ABSTRACT

Background: The primary objective of this study is to identify non-laboratory predictors for 30-day hospital readmission and 180-day in-hospital mortality rates among patients hospitalized with ischemic heart disease (IHD).

Research design and methods: This is a retrospective cohort study of hospitalized patients (≥ 40 years) with a primary diagnosis of IHD. Data were extracted from the Florida Agency for Health Care Administration dataset from 2006 to 2016. A machine learning approach was used to identify predictors of 30-day hospital readmission and 180-day in-hospital mortality.

Results: 346,390 patient records for incident IHD cases were identified. The top two predictors of 30-day readmission were the length of stay and the Elixhauser comorbidity index for readmission [ECI] (Area Under the Curve [AUC]=88%) using decision tree algorithms. For in-hospital mortality, the top two predictors were LOS and ECI (AUC=92%) using gradient boosting regressors. The cumulative 30-day readmission and the 180-day probability of mortality rates were 9.82% and 4.6% respectively.

Conclusions: Risk factors of 30-day readmission and 180-day mortality in hospitalized IHD patients identified by machine learning and their relative importance (value) will help pharmacists and other health care providers to prioritize their disease management strategies as they improve the care provided to IHD patients.

摘要

背景：本研究的主要目的是确定缺血性心脏病 (IHD) 住院患者 30 天再入院率和 180 天院内死亡率的非实验室预测因素。

研究设计和方法：这是一项对初步诊断为 IHD 的住院患者（≥ 40 岁）的回顾性队列研究。从 2006 年至 2016 年佛罗里达州卫生保健管理局数据集提取数据。使用机器学习方法来确定 30 天再入院和 180 天住院死亡率的预测因素。

结果：确定了 346,390 份 IHD 事件患者记录。30 天再入院的前两个预测因素是使用决策树算法的住院时间和再入院的 Elixhauser 合并症指数 [ECI]（曲线下面积 [AUC]=88%）。对于院内死亡率，使用梯度提升回归量的前两个预测因子是 LOS 和 ECI (AUC=92%)。累计 30 天再入院率和 180 天死亡率概率分别为 9.82% 和 4.6%。

结论：通过机器学习确定的住院 IHD 患者 30 天再入院和 180 天死亡率的危险因素及其相对重要性（价值）将帮助药剂师和其他医疗保健提供者在改善为患者提供的护理时优先考虑他们的疾病管理策略。 IHD 患者。