Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance. Issue 2 (26th February 2021)

• Record Type: Journal Article
• Authors: Statsenko, Yauhen; Al Zahmi, Fatmah; Habuza, Tetiana; Gorkom, Klaus Neidl-Van; Zaki, Nazar
• Is Part Of: BMJ open. Volume 11:Issue 2(2021)
• Publication Date: 2021-02-26
• Subjects: COVID-19 -- biotechnology & bioinformatics -- infectious diseases -- respiratory infections -- information technology -- biochemistry; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2020-044500
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Background: Despite the necessity, there is no reliable biomarker to predict disease severity and prognosis of patients with COVID-19. The currently published prediction models are not fully applicable to clinical use. Objectives: To identify predictive biomarkers of COVID-19 severity and to justify their threshold values for the stratification of the risk of deterioration that would require transferring to the intensive care unit (ICU). Methods: The study cohort (560 subjects) included all consecutive patients admitted to Dubai Mediclinic Parkview Hospital from February to May 2020 with COVID-19 confirmed by the PCR. The challenge of finding the cut-off thresholds was the unbalanced dataset (eg, the disproportion in the number of 72 patients admitted to ICU vs 488 non-severe cases). Therefore, we customised supervised machine learning (ML) algorithm in terms of threshold value used to predict worsening. Results: With the default thresholds returned by the ML estimator, the performance of the models was low. It was improved by setting the cut-off level to the 25th percentile for lymphocyte count and the 75th percentile for other features. The study justified the following threshold values of the laboratory tests done on admission: lymphocyte count <2.59×10 9 /L, and the upper levels for total bilirubin 11.9 μmol/L, alanine aminotransferase 43 U/L, aspartate aminotransferase 32 U/L, D-dimer 0.7 mg/L, activated partial thromboplastin time (aPTT) 39.9 s, creatine kinase 247 U/L, C reactive protein (CRP) 14.3 mg/L, lactate dehydrogenase 246 U/L, troponin 0.037 ng/mL, ferritin 498 ng/mL and fibrinogen 446 mg/dL. Conclusion: The performance of the neural network trained with top valuable tests (aPTT, CRP and fibrinogen) is admissible (area under the curve (AUC) 0.86; 95% CI 0.486 to 0.884; p<0.001) and comparable with the model trained with all the tests (AUC 0.90; 95% CI 0.812 to 0.902; p<0.001). Free online tool at https://med-predict.com illustrates the study results.