Algorithm for Individual Prediction of COVID-19–Related Hospitalization Based on Symptoms: Development and Implementation Study

Abstract

Background The COVID-19 pandemic has placed a huge strain on the health care system globally. The metropolitan area of Milan, Italy, was one of the regions most impacted by the COVID-19 pandemic worldwide. Risk prediction models developed by combining administrative databases and basic clinical data are needed to stratify individual patient risk for public health purposes. Objective This study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization. Methods A predictive algorithm was developed and applied to 36,834 patients with COVID-19 in Italy between March 8 and the October 9, 2020, in order to foresee their risk of hospitalization. Exposures considered were age, sex, comorbidities, and symptoms associated with COVID-19 (eg, vomiting, cough, fever, diarrhea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnea). The outcome was hospitalizations and emergency department admissions for COVID-19. Discrimination and calibration of the model were also assessed. Results The predictive model showed a good fit for predicting COVID-19 hospitalization (C-index 0.79) and a good overall prediction accuracy (Brier score 0.14). The model was well calibrated (intercept –0.0028, slope 0.9970). Based on these results, 118,804 patients diagnosed with COVID-19 from October 25 to December 11, 2020, were stratified into low, medium, and high risk for COVID-19 severity. Among the overall study population, 67,030 (56.42%) were classified as low-risk patients; 43,886 (36.94%), as medium-risk patients; and 7888 (6.64%), as high-risk patients. In all, 89.37% (106,179/118,804) of the overall study population was being assisted at home, 9% (10,695/118,804) was hospitalized, and 1.62% (1930/118,804) died. Among those assisted at home, most people (63,983/106,179, 60.26%) were classified as low risk, whereas only 3.63% (3858/106,179) were classified at high risk. According to ordinal logistic regression, the odds ratio (OR) of being hospitalized or dead was 5.0 (95% CI 4.6-5.4) among high-risk patients and 2.7 (95% CI 2.6-2.9) among medium-risk patients, as compared to low-risk patients. Conclusions A simple monitoring system, based on primary care data sets linked to COVID-19 testing results, hospital admissions data, and death records may assist in the proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

A Comparison Between Omicron and Earlier COVID-19 Variants' Disease Severity in the Milan Area, Italy

“…Samples of 2,267 PCR nasopharyngeal swab tests performed on subjects living in the territory of the Agency of Health Protection of the Metropolitan City (ATS) of Milan were genotyped to determine the specific SARS-CoV-2 variant between 2 December 2021 and 24 January 2022. We collected, through databases specifically developed since the beginning of the outbreak, information on the presence of typical COVID-19-related symptoms ( 19 ) (respiratory, fever, dyspnea, anosmia, ageusia, dysentery, muscle aches, asthenia, conjunctivitis, and headache), vaccination status, hospitalization, and recovery times (day difference between first positive test and first negative test). We obtained additional clinical and demographic information by record linkage to the Administrative Healthcare Databases of the ATS of Milan (sex, age, citizenship, and presence of comorbidities).…”

COVID-19 lessons to protect populations against future pandemics by implementing PPPM principles in healthcare

“…A prospective population-based cohort study indicated that a risk model involving age, gender, ethics, comorbidities and other factors showed a good performance in predicting COVID-19-related death and hospitalisation amongst the vaccinated population [ 33 ], which is beneficial for the targeted intervention and the deployment of public health policies. In addition, an algorithm to predict the risk of hospitalisation and stratify COVID-19 patients based on data from the Italian population has assisted in the allocation of medical resources [ 34 ]. A recursive mathematical model developed to predict virus transmissibility based on public data worldwide has evaluated the effectiveness of the quarantine strategies and provided guidance for health policy making [ 35 ].…”

Development of a prediction model for 30-day COVID-19 hospitalization and death in a national cohort of Veterans Health Administration patients – March 2022 - April 2023

“…Currently available COVID-19 risk prediction models have important limitations. First, most models were developed early in the pandemic (8)(9)(10)(11)(12)(13) and do not account for current circulating viral strains, population immunity from previous infection or vaccination, and treatments. This is particularly important as risk models are sensitive to population contextual factors under which they are developed.…”