A novel nomogram based on routine clinical indicators for screening for Wilson's disease

Area of Science:

• Hepatology
• Genetics
• Diagnostic Medicine

Background:

• Wilson's disease (WD) diagnosis lacks a unified predictive model.
• Early identification of WD is crucial for effective management.
• Patients with abnormal liver function require accurate diagnostic tools.

Purpose of the Study:

• To develop a predictive nomogram for Wilson's disease (WD).
• To enhance diagnostic accuracy using readily available clinical parameters.
• To improve early screening for WD in patients with abnormal liver function.

Main Methods:

• A nomogram was developed using data from 90 WD patients and 128 controls (July 2016-December 2020).
• Predictive variables were screened using least absolute shrinkage and selection operator (LASSO) and logistic regression.
• The nomogram incorporated serum copper, direct bilirubin, uric acid, cholinesterase, prealbumin, and reticulocyte percentage.

Main Results:

• The nomogram achieved high diagnostic accuracy, with an area under the curve (AUC) of 0.967 in the training cohort and 0.991 in the validation cohort.
• Key predictors included serum copper, direct bilirubin, uric acid, cholinesterase, prealbumin, and reticulocyte percentage.
• The model demonstrated 96% sensitivity and 87% specificity at an optimal cutpoint.

Conclusions:

• A novel nomogram effectively predicts Wilson's disease (WD) risk.
• This tool facilitates pre-symptomatic screening, bypassing the need for serum ceruloplasmin or urinary copper tests.
• The nomogram significantly improves screening efficiency for patients with abnormal liver function.