Identification of SARS-CoV-2 positivity using machine learning methods on blood count data: External validation of state-of-the-art models

Abstract

Introduction: The aim of the paper is to present the results from the process of external validation of a number of machine learning (ML) models that had been previously developed to detect SARS-CoV-2 virus positivity on both symptomatic and asymptomatic patients on the basis of the complete blood count (CBC) test. Methods: Briefly, models were trained using a dataset of 816 COVID-19 positive and 920 negative cases collected at the emergency departments of IRCCS Hospital San Raffaele and IRCCS Istituto Ortopedico Galeazzi. 21 parameters, including the results of the CBC analysis, age [60.9 (0.9) years], gender (57% males) and the presence of COVID-19 related symptoms were used. The validation regarded the evaluation of the error rate (through different metrics, including accuracy, sensitivity, specificity and the area under the curve (AUC)) of the models considered. This external validation was conducted on two well balanced datasets coming from two different hospitals in Northern Italy: Desio hospital and Bergamo Papa Giovanni XXIII hospital. 163 positive and 174 true negative patients from Desio, and 104 positive and 145 true negative from Bergamo were included in the validation. Results: The performance of the predictive models is satisfactory as we can report an average AUC of 95% for both external datasets. Conclusion: ML models have been applied to hematological parameters for a more rapid and cost-effective detection of the COVID-19 disease. We make the point that validated models may be useful in the management and early detection of potential COVID-19 patients.