Predictive Nomogram for Clinical Prognosis in Cervical Spondylotic Myelopathy With Intramedullary T2-Weighted Increased Signal Intensity: A Novel Digital Tool for Patient Prognosis Education - Wang, Jie; Li, Haopeng; Yang, Baohui (Frontiers in Public Health, 2022)
Aims:To establish a predictive nomogram for clinical prognosis in cervical spondylotic myelopathy (CSM) with intramedullary T2-weighted increased signal intensity (ISI). Methods: The clinical data of 680 patients with CSM with intramedullary T2-weighted ISI were retrospectively analyzed. The patients were divided into modeling group (476) and validation group (204) by using a random number table at a ratio of 7:3. The independent prognostic factors were screened using multivariate logistic regression analysis. The factors were subsequently incorporated into the establishment of the predictive nomogram. The area under the receiver operating characteristic curve (AUC) was undertaken to estimate the discrimination of the predictive nomogram. Calibration curve and Hosmer-Lemeshow test were used to assess the calibration of the predictive nomogram. The clinical usefulness of the predictive nomogram was evaluated by decision curve analysis (DCA). Results: Based on the preoperative Japanese Orthopaedic Association (JOA) score, maximal canal compromise (MCC), and maximal spinal cord compression (MSCC), we established a predictive nomogram. The AUCs in modeling group and validation group were 0.892(95%CI:0.861 0.924) and 0.885(95%CI:0.835 0.936), respectively, suggesting good discrimination of the nomogram. Calibration curves showed a favorable consistency between the predicted probability and the actual probability. In addition, the P value of the Hosmer-Lemeshow were 0.253 and 0.184, respectively, suggesting good calibration of the nomogram. DCA demonstrated that the nomogram had a good clinical usefulness. Conclusion: We established and validated a predictive nomogram for the clinical prognosis in CSM with intramedullary T2-weighted ISI. This predictive nomogram could help clinicians and patients identify high-risk patients and educate them about prognosis, thereby improving the prognosis of high-risk patients.