Can a Nomogram Predict Individual Hemiplegic Shoulder Pain in Stroke?

Why would you lazily just predict pain rather than solving the problem? Not a leader? Just a mouse? 

A nomogram, also called a nomograph, alignment chart, or abac, is a graphical calculating device, a two-dimensional diagram designed to allow the approximate graphical computation of a mathematical function.

Can a Nomogram Predict Individual Hemiplegic Shoulder Pain in Stroke?

A simple, practical nomogram has been developed for predicting the risk for hemiplegic shoulder pain during inpatient rehabilitation among patients who have experienced a stroke, according to a retrospective cohort study published in the Archives of Rehabilitation Research & Clinical Translation.

Researchers sought to develop and certify a nomogram for use in the prediction of hemiplegic shoulder pain during inpatient rehabilitation of individuals with stroke. It is well known that hemiplegic shoulder pain is a common and disabling adverse event that frequently occurs between 2 and 3 months poststroke. As a graphical display tool, a nomogram can be used to visualize the relative contributions of certain predictors to an outcome event.

In the study, medical records from a total of 376 patients who had experienced a stroke and were admitted to the rehabilitation department of a Chinese hospital between January 2018 and April 2021 were reviewed. The following inclusion criteria were utilized for participants.

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• First occurrence of a stroke confirmed by brain magnetic resonance imaging or computed tomography scan and 1-sided paralysis
• 18 years of age and older
• Medically stable for 48 hours and more poststroke
• Regular rehabilitation therapy on the upper limbs prior to the occurrence of hemiplegic shoulder pain

Detailed clinical characteristics were recorded on a self-designed Excel spreadsheet. Typical predictors of hemiplegic shoulder pain, which include sex, age, disease course on admission, length of stay, type of stroke (ischemic or hemorrhagic), side of the body affected (left or right), hypertension, diabetes, arm strength (via manual muscle testing), Brunnstrom stage, subluxation, hand edema, spasticity, and sensory disturbance were obtained at admission.

Overall, 30.05% (113 of 376) of inpatients developed hemiplegic shoulder pain. There were 5 independent predictors of hemiplegic shoulder pain included in the nomogram: subluxation, Brunnstrom stage, spasticity, sensory disturbance, and hand edema. The predictive value of the nomogram was good — with a C-index of 0.85 (95% CI, 0.81-0.89) and a corrected C-index of 0.84. Per the Homer-Lemeshow test, a good fit of the prediction nomogram was reported (χ²=13.854, P =.086). The calibration plot suggested good calibration ability of the nomogram, thus exhibiting good agreement between the forecasted probabilities and the actual observations.

The optimal cutoff value was 0.30, with a corresponding sensitivity and specificity of 0.73 and 0.83, respectively. According to decision curve analysis, the nomogram would add net clinical benefits if the threshold possibility of hemiplegic shoulder pain risk was between 5% and 88%.

A key limitation of the present study is its retrospective design, which may lead to potential biases and thus weaken the implications of the statistical analysis. Further, since external validation of the nomogram was not conducted in this study, additional studies are needed to validate the performance of the nomogram externally with the use of a multicenter cohort.

The researchers concluded that the nomogram for forecasting the risk for hemiplegic shoulder pain “ … exhibited satisfactory prediction performance and good clinical utility, potentially assisting clinicians in accurately predicting the patient’s risks of [hemiplegic shoulder pain] and the implementation of early interventions.” Additional studies from other medical centers are warranted, in order to verify the scope of clinical applications of the nomogram model. 

Disclosure: None of the study authors has declared affiliations with biotech, pharmaceutical, and/or device companies.  

Reference  

Feng J, Shen C, Zhang D, Yang W, Xu G. Development and validation of a nomogram to predict hemiplegic shoulder pain in patients with stroke: a retrospective cohort study. Arch Rehabil Res Clin Transl. Published online July 3, 2022. doi:10.1016/j.arrct.2022.100213