Machine Learning to Predict Disease Severity and Progression in Hospitalized COVID-19 Patients Using Laboratory Data on Admission

Year 2024, , 144 - 154, 29.10.2024

Gökhan Tazegül , Volkan Aydın , Elif Tükenmez Tigen Buket Erturk Sengel , Kübra Köksal , Buket Doğan , Sait Karakurt , Zehra Aysun Altıkardeş , Lütfiye Mülazimoğlu , Ali Serdar Fak , Abdulsamet Aktaş , Uluhan Sili , Abidin Gündoğdu Fethi Gül , Sena Tokay Tarhan , Emel Eryüksel , Mümine Topçu Berrin Aysevinç Songül Çeçen Düzel Tuba Güçtekin Derya Kocakaya , Beste Ozben , Halil Atas , Kürşat Tigen , Ahmet Altuğ Çinçin Bülent Mutlu , Alper Kepez , Mehmet Baran Balcan Ayla Erdoğan , Emre Çapar , Ömer Ataç , Beliz Bilgili , İsmail Cinel , Ahmet Akıcı , Haner Direskeneli

https://doi.org/10.46310/tjim.1502238

Abstract

Background: Herein, we aimed to develop and test machine learning (ML) models to predict disease severity and/or progression in hospitalized COVID-19 patients through baseline laboratory features.
Methods: In this retrospective study of hospitalized COVID-19 patients admitted to a tertiary care center, we evaluated routine admission data to determine the accuracy rates of different ML algorithms: k-nearest neighbor classifier, bagging classifier, random forest (RF), and decision tree. These models were compared over three outcomes: those who needed oxygen supplementation vs. who did not on admission (Analysis 1, n: 180), those who later developed oxygen requirement vs. those who did not (Analysis 2, n: 112), and those who needed invasive mechanical ventilation vs. those who did not during hospitalization (Analysis 3, n: 164).
Results: The median age of the patients was 55 (44-68) years, with males constituting 47.2% of the subjects. At admission, 37.8% of the patients required oxygen supplementation. During hospitalization, 17.5% needed mechanical ventilation, and 8.3% died. For all analyses, RF had the highest accuracy in classifying the need for oxygen supplementation on admission (89.4%) or during hospitalization (91.1%) and for invasive mechanical ventilation (92.2%). These were followed by a bagging classifier for Analysis 1 (88.3%) and Analysis 3 (91.0%) and by a decision tree for Analysis 2 (88.4%). C-reactive protein, monocyte distribution width, and high-sensitive troponin-T were the most crucial laboratory contributors to Analysis 1, Analysis 2, and Analysis 3, respectively.
Conclusion: Our study showed that ML algorithms could predict the need for oxygen supplementation and mechanical ventilation during hospitalization using baseline laboratory data, suggesting a slight superiority of RF, among others.

Keywords

accuracy, classifiers, COVID-19, inpatient, oxygen supplementation, random forest

Ethical Statement

This single-center retrospective study was approved by the institutional review board of the Turkish Ministry of Health’s COVID-19 Scientific Research Studies, and ethical approval was obtained from Marmara University Clinical Research Ethics Committee (Approval date: 27.04.2020, Approval number: 09.2020.487). This study was conducted per the Declaration of Helsinki and the Research and Publication Ethics, and patient data were anonymized before analysis.

Supporting Institution

Authors declare none.

Thanks

Authors declare none.

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