Liver cirrhosis prediction for patients with Wilson disease based on machine learning: a case-control study from southwest China

Application of attenuation coefficient in the assessment of hepatic involvement in children and adolescents with Wilson's disease

BACKGROUND

To investigate whether the attenuation coefficient (ATT) can be used as a noninvasive index to assess liver involvement in children and adolescents with Wilson's disease (WD).

METHODS

Children and adolescents diagnosed with WD were retrospectively collected from the First Affiliated Hospital of the Anhui University of Traditional Chinese Medicine between May 2022 and August 2022. The findings on ATT, Shear Wave Measurement (SWM), AST to platelet ratio index (APRI), and fibrosis 4 (FIB-4) score were obtained. The liver involvement of WD was classified into 3 groups based on serum levels of collagen type IV (CIV), hyaluronic acid (HA), laminin (LN) and precollagen type III N-terminal peptide (PIIINP): (1) Group1 (n = 25), no abnormalities in CIV, HA, LN and PIIINP; (2) Group2 (n = 19), elevation of 1 or 2 indexes in CIV, HA, LN, and PIIINP; Group3 (n = 18), elevation of 3 or 4 indicators in CIV, HA, LN, and PIIINP. The levels of ATT, SWM, APRI and FIB-4 were compared between the 3 groups; and correlation of ATT with SWM and triglyceride (TG) was performed using Spearman's correlation analysis. The Receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficacy of ATT alone and its combination with SWM, APRI, and FIB-4 in children and adolescents with WD.

RESULTS

A total of 62 children and adolescents with WD were retrospectively retrieved. ATT levels were significantly different in intergroup comparisons (P < 0.001). The ROC curve showed that the area under the curve (AUC) for the diagnosis of hepatic steatosis using ATT was 0.714, 0.712 and 0.867 in Group 1 versus Group 2, Group 2 versus Group 3, and Group 1 versus Group 3, respectively; the sensitivity for the diagnosis of hepatic steatosis in Group 1 versus Group 2 was 89.47% with the cutoff value of ATT of 0.73 dB/cm/MHz. No significant correlation found between ATT and TG (ρ = 0.154, P = 0.231). Compared to ATT alone, the combination of ATT with APRI and FIB-4 or the combination of ATT with SWM, APRI, and FIB-4 showed a better diagnostic efficacy in Group 1 versus Group 2 (both P = 0.038).

CONCLUSION

ATT could be used as a non-invasive index for the evaluation of liver steatosis in children and adolescents with WD, with a good clinical applicative value. Furthermore, ATT in combination with APRI, FIB-4, and SWM might have better diagnostic efficacy than ATT alone.

Construction of diagnostic prediction model for Wilson's disease

Background

Wilson's disease, also known as hepatolenticular degeneration, is a rare human autosomal recessive inherited disorder of copper metabolism. The clinical manifestations are diverse, and the diagnosis and treatment are often delayed. The purpose of this study is to establish a new predictive diagnostic model of Wilson's disease and evaluate its predictive efficacy by multivariate regression analysis of small trauma, good accuracy, low cost, and quantifiable serological indicators, in order to identify Wilson's disease early, improve the diagnosis rate, and clarify the treatment plan.

Methods

A retrospective analysis was performed on 127 patients with Wilson's disease admitted to the First People's Hospital of Yunnan Province from January 2003 to May 2022 as the experimental group and 73 patients with normal serological indicators who were not diagnosed with Wilson's disease. SPSS version 26.0 software was used for single factor screening and a multivariate binary logistic regression analysis to screen out independent factors. R version 4.1.0 software was used to establish an intuitive nomogram prediction model for the independent influencing factors included. The accuracy of the nomogram prediction model was evaluated and quantified by calculating the concordance index (C-index) and drawing the calibration curve. At the same time, the area under the curve (AUC) of the nomogram prediction model and the receiver operating characteristic (ROC) curve of the Leipzig score was calculated to compare the predictive ability of the nomogram model and the current Leipzig score for Wilson's disease.

Results

Alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (AKP), albumin (ALB), uric acid (UA), serum calcium (Ca), serum phosphorus (P), and hemoglobin (HGB) are closely related to the occurrence of Wilson's disease (p < 0.1). The prediction model of Wilson's disease contains seven independent predictors: ALT, AST, AKP, ALB, UA, Ca, and P. The AUC value of the prediction model was 0.971, and the C-index value was 0.972. The calibration curve was well fitted with the ideal curve. The nomogram prediction model had a good predictive effect on the occurrence of Wilson's disease; the ROC curve of Leipzig score was drawn, and the AUC value was calculated. The AUC of the Leipzig score was 0.969, indicating that the prediction model and the scoring system had predictive value, and the nomogram prediction model had a better predictive effect on the research objects of the center.

Conclusion

ALT, AST, AKP, ALB, UA, Ca, and P are independent predictors of Wilson's disease, and can be used as early predictors. Based on the nomogram prediction model, the optimal threshold was determined to be 0.698, which was an important reference index for judging Wilson's disease. Compared with the Leipzig score, the nomogram prediction model has a relatively high sensitivity and specificity and has a good clinical application value.