The journal of obstetrics and gynaecology research · 2026
Preoperative Prediction of the Risk of Intraoperative Hypothermia in Patients Undergoing Gynecological Laparoscopic Surgery: A Retrospective Cohort Study.
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Huang Li, Liu Bo, Lai Dechao, Xu Liansheng
Аннотация
BACKGROUND: Intraoperative hypothermia (IOH) is a common complication in patients undergoing gynecological laparoscopic surgery that could benefit from early accurate identification of high-risk individuals. We developed a prediction model to predict IOH using only routine preoperative variables.
METHODS: This study utilized a retrospective cohort dataset from a single center, including patients undergoing gynecological laparoscopic surgery. Candidate predictors were extracted from electronic health records (EHRs) and anesthesia records. Stepwise logistic regression based on the Akaike information criterion (AIC) was applied for feature selection. A multivariable logistic regression model was developed and evaluated using an internal train-test split of the same single-center dataset. A nomogram was constructed to visualize the prediction model. Model performance was assessed using the area under the curve (AUC), calibration curve, Hosmer-Lemeshow test, and decision curve analysis.
RESULTS: A total of 1092 patients were included in this study, of which 433 cases (39.7%) were diagnosed with IOH. After feature selection using stepwise logistic regression, the model retained six key predictors: age, fasting duration, basal temperature, emergency surgery, estimated duration of surgery, and anesthesia induction time. The multivariable logistic regression model achieved an AUC of 0.7731 in the training set and 0.7475 in the test set. The Hosmer-Lemeshow test indicated good calibration (χ2 = 3.8795, df = 8, p = 0.8678).
CONCLUSIONS: The logistic regression model performed well in predicting the risk of IOH in patients undergoing gynecological laparoscopic surgery. A nomogram was developed to provide an intuitive tool for preoperative risk screening.
METHODS: This study utilized a retrospective cohort dataset from a single center, including patients undergoing gynecological laparoscopic surgery. Candidate predictors were extracted from electronic health records (EHRs) and anesthesia records. Stepwise logistic regression based on the Akaike information criterion (AIC) was applied for feature selection. A multivariable logistic regression model was developed and evaluated using an internal train-test split of the same single-center dataset. A nomogram was constructed to visualize the prediction model. Model performance was assessed using the area under the curve (AUC), calibration curve, Hosmer-Lemeshow test, and decision curve analysis.
RESULTS: A total of 1092 patients were included in this study, of which 433 cases (39.7%) were diagnosed with IOH. After feature selection using stepwise logistic regression, the model retained six key predictors: age, fasting duration, basal temperature, emergency surgery, estimated duration of surgery, and anesthesia induction time. The multivariable logistic regression model achieved an AUC of 0.7731 in the training set and 0.7475 in the test set. The Hosmer-Lemeshow test indicated good calibration (χ2 = 3.8795, df = 8, p = 0.8678).
CONCLUSIONS: The logistic regression model performed well in predicting the risk of IOH in patients undergoing gynecological laparoscopic surgery. A nomogram was developed to provide an intuitive tool for preoperative risk screening.