COVID-19 disease progression according to initial symptoms. A telemedicine cohort study

Author:

Murillo-Villanueva Karla,Velázquez-Hernández Blanca,Jácome-Mondragón José A.,Cervantes-Llamas Judit J.,Talavera Juan O.

Abstract

AbstractObjectiveCOVID-19 progression to severe or critical illness may be related to initial clinical presentation. Main objective was to identify initial symptoms related to highest risk of disease progression, in mild or moderate suspected or confirmed COVID-19 patients or in asymptomatic subjects in contact with a recently diagnosed patient.Design and methodsHistoric cohort study of Mexican patients with suspected or confirmed mild or moderate COVID-19 or asymptomatic subjects in recent contact with positive patients. They sought medical attention in “Centro Médico ABC” or claimed for remote attention, and daily telemedicine follow up until recovery or illness progression, from April 17th to October 08th 2020. Data excerpted for analysis were sex, age, body mass index, comorbidities, and signs, and symptoms presented in first day of disease manifestations and during follow up. We used logistic regression to identify initial symptoms associated with progression disease and through a conjunctive consolidation analysis a symptom index was created.Results120 of 1635 patients (7.2%) had clinical progression disease. By logistic regression we found as initial symptoms related to progression: fever OR 3 (1.89-4.77, p<0.001), cough OR 2.34 (1.56-3.52, p<0.001), myalgias or arthralgias OR 1.69 (1.09-2.63, p=0.018), and fatigue OR 1.65 (1.08-2.53, p=0.019). Conjunctive consolidation was processed with the previous symptoms, and a 3 groups score resulted C-19PAIS Index: 1) Fever with cough or fever with fatigue, with a probability of progression disease of 29% (31/106 patients), 2) Fever or cough or fatigue or cough with fatigue, 10.7% (66/615 patients) and 3) No fever, no cough, no fatigue, 2% (23/914).ConclusionsInitial symptoms predict clinical progression in COVID-19 patients.

Publisher

Cold Spring Harbor Laboratory

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Computerized Intelligence and Mathematical Models for COVID-19 Diagnosis: A Review;Journal of Human Environment and Health Promotion;2023-06-01

2. Risk symptoms for progression in Covid-19. Telemedicine cohort study;Salud Pública de México;2023-01-02

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