Progression to Critical Illness and Death in Patients With Breakthrough Hospitalizations.

Background: Characterization of disease progression and outcomes after coronavirus disease 2019 (COVID-19)-related hospitalization in vaccinated compared with unvaccinated individuals is limited. Methods: This was a retrospective case-control study of symptomatic vaccinated (cases) and unvaccinated (controls) participants hospitalized for COVID-19 between December 30, 2020, and September 30, 2021, in Southeast Michigan. Hospitalized adult patients with lab-confirmed COVID-19 were identified through daily census report. Breakthrough infection was defined as detection of severe acute respiratory syndrome coronavirus 2 ≥14 days after completion of the primary vaccination series. The association between prior vaccination and critical COVID-19 illness (composite of intensive care unit [ICU] admission, invasive mechanical ventilation [IMV], 28-day mortality) was examined. Results: Two hundred ten (39%) fully vaccinated and 325 (61%) unvaccinated patients were evaluated. Compared with controls, cases were older, had more comorbidities (4 [3-7] vs 2 [1-4]; P < .001), and were more likely to be immunocompromised. Cases had less severe symptoms compared with controls (2 [1-2] vs 2 [2-3]; P < .001) and were less likely to progress to critical COVID-19 illness (33.3% vs 45.5%; P < .001); 28-day mortality was significantly lower in cases (11.0% vs 24.9%; P < .001). Symptom severity (odds ratio [OR], 2.59; 95% CI, 1.61-4.16; P < .001) and modified Sequential Organ Failure Assessment score on presentation (OR, 1.74; 95% CI, 1.48-2.06; P < .001) were independently associated with development of critical COVID-19 illness. Prior vaccination (OR, 0.528; 95% CI, 0.307-0.910; P = .020) was protective. Conclusions: COVID-19-vaccinated patients were less likely to develop critical COVID-19 illness and more likely to survive. Disease severity at presentation was a predictor of adverse outcomes regardless of vaccination status.

Long-term outcomes after robotic-assisted Ivor Lewis esophagectomy.

Robotic assistance has gained acceptance in thoracic procedures, including esophagectomy. There is a paucity of data regarding long-term outcomes for robotic esophagectomy. We previously reported our initial series of robot-assisted Ivor Lewis (RAIL) esophagectomy. We report long-term outcomes to assess the efficacy of the procedure. We performed a retrospective review of 112 consecutive patients who underwent a RAIL. Patient demographics, diagnosis, pathology, operative characteristics, post-operative complications, and long-term outcomes were documented. Descriptive statistical analysis was performed for all the variables. Primary endpoints were mortality and disease-free survival. Overall survival (OS) and disease-free survival (DFS) were calculated using the Kaplan-Meier method. Of the 112 patients, 106 had a diagnosis of cancer, with adenocarcinoma the dominant histology (87.5%). Of these 106 patients, 81 (76.4%) received neo-adjuvant chemoradiation. The 30-, 60-, and 90-day mortality was 1 (0.9%), 3 (2.7%), and 4 (3.6%), respectively. There were 9 anastomotic leaks (8%) and 18 (16.1%) patients had a stricture requiring dilation. All-patient OS at 1, 3, and 5 years was 81.4%, 60.5%, and 51.0%, respectively. For cancer patients, the 1-, 3-, and 5-year OS was 81.3%, 59.2%, and 49.4%, respectively, and the DFS was 75.3%, 42.3%, and 44.0%. We have shown that long-term outcomes after RAIL esophagectomy are similar to other non-robotic esophagectomies. Given the potential advantages of robotic assistance, our results are crucial to demonstrate that RAIL does not result in inferior outcomes.

Increasing Diversity in Cardiology: A Fellowship Director's Perspective.

Background Underrepresented-minorities (URM) remain few in number amongst practicing cardiologists and across cardiology fellowship training programs in the U.S. Increased diversity is needed across the entire field and is particularly necessary within graduate medical education cardiology fellowship training programs. Objectives This cross-sectional study was performed to identify which strategies were supported and implemented by cardiology fellowship program directors (PDs) to increase URM representation, to determine which entities hold the responsibility to increase diversity according to program directors, and to quantify URM representation in cardiology fellowship programs. Methods A 15-item survey was submitted to all American College of Graduate Medical Education (ACGME) accredited cardiology fellowship programs via electronic mail. Results Of 250 cardiology fellowship programs, 71 responses were received (28.4%). The number of URM faculty varied from 0-1 to more than six, and URM faculty held leadership roles in most programs (62.0%). A total of 16 respondents (22.5%) were URM program directors. Most respondents agreed that diversity was important to their training program (85.9%). The majority endorsed direct recruitment of URM applicants (60.6%), opportunities for applicants to connect with (54.9%) or be recruited by URM fellows (54.9%), holistic application review (67.6%), promoting mentorship by URM faculty (69.0%), URM faculty involvement in applicant interviewing (54.9%), and increased recruitment of URM faculty members (73.2%). Program directors allocated major responsibility to increase diversity to fellowship programs (71.8%), residency programs (63.3%), and medical schools (53.5%). Conclusions This study found that most cardiology programs have URM faculty in leadership roles, and nearly a quarter of the surveyed program directors were URMs. Cardiology program directors endorsed and employed numerous strategies to increase diversity and URM representation in fellowship programs. Additionally, program directors held fellowship training programs most responsible for increasing URM representation in the field of cardiology.

Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach.

Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance. In this study, we proposed a model that adopts a hybrid ensemble machine learning classifier structured from sequential minimal optimization and decision trees to identify risk factors contributing to fatal road injuries. The model was constructed, trained, tested, and validated using the Lebanese Road Accidents Platform (LRAP) database of 8482 road crash incidents, with fatality occurrence as the outcome variable. A sensitivity analysis was conducted to examine the influence of multiple factors on fatality occurrence. Seven out of the nine selected independent variables were significantly associated with fatality occurrence, namely, crash type, injury severity, spatial cluster-ID, and crash time (hour). Evidence gained from the model data analysis will be adopted by policymakers and key stakeholders to gain insights into major contributing factors associated with fatal road crashes and to translate knowledge into safety programs and enhanced road policies.