Identifying Medicare beneficiaries with Alzheimer's disease and related dementia using home health OASIS assessments

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 usin...

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Vydáno v:Journal of the American Geriatrics Society (JAGS) Ročník 71; číslo 10; s. 3229 - 3236
Hlavní autoři: Bélanger, Emmanuelle, Rosendaal, Nicole, Gutman, Roee, Lake, Derek, Santostefano, Christopher M., Meyers, David J., Gozalo, Pedro L.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Wiley Subscription Services, Inc 01.10.2023
Témata:
Accuracy
Aged
Algorithms
Alzheimer Disease - diagnosis
Alzheimer Disease - epidemiology
Alzheimer's disease
Beneficiaries
Cohort analysis
Confusion
Dementia
Dementia disorders
Diagnosis
Diagnostic tests
Disease
Elder care
Evaluation
Health
Health services
Hospitalization
Humans
Inpatient care
Mathematical models
Medical diagnosis
Medicare
Medicare Part C
Neurodegenerative diseases
Older people
Patient Discharge
Prediction models
Predictions
Regression analysis
Retrospective Studies
Statistical analysis
United States
Variables
United States
home healthcare
Alzheimer's disease diagnosis
measurement validity
ISSN:0002-8614, 1532-5415, 1532-5415
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Shrnutí: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.
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Concept and Design: Belanger, Gutman, Gozalo. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Belanger, Rosendaal, Gutman, Gozalo. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Belanger, Rosendaal, Gutman, Gozalo. Obtained funding: Gozalo. Administrative, technical, or material support: Gozalo. Supervision: Belanger, Gutman, Gozalo.
Author Contributions
ISSN:0002-8614
1532-5415
1532-5415
DOI:10.1111/jgs.18487