Reliability of a 2-Bout exercise test on a Wattbike cycle ergometer

Purpose: To determine the intraday and interday reliability of a 2 × 4-min performance test on a cycle ergometer (Wattbike) separated by 30 min of passive recovery (2 × 4MMP). Methods: Twelve highly trained cyclists (mean ± SD; age = 20 ± 2 y, predicted VO2max = 59.0 ± 3.6 mL · kg–1 · min–1) completed six 2 × 4MMP cycling tests on a Wattbike ergometer separated by 7 d. Mean power was measured to determine intraday (test 1 [T1] to test 2 [T2]) and interday reliability (weeks 1–6) over the repeated trials. Results: The mean intraday reliabilities of the 2 × 4MMP test, as expressed by the typical error of measurement (TEM, W) and coefficient of variation (CV, %) over the 6 wk, were 10.0 W (95% confidence limits [CL] 8.2–11.8), and 2.6% (95%CL 2.1–3.1), respectively. The mean interday reliability TEM and CV for T1 over the 6 wk were 10.4 W (95%CL 8.7–13.3) and 2.7% (95%CL 2.3–3.5), respectively, and 11.7 W (95%CL 9.8–15.1) and 3.0% (95%CL 2.5–3.9) for T2. Conclusion: The testing protocol performed on a Wattbike cycle ergometer in the current study is reproducible in highly trained cyclists. The high intraday and interday reliability make it a reliable method for monitoring cycling performance and for investigating factors that affect performance in cycling events

Supervised Machine Learning Approach to Identify Early Predictors of Poor Outcome in Patients with COVID-19 Presenting to a Large Quaternary Care Hospital in New York City

Background: The progression of clinical manifestations in patients with coronavirus disease 2019 (COVID-19) highlights the need to account for symptom duration at the time of hospital presentation in decision-making algorithms. Methods: We performed a nested case–control analysis of 4103 adult patients with COVID-19 and at least 28 days of follow-up who presented to a New York City medical center. Multivariable logistic regression and classification and regression tree (CART) analysis were used to identify predictors of poor outcome. Results: Patients presenting to the hospital earlier in their disease course were older, had more comorbidities, and a greater proportion decompensated (<4 days, 41%; 4–8 days, 31%; >8 days, 26%). The first recorded oxygen delivery method was the most important predictor of decompensation overall in CART analysis. In patients with symptoms for <4, 4–8, and >8 days, requiring at least non-rebreather, age ≥ 63 years, and neutrophil/lymphocyte ratio ≥ 5.1; requiring at least non-rebreather, IL-6 ≥ 24.7 pg/mL, and D-dimer ≥ 2.4 µg/mL; and IL-6 ≥ 64.3 pg/mL, requiring non-rebreather, and CRP ≥ 152.5 mg/mL in predictive models were independently associated with poor outcome, respectively. Conclusion: Symptom duration in tandem with initial clinical and laboratory markers can be used to identify patients with COVID-19 at increased risk for poor outcomes