Risk profiles for negative and positive COVID-19 hospitalized patients

Abstract

COVID-19 is a viral infection that affects people differently, where the majority of cases develop mild symptoms, some people require hospitalization, and unfortunately, a small number of patients perish. Hence, identifying risk factors is critical for physicians to make treatment decisions. The purpose of this article is to determine whether unsupervised analysis of risk factors in positive and negative COVID-19 subjects can aid in the identification of a set of reliable and clinically relevant risk profiles. Positive and negative patients hospitalized were randomly selected from the Mexican Open Registry between March and May 2020. Thirteen risk factors, three distinct outcomes, and COVID-19 test results were used to categorize registry patients. As a result, the dataset was reported via 6144 different risk profiles for each age group. The unsupervised learning method is proposed in this study to discover the most prevalent risk profiles. The data was partitioned into discovery (70%) and validation (30%) sets. The discovery set was analyzed using the partition around medoids (PAM) method, and the stable set of risk profiles was estimated using robust consensus clustering. The PAM models' reliability was validated by predicting the risk profile of subjects from the validation set and patients admitted in November 2020. In the validation set, the clinical relevance of the risk profiles was evaluated by determining the prevalence of three patient outcomes: pneumonia diagnosis, ICU admission, or death. Six positive and five negative COVID-19 risk profiles were identified, with significant statistical differences between them. As a result, PAM clustering with consensus mapping is a viable method for discovering unsupervised risk profiles in subjects with severe respiratory health problems.

A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study

“…Comorbidities such as cardiovascular disease, hypertension, and diabetes although are highly prevalent in older adults have been associated with worse outcomes in COVID-19 34,37,38 . Studies that rely on comorbidities to predict death based on ML usually rank age as one of the most influential variables 42,43 , in fact, a meta-analysis with 611,583 patients demonstrates an age-related increase in mortality. Thus, the highest mortality occurs in patients >80 years, in whom it was 6 times higher than in younger patients 44 .…”

Severe COVID-19 Outcomes in Five Latin American Countries in the Postvaccination Era

“…Previously published studies from both pre-and postvaccination periods [14,15] in Mexico reported that most of the COVID-19 patients who needed hospitalization, ICU care, and ventilatory support were males rather than females. This tendency was also observed in all countries in our study, consistent with a study in Colombia that included COVID-19 patients in 2020-2021 and reported a higher frequency of cases among males for all outcomes [16].…”

Assessment of Transfusion Practices Among Doctors During COVID-19 Pandemic Using Questionnaire-Based Survey

“…As there was none to minimal information on the transfusion requirement of COVID-19 patients, apprehension with regards to changes in the haematological profile, risk profile of COVID-19 patients and reduced number of blood donors, could have led to most doctors (71.8%) believing that shortage of blood components could also affect the transfusion management of COVID-19 patients [ 26 – 29 ].…”