P141. Hospital Factors Are More Important than Volume in Predicting Outcome and Failure to Rescue in Medicare Beneficiaries Undergoing Robotic Lobectomy
West Virginia University
Morgantown, WV
United States - Contact Me
Dr. Hayanga is a Professor in Cardiothoracic Surgery at the West Virginia University Medicine. He is the Vice Chair of Academic Affairs, Director of the WVU Heart & Vascular Institute ECMO Program and Medical Director of Research. Board certified in Cardiothoracic Surgery, General Surgery and Surgical Critical Care, he completed his surgical training at Johns Hopkins University and University of Michigan followed by cardiothoracic and transplant training at the University of Washington and University of Pittsburgh respectively. He served as an Alfred Sommer Scholar during his MPH at the Johns Hopkins School of Public Health, as a 2008 World Health Organization (WHO) Patient Safety Scholar, a Department of Health and Human Services (HHS) Fellow and Senior Medical Adviser to the Deputy Secretary in Washington, D.C. He holds a Master’s degree in Healthcare Leadership from Brown University and certificate in Artificial Intelligence and Business Strategy from MIT. He is an expert Health Policy panelist with RAND Corporation, member of the American Association of Thoracic Surgery (AATS) and editorial board member for Journal of Thoracic and Cardiovascular Surgery, Journal of Heart & Lung Transplantation and LUNG. He has authored over 350 peer-reviewed papers and his clinical work and research focuses on ECMO, transplantation and application of data analytics in prevention, diagnosis and mitigation of end-stage cardiopulmonary disease. He is a fellow of the American College of Surgeons, the Royal College of Surgeons and the American College of Chest Physicians.
Description
Surgical volume is known to influence failure to rescue (FTR), defined as death following a complication. Robotic lung surgery continues to expand despite outcome variability between hospitals. We sought to estimate the contribution of hospital-based factors on outcomes and FTR following robotic lobectomy.
METHODS
Using the Centers for Medicare and Medicaid Services inpatient claims database, we evaluated all patients aged 65 and older with a diagnosis of lung cancer undergoing robotic upper lobectomy between January 2018 and December 2020. We excluded patients who had a segmentectomy, sublobar, wedge and bronchoplastic resections, those with metastatic or non-malignant disease, and those with a history of neoadjuvant chemotherapy. Primary outcomes included FTR rate, length of stay (LOS), readmissions, conversion to open, complications, and costs. We analyzed hospitals by tertiles of volume (Low <9, Medium 9-20; High >20). We utilized the Medicare Mortality index (MMI), a marker of overall hospital performance, and analyzed hospitals by tertile (low <0.04; medium 0.04-0.13; high >0.13). Propensity score models were adjusted for confounding using goodness-of-fit.
RESULTS
Data pertaining to 4,317 patients who underwent robotic resection were analyzed. After propensity score balancing, patients from tertiles of lowest volume and highest MMI had higher costs ($34,222 vs. $30,316, p = 0.006) p < 0.001), as well as higher mortality (OR 7.46; 95% CI 2.67-28.2, p < 0.001) and death within 60 days (OR 17.1; 95% CI 5.43-87.4). Compared to high volume hospitals, low volume centers had the highest incidence of conversion to open, atelectasis, respiratory failure, hemorrhagic anemia, death, length of stay (LOS), and costs (each p<0.001). The C-statistic for volume as a predictor of FTR was 0.6. Hospitals in the highest tertile of MMI had highest incidence of conversion to open surgery (p=0.01), pneumothorax (p=0.02), atelectasis (p<0.001), and respiratory failure (p<0.001). They also had highest mortality, readmissions, LOS, costs (each p<0.001), and shortest survival (p<0.001). The C-statistic for MMI was 0.8. (Figure)
CONCLUSION
The MMI incorporates hospital-based factors in the adjudication of outcomes and may be a more sensitive predictor of FTR rates than volume alone. Combining MMI and volume may provide a metric that can guide quality improvement and cost effectiveness measures in hospitals seeking to implement robotic lung