Development and Validation of a Nomogram to Predict Hemiplegic Shoulder Pain in Patients With Stroke: A Retrospective Cohort Study

Abstract

Objective: The development and validation of a nomogram for the individualized prediction of hemiplegic shoulder pain (HSP) during the inpatient rehabilitation of patients with stroke.

Design: Retrospective cohort study.

Setting: The rehabilitation department at a tertiary hospital.

Participants: A total of 376 patients (N=376) with stroke admitted to inpatient rehabilitation from January 2018 to April 2021 were included in this study.

Interventions: Not applicable.

Main outcome measures: The outcome measure was shoulder pain on the patients' hemiplegic side occurring at rest or with movement during hospitalization.

Results: Among the 376 patients with stroke, 113 (30.05%) developed HSP. Five independent predictors were included in the nomogram: subluxation, Brunnstrom stage, hand edema, spasticity, and sensory disturbance. The nomogram was a good predictor, with a C-index of 0.85 (95% confidence interval, 0.81-0.89) and corrected C-index of 0.84. The Homer-Lemeshow test (χ²=13.854, P=.086) and calibration plot suggested good calibration ability of the nomogram. The optimal cutoff value for the predicted probability of HSP was 0.30 (sensitivity, 0.73; specificity, 0.83). Moreover, the decision curve analysis revealed that the nomogram would add net clinical benefits if the threshold possibility of HSP risk was from 5%-88%.

Conclusions: Our nomogram could accurately predict HSP, which may help clinicians accurately quantify the HSP risk in individuals and implement early interventions.

Keywords: CI, confidence interval; DCA, decision curve analysis; HSP, hemiplegic shoulder pain; Hemiplegia; Nomograms; OR, odds ratio; Rehabilitation; Risk factors; Shoulder Pain; Stroke.

Fig 1

Enrollment flow chart of the patients.

Fig 2

Nomogram for predicting the HSP. (A) Nomogram predicting the probability of HSP using subluxation, Brunnstrom stage, hand edema, spasticity, and sensory disturbance as independent predictive factors. Points are assigned for each factor, for which each value is assigned a score by plotting an upward line toward the points line and plotting the sum of 5 scores on the total point line. Subsequently, a vertical line is drawn till the predicted value line from the total points line to obtain the risk probability of HSP. For example, a patient with subluxation (100 points), Brunnstrom stage 1 (47 points), and sensory disturbance (31 points) but no hand edema (0 points) and spasticity (0 points) had a total score of 178 points. The risk probability of HSP would thus be approximately 79.5% (95% CI, 57.9%-91.6%). (B) The example of a screen from the web application developed from the prediction model reported in this study.

Fig 3

Nomogram calibration curve for predicting HSP. The x-axis designates the nomogram forecasted probability of HSP, whereas the y-axis designates the actual probability of HSP. The reference line is illustrated as 45°, indicating perfect calibration.

Fig 4

Decision curve for the nomogram. The x-axis illustrates the probability threshold, and the y-axis illustrates the net benefit.