Center for Health Research, Kaiser Permanente Hawaii, Honolulu, Hawaii.
Center for Health Research, Kaiser Permanente Northwest, Portland, Oregon.
J Am Geriatr Soc. 2019 Jul;67(7):1417-1422. doi: 10.1111/jgs.15872. Epub 2019 Mar 15.
To examine the use of electronic medical record (EMR) data to ascertain falls and develop a fall risk prediction model in an older population.
Retrospective longitudinal study using 10 years of EMR data (2004-2014). A series of 3-year cohorts included members continuously enrolled for a minimum of 3 years, requiring 2 years pre-fall (no previous record of a fall) and a 1-year fall risk period.
Kaiser Permanente Hawaii, an ambulatory setting.
A total of 57 678 adults, age 60 years and older.
Initial EMR searches were guided by current literature and geriatricians to understand coding sources of falls as our outcome. Falls were captured by two coding sources: International Classification of Diseases, Ninth Revision (ICD-9) codes (E880-889) and/or a fall listed as a "primary reason for visit." A comprehensive list of EMR predictors of falls were included into prediction models enabling statistical subset selection from many variables and modeling by logistic regression.
Although 72% of falls in the training data set were coded as "primary reason for visit," 22% of falls were coded as ICD-9 and 6% coded as both. About 80% were reported in face-to-face encounters (eg, emergency department). A total of 2164 individuals had a fall in the risk period. Using the 13 key predictors (age, comorbidities, female sex, other mental disorder, walking issues, Parkinson's disease, urinary incontinence, depression, polypharmacy, psychotropic and anticonvulsant medications, osteoarthritis, osteoporosis) identified through LASSO regression, the final model had a sensitivity of 67%, specificity of 69%, positive predictive value of 8%, negative predictive value of 98%, and area under the curve of .74.
This study demonstrated how the EMR can be used to ascertain falls and develop a fall risk prediction model with moderate sensitivity/specificity. Concurrent work with clinical providers to enhance fall documentation will improve the ability of the EMR to capture falls and consequently may improve the model to predict fall risk.
利用电子病历 (EMR) 数据确定跌倒事件，并为老年人群开发跌倒风险预测模型。
回顾性纵向研究，使用了 10 年的 EMR 数据（2004-2014 年）。一系列 3 年队列包括至少连续 3 年参保的成员，要求有 2 年的预跌倒期（无先前跌倒记录）和 1 年的跌倒风险期。
夏威夷 Kaiser Permanente，一个门诊环境。
共 57678 名年龄在 60 岁及以上的成年人。
最初的 EMR 搜索是根据当前文献和老年病学家的建议进行的，以了解我们的结果（跌倒）的编码来源。跌倒由两种编码来源捕获：国际疾病分类，第九版 (ICD-9) 代码（E880-889）和/或作为“就诊主要原因”列出的跌倒。纳入了大量预测跌倒的 EMR 预测因素的综合清单，这些因素可用于通过逻辑回归进行统计子集选择和建模。
尽管训练数据集中 72%的跌倒被编码为“就诊主要原因”，但 22%的跌倒被编码为 ICD-9，6%的跌倒同时被编码为 ICD-9 和“就诊主要原因”。约 80%的跌倒发生在面对面的就诊中（例如，急诊室）。在风险期内共有 2164 人发生跌倒。使用 LASSO 回归确定的 13 个关键预测因素（年龄、合并症、女性、其他精神障碍、行走问题、帕金森病、尿失禁、抑郁、多种药物治疗、精神药物和抗惊厥药物、骨关节炎、骨质疏松症），最终模型的敏感性为 67%，特异性为 69%，阳性预测值为 8%，阴性预测值为 98%，曲线下面积为.74。
本研究展示了如何利用 EMR 确定跌倒事件并开发具有中等敏感性/特异性的跌倒风险预测模型。与临床医生合作以增强跌倒记录将提高 EMR 捕获跌倒的能力，从而可能改进模型以预测跌倒风险。
