A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19
Authors: Murri Rita , Lenkowicz Jacopo , Masciocchi Carlotta , Iacomini Chiara , Fantoni Massimo , Damiani Andrea , Marchetti Antonio , Sergi Paolo Domenico Angelo , Arcuri Giovanni , Cesario Alfredo , Patarnello Stefano , Antonelli Massimo , Bellantone Rocco , Bernabei Roberto , Boccia Stefania , Calabresi Paolo , Cambieri Andrea , Cauda Roberto , Colosimo Cesare , Crea Filippo , De Maria Ruggero , De Stefano Valerio , Franceschi Francesco , Gasbarrini Antonio , Parolini Ornella , Richeldi Luca , Sanguinetti Maurizio , Urbani Andrea , Zega Maurizio , Scambia Giovanni , Valentini Vincenzo ,   The Gemelli against Covid Group, Armuzzi Alessandro , Barba Marta , Baroni Silvia , Bellesi Silvia , Bentivoglio Annarita , Biasucci Luigi Marzio , Biscetti Federico , Candelli Marcello , Capalbo Gennaro , Cattani Paola , Chiusolo Patrizia , Cingolani Antonella , Corbo Giuseppe , Covino Marcello , Cozzolino Angela Maria , D’Alfonso Marilena , De Angelis Giulia , De Pascale Gennaro , Frisullo Giovanni , Gabrielli Maurizio , Gambassi Giovanni , Garcovich Matteo , Gremese Elisa , Grieco Domenico Luca , Iaconelli Amerigo , Iorio Raffaele , Landi Francesco , Larici Annarita , Liuzzo Giovanna , Maviglia Riccardo , Miele Luca , Montalto Massimo , Natale Luigi , Nicolotti Nicola , Ojetti Veronica , Pompili Maurizio , Posteraro Brunella , Rapaccini Gianni , Rinaldi Riccardo , Rossi Elena , Santoliquido Angelo , Sica Simona , Tamburrini Enrica , Teofili Luciana , Testa Antonia , Tosoni Alberto , Trani Carlo , Varone Francesco , Verme Lorenzo Zileri Dal 
Published: October 2021

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.