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Predicting the Risk of Postoperative Complications of Schwannoma Surgery: Development and Assessment of a New Predictive NomogramFunding This work was supported by Wenzhou Science and Technology Bureau Project (Y20180498).
Background The aim of this study was to develop and internally validate a risk nomogram for postoperative complications of schwannoma surgery.
Methods From 2016 to 2020, we reviewed 83 patients who underwent schwannoma resection with a total number of 85 schwannomas. A predictive model was developed based on the dataset of this group. During model construction, univariate and multivariate logistic regression analysis were used to determine the independent predictors of postoperative complications. Assessment of the discriminative function, calibrating proficiency, and clinical usefulness of the predicting model was performed using C-index, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis. Internal validation was assessed using bootstrapping validation.
Results Predictors contained in the prediction nomogram included age, tumor location, symptoms, and surgical approach. The model displayed satisfying abilities of discrimination and calibration, with a C-index of 0.901 (95% confidence [CI]: 0.837–0.965). A high C-index value of 0.853 was achieved in the interval verification. Decision curve analysis showed that the nomogram was clinically useful when intervention was decided at the complication possibility threshold of 2%.
Conclusion This new risk nomogram for postoperative complications of schwannoma surgery has taken age, tumor location, symptoms, and surgical approach into account. It has reasonable predictive accuracy and can be conveniently used. It shall help patients understand the risk of postoperative complications before surgery, and offer guidance to surgeons in deciding on the surgical approach.
Ethics Approval and Consent to Participate
The current study was approved by The First Affiliated Hospital of Wenzhou Medical University Institutional Review Board (approval no. 2018–006).
* Contributed equally
Received: 10 December 2020
Accepted: 23 April 2021
Article published online:
10 May 2022
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