Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Res Nurs Health. 2022 Apr;45(2):230-239. doi: 10.1002/nur.22199. Epub 2021 Nov 24.
Healthcare disparities in the initial management of patients with acute coronary syndrome (ACS) exist. Yet, the complexity of interactions between demographic, social, economic, and geospatial determinants of health hinders incorporating such predictors in existing risk stratification models. We sought to explore a machine-learning-based approach to study the complex interactions between the geospatial and social determinants of health to explain disparities in ACS likelihood in an urban community. This study identified consecutive patients transported by Pittsburgh emergency medical service for a chief complaint of chest pain or ACS-equivalent symptoms. We extracted demographics, clinical data, and location coordinates from electronic health records. Median income was based on US census data by zip code. A random forest (RF) classifier and a regularized logistic regression model were used to identify the most important predictors of ACS likelihood. Our final sample included 2400 patients (age 59 ± 17 years, 47% Females, 41% Blacks, 15.8% adjudicated ACS). In our RF model (area under the receiver operating characteristic curve of 0.71 ± 0.03) age, prior revascularization, income, distance from hospital, and residential neighborhood were the most important predictors of ACS likelihood. In regularized regression (akaike information criterion = 1843, bayesian information criterion = 1912, χ = 193, df = 10, p < 0.001), residential neighborhood remained a significant and independent predictor of ACS likelihood. Findings from our study suggest that residential neighborhood constitutes an upstream factor to explain the observed healthcare disparity in ACS risk prediction, independent from known demographic, social, and economic determinants of health, which can inform future work on ACS prevention, in-hospital care, and patient discharge.
医疗保健在急性冠脉综合征 (ACS) 患者的初始管理中存在差异。然而，人口统计学、社会、经济和地理空间健康决定因素之间相互作用的复杂性阻碍了将这些预测因素纳入现有的风险分层模型中。我们试图探索一种基于机器学习的方法来研究地理空间和健康社会决定因素之间的复杂相互作用，以解释城市社区中 ACS 可能性的差异。这项研究确定了连续的患者由匹兹堡紧急医疗服务运送，主要抱怨胸痛或 ACS 等效症状。我们从电子健康记录中提取人口统计学、临床数据和位置坐标。中位数收入基于邮政编码的美国人口普查数据。随机森林 (RF) 分类器和正则化逻辑回归模型用于确定 ACS 可能性的最重要预测因素。我们的最终样本包括 2400 名患者（年龄 59±17 岁，47%为女性，41%为黑人，15.8%为 ACS 裁定）。在我们的 RF 模型中（接收者操作特征曲线下面积为 0.71±0.03），年龄、先前的血运重建、收入、距离医院的距离和居住社区是 ACS 可能性的最重要预测因素。在正则化回归（akaike 信息准则=1843，贝叶斯信息准则=1912，χ=193，df=10，p<0.001）中，居住社区仍然是 ACS 可能性的重要且独立的预测因素。我们的研究结果表明，居住社区是解释观察到的 ACS 风险预测中医疗保健差异的上游因素，独立于已知的人口统计学、社会和经济健康决定因素，这可以为 ACS 预防、住院护理和患者出院提供信息。
