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Background:
Systematic Review

Intraoperative Nerve Monitoring Parameters and Risk of Recurrent Laryngeal Nerve Injury in Thyroidectomy: A Systematic Review and Meta-Analysis

by
Shlomo Merchavy
1,
Kenan Kassem
1,
Rifat Awawde
2,
Uday Abd Elhadi
1 and
Alaa Safia
1,*
1
Head & Neck Surgery Unit, Department of Otolaryngology, Ziv Medical Center, Safed 13100, Israel
2
Department of Internal Medicine, Emek Medical Center, Afula 18317, Israel
*
Author to whom correspondence should be addressed.
Biomedicines 2025, 13(10), 2516; https://doi.org/10.3390/biomedicines13102516
Submission received: 11 August 2025 / Revised: 29 September 2025 / Accepted: 9 October 2025 / Published: 15 October 2025
(This article belongs to the Section Neurobiology and Clinical Neuroscience)
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Abstract

Background/Objectives: Recurrent laryngeal nerve injury (RLNI) is a major complication of thyroidectomy, affecting voice, airway protection, and quality of life. Intraoperative nerve monitoring (IONM) has been introduced to complement direct nerve visualization and reduce RLNI risk, but its efficacy remains controversial. This systematic review and meta-analysis aimed to determine RLNI prevalence with IONM, compare rates with historical no-IONM cohorts, perform head-to-head comparisons, and assess the influence of IONM characteristics. Methods: PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar were searched for studies reporting RLNI rates in thyroidectomy with and without IONM. Pooled prevalence estimates were calculated for transient and permanent unilateral and bilateral RLNI in IONM studies and historical controls. Direct meta-analysis estimated pooled odds ratios (ORs) for RLNI risk reduction. Subgroup analyses examined IONM type, monitoring model, stimulation amplitude, voltage, and neuromuscular blockade use; meta-regression identified influential parameters. Results: A total of 103 studies involving 132,212 patients met the criteria. Unilateral transient RLNI was lower with IONM (4%, 95% CI: 4–5%) than in historical controls (5%, 95% CI: 4–6%), while unilateral permanent RLNI was 1% in both groups. Bilateral RLNI was rare. Direct comparison showed a 38% reduction in transient unilateral RLNI (OR: 0.62, 95% CI: 0.42–0.79) and a 51% reduction in permanent unilateral RLNI (OR: 0.49, 95% CI: 0.34–0.70) with IONM. Continuous IONM, lower stimulation amplitudes (≤2 mA), and avoidance of neuromuscular blockade were protective. Conclusions: IONM significantly reduces RLNI risk, particularly for unilateral injuries, with optimal protection achieved through continuous monitoring, low stimulation amplitudes, and avoidance of neuromuscular blockade.

1. Introduction

Recurrent laryngeal nerve injury (RLNI) remains one of the most concerning complications of thyroid surgery, with implications for voice function, swallowing, and, in severe cases, airway compromise [1]. While meticulous surgical technique and direct nerve visualization are considered the standard of care for RLN preservation, intraoperative nerve monitoring (IONM) has been introduced as an adjunctive tool to reduce the risk of nerve injury [2,3,4]. Despite its increasing adoption, the efficacy of IONM in preventing RLNI remains a subject of ongoing debate.
Several systematic reviews and meta-analyses [2,4,5,6,7,8,9,10,11,12] have attempted to address this question, but their findings have been inconsistent. Some studies have reported that IONM is associated with a significant reduction in transient RLNI, particularly in high-risk cases such as bilateral thyroidectomy or reoperative thyroid surgery, while others have suggested that its benefits are marginal or nonexistent compared to direct visualization alone. For instance, Pisanu et al. [4] concluded that IONM did not significantly reduce overall RLNI rates when compared to nerve visualization, while Bergenfelz et al. [6] and Ku et al. [9] found that continuous IONM was associated with lower rates of permanent RLNI. Additionally, Bai and Chen [5] demonstrated that IONM significantly reduced both transient and permanent RLNI in high-risk cases, while Higgins et al. [2] found no significant difference between IONM and direct nerve visualization alone. These conflicting results underscore the need for a more comprehensive analysis that accounts for variations in IONM techniques, monitoring settings, and patient subgroups.
A key limitation of prior meta-analyses is their failure to systematically explore the impact of specific IONM parameters on RLNI outcomes. Most existing reviews have treated IONM as a homogeneous intervention, without differentiating between continuous and intermittent IONM, varying stimulation amplitudes, or the role of neuromuscular blockade in modulating IONM effectiveness. Additionally, prior studies have largely relied on pooled prevalence estimates, whereas direct head-to-head comparisons between IONM and no IONM within the same study cohorts have been underexplored.
This systematic review and meta-analysis aim to address these gaps by providing the most comprehensive synthesis of available evidence to date. First, this study quantifies the pooled prevalence of RLNI across studies that utilized IONM and compares these rates to a historical no-IONM control group. Second, it performs a direct head-to-head comparison in studies that reported outcomes for both IONM and no IONM within the same cohort, allowing for a more precise estimation of the risk reduction associated with IONM. Third, this study systematically investigates the impact of specific IONM parameters, including the type of IONM (continuous vs. intermittent), stimulation amplitude, voltage, neuromuscular blockade use, and IONM model, to determine how these factors influence RLNI risk. Finally, meta-regression analysis is employed to quantify the relative contribution of these variables and to identify optimal conditions for IONM utilization. By incorporating a broader dataset, applying rigorous subgroup analyses, and utilizing meta-regression to refine the findings, the findings of this research have the potential to not only clarify the efficacy of IONM but also to inform surgical best practices by identifying specific conditions under which IONM provides the greatest benefit.

2. Materials and Methods

2.1. Design and Literature Search

The study protocol was registered on PROSPERO (CRD42024556259). This post-hoc systematic review and meta-analysis followed the PRISMA [13] (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and AMSTAR (Assessing the methodological quality of systematic reviews) guidelines (Supplemental Material) [14]. The literature search was conducted across PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar (first 200 records) up to 17 July 2024. The search strategy, detailed in Table S1, was adapted for each database. References of included studies and related articles on PubMed and Google software were manually screened [15]. No restrictions were applied regarding the language of publication.

2.2. Selection Strategy

Studies were selected according to the PICOS framework [16] with the following inclusion criteria:
  • Population: Patients undergoing thyroidectomy.
  • Intervention: Thyroidectomy performed with intraoperative nerve monitoring (IONM), irrespective of approach or extent.
  • Comparison: Studies without IONM (historical control group) or direct head-to-head comparisons between IONM and non-IONM groups.
  • Outcome: The rates of unilateral and bilateral transient/permanent RLNI.
  • Study Design: All original observational or experimental studies with >20 cases.
The exclusion criteria were:
  • Non-original research.
  • Abstract-only publications.
  • Case reports or case series with <20 cases.
  • Duplicated records or studies with overlapping datasets.
  • Studies combining thyroid and parathyroid surgeries without stratified data for thyroidectomy.
  • Studies not reporting RLNI outcomes.
  • Studies reporting RLN invasion by thyroid cancer at baseline.
  • Studies not reporting whether or not IONM was used.
  • Animal studies.
  • Studies focusing on irrelevant outcomes (e.g., interventional, electromyographic, or diagnostic accuracy studies).

2.3. Data Collection and Outcomes

A structured data extraction sheet was created in Microsoft Excel and iteratively refined to accommodate extracted data. The final sheet consisted of four sections: study characteristics, patient and surgical data, outcome data, and methodological quality. Study characteristics included authors’ names, year of publication, country, study design, sample size, and follow-up period. Patient and surgical data included age, gender, and IONM details (type, model, amplitude, voltage, neuromuscular blockade use). Outcome data included the rates of unilateral and bilateral transient/permanent RLNI. The final part covered the methodological quality assessment.
Studies published in non-English languages (Croatian, German, Iranian) were translated as needed.

2.4. Risk of Bias Assessment

Randomized controlled trials (RCTs) were assessed using the revised Cochrane RoB-2 tool, while observational studies were evaluated with the Newcastle–Ottawa Scale (NOS). For NOS, studies were classified according to the AHRQ thresholds: good quality = 3–4 stars in selection and 1–2 stars in comparability and 2–3 stars in outcome/exposure; fair quality = 2 stars in selection and 1–2 stars in comparability and 2–3 stars in outcome/exposure; poor quality = 0–1 star in selection or 0 stars in comparability or 0–1 star in outcome/exposure. Study selection, data extraction, and quality assessment were performed independently by 2 reviewers, with conflicts revised and resolved by the senior author.

2.5. Statistical Analysis

All analyses were performed using STATA (Version 18, StataCorp, College station, TX, USA) following the predefined analysis plan. Pooled RLNI rates (unilateral, bilateral, transient, permanent) were calculated for studies using IONM, both overall and by subgroup (IONM type, IONM model, amplitude, voltage, and neuromuscular blockade use). These rates were then compared to a historical no-IONM group pooled from studies that did not utilize IONM. Although the overall follow-up durations reported by studies ranged from 0.06 to 75 months, this span often reflected other outcomes (e.g., reoperation, completion thyroidectomy). For RLNI, studies uniformly categorized outcomes as transient (typically resolving within 3–12 months) or permanent (persisting beyond 12–24 months). We therefore analyzed RLNI strictly as transient vs. permanent, consistent with clinical convention.
For studies providing direct head-to-head comparisons, risk estimates for RLNI (unilateral, bilateral, transient, permanent) were calculated using random-effects meta-analysis [17]. Subgroup analyses were conducted based on IONM type, model, amplitude, voltage, and neuromuscular blockade use. Meta-regression was performed to assess the impact of these factors on RLNI rates.
Heterogeneity was assessed using the I2 statistic, with significant heterogeneity defined as I2 > 40% [18]. Sensitivity analyses included Galbraith plots to identify outliers, and publication bias was examined using funnel plots and asymmetry tests. Meta-regression models adjusted for study-level covariates, assessing multicollinearity via variance inflation factors (VIF > 5 indicated problematic multicollinearity) [19]. The reference group for categorical covariates was chosen based on the most frequently reported subgroup. A minimum of 10 studies was required for subgroup and meta-regression analyses, provided significant heterogeneity was present [20]. Consistent with our a priori threshold (≥10 studies per model), RCT-only meta-analyses/meta-regressions were not performed due to fewer than 10 eligible trials and incomplete outcome reporting. Model fit was evaluated using adjusted R-squared, with higher values indicating better fit.
Certainty of evidence was assessed with GRADE for the direct head-to-head risk comparisons. We did not apply GRADE to the single-arm pooled prevalence analyses, as no validated GRADE extension currently exists for such designs.

3. Results

3.1. Literature Search Results

The literature search and screening process yielded 4966 citations, with 2151 duplicates identified using EndNote (Figure 1). After removing duplicates, 2815 articles remained, from which 2422 were excluded during title/abstract screening. We could not retrieve the full text for 51 articles, leaving 343 for full-text review. The corresponding/first authors of those papers were contacted three times through emails and ResearchGate; however, no response was received from them. A total of 144 articles were excluded during the full-text screening phase. In summary, the reasons included no report of IONM use (n = 96), followed by no reporting of RLNI (n = 52), protocols (n = 16), review articles (n = 14), EMG studies (n = 13), irrelevant outcome data (n = 9), and abstract-only publications (n = 9). The manual search of 16,514 articles yielded 155 papers, of which 152 were screened with no additional articles being identified. Finally, 103 studies were deemed eligible for data synthesis [6,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122].

3.2. Baseline Characteristics of Included Studies

The characteristics of included studies are summarized in Table 1. Most evidence was observational, with 78 retrospective cohorts, 13 prospective cohorts, and 1 cross-sectional study. Meanwhile, 11 RCTs were included. The United States accounted for the most investigated country (15 studies), followed by China (14 studies), France (8 studies), Italy (8 studies), and Turkey (8 studies), respectively. A total of 132,212 patients undergoing thyroidectomy were examined. In the 102,035 patients whom gender was disclosed, the majority were females (79,860 patients, 78.27%). The definition criteria of transient and permanent RLNI are provided in Table S2.

3.3. Methodological Quality of Included Studies

The summary of the methodological quality of included observational studies is provided in Table 2. Out of 92 studies, 44 (47.83%) had good quality; 43 (46.74%) had fair quality; and 5 (5.43%) had poor quality. Out of the 11 included RCTs, eight trials had low risk of bias while the remaining three had some concerns, mainly due to lack of an in-priori protocol to assess selective reporting.

3.4. Pooled RLNI Rates in IONM and Historical No-IONM Groups

The pooled prevalence of recurrent laryngeal nerve injury (RLNI) was assessed in both IONM-utilizing studies and historical cohorts where no intraoperative nerve monitoring was used (Table 3). Among 87 studies reporting unilateral transient RLNI, the pooled prevalence was 4% (95% CI: 4–5%) in the IONM group, which was lower than the 5% (95% CI: 4–6%) observed in the 61 studies within the historical cohort. Similarly, for unilateral permanent RLNI, data from 54 IONM studies yielded a pooled prevalence of 1% (95% CI: 1–1%), mirroring the findings from the 39 studies in the historical group.
For bilateral transient RLNI, the pooled prevalence in 11 IONM studies was 0% (95% CI: 0–0%), which was comparable to the 0% (95% CI: 0–0%) observed in 11 historical studies. Similarly, bilateral permanent RLNI was extremely rare, with 3 IONM studies reporting a 0% (95% CI: 0–0%) prevalence, comparable to 4 historical studies, which also showed 0% (95% CI: 0–0%).

3.5. Subgroup Analysis of RLNI Rates Based on IONM Characteristics

Subgroup analysis was conducted to determine how different IONM parameters influence RLNI prevalence (Table 3). When comparing IONM type, the prevalence of unilateral transient RLNI remained relatively stable across studies using continuous IONM (42 studies; 4%, 95% CI: 3–6%), intermittent IONM (16 studies; 5%, 95% CI: 3–6%), and those where the IONM type was not reported (17 studies; 5%, 95% CI: 3–6%). However, intermittent IONM appeared to be associated with a slightly lower prevalence of unilateral permanent RLNI (8 studies; 0%, 95% CI: 0–1%), compared to 1% (28 studies; 95% CI: 0–1%) in continuous IONM.
Analysis based on IONM model revealed notable variation. The AVALANCHE system had the highest pooled prevalence for unilateral transient RLNI (2 studies; 6%, 95% CI: 1–10%), while the CLEO nerve monitor (1 study) and Neurosign System (3 studies) exhibited some of the lowest rates (1–2%). For bilateral transient RLNI, the Medtronic NIM 3.0 system showed an increased prevalence (1 study; 4%, 95% CI: 0–9%) compared to other models.
When examining stimulation amplitude, lower amplitudes (<1 mA) were associated with an increased prevalence of unilateral transient RLNI (3 studies; 7%, 95% CI: 5–9%), whereas 1 mA stimulation showed a reduced rate (15 studies; 3%, 95% CI: 2–4%). A similar trend was noted for neuromuscular blockade use, where studies that explicitly did not use neuromuscular blockade reported a lower prevalence of unilateral transient RLNI (26 studies; 3%, 95% CI: 3–4%), compared to those using neuromuscular blockade (17 studies; 7%, 95% CI: 2–12%).
Lastly, stratification by stimulation voltage demonstrated that the 100 μV threshold was associated with a higher pooled prevalence of unilateral transient RLNI (14 studies; 5%, 95% CI: 3–6%), while a more moderate prevalence (3–4%) was observed with higher voltage thresholds (4 studies).

3.6. Direct Head-to-Head Comparison Between IONM and No IONM: Unilateral Transient RLNI

A direct comparison between studies that reported outcomes for both IONM and no IONM demonstrated a significant protective effect of IONM in reducing the risk of unilateral transient RLNI (Figure 2). The pooled OR for unilateral transient RLNI was 0.62 (95% CI: 0.42–0.79, p < 0.001, very low certainty). Despite the presence of substantial heterogeneity (I2 = 79.91%), the leave-one-out sensitivity analysis showed that results remained consistent across iterations (Figure S1). The risk of publication bias was insignificant (Egger’s p = 0.2373) (Figure S2).
Further subgroup analyses explored the influence of various IONM parameters on the protective effect (Figure 3). The reduction in RLNI risk was evident across different IONM types, with continuous IONM associated with an OR of 0.61 (95% CI: 0.44–0.83), while intermittent IONM exhibited a slightly attenuated effect (OR = 0.72, 95% CI: 0.42–1.22), though the latter did not reach statistical significance. When stratified by IONM model, the Medtronic NIM 2.0 system demonstrated the most pronounced protective effect, with an OR of 0.42 (95% CI: 0.24–0.73, p = 0.002). Similarly, the Medtronic NIM 3.0 system showed a borderline significant reduction in RLNI risk (OR = 0.55, 95% CI: 0.32–0.94, p = 0.028). The impact of stimulation amplitude was also examined, revealing that higher stimulation amplitudes (>3 mA) were associated with an increased RLNI risk, whereas amplitudes of ≤2 mA exhibited stronger protective effects. Additionally, the use of neuromuscular blockade appeared to diminish the efficacy of IONM, as evidenced by a higher OR of 1.36 (95% CI: 0.79–2.56, p = 0.076) in cases where neuromuscular blockade was used, suggesting that avoiding neuromuscular blockade may enhance the protective effect of IONM.

3.7. Direct Head-to-Head Comparison Between IONM and No IONM: Unilateral Permanent RLNI

A direct comparison between IONM and no IONM demonstrated a significant protective effect of IONM in reducing the risk of unilateral permanent RLNI (Figure 4). The pooled OR for unilateral permanent RLNI was 0.49 (95% CI: 0.34–0.70, p < 0.001, low certainty). Heterogeneity was low (I2 = 17.07%), and the leave-one-out sensitivity analysis showed no remarkable change in reported estimate (Figure S3). Publication bias was insignificant (Egger’s p = 0.5417) (Figure S4).
Further subgroup analysis was performed to examine the impact of different IONM parameters on the protective effect (Figure 5). The benefit of IONM was observed across various subgroups, with continuous IONM showing an OR of 0.61 (95% CI: 0.44–0.86), while intermittent IONM exhibited an even lower OR of 0.35 (95% CI: 0.14–0.89). Among IONM models, Medtronic NIM 2.0 provided the greatest risk reduction (OR = 0.41, 95% CI: 0.16–1.04), followed by the Medtronic Xomed 2.0 system (OR = 0.55, 95% CI: 0.16–1.93). However, subgroup comparisons did not yield statistically significant differences (p-values > 0.05), suggesting that the protective effect of IONM was largely consistent across different models and settings. Stimulation amplitude analysis revealed that lower amplitudes (≤2 mA) were associated with a greater protective effect, while amplitudes > 3 mA exhibited weaker risk reduction. Additionally, voltage did not appear to significantly modify the effect size.

3.8. Meta-Regression Analysis for the Direct Head-to-Head Comparison Between IONM and No IONM

To further explore the determinants of RLNI risk reduction associated with IONM, a meta-regression analysis was performed (Table 4). The models assessed the impact of IONM type, IONM model, stimulation amplitude, and neuromuscular blockade use on the risk of transient unilateral and bilateral RLNI. Due to multicollinearity, voltage was excluded from the final models.
For transient unilateral RLNI, continuous IONM was associated with a significantly lower risk compared to intermittent IONM (β = −1.196, p = 0.045), reinforcing the notion that real-time monitoring may provide superior nerve protection. Among IONM models, the Medtronic NIM 2.0 system exhibited the greatest reduction in RLNI risk (β = −1.931, p = 0.016), while the Medtronic Xomed 2.0 model was associated with an increased risk (β = 2.643, p = 0.004). The Medtronic (version not specified) system also showed a significant reduction in RLNI risk (β = −1.099, p = 0.045), whereas the Neurosign System, Inomed System, and CLEO nerve monitor did not demonstrate statistically significant effects. Stimulation amplitude, modeled as a continuous variable per mA increase, did not significantly influence the risk of RLNI (β = 0.925, p = 0.228). Similarly, the use of neuromuscular blockade was not significantly associated with RLNI risk in this model (β = −0.528, p = 0.172). The overall model fit indicated no residual heterogeneity (R2 = 100%; I2 = 0%), suggesting that the included predictors explained all the observed variability.
For transient bilateral RLNI, none of the included covariates reached statistical significance. Continuous IONM did not show a significant advantage over intermittent IONM (β = 1.015, p = 0.691). Among IONM models, neither the Medtronic NIM 2.0, Medtronic Xomed 2.0, nor the Neurosign System demonstrated a statistically significant effect. The model fit again suggested that all variability was explained by the included predictors (R2 = 100%; I2 = 0%), though the lack of significant findings indicates that the determinants of transient bilateral RLNI may be more complex or influenced by unmeasured factors.

4. Discussion

RLNI remains a significant complication of thyroid surgery, with serious implications for voice function, airway protection, and overall patient quality of life. While IONM has been widely adopted to mitigate this risk, its efficacy has remained a subject of debate. The findings of this systematic review and meta-analysis provide strong evidence supporting the protective role of IONM, particularly in reducing the incidence of transient and permanent unilateral RLNI. Additionally, this study highlights key determinants that influence the effectiveness of IONM, underscoring the importance of optimizing monitoring parameters to maximize its clinical benefit.

4.1. Comparison with Prior Evidence

The conclusions of this study build upon and refine the findings of previous meta-analyses that have assessed the impact of IONM on RLNI. Zheng et al. [12] conducted a meta-analysis incorporating over 36,000 nerves at risk and demonstrated a statistically significant reduction in total RLNI with IONM, with an odds ratio of 0.74 (95% CI: 0.59–0.92), particularly for transient injuries. Similarly, Rulli et al. [11] reported that IONM was associated with a reduction in transient RLNI, with a relative risk of 0.73 (95% CI: 0.54–0.98, p = 0.035), but found no significant effect on permanent RLNI. The present study aligns with these findings but extends the analysis further by incorporating extensive subgroup analyses and meta-regression, revealing the influence of IONM type, stimulation parameters, and neuromuscular blockade on RLNI outcomes.
While some prior meta-analyses have questioned the overall benefit of IONM, particularly with respect to permanent RLNI, others have suggested that the protective effects of IONM are most pronounced in high-risk surgical settings, such as bilateral thyroidectomies or oncologic resections. Bergenfelz et al. [6] found that although IONM did not significantly reduce the overall incidence of early RLNI, it was associated with a lower risk of permanent vocal cord palsy, with an odds ratio of 0.43 (95% CI: 0.19–0.93). Ku et al. [9] demonstrated that continuous IONM was particularly effective, reporting a permanent RLNI rate of only 0.05%. These findings are supported by the results of the present study, which confirm that continuous IONM provides superior nerve protection compared to intermittent IONM.
Conversely, other studies have reported conflicting findings. Pisanu et al. [4] and Higgins et al. [2] failed to identify a significant difference in RLNI rates when comparing IONM with direct nerve visualization alone. These discrepancies may stem from differences in surgical expertise, patient selection criteria, and variations in the application of IONM protocols. The meta-regression analysis in this study addresses this gap by identifying specific factors—such as the choice of IONM model, the applied stimulation amplitude, and the avoidance of neuromuscular blockade—that significantly influence RLNI risk reduction.

4.2. Key Contributions and Novel Insights

One of the primary strengths of this study lies in its comprehensive approach, integrating pooled prevalence estimates with direct head-to-head comparisons. By synthesizing data from over 130,000 patients, this study provides one of the most extensive analyses to date, offering high-powered evidence in favor of IONM. The findings demonstrate a 38% reduction in the odds of transient unilateral RLNI (OR: 0.62, 95% CI: 0.42–0.79, p < 0.001) and a 51% reduction in the odds of permanent unilateral RLNI (OR: 0.49, 95% CI: 0.34–0.70, p < 0.001). These results reinforce the role of IONM as a critical adjunct in thyroidectomy.
Beyond demonstrating the overall benefit of IONM, the findings of this study highlight the importance of optimization in monitoring parameters. Continuous IONM was associated with greater nerve protection compared to intermittent IONM. The Medtronic NIM 2.0 system exhibited the most substantial risk reduction, while the Medtronic Xomed 2.0 model was paradoxically associated with an increased risk of RLNI. Lower stimulation amplitudes (≤2 mA) were found to be more protective than higher amplitudes (>3 mA). Additionally, the use of neuromuscular blockade was found to diminish the efficacy of IONM, reinforcing the need for careful anesthetic management to ensure optimal monitoring performance. The meta-regression analysis quantitatively validated these findings, demonstrating that variations in IONM technique significantly influence RLNI risk.
While these subgroup and meta-regression findings help highlight potentially modifiable monitoring parameters, they should be interpreted with appropriate caution where contributing study counts are small and confidence intervals widen. In such cases, results are best viewed as hypothesis-generating signals that warrant confirmation in adequately powered prospective studies.
The geographic distribution of included studies (spanning Europe, Asia, and the Americas) is also noteworthy. Differences in surgical training, adoption of IONM technology, and perioperative standards across regions may have contributed to heterogeneity in reported RLNI outcomes. For example, European and East Asian centers often report higher adoption of continuous IONM, whereas many North American series rely on intermittent IONM. Such regional variation highlights the need for future multinational prospective studies with standardized protocols to ensure broader applicability of results.

4.3. Clinical Implications

The findings of this study have significant implications for surgical practice. While IONM has already been widely adopted in thyroid surgery, the focus should shift from merely using IONM to ensuring that it is applied in a standardized and optimized manner. The results suggest that rather than a binary decision regarding whether to use IONM, surgeons should focus on how IONM is implemented. Specifically, the data support the use of continuous IONM with optimized stimulation settings and the avoidance of neuromuscular blockade to maximize nerve protection.
These findings also have important implications for high-risk thyroidectomy cases. While the absolute risk of bilateral RLNI remains low, the results indicate that IONM reduces this risk, reinforcing its role in complex cases such as total thyroidectomy, re-operative thyroid surgery, and malignancy-related thyroid resections. In these scenarios, where the stakes of nerve injury are particularly high, the use of IONM may play a pivotal role in improving surgical safety.

4.4. Limitations and Future Directions

Despite its strengths, this study is not without limitations. One of the primary concerns is the heterogeneity among the included studies, particularly regarding differences in surgical technique, IONM protocols, and follow-up durations. We limited “no-IONM” prevalence comparators to cohorts that explicitly reported no IONM use; nonetheless, such contrasts can still be confounded by secular trends in technique, case selection, and perioperative care. Accordingly, we treat these contrasts as contextual rather than causal, and center our conclusions on the head-to-head analyses. Another limitation is the heterogeneity in how transient and permanent RLNI were defined across studies (Table S2), which prevented consistent categorization and precluded sensitivity analyses restricted to standardized definitions; this variability may have contributed to the observed heterogeneity in pooled estimates.
While the meta-regression analysis attempts to account for these variables, residual confounding remains possible. Additionally, publication bias is an inherent challenge in meta-analyses. Although the funnel plot analysis did not detect significant bias, the potential for selective reporting cannot be entirely excluded. Study-level covariates requested by the reviewer (year, RCT vs. cohort, surgeon/center volume) were reported too sparsely and inconsistently—especially within the <10 RCT subset—to support stable multivariable meta-regression; we therefore prioritized the prespecified IONM-parameter models and transparently report this limitation.
Despite pre-specified safeguards (e.g., conducting subgroup/meta-regression only when ≥10 studies were available and heterogeneity was present) and performing sensitivity checks, several subgroups still included a limited number of studies. Small subgroup samples increase imprecision, widen confidence intervals, and can heighten the probability of spurious or unstable estimates—particularly in the context of multiple comparisons. Accordingly, subgroup and meta-regression effects should be interpreted as exploratory and hypothesis-generating rather than definitive, pending confirmation in standardized, prospective datasets. Although all covariates were pre-specified in the protocol, we limited the presented analyses to IONM-related parameters; nonetheless, the performance of multiple subgroup and regression models may increase the risk of inflated type I error, and findings should therefore be interpreted with caution.
Future research should prioritize prospective, standardized studies with uniform IONM protocols, particularly in the form of randomized controlled trials comparing continuous versus intermittent IONM. Further investigation into machine-learning-assisted IONM interpretation may also offer new opportunities for real-time risk stratification and intraoperative decision-making. By refining these parameters, future studies can help further clarify the optimal use of IONM in thyroid surgery.

5. Conclusions

This systematic review and meta-analysis provide compelling evidence that IONM significantly reduces the risk of both transient and permanent RLNI. The findings clarify that the effectiveness of IONM is contingent upon how it is implemented rather than its mere presence or absence. Standardization of IONM protocols, including the use of continuous monitoring, appropriate stimulation parameters, and avoidance of neuromuscular blockade, is essential to maximize its protective effect. These results support the routine adoption of IONM in thyroidectomy, emphasizing the need for a structured and evidence-based approach to ensure optimal surgical outcomes. Future research should focus on refining these findings with high-quality prospective trials to further establish best practices in RLNI prevention.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines13102516/s1, Table S1. The search query employed in the literature search; Table S2. The definition criteria of transient and permanent recurrent laryngeal nerve injury (RLNI) reported in studies examining the impact of intraoperative nerve monitoring (IONM) during thyroidectomy; Figure S1. Leave-one-out sensitivity analysis of the direct head-to-head comparison between IONM and non-IONM regarding transient unilateral RLNI; Figure S2. Funnel plot of the publication bias of unilateral transient RLNI in studies reporting the direct head-to-head comparison between IONM and no IONM; Figure S3. Leave-one-out sensitivity analysis of the direct head-to-head comparison between IONM and non-IONM regarding transient unilateral RLNI; Figure S4. Funnel plot of the publication bias of unilateral transient RLNI in studies reporting the direct head-to-head comparison between IONM and no IONM.

Author Contributions

Conceptualization, S.M. and A.S.; methodology, K.K., R.A. and U.A.E.; software, U.A.E.; validation, S.M., K.K. and A.S.; formal analysis, K.K.; investigation, S.M., R.A. and K.K.; resources, U.A.E.; data curation, K.K.; writing—original draft preparation, K.K.; writing—review and editing, R.A., A.S. and S.M.; visualization, K.K.; supervision, A.S.; project administration, A.S. 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

Not applicable.

Acknowledgments

We would like to thank Abdelaziz Abdelaal for his help in constructing the analysis plan a priori.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
CRComplete resection
CIConfidence interval
EMGElectromyography
ETTEndotracheal tube
IONMIntraoperative nerve monitoring
LRLNILate recurrent laryngeal nerve injury
NMBNeuromuscular blockade
OROdds ratio
pp-value
PRLNIPermanent recurrent laryngeal nerve injury
RLNRecurrent laryngeal nerve
RLNIRecurrent laryngeal nerve injury
RRRisk ratio
SRLNISymptomatic recurrent laryngeal nerve injury
SSEPSomatosensory evoked potential
TcMEPTranscranial motor evoked potential
TRTotal resection
TSRLNITemporary symptomatic recurrent laryngeal nerve injury
VASVisual analogue scale

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Figure 1. PRISMA flow diagram showing the results of the database search and screening processes.
Figure 1. PRISMA flow diagram showing the results of the database search and screening processes.
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Figure 2. Forest plot showing the risk of unilateral transient RLNI in studies reporting direct head-to-head comparison between IONM and no IONM [6,23,24,26,33,36,37,41,42,43,44,48,50,52,54,55,56,57,58,59,61,63,64,66,67,70,71,72,73,74,76,77,84,86,87,90,92,94,96,97,100,101,104,108,110,111,112,114,116,118,119,120,121,122].
Figure 2. Forest plot showing the risk of unilateral transient RLNI in studies reporting direct head-to-head comparison between IONM and no IONM [6,23,24,26,33,36,37,41,42,43,44,48,50,52,54,55,56,57,58,59,61,63,64,66,67,70,71,72,73,74,76,77,84,86,87,90,92,94,96,97,100,101,104,108,110,111,112,114,116,118,119,120,121,122].
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Figure 3. Forest plot showing the risk of unilateral transient RLNI in studies reporting direct head-to-head comparison between IONM and no IONM, stratified by IONM-related parameters (type, model, amplitude/voltage, and neuromuscular blockade use).
Figure 3. Forest plot showing the risk of unilateral transient RLNI in studies reporting direct head-to-head comparison between IONM and no IONM, stratified by IONM-related parameters (type, model, amplitude/voltage, and neuromuscular blockade use).
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Figure 4. Forest plot showing the risk of unilateral permanent RLNI in studies reporting direct head-to-head comparison between IONM and no-IONM [23,24,25,26,33,36,37,43,44,50,55,56,57,58,63,64,66,68,70,74,76,77,84,86,101,104,108,114,116,118].
Figure 4. Forest plot showing the risk of unilateral permanent RLNI in studies reporting direct head-to-head comparison between IONM and no-IONM [23,24,25,26,33,36,37,43,44,50,55,56,57,58,63,64,66,68,70,74,76,77,84,86,101,104,108,114,116,118].
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Figure 5. Forest plot showing the risk of unilateral permanent RLNI in studies reporting direct head-to-head comparison between IONM and no IONM, stratified by IONM-related parameters (type, model, amplitude/voltage, and neuromuscular blockade use).
Figure 5. Forest plot showing the risk of unilateral permanent RLNI in studies reporting direct head-to-head comparison between IONM and no IONM, stratified by IONM-related parameters (type, model, amplitude/voltage, and neuromuscular blockade use).
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Table 1. Baseline characteristics of studies reporting the use of IONM during thyroid surgery and reporting RLNI as an outcome.
Table 1. Baseline characteristics of studies reporting the use of IONM during thyroid surgery and reporting RLNI as an outcome.
Author (YOP)CountryDesignYOISampleAgeGenderFU (mo)Neuromonitoring (IONM)
MeanSDMFYes/NoTypeModelAmplitudeVoltageNeuromuscular Blockade
Acun (2004a) [21]TurkeyRCT2001–200315243(24–77)39113120/152-----
Acun (2005) [22]TurkeyRC-17644(23–77)49127-0/176-----
Ahmed (2023) [23]IraqRCT2018–202015039-22128-75/75ContinuousNot reportedNot reportedNot reportedNo
Akici (2020) [24]TurkeyRC2012–2017273471238235-140/133ContinuousNot reported1.5 mANot reportedNo
Akkari (2014) [25]FranceRC2004–20126512.50.71649-40/25ContinuousMedtronic Xomed 2.0<1 mANot reportedNo
Alesina (2012) [26]GermanyRC1999–20112465512.537209-89/157ContinuousMedtronic Xomed 2.0<1 mANot reportedNo
Al-Hakami (2019) [27]KSARC2008–201745642.6(10–89)9935712456/0-Not reportedNot reportedNot reportedNo
Alhan (2015) [28]TurkeyRC2004–2012620481410951160/620-----
Alharbi (2018) [29]KSARC2011–201832042.259.5112208-0/620-Not reported--No
Alqahtani (2023) [30]KSARC2015–202143241.219.1763610.750/432-Not reported--No
Ambe (2014) [31]GermanyRC2006–201230554.314.2578227-305/0-Not reportedNot reportedNot reportedNo
Aygun (2022) [32]TurkeyRC2016–202187149.1713.42199672-871/0IntermittentMedtronic Xomed 2.01 mA100 μVYes
ContinuousMedtronic (version not specified)1 mA500 μVYes
Barczyński (2009) [33]PolandRCT2006–2007100051.614.68891212500/500ContinuousNeurosign System1 mANot reportedYes
Barczyński (2010) [34]PolandRCT2000–200360047.2215.6153517-0/600-Not reported--Yes
Barczyński (2012c) [35]PolandRCT2000–200419145.9(43.1–48.9)21170120/191-----
Barczyński (2012d) [36]PolandRCT2009–201021049.914.702016100/101ContinuousMedtronic NIM 3.01 mANot reportedYes
Barczyński (2014) [37]PolandRC1993–201285454.313.4687167-306/548ContinuousNeurosign System1 mANot reportedYes
Bawa (2021) [38]KSARC2013–201933938(29–48)59280-0/339-Not reported--Not reported
Bergenfelz (2016) [6]SwedenRC2009–2013525249(38–63)1050420263277/1975-Not reportedNot reportedNot reportedNot reported
Bertelli (2021) [39]BrazilRC201793--1479-93/0-Neurosoft (INTRO)Not reportedNot reportedNot reported
Bihain (2021) [40]FrancePC2013–201960352.8151374660.06367/236ContinuousMedtronic NIM 3.01 mA100 μVYes
Bryk (2024) [41]PolandRC-36752.2(18–79)55312-205/162ContinuousInomed System5 mANot reportedNo
Calò (2014a) [42]ItalyRC2007–2013656-----357/299ContinuousMedtronic Xomed 2.0Not reportedNot reportedNo
Calò (2014b) [43]ItalyRC2007–20122034-----1041/993ContinuousMedtronic Xomed 2.0Not reportedNot reportedNo
Chan (2006) [44]ChinaRC2002–200563949(8–93)133506-501/499ContinuousNeurosign System1.5 mANot reportedNo
Chen (2022a) [45]ChinaRC2019–202011041.17.254664-110/0-Not reportedNot reportedNot reportedNot reported
Chiang (2004) [46]TaiwanRC1986–200252142(17–78)118403-0/521-Not reported--Not reported
Chiang (2011) [47]TaiwanRC2006–2009231-----231/0ContinuousMedtronic Xomed 2.01 mANot reportedNo
Chuang (2013) [48]TaiwanRC2001–201071-(22.8–85)1259-56/15ContinuousMedtronic (version not specified)1 mA700–1500 μVNo
Dedhia (2020) [49]USARC2000–2018109650.10.7834175561096/0-Not reportedNot reportedNot reportedNot reported
Dionigi (2009) [50]ItalyRCT2004–20077240.5(19–77)106212--Medtronic NIM 2.0---
Dralle (2004) [51]GermanyRC1998–200116,448-----12,166/17,832ContinuousNeurosign System5 mANot reportedNo
Erçetin (2019) [52]TurkeyPC2008–201674847.81313066512398/397IntermittentMedtronic (version not specified)1.5 mANot reportedNo
Farizon (2017) [53]FranceRC2012–201519553.4(14–88)3416112195/0ContinuousMedtronic (version not specified)<1 mA100 μVNo
Fassari (2024) [54]ItalyRC-30048.611.9121179-150/150IntermittentMedtronic (version not specified)2 mANot reportedNo
Fei (2022) [55]ChinaRC2013–201810632.256.83---54/52Intermittent/ContinuousMedtronic (version not specified)2 mA100 μVYes
Formanez (2016) [56]PhilippinesRC2009–201423741(20–65)741636109/128-Not reportedNot reportedNot reportedNot reported
Frattini (2010) [57]ItalyRC-15240.6(19–77)67851276/76ContinuousMedtronic (version not specified)3 mANot reportedNo
Gremillion (2012) [58]USARC2007–2010119-----31/88ContinuousNot reportedNot reportedNot reportedNot reported
Gunn (2020) [59]USARC2016–201711,37053(41–63)2476889417031/4230ContinuousNot reportedNot reportedNot reportedNot reported
Gür (2019) [60]TurkeyRC2014–201745652.8(18–82)106350-456/0ContinuousAVALANCHE2 mA100 μVNo
Gutierrez-Alvarez (2023) [61]UASRC2019–2022218--39179-150/68ContinuousMedtronic NIM 3.0Not reportedNot reportedNo
Hamilton (2019) [62]UKRC2014–2016256-27–8627169---APS Medtronic---
Hei (2016a) [63]ChinaRCT2012–20147047.59.91654641/43IntermittentMedtronic NIM 2.0Not reportedNot reportedYes
Hei (2016b) [64]ChinaRC2009–20119745.3511.231978-46/51IntermittentMedtronic NIM 2.02 mANot reportedYes
Hu (2016) [65]ChinaRC2003–2014555955(9–87)714484560/5559-Not reported--Not reported
Iqbal (2016) [66]PakistanRCT2013–2014150-13–605397-75/75-Not reportedNot reportedNot reportedNot reported
Jawad (2018) [67]BaghdadPC2012–201613237.358.374785664/54-Not reportedNot reportedNot reportedNot reported
Joliat (2017) [68]SwitzerlandRC2005–20134515043–631251128/55-Not reportedNot reportedNot reportedNot reported
Jonas (2006) [69]FrancePC1999–200493750.8(24–83)--12--Neurosign System---
Kai (2017) [70]ChinaRC2013–201652265.660.3122430-340/212ContinuousMedtronic NIM 3.01 mA100 μVYes
Karpathiotakis (2022) [71]ItalyPC2018–202010055(43–65)1783650/50IntermittentMedtronic NIM 3.0Not reported100 μVNo
Khan (2022) [122]PakistanCS2020–20217044.43-1852657/23-Not reportedNot reportedNot reportedNot reported
Kim (2021) [72]USARC2016–201817,6105215390413706-11,248/6362-Not reportedNot reportedNot reportedNot reported
Kuryga (2021) [73]PolandRC2005–2012123549.5-169106536182/1052ContinuousCLEO nerve monitor1 mANot reportedYes
Landerholm (2014) [74]SwedenPC1984–201197354.716.12421080120/973-Not reported--Not reported
Lenay-Pinon (2021) [75]FranceRC2013–2019102653(18–81)26676012--Inomed System---
Leow (2020) [76]SingaporeRC2014–201826149.212.568193-108/153IntermittentMedtronic NIM 3.0Not reportedNot reportedNo
Ling (2020) [77]ChinaRC2012–2017169652(40–59)28075361104/592IntermittentMedtronic NIM 3.02 mA100 μVYes
Liu (2020) [78]ChinaRC2017–2019235051.913.3371188762350/0IntermittentMedtronic NIM 3.01 mA100 μVNo
Liu (2021) [79]ChinaRC2012–201941535.45(19–48)14046415/0IntermittentMedtronic NIM 3.01 mA100 μVYes
Machens (2018) [80]GermanyRC1994–20171676.9-78896167/0ContinuousNot reportedNot reportedNot reportedNot reported
Mahoney (2021) [81]USARC2016–201711,552≥65-25149038-7130/4422-Not reportedNot reportedNot reportedNot reported
Maksimoski (2022) [82]USARC2012–2017102513.9-228797-795/230-Not reportedNot reportedNot reportedNot reported
Marin Arteaga (2018) [83]SwitzerlandRC2012–201610015516.8356661001/0ContinuousMedtronic NIM 3.0Not reported100 μVNo
Maurer (2020) [84]GermanyRC2017–2019180844(14–80)3301478-3409/16Intermittent/ContinuousNot reportedNot reportedNot reportedNot reported
Messenbaeck (2018) [85]AustriaRC-24645.6(21–73)29217-246/0ContinuousMedtronic (version not specified)Not reportedNot reportedNot reported
Mirallié (2018) [86]FrancePC2012–2014132851.2(18–80)26710616807/521ContinuousMedtronic (version not specified)Not reportedNot reportedYes
Mizuno (2019) [87]JapanRC2008–2017508457.714.6152842761849/4955-Not reportedNot reportedNot reportedNot reported
Mohammad (2022) [88]KuwaitRC2016–20191974923–85711266171/26ContinuousMedtronic NIM 3.02 mA100 μVNo
Moreira (2020) [89]AustraliaRC2010–20171003--220783-1003/0ContinuousMedtronic Xomed 2.0Not reportedNot reportedNot reported
Muhammad (2021) [90]MalaysiaRCT20162554.2-733-20/20ContinuousMedtronic NIM 3.01 mANot reportedYes
Nagaoka (2022) [91]JapanRC2016–202010036.2-199625/75-Not reportedNot reportedNot reportedNot reported
Nayyar (2020) [92]IndiaRC2017–2019228--150250-150/250-Not reportedNot reportedNot reportedNot reported
Paek (2022) [93]KoreaRC2013–201431542.459.9702456315/0ContinuousMedtronic NIM 3.0Not reportedNot reportedNot reported
Pei (2021) [94]ChinaRC2010–202010949.5614.984861-65/44ContinuousMedtronic NIM 3.02 mANot reportedNot reported
Périé (2013) [95]FrancePC2007–201110047.116–8119816--Neurosign System---
Porseyedi (2012) [96]IranRC2005–201156640.26-124442-337/229-Not reportedNot reportedNot reportedNot reported
Prokopakis (2013) [97]GreeceRC2004–2011976147–752077---Medtronic (version not specified)---
Raval (2009) [98]USARC2000–20073112.2(5–17)625623/8ContinuousMedtronic (version not specified)Not reportedNot reportedNot reported
Razavi (2018) [99]USARC2016–20172741.312.24233--Medtronic (version not specified)---
Ritter (2021) [100]IsraelRC2001–201911313.53.9298412--Medtronic NIM 2.0---
Robertson (2004) [101]USARC1999–200216544.4-54182-82/83ContinuousMedtronic Xomed 2.0Not reportedNot reportedNot reported
Rudolph (2014) [102]FranceRC1991–200649439-414536494/0-Not reportedNot reportedNot reportedNot reported
Russell (2021) [103]USARC2017–202053344(10–84)904436533/0ContinuousMedtronic NIM 3.0Not reportedNot reportedNot reported
Sanguinetti (2014) [104]ItalyRC2012350-----105/245ContinuousMedtronic Xomed 2.0Not reportedNot reportedNot reported
Sarkis (2017) [105]AustraliaRC1990–20147406----37406/0ContinuousNot reportedNot reportedNot reportedNot reported
Schneider (2019) [106]AustriaPC2012–20164707--12123495124707/0IntermittentAVALANCHE2 mANot reportedNot reported
Sena (2019) [107]ItalyRC2009–201823752.7-89199---Medtronic NIM 3.0---
Shindo (2007) [108]USARC1998–2005684-----671/372ContinuousMedtronic (version not specified)Not reportedNot reportedNo
Snyder (2010) [109]USARC2003–2009124257.3----1242/0-Not reportedNot reportedNot reportedNot reported
Snyder (2013) [110]USARC2004–2011193652-6852750-3354/81ContinuousMedtronic (version not specified)1 mA150 μVNo
Sopiński (2017) [111]ChinaRCT2014–20168057.959.35476-27/53IntermittentInomed SystemNot reportedNot reportedYes
Stevens (2012) [112]USAPC2004–20089148.4512.93754639/52ContinuousMedtronic (version not specified)Not reported100 μVNot reported
Tabriz (2024) [113]GermanyRC2016–2020114752(13–90)293854-1147/0IntermittentNot reportedNot reportedNot reportedNot reported
Vasileiadis (2016) [114]GreeceRC2002–2012256651.3514.185282028121481/1075IntermittentMedtronic NIM 2.01 mANot reportedNo
Velayutham (2022) [115]IndiaPC2017–201984-----84/0ContinuousMedtronic NIM 3.02.5 mA500 μVNo
Wojtczak (2017) [116]PolandPC2011–201463253.9413.871175156236/396IntermittentMedtronic NIM 3.01.5 mANot reportedNot reported
Wu (2017) [117]TaiwanPC2012–201432350(16–83)632606--Medtronic NIM 3.0---
Wu (2018) [118]USARC2006–201538038.514.0471309-288/92ContinuousMedtronic (version not specified)<1 mA100 μVNot reported
Xu (2023) [119]ChinaRC2015–202141637.87.87--6416/0IntermittentMedtronic (version not specified)3 mANot reportedNot reported
Yu (2020) [120]ChinaRC2016–2017935024–782271-93/0ContinuousMedtronic (version not specified)Not reportedNot reportedYes
Yuksekdag (2019) [121]TurkeyRC2014–20182605132–67--6--Medtronic NIM 3.0---
YOP: year of publication; RC: retrospective cohort; RCT: randomized controlled trial; PC: prospective cohort; IONM: intraoperative nerve monitoring; RLNI: recurrent laryngeal nerve injury; SD: standard deviation; M: male; F: female; FU: follow-up; mo: month: NR: not reported.
Table 2. A summary of the methodological quality of included observational studies using the Newcastle–Ottawa Scale.
Table 2. A summary of the methodological quality of included observational studies using the Newcastle–Ottawa Scale.
Author (YOP)SelectionComparabilityOutcomeOverall Rating
Representativeness of the Exposed CohortSelection of the Non-Exposed CohortAscertainment of ExposureDemonstration That Outcome of Interest Was Not Present at Start of StudyDesignAnalysisAssessment of OutcomeWas Follow-Up Long Enough for Outcomes to Occur?Adequacy of Follow-Up of Cohorts
Acun (2005) [21]YesYesYesYesYesNoYesYesYesGood
Akici (2020) [24]YesYesYesYesYesNoYesYesYesGood
Akkari (2014) [25]NoYesYesYesYesNoYesNoNoPoor
Alesina (2012) [26]YesYesYesYesYesNoYesNoNoFair
AlHakami (2019) [27]YesYesYesYesYesNoYesNoNoFair
Alhan (2015) [28]YesYesYesYesYesNoYesNoNoFair
Alharbi (2018) [29]YesYesYesYesYesNoYesNoNoFair
Alqahtani (2023) [30]YesYesYesYesYesNoYesNoNoFair
Ambe (2014) [31]YesYesYesYesYesNoYesNoNoFair
Aygun (2022) [32]YesYesYesYesYesNoYesNoNoFair
Barczyński (2014) [33]YesYesYesYesYesNoYesNoNoFair
Bawa (2021) [38]YesYesYesYesYesNoYesNoNoFair
Bergenfelz (2016) [6]YesYesYesYesYesNoYesNoNoFair
Bertelli (2021) [39]NoYesYesYesYesNoYesYesYesFair
Bihain (2021) [40]YesYesYesYesYesNoYesNoNoFair
Bryk (2024) [41]YesYesYesYesYesNoYesNoNoFair
Calò (2014a) [42]YesYesYesYesYesNoYesYesYesGood
Calò (2014b) [43]YesYesYesYesYesNoYesNoNoFair
Chan (2006) [44]YesYesYesYesYesNoYesNoNoFair
Chen (2022a) [45]YesYesYesYesYesNoYesYesYesGood
Chiang (2004) [46]YesYesYesYesYesNoYesNoNoFair
Chiang (2011) [47]YesYesYesYesYesNoYesNoNoFair
Chuang (2013) [48]NoYesYesYesYesNoYesYesYesFair
Dedhia (2020) [49]YesYesYesYesYesNoYesYesYesGood
Dralle (2004) [51]YesYesYesYesYesNoYesYesYesGood
Erçetin (2019) [52]YesYesYesYesYesNoYesYesYesGood
Farizon (2017) [53]YesYesYesYesYesNoYesNoNoFair
Fassari (2024) [54]YesYesYesYesYesNoYesYesYesGood
Fei (2022) [55]YesYesYesYesYesNoYesYesYesGood
Formanez (2016) [56]YesYesYesYesYesNoYesNoNoFair
Frattini (2010) [57]YesYesYesYesYesNoYesNoNoFair
Gremillion (2012) [58]YesYesYesYesYesNoYesYesYesGood
Gunn (2020) [59]YesYesYesYesYesNoYesNoNoFair
Gür (2019) [60]YesYesYesYesYesNoYesYesYesGood
GutierrezAlvarez (2023) [61]YesYesYesYesYesNoYesYesYesGood
Hamilton (2019) [62]YesYesYesYesYesNoYesYesYesGood
Hei (2016b) [63]NoYesYesYesYesNoYesNoNoPoor
Hu (2016) [64]YesYesYesYesYesNoYesYesYesGood
Jawad (2018) [67]YesYesYesYesYesNoYesNoNoFair
Joliat (2017) [68]YesYesYesYesYesNoYesNoNoFair
Jonas (2006) [69]YesYesYesYesYesNoYesNoNoFair
Kai (2017) [70]YesYesYesYesYesNoYesYesYesGood
Karpathiotakis (2022) [71]YesYesYesYesYesNoYesYesYesGood
Khan (2022) [122]YesYesYesYesYesNoYesYesYesGood
Kim (2021) [72]YesYesYesYesYesNoYesYesYesGood
Kuryga (2021) [73]YesYesYesYesYesNoYesYesYesGood
Landerholm (2014) [74]YesYesYesYesYesNoYesNoNoFair
LenayPinon (2021) [75]YesYesYesYesYesNoYesYesYesGood
Leow (2020) [76]YesYesYesYesYesNoYesYesYesGood
Ling (2020) [77]YesYesYesYesYesNoYesYesYesGood
Liu (2020) [78]YesYesYesYesYesNoYesNoNoFair
Liu (2021) [79]YesYesYesYesYesNoYesYesYesGood
Machens (2018) [80]YesYesYesYesYesNoYesYesYesGood
Mahoney (2021) [81]YesYesYesYesYesNoYesYesYesGood
Maksimoski (2022) [82]YesYesYesYesYesNoYesYesYesGood
Marin Arteaga (2018) [83]YesYesYesYesYesNoYesNoNoFair
Maurer (2020) [84]YesYesYesYesYesNoYesNoNoFair
Messenbaeck (2018) [85]YesYesYesYesYesNoYesYesYesGood
Mirallié (2018) [86]YesYesYesYesYesNoYesNoNoFair
Mizuno (2019) [87]YesYesYesYesYesNoYesNoNoFair
Mohammad (2022) [88]YesYesYesYesYesNoYesYesYesGood
Moreira (2020) [89]YesYesYesYesYesNoYesYesYesGood
Nagaoka (2022) [91]YesYesYesYesYesNoYesYesYesGood
Nayyar (2020) [92]YesYesYesYesYesNoYesNoNoFair
Paek (2022) [93]YesYesYesYesYesNoYesNoNoFair
Pei (2021) [94]YesYesYesYesYesNoYesNoNoFair
Périé (2019) [95] YesYesYesYesYesNoYesYesYesGood
Porseyedi (2012) [96]YesYesYesYesYesNoYesYesYesGood
Prokopakis (2013) [97]NoYesYesYesYesNoYesNoNoPoor
Raval (2009) [98]NoYesYesYesYesNoYesNoNoPoor
Razavi (2018) [99]NoYesYesYesYesNoYesYesYesFair
Ritter (2021) [100]YesYesYesYesYesNoYesNoNoFair
Robertson (2004) [101]YesYesYesYesYesNoYesNoNoFair
Rudolph (2014) [102]YesYesYesYesYesNoYesNoNoFair
Russell (2021) [103]YesYesYesYesYesNoYesYesYesGood
Sanguinetti (2014) [104]YesYesYesYesYesNoYesYesYesGood
Sarkis (2017) [105]YesYesYesYesYesNoYesYesYesGood
Schneider (2019) [106]YesYesYesYesYesNoYesYesYesGood
Sena (2019) [107]YesYesYesYesYesNoYesYesYesGood
Shindo (2007) [108]YesYesYesYesYesNoYesNoNoFair
Snyder (2010) [109]YesYesYesYesYesNoYesYesYesGood
Snyder (2013) [110]YesYesYesYesYesNoYesYesYesGood
Stevens (2012) [112]NoYesYesYesYesNoYesYesYesFair
Tabriz (2024) [113]YesYesYesYesYesNoYesYesYesGood
Vasileiadis (2016) [114]YesYesYesYesYesNoYesNoNoFair
Velayutham (2022) [115]NoYesYesYesYesNoYesNoNoPoor
Wojtczak (2017) [116]YesYesYesYesYesNoYesYesYesGood
Wu (2017) [117]YesYesYesYesYesNoYesNoNoFair
Wu (2018) [118]YesYesYesYesYesNoYesYesYesGood
Xu (2023) [119]YesYesYesYesYesNoYesYesYesGood
Yu (2020) [120]NoYesYesYesYesNoYesYesYesFair
Yuksekdag (2019) [121]YesYesYesYesYesNoYesYesYesGood
Table 3. A summary of the pooled prevalence rate of RLNI in studies using IONM and historical cohorts (no IONM use) with subsets based on IONM details.
Table 3. A summary of the pooled prevalence rate of RLNI in studies using IONM and historical cohorts (no IONM use) with subsets based on IONM details.
Unilateral RLNIBilateral RLNI
TransientPermanentTransientPermanent
IONMHistoricalIONMHistoricalIONMHistoricalIONMHistorical
Main-PooledStudies87615439111134
Patients11,2486362740655594955495513522341
Proportion (95% CI)4% (4–5)5% (4–6)1% (1–1)1% (1–1)0% (0–0)0% (0–0)0% (0–0)0% (0–0)
IONM TypeContinuous4% (3–6) 1% (0–1) 0% (0–1)
Intermittent5% (3–6)0% (0–1)4% (0–9)
Not reported5% (3–6)1% (1–2)0% (0–0)
IONM ModelAVALANCHE6% (1–10)1% (1–1)-
CLEO nerve monitor1% (0–2)-1% (0–2)
Inomed System10% (0–23)--
Medtronic (version not specified)7% (1–12)1% (0–2)1% (0–1)
Medtronic NIM 2.02% (0–5)0% (0–1)-
Medtronic NIM 3.04% (3–5)0% (0–1)4% (0–9)
Medtronic Xomed 2.04% (2–6)0% (0–0)0% (0–0)
Neurosign System2% (2–3)2% (0–3)-
Neurosoft (INTRO)3% (0–7)1% (0–3)-
Not reported5% (3–6)1% (0–1)0% (0–0)
Amplitude<1 mA7% (5–9)0% (0–1)1% (0–2)
1 mA3% (2–4)0% (0–1)1% (0–2)
1.5 mA3% (2–4)1% (0–2)-
2 mA5% (3–7)1% (0–1)-
2.5 mA5% (1–9)--
3 mA4% (2–6)1% (0–4)1% (0–2)
5 mA5% (2–8)4% (3–4)-
Not reported5% (4–7)1% (0–1)0% (0–0)
Voltage100 μV5% (3–6)0% (0–1)4% (0–9)
150 μV3% (2–3)--
500 μV4% (3–6)--
700–1500 μV4% (0–9)--
Not reported4% (3–5)1% (1–1)0% (0–0)
Neuromuscular blockadeNo3% (3–4)1% (1–2)0% (0–0)
Yes7% (2–12)1% (0–2)1% (0–1)
Not reported5% (4–6)0% (0–1)0% (0–0)
Historical cohorts refer to the studies that reported no use of IONM either before IONM introduction or for not using it after production. CI: confidence interval; IONM: intraoperative nerve monitoring; RLNI: recurrent laryngeal nerve injury.
Table 4. Meta-regression models predicting the risk of unilateral and bilateral transient RLNI based on IONM characteristics.
Table 4. Meta-regression models predicting the risk of unilateral and bilateral transient RLNI based on IONM characteristics.
Transient Unilateral RLNICoefficientSEZp ValueLow CIHigh CI
  Continuous IONM (vs. Intermittent)−1.1960.597−2.0000.045−2.365−0.027
  IONM Model (Reference: Medtronic NIM 3.0)
    Medtronic (version not specified)−1.0990.549−2.0000.045−2.176−0.022
    Medtronic Xomed 2.02.6430.9162.8900.0040.8474.438
    Neurosign System0.5390.4401.2200.221−0.3241.402
    Medtronic NIM 2.0−1.9310.801−2.4100.016−3.500−0.362
    Inomed System−2.9243.059−0.9600.339−8.9193.071
    CLEO nerve monitor0.9891.1340.8700.383−1.2333.211
  Amplitude (per mA change)0.9250.7681.2000.228−0.5802.429
  Neuromuscular blockade use (vs. non)−0.5280.387−1.3600.172−1.2860.230
  Constant−0.5171.438−0.3600.719−3.3352.301
  Model FitR2 = 100%; I2 = 0%
Transient Bilateral RLNICoefficientSEZp ValueLow CIHigh CI
  Continuous IONM (vs. Intermittent)1.0152.5530.4000.691−3.9896.019
  IONM Model (Reference: Medtronic NIM 3.0)
    Medtronic (version not specified)−1.2552.418−0.5200.604−5.9953.484
    Medtronic Xomed 2.01.4981.7010.8800.378−1.8364.831
    Neurosign System0.2620.8580.3000.761−1.4201.944
    Medtronic NIM 2.0−0.1722.283−0.0800.940−4.6464.302
  Amplitude (per mA change)1.6972.0930.8100.417−2.4055.799
  Neuromuscular blockade use (vs. non)−0.0120.719−0.0200.986−1.4211.396
  Constant−3.4364.202−0.8200.414−11.6724.800
  Model FitR2 = 100%; I2 = 0%
SE: standard error; CI: confidence interval; IONM: intraoperative nerve monitoring; RLNI: recurrent laryngeal nerve injury.
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Merchavy, S.; Kassem, K.; Awawde, R.; Abd Elhadi, U.; Safia, A. Intraoperative Nerve Monitoring Parameters and Risk of Recurrent Laryngeal Nerve Injury in Thyroidectomy: A Systematic Review and Meta-Analysis. Biomedicines 2025, 13, 2516. https://doi.org/10.3390/biomedicines13102516

AMA Style

Merchavy S, Kassem K, Awawde R, Abd Elhadi U, Safia A. Intraoperative Nerve Monitoring Parameters and Risk of Recurrent Laryngeal Nerve Injury in Thyroidectomy: A Systematic Review and Meta-Analysis. Biomedicines. 2025; 13(10):2516. https://doi.org/10.3390/biomedicines13102516

Chicago/Turabian Style

Merchavy, Shlomo, Kenan Kassem, Rifat Awawde, Uday Abd Elhadi, and Alaa Safia. 2025. "Intraoperative Nerve Monitoring Parameters and Risk of Recurrent Laryngeal Nerve Injury in Thyroidectomy: A Systematic Review and Meta-Analysis" Biomedicines 13, no. 10: 2516. https://doi.org/10.3390/biomedicines13102516

APA Style

Merchavy, S., Kassem, K., Awawde, R., Abd Elhadi, U., & Safia, A. (2025). Intraoperative Nerve Monitoring Parameters and Risk of Recurrent Laryngeal Nerve Injury in Thyroidectomy: A Systematic Review and Meta-Analysis. Biomedicines, 13(10), 2516. https://doi.org/10.3390/biomedicines13102516

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