Department of Anesthesiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, PR China.
Department of Anesthesiology, Sun Yat-sen Memorial Hospital of Sun Yat-Sen University, Guangzhou 510120, PR China.
Gynecol Oncol. 2024 Jun;185:156-164. doi: 10.1016/j.ygyno.2024.02.009. Epub 2024 Feb 29.
Hypothermia is highly common in patients undergoing gynecological surgeries under general anesthesia, so the length of hospitalization and even the risk of mortality are substantially increased. Our aim was to develop a simple and practical model to preoperatively identify gynecological surgery patients at risk of intraoperative hypothermia.
In this retrospective study, we collected data from 802 patients who underwent gynecological surgery at three medical centers from June 2022 to August 2023. We further allocated the patients to a training group, an internal validation group, or an external validation group. The preliminary predictive factors for intraoperative hypothermia in gynecological patients were determined using the least absolute shrinkage and selection operator (LASSO) method. The final predictive factors were subsequently identified through multivariate logistic regression analysis, and a nomogram for predicting the occurrence of hypothermia was established.
A total of 802 patients were included, with 314 patients in the training cohort (mean age 48.5 ± 12.6 years), 130 patients in the internal validation cohort (mean age 49.9 ± 12.5 years), and 358 patients in the external validation cohort (mean age 47.6 ± 14.0 years). LASSO regression and multivariate logistic regression analyses indicated that body mass index, minimally invasive surgery, baseline heart rate, baseline body temperature, history of previous surgery, and aspartate aminotransferase level were associated with intraoperative hypothermia in gynecological surgery patients. This nomogram was constructed based on these six variables, with a C-index of 0.712 for the training cohort.
We established a practical predictive model that can be used to preoperatively predict the occurrence of hypothermia in gynecological surgery patients.
chictr.org.cn, identifier ChiCTR2300071859.
全麻下妇科手术患者常发生低体温，这会显著延长住院时间甚至增加死亡率。本研究旨在建立一种简单实用的模型，以便术前识别妇科手术患者术中发生低体温的风险。
本回顾性研究收集了 2022 年 6 月至 2023 年 8 月期间在 3 家医疗中心接受妇科手术的 802 例患者的数据。我们将患者分为训练组、内部验证组和外部验证组。采用最小绝对值收缩和选择算子（LASSO）法确定妇科手术患者术中低体温的初步预测因素。然后通过多变量逻辑回归分析确定最终预测因素，并建立预测低体温发生的列线图。
共纳入 802 例患者，其中训练组 314 例（平均年龄 48.5±12.6 岁），内部验证组 130 例（平均年龄 49.9±12.5 岁），外部验证组 358 例（平均年龄 47.6±14.0 岁）。LASSO 回归和多变量逻辑回归分析表明，体重指数、微创手术、基础心率、基础体温、既往手术史和天冬氨酸氨基转移酶水平与妇科手术患者术中低体温有关。该列线图基于这 6 个变量构建，在训练组中的 C 指数为 0.712。
我们建立了一种实用的预测模型，可用于术前预测妇科手术患者低体温的发生。
chictr.org.cn，标识符 ChiCTR2300071859。
