Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data.

ABSTRACT:

Background

Although risk adjustment remains a cornerstone for comparing outcomes across hospitals, optimal strategies continue to evolve in the presence of many confounders. We compared conventional regression-based model to approaches particularly suited to leveraging big data.

Methods and results

We assessed hospital all-cause 30-day excess mortality risk among 8952 adults undergoing percutaneous coronary intervention between October 1, 2011, and September 30, 2012, in 24 Massachusetts hospitals using clinical registry data linked with billing data. We compared conventional logistic regression models with augmented inverse probability weighted estimators and targeted maximum likelihood estimators to generate more efficient and unbiased estimates of hospital effects. We als

SUBMITTER: Spertus JV 

PROVIDER: S-EPMC5341139 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature