# Development and validation of a predictive nomogram for cerebral white matter hyperintensities: insights from a comprehensive clinical and laboratory analysis

## TL;DR

This study developed a predictive model for cerebral white matter hyperintensities using clinical and lab data, showing good accuracy for early risk assessment.

## Contribution

A validated nomogram integrating clinical and biochemical markers for predicting WMH is presented.

## Key findings

- The model achieved an AUC of 0.783 in the training cohort and 0.762 in the validation cohort.
- Key predictors included age, stroke history, hypertension, and specific biochemical markers like homocysteine.
- Calibration and decision curve analysis confirmed the model's clinical utility for risk stratification.

## Abstract

White matter hyperintensities (WMH) are key imaging markers of cerebral small vessel disease (CSVD), associated with cognitive decline and stroke risk. An accurate predictive model is needed for early risk assessment.

This retrospective study utilized data from 587 patients undergoing cranial magnetic resonance imaging (MRI) at Hebei University’s Neurology Department. A predictive model for WMH was developed using a combination of clinical and laboratory parameters through Least Absolute Shrinkage and Selection Operator (LASSO) regression and binary logistic regression analysis. The model’s performance was evaluated using area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and decision curve analysis (DCA).

Key predictors included age, history of stroke, hypertension, triiodothyronine levels, albumin- globulin ratio, and homocysteine. The nomogram achieved an AUC of 0.783 (95% CI: 0.738–0.829) in the training cohort and 0.762 (95% CI: 0.690–0.834) in the validation cohort. Calibration and DCA confirmed the model’s clinical applicability.

This study presents a validated nomogram for predicting WMH, integrating clinical and biochemical markers. The model demonstrated robust predictive accuracy and potential for early risk stratification. Future studies should focus on multi-center validation and expanded risk factor inclusion.