Murray A. Mittleman

This study is the first to show that cardiac parasympathetic function, assessed via heart rate fragmentation (HRF), is independently associated with major adverse cardiovascular events (MACEs) in individuals traditionally considered low-risk due to nondetectable or minimal coronary artery calcification (CAC). HRF outperforms both the Framingham and MESA-CAC risk scores in the lowest-risk subgroup over the short term (<10 yr).

BACKGROUND: Hypotensive episodes detected by 24-hour ambulatory blood pressure (BP) monitoring capture daily cumulative hypotensive stress and could be clinically relevant to cognitive impairment, but this relationship remains unclear. METHODS: We included participants from the Systolic Blood Pressure Intervention Trial (receiving intensive or standard BP treatment) who had 24-hour ambulatory BP monitoring measured near the 27-month visit and subsequent biannual cognitive assessments. We evaluated the associations of hypotensive episodes (defined as systolic BP drops of ≥20 mm Hg between 2 consecutive measurements that reached Z scores, −0.249 [95% CI, −0.380 to −0.119]) and their faster declines (difference in Z score changes, −0.128 [95% CI, −0.231 to −0.026]). A consistent dose-response association was also observed for longer hypotensive duration with worse Montreal Cognitive Assessment and digit symbol coding scores. The association with digit symbol coding scores remained significant after further adjusting for 24-hour average BP and variability and was not observed for hypotension defined by clinic, orthostatic, or 24-hour average BP. Intensive BP treatment increased 24-hour hypotensive episodes and modified its association with the decline in digit symbol coding score. CONCLUSION: Twenty-four–hour hypotensive episodes were associated with worse cognitive function, especially in processing speed, and could be a novel marker for optimal BP control and dementia prevention.

Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study assessed seven statistical and machine learning algorithms–Lasso, Ridge, Elastic Net, Cox Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests in a post-policy cohort of 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database who received their first transplant on or after October 18, 2018. A cross-validation framework was designed in mlr. Model performance was also compared in a seasonally-matched pre-policy cohort. In the post-policy cohort, Random Survival Forests and Cox Gradient Boost had the highest performances with C-indices of 0.628 and 0.627. The relative importance of some predictive variables differed between the pre-policy and post-policy cohorts, such as the absence of ECMO in the post-policy cohort. Survival machine learning models provide reasonable prediction of 1-year posttransplant mortality outcomes and continual updating of prediction models is warranted in the contemporary era.

Background It remains unknown whether estimated glomerular filtration rate (eGFR) using the refit Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation without a term for race is associated with mortality and the need for kidney replacement therapy (KRT) differentially between Black and White heart transplant recipients. Methods We studied 25,900 adults included in the Scientific Registry of Transplant Recipients. We classified recipients into six categories of eGFR (< 30, 30 to < 45, 45 to < 60, 60 to < 90, 90 to < 120, ≥ 120 ml/min/1.73 m²) using the race-neutral CKD-EPI refit equation, and assessed survival with multivariable adjusted Cox proportional hazards regression. Results The association between pre-transplant race-neutral eGFR and mortality varied by race (P_interaction = 0.006). Compared to White patients with an eGFR of 90–120 ml/min/1.73 m², the mortality rates were 57% (95% CI 1.25, 1.98), 29% (95% CI 1.11, 1.51), 34% (95% CI 1.19, 1.52), and 19% (95% CI 1.06, 1.33) higher in Black patients with an eGFR less than 30, 30–45, 45–60, and 60–90 ml/min/1.73m², respectively; and 53% (95% CI 1.28, 1.82), 49% (95% CI 1.33, 1.66), and 23% (95% CI 1.11, 1.35) higher among White patients with an eGFR less than 30, 30–45, and 45–60 ml/min/1.73 m², respectively. The association between pre-transplant eGFR and the need for KRT during follow-up was similar between Black and White patients (P_interaction = 0.57). Conclusions Worsening pre-transplant eGFR using the new race-neutral CKD-EPI refit equation was associated with a higher rate of post-heart transplant mortality and KRT in Black and White recipients. The racial disparity in post-heart transplant mortality was narrower in the setting of severe kidney dysfunction. Graphical abstract

Background: Many individuals eligible for statin therapy decline treatment, often due to fear of adverse effects. Misinformation about statins is common and drives statin reluctance, but its prevalence on social media platforms, such as Twitter (now X) remains unclear. Social media bots are known to proliferate medical misinformation, but their involvement in statin‐related discourse is unknown. This study examined temporal trends in volume, author type (bot or human), and sentiment of statin‐related Twitter posts (tweets).Methods and Results: We analyzed original tweets with statin‐related terms from 2010 to 2022 using a machine learning–derived classifier to determine the author's bot probability, natural language processing to assign each tweet a negative or positive sentiment, and manual qualitative analysis to identify statin skepticism in a random sample of all tweets and in highly influential tweets. We identified 1 155 735 original statin‐related tweets. Bots produced 333 689 (28.9%), humans produced 699 876 (60.6%), and intermediate probability accounts produced 104 966 (9.1%). Over time, the proportion of bot tweets decreased from 47.8% to 11.3%, and human tweets increased from 43.6% to 79.8%. The proportion of negative‐sentiment tweets increased from 27.8% to 43.4% for bots and 30.9% to 38.4% for humans. Manually coded statin skepticism increased from 8.0% to 19.0% for bots and from 26.0% to 40.0% for humans.Conclusions: Over the past decade, humans have overtaken bots as generators of statin‐related content on Twitter. Negative sentiment and statin skepticism have increased across all user types. Twitter may be an important forum to combat statin‐related misinformation.

Introduction: Post-transplant diabetes mellitus (PTDM) is a common complication among cardiac transplant recipients, causing diabetes-related complications and death. While certain maintenance immunosuppressive drugs increase PTDM risk, it is unclear whether induction immunosuppression can do the same. Therefore, we evaluated whether induction immunosuppression with IL-2 receptor antagonists, polyclonal anti-lymphocyte antibodies, or Alemtuzumab given in the peri-transplant period is associated with PTDM.Methods: We used the Scientific Registry of Transplant Recipients database to conduct a cohort study of US adults who received cardiac transplants between January 2008-December 2018. We excluded patients with prior or multiple organ transplants and those with a history of diabetes, resulting in 17,142 recipients. We created propensity-matched cohorts (n=7,412) using predictors of induction immunosuppression and examined the association between post-transplant diabetes and induction immunosuppression by estimating hazard ratios using Cox proportional-hazards models.Results: In the propensity-matched cohort, the average age was 52.5 (SD=13.2) years, 28.7% were female and 3,706 received induction immunosuppression. There were 867 incident cases of PTDM during 26,710 person-years of follow-up (32.5 cases/1,000 person-years). There was no association between induction immunosuppression and post-transplant diabetes (Hazard Ratio= 1.04, 95% confidence interval 0.91 – 1.19). Similarly, no associations were observed for each class of induction immunosuppression agents and post-transplant diabetes.Conclusion: The use of contemporary induction immunosuppression in cardiac transplant patients was not associated with post-transplant diabetes.