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验证和改进萨加落风险模型:一个多中心回顾性观察性研究
Masaki Tago1, Risa Hirata1, Naoko E Katsuki1
1Department of General Medicine, Saga University Hospital, Saga, Japan.
Clinical interventions in aging
|February 13, 2024
概括
萨加摔倒风险模型2 (SFRM2) 在预测患者在不同医院环境中的摔倒方面显示出中等准确性. 调整其系数提高了预测的准确性,创造了SFRM2.1,一个更可靠的工具来评估跌倒风险.
科学领域:
- 老年学是一门学科.
- 改善医疗保健质量 改善医疗保健质量
- 患者安全 患者安全
背景情况:
- 医院内摔倒对患者安全构成重大风险,并导致医疗保健成本增加.
- 当前的秋季预测模型在不同患者群体和医疗保健环境中应用时,可能存在准确性的局限性.
研究的目的:
- 在多家医院环境中,包括慢性护理设施,对Saga Fall Risk Model 2 (SFRM2) 进行外部验证.
- 通过调整其系数和重新验证修改后的模型来提高SFRM2的准确性.
主要方法:
- 一项回顾性研究包括2018年4月至2021年3月期间来自八家医院 (急性,慢性,三级护理) 的124,521名住院患者.
- 萨加风险模型2 (SFRM2) 得到了验证,其系数使用后勤回归对三分之二的数据进行了调整,以开发SFRM2.1.
- 使用曲线下的面积 (AUC) 评估了这两种模型的准确性.
主要成果:
- 最初的SFRM2显示AUC为0.687 (95%CI:0.678-0.697). 这是一个很好的结果.
- 修改后的模型,SFRM2.1,使用三分之二的数据开发并对剩余的三分之一 (42,731名患者) 进行验证,实现了明显更高的AUC0.745 (95%CI:0.731-0.758).
- 对于SFRM2的收缩系数为0.996,这表明在初始验证中过拟合是最小的.
结论:
- SFRM2在预测各种医院类型和患者人口统计学中的住院倒闭方面表现出中等准确性.
- 调整模型的系数,就像在SFRM2.1中所做的那样,可以显著提高模型的预测准确性.
- 改进后的SFRM2.1为在不同临床环境中识别患有跌倒风险的患者提供了更强大的工具.
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