Prediction Model for Extended Hospital Stay Among Medicare Beneficiaries After Percutaneous Coronary Intervention

Brittany N. Burton, Boya Abudu, Dennis J. Danforth, Saatchi Patell, Lizett Wilkins y Martinez, Byron Fergerson, Ahmad Elsharydah, Rodney A. Gabriel

Research output: Contribution to journalArticle

Abstract

Objective: The authors conducted a retrospective analysis to develop a predictive model consisting of factors associated with extended hospital stay among Medicare beneficiaries undergoing percutaneous coronary intervention (PCI). Design: Retrospective cohort study. Setting: Multi-institutional. Participants: Data were obtained from the National (Nationwide) Inpatient Sample registry from 2013 to 2014 over a 2-year period. Interventions: None. Measurements and Main Results: The primary outcome was extended hospital stay, which was defined as an inpatient stay greater than 75th percentile for the cohort (≥5 d), among Medicare beneficiaries (fee-for-service and managed care) undergoing PCI. A multivariable logistic regression analysis was built on a training set to develop the predictive model. The authors evaluated model performance with area under the receiver operating characteristic curve (AUC) and performed k-folds cross-validation to calculate the average AUC. The final analysis included 91,880 patients. Inpatient hospital length of stay ranged from 0 to 247 days, with 3 and 5 days as the median and 3rd quartile hospital stay, respectively. The final multivariable analysis suggested that sociodemographic variables, hospital-related factors, and comorbidities were associated with a greater odds of extended hospital stay (all p < 0.05). The use of PCI with drug-eluting stent was associated with a 31% decrease in extended hospital stay (odds ratio 0.69, 95% confidence interval 0.66-0.72; p < 0.001). Model discrimination was deemed excellent with an AUC (95% confidence interval) of 0.814 (0.811-0.817) and 0.809 (0.799-0.819) for the training and testing sets, respectively. Conclusion: The authors’ predictive model identified risk factors that have a higher probability of extended hospital stay. This model can be used to improve periprocedural optimization and improved discharge planning, which may help to decrease costs associated with PCIs. Management of Medicare beneficiaries after PCI calls for a multidisciplinary approach among healthcare teams and hospital administrators.

Original languageEnglish (US)
JournalJournal of Cardiothoracic and Vascular Anesthesia
DOIs
StatePublished - Jan 1 2019

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Percutaneous Coronary Intervention
Medicare
Length of Stay
Area Under Curve
Inpatients
Confidence Intervals
Hospital Administrators
Fee-for-Service Plans
Patient Care Team
Drug-Eluting Stents
Patient Discharge
Managed Care Programs
ROC Curve
Registries
Comorbidity
Cohort Studies
Retrospective Studies
Logistic Models
Odds Ratio
Regression Analysis

Keywords

  • hospital stay
  • Medicare
  • percutaneous coronary intervention
  • prediction model

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Anesthesiology and Pain Medicine

Cite this

Prediction Model for Extended Hospital Stay Among Medicare Beneficiaries After Percutaneous Coronary Intervention. / Burton, Brittany N.; Abudu, Boya; Danforth, Dennis J.; Patell, Saatchi; Wilkins y Martinez, Lizett; Fergerson, Byron; Elsharydah, Ahmad; Gabriel, Rodney A.

In: Journal of Cardiothoracic and Vascular Anesthesia, 01.01.2019.

Research output: Contribution to journalArticle

Burton, Brittany N. ; Abudu, Boya ; Danforth, Dennis J. ; Patell, Saatchi ; Wilkins y Martinez, Lizett ; Fergerson, Byron ; Elsharydah, Ahmad ; Gabriel, Rodney A. / Prediction Model for Extended Hospital Stay Among Medicare Beneficiaries After Percutaneous Coronary Intervention. In: Journal of Cardiothoracic and Vascular Anesthesia. 2019.
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AU - Wilkins y Martinez, Lizett

AU - Fergerson, Byron

AU - Elsharydah, Ahmad

AU - Gabriel, Rodney A.

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