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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Veröffentlicht in:	Journal of the American Geriatrics Society (JAGS) Jg. 71; H. 10; S. 3229 - 3236
Hauptverfasser:	Bélanger, Emmanuelle, Rosendaal, Nicole, Gutman, Roee, Lake, Derek, Santostefano, Christopher M., Meyers, David J., Gozalo, Pedro L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Wiley Subscription Services, Inc 01.10.2023
Schlagworte:	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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Abstract	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.
AbstractList	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. BackgroundHome 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).MethodsWe 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.ResultsThe 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.ConclusionsThe 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. 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).BACKGROUNDHome 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.METHODSWe 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.RESULTSThe 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.CONCLUSIONSThe 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.
Author	Meyers, David J. Lake, Derek Gozalo, Pedro L. Gutman, Roee Bélanger, Emmanuelle Rosendaal, Nicole Santostefano, Christopher M.
AuthorAffiliation	c Department of Biostatistics, Brown University School of Public Health, RI, USA a Center for Gerontology and Healthcare Research, Brown University School of Public Health, RI, USA b Department of Health Services, Policy & Practice, Brown University School of Public Health, RI, USA
AuthorAffiliation_xml	– name: b Department of Health Services, Policy & Practice, Brown University School of Public Health, RI, USA – name: a Center for Gerontology and Healthcare Research, Brown University School of Public Health, RI, USA – name: c Department of Biostatistics, Brown University School of Public Health, RI, USA
Author_xml	– sequence: 1 givenname: Emmanuelle orcidid: 0000-0001-7301-0451 surname: Bélanger fullname: Bélanger, Emmanuelle organization: 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 – sequence: 2 givenname: Nicole surname: Rosendaal fullname: Rosendaal, Nicole organization: Center for Gerontology and Healthcare Research Brown University School of Public Health Providence Rhode Island USA – sequence: 3 givenname: Roee surname: Gutman fullname: Gutman, Roee organization: Department of Biostatistics Brown University School of Public Health Providence Rhode Island USA – sequence: 4 givenname: Derek orcidid: 0000-0002-6250-8737 surname: Lake fullname: Lake, Derek organization: Center for Gerontology and Healthcare Research Brown University School of Public Health Providence Rhode Island USA – sequence: 5 givenname: Christopher M. surname: Santostefano fullname: Santostefano, Christopher M. organization: Center for Gerontology and Healthcare Research Brown University School of Public Health Providence Rhode Island USA – sequence: 6 givenname: David J. orcidid: 0000-0002-4081-1751 surname: Meyers fullname: Meyers, David J. organization: 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 – sequence: 7 givenname: Pedro L. surname: Gozalo fullname: Gozalo, Pedro L. organization: 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
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 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
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SubjectTerms	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
Title	Identifying Medicare beneficiaries with Alzheimer's disease and related dementia using home health OASIS assessments
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