所有原因的再入院或潜在的可避免的再入院:哪一个更可预测,使用脆弱性,并发症和ADL?
Seigo Mitsutake1, Tatsuro Ishizaki1, Shohei Yano1,2
1Human Care Research Team, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan.
Innovation in aging
|June 21, 2023
概括
潜在可避免的再接收 (PAR) 比所有原因的再接收 (ACR) 更可预测,使用评估脆弱性,并发症和日常生活活动的行政数据工具. 这一发现有助于识别高风险患者进行有针对性的过渡期护理干预.
科学领域:
- 老年医学 老年医学
- 医疗保健服务研究 医疗服务研究
- 医疗信息学 医疗信息学
背景情况:
- 医疗保健改革越来越多地关注潜在可避免的再录取 (PAR),而不是所有原因的再录取 (ACR).
- 对于 PAR 的行政数据工具的预测实用性仍未得到充分探索.
- 了解PAR的预测因素对于改善患者的治疗结果和降低医疗保健成本至关重要.
研究的目的:
- 为了比较30天所有原因再入院 (ACR) 与30天潜在可避免的再入院 (PAR) 的可预测性.
- 评估行政数据工具 (脆弱性,并发症,ADL) 在预测30天ACR和PAR方面的有效性.
- 使用这些工具来确定哪种类型的 readmission 是更可预测的.
主要方法:
- 在日本东京的一家大型急性护理医院 (2016年7月至2021年2月) 住院的70岁以上的患者进行了回顾性队列研究.
- 利用行政数据来评估医院脆弱性风险评分,查尔森并发症指数和入院时的巴特尔指数.
- 使用各种变量组合构建了后勤回归模型,以预测30天的ACR和PAR.
主要成果:
- 潜在可避免的再接收 (PAR) 显示出比所有原因的再接收 (ACR) 更高的可预测性.
- 与30天ACR (0.73) 模型相比,30天PAR的完整预测模型显示出优越的歧视 (C统计:0.79).
- 在 PAR 预测模型中,在不同变量组合中观察到持续更好的歧视.
结论:
- 在使用评估脆弱性,并发症和日常生活活动 (ADL) 的行政数据时,潜在可避免的再入院 (PAR) 比所有原因的再入院 (ACR) 更可预测.
- 开发的PAR预测模型可以帮助精确识别高风险患者.
- 这有助于实施有针对性的过渡期护理干预措施,以改善患者管理.
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