Suppr超能文献

“药物跌倒风险评分”的评估

Evaluation of the "medication fall risk score".

作者信息

Yazdani Cyrus, Hall Scott

机构信息

Department of Pharmacy Services, HonorHealth John C. Lincoln Medical Center, Phoenix, AZ.

出版信息

Am J Health Syst Pharm. 2017 Jan 1;74(1):e32-e39. doi: 10.2146/ajhp150745. Epub 2016 Dec 22.

Abstract

PURPOSE

Results of a study evaluating the predictive validity of a fall screening tool in hospitalized patients are reported.

METHODS

Administrative claims data from two hospitals were analyzed to determine the discriminatory ability of the "medication fall risk score" (RxFS), a medication review fall-risk screening tool that is designed for use in conjunction with nurse-administered tools such as the Morse Fall Scale (MFS). Through analysis of data on administered medications and documented falls in a population of adults who underwent fall-risk screening at hospital admission over a 15-month period (n = 33,058), the predictive value of admission MFS scores, alone or in combination with retrospectively calculated RxFS-based risk scores, was assessed. Receiver operating characteristic (ROC) curve analysis and net reclassification improvement (NRI) analysis were used to evaluate improvements in risk prediction with the addition of RxFS data to the prediction model.

RESULTS

The area under the ROC curve for the predictive model for falls compromising both MFS and RxFS scores was computed as 0.8014, which was greater than the area under the ROC curve associated with use of the MFS alone (0.7823, p = 0.0030). Screening based on MFS scores alone had 81.25% sensitivity and 61.37% specificity. Combined use of RxFS and MFS scores resulted in 82.42% sensitivity and 66.65% specificity (NRI = 0.0587, p = 0.0003).

CONCLUSION

Reclassification of fall risk based on coadministration of the MFS and the RxFS tools resulted in a modest improvement in specificity without compromising sensitivity.

摘要

目的

报告一项评估跌倒筛查工具对住院患者预测效度的研究结果。

方法

分析了两家医院的管理索赔数据,以确定“药物跌倒风险评分”(RxFS)的辨别能力,RxFS是一种药物审查跌倒风险筛查工具,旨在与护士管理的工具(如莫尔斯跌倒量表(MFS))联合使用。通过分析在15个月期间入院时接受跌倒风险筛查的成年人群(n = 33,058)中所使用药物和记录的跌倒数据,评估入院时MFS评分单独或与基于RxFS回顾性计算的风险评分相结合的预测价值。采用受试者工作特征(ROC)曲线分析和净重新分类改善(NRI)分析来评估在预测模型中添加RxFS数据后风险预测的改善情况。

结果

包含MFS和RxFS评分的跌倒预测模型的ROC曲线下面积计算为0.8014,大于仅使用MFS时的ROC曲线下面积(0.7823,p = 0.0030)。仅基于MFS评分进行筛查时,敏感性为81.25%,特异性为61.37%。联合使用RxFS和MFS评分时,敏感性为82.42%,特异性为66.65%(NRI = 0.0587,p = 0.0003)。

结论

基于同时使用MFS和RxFS工具对跌倒风险进行重新分类,在不影响敏感性的情况下,特异性有适度提高。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验