An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study

Autor: Maria Elena Laino, Elena Generali, Tobia Tommasini, Giovanni Angelotti, Alessio Aghemo, Antonio Desai, Pierandrea Morandini, Giulio G. Stefanini, Ana Lleo, Antonio Voza, Victor Savevski
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Archives of Medical Science, Vol 18, Iss 3, Pp 587-595 (2022)
Druh dokumentu: article
ISSN: 1734-1922
1896-9151
DOI: 10.5114/aoms/144980
Popis: Introduction Identifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning. Material and methods We conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation. Results 1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality. Conclusions Machine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.
Databáze: Directory of Open Access Journals
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