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Published on: January 6, 2011
Construction and Validation of a Risk Prediction Model Incorporating Temporal Muscle Thickness for Adverse Outcome in
Huanpeng Wang1, Yanchun Wu2, Xiaojia Wu1
1Department of Radiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Background:
Sarcopenia significantly impacts stroke prognosis. Temporal muscle thickness (TMT) is an emerging metric for sarcopenia.
Objectives:
To developed a TMT-incorporated model to predict 6-month adverse outcomes in acute ischemic stroke (AIS).
Design:
In this retrospective study, 479 AIS patients were divided into training (n=283), test (n=120), and external validation cohorts (n=76).
Methods:
A combined model was constructed to predict adverse outcomes in the training and test cohorts using LASSO regression analysis. Model performance was assessed via calculating accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score.
Results:
The proportion of patients with an adverse outcomes in the training and test sets was 18.02% vs 17.50%, respectively (P = 0.90). Ischemic stroke event, admission NIHSS score, BI score, TMT were used to construct the prediction model. The combined model presented good discriminatory potential in the training and test sets (AUC = 0.929 and 0.930, respectively), which was verified in the external validation cohort (AUC = 0.902). For the combined model, the P values of the Hosmer-Lemeshow test in the training set, the test set, and the external validation were < 0.001 (χ2 = 44.007), 0.472 (χ2 = 7.611), and <0.001 (χ2 = 4919.666), respectively. The combined model showed good calibration and discrimination. The clinical usefulness of the model was confirmed by decision curve analysis.
Conclusion:
This study developed a combined model incorporating ischemic stroke event, admission NIHSS score, BI score, TMT and infarct volume to predict 6-month adverse outcomes in AIS patients, providing clinicians with a practical tool for treatment decisions and prognosis assessment.
