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Incorporating the Present-on-Admission Indicator to Predict In-hospital Mortality Through Elixhauser Measures

A Medicare Data Analysis

Datos Bibliográficos

ID9103845
AutoresJianfang Liu (0000-0002-9235-3166, Columbia University School of Nursing, New York, NY, autor de correspondencia), Ani Bilazarian (0000-0002-7095-5369, McKinsey and Company Inc., New York, NY), Madeline M Pollifrone (0000-0001-8342-2003, Columbia University School of Nursing, New York, NY, autor de correspondencia), Sunmoo Yoon (0000-0001-9413-8980, Columbia University Irving Medical Center, New York, NY), Rachel Siegel (0009-0007-1839-0085, Columbia University School of Nursing, New York, NY, autor de correspondencia), Lusine Poghosyan (0000-0002-0529-8171, Stone Foundation and Elise D. Fish Professor of Nursing, Columbia University School of Nursing and Professor of Health Policy and Management, Mailman School of Public Health, Columbia University, Executive Director, Center for Healthcare Delivery Research and Innovations (HDRI), New York, NY)
Año2025
Volumen63
Número12
Páginas929-935
Fecha de publicación2025-12-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaMedical Care (JOURNAL)
Identificadores de la revistaISSN: 0025-7079 • E-ISSN: 1537-1948
EditorialOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000002192
PMID40846637
OpenAlexW4413441217
IdiomaEN
Referencias citadas16

BACKGROUND: In 2021, the Agency for Health Care Research and Quality (AHRQ) updated its guidelines for using the Present-on-Admission (POA) indicator in the Elixhauser comorbidity index. This update helps distinguish pre-existing comorbidities from complications that arise after hospital admission, improving the validity of hospital performance assessments and more accurately measuring patients' severity of illness upon admission. OBJECTIVE: To evaluate differences in comorbidity prevalence and the predictive performance of the Elixhauser Comorbidity Index for in-hospital mortality at admission under 3 comorbidity coding guidelines, including one that ignores the POA indicator. RESEARCH DESIGN: A retrospective analysis of inpatient administrative data on Medicare beneficiaries. SUBJECTS: The dataset included 1,810,106 adult Medicare inpatient admissions across 6 U.S. states between 2017 and 2019. METHODS: Elastic net models were applied to predict in-hospital mortality using 3 approaches to coding comorbidities: (1) No-POA (including all conditions as admission comorbidities), (2) Full-POA (including only POA conditions as comorbidities), and (3) the 2021 AHRQ Partial-POA (applying POA to a subset of conditions to code comorbidities). Results: C-statistics were 0.800 (0.797-0.804), 0.768 (0.763-0.771), and 0.786 (0.781-0.790) for No-POA, full-POA, and 2021 AHRQ partial-POA guidelines, respectively. CONCLUSION: Ignoring the POA inflated model performance by misclassifying complications as admission comorbidities. The 2021 Partial-POA guidelines achieved intermediate C-statistics while ensuring internal validity by accurately measuring illness severity at admission. This supports improved hospital evaluations, care quality, resource allocation, tailored intervention, and reimbursement. The elastic net model shows promise as a standard for predicting in-hospital mortality with the Elixhauser comorbidity measure

Statistics · Chronic Disease Management Strategies · Emergency Medicine · Mathematics · Medicine · Primary Care and Health Outcomes · Sepsis Diagnosis and Treatment

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