Development and evaluation of a novel predictive nomogram for assessing the risk of intraoperative hypothermia in patients undergoing thoracoscopic pulmonary tumor surgery

• Published in: Heliyon Vol. 9; no. 12; p. e22574
• Main Authors: Zhang, Baiyi, Pan, Ai-fen
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-12-2023; Elsevier
• Subjects: Nomogram; Predictors; Thoracoscopic pulmonary tumor surgery; Unplaned intraoperative hypothermia; Thoracoscopic pulmonary tumor surgery; Unplaned intraoperative hypothermia; Nomogram; Predictors
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2023.e22574

The prevalence of unplanned intraoperative hypothermia during thoracoscopic pulmonary tumor resection under general anesthesia is considerable, which may result in numerous adverse reactions.
The aim of this study was to develop and validate a nomogram-based prediction model for assessing the risk of intraoperative hypothermia in patients undergoing thoracoscopic pulmonary tumor resection under general anesthesia.
This was a retrospective study conducted at a tertiary class A hospital.
The study included 506 adult patients who underwent thoracoscopic lung tumor resection under general anesthesia in 2022.
The clinical data of 506 patients who underwent thoracoscopic pulmonary tumor surgery from January 1 to December 31, 2022 were collected and randomly divided into the modeling group (n = 356) and the validation group (n = 50). The data of 356 patients were used establish a prediction model for intraoperative hypothermia. A total of 17 factors covering patient demographics, disease characteristics, and surgical conditions were gathered. The least absolute shrinkage and selection operator regression model was utilized to optimize the risk model's features. Multivariate logistic regression analysis was employed to construct the final predictive model.
Gender, body mass index, preoperative body temperature and operation time were used as predictors to construct the nomogram. The C-index of the model was 0.831 (95%CI: 0.799–0.863). The C-index of external validation was 0.820, which verified the calibration of the model. Decision curve analysis validated the clinical utility of the nomogram, particularly when using a threshold probability of unplanned intraoperative hypothermia 1 %.-74 %.
The nomogram constructed in this study can effectively predict the risk of intraoperative hypothermia in patients undergoing thoracoscopic lung tumor resection under general anesthesia. The nomogram incorporated readily available predictors such as sex, body mass index, preoperative body temperature, and duration of surgery.