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识别老年人术后全身炎症反应综合征的易感和高危人群:基于机器学习的预测模型。

Identification of a Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Older Adults: Machine Learning-Based Predictive Model.

机构信息

Department of Pharmacy, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

出版信息

J Med Internet Res. 2024 Nov 22;26:e57486. doi: 10.2196/57486.

Abstract

BACKGROUND

Systemic inflammatory response syndrome (SIRS) is a serious postoperative complication among older adult surgical patients that frequently develops into sepsis or even death. Notably, the incidences of SIRS and sepsis steadily increase with age. It is important to identify the risk of postoperative SIRS for older adult patients at a sufficiently early stage, which would allow preemptive individualized enhanced therapy to be conducted to improve the prognosis of older adult patients. In recent years, machine learning (ML) models have been deployed by researchers for many tasks, including disease prediction and risk stratification, exhibiting good application potential.

OBJECTIVE

We aimed to develop and validate an individualized predictive model to identify susceptible and high-risk populations for SIRS in older adult patients to instruct appropriate early interventions.

METHODS

Data for surgical patients aged ≥65 years from September 2015 to September 2020 in 3 independent medical centers were retrieved and analyzed. The eligible patient cohort in the Third Affiliated Hospital of Sun Yat-sen University was randomly separated into an 80% training set (2882 patients) and a 20% internal validation set (720 patients). We developed 4 ML models to predict postoperative SIRS. The area under the receiver operating curve (AUC), F score, Brier score, and calibration curve were used to evaluate the model performance. The model with the best performance was further validated in the other 2 independent data sets involving 844 and 307 cases, respectively.

RESULTS

The incidences of SIRS in the 3 medical centers were 24.3% (876/3602), 29.6% (250/844), and 6.5% (20/307), respectively. We identified 15 variables that were significantly associated with postoperative SIRS and used in 4 ML models to predict postoperative SIRS. A balanced cutoff between sensitivity and specificity was chosen to ensure as high a true positive as possible. The random forest classifier (RF) model showed the best overall performance to predict postoperative SIRS, with an AUC of 0.751 (95% CI 0.709-0.793), sensitivity of 0.682, specificity of 0.681, and F score of 0.508 in the internal validation set and higher AUCs in the external validation-1 set (0.759, 95% CI 0.723-0.795) and external validation-2 set (0.804, 95% CI 0.746-0.863).

CONCLUSIONS

We developed and validated a generalizable RF model to predict postoperative SIRS in older adult patients, enabling clinicians to screen susceptible and high-risk patients and implement early individualized interventions. An online risk calculator to make the RF model accessible to anesthesiologists and peers around the world was developed.

摘要

背景

全身炎症反应综合征(SIRS)是老年外科患者术后的一种严重并发症,常发展为脓毒症甚至死亡。值得注意的是,SIRS 和脓毒症的发生率随着年龄的增长而稳步上升。重要的是要在足够早的阶段识别老年患者术后发生 SIRS 的风险,以便进行预防性个体化强化治疗,从而改善老年患者的预后。近年来,机器学习(ML)模型已被研究人员用于许多任务,包括疾病预测和风险分层,具有很好的应用潜力。

目的

我们旨在开发和验证一种个体化预测模型,以识别老年患者发生 SIRS 的易感和高危人群,从而指导适当的早期干预。

方法

检索并分析了 2015 年 9 月至 2020 年 9 月来自 3 家独立医疗中心≥65 岁手术患者的数据。中山大学附属第三医院的合格患者队列被随机分为 80%的训练集(2882 例患者)和 20%的内部验证集(720 例患者)。我们开发了 4 种 ML 模型来预测术后 SIRS。使用受试者工作特征曲线下面积(AUC)、F 分数、Brier 分数和校准曲线来评估模型性能。在另外 2 个分别包含 844 例和 307 例病例的独立数据集上进一步验证表现最佳的模型。

结果

3 家医疗中心的 SIRS 发生率分别为 24.3%(876/3602)、29.6%(250/844)和 6.5%(20/307)。我们确定了 15 个与术后 SIRS 显著相关的变量,并将其用于 4 种 ML 模型来预测术后 SIRS。选择一个在敏感性和特异性之间平衡的截断值,以确保尽可能高的真阳性率。随机森林分类器(RF)模型在预测术后 SIRS 方面表现出最佳的整体性能,在内部验证集中的 AUC 为 0.751(95%CI 0.709-0.793),敏感性为 0.682,特异性为 0.681,F 分数为 0.508,在外部验证集 1(0.759,95%CI 0.723-0.795)和外部验证集 2(0.804,95%CI 0.746-0.863)中的 AUC 更高。

结论

我们开发并验证了一种可推广的 RF 模型,用于预测老年患者术后 SIRS,使临床医生能够筛选易感和高危患者,并实施早期个体化干预。我们开发了一个在线风险计算器,使 RF 模型可供世界各地的麻醉师和同行使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9775/11624453/176a5fcd8487/jmir_v26i1e57486_fig1.jpg

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