Literature DB >> 33901296

Identifying Medicare beneficiaries with dementia.

Lidia M V R Moura1,2, Natalia Festa3, Mary Price4, Margarita Volya4, Nicole M Benson4,5, Sahar Zafar1, Max Weiss4, Deborah Blacker6,7, Sharon-Lise Normand8,9, Joseph P Newhouse8,10,11,12, John Hsu4,8.   

Abstract

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.
© 2021 The American Geriatrics Society.

Entities:  

Keywords:  Medicare claims; dementia; dementia prevalence; electronic health record; validation

Mesh:

Year:  2021        PMID: 33901296      PMCID: PMC8373730          DOI: 10.1111/jgs.17183

Source DB:  PubMed          Journal:  J Am Geriatr Soc        ISSN: 0002-8614            Impact factor:   7.538


  23 in total

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Authors:  Marilyn S Albert; Steven T DeKosky; Dennis Dickson; Bruno Dubois; Howard H Feldman; Nick C Fox; Anthony Gamst; David M Holtzman; William J Jagust; Ronald C Petersen; Peter J Snyder; Maria C Carrillo; Bill Thies; Creighton H Phelps
Journal:  Alzheimers Dement       Date:  2011-04-21       Impact factor: 21.566

2.  Identification of dementia: agreement among national survey data, medicare claims, and death certificates.

Authors:  Truls Ostbye; Donald H Taylor; Elizabeth C Clipp; Lynn Van Scoyoc; Brenda L Plassman
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3.  Integrating Medical and Nonmedical Services - The Promise and Pitfalls of the CHRONIC Care Act.

Authors:  Amber Willink; Eva H DuGoff
Journal:  N Engl J Med       Date:  2018-06-07       Impact factor: 91.245

4.  Stratifying risk for dementia onset using large-scale electronic health record data: A retrospective cohort study.

Authors:  Thomas H McCoy; Larry Han; Amelia M Pellegrini; Rudolph E Tanzi; Sabina Berretta; Roy H Perlis
Journal:  Alzheimers Dement       Date:  2020-01-16       Impact factor: 21.566

5.  Future Monetary Costs of Dementia in the United States under Alternative Dementia Prevalence Scenarios.

Authors:  Michael D Hurd; Paco Martorell; Kenneth Langa
Journal:  J Popul Ageing       Date:  2015-03

6.  Monetary costs of dementia in the United States.

Authors:  Michael D Hurd; Paco Martorell; Adeline Delavande; Kathleen J Mullen; Kenneth M Langa
Journal:  N Engl J Med       Date:  2013-04-04       Impact factor: 91.245

7.  Health Care Utilization, Care Satisfaction, and Health Status for Medicare Advantage and Traditional Medicare Beneficiaries With and Without Alzheimer Disease and Related Dementias.

Authors:  Sungchul Park; Lindsay White; Paul Fishman; Eric B Larson; Norma B Coe
Journal:  JAMA Netw Open       Date:  2020-03-02

8.  Analysis of dementia in the US population using Medicare claims: Insights from linked survey and administrative claims data.

Authors:  Yi Chen; Bryan Tysinger; Eileen Crimmins; Julie M Zissimopoulos
Journal:  Alzheimers Dement (N Y)       Date:  2019-06-06

9.  Comparison of Methods for Algorithmic Classification of Dementia Status in the Health and Retirement Study.

Authors:  Kan Z Gianattasio; Qiong Wu; M Maria Glymour; Melinda C Power
Journal:  Epidemiology       Date:  2019-03       Impact factor: 4.822

10.  Interrater reliability: the kappa statistic.

Authors:  Mary L McHugh
Journal:  Biochem Med (Zagreb)       Date:  2012       Impact factor: 2.313

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4.  Evaluation of Claims-Based Ascertainment of Alzheimer Disease and Related Dementias Across Health Care Settings.

Authors:  Natalia Festa; Mary Price; Lidia M V R Moura; Deborah Blacker; Sharon-Lise Normand; Joseph P Newhouse; John Hsu
Journal:  JAMA Health Forum       Date:  2022-04-22

5.  Development and Validation of a Deep Learning Model for Earlier Detection of Cognitive Decline From Clinical Notes in Electronic Health Records.

Authors:  Liqin Wang; John Laurentiev; Jie Yang; Ying-Chih Lo; Rebecca E Amariglio; Deborah Blacker; Reisa A Sperling; Gad A Marshall; Li Zhou
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6.  Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study.

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