Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, Rhode Island, USA.
Department of Health Services, Policy & Practice, Brown University School of Public Health, Providence, Rhode Island, USA.
J Am Geriatr Soc. 2023 Oct;71(10):3229-3236. doi: 10.1111/jgs.18487. Epub 2023 Jun 26.
Home health services are an important site of care following hospitalization among Medicare beneficiaries, providing health assessments that can be leveraged to detect diagnoses that are not available in other data sources. In this work, we aimed to develop a parsimonious and accurate algorithm using home health outcome and assessment information set (OASIS) measures to identify Medicare beneficiaries with a diagnosis of Alzheimer's disease and related dementia (ADRD).
We conducted a retrospective cohort study of Medicare beneficiaries with a complete OASIS start of care assessment in 2014, 2016, 2018, or 2019 to determine how well the items from various versions could identify those with an ADRD diagnosis by the assessment date. The prediction model was developed iteratively, comparing the performance of different models in terms of sensitivity, specificity, and accuracy of prediction, from a multivariable logistic regression model using clinically relevant variables, to regression models with all available variables and predictive modeling techniques, to estimate the best performing parsimonious model.
The most important predictors of having a diagnosis of ADRD by the start of care OASIS assessment were a prior discharge diagnosis of ADRD among those admitted from an inpatient setting, and frequently exhibiting symptoms of confusion. Results from the parsimonious model were consistent across the four annual cohorts and OASIS versions with high specificity (above 96%), but poor sensitivity (below 58%). The positive predictive value was high, over 87% across study years.
The proposed algorithm has high accuracy, requires a single OASIS assessment, is easy to implement without sophisticated statistical models, and can be used across four OASIS versions and in situations where claims are not available to identify individuals with a diagnosis of ADRD, including the growing population of Medicare Advantage beneficiaries.
家庭健康服务是医疗保险受益人住院后重要的护理场所，提供健康评估，可用于发现其他数据源中无法获得的诊断。在这项工作中，我们旨在使用家庭健康结局和评估信息集（OASIS）措施开发一个简洁而准确的算法，以识别患有阿尔茨海默病和相关痴呆症（ADRD）的医疗保险受益人。
我们对 2014 年、2016 年、2018 年或 2019 年接受完整 OASIS 开始护理评估的医疗保险受益人进行了回顾性队列研究，以确定不同版本的项目如何能够在评估日期确定患有 ADRD 诊断的患者。该预测模型是通过使用临床相关变量的多变量逻辑回归模型、具有所有可用变量的回归模型和预测建模技术的迭代比较来开发的，比较了不同模型在敏感性、特异性和预测准确性方面的性能，以确定表现最佳的简洁模型。
在开始护理 OASIS 评估时患有 ADRD 诊断的最重要预测因素是从住院环境入院的患者中存在 ADRD 的先前出院诊断，以及经常表现出困惑的症状。从简洁模型中得出的结果在四个年度队列和 OASIS 版本中具有高度的特异性（超过 96%），但敏感性较差（低于 58%）。阳性预测值很高，在研究年份中超过 87%。
该算法具有较高的准确性，只需要进行一次 OASIS 评估，易于实施，无需复杂的统计模型，并且可以在四个 OASIS 版本中使用，也可以在没有索赔的情况下识别患有 ADRD 的患者，包括不断增长的医疗保险优势受益人群。
