Machine Learning-Based Prediction of In-Hospital Mortality in Severe COVID-19 Patients Using Hematological Markers.

Journal: The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale
Published Date: Jan 1, 2025

Abstract

The mortality rate is very high in patients with severe COVID-19. Nearly 32% of COVID-19 patients are critically ill, with mortality rates ranging from 8.1% to 33%. Early risk factor detection makes it easier to get the right care and estimate the prognosis. This study aimed to develop and validate a model to predict the risk of mortality based on hematological parameters at hospital admission in patients with severe COVID-19. The study retrospectively collected clinical data and laboratory test results from 396 and 112 patients with severe COVID-19 in two tertiary care hospitals as Cohort 1 and Cohort 2, respectively. Cohort 1 was to train the model. The LASSO method was used to screen features. The models built by nine machine learning algorithms were compared to screen the best algorithm and model. The model was visualized using nomogram, followed by trend analyses, and finally subgroup analyses. Cohort 2 was for external validation. In Cohort 1, the model developed by the LR algorithm performed the best, with an AUC of 0.852 (95% CI: 0.750-0.953). Five features were included in the model, namely, D-dimer, platelets, neutrophil count, lymphocyte count, and activated partial thromboplastin time. The mode had higher diagnostic accuracy in patients with severe COVID-19 > 65 years of age (AUC = 0.814), slightly lower than in patients with severe COVID-19 ≤ 65 years of age (AUC = 0.875). The ability of the model to predict the occurrence of mortality was validated in Cohort 2 (AUC = 0.841). The risk prediction model for mortality for patients with severe COVID-19 was constructed by the LR algorithm using only hematological parameters in this study. The model contributes to the timely and accurate stratification and management of patients with severe COVID-19.

Authors

  • Rongrong Dong
    Department of Laboratory Medicine, First Hospital of Jilin University, Changchun, 130021, China.
  • Han Yao
    School of Health Policy and Management, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
  • Taoran Chen
    Department of Laboratory Medicine, First Hospital of Jilin University, Changchun 130021, Jilin, China.
  • Wenjing Yang
    State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha 410073, China.
  • Qi Zhou
  • Jiancheng Xu
    School of Electronics and Information, Northwestern Polytechnic University, Xi'an, Shaanxi 710016, China.

Keywords

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