Key Finding
A seven-factor nomogram predicted post-stroke spasticity risk with 0.844 AUC accuracy using routine clinical markers including C-reactive protein, albumin, creatine kinase, fasting glucose, hyperlipidemia, sleep disorders, and muscle strength scores.
Researchers developed a prediction tool to identify stroke patients at higher risk of developing post-stroke spasticity (PSS), a condition where muscles become stiff and difficult to control after a stroke. This retrospective study analyzed routine clinical data from stroke patients to create a nomogram—a visual chart that calculates individual risk scores.
The researchers identified seven key factors that predict PSS development: C-reactive protein levels (a marker of inflammation), albumin levels (a protein in blood), creatine kinase (an enzyme indicating muscle damage), fasting blood sugar, presence of high cholesterol, sleep disorders, and muscle strength scores at hospital admission. By combining these readily available measurements, the prediction model showed excellent accuracy, with an area under the curve of 0.844 in the training group and 0.842 in the validation group, indicating strong predictive ability.
For patients considering acupuncture, this research is relevant because identifying high-risk individuals early could allow for timely intervention. While this study focused on conventional risk factors and didn't examine acupuncture specifically, post-stroke spasticity is a condition that some patients explore acupuncture for managing. Early identification of PSS risk using this tool could help healthcare providers and patients make informed decisions about complementary therapies, including acupuncture, as part of a comprehensive rehabilitation plan. The model needs further validation across multiple medical centers before widespread clinical use, but it represents an important step toward personalized stroke recovery care. If considering acupuncture for post-stroke symptoms, work with a licensed acupuncturist experienced in neurological rehabilitation.
This retrospective study developed and validated a nomogram for predicting post-stroke spasticity (PSS) risk using LASSO-logistic regression analysis. Seven predictors were identified: C-reactive protein, albumin, creatine kinase, fasting blood glucose, hyperlipidemia, sleep disorders, and manual muscle testing scores at admission. The model demonstrated strong discriminative ability with AUC values of 0.844 (95% CI: 0.793-0.896) in the training set and 0.842 (95% CI: 0.765-0.920) in the validation set. Calibration curves showed excellent agreement between predicted and observed probabilities, with minimal risk overestimation in validation. Decision curve analysis and clinical impact curves supported clinical utility across various threshold probabilities. The nomogram, based on routine clinical parameters, provides clinicians with a practical decision-making tool for early PSS risk stratification. Multi-center external validation is needed to confirm generalizability. Sample size was not specified in the abstract.
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