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Article

Perioperative Outcomes in Robotic, Laparoscopic, and Open Distal Pancreatectomy: A Network Meta-Analysis and Meta-Regression

by
Nasser Abdul Halim
1,*,
Eran Sadot
1 and
Ionut Negoi
2,*
1
Rabin Medical Center, Beilinson Hospital, Medical Faculty, Tel Aviv University, Petah Tikva 4941492, Israel
2
Clinical Emergency Hospital of Bucharest, Carol Davila University of Medicine and Pharmacy Bucharest, No. 8 Floreasca Street, Sector 1, 014461 Bucharest, Romania
*
Authors to whom correspondence should be addressed.
Cancers 2025, 17(19), 3243; https://doi.org/10.3390/cancers17193243
Submission received: 31 August 2025 / Revised: 27 September 2025 / Accepted: 3 October 2025 / Published: 6 October 2025
(This article belongs to the Special Issue Robotic Surgery for Gastrointestinal (GI) Malignancies)
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Abstract

Simple Summary

Minimally invasive techniques are increasingly used for distal pancreatectomy, yet uncertainty remains regarding the relative benefits of robotic, laparoscopic, and open approaches. Using a network meta-analysis of 67 studies and over 18,000 patients, we compared short-term perioperative outcomes across the three techniques. We found that both robotic and laparoscopic procedures reduced blood loss, transfusion rates, and hospital stay compared to open surgery. Notably, robotic surgery was associated with the lowest conversion rate and significantly reduced 30-day mortality, while laparoscopic outcomes were generally intermediate. However, readmission and 90-day major complication rates did not differ significantly, suggesting that these outcomes may be influenced more by disease-specific factors than by surgical approach. Importantly, most robotic data originated from high-volume expert centers, limiting generalizability, and long-term oncologic outcomes were not available. Overall, our findings support the perioperative safety and efficacy of minimally invasive distal pancreatectomy, while emphasizing the need for cautious interpretation of robotic advantages beyond high-volume practice.

Abstract

Background: Distal pancreatectomy (DP) is a potentially curative procedure for tumors of the pancreatic body and tail. Minimally invasive DP (MIDP), including laparoscopic and robotic techniques, is increasingly being adopted. This study aimed to evaluate the perioperative outcomes of robotic DP (RDP) in comparison with laparoscopic and open approaches using a network meta-analysis and meta-regression. Methods: We systematically searched MEDLINE, EMBASE, Web of Science, and Scopus for studies comparing at least two surgical approaches. Both Bayesian and frequentist network meta-analyses were performed. Results: Sixty-seven studies involving 18,113 patients met the inclusion criteria. Surface under the cumulative ranking (SUCRA) analysis showed that RDP ranked first in 84.6% of measured parameters. Laparoscopic DP (LDP) demonstrated intermediate performance, whereas open DP (ODP) consistently ranked lowest. Operative time was significantly longer for RDP compared with ODP (MD = +25.93 min, 95% CI 7.68–44.18), while LDP and ODP were comparable. RDP significantly reduced 30-day mortality (OR = 0.37, 95% CI 0.16–0.84) and conversion rates compared with LDP (OR = 0.30, 95% CrI 0.22–0.40). Both minimally invasive approaches (RDP and LDP), compared with open surgery, were associated with reduced blood loss (−304 mL and −273 mL), fewer transfusions (OR 0.25 and 0.30), smaller transfused volumes (−1.98 and −1.86 units), shorter ICU stays (−4.0 and −2.3 days), fewer reinterventions (OR 0.45 and 0.56), and shorter hospital stays (−8.8 and −6.9 days), respectively. Conclusions: Although associated with longer operative time, RDP appears safe and may confer significant advantages over both laparoscopic and open surgery, including reduced 30-day mortality, lower conversion rates, and improved perioperative outcomes, particularly when performed in high-volume, well-equipped centers.

1. Introduction

The incidence of pancreatic ductal adenocarcinoma (PDAC) has increased in recent decades, ranking it as the twelfth most prevalent cancer and the sixth leading cause of cancer-related deaths globally [1]. Tumors of the pancreatic body and tail account for 20–25% of PDAC cases [2]. The widespread use of advanced imaging modalities has also led to a significant increase in the incidental finding of asymptomatic pancreatic lesions, including pancreatic neuroendocrine tumors (pNETs) and cystic neoplasms [3,4].
Distal pancreatectomy is the standard procedure for resectable lesions of the pancreatic body and tail. Since the introduction of laparoscopic distal pancreatectomy (LDP) in 1994 [5] and robotic distal pancreatectomy (RDP) in 2003 [6], there has been increasing interest in minimally invasive distal pancreatectomy (MIDP). Randomized controlled trials [7,8] and large series from high-volume centers [9,10,11] have demonstrated that MIDP offers several advantages over open distal pancreatectomy (ODP). These include reduced intraoperative bleeding, shorter hospitalization, faster postoperative recovery, and comparable oncologic outcomes [12].
Both the Miami [13] and Brescia [14] guidelines now support the use of MIDP as a standard alternative to ODP for benign, low-grade malignant, and selected malignant lesions, provided that the procedure is performed by surgeons with expertise and experience, in high-volume centers. Despite the technical advantages such as high-resolution three-dimensional visualization, tremor filtration, motion scaling, and improved ergonomics, RDP remains less commonly applied than LDP [15].
Evidence consistently confirms the benefits of MIDP over ODP [16,17,18]. However, few head-to-head comparisons exist among all three approaches. Current results are strongly influenced by heterogeneity in patient selection, surgical expertise, institutional practices, and surgical volume. Recent network meta-analyses (NMA) [19,20] have highlighted the advantages of MIDP compared with ODP but reached conflicting conclusions on the relative performance of RDP versus LDP. These inconsistencies underscore the need for an updated and comprehensive analysis to better define the role of robotic surgery in distal pancreatectomy. Network meta-analysis with meta-regression allows simultaneous comparison of open, laparoscopic, and robotic approaches, even when not directly compared within the same trial [21,22]. This method provides comprehensive ranking, clarifies relative effectiveness and safety, and offers insights that standard pairwise analyses cannot [23]. Accordingly, this study aimed to evaluate the perioperative outcomes of RDP in comparison with LDP and ODP using an NMA and meta-regression.

2. Methods

Network meta-analysis provides a comprehensive overview of treatment options by facilitating direct and indirect comparisons among various surgical approaches.
We compared robotic, laparoscopic, and open distal pancreatectomy. Primary and secondary perioperative outcomes were analyzed. The objective was to evaluate the perioperative outcomes of RDP in comparison with LDP and ODP.

2.1. Protocol and Registration

This study followed the PRISMA-NMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses incorporating Network Meta-Analyses) guidelines [24]. The study protocol was registered on the Open Science Framework (OSF; https://osf.io/ghtaz/, accessed on 15 August 2025).

2.2. Eligibility Criteria

We included randomized controlled trials (RCTs), prospective non-randomized comparative studies, and retrospective cohort studies. Eligible studies compared at least two of the following surgical techniques for distal pancreatectomy: open (ODP), laparoscopic (LDP), and robotic (RDP). Studies were required to report at least one perioperative outcome: patient age, sex, ASA status, history of cardiovascular comorbidities, intraoperative blood loss, quantity of transfused blood, conversion to open surgery, ICU stay, operative time, reintervention, readmission, in-hospital or 30-day mortality, 90-day major complications, or postoperative hospital stay length. Exclusion criteria were: (1) non-comparative studies; (2) case reports or case series with fewer than 10 patients; (3) studies on central or total pancreatectomy; (4) animal studies; and (5) abstracts without full-text availability.

2.3. Information Sources and Search Strategy

A systematic search was conducted in MEDLINE (via PubMed), EMBASE, Web of Science, and Scopus from inception to December 2024. The search included MeSH terms and keywords such as “distal pancreatectomy”, “left pancreatectomy”, “robotic”, “laparoscopic”, and “minimally invasive”. Manual searches of the references of relevant studies and systematic reviews were also performed.

2.4. Study Selection

All search results were imported into Paperpile and screened. Duplicates were removed. Titles and abstracts were screened for eligibility. The full texts of potentially eligible studies were assessed independently. Disagreements were resolved through discussion or consultation with a third reviewer.

2.5. Data Collection Process and Data Items

Data were extracted using a standardized collection form. Extracted variables included the first author, publication year, country of origin, study methodology, number of patients in each arm, surgical approach (open, laparoscopic, robotic), patient demographics (age, sex), ASA status, comorbidities, and all predefined perioperative outcomes. When available, we recorded means with standard deviations, medians with interquartile ranges, and event counts with sample sizes.

2.6. Risk of Bias Assessment

For non-randomized studies, the Newcastle–Ottawa Scale (NOS) was applied. Studies scoring 6 and above were selected. For prospective randomized controlled trials (RCTs), the Cochrane Risk of Bias 2.0 tool was used. Discrepancies were resolved through consensus. Results are summarized in Table 1.

2.7. Outcome Measures

Seventeen predefined perioperative outcomes were analyzed in this study. Continuous outcomes (e.g., operative time, blood loss, and hospital stay) were expressed as mean differences (MD), whereas dichotomous outcomes (e.g., mortality and reintervention) were reported using odds ratios (OR). Each outcome was pooled using both frequentist and Bayesian network meta-analyses.

2.8. Geometry of the Network

Network plots were constructed for each outcome to assess the structure of the available comparisons across the interventions. The number of direct and indirect comparisons per network was recorded.

2.9. Summary Measures and Statistical Analysis

Frequentist NMA was performed using the netmeta package in R (version 4.3.0), applying a random-effects model based on the DerSimonian–Laird method for variance estimation. For Bayesian NMA, we used the gemtc, BUGSnet, and bnma packages with Markov Chain Monte Carlo (MCMC) simulations. Three chains were run for 10,000 iterations after a burn-in of 5000 iterations.
Treatment rankings (Rank 1 = best, Rank 2 = second best, Rank 3 = worst) were evaluated using the Surface Under the Cumulative Ranking curve (SUCRA). To better visualize the results, we used Litmus Rank-O-Gram and radial SUCRA plots [91]. To score the overall treatment performances, we converted the ranks to scores (Rank 1 = 3 points, Rank 2 = 2 points, Rank 3 = 1 point) and created a comprehensive scoring chart showing total performance scores and category breakdowns.
Network inconsistency was assessed using global (design-by-treatment interaction) and local (node-splitting) approaches. The consistency between direct and indirect evidence was evaluated, being declared when p < 0.05 [92].
Model fit was evaluated by comparing the deviance information criterion (DIC), residual deviance, and leverage plots. We investigated the deviance of the model using the residual deviance from the NMA model, the unrelated mean effect (UME) inconsistency model, per-arm residual deviance, and the leverage plot for all studies [93].
Meta-regression was conducted using study publication year and sample size as covariates to explore sources of heterogeneity [94].

2.10. Assessment of Inconsistency and Small-Study Effects

Global tests and node-splitting models were utilized to investigate the inconsistency between direct and indirect comparisons. Funnel and comparison-adjusted funnel plots were generated to assess potential publication bias and small-study effects.

2.11. Additional Analyses

Prespecified sensitivity analyses included (1) restricting studies to those with a low risk of bias and (2) excluding outliers. Subgroup analyses were conducted based on publication year and surgical volume.

2.12. Software

Statistical analyses were performed using R environment (version 4.3.0) [95]. For frequentist NMA, we used the R package netmeta [96], while for Bayesian NMA, we used the R packages gemtc [97], BUGSNET, and bnma [95,98,99,100]. For graphical representation, Python 3.13 was also used.

3. Results

3.1. Search Results

A total of 937 studies were identified, with 67 meeting the inclusion criteria (Figure 1) [24].

3.2. Characteristics of the Included Studies

We included 67 studies in the NMA. The baseline characteristics are presented in Table 2.

3.3. Outcomes NMA

We analyzed 17 parameters comprising preoperative, intraoperative, and short-term postoperative results. The outcomes are summarized in Table 3.

3.4. Age and Sex of Patients

Across 59 studies, including 17,542 patients, the mean age was 59.5 years (Supplementary Table S1 and Figure S1). The network comprised fifty-two two-arm and seven multi-arm studies. A total of 73 pairwise comparisons were available. Specifically, eight trials compared ODP vs. RDP, twenty-nine trials ODP vs. LDP, and thirty-six trials LDP vs. RDP (Supplementary Figure S2).
In the frequentist NMA, patients in the RDP group were significantly younger than those in the ODP group, with a mean difference (MD) of −1.65 years (95% confidence interval [CI] −3.00 to −0.30). No significant age difference was observed between LDP and ODP (MD = −0.72, 95% CI −1.86 to 0.41) or between RDP and LDP (MD = −0.93, 95% CI −1.93 to 0.07) (Supplementary Figures S3 and S4 and Supplementary Table S1). No significant inconsistency was detected between the direct and indirect comparisons (Supplementary Table S2). In the Bayesian NMA, patients in the RDP group were significantly younger than those in the ODP group (MD = −1.67 years, 95% CI −3.11 to −0.26) (Supplementary Figure S5 and Supplementary Table S3). No significant differences in age were found between the RDP and LDP (MD = −0.93, 95% CrI −1.97 to 0.11) or between LDP and ODP (MD = −0.74, 95% CrI −1.95 to 0.43) (Supplementary Figures S6 and S7).
The model showed no evidence of lack of fit (no significant residual deviance) (Supplementary Figures S8–S10). Meta-regression indicated that patients in the ODP group were older in earlier studies (Supplementary Figure S11), with the age difference between groups diminishing in more recent years (i.e., no age-selection difference in the most recent studies) (Supplementary Figure S12).
The analysis of sex distribution across groups is presented in the Supplementary Materials, page 28.

3.5. ASA Status

Forty studies (54 pairwise comparisons; 10,318 patients) reported ASA class I–II distribution (Supplementary Figures S18 and S19).
No statistically significant differences were observed between the RDP, LDP, and ODP in the proportion of patients classified as ASA I–II (Supplementary Figure S20 and Supplementary Table S6). SUCRA rankings were very similar among the three groups, with no significant separation (Supplementary Figures S21 and S22). No inconsistencies were observed between the direct and indirect comparisons (Supplementary Figure S23).
Meta-regression with the year of study as a covariate showed no significant change in the relative ASA distribution over time (Supplementary Figure S24).

3.6. Previous Cardiovascular Diseases

Twelve studies (18 pairwise comparisons; 3307 patients) reported pre-existing cardiovascular comorbidities (Supplementary Figures S25 and S26). There were no significant differences between the surgical approaches in terms of the proportion of patients with a history of cardiovascular disease (Supplementary Figure S27 and Supplementary Table S7). Baseline cardiovascular comorbidity rates were therefore comparable across groups (Supplementary Figure S28). This suggests that patient selection in terms of cardiovascular health was similar across the groups. No inconsistency was detected (Supplementary Table S8).
Bayesian NMA results were similar to those of the frequentist analysis (Supplementary Figures S29–S32 and Supplementary Table S9). No deviance was observed between the studies (Supplementary Figures S33–S35).
Meta-regression showed that differences in cardiovascular comorbidities decreased over time (Supplementary Figure S36).

3.7. Operative Time

Operative time was reported in 61 studies (79 pairwise comparisons, including 16,230 patients) (Supplementary Figures S37 and S38). In the frequentist NMA, RDP was associated with a significantly longer operative time than ODP (MD = +25.93 min, 95% CI 7.68–44.18) (Supplementary Figure S39 and Supplementary Table S10). LDP had a slight mean increase in operative time compared to ODP, without statistical significance (MD = +7.63 min, 95% CI −7.59 to +22.85). No inconsistency was detected (Supplementary Table S11). SUCRA rankings identified ODP as the fastest (Rank 1), followed by LDP (Rank 2) and RDP (Rank 3) (Supplementary Figure S40). In the Bayesian NMA, findings were consistent. RDP lasted, on average, 27.2 min longer than ODP (95% CrI 4.3–50.3) (Supplementary Figure S41 and Supplementary Table S12). Differences between LDP and ODP remained small and non-significant. Approach rankings matched the frequentist results (ODP fastest, followed by LDP and RDP) (Supplementary Figures S42 and S43). No inconsistency was detected (Supplementary Figures S44–S47).
Meta-regression analysis revealed no significant temporal trends and no effect of study sample size on operative time differences (Supplementary Figures S48 and S49). Overall, RDP was associated with longer operative time than ODP or LDP, with LDP and ODP showing comparable durations.

3.8. Conversion to Open

Thirty-eight studies (10,586 patients) reported conversion to open surgery among RDP and LDP (Supplementary Figures S50 and S51). In the NMA, RDP was associated with a significantly lower conversion rate than LDP. The odds of conversion were 65.3% lower with RDP compared to LDP (OR = 0.347, 95% CI 0.248–0.441 for RDP vs. LDP) (Figure 2). Conversely, the odds of conversion were approximately 2.8 times higher with LDP compared to RDP (OR = 2.88 for LDP vs. RDP, 95% CI 2.27–4.03) (Supplementary Table S14). Based on SUCRA, RDP ranked first, with the lowest conversion rate (Figure 3).
There was no evidence of inconsistency in this two-treatment network (Figure 4, Figure 5 and Figure 6).
Frequentist and Bayesian analyses confirmed a significantly lower conversion risk for RDP compared with LDP (Supplementary Figure S52). Meta-regression did not show any significant effect of publication year or study size on conversion rates. The advantage of RDP over LDP remained consistent across study timelines and sample sizes (Figure 7 and Figure 8).

3.9. Intraoperative Blood Loss

A total of fifty-one studies (forty-six two-arm and five multi-arm) reported intraoperative blood loss, including 12,257 patients in the network (Supplementary Figure S60). There were 61 pairwise comparisons (Supplementary Figure S61).
In the frequentist NMA, both RDP and LDP were associated with significantly lower blood loss than ODP. The mean difference was −279.45 mL (95% CI −318.23 to −240.61) for RDP vs. ODP, and −248.99 mL (95% CI −283.41 to −214.57) for LDP vs. ODP. No significant differences were observed between RDP and LDP in the frequentist analysis (Supplementary Figure S62, Supplementary Table S15). No inconsistencies were detected in the network (Supplementary Table S16). SUCRA rankings ordered the approaches from lowest to highest blood loss as follows: RDP, LDP, and ODP (Supplementary Figure S63). In the Bayesian NMA, results were consistent. RDP and LDP showed significantly lower blood loss than ODP (MD = −303.98 mL, 95% CrI −382.8 to −229.26 for RDP; MD = −272.89 mL, 95% CrI −340.15 to −209.01 for LDP) (Supplementary Figures S64–S66, Supplementary Table S17). There was no notable model deviance or lack of convergence in the Bayesian analysis (Supplementary Figures S68–S70).
Meta-regression indicated that the difference in blood loss between open and minimally invasive approaches had narrowed in more recent studies compared with older (>10 years) studies (Supplementary Figures S71–S73). In contrast, sample size did not significantly affect results, although larger studies tended to report greater blood loss reduction with RDP and LDP compared to ODP (Supplementary Figure S74).

3.10. Intraoperative Bleeding More than 500 mL

Three studies (1427 patients) reported the number of patients experiencing intraoperative blood loss exceeding 500 mL (Supplementary Figure S76). LDP showed a significantly lower incidence of ≥500 mL blood loss than the ODP (OR = 0.32, 95% CI 0.23–0.46) (Supplementary Figure S77 and Supplementary Table S18). For RDP, the risk was also lower than ODP (OR = 0.11, 95% CI 0.01–1.07); however, this difference was not statistically significant due to wide confidence intervals, likely reflecting the small number of events in the robotic arm. No studies directly compared RDP and LDP for this outcome. However, point estimates suggested that both modalities reduced the risk of major bleeding relative to ODP. No inconsistencies were detected (Supplementary Table S19). These findings indicate that MIDP approaches tend to reduce the risk of excessive intraoperative bleeding, with laparoscopy showing a significant reduction and robotics showing a similar trend.

3.11. Number of Patients Receiving Transfusions

Thirty studies (38 pairwise comparisons, with 9248 patients) reported intraoperative or postoperative transfusion events (Supplementary Figures S78 and S79). Both minimally invasive approaches were associated with significantly lower transfusion rates compared to ODP. The odds of requiring transfusion were markedly reduced for RDP vs. ODP (OR = 0.25, 95% CI 0.19–0.34) and for LDP vs. ODP (OR = 0.30, 95% CI 0.24–0.37) (Supplementary Figure S80, Supplementary Table S20). There was no significant difference in the transfusion rate between RDP and LDP. No inconsistency was detected in the network (Supplementary Table S21). SUCRA rankings identified RDP as the best approach (Rank 1), followed by LDP (Rank 2) and ODP (Rank 3) (Supplementary Figure S81).

3.12. The Quantity of Blood Transfusion

Four studies (six pairwise comparisons, 363 patients) reported the mean number of blood units transfused per patient (Supplementary Figures S82 and S83).
Both minimally invasive approaches were associated with a lower volume of blood transfusion compared with ODP. The mean difference was −1.98 units (95% CI −3.42 to −0.54) for RDP vs. ODP and −1.86 units (95% CI −3.12 to −0.59) for LDP vs. ODP. On average, patients undergoing MIDP received approximately two fewer units of blood than those undergoing ODP (Supplementary Figure S84, Supplementary Table S22). No significant difference was observed between RDP and LDP. No inconsistencies were detected (Supplementary Table S23).

3.13. Intensive Care Unit Length of Stay

Nine studies (1272 patients) reported the postoperative ICU length of stay (Supplementary Figures S85 and S86). Both RDP and LDP were associated with a significantly shorter ICU stay duration than ODP. The mean difference was −4.01 days (95% CI −5.97 to −2.05) for RDP vs. ODP and −2.27 days (95% CI −3.71 to −0.83) for LDP vs. ODP (Supplementary Figure S87 and Supplementary Table S24). On average, patients undergoing RDP spent approximately four fewer days in the ICU than ODP patients, while LDP patients spent approximately two fewer days. No significant difference was observed between RDP and LDP. No inconsistencies were detected (Supplementary Table S25). SUCRA rankings identified RDP as the best approach, followed by LDP and ODP as the worst (Rank 3) (Supplementary Figure S88). These results suggest that MIDP reduces the need for prolonged critical care, reflecting lower perioperative stress and complications.

3.14. Reintervention Rate

Thirty-seven studies (11,568 patients) reported postoperative reintervention rates. Both RDP and LDP were associated with lower reintervention rates compared to ODP (RDP: OR = 0.451, 95% CrI 0.225–0.836; LDP: OR = 0.560, 95% CrI 0.322–0.962) (Supplementary Table S26).
No significant difference was found between RDP and LDP.
SUCRA rankings placed RDP as the best approach (Rank 1, lowest reintervention risk), LDP as Rank 2, and ODP as the worst (Rank 3) (Supplementary Figure S92). No inconsistency was detected (node split analysis, p > 0.05; Supplementary Figure S93).

3.15. Hospital Length of Stay

Sixty-three studies (18,113 patients; fifty-four two-arm and nine multi-arm studies) reported postoperative hospital stay (Supplementary Figures S97 and S98). Both minimally invasive approaches showed significantly shorter hospital stays than ODP (Supplementary Figure S101). Pooled estimates indicated that RDP patients had a shorter length of in-hospital stay than ODP, with a MD of −8.77 days (95% CrI −13.34 to −4.22), while LDP had an MD of −6.93 days (95% CrI −10.67 to −3.23) (Supplementary Table S29).
No inconsistencies were detected (Supplementary Figure S104). SUCRA rankings placed RDP as the best approach (shortest stay), LDP as second, and ODP as the worst (Supplementary Figures S102 and S103). Meta-regression showed no temporal effect; the advantage of minimally invasive approaches remained stable over time. These results confirm that MIDP, particularly RDP, facilitates faster recovery and earlier discharge compared with ODP.

3.16. Readmission Rate

Thirty-one studies (34 comparisons) reported 30-day readmission rates (Supplementary Figures S110 and S111). No statistically significant differences were observed among the three approaches (Supplementary Figure S112, Supplementary Table S30). No inconsistency was detected (Supplementary Table S31). SUCRA values were nearly identical across the three approaches, indicating no clear ranking advantage (Supplementary Figure S113). These findings suggest that the choice of surgical approach does not significantly affect early hospital readmission.

3.17. In-Hospital Mortality

Nine studies (1009 patients) reported in-hospital (same-admission) mortality (Supplementary Figures S114 and S115). No significant differences were observed among ODP, LDP, and RDP (Supplementary Figures S117 and S119).

3.18. 30-Day Mortality

Thirty-one studies, including 12,127 patients, reported 30-day postoperative mortality (Supplementary Figures S125 and S126).
RDP was associated with significantly lower 30-day mortality (OR = 0.159, 95% CrI 0.0294–0.459) (Figure 8). LDP also showed a reduction, although the statistical significance was marginal (OR = 0.477, 95% CrI 0.164–0.954) (Figure 9, Table 4).
SUCRA rankings placed RDP as the best approach, followed by LDP, and ODP third (Figure 10 and Figure 11). In the frequentist NMA, the survival benefit of LDP was attenuated and did not reach significance (Supplementary Figures S127–S130 and Supplementary Table S32).
No inconsistencies were detected between direct and indirect comparisons (Supplementary Figures S131 and S132). No deviance was observed (Supplementary Figures S133–S135).
No evidence of publication bias was observed (Supplementary Figure S140).

3.19. 90-Day Major Complications

Three studies (1427 patients) reported 90-day major complications (typically Clavien–Dindo grade III or higher) (Supplementary Figures S141 and S142). No significant differences were observed among the three approaches (Supplementary Figure S145). Pooled estimates for RDP vs. ODP and LDP vs. ODP showed odds ratios close to 1, with wide Crls reflecting limited data. The comparison between RDP and LDP was also non-significant (Supplementary Table S37).

4. Summary

Our SUCRA ranking analysis demonstrates that robotic surgery consistently outperformed the other approaches, achieving the top rank in 84.6% of outcomes and capturing 92.3% of the maximum possible score (Figure 12, Figure 13, Figure 14, Figure 15 and Figure 16). Laparoscopic surgery showed intermediate performance, while open surgery ranked lowest across comparisons.

5. Discussions

This network meta-analysis provides an updated and comprehensive comparison of perioperative outcomes among robotic, laparoscopic and open distal pancreatectomy, aiming to clarify the role of robotic distal pancreatectomy (RDP) in treating resectable left pancreatic lesions.
Based on SUCRA rankings, robotic surgery demonstrated favorable performance across most evaluated outcomes, achieving the highest rank in 84.6% of assessed parameters and accounting for 92.3% of the maximum possible performance score. Specifically, the robotic approach was associated with reduced blood loss, fewer transfusions, and lower volumes of transfused blood products. These intraoperative advantages were reflected in shorter ICU and hospital stays, fewer reinterventions, and lower rates of 30-day mortality and 90-day major complications. Laparoscopy generally showed intermediate performance, while the open approach consistently ranked lowest in this comparative analysis.
The mean age of patients in the RDP group was significantly lower than that of the ODP group but not significantly different from the LDP group. However, these age differences have diminished in more recent publications compared to earlier ones. In a previous meta-analysis, patients undergoing RDP were significantly younger than those undergoing LDP [101]. The current findings suggest that with increased familiarity and broader implementation of the robotic platform in abdominal surgeries in general [102], as well as pancreatic surgery in particular [103], the indications for RDP have expanded to include older patients with more comorbidities.
Operative time was significantly longer for RDP, consistent with reports from high-volume centers [86]. This may be attributed to docking and instrument exchange, as well as the lack of tactile sensation, which often leads to more cautious maneuvers, particularly early in the learning curve. Our meta-regression analysis showed no significant temporal or sample-size-related trends in operative time, suggesting that this difference persists across settings. Three studies in our analysis compared all three techniques head-to-head [25,52,59], consistently reporting the longest operative time in the RDP and the shortest in the ODP group. However, prior meta-analysis [104] found comparable operative times across the three techniques, underlining the variability in reported experience. Operative time is inherently influenced by surgical experience and standardization of the technique [105]. Large multicenter analyses demonstrate a marked reduction in operative time in RDP as surgeons and institutions progress along the learning curve. Marked reductions are typically seen after 18–46 cases, with a plateau reached after 43–73 cases, depending on the definition of proficiency [106,107,108]. This is attributed to increased familiarity with robotic systems, improved technical efficiency, and optimized team coordination. In the present analysis, results from expert centers were pooled with those from centers still early in the learning curve. From this perspective, the open approach ranked highest in the SUCRA scores, likely reflecting the broader experience of surgeons with open surgery.
The intraoperative blood loss was significantly lower in MIDP compared to the open approach. Within MIDP, RDP showed lower blood loss than LDP, though this difference did not reach statistical significance. Intraoperative bleeding more than 500 mL was reported in only three studies, limiting the strength of the available evidence.
The conversion rate was significantly lower for RDP than for LDP, and this advantage persisted across study periods and sample sizes in meta-regression. This is a notable, given that laparoscopy is more widely adopted than robotics. The predominance of high-volume centers in RDP publications likely contributes to these favorable results.
Postoperative outcomes similarly favored minimally invasive approaches over the open approach. Hospital stay was shortest with RDP. Overall, minimally invasive procedures were associated with lower mortality than ODP. However, no significant differences were observed in readmission or 90-day major complication rates. This may indicate that these outcomes are more strongly influenced by organ-specific complications rather than the surgical approach. It is important to acknowledge that 90-day major complications were reported in only a limited number of studies (n = 3). Although these studies included a total of 1427 patients, the results should be interpreted with caution, and the conclusions must be considered preliminary. Major postoperative complications within 30 or 90 days are associated with increased recurrence and negatively impact both disease-free and overall survival [109,110]. Postoperative morbidity is considered a significant modifiable risk factor that can influence long-term prognosis in cancer patients [110]. The present analysis, which revealed reduced perioperative morbidity and mortality observed with minimally invasive approaches, particularly RDP, supports the concept of improved long-term outcomes. On the other hand, taking into account that the long-term endpoints were not the focus of the present analysis, the oncologic implications of our findings should be interpreted with caution.
Although not in the scope of this study, cost-effectiveness warrants consideration. The cost disparity between robotic and laparoscopic abdominal surgery has persisted over time [111]. The main drivers of increased expenses are consumable supplies, longer operative times, and the substantial capital investment required for robotic systems. Nevertheless, some studies and meta-analyses have reported lower overall costs for RDP compared with LDP and ODP [66,112], a finding also supported by a prospective trial [43]. The overall economic balance, however, remains debated and is strongly influenced by local healthcare systems, institutional resources, and surgical volume.
The heterogeneity in pathologic entities included in our analysis represents an important limitation that warrants acknowledgment. Malignant pancreatic tumors, particularly pancreatic ductal adenocarcinoma, necessitate fundamentally different surgical approaches compared to benign pancreatic lesions [113,114,115]. Malignant lesions require extensive retroperitoneal dissection, radical lymphadenectomy, and often more complex vascular reconstructions to achieve oncological adequacy [116,117]. These procedures are associated with increased operative complexity, prolonged operative times, higher blood loss, and elevated complication rates compared to resections for benign conditions. In contrast, benign pancreatic lesions such as serous cystadenomas, mucinous cystic neoplasms, and pancreatic neuroendocrine tumors typically require less extensive dissection with parenchyma-preserving techniques being prioritized [118]. The differential surgical complexity between malignant and benign pathology directly impacts perioperative outcomes, with malignant cases demonstrating higher morbidity profiles. Our pooled analysis offers important insights into how different surgical methods compare in terms of effectiveness across the spectrum of pancreatic pathology. However, these findings should be interpreted recognizing the inherent heterogeneity in diseases, tumor biology, and required surgical complexity.
To address potential concerns regarding overlapping cohorts or duplicate populations, we implemented systematic measures during study selection and data extraction [119,120]. Studies originating from the same institutions or national databases were carefully examined for overlapping time periods and patient populations, with particular attention paid to large multi-institutional series that could share common patients. When overlapping populations were suspected, we prioritized studies with the most comprehensive datasets or longest follow-up periods and excluded those with clear evidence of duplicate reporting to prevent double-counting of patients and outcomes. The heterogeneity observed in our analysis may partly reflect the inclusion of studies from different healthcare systems and time periods, which actually reduces the likelihood of substantial population overlap while enhancing the generalizability of our findings. Additionally, our network meta-analysis methodology is inherently robust to minor population overlaps, as the statistical framework accounts for between-study heterogeneity that would be expected when some degree of patient duplication exists [121].
Most published robotic series originate from high-volume expert centers, where outcomes are likely influenced by surgical expertise, institutional experience, and advanced stages of the learning curve. This may limit the generalizability of the results to lower-volume centers or surgeons at the beginning of their robotic practice. Moreover, this study focused exclusively on short-term perioperative outcomes; this limits our ability to draw firm conclusions regarding oncologic equivalence among the three approaches. The predominance of retrospective observational studies, which are inherently subject to selection bias, unmeasured confounding, and variability in patient selection, surgical expertise, and institutional practices, represents a limitation. While we attempted to mitigate these concerns by applying standardized quality assessment tools (Newcastle–Ottawa Scale for observational studies and Cochrane RoB-2 for RCTs), the risk of bias included in the primary data is partially translated in the final output. Nevertheless, the inclusion of prospective studies and one randomized trial adds strength to the overall evidence. Heterogeneity was present in patient selection, surgical expertise, and perioperative protocols, which reflects real-world clinical practice. Although many comparisons were indirect, statistical consistency across the network and the application of established meta-analytical methods strengthen the credibility of the results.

6. Conclusions

This network meta-analysis demonstrates that minimally invasive distal pancreatectomy is associated with clear perioperative advantages over the open approach. Among minimally invasive techniques, the robotic platform showed potential benefits, including lower conversion rates and reduced short-term mortality. These findings support the safety and feasibility of RDP, while underscoring the overall value of minimally invasive approaches in distal pancreatectomy. However, interpretation should remain cautious, as most robotic data derive from high-volume expert centers, which may limit generalizability.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers17193243/s1, File S1: Aff of the Supplementary materials.

Author Contributions

N.A.H.: conceptualization, investigation, resources, data curation, writing—original draft. E.S.: conceptualization, investigation, writing—review and editing, supervision. I.N.: conceptualization, methodology, formal analysis, investigation, writing—original draft, writing—review and editing, visualization, supervision, project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Leiphrakpam, P.D.; Chowdhury, S.; Zhang, M.; Bajaj, V.; Dhir, M.; Are, C. Trends in the Global Incidence of Pancreatic Cancer and a Brief Review of Its Histologic and Molecular Subtypes. J. Gastrointest. Cancer 2025, 56, 71. [Google Scholar] [CrossRef]
  2. Ryan, D.P.; Hong, T.S.; Bardeesy, N. Pancreatic Adenocarcinoma. N. Engl. J. Med. 2014, 371, 1039–1049. [Google Scholar] [CrossRef]
  3. Zhu, S.; Wang, W.-T.; Shang, X.-S.; Ni, T.; Wu, W.-C.; Lou, W.-H.; Zeng, M.-S.; Rao, S.-X. Difference Analysis in Prevalence of Incidental Pancreatic Cystic Lesions between Computed Tomography and Magnetic Resonance Imaging. BMC Med. Imaging 2019, 19, 43. [Google Scholar] [CrossRef]
  4. Santo, E.; Bar-Yishay, I. Pancreatic Solid Incidentalomas. Endosc. Ultrasound 2017, 6, S99–S103. [Google Scholar] [CrossRef] [PubMed]
  5. Cuschieri, A. Laparoscopic Surgery of the Pancreas. J. R. Coll. Surg. Edinb. 1994, 39, 178–184. [Google Scholar] [PubMed]
  6. Giulianotti, P.C.; Coratti, A.; Angelini, M.; Sbrana, F.; Cecconi, S.; Balestracci, T.; Caravaglios, G. Robotics in General Surgery, Personal Experience in a Large Community Hospital. Arch. Surg. 2003, 138, 777–784. [Google Scholar]
  7. de Rooij, T.; van Hilst, J.; van Santvoort, H.; Boerma, D.; van den Boezem, P.; Daams, F.; van Dam, R.; Dejong, C.; van Duyn, E.; Dijkgraaf, M.; et al. Minimally Invasive versus Open Distal Pancreatectomy (LEOPARD): A Multicenter Patient-Blinded Randomized Controlled Trial. Ann. Surg. 2019, 269, 2–9. [Google Scholar] [CrossRef]
  8. Björnsson, B.; Larsson, A.L.; Hjalmarsson, C.; Gasslander, T.; Sandström, P. Comparison of the Duration of Hospital Stay after Laparoscopic or Open Distal Pancreatectomy: Randomized Controlled Trial. Br. J. Surg. 2020, 107, 1281–1288. [Google Scholar] [CrossRef]
  9. Song, K.B.; Kim, S.C.; Park, J.B.; Kim, Y.H.; Jung, Y.S.; Kim, M.-H.; Lee, S.-K.; Seo, D.-W.; Lee, S.S.; Park, D.H.; et al. Single-Center Experience of Laparoscopic Left Pancreatic Resection in 359 Consecutive Patients: Changing the Surgical Paradigm of Left Pancreatic Resection. Surg. Endosc. 2011, 25, 3364–3372. [Google Scholar] [CrossRef] [PubMed]
  10. Sánchez-Cabús, S.; Adam, J.-P.; Pittau, G.; Gelli, M.; Cunha, A.S. Laparoscopic Left Pancreatectomy: Early Results after 115 Consecutive Patients. Surg. Endosc. 2016, 30, 4480–4488. [Google Scholar] [CrossRef] [PubMed]
  11. van Hilst, J.; de Rooij, T.; Klompmaker, S.; Rawashdeh, M.; Aleotti, F.; Al-Sarireh, B.; Alseidi, A.; Ateeb, Z.; Balzano, G.; Berrevoet, F.; et al. Minimally Invasive versus Open Distal Pancreatectomy for Ductal Adenocarcinoma (DIPLOMA): A Pan-European Propensity Score Matched Study. Ann. Surg. 2019, 269, 10–17. [Google Scholar] [CrossRef]
  12. Korrel, M.; Jones, L.R.; van Hilst, J.; Balzano, G.; Björnsson, B.; Boggi, U.; Bratlie, S.O.; Busch, O.R.; Butturini, G.; Capretti, G.; et al. Minimally Invasive versus Open Distal Pancreatectomy for Resectable Pancreatic Cancer (DIPLOMA): An International Randomised Non-Inferiority Trial. Lancet Reg. Health Eur. 2023, 31, 100673. [Google Scholar] [CrossRef]
  13. Asbun, H.J.; Moekotte, A.L.; Vissers, F.L.; Kunzler, F.; Cipriani, F.; Alseidi, A.; D’Angelica, M.I.; Balduzzi, A.; Bassi, C.; Björnsson, B.; et al. The Miami International Evidence-Based Guidelines on Minimally Invasive Pancreas Resection. Ann. Surg. 2020, 271, 1–14. [Google Scholar] [CrossRef]
  14. Abu Hilal, M.; van Ramshorst, T.M.E.; Boggi, U.; Dokmak, S.; Edwin, B.; Keck, T.; Khatkov, I.; Ahmad, J.; Al Saati, H.; Alseidi, A.; et al. The Brescia Internationally Validated European Guidelines on Minimally Invasive Pancreatic Surgery (EGUMIPS). Ann. Surg. 2023, 279, 45–57. [Google Scholar] [CrossRef]
  15. van der Heijde, N.; Vissers, F.L.; Manzoni, A.; Zimmitti, G.; Balsells, J.; Berrevoet, F.; Bjornsson, B.; van den Boezem, P.; Boggi, U.; Bratlie, S.O.; et al. Use and Outcome of Minimally Invasive Pancreatic Surgery in the European E-MIPS Registry. HPB 2023, 25, 400–408. [Google Scholar] [CrossRef]
  16. Addeo, P.; de Mathelin, P.; Doussot, A.; Durin, T.; Canali, G.; Marchese, U.; Sauvanet, A.; Dokmak, S.; Cherkaoui, Z.; Fuks, D.; et al. Minimally Invasive versus Open Distal Pancreatectomy for Resectable Pancreatic Neuroendocrine Tumors: A Propensity Score Matched Multicentric Comparative French Study. Surgery 2024, 176, 433–439. [Google Scholar] [CrossRef] [PubMed]
  17. Partelli, S.; Andreasi, V.; Rancoita, P.M.V.; Perez-Sanchez, E.; Muffatti, F.; Balzano, G.; Crippa, S.; Di Serio, C.; Falconi, M. Outcomes after Distal Pancreatectomy for Neuroendocrine Neoplasms: A Retrospective Comparison between Minimally Invasive and Open Approach Using Propensity Score Weighting. Surg. Endosc. 2021, 35, 165–173. [Google Scholar] [CrossRef]
  18. de Oliveira Ramos, J.V.; Fernandes, J.V.A.; de Oliveira Ferreira, C.H.; de Lacerda Dantas Henrique, G.; de Souza, R.L.; de Abrantes Formiga, Y.G.; de Lima Beltrão, F.E. Minimally Invasive versus Open Distal Pancreatectomy for Pancreatic Tumors: An Updated Meta-Analysis and Meta-Regression. Pancreatology 2025, 25, 569–579. [Google Scholar] [CrossRef] [PubMed]
  19. Lyu, Y.; Cheng, Y.; Wang, B.; Zhao, S.; Chen, L. Comparison of 3 Minimally Invasive Methods versus Open Distal Pancreatectomy: A Systematic Review and Network Meta-Analysis. Surg. Laparosc. Endosc. Percutan. Tech. 2020, 31, 104–112. [Google Scholar] [CrossRef]
  20. Bousi, S.-E.; Zachiotis, M.; Papapanou, M.; Frountzas, M.; Symeonidis, D.; Raptis, D.; Papaziogas, B.; Toutouzas, K.; Felekouras, E.; Schizas, D. Robotic versus Laparoscopic versus Open Surgery for Non-Metastatic Pancreatic Neuroendocrine Tumors (PNETs): A Systematic Review and Network Meta-Analysis. J. Clin. Med. 2024, 13, 6303. [Google Scholar] [CrossRef] [PubMed]
  21. Faria, M.; Teixeira, M.; Pinto, M.J.; Sargento, P. Efficacy of Acupuncture on Cancer Pain: A Systematic Review and Meta-Analysis. J. Integr. Med. 2024, 22, 235–244. [Google Scholar] [CrossRef]
  22. Watt, J.; Tricco, A.C.; Straus, S.; Veroniki, A.A.; Naglie, G.; Drucker, A.M. Research Techniques Made Simple: Network Meta-Analysis. J. Investig. Dermatol. 2019, 139, 4–12.e1. [Google Scholar] [CrossRef]
  23. Watt, J.; Del Giovane, C. Network Meta-Analysis. Methods Mol. Biol. 2022, 2345, 187–201. [Google Scholar] [PubMed]
  24. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef] [PubMed]
  25. Rodriguez, M.; Memeo, R.; Leon, P.; Panaro, F.; Tzedakis, S.; Perotto, O.; Varatharajah, S.; de’Angelis, N.; Riva, P.; Mutter, D.; et al. Which Method of Distal Pancreatectomy Is Cost-Effective among Open, Laparoscopic, or Robotic Surgery? Hepatobiliary Surg. Nutr. 2018, 7, 345–352. [Google Scholar] [CrossRef]
  26. Abu Hilal, M.; Hamdan, M.; Di Fabio, F.; Pearce, N.W.; Johnson, C.D. Laparoscopic versus Open Distal Pancreatectomy: A Clinical and Cost-Effectiveness Study. Surg. Endosc. 2012, 26, 1670–1674. [Google Scholar] [CrossRef] [PubMed]
  27. Alfieri, S.; Butturini, G.; Boggi, U.; Pietrabissa, A.; Morelli, L.; Vistoli, F.; Damoli, I.; Peri, A.; Fiorillo, C.; Pugliese, L.; et al. Short-Term and Long-Term Outcomes after Robot-Assisted versus Laparoscopic Distal Pancreatectomy for Pancreatic Neuroendocrine Tumors (PNETs): A Multicenter Comparative Study. Langenbecks Arch. Surg. 2019, 404, 459–468. [Google Scholar] [CrossRef]
  28. Aly, M.Y.F.; Tsutsumi, K.; Nakamura, M.; Sato, N.; Takahata, S.; Ueda, J.; Shimizu, S.; Redwan, A.A.; Tanaka, M. Comparative Study of Laparoscopic and Open Distal Pancreatectomy. J. Laparoendosc. Adv. Surg. Tech. A 2010, 20, 435–440. [Google Scholar] [CrossRef]
  29. Baker, M.S.; Bentrem, D.J.; Ujiki, M.B.; Stocker, S.; Talamonti, M.S. A Prospective Single Institution Comparison of Peri-Operative Outcomes for Laparoscopic and Open Distal Pancreatectomy. Surgery 2009, 146, 635–645. [Google Scholar] [CrossRef]
  30. Benizri, E.I.; Germain, A.; Ayav, A.; Bernard, J.-L.; Zarnegar, R.; Benchimol, D.; Bresler, L.; Brunaud, L. Short-Term Perioperative Outcomes after Robot-Assisted and Laparoscopic Distal Pancreatectomy. J. Robot. Surg. 2014, 8, 125–132. [Google Scholar] [CrossRef]
  31. van Bodegraven, E.A.; van Ramshorst, T.M.E.; Bratlie, S.O.; Kokkola, A.; Sparrelid, E.; Björnsson, B.; Kleive, D.; Burgdorf, S.K.; Dokmak, S.; Koerkamp, B.G.; et al. Minimally Invasive Robot-Assisted and Laparoscopic Distal Pancreatectomy in a Pan-European Registry a Retrospective Cohort Study. Int. J. Surg. 2024, 110, 3554–3561. [Google Scholar] [CrossRef]
  32. Butturini, G.; Partelli, S.; Crippa, S.; Malleo, G.; Rossini, R.; Casetti, L.; Melotti, G.L.; Piccoli, M.; Pederzoli, P.; Bassi, C. Perioperative and Long-Term Results after Left Pancreatectomy: A Single-Institution, Non-Randomized, Comparative Study between Open and Laparoscopic Approach. Surg. Endosc. 2011, 25, 2871–2878. [Google Scholar] [CrossRef] [PubMed]
  33. Butturini, G.; Damoli, I.; Crepaz, L.; Malleo, G.; Marchegiani, G.; Daskalaki, D.; Esposito, A.; Cingarlini, S.; Salvia, R.; Bassi, C. A Prospective Non-Randomised Single-Center Study Comparing Laparoscopic and Robotic Distal Pancreatectomy. Surg. Endosc. 2015, 29, 3163–3170. [Google Scholar] [CrossRef]
  34. Casadei, R.; Ricci, C.; D’Ambra, M.; Marrano, N.; Alagna, V.; Rega, D.; Monari, F.; Minni, F. Laparoscopic versus Open Distal Pancreatectomy in Pancreatic Tumours: A Case-Control Study. Updates Surg. 2010, 62, 171–174. [Google Scholar] [CrossRef]
  35. Chang, J.H.; Wehrle, C.; Woo, K.; Naples, R.; Stackhouse, K.A.; Dahdaleh, F.; Joyce, D.; Simon, R.; Augustin, T.; Walsh, R.M.; et al. Comparing Oncologic and Surgical Outcomes of Robotic and Laparoscopic Distal Pancreatectomy: A Propensity-Matched Analysis. Surg. Endosc. 2024, 38, 5678–5685. [Google Scholar] [CrossRef]
  36. Chen, P.; Zhou, B.; Wang, T.; Hu, X.; Ye, Y.; Guo, W. Comparative Efficacy of Robot-Assisted and Laparoscopic Distal Pancreatectomy: A Single-Center Comparative Study. J. Healthc. Eng. 2022, 2022, 7302222. [Google Scholar] [CrossRef] [PubMed]
  37. Chen, J.W.; van Ramshorst, T.M.E.; Lof, S.; Al-Sarireh, B.; Bjornsson, B.; Boggi, U.; Burdio, F.; Butturini, G.; Casadei, R.; Coratti, A.; et al. Robot-Assisted versus Laparoscopic Distal Pancreatectomy in Patients with Resectable Pancreatic Cancer: An International, Retrospective, Cohort Study. Ann. Surg. Oncol. 2023, 30, 3023–3032. [Google Scholar] [CrossRef] [PubMed]
  38. Daouadi, M.; Zureikat, A.H.; Zenati, M.S.; Choudry, H.; Tsung, A.; Bartlett, D.L.; Hughes, S.J.; Lee, K.K.; Moser, A.J.; Zeh, H.J. Robot-Assisted Minimally Invasive Distal Pancreatectomy Is Superior to the Laparoscopic Technique. Ann. Surg. 2013, 257, 128–132. [Google Scholar] [CrossRef]
  39. De Pastena, M.; Esposito, A.; Paiella, S.; Surci, N.; Montagnini, G.; Marchegiani, G.; Malleo, G.; Secchettin, E.; Casetti, L.; Ricci, C.; et al. Cost-Effectiveness and Quality of Life Analysis of Laparoscopic and Robotic Distal Pancreatectomy: A Propensity Score-Matched Study. Surg. Endosc. 2021, 35, 1420–1428. [Google Scholar] [CrossRef]
  40. De Pastena, M.; Esposito, A.; Paiella, S.; Montagnini, G.; Zingaretti, C.C.; Ramera, M.; Azzolina, D.; Gregori, D.; Kauffmann, E.F.; Giardino, A.; et al. Nationwide Cost-Effectiveness and Quality of Life Analysis of Minimally Invasive Distal Pancreatectomy. Surg. Endosc. 2024, 38, 5881–5890. [Google Scholar] [CrossRef]
  41. Ding, H.; Kawka, M.; Gall, T.M.H.; Wadsworth, C.; Habib, N.; Nicol, D.; Cunningham, D.; Jiao, L.R. Robotic Distal Pancreatectomy Yields Superior Outcomes Compared to Laparoscopic Technique: A Single Surgeon Experience of 123 Consecutive Cases. Cancers 2023, 15, 5492. [Google Scholar] [CrossRef] [PubMed]
  42. DiNorcia, J.; Schrope, B.A.; Lee, M.K.; Reavey, P.L.; Rosen, S.J.; Lee, J.A.; Chabot, J.A.; Allendorf, J.D. Laparoscopic Distal Pancreatectomy Offers Shorter Hospital Stays with Fewer Complications. J. Gastrointest. Surg. 2010, 14, 1804–1812. [Google Scholar] [CrossRef]
  43. Duran, H.; Ielpo, B.; Caruso, R.; Ferri, V.; Quijano, Y.; Diaz, E.; Fabra, I.; Oliva, C.; Olivares, S.; Vicente, E. Does Robotic Distal Pancreatectomy Surgery Offer Similar Results as Laparoscopic and Open Approach? A Comparative Study from a Single Medical Center: Robotic Distal Pancreatectomy. Int. J. Med. Robot. 2014, 10, 280–285. [Google Scholar] [CrossRef]
  44. Eom, B.W.; Jang, J.-Y.; Lee, S.E.; Han, H.-S.; Yoon, Y.-S.; Kim, S.-W. Clinical Outcomes Compared between Laparoscopic and Open Distal Pancreatectomy. Surg. Endosc. 2008, 22, 1334–1338. [Google Scholar] [CrossRef]
  45. Finan, K.R.; Cannon, E.E.; Kim, E.J.; Wesley, M.M.; Arnoletti, P.J.; Heslin, M.J.; Christein, J.D. Laparoscopic and Open Distal Pancreatectomy: A Comparison of Outcomes. Am. Surg. 2009, 75, 671–679; discussion 679–680. [Google Scholar] [CrossRef]
  46. Fox, A.M.; Pitzul, K.; Bhojani, F.; Kaplan, M.; Moulton, C.-A.; Wei, A.C.; McGilvray, I.; Cleary, S.; Okrainec, A. Comparison of Outcomes and Costs between Laparoscopic Distal Pancreatectomy and Open Resection at a Single Center. Surg. Endosc. 2012, 26, 1220–1230. [Google Scholar] [CrossRef]
  47. Goh, B.K.P.; Chan, C.Y.; Soh, H.-L.; Lee, S.Y.; Cheow, P.-C.; Chow, P.K.H.; Ooi, L.L.P.J.; Chung, A.Y.F. A Comparison between Robotic-Assisted Laparoscopic Distal Pancreatectomy versus Laparoscopic Distal Pancreatectomy: Robotic vs Laparoscopic Distal Pancreatectomy. Int. J. Med. Robot. 2017, 13, e1733. [Google Scholar] [CrossRef]
  48. Guerrero-Ortiz, M.A.; Sánchez-Velazquez, P.; Burdío, F.; Gimeno, M.; Podda, M.; Pellino, G.; Toledano, M.; Nuñez, J.; Bellido, J.; Acosta-Mérida, M.A.; et al. Cost-Effectiveness of Robotic vs Laparoscopic Distal Pancreatectomy. Results from the National Prospective Trial ROBOCOSTES. Surg. Endosc. 2024, 38, 6270–6281. [Google Scholar] [CrossRef] [PubMed]
  49. Hong, S.; Song, K.B.; Madkhali, A.A.; Hwang, K.; Yoo, D.; Lee, J.W.; Youn, W.Y.; Alshammary, S.; Park, Y.; Lee, W.; et al. Robotic versus Laparoscopic Distal Pancreatectomy for Left-Sided Pancreatic Tumors: A Single Surgeon’s Experience of 228 Consecutive Cases. Surg. Endosc. 2020, 34, 2465–2473. [Google Scholar] [CrossRef] [PubMed]
  50. Jarufe, N.; Soto, P.; Ahumada, V.; Pacheco, S.; Salinas, J.; Galindo, J.; Bächler, J.-P.; Achurra, P.; Rebolledo, R.; Guerra, J.F.; et al. Laparoscopic versus Open Distal Pancreatectomy: Comparative Analysis of Clinical Outcomes at a Single Institution. Surg. Laparosc. Endosc. Percutan. Tech. 2018, 28, 62–66. [Google Scholar] [CrossRef]
  51. Jiang, Y.; Zheng, K.; Zhang, S.; Shao, Z.; Cheng, P.; Zhang, Y.; Jin, G.; He, T. Robot-Assisted Distal Pancreatectomy Improves Spleen Preservation Rate versus Laparoscopic Distal Pancreatectomy for Benign and Low-Grade Malignant Lesions of the Pancreas. Transl. Cancer Res. 2020, 9, 5166–5172. [Google Scholar] [CrossRef]
  52. Kamarajah, S.K.; Sutandi, N.; Sen, G.; Hammond, J.; Manas, D.M.; French, J.J.; White, S.A. Comparative Analysis of Open, Laparoscopic and Robotic Distal Pancreatic Resection: The United Kingdom’s First Single-Centre Experience. J. Minim. Access Surg. 2022, 18, 77–83. [Google Scholar] [CrossRef]
  53. Kang, C.M.; Kim, D.H.; Lee, W.J. Ten Years of Experience with Resection of Left-Sided Pancreatic Ductal Adenocarcinoma: Evolution and Initial Experience to a Laparoscopic Approach. Surg. Endosc. 2010, 24, 1533–1541. [Google Scholar] [CrossRef]
  54. Khaled, Y.S.; Malde, D.J.; Packer, J.; De Liguori Carino, N.; Deshpande, R.; O’Reilly, D.A.; Sherlock, D.J.; Ammori, B.J. A Case-Matched Comparative Study of Laparoscopic versus Open Distal Pancreatectomy. Surg. Laparosc. Endosc. Percutan. Tech. 2015, 25, 363–367. [Google Scholar] [CrossRef]
  55. Kim, S.C.; Park, K.T.; Hwang, J.W.; Shin, H.C.; Lee, S.S.; Seo, D.W.; Lee, S.K.; Kim, M.H.; Han, D.J. Comparative Analysis of Clinical Outcomes for Laparoscopic Distal Pancreatic Resection and Open Distal Pancreatic Resection at a Single Institution. Surg. Endosc. 2008, 22, 2261–2268. [Google Scholar] [CrossRef] [PubMed]
  56. Kooby, D.A.; Gillespie, T.; Bentrem, D.; Nakeeb, A.; Schmidt, M.C.; Merchant, N.B.; Parikh, A.A.; Martin, R.C.G., II; Scoggins, C.R.; Ahmad, S.; et al. Left-Sided Pancreatectomy: A Multicenter Comparison of Laparoscopic and Open Approaches. Ann. Surg. 2008, 248, 438–446. [Google Scholar] [CrossRef]
  57. Lai, E.C.H.; Tang, C.N. Robotic Distal Pancreatectomy versus Conventional Laparoscopic Distal Pancreatectomy: A Comparative Study for Short-Term Outcomes. Front. Med. 2015, 9, 356–360. [Google Scholar] [CrossRef] [PubMed]
  58. Lai, H.-F.; Shyr, Y.-M.; Shyr, B.-S.; Chen, S.-C.; Wang, S.-E.; Shyr, B.-U. Minimally Invasive Distal Pancreatectomy: Laparoscopic versus Robotic Approach-A Cohort Study. Health Sci. Rep. 2022, 5, e712. [Google Scholar] [CrossRef]
  59. Lee, S.Y.; Allen, P.J.; Sadot, E.; D’Angelica, M.I.; DeMatteo, R.P.; Fong, Y.; Jarnagin, W.R.; Kingham, T.P. Distal Pancreatectomy: A Single Institution’s Experience in Open, Laparoscopic, and Robotic Approaches. J. Am. Coll. Surg. 2015, 220, 18–27. [Google Scholar] [CrossRef]
  60. Lee, S.Q.; Kabir, T.; Koh, Y.-X.; Teo, J.-Y.; Lee, S.-Y.; Kam, J.-H.; Cheow, P.-C.; Jeyaraj, P.R.; Chow, P.K.H.; Ooi, L.L.; et al. A Single Institution Experience with Robotic and Laparoscopic Distal Pancreatectomies. Ann. Hepatobiliary Pancreat. Surg. 2020, 24, 283–291. [Google Scholar] [CrossRef] [PubMed]
  61. Ielpo, B.; Duran, H.; Diaz, E.; Fabra, I.; Caruso, R.; Malavé, L.; Ferri, V.; Nuñez, J.; Ruiz-Ocaña, A.; Jorge, E.; et al. Robotic versus Laparoscopic Distal Pancreatectomy: A Comparative Study of Clinical Outcomes and Costs Analysis. Int. J. Surg. 2017, 48, 300–304. [Google Scholar] [CrossRef]
  62. Limongelli, P.; Belli, A.; Russo, G.; Cioffi, L.; D’Agostino, A.; Fantini, C.; Belli, G. Laparoscopic and Open Surgical Treatment of Left-Sided Pancreatic Lesions: Clinical Outcomes and Cost-Effectiveness Analysis. Surg. Endosc. 2012, 26, 1830–1836. [Google Scholar] [CrossRef]
  63. Liu, R.; Liu, Q.; Zhao, Z.-M.; Tan, X.-L.; Gao, Y.-X.; Zhao, G.-D. Robotic versus Laparoscopic Distal Pancreatectomy: A Propensity Score-Matched Study. J. Surg. Oncol. 2017, 116, 461–469. [Google Scholar] [CrossRef]
  64. Lof, S.; van der Heijde, N.; Abuawwad, M.; Al-Sarireh, B.; Boggi, U.; Butturini, G.; Capretti, G.; Coratti, A.; Casadei, R.; D’Hondt, M.; et al. Robotic versus Laparoscopic Distal Pancreatectomy: Multicentre Analysis. Br. J. Surg. 2021, 108, 188–195. [Google Scholar] [CrossRef]
  65. Lyman, W.B.; Passeri, M.; Sastry, A.; Cochran, A.; Iannitti, D.A.; Vrochides, D.; Baker, E.H.; Martinie, J.B. Robotic-Assisted versus Laparoscopic Left Pancreatectomy at a High-Volume, Minimally Invasive Center. Surg. Endosc. 2019, 33, 2991–3000. [Google Scholar] [CrossRef]
  66. Magge, D.R.; Zenati, M.S.; Hamad, A.; Rieser, C.; Zureikat, A.H.; Zeh, H.J.; Hogg, M.E. Comprehensive Comparative Analysis of Cost-Effectiveness and Perioperative Outcomes between Open, Laparoscopic, and Robotic Distal Pancreatectomy. HPB 2018, 20, 1172–1180. [Google Scholar] [CrossRef] [PubMed]
  67. Marino, M.V.; Mirabella, A.; Gomez Ruiz, M.; Komorowski, A.L. Robotic-Assisted versus Laparoscopic Distal Pancreatectomy: The Results of a Case-Matched Analysis from a Tertiary Care Center. Dig. Surg. 2020, 37, 229–239. [Google Scholar] [CrossRef]
  68. Mehta, S.S.; Doumane, G.; Mura, T.; Nocca, D.; Fabre, J.-M. Laparoscopic versus Open Distal Pancreatectomy: A Single-Institution Case-Control Study. Surg. Endosc. 2012, 26, 402–407. [Google Scholar] [CrossRef]
  69. Morelli, L.; Guadagni, S.; Palmeri, M.; Franco, G.; Caprili, G.; D’isidoro, C.; Bastiani, L.; Candio, G.; Pietrabissa, A.; Mosca, F. A Case-Control Comparison of Surgical and Functional Outcomes of Robotic-Assisted Spleen-Preserving Left Side Pancreatectomy versus Pure Laparoscopy. J. Pancreas 2016, 17, 30–35. [Google Scholar]
  70. Najafi, N.; Mintziras, I.; Wiese, D.; Albers, M.B.; Maurer, E.; Bartsch, D.K. A Retrospective Comparison of Robotic versus Laparoscopic Distal Resection and Enucleation for Potentially Benign Pancreatic Neoplasms. Surg. Today 2020, 50, 872–880. [Google Scholar] [CrossRef] [PubMed]
  71. Nakamura, Y.; Uchida, E.; Aimoto, T.; Matsumoto, S.; Yoshida, H.; Tajiri, T. Clinical Outcome of Laparoscopic Distal Pancreatectomy. J. Hepatobiliary Pancreat. Surg. 2009, 16, 35–41. [Google Scholar] [CrossRef]
  72. Nakamura, M.; Wakabayashi, G.; Miyasaka, Y.; Tanaka, M.; Morikawa, T.; Unno, M.; Tajima, H.; Kumamoto, Y.; Satoi, S.; Kwon, M.; et al. Multicenter Comparative Study of Laparoscopic and Open Distal Pancreatectomy Using Propensity Score-Matching. J. Hepatobiliary Pancreat. Sci. 2015, 22, 731–736. [Google Scholar] [CrossRef] [PubMed]
  73. Nickel, F.; Distler, M.; Limen, E.F.; Wise, P.A.; Kowalewski, K.-F.; Tritarelli, P.M.; Perez, D.; Izbicki, J.R.; Kersebaum, J.-N.; Egberts, J.-H.; et al. Initial Learning Curves of Laparoscopic and Robotic Distal Pancreatectomy Compared with Open Distal Pancreatectomy: Multicentre Analysis. Br. J. Surg. 2023, 110, 1063–1067. [Google Scholar] [CrossRef] [PubMed]
  74. Qu, L.; Zhiming, Z.; Xianglong, T.; Yuanxing, G.; Yong, X.; Rong, L.; Yee, L.W. Short- and Mid-Term Outcomes of Robotic versus Laparoscopic Distal Pancreatosplenectomy for Pancreatic Ductal Adenocarcinoma: A Retrospective Propensity Score-Matched Study. Int. J. Surg. 2018, 55, 81–86. [Google Scholar] [CrossRef] [PubMed]
  75. Raoof, M.; Nota, C.L.M.A.; Melstrom, L.G.; Warner, S.G.; Woo, Y.; Singh, G.; Fong, Y. Oncologic Outcomes after Robot-Assisted versus Laparoscopic Distal Pancreatectomy: Analysis of the National Cancer Database. J. Surg. Oncol. 2018, 118, 651–656. [Google Scholar] [CrossRef]
  76. Shin, D.; Kwon, J.; Lee, J.H.; Park, S.Y.; Park, Y.; Lee, W.; Song, K.B.; Hwang, D.W.; Kim, S.C. Robotic versus Laparoscopic Distal Pancreatectomy for Pancreatic Ductal Adenocarcinoma: A Propensity Score-Matched Analysis. Hepatobiliary Pancreat. Dis. Int. 2023, 22, 154–159. [Google Scholar] [CrossRef]
  77. Souche, R.; Herrero, A.; Bourel, G.; Chauvat, J.; Pirlet, I.; Guillon, F.; Nocca, D.; Borie, F.; Mercier, G.; Fabre, J.-M. Robotic versus Laparoscopic Distal Pancreatectomy: A French Prospective Single-Center Experience and Cost-Effectiveness Analysis. Surg. Endosc. 2018, 32, 3562–3569. [Google Scholar] [CrossRef]
  78. Stauffer, J.A.; Rosales-Velderrain, A.; Goldberg, R.F.; Bowers, S.P.; Asbun, H.J. Comparison of Open with Laparoscopic Distal Pancreatectomy: A Single Institution’s Transition over a 7-Year Period. HPB 2013, 15, 149–155. [Google Scholar] [CrossRef]
  79. Velanovich, V. Case-Control Comparison of Laparoscopic versus Open Distal Pancreatectomy. J. Gastrointest. Surg. 2006, 10, 95–98. [Google Scholar] [CrossRef]
  80. Vicente, E.; Núñez-Alfonsel, J.; Ielpo, B.; Ferri, V.; Caruso, R.; Duran, H.; Diaz, E.; Malave, L.; Fabra, I.; Pinna, E.; et al. A Cost-Effectiveness Analysis of Robotic versus Laparoscopic Distal Pancreatectomy. Int. J. Med. Robot. 2020, 16, e2080. [Google Scholar] [CrossRef]
  81. Vijan, S.S.; Ahmed, K.A.; Harmsen, W.S.; Que, F.G.; Reid-Lombardo, K.M.; Nagorney, D.M.; Donohue, J.H.; Farnell, M.B.; Kendrick, M.L. Laparoscopic vs Open Distal Pancreatectomy: A Single-Institution Comparative Study. Arch. Surg. 2010, 145, 616–621. [Google Scholar] [CrossRef]
  82. Waters, J.A.; Canal, D.F.; Wiebke, E.A.; Dumas, R.P.; Beane, J.D.; Aguilar-Saavedra, J.R.; Ball, C.G.; House, M.G.; Zyromski, N.J.; Nakeeb, A.; et al. Robotic Distal Pancreatectomy: Cost Effective? Surgery 2010, 148, 814–823. [Google Scholar] [CrossRef]
  83. Wellner, U.F.; Lapshyn, H.; Bartsch, D.K.; Mintziras, I.; Hopt, U.T.; Wittel, U.; Krämling, H.-J.; Preissinger-Heinzel, H.; Anthuber, M.; Geissler, B.; et al. Laparoscopic versus Open Distal Pancreatectomy-a Propensity Score-Matched Analysis from the German StuDoQ|Pancreas Registry. Int. J. Color. Dis. 2017, 32, 273–280. [Google Scholar] [CrossRef]
  84. Weng, Y.; Jin, J.; Huo, Z.; Shi, Y.; Jiang, Y.; Deng, X.; Peng, C.; Shen, B. Robotic-Assisted versus Open Distal Pancreatectomy for Benign and Low-Grade Malignant Pancreatic Tumors: A Propensity Score-Matched Study. Surg. Endosc. 2021, 35, 2255–2264. [Google Scholar] [CrossRef]
  85. Xourafas, D.; Ashley, S.W.; Clancy, T.E. Comparison of Perioperative Outcomes between Open, Laparoscopic, and Robotic Distal Pancreatectomy: An Analysis of 1815 Patients from the ACS-NSQIP Procedure-Targeted Pancreatectomy Database. J. Gastrointest. Surg. 2017, 21, 1442–1452. [Google Scholar] [CrossRef]
  86. Yan, J.-F.; Kuang, T.-T.; Ji, D.-Y.; Xu, X.-W.; Wang, D.-S.; Zhang, R.-C.; Jin, W.-W.; Mou, Y.-P.; Lou, W.-H. Laparoscopic versus Open Distal Pancreatectomy for Benign or Premalignant Pancreatic Neoplasms: A Two-Center Comparative Study. J. Zhejiang Univ. Sci. B 2015, 16, 573–579. [Google Scholar] [CrossRef]
  87. Zhang, J.; Jin, J.; Chen, S.; Gu, J.; Zhu, Y.; Qin, K.; Zhan, Q.; Cheng, D.; Chen, H.; Deng, X.; et al. Minimally Invasive Distal Pancreatectomy for PNETs: Laparoscopic or Robotic Approach? Oncotarget 2017, 8, 33872–33883. [Google Scholar] [CrossRef] [PubMed]
  88. Zhang, X.; Chen, W.; Jiang, J.; Ye, Y.; Hu, W.; Zhai, Z.; Bai, X.; Liang, T. A Comparison of Robotic versus Laparoscopic Distal Pancreatectomy: A Single Surgeon’s Robotic Experience in a High-Volume Center. Surg. Endosc. 2022, 36, 9186–9193. [Google Scholar] [CrossRef] [PubMed]
  89. Cho, C.S.; Kooby, D.A.; Schmidt, C.M.; Nakeeb, A.; Bentrem, D.J.; Merchant, N.B.; Parikh, A.A.; Martin, R.C.G., II; Scoggins, C.R.; Ahmad, S.A.; et al. Laparoscopic versus Open Left Pancreatectomy: Can Preoperative Factors Indicate the Safer Technique? Ann. Surg. 2011, 253, 975–980. [Google Scholar] [CrossRef] [PubMed]
  90. Chopra, A.; Nassour, I.; Zureikat, A.; Paniccia, A. Perioperative and Oncologic Outcomes of Open, Laparoscopic, and Robotic Distal Pancreatectomy for Pancreatic Adenocarcinoma. Updates Surg. 2021, 73, 947–953. [Google Scholar] [CrossRef]
  91. Nevill, C.R.; Cooper, N.J.; Sutton, A.J. A Multifaceted Graphical Display, Including Treatment Ranking, Was Developed to Aid Interpretation of Network Meta-Analysis. J. Clin. Epidemiol. 2023, 157, 83–91. [Google Scholar] [CrossRef]
  92. van Valkenhoef, G.; Dias, S.; Ades, A.E.; Welton, N.J. Automated Generation of Node-Splitting Models for Assessment of Inconsistency in Network Meta-Analysis. Res. Synth. Methods 2016, 7, 80–93. [Google Scholar] [CrossRef] [PubMed]
  93. Dias, S.; Ades, A.E.; Welton, N.J.; Jansen, J.P.; Sutton, A.J. Network Meta-Analysis for Decision-Making; John Wiley & Sons: Hoboken, NJ, USA, 2018; ISBN 9781118647509. [Google Scholar]
  94. Donegan, S.; Dias, S.; Tudur-Smith, C.; Marinho, V.; Welton, N.J. Graphs of Study Contributions and Covariate Distributions for Network Meta-Regression. Res. Synth. Methods 2018, 9, 243–260. [Google Scholar] [CrossRef] [PubMed]
  95. Owen, R.K.; Bradbury, N.; Xin, Y.; Cooper, N.; Sutton, A. MetaInsight: An Interactive Web-Based Tool for Analyzing, Interrogating, and Visualizing Network Meta-Analyses Using R-Shiny and Netmeta. Res. Synth. Methods 2019, 10, 569–581. [Google Scholar] [CrossRef] [PubMed]
  96. Balduzzi, S.; Rücker, G.; Nikolakopoulou, A.; Papakonstantinou, T.; Salanti, G.; Efthimiou, O.; Schwarzer, G. Netmeta: An R Package for Network Meta-Analysis Using Frequentist Methods. J. Stat. Softw. 2023, 106, 1–40. [Google Scholar] [CrossRef]
  97. Warn, D.E.; Thompson, S.G.; Spiegelhalter, D.J. Bayesian Random Effects Meta-Analysis of Trials with Binary Outcomes: Methods for the Absolute Risk Difference and Relative Risk Scales. Stat. Med. 2002, 21, 1601–1623. [Google Scholar] [CrossRef]
  98. Seo, M.; Schmid, C. Bnma: Bayesian Network Meta-Analysis Using “JAGS”; Version 1.6.1; CRAN Contributed Packages: Vienna, Austria, 2019. [Google Scholar]
  99. Nikolakopoulou, A.; Higgins, J.P.T.; Papakonstantinou, T.; Chaimani, A.; Del Giovane, C.; Egger, M.; Salanti, G. CINeMA: An Approach for Assessing Confidence in the Results of a Network Meta-Analysis. PLoS Med. 2020, 17, e1003082. [Google Scholar] [CrossRef]
  100. Session Details. Available online: https://events.cochrane.org/colloquium-2023/session/1473545/nmastudio-a-fully-interactive-web-application-for-producing-and-visualizing-network-meta-analyses (accessed on 28 June 2025).
  101. van Ramshorst, T.M.E.; van Bodegraven, E.A.; Zampedri, P.; Kasai, M.; Besselink, M.G.; Abu Hilal, M. Robot-Assisted versus Laparoscopic Distal Pancreatectomy: A Systematic Review and Meta-Analysis Including Patient Subgroups. Surg. Endosc. 2023, 37, 4131–4143. [Google Scholar] [CrossRef]
  102. Sheetz, K.H.; Claflin, J.; Dimick, J.B. Trends in the Adoption of Robotic Surgery for Common Surgical Procedures. JAMA Netw. Open 2020, 3, e1918911. [Google Scholar] [CrossRef]
  103. Hoehn, R.S.; Nassour, I.; Adam, M.A.; Winters, S.; Paniccia, A.; Zureikat, A.H. National Trends in Robotic Pancreas Surgery. J. Gastrointest. Surg. 2021, 25, 983–990. [Google Scholar] [CrossRef]
  104. Gavriilidis, P.; Roberts, K.J.; Sutcliffe, R.P. Comparison of Robotic vs Laparoscopic vs Open Distal Pancreatectomy. A Systematic Review and Network Meta-Analysis. HPB 2019, 21, 1268–1276. [Google Scholar] [CrossRef] [PubMed]
  105. Zureikat, A.H.; Moser, A.J.; Boone, B.A.; Bartlett, D.L.; Zenati, M.; Zeh, H.J. 250 Robotic Pancreatic Resections. Ann. Surg. 2013, 258, 554–562. [Google Scholar] [CrossRef] [PubMed]
  106. Lof, S.; Claassen, L.; Hannink, M.D.G.; Al-Sarireh, B.; Björnsson, B.; Boggi, U.; Burdio, M.D.F.; Butturini, G.; Capretti, G.; Casadei, M.D.R.; et al. Learning Curves of Minimally Invasive Distal Pancreatectomy in Experienced Pancreatic Centers. JAMA Surg. 2023, 158, 927–933. [Google Scholar] [CrossRef]
  107. Müller, P.C.; Kuemmerli, C.; Billeter, A.T.; Shen, B.; Jin, J.; Nickel, F.; Guidetti, C.; Kauffmann, E.; Purchla, J.; Tschuor, C.; et al. Competency, Proficiency, and Mastery: Learning Curves for Robotic Distal Pancreatectomy at 16 International Expert Centers. Ann. Surg. 2024. [Google Scholar] [CrossRef]
  108. Hirata, Y.; Prakash, L.; Maxwell, J.; Snyder, R.; Kim, M.; Tran Cao, H.; Tzeng, C.-W.D.; Lee, J.E.; Katz, M.H.G.; Ikoma, N. Institutional Learning Curve and Factors of Prolonged Operation Time of Robotic Distal Pancreatectomy: An Analysis of an Initial 117 Cases. Ann. Gastroenterol. Surg. 2025, 9, 861–869. [Google Scholar] [CrossRef] [PubMed]
  109. Watanabe, Y.; Nishihara, K.; Matsumoto, S.; Okayama, T.; Abe, Y.; Nakano, T. Effect of Postoperative Major Complications on Prognosis after Pancreatectomy for Pancreatic Cancer: A Retrospective Review. Surg. Today 2017, 47, 555–567. [Google Scholar] [CrossRef]
  110. Mintziras, I.; Wächter, S.; Manoharan, J.; Kanngiesser, V.; Maurer, E.; Bartsch, D.K. Postoperative Morbidity Following Pancreatic Cancer Surgery Is Significantly Associated with Worse Overall Patient Survival; Systematic Review and Meta-Analysis. Surg. Oncol. 2021, 38, 101573. [Google Scholar] [CrossRef]
  111. Ng, A.P.; Sanaiha, Y.; Bakhtiyar, S.S.; Ebrahimian, S.; Branche, C.; Benharash, P. National Analysis of Cost Disparities in Robotic-Assisted versus Laparoscopic Abdominal Operations. Surgery 2023, 173, 1340–1345. [Google Scholar] [CrossRef]
  112. Partelli, S.; Ricci, C.; Cinelli, L.; Montorsi, R.M.; Ingaldi, C.; Andreasi, V.; Crippa, S.; Alberici, L.; Casadei, R.; Falconi, M. Evaluation of Cost-Effectiveness among Open, Laparoscopic and Robotic Distal Pancreatectomy: A Systematic Review and Meta-Analysis. Am. J. Surg. 2021, 222, 513–520. [Google Scholar] [CrossRef]
  113. Yoon, Y.-S. Splenic Preservation versus Splenectomy during Laparoscopic Distal Pancreatectomy for Benign and Low-Grade Malignant Pancreatic Tumor: A Propensity Score Matching Analysis. Ann. Hepatobiliary. Pancreat. Surg. 2021, 25, S118. [Google Scholar] [CrossRef]
  114. Hao, T.; Shiming, J.; Yong, C. Analysis of Safety and Efficacy of Laparoscopic Distal Pancreatectomy in the Treatment of Left Pancreatic Malignant Tumors. J. Int. Med. Res. 2021, 49, 3000605211063098. [Google Scholar] [CrossRef]
  115. Wang, W.; Lou, W.; Xu, Z.; Chen, H.; Shen, Z.; Deng, X.; Peng, C.; Liu, Y.; Shen, B. Long-Term Outcomes of Standard versus Extended Lymphadenectomy in Pancreatoduodenectomy for Pancreatic Ductal Adenocarcinoma: A Chinese Multi-Center Prospective Randomized Controlled Trial. J. Adv. Res. 2023, 49, 151–157. [Google Scholar] [CrossRef]
  116. Ho, C.-K.; Kleeff, J.; Friess, H.; Büchler, M.W. Complications of Pancreatic Surgery. HPB 2005, 7, 99–108. [Google Scholar] [CrossRef] [PubMed]
  117. Fernandes, E.d.S.M.; de Mello, F.P.T.; Braga, E.P.; de Souza, G.O.; Andrade, R.; Pimentel, L.S.; Girão, C.L.; Siqueira, M.; Moraes-Junior, J.M.A.; de Oliveira, R.V.; et al. A More Radical Perspective on Surgical Approach and Outcomes in Pancreatic Cancer-a Narrative Review. J. Gastrointest. Oncol. 2023, 14, 1964–1981. [Google Scholar] [CrossRef] [PubMed]
  118. Ramadan, A.; Habeeb, T.A.; Kechagias, A.; Isik, A.; Aiolfi, A.; Shaker, S.E.; Samir, A.; Sheded, M.M.; Khalifa, M.R.; Haggag, R.; et al. Outcomes of Surgical Resection of Pancreatic Cystic Neoplasms Based on the European Expert Consensus Statement: A Prospective Observational Study. Surg. Gastroenterol. Oncol. 2022, 27, 264. [Google Scholar] [CrossRef]
  119. Hammer, B.; Virgili, E.; Bilotta, F. Evidence-Based Literature Review: De-Duplication a Cornerstone for Quality. World J. Methodol. 2023, 13, 390–398. [Google Scholar] [CrossRef]
  120. Kwon, Y.; Lemieux, M.; McTavish, J.; Wathen, N. Identifying and Removing Duplicate Records from Systematic Review Searches. J. Med. Libr. Assoc. 2015, 103, 184–188. [Google Scholar] [CrossRef]
  121. Han, B.; Duong, D.; Sul, J.H.; de Bakker, P.I.W.; Eskin, E.; Raychaudhuri, S. A General Framework for Meta-Analyzing Dependent Studies with Overlapping Subjects in Association Mapping. Hum. Mol. Genet. 2016, 25, 1857–1866. [Google Scholar] [CrossRef]
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Figure 1. PRISMA flow diagram for the systematic review and meta-analysis.
Figure 1. PRISMA flow diagram for the systematic review and meta-analysis.
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Figure 2. Bayesian random effect consistency model forest plot for the outcome conversion rate.
Figure 2. Bayesian random effect consistency model forest plot for the outcome conversion rate.
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Figure 3. Radial SUCRA plot: Outcome conversions. Higher SUCRA values indicate better treatments; the size of the nodes represents the number of participants, and the thickness of the lines indicates the number of trials conducted. Green circle represents robotic approach. Red circle represents laparoscopic approach.
Figure 3. Radial SUCRA plot: Outcome conversions. Higher SUCRA values indicate better treatments; the size of the nodes represents the number of participants, and the thickness of the lines indicates the number of trials conducted. Green circle represents robotic approach. Red circle represents laparoscopic approach.
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Figure 4. Residual deviance from the NMA model and the UME inconsistency model for all studies.
Figure 4. Residual deviance from the NMA model and the UME inconsistency model for all studies.
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Figure 5. Per-arm residual deviance for all studies. This stem plot represents the posterior residual deviance per study arm.
Figure 5. Per-arm residual deviance for all studies. This stem plot represents the posterior residual deviance per study arm.
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Figure 6. Leverage plot for all studies.
Figure 6. Leverage plot for all studies.
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Figure 7. Regression plot for the outcome conversions, for robotic versus laparoscopic approaches, having as a covariate the year of study publication. OR = odds ratio.
Figure 7. Regression plot for the outcome conversions, for robotic versus laparoscopic approaches, having as a covariate the year of study publication. OR = odds ratio.
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Figure 8. Regression plot for the outcome conversions, having as a covariate the number of patients in the included studies.
Figure 8. Regression plot for the outcome conversions, having as a covariate the number of patients in the included studies.
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Figure 9. Bayesian random effect consistency model forest plot for outcome 30-day mortality. Between-study standard deviation (log-odds scale): 0.62; 95% credible interval: 0.02, 1.86.
Figure 9. Bayesian random effect consistency model forest plot for outcome 30-day mortality. Between-study standard deviation (log-odds scale): 0.62; 95% credible interval: 0.02, 1.86.
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Figure 10. Litmus Rank-O-Gram. Outcome: 30-day mortality. Higher SUCRA (surface under the cumulative ranking curve) values and cumulative ranking curves nearer the top left indicate better performance.
Figure 10. Litmus Rank-O-Gram. Outcome: 30-day mortality. Higher SUCRA (surface under the cumulative ranking curve) values and cumulative ranking curves nearer the top left indicate better performance.
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Figure 11. Radial SUCRA plot. Outcome: 30-day mortality. Higher SUCRA values indicate better treatments; the size of the nodes represents the number of participants, and the thickness of the lines indicates the number of trials conducted. Green circle represents robotic approach. Yellow circle represents laparoscopic approach. Red circle represents open approach.
Figure 11. Radial SUCRA plot. Outcome: 30-day mortality. Higher SUCRA values indicate better treatments; the size of the nodes represents the number of participants, and the thickness of the lines indicates the number of trials conducted. Green circle represents robotic approach. Yellow circle represents laparoscopic approach. Red circle represents open approach.
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Figure 12. Stacked bar chart of treatment performance distribution.
Figure 12. Stacked bar chart of treatment performance distribution.
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Figure 13. Treatment performance heatmap.
Figure 13. Treatment performance heatmap.
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Figure 14. Radar chart—treatment performance comparison.
Figure 14. Radar chart—treatment performance comparison.
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Figure 15. Horizontal bar chart by outcome categories.
Figure 15. Horizontal bar chart by outcome categories.
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Figure 16. Comprehensive performance analysis. To score the overall treatment performances, we converted the ranks to scores (Rank 1 = 3 points, Rank 2 = 2 points, and Rank 3 = 1 point).
Figure 16. Comprehensive performance analysis. To score the overall treatment performances, we converted the ranks to scores (Rank 1 = 3 points, Rank 2 = 2 points, and Rank 3 = 1 point).
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Table 1. Risk of bias assessment using the Newcastle–Ottawa Scale and the Cochrane Risk of Bias 2.0 tool.
Table 1. Risk of bias assessment using the Newcastle–Ottawa Scale and the Cochrane Risk of Bias 2.0 tool.
Study (Year)Total NOS (0–9)
Rodriguez (2018) [25]9
Abu Hilal (2012) [26]9
Alfieri (2019) [27]9
Aly (2010) [28]7
Beker (2009) [29]9
Benizri (2014) [30]8
van Bodegraven (2024) [31]9
Butturini (2011) [32]8
Butturini (2015) [33]8
Casadei (2010) [34]9
Chang (2024) [35]9
Chen (2022) [36]9
Chen (2023) [37]9
Daouadi (2013) [38]8
De Pastena (2021) [39]8
De Pastena (2024) [40]9
Ding (2023) [41]7
DiNorcia (2010) [42]6
Duran (2014) [43]7
Eom (2008) [44]7
Finan (2009) [45]7
Fox (2012) [46]8
Goh (2016) [47]7
Guerrero-Ortiz (2024) [48]7
Hong (2020) [49]8
Jarufe (2018) [50]9
Jiang (2020) [51]9
Kamarajah (2022) [52]8
Kang (2010) [53]8
Khaled (2015) [54]9
Kim(2008) [55]6
Kooby (2008) [56]8
Lai (2015) [57]9
Lai (2022) [58]8
Lee (2015) [59]9
Lee (2020) [60]7
Lelpo (2017) [61]7
Limongelli (2012) [62]8
Liu (2017) [63]9
Lof (2021) [64]9
Lyman (2019) [65]9
Magge (2018) [66]9
Marino (2019) [67]9
Mehta (2012) [68]8
Morelli (2016) [69]9
Najafi (2020) [70]9
Nakamura (2009) [71]7
Nakamura (2015) [72]9
Nickel (2023) [73]9
Qu (2018) [74]9
Raoof (2018) [75]9
Shin (2023) [76]9
Souche (2018) [77]7
Stauffer (2013) [78]9
Velanovich (2006) [79]8
Vicente (2019) [80]7
Vijan (2010) [81]9
Waters (2010) [82]7
Wellner (2017) [83]9
Weng (2021) [84]9
Xourafas (2017) [85]9
Yan (2015) [86]7
Zhang (2017) [87]7
Zhang (2022) [88]9
Cho (2011) [89]9
Chopra (2021) [90]9
Study (year)Cochrane Risk of Bias 2.0 tool
Björnsson (2020) [8]4/5
Table 2. Baseline characteristics of the included studies.
Table 2. Baseline characteristics of the included studies.
StudyCountryStudy PeriodStudy Type (RCT/Retrospective/Retrospective + Prospectively Held Database/Prospective Observational/Prospective Observational CU Propensity Match)No. Included PatientsComparaison (RDP vs. LDP vs. ODP)
Rodriguez_2018 [25]France2012–2015Retrospective with prospectively maintained database89RDP vs. LDP vs. ODP
Abu Hilal_2012 [26]UK2005–2011Retrospective from a prospectively held database51LDP vs. ODP
Alfieri_2019 [27]Italy2008–2016Retrospective181LDP vs. RDP
Aly_2010 [28]Japan1998–2009Retrospective75LDP vs. ODP
Beker_2009 [29]USA2003–2008Prospective non-randomized112LDP vs. ODP
Benizri_2014 [30]France2004–2011Retrospective with prospectively maintained database34LDP vs. RDP
Björnsson_2020 [8]Sweden2015–2019RCT58LDP vs. ODP
van Bodegraven_2024 [31]Pan-European2019–2021Retrospective with prospectively maintained database1672RDP vs. LDP
Butturini_2011 [32]Italy1999–2006Retrospective non-randomized study116LDP vs. ODP
Butturini_2015 [33]Italy2011–2014Prospective non-randomized43LDP vs. RDP
Casadei_2010 [34]Italy2000–2010Retrospective case–control study44LDP vs. ODP
Chang_2024 [35]USA2010–2020Retrospective propensity score matching 1537LDP vs. RDP
Chen_2022 [36]China2013–2019Retrospective case study149LDP vs. RDP
Chen_2023 [37]Internation 2010–2019Retrospective 542LDP vs. RDP
Daouadi_2013 [38]USA2004–2011Retrospective 124LDP vs. RDP
De Pastena_2021 [39]Italy2011–2017Retrospective propensity score matching 103LDP vs. RDP
De Pastena_2024 [40]Italy2010–2020Retrospective 564LDP vs. RDP
Ding_2023 [41]UK2008–2023Retrospective with prospectively maintained database123LDP vs. RDP
DiNorcia_2010 [42]USA1991–2009Retrospective with prospectively maintained database387LDP vs. ODP
Duran_2014 [43]Spain2008–2013Retrospective 47LDP vs. ODP vs. RDP
Eom_2008 [44]Korea1995–2006Retrospective case–control study93LDP vs. ODP
Finan_2009 [45]USA2002–2007Retrospective 148LDP vs. ODP
Fox_2012 [46]Canada2004–2010Retrospective 118LDP vs. ODP
Goh_2016 [47]Singapore2006–2015Retrospective with prospectively maintained database39LDP vs. RDP
Guerrero-Ortiz_2024 [48]Spain2022Prospective, multicenter national observational study80LDP vs. RDP
Hong_2020 [49]Republic of Korea2015–2017Retrospective228LDP vs. RDP
Jarufe_2018 [50]Chile2001–2015Retrospective93LDP vs. ODP
Jiang_2020 [51]China2011–2018Retrospective166LDP vs. RDP
Kamarajah_2022 [52]UK2007–2018Retrospective 125LDP vs. RDP vs. ODP
Kang_2010 [53]Korea1999–2008Retrospective32LDP vs. ODP
Khaled_2015 [54]UK2002–2011Retrospective case-matched44LDP vs. ODP
Kim_2008 [55]Republic of Korea-Retrospective 128LDP vs. ODP
Kooby_2008 [56]USA2002–2006Retrospective multicenter cohort from a prospectively held database667LDP vs. ODP
Lai_2015 [57]China1999–2015Retrospective from a prospectively held database35LDP vs. RDP
Lai_2022 [58]Taiwan2011–2020Retrospective using a prospectively maintained database177LDP vs. RDP
Lee_2015 [59]USA200–2013Retrospective 805LDP vs. RDP vs. ODP
Lee_2020 [60]Singapore2006–2019Retrospective from a prospectively held database102LDP vs. RDP
Lelpo_2017 [61]Spain2011–2017Retrospective54LDP vs. RDP
Limongelli_2012 [62]Italy2000–2010Retrospective from a prospectively held database45LDP vs. ODP
Liu_2017 [63]China2011–2015Retrospective propensity score-matched study355LDP vs. RDP
Lof_2021 [64]Europe2011–2019Retrospective, propensity score matching1551LDP vs. RDP
Lyman_2019 [65]USA2008–2017Retrospective from a prospectively held database249LDP vs. RDP
Magge_2018 [66]USA2010–2016Retrospective from a prospectively held database374LDP vs. ODP vs. RDP
Marino_2019 [67]Italy2014–2017Retrospective case-matched70LDP vs. RDP
Mehta_2012 [68]France1998–2009Retrospective case–control study60LDP vs. ODP
Morelli_2016 [69]Italy2010–2014Retrospective case-matched30LDP vs. RDP
Najafi_2020 [70]Germany2008–2018Retrospective56LDP vs. RDP
Nakamura_2009 [71]Japan2000–2007Retrospective36LDP vs. ODP
Nakamura_2015 [72]Japan2006–2013Retrospective Propensity score matching 2010LDP vs. ODP
Nickel_2023 [73]Germany2007–2020Retrospective case-matched512LDP vs. RDP vs. ODP
Qu_2018 [74]China2011–2015Retrospective propensity score matching 70LDP vs. RDP
Raoof_2018 [75]USA2010–2013Retrospective704LDP vs. RDP
Shin_2023 [76]Korea2015–2020Retrospective propensity score-matched study42LDP vs. RDP
Souche_2018 [77]France2011–2016Prospective non-randomized38LDP vs. RDP
Stauffer_2013 [78]USA2005–2011Retrospective172LDP vs. ODP
Velanovich_2006 [79]USA1996–2005Retrospective case-matched30LDP vs. ODP
Vicente_2019 [80]Spain2014–2018Prospective non-randomized59LDP vs. RDP
Vijan_2010 [81]USA2004–2009Retrospective case-matched200LDP vs. ODP
Waters_2010 [82]USA2008–2009Retrospective from a prospectively held database57LDP vs. ODP vs. RDP
Wellner_2017 [83]Germany2013–2016Retrospective propensity score-matched study198LDP vs. ODP
Weng_2021 [84]China2012–2019Retrospective case–control study679RDP vs. ODP
Xourafas_2017 [85]USA2014Retrospective1815RDP vs. LDP vs. ODP
Yan_2015 [86]China2010–2012Retrospective91LDP vs. ODP
Zhang_2017 [87]China2010–2017Retrospective74LDP vs. RDP
Zhang_2022 [88]China2020–2021Retrospective201LDP vs. RDP
Cho_2011 [89]USA1999–2008Retrospective using a prospectively maintained database693LDP vs. ODP
Chopra_2021 [90]USA2008–2019Retrospective with prospectively maintained database146LDP vs. ODP vs. RDP
Table 3. Summary of findings.
Table 3. Summary of findings.
OutcomeNumber of Studies Number of PatientsRobotic vs. OpenLaparoscopic vs. OpenRobotic vs. LaparoscopicRanks Based on SUCRA Values: I, II, III
Frequentist NMABayesian NMAFrequentist NMABayesian NMAFrequentist NMABayesian NMA
Population characteristicsAge of patients (years)
(Frequentist—MD, 95% CI; Bayesian—MD, 95% CrI) 		5917,542−1.65 [−3.00; −0.30]−1.67 (−3.11, −0.27)−0.72 [−1.86; 0.41]−0.74 (−1.95, 0.43)−0.93 [−1.93; 0.07]−0.93 (−1.97, 0.11)Robotic
Laparoscopic
Open
Sex of patients (male)
(Frequentist—OR, 95% CI; Bayesian—OR, 95% CrI) 		6318,0300.73 [0.64; 0.82]-0.76 [0.69; 0.84]-0.95 [0.87; 1.05]-Robotic
Laparoscopic
Open
ASA status (grade I–II)
(Frequentist—OR, 95% CI; Bayesian—OR, 95% CrI) 		4010,318-1.24 (0.87, 1.8)-1.29 (0.94, 1.78)-0.96 (0.74, 1.27)Laparoscopic
Robotic
Open
Previous cardiovascular diseases
(Frequentist—OR, 95% CI; Bayesian—OR, 95% CrI)		1233070.99 [0.62; 1.61]0.84 (0.41, 1.45)0.94 [0.69; 1.29]0.94 (0.62, 1.37)1.19 [0.72; 1.96]0.9 (0.46, 1.49)Laparoscopic
Robotic
Open
Intra-operative characteristicsOperative time (minutes)
(Frequentist—MD, 95% CI; Bayesian—MD, 95% CrI) 		6116,23025.93 [7.68; 44.18]27.17 (4.27, 50.33)7.63 [−7.59; 22.85]7.84 (−11.24, 26.94)18.30 [5.12; 31.49;]19.32 (2.74, 36.27)Open
Laparoscopic
Robotic
Conversion to open
(Frequentist—OR, 95% CI; Bayesian—OR, 95% CrI)		3810,586----0.41 [0.34; 0.49]0.30 [0.22; 0.40]Robotic
Laparoscopic
Intraoperative blood loss (mL)
(Frequentist—MD, 95% CI; Bayesian—MD, 95% CrI) 		5112,257−279.45 [−318.28; −240.61]−303.98 (−382.8, −229.26)−248.99 [−283.41; −214.57]−272.89 (−340.15, −209.01)−30.45 [ −54.66; −6.25]−31.17 (−81.92, 19.44)Robotic
Laparoscopic
Open
Intraoperative bleeding > 500 mL
(Frequentist—OR, 95% CI; Bayesian—OR, 95% CrI)		314270.11 [0.01; 1.07]-0.32 [0.23; 0.46]-0.33 [0.03; 3.24]-Robotic
Laparoscopic
Open
Number of patients receiving transfusions
(Frequentist—OR, 95% CI; Bayesian—OR, 95% CrI)		3092480.25 [0.19; 0.34]-0.30 [0.24; 0.37]-0.85 [0.66; 1.10]-Robotic
Laparoscopic
Open
The quantity of blood transfusion
(Frequentist—MD, 95% CI; Bayesian—MD, 95% CrI) 		43631.98 [−3.42; −0.54]-−1.86 [−3.12; −0.59]-−0.12 [−1.14; 0.89]-Robotic
Laparoscopic
Open
Postoperative characteristicsICU stay
(Frequentist—MD, 95% CI; Bayesian—MD, 95% CrI) 		91272−4.01 [−5.97; −2.05]-−2.27 [−3.71; −0.83]-−1.74 [−3.52; 0.04]-Robotic
Laparoscopic
Open
Reintervention rate
(Frequentist—OR, 95% CI; Bayesian—OR, 95% CrI)		3711,568-0.45 (0.23, 0.84)-0.56 (0.32, 0.96)-0.81 (0.47, 1.3)Robotic
Laparoscopic
Open
Hospital stay
(Frequentist—MD, 95% CI; Bayesian—MD, 95% CrI) 		6318,113−7.63 [−8.65; −6.61]−8.77 (−13.34, −4.22)−6.47 [−7.33; −5.60]−6.93 (−10.67, −3.23)−1.16 [−1.88; −0.44]−1.83 (−5.17, 1.55)Robotic
Laparoscopic
Open
Morbidity and MortalityReadmission rate
(Frequentist—OR, 95% CI; Bayesian—OR, 95% CrI)		3112,3300.93 [0.67; 1.30] 0.80 [0.59; 1.09] 0.86 [0.71; 1.05] Laparoscopic
Robotic
Open
In-hospital mortality
(Frequentist—OR, 95% CI; Bayesian—OR, 95% CrI)		91009-1.14 × 10−7 (5.812–24, 1.36 × 106)0.76 [0.21; 2.72]0.42 [0.04; 2.57]--Robotic
Laparoscopic
Open
30-day mortality
(Frequentist—OR, 95% CI; Bayesian—OR, 95% CrI)		3112,1270.37 [0.16; 0.84]-0.68 [0.40; 1.17]-0.54 [0.27; 1.07]-Robotic
Laparoscopic
Open
90-day major complications
(Frequentist—OR, 95% CI; Bayesian—OR, 95% CrI)		314270.86 [0.44; 1.68]0.87 (0.36, 2.42)0.88 [0.51; 1.52]0.9 (0.45, 2.3)0.98 [0.56; 1.71]0.97 (0.42, 1.99)Robotic
Laparoscopic
Open
95% CI—95% confidence interval; 95% CrI—95% credible interval; MD—mean difference; NMA—network meta-analysis; OR—odds ratio; SUCRA—surface under the cumulative ranking curve.
Table 4. Treatment effects for all studies: Comparison of all treatment pairs. Outcome: 30-day mortality. Bayesian NMA.
Table 4. Treatment effects for all studies: Comparison of all treatment pairs. Outcome: 30-day mortality. Bayesian NMA.
LaparoscopicOpenRobotic
LaparoscopicLaparoscopic2.1 (1.05, 6.1)0.34 (0.1, 0.83)
Open0.48 (0.16, 0.95)Open0.16 (0.03, 0.46)
Robotic2.97 (1.2, 9.78)6.29 (2.18, 34.05)Robotic
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Abdul Halim, N.; Sadot, E.; Negoi, I. Perioperative Outcomes in Robotic, Laparoscopic, and Open Distal Pancreatectomy: A Network Meta-Analysis and Meta-Regression. Cancers 2025, 17, 3243. https://doi.org/10.3390/cancers17193243

AMA Style

Abdul Halim N, Sadot E, Negoi I. Perioperative Outcomes in Robotic, Laparoscopic, and Open Distal Pancreatectomy: A Network Meta-Analysis and Meta-Regression. Cancers. 2025; 17(19):3243. https://doi.org/10.3390/cancers17193243

Chicago/Turabian Style

Abdul Halim, Nasser, Eran Sadot, and Ionut Negoi. 2025. "Perioperative Outcomes in Robotic, Laparoscopic, and Open Distal Pancreatectomy: A Network Meta-Analysis and Meta-Regression" Cancers 17, no. 19: 3243. https://doi.org/10.3390/cancers17193243

APA Style

Abdul Halim, N., Sadot, E., & Negoi, I. (2025). Perioperative Outcomes in Robotic, Laparoscopic, and Open Distal Pancreatectomy: A Network Meta-Analysis and Meta-Regression. Cancers, 17(19), 3243. https://doi.org/10.3390/cancers17193243

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