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A Nomogram Model Integrating Inflammation Markers for Predicting the Risk of Recurrent Sciatica After Selective Nerve
1Department of Pain Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People's Republic of China.
Background:
Lumbar disc herniation (LDH) usually c auses sciatica. Although selective nerve root block (SNRB) is an effective, highly target-oriented interventional procedure for patients with LDH, accurately predicting the risk of sciatica recurrence in such patients after SNRB remains a major challenge.
Objective:
We aimed to construct a nomogram model by integrating clinical data, imaging features and inflammation markers that could predict recurrent sciatica following SNRB in LDH patients, which fill the inflammation data gaps during model construction.
Methods:
In total, 121 sciatica patients were enrolled and assigned to the recurrence group (n = 41) and non-recurrence group (n = 80). By performing the logistic regression analyses, we identified risk factors serving as independent predictors and constructed the nomogram prediction model. Then, the performance and clinical practicality of the nomogram model were validated by performing the receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA). The bootstrap method was applied for the internal validation of the nomogram model.
Results:
Preoperative sensory symptoms (odds ratio [OR] [95% confidence interval (CI)]: 2.933 [1.211-7.353]), type of herniation (OR [95% CI]: 2.712 [1.261-6.109]), and systemic inflammation response index (OR [95% CI]: 2.447 [1.065-6.271]) were included in the nomogram for predicting unfavorable outcomes following sciatica. The nomogram AUC was 0.764, and the prognostic precision, validated using the bootstrap method, reached 0.756. The ROC and calibration curve analyses, and DCA also produced excellent results, exhibiting favorable predictive performance and clinical benefit.
Conclusion:
This study thus identified risk factors that predict unfavorable outcomes after sciatica and developed a risk prediction model based on clinical, radiologic, and inflammatory factors, thereby facilitating early predictions by clinicians and offering an individualized medical interventions for patients with recurrent sciatica in early stages.
