Impact of Hospital Practice and Staffing Differences on Transesophageal Echocardiography Use in Cardiac Valve or Coronary Artery Bypass Graft Surgery

Abstract

Objective: To identify and quantify the predictors of intraoperative TEE use among patients undergoing cardiac valve or isolated coronary artery bypass graft (CABG) surgery.

Design: Observational cohort study.

Setting: This study used Centers for Medicare and Medicaid Services (CMS) administrative claims dataset of beneficiaries undergoing valve or isolated CABG surgery between 2013 – 2015.

Participants: Adults aged ≥65 years of age undergoing cardiac valve or isolated CABG surgery.

Interventions: Generalized linear mixed model (GLMM) analyses were used to examine the relationship between TEE and patient characteristics, hospital factors, and staffing differences while accounting for clustering within hospitals. The proportion of variation in TEE use attributable to patient-level characteristics were quantified using odds ratios. Hospital-level factors and staffing differences were quantified using median odds ratios (MOR) and interval odds ratios (IOR).

Measurements and Main Results: Among 261,860 patients (123,702 valve and 138,158 isolated CABG), the GLMM analysis demonstrated that the strongest predictor for intraoperative TEE use was the hospital where the surgery occurred (MOR for TEE of 2.57 in valve and 4.16 in isolated CABG). The TEE staffing variable reduced the previously unexplained across-hospital variability by 9% in valve and 21% in isolated CABG, and hospitals with anesthesiologist TEE staffing (vs mixed) were more likely to use TEE in both valve (MOR for TEE of 1.21 in valve and 1.84 in isolated CABG).

Conclusions: Hospital practice was the strongest predictor for TEE use overall, and in isolated CABG surgery, hospitals with anesthesiologist TEE staffing was a primary predictor for TEE use.

Bo Zhang

Assistant Professor of Biostatistics

My research interests include design of observational studies, instrumental variables, application of causal inference in medicine and applied statistics in general.