Identifying Medicare beneficiaries with dementia.


Journal

Journal of the American Geriatrics Society
ISSN: 1532-5415
Titre abrégé: J Am Geriatr Soc
Pays: United States
ID NLM: 7503062

Informations de publication

Date de publication:
08 2021
Historique:
revised: 02 04 2021
received: 22 02 2021
accepted: 03 04 2021
pubmed: 27 4 2021
medline: 14 4 2022
entrez: 26 4 2021
Statut: ppublish

Résumé

No data exist regarding the validity of International Classification of Disease (ICD)-10 dementia diagnoses against a clinician-adjudicated reference standard within Medicare claims data. We examined the accuracy of claims-based diagnoses with respect to expert clinician adjudication using a novel database with individual-level linkages between electronic health record (EHR) and claims. In this retrospective observational study, two neurologists and two psychiatrists performed a standardized review of patients' medical records from January 2016 to December 2018 and adjudicated dementia status. We measured the accuracy of three claims-based definitions of dementia against the reference standard. Mass-General-Brigham Healthcare (MGB), Massachusetts, USA. From an eligible population of 40,690 fee-for-service (FFS) Medicare beneficiaries, aged 65 years and older, within the MGB Accountable Care Organization (ACO), we generated a random sample of 1002 patients, stratified by the pretest likelihood of dementia using administrative surrogates. None. We evaluated the accuracy (area under receiver operating curve [AUROC]) and calibration (calibration-in-the-large [CITL] and calibration slope) of three ICD-10 claims-based definitions of dementia against clinician-adjudicated standards. We applied inverse probability weighting to reconstruct the eligible population and reported the mean and 95% confidence interval (95% CI) for all performance characteristics, using 10-fold cross-validation (CV). Beneficiaries had an average age of 75.3 years and were predominately female (59%) and non-Hispanic whites (93%). The adjudicated prevalence of dementia in the eligible population was 7%. The best-performing definition demonstrated excellent accuracy (CV-AUC 0.94; 95% CI 0.92-0.96) and was well-calibrated to the reference standard of clinician-adjudicated dementia (CV-CITL <0.001, CV-slope 0.97). This study is the first to validate ICD-10 diagnostic codes against a robust and replicable approach to dementia ascertainment, using a real-world clinical reference standard. The best performing definition includes diagnostic codes with strong face validity and outperforms an updated version of a previously validated ICD-9 definition of dementia.

Sections du résumé

BACKGROUND/OBJECTIVES
No data exist regarding the validity of International Classification of Disease (ICD)-10 dementia diagnoses against a clinician-adjudicated reference standard within Medicare claims data. We examined the accuracy of claims-based diagnoses with respect to expert clinician adjudication using a novel database with individual-level linkages between electronic health record (EHR) and claims.
DESIGN
In this retrospective observational study, two neurologists and two psychiatrists performed a standardized review of patients' medical records from January 2016 to December 2018 and adjudicated dementia status. We measured the accuracy of three claims-based definitions of dementia against the reference standard.
SETTING
Mass-General-Brigham Healthcare (MGB), Massachusetts, USA.
PARTICIPANTS
From an eligible population of 40,690 fee-for-service (FFS) Medicare beneficiaries, aged 65 years and older, within the MGB Accountable Care Organization (ACO), we generated a random sample of 1002 patients, stratified by the pretest likelihood of dementia using administrative surrogates.
INTERVENTION
None.
MEASUREMENTS
We evaluated the accuracy (area under receiver operating curve [AUROC]) and calibration (calibration-in-the-large [CITL] and calibration slope) of three ICD-10 claims-based definitions of dementia against clinician-adjudicated standards. We applied inverse probability weighting to reconstruct the eligible population and reported the mean and 95% confidence interval (95% CI) for all performance characteristics, using 10-fold cross-validation (CV).
RESULTS
Beneficiaries had an average age of 75.3 years and were predominately female (59%) and non-Hispanic whites (93%). The adjudicated prevalence of dementia in the eligible population was 7%. The best-performing definition demonstrated excellent accuracy (CV-AUC 0.94; 95% CI 0.92-0.96) and was well-calibrated to the reference standard of clinician-adjudicated dementia (CV-CITL <0.001, CV-slope 0.97).
CONCLUSION
This study is the first to validate ICD-10 diagnostic codes against a robust and replicable approach to dementia ascertainment, using a real-world clinical reference standard. The best performing definition includes diagnostic codes with strong face validity and outperforms an updated version of a previously validated ICD-9 definition of dementia.

Identifiants

pubmed: 33901296
doi: 10.1111/jgs.17183
pmc: PMC8373730
mid: NIHMS1704529
doi:

Types de publication

Journal Article Observational Study Research Support, N.I.H., Extramural Validation Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

2240-2251

Subventions

Organisme : NIA NIH HHS
ID : NIH-NIA 5K08AG053380-02
Pays : United States
Organisme : NINDS NIH HHS
ID : NIH-NINDS K23NS114201
Pays : United States
Organisme : NIA NIH HHS
ID : P01 AG032952
Pays : United States
Organisme : NLM NIH HHS
ID : NLM T15 LM007092
Pays : United States
Organisme : NIA NIH HHS
ID : NIH-NIA 2P01AG032952-11
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG076478
Pays : United States
Organisme : NIA NIH HHS
ID : K08 AG053380
Pays : United States
Organisme : NIA NIH HHS
ID : NIH-NIA 5R01AG062282-02
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG062282
Pays : United States

Informations de copyright

© 2021 The American Geriatrics Society.

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Auteurs

Lidia M V R Moura (LMVR)

Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA.

Natalia Festa (N)

Department of Internal Medicine, Section of Geriatric Medicine, Yale School of Medicine, New Haven, Connecticut, USA.

Mary Price (M)

Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Margarita Volya (M)

Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Nicole M Benson (NM)

Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA.

Sahar Zafar (S)

Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.

Max Weiss (M)

Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Deborah Blacker (D)

Department of Psychiatry, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, USA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Sharon-Lise Normand (SL)

Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Joseph P Newhouse (JP)

Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA.
Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Division of Health Policy Research and Education, Harvard Kennedy School, Cambridge, Massachusetts, USA.
Programs on Health Care, Health Economics, Productivity, and Children, National Bureau of Economic Research, Cambridge, Massachusetts, USA.

John Hsu (J)

Mongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA.

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