Cranial reconstruction after decompressive craniectomy: Prediction of complications using fuzzy logic

Abstract

Introduction: Cranial reconstruction after decompressive craniectomy (DC) has been shown to be associated with a relatively high complication rate (16.4%-34%) compared with standard neurosurgical procedures (2%-5%). Most studies that have previously attempted to formulate a multivariate model for identifying factors predictive of postoperative complications of cranioplasty either were unsuccessful or yielded conflicting results. Therefore, fuzzy logic-based fuzzy inference system (FIS), which has proven to be a useful tool for risk prediction in medical and surgical conditions, was used in this study to identify predictors of complications of cranioplasty. Methods: A retrospective chart review of all the patients who underwent DC followed by elective cranioplasty at Aga Khan University Hospital, during a 10-year period (2000-2010), was carried out to collect data on 24 carefully selected preoperative variables or inputs. The proposed FIS had 24 inputs, 3 outputs, and a set of 7 fuzzy-based rules. All inputs were assigned degrees of membership, and complications were further divided into "severe," "minor," and "least" output classes with each of them representing 2 membership functions: "less" and "more." For each set of inputs, a specific portion of the hypersurface was masked out. The centroid of this subsurface represented the defuzzified output corresponding to 1 percentage value for each output. The maximum of these outputs for each of the 3 output classes was selected to be the final output class. Each output class was compared to the actual outcome of patients, and positive predictive value, negative predictive value, sensitivity, and specificity of FIS for predicting complications were calculated. Results: A total of 89 patients (mean [SD] age, 33.1 [15.0] y; male-to-female ratio, 3:1) were included in the study. The common postoperative complications included seizures (14.6%), cerebrospinal fluid leak (4.5%), neurologic deficits (3.4%), hydrocephalus (3.4%), superficial wound infection (3.4%), and osteomyelitis (2.2%). The FIS correctly identified all 7 patients who developed severe complications after cranioplasty (true positives) and all 82 patients who did not develop severe complications (true negatives). Thus, the FIS has a sensitivity and specificity of 100% in predicting severe complications. Conclusions: Our study shows that the procedure of cranioplasty is associated with a high complication rate and that FIS has a 100% sensitivity and specificity in predicting severe complications after cranioplasty. It will prove to be an invaluable tool for clinicians once the results are validated by a similar prospective study with a larger sample size.