The Effect of Naturally Acquired Immunity on Mortality Predictors: A Focus on Individuals with New Coronavirus

Background/Objectives: The spread of the COVID-19 pandemic has spurred the development of advanced healthcare tools to effectively manage patient outcomes. This study aims to identify key predictors of mortality in hospitalized patients with some level of natural immunity, but not yet vaccinated, using machine learning techniques. Methods: A total of 363 patients with COVID-19 admitted to Río Hortega University Hospital in Spain between the second and fourth waves of the pandemic were included in this study. Key characteristics related to both the patient's previous status and hospital stay were screened using the Random Forest (RF) machine learning technique. Results: Of the 19 variables identified as having the greatest influence on predicting mortality, the most powerful ones could be identified at the time of hospital admission. These included the assessment of severity in community-acquired pneumonia (CURB-65) scale, age, the Glasgow Coma Scale (GCS), and comorbidities, as well as laboratory results. Some variables associated with hospitalization and intensive care unit (ICU) admission (acute renal failure, shock, PRONO sessions and the Acute Physiology and Chronic Health Evaluation [APACHE-II] scale) showed a certain degree of significance. The Random Forest (RF) method showed high accuracy, with a precision of >95%. Conclusions: This study shows that natural immunity generates significant changes in the evolution of the disease. As has been shown, machine learning models are an effective tool to improve personalized patient care in different periods.