Early changes in laboratory tests predict liver function damage in patients with moderate coronavirus disease 2019: a retrospective multicenter study

Abstract

Background: Most patients with coronavirus disease 2019 demonstrate liver function damage. In this study, the laboratory test data of patients with moderate coronavirus disease 2019 were used to establish and evaluate an early prediction model to assess the risk of liver function damage.

Methods: Clinical data and the first laboratory examination results of 101 patients with moderate coronavirus disease 2019 were collected from four hospitals' electronic medical record systems in Jilin Province, China. Data were randomly divided into training and validation sets. A logistic regression analysis was used to determine the independent factors related to liver function damage in patients in the training set to establish a prediction model. Model discrimination, calibration, and clinical usefulness were evaluated in the training and validation sets.

Results: The logistic regression analysis showed that plateletcrit, retinol-binding protein, and carbon dioxide combining power could predict liver function damage (P < 0.05 for all). The receiver operating characteristic curve showed high model discrimination (training set area under the curve: 0.899, validation set area under the curve: 0.800; P < 0.05). The calibration curve showed a good fit (training set: P = 0.59, validation set: P = 0.19; P > 0.05). A decision curve analysis confirmed the clinical usefulness of this model.

Conclusions: In this study, the combined model assesses liver function damage in patients with moderate coronavirus disease 2019 performed well. Thus, it may be helpful as a reference for clinical differentiation of liver function damage. Trial registration retrospectively registered.

Keywords: Carbon dioxide combining power; Coronavirus disease 2019; Liver function; Nomogram; Plateletcrit; Predict; Retinol-binding protein.

Fig. 1

a Correlation heat map of 10 significantly different non liver function indicators in the training set. b Forest plot of laboratory indicators based on the univariate logistic regression analysis. *Significant correlation between the two indicators, P < 0.001. CO₂-CP, Carbon dioxide combining power, mmol/L; RBP, Retinol-binding protein, mg/L; PCT, Plateletcrit, ‱; MO%, Monocyte percentage, %; PT, Prothrombin time, s; Cl, Chloride, mmol/L; Ca, Calcium, mg/dL; HDL-C, High-density lipoprotein cholesterol, mmol/L; hsCRP, High-sensitivity C-reactive protein, mg/L; LDH, Lactate dehydrogenase, U/L

Fig. 2

Nomogram to illustrate how PCT, RBP, and CO₂-CP on admission are related to liver function damage. CO₂-CP, Carbon dioxide combining power, mmol/L; RBP, Retinol-binding protein, mg/L; PCT, Plateletcrit, ‱

Fig. 3

a Receiver operating characteristic curve. Training set area under the curve: 0.899, validation set area under the curve: 0.800. b Calibration curve

Fig. 4

Decision curve analysis. a Training set. b Validation set. CO₂-CP, Carbon dioxide combining power, mmol/L; RBP, Retinol-binding protein, mg/L; PCT, Plateletcrit, ‱