Gao Zhenyue, Liu Xiaoli, Kang Yu, Hu Pan, Zhang Xiu, Li Mengwei, Peng Yumeng, Yan Wei, Yan Muyang, Yu Pengming, Zhang Zhengbo, Zhang Qing, Xiao Wendong
Medical Innovation Research Department, The General Hospital of PLA, Beijing, China.
Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.
PLoS One. 2025 Sep 3;20(9):e0327979. doi: 10.1371/journal.pone.0327979. eCollection 2025.
There is limited evidence on how social determinants of health (SDOH) and physical frailty (PF) influence mortality prediction in heart failure (HF), particularly for in-hospital, 90-day, and 1-year outcomes. This study aims to develop explainable machine learning (ML) models to assess the prognostic value of SDOH and PF at multiple time points. We analyzed data from adult patients admitted to the intensive care unit (ICU) for the first time with a diagnosis of HF. Key variables extracted from electronic health records included SDOH (e.g., primary language, insurance type), PF indicators (Braden mobility, nutrition, activity, and fall risk scores), vital signs, laboratory tests, and lung sounds (LS) from both ICU admission and discharge. We employed the eXtreme Gradient Boosting (XGBoost) algorithm to build models for short- and long-term mortality prediction, and used SHapley Additive exPlanations (SHAP) to interpret model outputs and quantify the importance of each feature. The observed mortality rates were 14.8% in-hospital (n = 12,856), 7.0% at 90 days (n = 10,990), and 13.5% at 1 year (n = 10,221). The prediction models achieved area under the receiver operating characteristic curve (AUROC) scores of 0.836 (95% CI: 0.831-0.844) for in-hospital, 0.790 (95% CI: 0.780-0.800) for 90-day, and 0.789 (95% CI: 0.780-0.799) for 1-year mortality. These models outperformed baseline ML algorithms and conventional clinical risk scores. Key predictors of HF outcomes included age, fall risk, primary language, blood urea nitrogen, comorbidities, urine output, insurance type, and LS findings. Incorporating PF at ICU admission and discharge, along with SDOH such as language proficiency and insurance status, could enhance the identification of high-risk HF patients and may inform targeted interventions.
关于健康的社会决定因素（SDOH）和身体虚弱（PF）如何影响心力衰竭（HF）的死亡率预测，证据有限，尤其是对于住院、90天和1年的结局。本研究旨在开发可解释的机器学习（ML）模型，以评估SDOH和PF在多个时间点的预后价值。我们分析了首次因HF诊断入住重症监护病房（ICU）的成年患者的数据。从电子健康记录中提取的关键变量包括SDOH（如主要语言、保险类型）、PF指标（Braden活动能力、营养、活动和跌倒风险评分）、生命体征、实验室检查以及ICU入院和出院时的肺部声音（LS）。我们采用极端梯度提升（XGBoost）算法构建短期和长期死亡率预测模型，并使用Shapley加性解释（SHAP）来解释模型输出并量化每个特征的重要性。观察到的住院死亡率为14.8%（n = 12,856），90天时为7.0%（n = 10,990），1年时为13.5%（n = 10,221）。预测模型的受试者工作特征曲线下面积（AUROC）得分在住院时为0.836（95%CI：0.831 - 0.844），90天时为0.790（95%CI：0.780 - 0.800），1年时为0.789（95%CI：0.780 - 0.799）。这些模型优于基线ML算法和传统临床风险评分。HF结局的关键预测因素包括年龄、跌倒风险、主要语言、血尿素氮、合并症、尿量、保险类型和LS检查结果。在ICU入院和出院时纳入PF，以及语言能力和保险状况等SDOH，可增强对高危HF患者的识别，并可为有针对性的干预提供依据。
