The Impact of Multidisciplinary Preoperative Optimization Program on Postoperative Outcomes Among Surgical Oncology Patients

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript titled “The Impact of Multidisciplinary Preoperative Optimization Program on Postoperative Outcomes among Surgical Oncology Patients” is well written and presented.

This study shows a preoperative optimization programs effectively improve surgical outcomes by addressing modifiable risk factors such as smoking, anemia, hyperglycemia, and malnutrition. Their adoption can reduce complications, enhance recovery, and ensure higher-quality care.

This study is limited by its retrospective, single-center design, potential selection bias, and short-term follow-up of only 30 days. Patient adherence, unmeasured confounders, and the impact of COVID-19 may also have influenced the outcomes.

This manuscript is recommended for revision to improve clarity and incorporate specific feedback, authors please provide explanations:

1. This study relied on retrospective chart review, which introduces the possibility of incomplete or inaccurate documentation. Missing data could have influenced baseline assessments and postoperative outcome reporting.
2. The study is conducted at a single tertiary cancer center, which limits the generalizability of the findings to other institutions with different patient populations, resources, or perioperative practices.
3. Patients were not randomized here. The assignment to pre-implementation and post-implementation cohorts was based on the date of surgery, which may bring the confounding factors (e.g., changes in surgical teams, hospital policies, or pandemic-related effects during 2020–2021).
4. Although key modifiable risk factors (anemia, nutrition, smoking, diabetes) were addressed, but there are other potential confounders such as physical activity, psychosocial status, or socioeconomic factors were not evaluated.
5. Postoperative outcomes were only assessed up to 30 days. Long-term effects of optimization on survival, cancer recurrence, and quality of life remain unknown.
6. The study did not measure patient adherence to optimization strategies (e.g., compliance with nutritional support or smoking cessation). Similarly, variations in how interventions were delivered by providers could have affected outcomes.
7. The relatively small subgroup sizes for certain risk factors (e.g., malnourished patients) have limited statistical power to detect differences in those populations.

 

Author Response

We thank the reviewer for their thoughtful and constructive comments. We appreciate the recognition of the relevance and rigor of our study. We have carefully considered all suggestions and have revised the manuscript to improve clarity, provide additional explanations, and address potential limitations. Specifically, we have:

Reviewer Comment:

This study relied on retrospective chart review, which introduces the possibility of incomplete or inaccurate documentation. Missing data could have influenced baseline assessments and postoperative outcome reporting.

Response:
We thank the reviewer for this important observation. As noted in the first paragraph of the Results section, we explicitly reported the issue of incomplete data. Out of 2,465 general oncology patients evaluated, 243 patients (9.9%) had incomplete information regarding optimization status and were therefore excluded from the final analysis. By clearly identifying and excluding cases with missing baseline or optimization data, we minimized potential bias from incomplete documentation.
we add this statement to the exclusion criteria: Exclusion criteria: Pediatric patients, prior neoadjuvant therapy, optimized individuals, disease recurrence, urgent or non-primary diagnosis–related surgery, pregnant patients, and those with incomplete data regarding optimization status or outcomes

Regarding postoperative outcomes, all events were captured through our institutional NSQIP database, which ensures standardized data abstraction and follow-up. Therefore, no missing postoperative outcome data were observed in this study.

 

Reviewer Comment:

The study is conducted at a single tertiary cancer center, which limits the generalizability of the findings to other institutions with different patient populations, resources, or perioperative practices.

Response:
We fully agree with the reviewer that conducting the study at a single tertiary cancer center may limit the generalizability of the findings to other institutions with different resources, populations, or perioperative practices. This limitation has already been acknowledged in the manuscript. However, it is important to note that King Hussein Cancer Center is a high-volume institution and the only specialized tertiary referral center for oncology patients in Jordan. The preoperative optimization program was implemented across a broad spectrum of elective general surgical oncology cases, which enhances the robustness of the findings within this context.

 

Reviewer Comment:

Patients were not randomized here. The assignment to pre-implementation and post-implementation cohorts was based on the date of surgery, which may bring the confounding factors (e.g., changes in surgical teams, hospital policies, or pandemic-related effects during 2020–2021).

Response:
We fully agree with the reviewer. Randomization was not feasible, as this was a hospital-wide quality-improvement program implemented in real time. Therefore, assignment to pre-implementation and post-implementation cohorts was determined by the program’s launch timeline. To mitigate potential confounding, we adjusted for baseline characteristics and comorbidities in the multivariate analysis (adjusted odds ratio).

Regarding the pandemic, its impact spanned the entire study period. To account for potential rescheduling or delays in surgery, we included “time to surgery” in the analysis, and no significant differences were observed between cohorts. Nonetheless, we acknowledge this as a potential limitation and will clarify it in the manuscript. (Discussion – Line: 387- 395):
“Patients were not randomized, as assignment to pre- and post-implementation cohorts was determined by the hospital-wide launch of the program. Although baseline characteristics and comorbidities were adjusted for in multivariate analyses, residual confounding factors such as temporal changes in surgical teams, hospital policies, or broader healthcare system dynamics cannot be fully excluded. Furthermore, while the study period overlapped with the COVID-19 pandemic, we accounted for its potential impact by including “time to surgery” in the analysis, which revealed no significant differences between cohorts; nevertheless, pandemic-related influences remain a potential source of bias”

 

Reviewer Comment:

Although key modifiable risk factors (anemia, nutrition, smoking, diabetes) were addressed, but there are other potential confounders such as physical activity, psychosocial status, or socioeconomic factors were not evaluated.

Response:
We agree that such factors may impact outcomes but were not systematically documented in our dataset. This is a limitation we have now explicitly acknowledged. We also note that the focus of this program was specifically on modifiable clinical risk factors that could be feasibly optimized preoperatively within a short timeframe. Future research incorporating broader patient-reported and social determinants of health is warranted. (Discussion – Line: 395-400):

“Other potential confounders, such as physical activity, psychosocial status, or socioeconomic factors, were not evaluated, as these were not systematically captured in our dataset. While the program was designed to target clinically modifiable risk factors that could be feasibly optimized in the preoperative window, the lack of broader patient-reported or social determinants of health is a limitation that may influence generalizability and outcome interpretation.”

 

Reviewer Comment:

Postoperative outcomes were only assessed up to 30 days. Long-term effects of optimization on survival, cancer recurrence, and quality of life remain unknown.

Response:
We agree and already have clarified this in the revised Limitations section. Our primary aim was to evaluate short-term surgical morbidity, consistent with common perioperative quality measures. However, we agree that future prospective studies should assess long-term outcomes including survival, recurrence, functional recovery, and quality of life.

 

Reviewer Comment:

The study did not measure patient adherence to optimization strategies (e.g., compliance with nutritional support or smoking cessation). Similarly, variations in how interventions were delivered by providers could have affected outcomes.

Response:
We appreciate the reviewer’s comment regarding program adherence. As detailed in the “Program Adherence” section (Table 2), adherence to the preoperative optimization program was systematically documented and demonstrated significant differences between the optimized and control groups across all intervention domains. Nutritional, endocrine, anemia, and smoking cessation interventions were consistently implemented in the optimized group, with corresponding improvements in clinical outcomes (e.g., higher smoking quit rates and more frequent use of IV iron therapy). However, future studies using standardized treatment protocols are warranted to further minimize variability in intervention delivery and optimize reproducibility across centers. (Discussion - line: 403 - 404)

 

Reviewer Comment:

The relatively small subgroup sizes for certain risk factors (e.g., malnourished patients) have limited statistical power to detect differences in those populations.

Response:
We agree, as noted in the limitations section. Although trends were consistent across subgroups, the relatively small sample sizes in certain categories may have limited the statistical power to detect significant differences. Future studies with larger cohorts are warranted to validate these subgroup findings.

Reviewer 2 Report

Comments and Suggestions for Authors

This is an intersting and well-written original study. Although the topic is common and the concept seems simplistic, the methodology is reported in a detailed way, while the sample size is large. Morover, the conclusions are supported by the results. Comments for the authors:

1) Have the authors performed a power analysis (sample size calculation)?

2) Was the mean ASA score comparable between the two groups? This could represent a serious confounding factor. The patients in one group had regulated comorbidities, and the patients in the other group had unregulated ones, but the comorbidities in the unadjusted group could be more severe.

3) Were the surgeries between the two groups of the same severity? This could also represent a serious confounding factor.

4) The authors should provide more details about the surgeries (intraoperative characteristics) of their cohort, with some statistics about the surgical procedures and their severity.

Author Response

We sincerely thank the reviewer for the positive and constructive feedback. We appreciate the recognition of the study’s detailed methodology, large sample size, and the alignment of our conclusions with the results. We have carefully considered all comments and have made clarifications and revisions where appropriate to further enhance the clarity and rigor of the manuscript.

Reviewer Comment 1:

Have the authors performed a power analysis (sample size calculation)?

Response:
We thank the reviewer for this valuable comment. As this was a retrospective quality-improvement analysis, we did not perform an a priori power calculation. Instead, we included all eligible oncology patients undergoing elective surgery during the study period (January 2020–December 2021). However, the sample size of 503 patients provided sufficient statistical power to detect clinically meaningful differences between groups, as reflected in the significant associations demonstrated in our results.

 

Reviewer Comment 2:

Was the mean ASA score comparable between the two groups? This could represent a serious confounding factor. The patients in one group had regulated comorbidities, and the patients in the other group had unregulated ones, but the comorbidities in the unadjusted group could be more severe.

Response:
We thank the reviewer for this observation. The ASA classification was collected and is presented in Table 4, showing comparable distribution between the pre-implementation and post-implementation groups. As shown in Table 4, the ASA scores and comorbidity profiles (Table 1) were not statistically different between the control and optimized groups. This indicates that the baseline severity of comorbidities was comparable, and therefore differences in postoperative outcomes are unlikely to be attributable to imbalance in underlying health status. Furthermore, multivariable analyses adjusted for baseline characteristics to account for any residual confounding.

 

Reviewer Comment 3:

Were the surgeries between the two groups of the same severity? This could also represent a serious confounding factor.

Response:
We thank the reviewer for raising this important point. Surgical severity was also collected and reported in Table 4. While there was some variation in case complexity, this variable was identified as a confounder in the univariable analysis and therefore included in the adjusted multivariable logistic regression. Thus, differences in surgical severity were accounted for in our reported adjusted odds ratios. We have clarified this in the Methods and Results sections.

 

Reviewer Comment 4:

The authors should provide more details about the surgeries (intraoperative characteristics) of their cohort, with some statistics about the surgical procedures and their severity.

Response:
We appreciate the reviewer’s suggestion. Details of the surgical procedures and severity are presented in Table 4, which provides an overview of case distribution by procedure type and complexity. To improve clarity, we have now elaborated in the Results section to explicitly summarize these intraoperative characteristics with reference to Table 4.

 

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

No additional comments.