Daily ReportSep 17, 2026
Anesthesiology, September 17 edition
We read 27 papers and selected 3.
Summary
Today's most impactful studies address neuromuscular monitoring, objective assessment of anesthetic analgesia, and prediction of inadvertent intraoperative hypothermia. The strongest contributions combine randomized prospective methodology or broad evidence synthesis with direct implications for monitoring, perioperative safety, and future validation studies.
Research Themes
- Neuromuscular monitoring and recovery assessment
- Objective monitoring of analgesia during general anesthesia
- Risk prediction and prevention of intraoperative hypothermia
Selected Articles
1. Prospective, randomized comparison of electromyographic neuromuscular responses from the adductor pollicis and abductor digiti minimi muscles during rocuronium-induced neuromuscular block.
In 44 patients, simultaneous electromyographic monitoring of the adductor pollicis and abductor digiti minimi showed no overall differences in rocuronium onset or sugammadex recovery to a train-of-four ratio of at least 0.9 or 1.0. However, when the dominant right hand was monitored, abductor digiti minimi recovery was approximately 20% to 25% faster than adductor pollicis recovery.
Impact: This study directly tests whether a commonly used alternative monitoring muscle can substitute for the guideline-preferred adductor pollicis. Its clinically actionable finding is that hand dominance and monitoring side may affect recovery interpretation, despite broad equivalence between muscles.
Clinical Implications: The abductor digiti minimi can generally be used for routine electromyographic neuromuscular monitoring, particularly on the non-dominant arm. Clinicians should interpret recovery thresholds cautiously when monitoring the dominant hand and should avoid assuming complete interchangeability in all settings.
Key Findings
- No significant overall differences were found between adductor pollicis and abductor digiti minimi for rocuronium block onset.
- No significant overall differences were found in recovery to a train-of-four ratio of at least 0.9 or to 1.0 after sugammadex.
- On the dominant right hand, abductor digiti minimi recovery was 93 seconds faster to a train-of-four ratio of at least 0.9 and 105 seconds faster to a ratio of 1.0.
Methodological Strengths
- Prospective randomized selection of the monitored hand and simultaneous electromyographic recordings reduced measurement and allocation bias.
- The study assessed clinically meaningful onset and quantitative recovery endpoints using standardized train-of-four thresholds.
Limitations
- The sample size was modest at 44 patients, limiting precision and subgroup analyses.
- The findings may not generalize to other neuromuscular blockers, monitoring technologies, or patients with neuromuscular disease.
Future Directions: Larger multicenter studies should examine hand dominance, electrode placement, skin impedance, and different neuromuscular blocking and reversal agents. Studies should also determine whether the observed recovery-time differences affect extubation decisions or residual paralysis detection.
BACKGROUND: Many clinicians monitor the abductor digiti minimi (ADM) muscle during surgery using electromyography (EMG) for ease and consistency of electrode placement, while practice guidelines recommend monitoring of responses at the adductor pollicis (AP) muscle. We compared the onset time of rocuronium neuromuscular block and the sugammadex-induced recovery time recorded simultaneously from the AP and ADM muscles. METHODS: In 44 consenting patients undergoing general anesthesia, simultaneous EMG recording of ADM and AP muscles was performed.
2. Contact heat evoked potentials as a measure of the analgesic component of anesthesia - a patient study.
In 119 analyzed patients randomized to four remifentanil infusion rates, remifentanil dose correlated inversely with pain ratings and modestly with contact heat evoked potential amplitude. However, contact heat evoked potentials did not correlate with pain ratings, were not consistently detectable, and disappeared after propofol-induced loss of responsiveness, indicating that they cannot directly distinguish analgesia from sedation.
Impact: The study rigorously evaluates a proposed physiologic biomarker for analgesia and reports an important negative result. Demonstrating that contact heat evoked potentials are confounded by sedation helps prevent premature adoption of an unreliable intraoperative analgesia monitor.
Clinical Implications: Contact heat evoked potentials should not currently be used as a standalone monitor of the analgesic component of general anesthesia. Clinical teams should continue to integrate hemodynamic responses, validated behavioral or nociception measures, drug dosing, and overall anesthetic context.
Key Findings
- Remifentanil dose showed a strong inverse correlation with visual analog scale pain ratings.
- Remifentanil dose correlated with contact heat evoked potential amplitude, but contact heat evoked potential amplitude did not correlate with pain ratings.
- Contact heat evoked potentials were no longer detectable after propofol-induced loss of responsiveness, indicating substantial sensitivity to sedation.
Methodological Strengths
- The randomized allocation across four predefined remifentanil infusion rates enabled dose-response assessment.
- Pain ratings, evoked potentials, sedation scores, and middle-latency auditory evoked potentials were assessed together, allowing evaluation of analgesic-sedative confounding.
Limitations
- Contact heat evoked potentials could not be detected in all patients at all time points, limiting reliability and completeness of the outcome.
- The experimental setting used healthy ASA physical status I or II adults and may not represent surgical patients or other anesthetic regimens.
Future Directions: Future research should identify physiologic signals that remain measurable during sedation and specifically separate nociception from consciousness. Multimodal algorithms combining evoked potentials with autonomic, electroencephalographic, and pharmacologic data may be more informative than a single electrophysiologic signal.
To assess the applicability of contact heat evoked potentials (CHEPs) as a direct measure of analgesia, we analyzed the following endpoints. (1) Correlation between remifentanil-induced changes in VAS and CHEP amplitudes. (2) Correlation between CHEP amplitude reduction and remifentanil infusion rate. Furthermore, we investigated the sedative effects of remifentanil and the effect of propofol-induced loss of responsiveness (PI-LOR) on CHEPs. After determination of the individual pain threshold (visual analog scale (VAS) of 10), 120 adult ASA physical status I or II patients randomly received remifentanil in one of four predefined infusion rates.
3. Risk prediction models for inadvertent intraoperative hypothermia in surgical patients: a systematic review and meta-analysis.
This systematic review and meta-analysis included 47 studies and 77 prediction models for inadvertent intraoperative hypothermia. Model discrimination was generally good, with AUC values from 0.683 to 0.968, but most studies had high risk of bias under PROBAST. Age, baseline temperature, operating-room temperature, anesthesia duration, and surgical duration were among the most consistently identified predictors.
Impact: This work maps the current prediction-model landscape for a common and preventable perioperative complication while explicitly identifying limitations that may impede clinical implementation. Its emphasis on external validation, standardized predictors, and reporting quality provides a practical research agenda.
Clinical Implications: Existing models may help identify patients at increased risk of intraoperative hypothermia and guide targeted warming strategies, but they should not be adopted uncritically. Institutions should assess local calibration and prioritize models with transparent predictors, robust validation, and clinically usable thresholds.
Key Findings
- Forty-seven studies containing 77 prediction models were included.
- Model AUC values ranged from 0.683 to 0.968, and 51 models had AUC values above 0.700.
- Higher age, longer anesthesia duration, and longer surgical duration were associated with increased risk, whereas higher preoperative temperature, BMI, operating-room temperature, and preoperative heart rate were associated with lower risk.
- Most included studies had high risk of bias according to PROBAST.
Methodological Strengths
- The review searched multiple international and Chinese databases and used independent screening and data extraction.
- Risk of bias was assessed with PROBAST, and predictor effects were quantitatively synthesized through meta-analysis.
Limitations
- Most included prediction-model studies had high risk of bias, limiting confidence in their reported performance.
- Heterogeneity in predictor definitions, outcome thresholds, perioperative settings, and validation methods limits direct comparison and immediate clinical implementation.
Future Directions: Future studies should use transparent reporting standards, prespecified predictor sets, contemporary perioperative data, and rigorous internal and external validation. Prospective impact studies are needed to determine whether model-guided warming reduces hypothermia, complications, resource use, or costs.
OBJECTIVE: To systematically evaluate and meta-analyze the available risk prediction models for inadvertent intraoperative hypothermia (IIH) in surgical patients, and to summarize the methodological quality, predictive performance, and identified predictors. METHODS: We searched databases including PubMed, Embase, Web of Science, Cochrane Library, CNKI, and Wanfang from inception until December 30, 2025 for studies on IIH risk prediction models. Two researchers independently screened the literature, extracted data, and assessed the risk of bias using the PROBAST tool. A meta-analysis was performed on identified predictors. RESULTS: A total of 47 studies comprising 77 prediction models were included. The area under the receiver operating characteristic curve (AUC) for the models ranged from 0.683 to 0.968, with 51 models demonstrating good predictive performance (AUC > 0.700).