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Esophageal Heat Transfer for Patient Temperature Control and Targeted Temperature Management
Published on: November 21, 2017
Risk prediction models for inadvertent intraoperative hypothermia in surgical patients: a systematic review and
Pengfei Yang1, Mengru Liu2, Qi Wang3
1Department of Nursing, Shanghai General Hospital, Shanghai Jiao Tong University, No 86 Wujin Road, Shanghai, 200080, China.
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). The most common predictors included age, baseline body temperature, intraoperative fluid volume, duration of anesthesia, operating room temperature, duration of surgery, intraoperative blood loss, volume of irrigation fluid, type of anesthesia, hypothyroidism, and type of surgery. Meta-analyses identified preoperative temperature (OR: 0.29, 95% CI: 0.21-0.42), BMI (OR: 0.82, 95%CI: 0.78-0.87), operating room temperature (OR: 0.50, 95% CI: 0.36-0.68), and preoperative heart rate (OR: 0.98, 95% CI: 0.97-0.99) as protective factors, while anesthesia duration (OR: 1.01, 95% CI: 1.00-1.02), surgical duration (OR: 1.02, 95% CI: 1.01-1.03), and age (OR: 1.04, 95% CI: 1.02-1.06) were associated with increased risk. The PROBAST assessment indicated a high risk of bias in most included studies.
Conclusion:
Prediction models for IIH generally exhibit good predictive performance; however, their methodological quality requires improvement. Future research should adhere to established reporting guidelines, employ rigorous internal and external validation strategies, and aim for a standardized core set of predictors to improve model comparability and clinical applicability.
