Objective: To investigate the risk factors for inadvertent perioperative hypothermia(IPH) in gynecological malignancy patients under general anesthesia and establish a prediction model. Methods: A total of 420 gynecological malignancy patients who underwent surgery under general anesthesia at our hospital from June 2022 to December 2023 were selected and divided into modeling set and validation set at a ratio of 6∶4. General demographic data, past medical history, surgery-related information, laboratory findings, and other data were collected. Patients were classified into IPH group and non-IPH group based on whether IPH occurred. Univariate and binary Logistic regression analyses were used to screen for influencing factors of IPH in gynecological malignancy patients under general anesthesia. A nomogram prediction model was established using R language, and its discriminative ability and calibration were evaluated using receiver operating characteristic(ROC) curve, Hosmer-Lemeshow goodness-of-fit test, and calibration curve. Decision curve analysis was used to determine the clinical effectiveness of the model. Results: In the modeling set, univariate analysis showed that the IPH group had higher proportions of age≥60 years, longer operation time, longer anesthesia time, larger intraoperative fluid infusion volume, and larger intraoperative peritoneal lavage volume compared with the non-IPH group, while body mass index, preoperative hematocrit, and preoperative hemoglobin levels were lower(P<0.05). Binary Logistic regression analysis showed that age, operation time, anesthesia time, intraoperative fluid infusion volume, and intraoperative peritoneal lavage volume were risk factors for IPH in gynecological malignancy patients under general anesthesia, while body mass index and preoperative hematocrit were protective factors(P<0.05). The ROC curve of the modeling set showed that the area under the curve(AUC) of the nomogram prediction model was 0.927(0.894-0.960), and the ROC curve of the validation set showed that the AUC was 0.941(0.904-0.978), indicating good discriminative ability of the model. The Hosmer-Lemeshow goodness-of-fit test and calibration curve of both modeling and validation sets showed good calibration ability of the model. Decision curve analysis showed that the model had good clinical net benefit. Conclusion: The influencing factors for IPH in gynecological malignancy patients under general anesthesia included age, body mass index, operation time, anesthesia time, intraoperative fluid infusion volume, intraoperative peritoneal lavage volume, and preoperative hematocrit. Based on these factors,the prediction model was established and demonstrated good predictive performance and clinical utility. |
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