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

• Published in: Journal of cardiothoracic and vascular anesthesia 2019-11, Vol.33 (11), p.3035-3041
• Main Authors: Burton, Brittany N., Abudu, Boya, Danforth, Dennis J., Patell, Saatchi, Wilkins y Martinez, Lizett, Fergerson, Byron, Elsharydah, Ahmad, Gabriel, Rodney A.
• Format: Article
• Language: English
• Subjects: hospital stay; Medicare; percutaneous coronary intervention; prediction model
• ISSN: 1053-0770; 1532-8422
• DOI: 10.1053/j.jvca.2019.04.022

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). Retrospective cohort study. Multi-institutional. Data were obtained from the National (Nationwide) Inpatient Sample registry from 2013 to 2014 over a 2-year period. None. 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. 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.