Características e fatores de risco para mortalidade por COVID-19 em pacientes hospitalizados: Uma abordagem das variáveis prognósticas através da análise de componentes principais categóricos (CAPTCA)

Abstract

Introduction: The pandemic caused by SARS-CoV-2 brought a wide diversity of symptoms, clinical progression, and mortality among patients affected by the virus. Notably, hospitalization has been necessary only for patients with severe or critical symptoms. Recognizing patterns among variables used in the prognosis of COVID-19 can increase the accuracy of predicting patient outcomes. Through Categorical Principal Component Analysis (CAPTCA), large data groups are reduced to a smaller set of components with the least possible loss of information. Furthermore, CATPCA can efficiently manage missing data, a common challenge in research and data analysis. Objective: To identify risk factors for mortality in patients hospitalized for COVID-19. Methods: Records of 205 patients were evaluated, investigating demographic, clinical, and medical characteristics. The data were structured and analyzed using descriptive, exploratory, and multivariate statistical tests. Results: This research, conducted between September and December 2022, showed that out of the 205 patients studied, 37 died. It also revealed that mortality in COVID-19 patients is influenced by a variety of factors. Among them, length of hospital stay, advanced age, and severity of symptoms, such as high temperature and low oxygen saturation, were prominent. The need for ICU admission emerged as a strong indicator of risk of death. Additionally, comorbidities and risk behaviors, like smoking, obesity, nephropathies, neurological diseases, and prior use of medication for pulmonary or psychiatric diseases, significantly increased the risk of fatal outcomes. Through Categorical Principal Component Analysis, 14 fundamental dimensions were identified from the initial set of 72 variables. These dimensions comprehensively reflect the behavior of the total set of variables, explaining 36.91% of their variation. Conclusion: This study presents a comprehensive analysis of the factors contributing to mortality in patients hospitalized for COVID-19. This research not only confirms some of the observations made in previous studies, such as the role of advanced age and the need for ICU admission as predictive factors for fatal outcomes but also introduces new evidence on the influence of comorbidities and risk behaviors. Notably, ICU admission proved to be one of the most critical indicators for mortality. This result signals the need for more efficient screening guidelines to optimize the use of limited hospital resources. Evidence brought by the results, such as the presence of comorbidities like nephropathies, neurological diseases, and prior use of medication for pulmonary or psychiatric diseases, points to the importance of integrated management of these conditions in clinical decision-making in cases of COVID-19 patients. The results obtained through multivariate techniques are of profound relevance, as they support a method for developing more agile and lower-cost prognostic assessment protocols. The practical implications of this work transcend the academic sphere, aiming to directly influence clinical practices and public health policies in response to future pandemics.