%0 Journal Article
%@ 2369-2960
%I JMIR Publications
%V 7
%N 11
%P e29504
%T Algorithm for Individual Prediction of COVID-19–Related Hospitalization Based on Symptoms: Development and Implementation Study
%A Murtas,Rossella
%A Morici,Nuccia
%A Cogliati,Chiara
%A Puoti,Massimo
%A Omazzi,Barbara
%A Bergamaschi,Walter
%A Voza,Antonio
%A Rovere Querini,Patrizia
%A Stefanini,Giulio
%A Manfredi,Maria Grazia
%A Zocchi,Maria Teresa
%A Mangiagalli,Andrea
%A Brambilla,Carla Vittoria
%A Bosio,Marco
%A Corradin,Matteo
%A Cortellaro,Francesca
%A Trivelli,Marco
%A Savonitto,Stefano
%A Russo,Antonio Giampiero
%+ Epidemiology Unit, Agency for the Protection of Health of the Metropolitan Area of Milan, Via Conca del Naviglio 45, Milan, 20123, Italy, 39 0285782111, agrusso@ats-milano.it
%K COVID-19
%K severe outcome
%K prediction
%K monitoring system
%K symptoms
%K risk prediction
%K risk
%K algorithms
%K prediction models
%K pandemic
%K digital data
%K health records
%D 2021
%7 15.11.2021
%9 Original Paper
%J JMIR Public Health Surveill
%G English
%X 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. 
%M 34543227
%R 10.2196/29504
%U https://publichealth.jmir.org/2021/11/e29504
%U https://doi.org/10.2196/29504
%U http://www.ncbi.nlm.nih.gov/pubmed/34543227