Details:

Alternative Title:
Med Care
Publisher's site:
http://dx.doi.org/10.1097/MLR.0000000000001767
Personal Author:
Moura, Lidia M.V.R. ; Zafar, Sahar ; Benson, Nicole M. ; Festa, Natalia ; Price, Mary ; ... More +
Moura, Lidia M.V.R. ; Zafar, Sahar ; Benson, Nicole M. ; Festa, Natalia ; Price, Mary ; Donahue, Maria A. ; Normand, Sharon-Lise ; Newhouse, Joseph P. ; Blacker, Deborah ; Hsu, John Less -
Description:
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 ICD-10 diagnostic codes.

Methods:

Using a linked EHR-Medicare claims dataset, two neurologists and two psychiatrists performed a standardized review of EHR records between 2016–2018 for a stratified random sample of 1,002 patients among 40,690 eligible subjects. Reviewers adjudicated delirium status (reference standard) during this three-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 (CITL), calibration slope, and the area-under-receiver-operating-curve (AUROC) 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 three years. While 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-CITL <0.001, CV-slope 0.94, and CV-AUC [0.88 95% CI: 0.84–0.91]).

Conclusions:

A delirium classification model using Medicare administrative data and ICD-10 diagnosis codes can identify beneficiaries with delirium in large datasets.


Subject:
[+]
Aged Antipsychotic Agents Article Delirium Delirium Prevalence Electronic Health Record Electronic Health Records Female Humans International Classification Of Diseases Medicare Medicare Claims United States Validation Delirium
Source:
Med Care. 60(11):852-859
Pubmed ID:
36043702
Pubmed Central ID:
PMC9588515
Document Type:
Journal Article
Funding:
K08 AG053380/AG/NIA NIH HHSUnited States/ ; R01 AG062282/AG/NIA NIH HHSUnited States/ ; R01 AG073410/AG/NIA NIH HHSUnited States/ ; U48 DP006377/DP/NCCDPHP CDC HHSUnited States/ ; P01 AG032952/AG/NIA NIH HHSUnited States/ ; ... More +
K08 AG053380/AG/NIA NIH HHSUnited States/ ; R01 AG062282/AG/NIA NIH HHSUnited States/ ; R01 AG073410/AG/NIA NIH HHSUnited States/ ; U48 DP006377/DP/NCCDPHP CDC HHSUnited States/ ; P01 AG032952/AG/NIA NIH HHSUnited States/ ; K23 NS114201/NS/NINDS NIH HHSUnited States/ ; T15 LM007092/LM/NLM NIH HHSUnited States/ ; U01 AG076478/AG/NIA NIH HHSUnited States/ ; P30 AG062421/AG/NIA NIH HHSUnited States/ ; P01 AG036694/AG/NIA NIH HHSUnited States/ ; U01 AG032984/AG/NIA NIH HHSUnited States/ ; U24 NS100591/NS/NINDS NIH HHSUnited States/ ; R01 AG058063/AG/NIA NIH HHSUnited States/ ; R01 AG063975/AG/NIA NIH HHSUnited States/ ; R01 AG066793/AG/NIA NIH HHSUnited States/ ; T32 MH017119/MH/NIMH NIH HHSUnited States/ ; U01 AG068221/AG/NIA NIH HHSUnited States/ ; R01 AG048351/AG/NIA NIH HHSUnited States/ ; K08 AG053380/AG/NIA NIH HHSUnited States/ ; R01 AG073410/AG/NIA NIH HHSUnited States/ ; TL1 TR001864/TR/NCATS NIH HHSUnited States/ Less -
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:4c64921799d9dbcf6369835ca0e42350c1a7817aa3765d8e084083ca8a4c30e4
File Type:

• Supporting Files:
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