ArticleJMIR public health and surveillance2021
Algorithm for Individual Prediction of COVID-19-Related Hospitalization Based on Symptoms: Development and Implementation Study.
Article in JMIR public health and surveillance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Telemedicine and Mortality Reduction During COVID-19: Telemonitoring as a Key Strategy for Emergency Health care Preparedness.Telemedicine reports · 2025Article
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- COVID-19 lessons to protect populations against future pandemics by implementing PPPM principles in healthcare.The EPMA journal · 2023Review
- Hospital Antibiotic Use during COVID-19 Pandemic in Italy.Antibiotics (Basel, Switzerland) · 2023Article
- Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- A Comparison Between Omicron and Earlier COVID-19 Variants' Disease Severity in the Milan Area, Italy.Frontiers in epidemiology · 2022Article
- Validation of a simple risk stratification tool for COVID-19 mortality.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
19 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectiveThis study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization.
methodsA 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.
resultsThe 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.
conclusionsA 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.
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