紧急海斯特戴维斯量表的预测模型开发和验证©用于跌倒风险筛查

IF 0.8 Q4 NURSING
Stella Kientz, A. Hester
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引用次数: 2

摘要

跌倒是医疗保健中最严重的不良事件之一。由于各种情况,到急诊科就诊的病人可能有跌倒的危险。临床医生快速准确地确定跌倒风险的能力对于实施适当的跌倒预防工作至关重要。方法:采用二次资料分析,构建初始急诊Hester Davis量表(eHDS)跌倒风险筛查模型。该模型首先在美国中南部的一个城市学术医疗中心和美国西海岸的一个大型城市急诊科(ED)进行回顾性开发,然后进行前瞻性验证。回顾性样本包括152名跌倒者。前瞻性样本包括13名跌倒者和216名非跌倒者的随机样本。统计分析包括描述性统计、敏感性、特异性和受试者工作特征(ROC)曲线程序。结果:回顾性分析包括152例经历预期生理事件的ED患者的数据,揭示了三个显著的筛选变量,包括活动能力、药物和行为。前瞻性数据包括从两家参与医院的110,445次急诊就诊中随机抽取216名非住院患者和13名住院患者。导出的工具正确地识别了所有13个预期的生理下降的风险。ROC曲线分析的使用和解释有助于进一步评价。讨论:eHDS可以准确地识别在ED中有跌倒风险的成年人,可以是预防跌倒和随后的跌倒相关费用的第一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predictive Model Development and Validation of the Emergency Hester Davis Scale© for Fall Risk Screening
Introduction: Falls are among the most serious adverse events in healthcare. Patients presenting to the emergency department may be at risk for falls due to various conditions. The ability for clinicians to quickly and accurately determine fall risk is vital to the implementation of appropriate fall prevention efforts. Methods: A secondary data analysis was used to construct the initial emergency Hester Davis Scale (eHDS) fall risk screening model. The model was fi rst retrospectively developed and then prospectively validated concurrently in an urban academic medical center in the south-central United States and in a large, urban emergency department (ED) on the west coast of the United States. The retrospective sample included 152 fallers. The prospective sample included 13 fallers and a random sample of 216 non-fallers. Statistical analyses included descriptive statistics, sensitivity, speci fi city, and receiver operating characteristic (ROC) curve procedures. Results: Retrospective analysis of data including 152 ED fallers experiencing anticipated physiologic events revealed three salient screening variables including mobility, medications, and behavior. Prospective data included a random sample of 216 non-fallers and 13 fallers from the two participating hospitals from 110,445 combined ED visits. The derived tool correctly identi fi ed risk in all 13 anticipated physiologic falls. The use and interpretation of the ROC curve analysis contributed to further evaluation. Discussion: The eHDS can accurately identify adults at risk for falling in the ED and can be the fi rst step in preventing falls and subsequent fall-related costs.
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