# Development and validation of a nomogram for early prediction of post-stroke shoulder–hand syndrome: a retrospective cohort study

## TL;DR

This study creates a tool to predict shoulder-hand syndrome after stroke, using patient data to help doctors develop prevention plans.

## Contribution

The study introduces a validated nomogram for early prediction of post-stroke shoulder-hand syndrome using clinical features.

## Key findings

- The nomogram achieved an AUC of 0.777 in the training set and 0.698 in the validation set.
- Key predictors included sex, occupation, residence, and several medical conditions and lab values.
- The model can predict SHS occurrence within 6 months after stroke with reasonable accuracy.

## Abstract

Shoulder-hand syndrome (SHS) is a prevalent complication following strokes. At present, there is no established and dependable method for early prediction of SHS risk. Thus, we undertook this study to create and validate a nomogram for early prediction of SHS following ischemic stroke (IS), with the aim of informing the development of SHS-specific follow-up protocols in clinical practice.

We retrospectively collected data on IS patients admitted to the Affiliated Panyu Central Hospital of Guangzhou Medical University from October 1, 2019 to March 31, 2024. The data was randomly split into a training set and a validation set in a 7:3 ratio, and LASSO regression was used to filter the modeling variables. In addition, the consistency index, area under the receiver operating characteristic curve (AUC), and calibration curve were used to verify the accuracy and discriminant power of the nomogram.

A total of 514 patients were enrolled in our study. Significant predictors contained sex, occupation, residence, osteoarthritis, gouty arthritis, myodynamia, heart rate, neutrophils, blood glucose, aspartate aminotransferase, and activated partial thromboplastin time. The AUC for the model constructed on the basis of these predictors was 0.777 (95% CI: 0.727–0.826) in the training set and 0.698 (95% CI: 0.615–0.781) in the validation set.

The nomogram constructed on the basis of common clinical features has a high performance in predicting the occurrence of SHS within 6 months after stroke. It can provide a reference for the development of specific prevention programs during clinical practice.