COVID-19 Vaccination Priorities Based on Risk of In-Hospital Death: Recommendation Based on Artificial Intelligence

Background: Defining priority vaccination groups is a critical factor to reduce mortality rates.Methods: We aimed to identify priority-population groups for COVID-19 vaccination, based on in-hospital risk of death by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older) with quantitative RT-PCR-confirmed COVID-19, who were hospitalized in one of the 336 Brazilian hospitals from March 19th, 2020 to March 22nd, 2021. Independent variables encompassed age, sex and chronic comorbidities grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Vaccination priority-population groups were formed based on different levels of in-hospital risk of death due to COVID-19, based on the developed ML model, by taking into consideration the independent variables ​​. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05).Findings: Patients’ mean age was 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the ML model developed in the current study.Interpretation: Population priorities for vaccination based on risk of in-hospital death, which can be easily used by health system managers, were defined based on ML.Funding: Medical Sciences School of Minas Gerais, Lucas Machado Educational Foundation.Declaration of Interest: We declare no competing interests.Ethical Approval: The present study was approved by the Ethics and Research Committee of the Medical Sciences School of Minas Gerais (CAEE: 29000819.0.0000.5134).