Abstract
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.
Keywords
peripheral schwannoma - complications - predictors - nomogram
