Machine Learning-Based Models Incorporating Social Determinants of Health vs Traditional Models for Predicting In-Hospital Mortality in Patients With Heart Failure
Machine Learning-Based Models Incorporating Social Determinants of Health vs Traditional Models for Predicting In-Hospital Mortality in Patients With Heart Failure
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DOI：
10.1001/jamacardio.2022.1900
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发表时间：
2022-07-06
期刊：
影响因子：
24
通讯作者：
Pandey, Ambarish
中科院分区：
文献类型：
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作者：
Segar, Matthew W.;Hall, Jennifer L.;Pandey, Ambarish
重要性预测心力衰竭(HF)患者住院死亡率的传统模型使用Logistic回归，没有考虑健康的社会决定因素(SDOH)。目的开发和验证新的机器学习(ML)模型，该模型包含SDOH。设计、设置和参与者这项回顾性研究使用了GET WITH指南-心力衰竭(GWTG-HF)登记的数据来确定2010年1月1日至2020年12月31日期间心力衰竭的住院情况。这项研究包括在研究期间在GWTG-HF参与中心住院的急性失代偿性心衰患者。数据分析于2021年1月6日至2022年4月26日进行。在2005年至2014年的社区动脉粥样硬化风险(ARIC)研究的住院队列中进行了外部验证。MAIN结果和测量采用基于随机森林的ML方法来开发种族特定和种族不可知的模型来预测住院死亡率。结果训练数据集包括GWTG-HF登记的123 634名心力衰竭住院患者(平均年龄71[13]岁；女性58356例[47.2%]；男性65278例[52.8%])。患者被分析为两类：黑人(23453例(19.0%))和非黑人(2121例(2.1%)亚裔；91154例(91.0%)白人，以及6906例(6.9%)其他种族和民族)。ML模型在内部测试子集(n=82 420)(C统计，黑人患者0.81，非黑人患者0.82)和在协变量错失率小于50%的真实队列中(n=553506；C统计，黑人患者0.74，非黑人患者0.75)中表现出优异的性能。在外部验证队列中(ARIC登记；n=1205名黑人患者和2264名非黑人患者)，ML模型显示出高度的区分性和充分的校正(C统计，分别为0.79和0.80)。此外，ML模型的性能优于传统的GWTG-HF风险评分模型(C指数，两个种族组均为0.69)和其他以RACE为协变量的重新推导的Logistic回归模型。在GWTG-HF和外部验证队列中，使用种族特定和种族不可知的方法，ML模型的性能是相同的。在GWTG-HF队列中，在只有临床协变量的ML模型中加入邮政编码水平的SDOH参数与黑人患者更好的区分度、预后效用(使用决策曲线进行评估)和模型重分类指标(净重分类改善，0.22[95%CI，0.14-0.30]；P
IMPORTANCE Traditional models for predicting in-hospital mortality for patients with heart failure (HF) have used logistic regression and do not account for social determinants of health (SDOH).OBJECTIVE To develop and validate novel machine learning (ML) models for HF mortality that incorporate SDOH.DESIGN, SETTING, AND PARTICIPANTS This retrospective study used the data from the Get With The Guidelines-Heart Failure (GWTG-HF) registry to identify HF hospitalizations between January 1, 2010, and December 31, 2020. The study included patients with acute decompensated HF who were hospitalized at the GWTG-HF participating centers during the study period. Data analysis was performed January 6, 2021, to April 26, 2022. External validation was performed in the hospitalization cohort from the Atherosclerosis Risk in Communities (ARIC) study between 2005 and 2014.MAIN OUTCOMES AND MEASURES Random forest-based ML approacheswere used to develop race-specific and race-agnostic models for predicting in-hospital mortality. Performance was assessed using C index (discrimination), regression slopes for observed vs predicted mortality rates (calibration), and decision curves for prognostic utility.RESULTS The training data set included 123 634 hospitalized patients with HF who were enrolled in the GWTG-HF registry (mean [SD] age, 71 [13] years; 58 356 [47.2%] female individuals; 65 278 [52.8%] male individuals. Patients were analyzed in 2 categories: Black (23 453 [19.0%]) and non-Black (2121 [2.1%] Asian; 91 154 [91.0%] White, and 6906 [6.9%] other race and ethnicity). TheML models demonstrated excellent performance in the internal testing subset (n = 82 420) (C statistic, 0.81 for Black patients and 0.82 for non-Black patients) and in the real-world-like cohort with less than 50% missingness on covariates (n = 553 506; C statistic, 0.74 for Black patients and 0.75 for non-Black patients). In the external validation cohort (ARIC registry; n = 1205 Black patients and 2264 non-Black patients), ML models demonstrated high discrimination and adequate calibration (C statistic, 0.79 and 0.80, respectively). Furthermore, the performance of the ML models was superior to the traditional GWTG-HF risk score model (C index, 0.69 for both race groups) and other rederived logistic regression models using race as a covariate. The performance of the ML models was identical using the race-specific and race-agnostic approaches in the GWTG-HF and external validation cohorts. In the GWTG-HF cohort, the addition of zip code-level SDOH parameters to the ML model with clinical covariates only was associated with better discrimination, prognostic utility (assessed using decision curves), and model reclassification metrics in Black patients (net reclassification improvement, 0.22 [95% CI, 0.14-0.30]; P