Shamim Muhammad Shahzad, Enam Syed Ather, Qidwai Uvais
Section of Neurosurgery, Department of Surgery, Aga Khan University Hospital, P.O. Box 3500, Stadium Road, Karachi 74800, Pakistan.
Surg Neurol. 2009 Dec;72(6):565-72; discussion 572. doi: 10.1016/j.surneu.2009.07.012.
Despite a lot of research into patient selection, a significant number of patients fail to benefit from surgery for symptomatic lumbar disk herniation. We have used Fuzzy Logic-based fuzzy inference system (FIS) for identifying patients unlikely to improve after disk surgery and explored FIS as a tool for surgical outcome prediction.
Data of 501 patients were retrospectively reviewed for 54 independent variables. Sixteen variables were short-listed based on heuristics and were further classified into memberships with degrees of membership within each. A set of 11 rules was formed, and the rule base used individual membership degrees and their values mapped from the membership functions to perform Boolean Logical inference for a particular set of inputs. For each rule, a decision bar was generated that, when combined with the other rules in a similar way, constituted a decision surface. The FIS decisions were then based on calculating the centroid for the resulting decision surfaces and thresholding of actual centroid values. The results of FIS were then compared with eventual postoperative patient outcomes based on clinical follow-ups at 6 months to evaluate FIS as a predictor of poor outcome.
Fuzzy inference system has a sensitivity of 88% and specificity of 86% in the prediction of patients most likely to have poor outcome after lumbosacral miscrodiskectomy. The test thus has a positive predictive value of 0.36 and a negative predictive value of 0.98.
Fuzzy inference system is a sensitive method of predicting patients who will fail to improve with surgical intervention.
尽管对患者选择进行了大量研究，但仍有相当数量的有症状腰椎间盘突出症患者未能从手术中获益。我们使用基于模糊逻辑的模糊推理系统（FIS）来识别椎间盘手术后不太可能改善的患者，并探索将FIS作为手术结果预测的工具。
回顾性分析501例患者的54个独立变量的数据。基于启发式方法筛选出16个变量，并进一步将其分类为具有各自隶属度的隶属度。形成了一组11条规则，规则库使用各个隶属度及其从隶属函数映射的值，对特定的一组输入执行布尔逻辑推理。对于每条规则，生成一个决策条，当以类似方式与其他规则组合时，构成一个决策面。然后，FIS决策基于计算所得决策面的质心以及对实际质心值进行阈值处理。然后将FIS的结果与基于6个月临床随访的最终术后患者结果进行比较，以评估FIS作为不良结果预测指标的性能。
在预测腰骶部显微椎间盘切除术后最可能出现不良结果的患者方面，模糊推理系统的灵敏度为88%，特异度为86%。该测试的阳性预测值为0.36，阴性预测值为0.98。
模糊推理系统是预测手术干预后无法改善的患者的一种敏感方法。
