Dataset Information

Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance.

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 <2.59×10⁹/L, and the upper levels for total bilirubin 11.9 ?mol/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.

SUBMITTER: Statsenko Y

PROVIDER: S-EPMC7918887 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance.

Statsenko Yauhen Y Al Zahmi Fatmah F Habuza Tetiana T Gorkom Klaus Neidl-Van KN Zaki Nazar N

BMJ open 20210226 2

<h4>Background</h4>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.<h4>Objectives</h4>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).<h4>Methods</h4>The study cohort (560 subjects ...[more]

PMID: 33637550

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Dataset Information

Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance.

Background

Objectives

Methods

Results

Conclusion

Publications

Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance.

Similar Datasets

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