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Identifying Medicare Beneficiaries with Delirium
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11 01 2022
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Source: Med Care. 60(11):852-859
[PDF-815.06 KB]
Details:
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Alternative Title:Med Care
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Personal Author:
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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.
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Pubmed ID:36043702
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Pubmed Central ID:PMC9588515
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Volume:60
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Issue:11
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