Race, substance use, and evaluation for heart transplantation: Insights from a large urban medical center.

It is unknown whether racial disparities in access to heart transplantation (HT) are amplified when coupled with substance use. We examined patients evaluated for HT over 8 years at an urban transplant center. We evaluated substance use and race/ethnicity as independent and interactive predictors of HT and left ventricular assist device (LVAD) implantation. Of 1,148 patients evaluated for HT, substance use was cited as an ineligibility factor in 151 (13%) patients, 16 (11%) of whom ultimately received HT. Significantly more non-Hispanic Black (NHB) patients were deemed ineligible due to substance use (n = 59, 19%) compared to other races/ethnicities (non-Hispanic white: n = 68, 12%; other race/ethnicity: n = 24, p = 0.002). No racial differences were observed in the likelihood of HT among patients initially excluded for substances, but more NHB patients ultimately received LVAD than the other racial groups. This study encourages greater awareness of the role of substance use and race in the HT evaluation.

Augmented Intelligence to Identify Patients With Advanced Heart Failure in an Integrated Health System.

BACKGROUND: Timely referral for specialist evaluation in patients with advanced heart failure (HF) is a Class 1 recommendation. However, the transition from stage C HF to advanced or stage D HF often goes undetected in routine care, resulting in delayed referral and higher mortality rates. OBJECTIVES: The authors sought to develop an augmented intelligence-enabled workflow using machine learning to identify patients with stage D HF and streamline referral. METHODS: We extracted data on HF patients with encounters from January 1, 2007, to November 30, 2020, from a HF registry within a regional, integrated health system. We created an ensemble machine learning model to predict stage C or stage D HF and integrated the results within the electronic health record. RESULTS: In a retrospective data set of 14,846 patients, the model had a good positive predictive value (60%) and low sensitivity (25%) for identifying stage D HF in a 100-person, physician-reviewed, holdout test set. During prospective implementation of the workflow from April 1, 2021, to February 15, 2022, 416 patients were reviewed by a clinical coordinator, with agreement between the model and the coordinator in 50.3% of stage D predictions. Twenty-four patients have been scheduled for evaluation in a HF clinic, 4 patients started an evaluation for advanced therapies, and 1 patient received a left ventricular assist device. CONCLUSIONS: An augmented intelligence-enabled workflow was integrated into clinical operations to identify patients with advanced HF. Endeavors such as this require a multidisciplinary team with experience in design thinking, informatics, quality improvement, operations, and health information technology, as well as dedicated resources to monitor and improve performance over time.