预测慢性病患者住院的情况
Steph Karpinski, Scott Sibbel, Kathryn Gray
1Davita, Inc, 825 S 8th St, Ste 300, Minneapolis, MN 55404.
The American journal of managed care
|September 20, 2023
概括
患有慢性病 (CKD) 的患者面临更高的住院风险. 使用医学声明的新算法可以识别高风险的CKD患者,以更好地分配资源和改善临床结果.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 预测分析是一种预测分析.
背景情况:
- 与普通人群相比,患有慢性病 (CKD) 的患者住院率较高.
- 慢性病患者的住院治疗与增加的医疗费用,发病率和死亡率有关.
- 识别高风险CKD个体对于提高临床结果和优化医疗保健资源配置至关重要.
研究的目的:
- 开发和验证一种预测算法,用于识别近期住院高风险的3-5期CKD患者.
- 为了利用医疗保险索赔数据,在CKD人口中进行风险分层.
- 为管理CKD患者群体的临床计划创建一个决策支持工具.
主要方法:
- 一个回顾性,观察性队列研究利用医疗保险A部分和B部分的索赔从2017-2018年.
- 一个由5万名患有3-5期CKD的独特患者组成的数据集被分为训练 (4万) 和测试 (1万) 组.
- 开发了一个带有399个输入特征的梯度增强机器模型,以预测90天内所有原因的住院治疗.
主要成果:
- 最终的梯度增强模型实现了0.73的曲线下面积 (AUC) 预测住院治疗,在训练和测试组中一致.
- 该模型在分别达到10%,20%和30%的值时显示出0.306,0.240和0.216的积极预测值.
- 这些值的灵敏度分别为0.288,0.453和0.609,表明该模型能够识别出相当一部分风险患者.
结论:
- 通过使用医疗索赔数据成功开发了一种算法,以识别患有CKD 3-5阶段的医疗保险患者,他们有住院的高风险.
- 开发的算法显示了其作为临床方案的决策支持工具的潜力,该方案专注于CKD患者管理.
- 这种工具可以帮助积极管理慢性病患者,可能减少住院治疗和改善护理协调.
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