Development and Validation of a Nomogram to Predict Hemiplegic Shoulder Pain in Patients With Stroke: A Retrospective Cohort Study.
Jinfa Feng, Chao Shen, Dawei Zhang and 2 others
PMID 36123984WHAT IT FOUND
A prediction tool using five admission findings accurately forecasts hemiplegic shoulder pain in stroke patients.
Shoulder subluxation, low Brunnstrom stage, sensory loss, spasticity, and hand edema each independently raise risk, allowing clinicians to identify high-risk patients early.
Key findings
01Shoulder subluxation was the strongest independent predictor of hemiplegic shoulder pain, with an odds ratio of 10.137.
02The nomogram demonstrated excellent discrimination with a bootstrap-corrected C-index of 0.84 and good calibration.
03Sensory disturbance, spasticity, and hand edema were also independent predictors, with odds ratios of 1.982, 2.065, and 2.316 respectively.
STILL TO COME
How it was doneWhat they foundWhat it means for PTsWhat it means for OTs
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What it does not show
The study was retrospective, which may introduce biases in data recording and completeness. The nomogram was only internally validated; it has not been tested on patients from other hospitals or settings, so its accuracy in different populations is unknown. Some potential risk factors, such as depression, hemineglect, and shoulder range of motion, were not included in the analysis. The study was conducted at a single center, limiting the generalizability of the findings.
Declared interests
The authors declared no conflicts of interest. The study was approved by the hospital's ethics committee, and informed consent was waived due to the retrospective nature of the work.
The easy way to misread this
Do not use this nomogram as a diagnostic tool or assume it will perform equally well in your clinic without external validation. The model was built on data from a single center and has only undergone internal validation, meaning its predictive accuracy may drop when applied to a different population.