Chacin Suarez, Audry S

Background Cardiac rehabilitation (CR) leads to increased exercise capacity (VO 2 peak) and risk reduction of major adverse cardiovascular events (MACEs). Women exhibit blunted VO 2 peak improvements following CR. The purpose was to examine sex differences in the clinical predictors of VO 2 peak improvement and odds of MACEs following CR. Methods From 1999 to 2017, all consecutive patients undergoing cardiopulmonary exercise testing before and after CR were included. Sex differences in peak exercise metrics in response to CR were compared. Multiple logistic regression models were fit to assess clinical predictors of VO 2 peak improvement (defined as >0% from pre‐CR) and MACEs following CR for men and women. Results A total of 513 men and 158 women were included. Men had greater increases in VO 2 peak than women following CR ( P =0.001). For men, independent predictors of VO 2 peak improvement following CR included higher CR session attendance (odds ratio [OR], 1.04 [95% CI, 1.02–1.06]), peripheral artery disease (OR, 0.47 [95% CI, 0.25–0.86]), and pre‐CR VO 2 peak (OR, 0.98 [95% CI, 0.97–0.99]) (all, P <0.02). Independent predictors of lower odds of MACEs following CR in men included higher number of CR sessions (OR, 0.97 [95% CI, 0.95–0.99]), pre‐CR VO 2 peak (OR, 0.98 [95% CI, 0.97–0.99]), surgical CR indication (OR, 0.34 [95% CI, 0.18–0.68]), and no history of peripheral artery disease (OR, 2.94 [95% CI, 1.60–5.38]) (all, P <0.02). For women, the selected clinical characteristics were not statistically significant independent predictors of VO 2 peak improvement or odds of MACEs (except for diabetes) following CR. Conclusions These findings suggest that clinical predictors of VO 2 peak improvement and odds of MACEs following CR are sex specific.

Background Cardiac rehabilitation (CR) is a comprehensive secondary cardiovascular disease program with structured lifestyle interventions to reduce morbidity and mortality. The American Heart Association cardiovascular health (CVH) framework measures health‐promoting behaviors and clinical factors, but it has not been rigorously evaluated in the CR setting. Methods This retrospective cohort study analyzed patients attending CR from January 2018 to September 2020. Patients were evaluated at baseline (pre‐CR) and completion (post‐CR) using 3 clinical factors (blood pressure, cholesterol, and hemoglobin A1c) and 4 health behaviors (smoking, body mass index, physical activity, and diet). CVH score was computed as a composite of each Life's Simple 7 component by assigning 0 points for poor, 1 point for intermediate, or 2 points for ideal (range 0–14 points). CVH scores were further categorized as poor (0–6 points), intermediate (7–8 points), and ideal (9–14 points). Missing data in the analysis were accounted for using a multiple imputation procedure. Results Patients (N=937) were aged 64.0±13.4 years old, 34% women, and attended 11±12 CR sessions. Pre‐CR, 97.2% had poor CVH scores, 2.8% had intermediate scores, and none met ideal CVH criteria. Post‐CR, there was a reduction in poor scores across all metrics except for hemoglobin A1c, which increased (40.6%–43.5%). Younger patients showed improvement in hemoglobin A1c, while older patients improved in body mass index and blood pressure. Conclusions Our study demonstrates the efficacy of CR in improving CVH but underscores the need for better blood glucose management. Tailored interventions based on age and sex may further optimize outcomes for CR participants.

Purpose The purpose of the present study was to investigate body mass index, multi-morbidity, and COVID-19 Risk Score as predictors of severe COVID-19 outcomes. Patients Patients from this study are from a well-characterized patient cohort collected at Mayo Clinic between January 1, 2020 and May 23, 2020; with confirmed COVID-19 diagnosis defined as a positive result on reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assays from nasopharyngeal swab specimens. Measures Demographic and clinical data were extracted from the electronic medical record. The data included: date of birth, gender, ethnicity, race, marital status, medications (active COVID-19 agents), weight and height (from which the Body Mass Index (BMI) was calculated, history of smoking, and comorbid conditions to calculate the Charlson Comorbidity Index (CCI) and the U.S Department of Health and Human Services (DHHS) multi-morbidity score. An additional COVID-19 Risk Score was also included. Outcomes included hospital admission, ICU admission, and death. Results Cox proportional hazards models were used to determine the impact on mortality or hospital admission. Age, sex, and race (white/Latino, white/non-Latino, other, did not disclose) were adjusted for in the model. Patients with higher COVID-19 Risk Scores had a significantly higher likelihood of being at least admitted to the hospital (HR = 1.80; 95% CI = 1.30, 2.50; P < .001), or experiencing death or inpatient admission (includes ICU admissions) (HR = 1.20; 95% CI = 1.02, 1.42; P = .028). Age was the only statistically significant demographic predictor, but obesity was not a significant predictor of any of the outcomes. Conclusion Age and COVID-19 Risk Scores were significant predictors of severe COVID-19 outcomes. Further work should examine the properties of the COVID-19 Risk Factors Scale.

Objective To describe the process and outcome of creating a patient cohort in the early stages of the COVID-19 pandemic in order to better understand the process of and predict the outcomes of COVID-19. Patients and Methods A total of 1169 adults aged 18 years of age or older who tested positive in Mayo Clinic Rochester or the Mayo Clinic Midwest Health System between January 1 and May 23 of 2020. Results Patients were on average 43.9 years of age and 50.7% were female. Most patients were white (69.0%), and Blacks (23.4%) and Asians (5.8%) were also represented in larger numbers. Hispanics represented 16.3% of the sample. Just under half of patients were married (48.4%). Common comorbid conditions included: cardiovascular diseases (25.1%), dyslipidemia (16.0%), diabetes mellitus (11.2%), chronic obstructive pulmonary disease (6.6%), asthma (7.5%), and cancer (5.1%). All other comorbid conditions were less the 5% in prevalence. Data on 3 comorbidity indices are also available including the: DHHS multi-morbidity score, Charlson Comorbidity Index, and Mayo Clinic COVID-19 Risk Factor Score. Conclusion In addition to managing the ever raging pandemic and growing death rates, it is equally important that we develop adequate resources for the investigation and understanding of COVID-19-related predictors and outcomes.