Predictors of disease severity, clinical course, and therapeutic outcome in COVID-19 patients: our experience with 1,700 patients

Autor: Z, Ergenc, H, Ergenc, S, Araç, I H, Tör, M, Usanmaz, E, Alkılınç, C, Karacaer, T, Kaya, A, Nalbant, S, Görgün, A, Öztürk, I, Yıldırım
Rok vydání: 2022
Předmět:
Zdroj: European review for medical and pharmacological sciences. 26(21)
ISSN: 2284-0729
Popis: Our study aimed at investigating the impacts of demographic, hematological, and biochemical factors on the clinical course and the prognostic outcome in adult COVID-19 patients.This retrospective study was performed in the internal medicine departments of two hospitals, and data were extracted from the medical files of 1,700 adult COVID-19 patients (836 females, 49.2%; 864 males, 50.8%) with an average age of 48.23 ± 16.68 (range: 18-93). Clinical data included baseline descriptives, prior medical history, admission date, treatment, and hematological and biochemical blood test results. The relationship between the survival, length of hospitalization, hematological, and biochemical parameters was investigated.Advanced age (p0.001), presence of at least on comorbid disease (p=0.045), increased length of hospitalization (p=0.006), elevated white blood cell (p=0.001) and neutrophil (p=0.002) counts, increased serum levels of glucose (p=0.027), blood urea nitrogen (p0.001), AST (p=0.006), LDH (p0.001), CRP (p0.001), and D-dimer (p=0.001). In contrast, diminution of serum levels of albumin (p0.001), ALT (p=0.028), calcium (p=0.022), and platelet count (p=0.010) were associated with increased mortality. There was a positive and weak relationship between serum D-dimer levels and length of hospitalization.Our data imply that identifying and validating indicators that predict COVID-19 disease progression to improve health outcomes is crucial. Age, comorbidities, immunological response, radiographic abnormalities, laboratory markers, and signs of organ dysfunction may all predict poor outcomes individually or collectively. Identifying characteristics that predict COVID-19 problems is critical to guiding clinical management, improving patient outcomes, and allocating limited resources.
Databáze: OpenAIRE