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

Background 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. Methods 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. Results 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. Conclusion 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.