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. 2023 Nov;27(22):3423-3430.
doi: 10.1111/jcmm.17864. Epub 2023 Oct 26.

A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters

S Gómez-Rojas  1 , G Pérez Segura  1 , J Ollé  2 , G Carreño Gómez-Tarragona  1 , J González Medina  3 , J M Aguado  4   5 , E Vera Guerrero  1 , M Poza Santaella  1 , J Martínez-López  1   5
Affiliations

A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters

S Gómez-Rojas et al. J Cell Mol Med. 2023 Nov.

Abstract

Monocytes and neutrophils play key roles in the cytokine storm triggered by SARS-CoV-2 infection, which changes their conformation and function. These changes are detectable at the cellular and molecular level and may be different to what is observed in other respiratory infections. Here, we applied machine learning (ML) to develop and validate an algorithm to diagnose COVID-19 using blood parameters. In this retrospective single-center study, 49 hemogram parameters from 12,321 patients with clinical suspicion of COVID-19 and tested by RT-PCR (4239 positive and 8082 negative) were analysed. The dataset was randomly divided into training and validation sets. Blood cell parameters and patient age were used to construct the predictive model with the support vector machine (SVM) tool. The model constructed from the training set (5936 patients) achieved an accuracy for diagnosis of SARS-CoV-2 infection of 0.952 (95% CI: 0.875-0.892). Test sensitivity and specificity was 0.868 and 0.899, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.896 and 0.872, respectively (prevalence 0.50). The validation set model (4964 patients) achieved an accuracy of 0.894 (95% CI: 0.883-0.903). Test sensitivity and specificity was 0.8922 and 0.8951, respectively, with a positive (PPV) and negative (NPV) predictive value of 0.817 and 0.94, respectively (prevalence 0.34). The area under the receiver operating characteristic curve was 0.952 for the algorithm performance. This algorithm may allow to rule out COVID-19 diagnosis with 94% of probability. This represents a great advance for early diagnostic orientation and guiding clinical decisions.

Keywords: COVID-19; SARS-CoV-2; cell morphological data; cell population data; hemogram; machine learning.

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Conflict of interest statement

Beckman Coulter S.A® has participated in part of the funding of these research study. There are no other conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Flow chart shows the distribution of the patients included in the study, a total of 12,321. Patients with infectious or respiratory symptons are divided in a negative or positive group according to PCR result. In the subgroup of hospitalized patients, patients are classified based on clinical severity: ventilatory failure, admitted to critical care unit (CCU) or exitus.
FIGURE 2
FIGURE 2
ROC curve: Results from the validation test. The accuracy for COVID‐19 infection prediction with an AUC of 0.9524.
FIGURE 3
FIGURE 3
Lists the most important blood parameters (as estimated by ReliefF) and their frequencies. Abbreviations are indicated in Appendix S1.
See this image and copyright information in PMC

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