使用行政和索赔数据预测COPD医疗补助患者的医院再入院情况
Daniel F Heitjan1, Yifei Wang2, Jaehyeon Yun3
1Department of Statistics and Data Science, Southern Methodist University, Dallas, Texas; and Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas. dheitjan@smu.edu.
Respiratory care
|March 26, 2024
概括
这项研究开发了一种预测模型,用于慢性阻塞性肺部疾病 (COPD) 的住院医疗补助患者的30天全因再入院. 该模型准确地识别了针对性干预的高风险患者.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 肺部病理学 肺部病理学
- 医疗信息学 医疗信息学
背景情况:
- 患有慢性阻塞性肺病 (COPD) 的住院患者面临着高的再入院率.
- 患有COPD的医疗补助受益人代表了一个有着大量医疗利用的弱势群体.
- 预测30天所有原因的再入院对于改善患者的治疗结果和管理医疗保健成本至关重要.
研究的目的:
- 开发和验证30天所有原因再入院的预测模型,用于诊断为COPD的住院医疗补助患者.
- 确定导致该特定患者队列再接收风险的关键因素.
- 为医疗保健提供者提供一个工具,以便在退院后主动管理COPD患者.
主要方法:
- 美国7个州 (2016-2019) 的医疗补助申请数据的回顾性分析.
- 纳入标准:国际疾病分类,第十次修订 (ICD-10) COPD 的代码.
- 混合效应的后勤回归模型被用来预测30天的再入院,包括患者人口统计,并发病,医疗保健利用率和住院细节.
- 用图形评估和接收器操作特征 (ROC) 曲线下的面积来评估模型性能.
主要成果:
- 该研究分析了来自9,437名受试者的12,283例COPD住院病例.
- 总共有2534名 (20.6%) 患者经历了30天的再入院.
- 最终的预测模型包括人口统计,并发症,病史,入院/出院变量,停留时间和入院/出院季节.
- 该模型显示了可接受的预测准确性,ROC曲线下的面积为0.702.
结论:
- 开发的模型准确地识别了30天再入院的高风险的COPD住院医疗补助患者.
- 这种预测工具可以为制定有针对性的放免后干预措施提供信息,以减少再接收.
- 该模型可以帮助标准化不同地点或随时间推移的再接收率的比较.
- 它支持以患者为中心的方法来照顾COPD患者.
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