Identifying Medicare Beneficiaries With Delirium.
Journal
Medical care
ISSN: 1537-1948
Titre abrégé: Med Care
Pays: United States
ID NLM: 0230027
Informations de publication
Date de publication:
01 11 2022
01 11 2022
Historique:
pubmed:
1
9
2022
medline:
18
10
2022
entrez:
31
8
2022
Statut:
ppublish
Résumé
Each year, thousands of older adults develop delirium, a serious, preventable condition. At present, there is no well-validated method to identify patients with delirium when using Medicare claims data or other large datasets. We developed and assessed the performance of classification algorithms based on longitudinal Medicare administrative data that included International Classification of Diseases, 10th Edition diagnostic codes. Using a linked electronic health record (EHR)-Medicare claims dataset, 2 neurologists and 2 psychiatrists performed a standardized review of EHR records between 2016 and 2018 for a stratified random sample of 1002 patients among 40,690 eligible subjects. Reviewers adjudicated delirium status (reference standard) during this 3-year window using a structured protocol. We calculated the probability that each patient had delirium as a function of classification algorithms based on longitudinal Medicare claims data. We compared the performance of various algorithms against the reference standard, computing calibration-in-the-large, calibration slope, and the area-under-receiver-operating-curve using 10-fold cross-validation (CV). Beneficiaries had a mean age of 75 years, were predominately female (59%), and non-Hispanic Whites (93%); a review of the EHR indicated that 6% of patients had delirium during the 3 years. Although several classification algorithms performed well, a relatively simple model containing counts of delirium-related diagnoses combined with patient age, dementia status, and receipt of antipsychotic medications had the best overall performance [CV- calibration-in-the-large <0.001, CV-slope 0.94, and CV-area under the receiver operating characteristic curve (0.88 95% confidence interval: 0.84-0.91)]. A delirium classification model using Medicare administrative data and International Classification of Diseases, 10th Edition diagnosis codes can identify beneficiaries with delirium in large datasets.
Sections du résumé
BACKGROUND
Each year, thousands of older adults develop delirium, a serious, preventable condition. At present, there is no well-validated method to identify patients with delirium when using Medicare claims data or other large datasets. We developed and assessed the performance of classification algorithms based on longitudinal Medicare administrative data that included International Classification of Diseases, 10th Edition diagnostic codes.
METHODS
Using a linked electronic health record (EHR)-Medicare claims dataset, 2 neurologists and 2 psychiatrists performed a standardized review of EHR records between 2016 and 2018 for a stratified random sample of 1002 patients among 40,690 eligible subjects. Reviewers adjudicated delirium status (reference standard) during this 3-year window using a structured protocol. We calculated the probability that each patient had delirium as a function of classification algorithms based on longitudinal Medicare claims data. We compared the performance of various algorithms against the reference standard, computing calibration-in-the-large, calibration slope, and the area-under-receiver-operating-curve using 10-fold cross-validation (CV).
RESULTS
Beneficiaries had a mean age of 75 years, were predominately female (59%), and non-Hispanic Whites (93%); a review of the EHR indicated that 6% of patients had delirium during the 3 years. Although several classification algorithms performed well, a relatively simple model containing counts of delirium-related diagnoses combined with patient age, dementia status, and receipt of antipsychotic medications had the best overall performance [CV- calibration-in-the-large <0.001, CV-slope 0.94, and CV-area under the receiver operating characteristic curve (0.88 95% confidence interval: 0.84-0.91)].
CONCLUSIONS
A delirium classification model using Medicare administrative data and International Classification of Diseases, 10th Edition diagnosis codes can identify beneficiaries with delirium in large datasets.
Identifiants
pubmed: 36043702
doi: 10.1097/MLR.0000000000001767
pii: 00005650-202211000-00009
pmc: PMC9588515
mid: NIHMS1826475
doi:
Substances chimiques
Antipsychotic Agents
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
852-859Subventions
Organisme : NIA NIH HHS
ID : U19 AG062682
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG063975
Pays : United States
Organisme : NLM NIH HHS
ID : T15 LM007092
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG073410
Pays : United States
Organisme : ACL HHS
ID : U48DP006377
Pays : United States
Organisme : NINDS NIH HHS
ID : U24 NS100591
Pays : United States
Organisme : NINDS NIH HHS
ID : K23 NS114201
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG066793
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 : R01 AG058063
Pays : United States
Organisme : NIA NIH HHS
ID : T32 AG051108
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG068221
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG032984
Pays : United States
Organisme : NIA NIH HHS
ID : P30 AG062421
Pays : United States
Organisme : NIA NIH HHS
ID : P01 AG036694
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG048351
Pays : United States
Organisme : NCCDPHP CDC HHS
ID : U48 DP006377
Pays : United States
Organisme : NCATS NIH HHS
ID : TL1 TR001864
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG062282
Pays : United States
Organisme : NIA NIH HHS
ID : P01 AG032952
Pays : United States
Organisme : NIMH NIH HHS
ID : T32 MH017119
Pays : United States
Informations de copyright
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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