Hartford Hospital, Inc.

Chagas cardiomyopathy (CCM) is a significant cause of ventricular arrhythmias and sudden cardiac death (SCD). Although, implantable cardiac defibrillators (ICD) have been used for all forms of non-ischemic cardiomyopathy (NICM), studies on ICD efficacy in CCM are scarce.

The present study aims to compare the long-term outcomes, mortality rates, and the occurrence of tachycardia therapies after ICD implantation in patients with CCM and NICM.

The study was conducted over an 18-year period beginning in 2003. The primary outcome of this study was the difference in appropriate ICD therapies and mortality among patients in a single center receiving implant for CCM or NICM management. As a secondary outcome, we compared inappropriate shocks, presence of incessant ventricular tachycardia/electrical storm, and SCD.

The study included 207 patients (117 with CCM and 90 with NICM). The median follow-up time was 61 months [25-121] in the CCM group and 56.5 months [23-119] in the NICD group. During follow up, 39.3 % (46 patients) died in the CCM group and 5.6 % (5 patients) in the NICM group. Appropriate shocks, appropriate therapies, ATP, electrical storm and inappropriate shocks were all more frequent in patients with CCM.

CCM patients experienced higher mortality and more frequent appropriate ICD interventions compared to patients with NICM. ICDs appear effective and safe for long-term management in CCM.

As a powerful tool for discovering and documenting injury, the forensic autopsy has been incorporated into trauma care quality improvement. Autopsy findings are used to determine the Abbreviated Injury Scale (AIS) and Injury Severity Score (ISS) to assess a patient's trauma severity. Clinical trauma services use autopsy reports to educate, improve patient care, and strengthen the information used in epidemiological and injury prevention studies. However, there is sometimes a disconnect between the forensic pathologist's injury descriptions and the information needed by the trauma service and for AIS/ISS coding.We formed a collaboration between forensic pathologists and trauma teams to improve this communication. Accordingly, we describe injuries that are commonly overlooked or poorly described in autopsy reports, useful injury terminology, opportunities for trauma care improvement revealed by autopsies, and which medical examiner/coroner cases will most benefit from attention to specific injury details. We demonstrate how differences in injury description impact AIS/ISS results, and distill the detailed AIS manual into a pragmatic, concise, “quick reference” autopsy-directed guide for the forensic pathologist. Collaborations between trauma teams and forensic pathologists benefit each other and help communicate autopsy findings in a more clinically useful way to stakeholders to further benefit the living.

Missing data decreasing study power and introducing bias, thereby undermining a registry's ability to draw valid inferences. We evaluated how missing data are reported and addressed in heart transplantation (HT) studies using the United Network for Organ Sharing (UNOS) database.

We conducted a systematic literature search of Medline from January 1, 2018 through August 22, 2023 and included studies that used the UNOS database to evaluate adult (≥18 years) de novo HT recipients. We collected details on the study population, timeframe, primary end-point, use of missing data, and whether and what methods were used to handle missing data. Approaches were classified as variable selection, complete case analysis (CCA), missing indicator method, single imputation, or multiple imputation.

Of the 229 included studies, 67 (29.3%) limited their cohorts to those without missing data for the outcome or key variables and 93 (40.6%) reported missing data in their final cohort. 78 (34.1%) studies reported how they handled missing data in their statistical modeling. Of these, CCA was most used (n = 41, 52.6%) followed by multiple imputation (n = 22, 28.2%), and other methods (n = 15, 19.2%). Thirty-one (13.5%) studies reported removing covariates from their analysis because of missingness.

Merely a third of the identified UNOS database studies reported how they handled missing data in their analysis, with strategies varying. Although no singular approach to handling missing data exists, methods are available that can improve upon the most used approaches. Future best practices should include explicit reporting of missingness, detailed methods, and sensitivity checks.