Awarding additional MELD points to the shortest waitlist candidates improves sex disparity in access to liver transplant in the United States.

Since the introduction of the MELD-based allocation system, women are now 30% less likely than men to undergo liver transplant (LT) and have 20% higher waitlist mortality. These disparities are in large part due to height differences in men and women though no national policies have been implemented to reduce sex disparities. Patients were identified using the Scientific Registry of Transplant Recipients (SRTR) from 2014 to 2019. Patients were categorized into five groups by first dividing into thirds by height then dividing the shortest third into three groups to capture more granular differences in the most disadvantaged patients (<166 cm). We then used LSAM to model waitlist outcomes in five versions of awarding additional MELD points to shorter candidates compared to current policy. We identified two proposed policy changes LSAM scenarios that resulted in improvement in LT and death percentage for the shortest candidates with the least negative impact on taller candidates. In conclusion, awarding an additional 1-2 MELD points to the shortest 8% of LT candidates would improve waitlist outcomes for women. This strategy should be considered in national policy allocation to address sex-based disparities in LT.

Machine-learning algorithms for predicting hospital re-admissions in sickle cell disease.

Reducing preventable hospital re-admissions in Sickle Cell Disease (SCD) could potentially improve outcomes and decrease healthcare costs. In a retrospective study of electronic health records, we hypothesized Machine-Learning (ML) algorithms may outperform standard re-admission scoring systems (LACE and HOSPITAL indices). Participants (n = 446) included patients with SCD with at least one unplanned inpatient encounter between January 1, 2013, and November 1, 2018. Patients were randomly partitioned into training and testing groups. Unplanned hospital admissions (n = 3299) were stratified to training and testing samples. Potential predictors (n = 486), measured from the last unplanned inpatient discharge to the current unplanned inpatient visit, were obtained via both data-driven methods and clinical knowledge. Three standard ML algorithms, Logistic Regression (LR), Support-Vector Machine (SVM), and Random Forest (RF) were applied. Prediction performance was assessed using the C-statistic, sensitivity, and specificity. In addition, we reported the most important predictors in our best models. In this dataset, ML algorithms outperformed LACE [C-statistic 0·6, 95% Confidence Interval (CI) 0·57-0·64] and HOSPITAL (C-statistic 0·69, 95% CI 0·66-0·72), with the RF (C-statistic 0·77, 95% CI 0·73-0·79) and LR (C-statistic 0·77, 95% CI 0·73-0·8) performing the best. ML algorithms can be powerful tools in predicting re-admission in high-risk patient groups.