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.
【저자키워드】 machine learning, COVID, outcomes, 【초록키워드】 COVID-19, coronavirus disease, IL-6, hospital, Comorbidities, CRP, D-dimer, oxygen, Symptom, progression, outcome, New York City, Laboratory, Predictive model, symptom duration, Patient, Logistic regression, age, Follow-up, predictor, marker, Analysis, clinical manifestation, Older, Algorithms, increased risk, disease course, highlight, initial, greater, identify, performed, proportion, can be used, were used, presenting, recorded, had more, patients with COVID-19, 【제목키워드】 New York City, Poor, with COVID-19,