The 2018 Revised United Network for Organ Sharing Heart Allocation System (HAS) was proposed to reclassify status 1A candidates into groups of decreasing acuity; however, it does not take into account factors such as body mass index (BMI) and blood group which influence waitlist (WL) outcomes. We sought to validate patient prioritization in the new HAS at our center. We retrospectively evaluated patients listed for heart transplantation (n = 214) at Emory University Hospital from 2011 to 2017. Patients were reclassified into the 6-tier HAS. Multistate modeling and competing risk analysis were used to compare outcomes of transplantation and WL death/deterioration between new tiers. Additionally, a stratified sensitivity analysis by BMI and blood group was performed. Compared with tier 4 patients, there was progressively increasing hazard of WL death/deterioration in tier 3 (HR: 2.52, 95% CI: 1.37-4.63, P =.003) and tier 2 (HR: 5.03, 95% CI: 1.99-12.70, P <.001), without a difference in transplantation outcome. When stratified by BMI and blood group, this hierarchical association was not valid in patients with BMI ≥30 kg/m2 and non-O blood groups in our cohort. Therefore, the 2018 HAS accurately prioritizes the sickest patients in our cohort. Factors such as BMI and blood group influence this relationship and iterate that the system can be further refined.

Objectives: We investigated sex-based differences in eligibility for and outcomes after receipt of advanced heart failure (HF) therapies. Background: Although women are more likely to die from HF than men, registry data suggest that women are less likely to receive heart transplant (HT) or left ventricular assist device (LVAD) for largely unknown reasons. Methods: We performed a single-center retrospective cohort study of patients evaluated for advanced HF therapies from 2012 to 2016. Logistic regression was used to determine the association of sex with eligibility for HT/LVAD. Competing risks and Kaplan-Meier analysis were used to examine survival. Results: Of 569 patients (31% women) evaluated, 223 (39.2%) were listed for HT and 81 (14.2%) received destination (DT) LVAD. Women were less likely to be listed for HT (adjusted odds ratio [OR] 0.36, 95% confidence interval [CI] 0.21-0.61; P <.0001), based on allosensitization (P <.0001) and obesity (P =.02). Women were more likely to receive DT LVAD (adjusted OR 2.29, 95% CI 1.23-4.29; P =.01). Survival was similar between men and women regardless of whether they received HT and DT LVAD or were ineligible for therapy. Conclusion: Women are less likely to be HT candidates, but more likely to receive DT LVAD.

Background--With the recent implementation of the Medicare Quality Payment Program, providers face increasing accountability for delivering high-quality care. Such pay-for-performance programs aim to leverage systematic data captured by electronic health record (EHR) systems to measure performance; however, the fidelity of EHR query for assessing performance has not been validated compared with manual chart review. We sought to determine whether our institution's methodology of EHR query could accurately identify cases in which providers failed to prescribe statins for eligible patients with coronary artery disease. Methods and Results--A total of 9459 patients with coronary artery disease were seen at least twice at the Emory Clinic between July 2014 and June 2015, of whom 1338 (14.1%, 95% confidence interval 13.5-14.9%) had no statin prescription or exemption per EHR query. A total of 120 patient cases were randomly selected and reviewed by 2 physicians for further adjudication. Of the 120 cases initially classified as statin prescription failures, only 21 (17.5%; 95% confidence interval, 11.7-25.3%) represented true failure following physician review. Conclusions--Sole reliance on EHR data query to measure quality metrics may lead to significant errors in assessing provider performance. Institutions should be cognizant of these potential sources of error, provide support to medical providers, and form collaborative data management teams to promote and improve meaningful use of EHRs. We propose actionable steps to improve the accuracy of EHR data query that require hypothesis testing and prospective validation in future studies.