机器学习模型是否比传统统计模型更能提高腹部手术部位感染的预测效果？
Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?
机构信息
Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Department of Research and Medical Innovation, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Bangkok, Thailand.
出版信息
J Int Med Res. 2024 Nov;52(11):3000605241293696. doi: 10.1177/03000605241293696.
OBJECTIVE
To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches.
METHODS
A retrospective analysis of hospital database-derived data from patients that had undergone gastrointestinal, colorectal and hernia surgeries (identified by ICD-9-CM). The SENIC index was calculated and fitted in an LR. MLs were developed using decision-tree (DT), random forest (RF), extreme-gradient-boosting (XGBoost) and Naïve Bayes (NB).
RESULTS
The prevalence of an SSI was 3.21% (404 of 12 596 surgeries; 95% confidence interval [CI] 2.91%, 3.53%). The C-statistic for the original SENIC model was 0.668 (95% CI 0.648, 0.688) with an observed/expected (O/E) ratio of 0.998 (interquartile range [IQR] 0.750, 1.047). An updated-SENIC-LR model with six predictors had a C-statistic of 0.768 (95% CI 0.745, 0.790) and O/E ratio of 0.999 (IQR 0.976, 1.004). The performance of MLs considering 14 predictors was poorer than the updated-SENIC-LR with C-statistics of 0.679, 0.675, 0.656 and 0.651 for NB, XGBoost, RF and DT, respectively. Overfitting was detected for ML approaches, particularly for DT, RF and XGBoost.
CONCLUSION
The updated-SENIC-LR model and NB may be useful for monitoring SSI risk following abdominal surgery.
目的
通过修订和更新使用逻辑回归（LR）和机器学习（ML）方法的手术部位感染（SSI）医院感染控制（SENIC）模型的研究，对其进行外部验证。
方法
对来自接受胃肠道、结直肠和疝手术的患者的医院数据库衍生数据进行回顾性分析（通过 ICD-9-CM 识别）。计算 SENIC 指数并拟合到 LR 中。使用决策树（DT）、随机森林（RF）、极端梯度增强（XGBoost）和朴素贝叶斯（NB）开发 ML。
结果
SSI 的患病率为 3.21%（12596 例手术中的 404 例；95%置信区间[CI] 2.91%，3.53%）。原始 SENIC 模型的 C 统计量为 0.668（95%CI 0.648，0.688），观察到/预期（O/E）比值为 0.998（四分位距[IQR] 0.750，1.047）。具有六个预测因子的更新-SENIC-LR 模型的 C 统计量为 0.768（95%CI 0.745，0.790），O/E 比值为 0.999（IQR 0.976，1.004）。考虑 14 个预测因子的 ML 性能均不如更新后的 SENIC-LR，C 统计量分别为 NB、XGBoost、RF 和 DT 的 0.679、0.675、0.656 和 0.651。ML 方法存在过拟合现象，尤其是 DT、RF 和 XGBoost。
结论
更新后的 SENIC-LR 模型和 NB 可能有助于监测腹部手术后的 SSI 风险。
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