CC:用于危重患者新发心房颤动预测的可解释机器学习模型：一项多中心研究

Abstract

Background

New-onset atrial fibrillation (NOAF) is the most common arrhythmia in critically ill patients admitted to intensive care and is associated with poor prognosis and disease burden. Identifying high-risk individuals early is crucial. This study aims to create and validate a NOAF prediction model for critically ill patients using machine learning (ML).

新发心房颤动（NOAF）是重症监护病房（ICU）收治的危重患者中最常见的心律失常，与不良预后和疾病负担相关。早期识别高风险个体至关重要。本研究旨在利用机器学习（ML）构建并验证一种用于危重患者的NOAF预测模型。

Methods

The data came from two non-overlapping datasets from the Medical Information Mart for Intensive Care (MIMIC), with MIMIC-IV used for training and subset of MIMIC-III used as external validation. LASSO regression was used for feature selection. Eight ML algorithms were employed to construct the prediction model. Model performance was evaluated based on identification, calibration, and clinical application. The SHapley Additive exPlanations (SHAP) method was used for visualizing model characteristics and individual case predictions.

数据来源于医疗信息市场重症监护（MIMIC）的两个非重叠数据集，其中MIMIC-IV用于训练，MIMIC-III的子集用于外部验证。采用LASSO回归进行特征选择。使用八种ML算法构建预测模型。模型性能基于识别、校准和临床应用进行评估。采用SHapley加性解释（SHAP）方法可视化模型特征和个体病例预测。

Results

Among 16,528 MIMIC-IV patients, 1520 (9.2%) developed AF post-ICU admission. A model with 23 variables was built, with XGBoost performing best, achieving an AUC of 0.891 (0.873–0.888) in validation and 0.769 (0.756–0.782) in external validation. Key predictors included age, mechanical ventilation, urine output, sepsis, blood urea nitrogen, percutaneous arterial oxygen saturation, continuous renal replacement therapy and weight. A risk probability greater than 0.6 was defined as high risk. A friendly user interface had been developed for clinician use.

在16,528名MIMIC-IV患者中，1520名（9.2%）在ICU入院后发生AF。构建了一个包含23个变量的模型，其中XGBoost表现最佳，在验证集中的AUC为0.891（0.873–0.888），在外部验证集中的AUC为0.769（0.756–0.782）。关键预测因素包括年龄、机械通气、尿量、败血症、血尿素氮、经皮动脉血氧饱和度、连续肾脏替代治疗和体重。风险概率大于0.6被定义为高风险。已开发出友好的用户界面供临床医生使用。

Conclusion

We developed a ML model to predict the risk of NOAF in critically ill patients without cardiac surgery and validated its potential as a clinically reliable tool. SHAP improves the interpretability of the model, enables clinicians to better understand the causes of NOAF, helps clinicians to prevent it in advance and improves patient outcomes.

我们开发了一种ML模型，用于预测未接受心脏手术的危重患者发生NOAF的风险，并验证了其作为临床可靠工具的潜力。SHAP提高了模型的可解释性，使临床医生能够更好地理解NOAF的成因，帮助临床医生提前预防并改善患者预后。

相关链接：https://mp.weixin.qq.com/s/-us3iQhlgJaexWS1dwHfeQ