Machine learning for prediction of in-hospital mortality in coronavirus disease 2019 patients: results from an Italian multicenter study
Authors: Vezzoli Marika , Inciardi Riccardo Maria , Oriecuia Chiara , Paris Sara , Murillo Natalia Herrera , Agostoni Piergiuseppe , Ameri Pietro , Bellasi Antonio , Camporotondo Rita , Canale Claudia , Carubelli Valentina , Carugo Stefano , Catagnano Francesco , Danzi Giambattista , Dalla Vecchia Laura , Giovinazzo Stefano , Gnecchi Massimiliano , Guazzi Marco , Iorio Anita , La Rovere Maria Teresa , Leonardi Sergio , Maccagni Gloria , Mapelli Massimo , Margonato Davide , Merlo Marco , Monzo Luca , Mortara Andrea , Nuzzi Vincenzo , Pagnesi Matteo , Piepoli Massimo , Porto Italo , Pozzi Andrea , Provenzale Giovanni , Sarullo Filippo , Senni Michele , Sinagra Gianfranco , Tomasoni Daniela , Adamo Marianna , Volterrani Maurizio , Maroldi Roberto , Metra Marco , Lombardi Carlo Mario , Specchia Claudia 
Published: June 2022

Background

Several risk factors have been identified to predict worse outcomes in patients affected by SARS-CoV-2 infection. Machine learning algorithms represent a novel approach to identifying a prediction model with a good discriminatory capacity to be easily used in clinical practice. The aim of this study was to obtain a risk score for in-hospital mortality in patients with coronavirus disease infection (COVID-19) based on a limited number of features collected at hospital admission.

Methods and results

We studied an Italian cohort of consecutive adult Caucasian patients with laboratory-confirmed COVID-19 who were hospitalized in 13 cardiology units during Spring 2020. The Lasso procedure was used to select the most relevant covariates. The dataset was randomly divided into a training set containing 80% of the data, used for estimating the model, and a test set with the remaining 20%. A Random Forest modeled in-hospital mortality with the selected set of covariates: its accuracy was measured by means of the ROC curve, obtaining AUC, sensitivity, specificity and related 95% confidence interval (CI). This model was then compared with the one obtained by the Gradient Boosting Machine (GBM) and with logistic regression. Finally, to understand if each model has the same performance in the training and test set, the two AUCs were compared using the DeLong's test. Among 701 patients enrolled (mean age 67.2 ± 13.2 years, 69.5% male individuals), 165 (23.5%) died during a median hospitalization of 15 (IQR, 9–24) days. Variables selected by the Lasso procedure were: age, oxygen saturation, PaO2/FiO2, creatinine clearance and elevated troponin. Compared with those who survived, deceased patients were older, had a lower blood oxygenation, lower creatinine clearance levels and higher prevalence of elevated troponin (all P < 0.001). The best performance out of the samples was provided by Random Forest with an AUC of 0.78 (95% CI: 0.68–0.88) and a sensitivity of 0.88 (95% CI: 0.58–1.00). Moreover, Random Forest was the unique model that provided similar performance in sample and out of sample (DeLong test P = 0.78).

Conclusion

In a large COVID-19 population, we showed that a customizable machine learning-based score derived from clinical variables is feasible and effective for the prediction of in-hospital mortality.