Predicting Hospital Readmission in Medicaid Patients With COPD Using Administrative and Claims Data
Copyright © 2024 by Daedalus Enterprises..
BACKGROUND: The goals of this study were to develop a model that predicts the risk of 30-d all-cause readmission in hospitalized Medicaid patients diagnosed with COPD and to create a predictive model in a retrospective study of a population cohort.
METHODS: We analyzed 2016-2019 Medicaid claims data from 7 United States states. A COPD admission was one in which either the admission diagnosis or the first or second clinical (discharge) diagnosis bore an International Classification of Diseases, Tenth Revision code for COPD. A readmission was an admission for any condition (not necessarily COPD) that occurred within 30 d of a COPD discharge. We estimated a mixed-effects logistic model to predict 30-d readmission from patient demographic data, comorbidities, past health care utilization, and features of the index hospitalization. We evaluated model fit graphically and measured predictive accuracy by the area under the receiver operating characteristic (ROC) curve.
RESULTS: Among 12,283 COPD hospitalizations contributed by 9,437 subjects, 2,534 (20.6%) were 30-d readmissions. The final model included demographics, comorbidities, claims history, admission and discharge variables, length of stay, and seasons of admission and discharge. The observed versus predicted plot showed reasonable fit, and the estimated area under the ROC curve of 0.702 was robust in sensitivity analyses.
CONCLUSIONS: Our model identified with acceptable accuracy hospitalized Medicaid patients with a diagnosis of COPD who are at high risk of readmission. One can use the model to develop post-discharge management interventions for reducing readmissions, for adjusting comparisons of readmission rates between sites/providers or over time, and to guide a patient-centered approach to patient care.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:69 |
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| Enthalten in: |
Respiratory care - 69(2024), 5 vom: 22. Apr., Seite 541-548 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Heitjan, Daniel F [VerfasserIn] |
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| Links: |
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| Themen: |
Burden of COPD |
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| Anmerkungen: |
Date Revised 22.04.2024 published: Electronic Citation Status In-Process |
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| doi: |
10.4187/respcare.11455 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a BACKGROUND: The goals of this study were to develop a model that predicts the risk of 30-d all-cause readmission in hospitalized Medicaid patients diagnosed with COPD and to create a predictive model in a retrospective study of a population cohort | ||
| 520 | |a METHODS: We analyzed 2016-2019 Medicaid claims data from 7 United States states. A COPD admission was one in which either the admission diagnosis or the first or second clinical (discharge) diagnosis bore an International Classification of Diseases, Tenth Revision code for COPD. A readmission was an admission for any condition (not necessarily COPD) that occurred within 30 d of a COPD discharge. We estimated a mixed-effects logistic model to predict 30-d readmission from patient demographic data, comorbidities, past health care utilization, and features of the index hospitalization. We evaluated model fit graphically and measured predictive accuracy by the area under the receiver operating characteristic (ROC) curve | ||
| 520 | |a RESULTS: Among 12,283 COPD hospitalizations contributed by 9,437 subjects, 2,534 (20.6%) were 30-d readmissions. The final model included demographics, comorbidities, claims history, admission and discharge variables, length of stay, and seasons of admission and discharge. The observed versus predicted plot showed reasonable fit, and the estimated area under the ROC curve of 0.702 was robust in sensitivity analyses | ||
| 520 | |a CONCLUSIONS: Our model identified with acceptable accuracy hospitalized Medicaid patients with a diagnosis of COPD who are at high risk of readmission. One can use the model to develop post-discharge management interventions for reducing readmissions, for adjusting comparisons of readmission rates between sites/providers or over time, and to guide a patient-centered approach to patient care | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Yun, Jaehyeon |e verfasserin |4 aut | |
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