Identifying Medicare Beneficiaries With Delirium.
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 a...
| Publicado en: | Medical Care Vol. 60; no. 11; pp. 852 - 860 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Nov2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159657631&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159657631 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00257079 21B jtl: Medical Care issn: 00257079 maglogo: N pubinfo: dt: Nov2022 vid: 60 iid: 11 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 159657631 159657631 NLM36043702 159657631 10.1097/MLR.0000000000001767 NLM36043702 159657631 ppf: 852 ppct: 8 formats: tig: atl: Identifying Medicare Beneficiaries With Delirium. aug: au: 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 affil: Neurology sug: subj: Delirium Diagnosis Delirium Epidemiology Antipsychotic Agents Medicare United States International Classification of Diseases Aged Female Human Aged: 65+ years Female ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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