The Novel “CLASS” Algorithmic Scale for Patient Selection in Meningioma Surgery

Objective: Meningioma patients with incidental tumors, with significant medical comorbidity, or elderly patients with minimal symptoms present a challenging dilemma to neurosurgeons with regard to surgical recommendations. A novel algorithmic scale was developed to help in the decision-making process in patients with meningiomas.

Method: The “CLASS” scale is developed based on balancing the risks (Comorbidity, Location, Age) against benefits (Size, Symptoms/signs) of surgery. Each risk factor is graded from −2 to 0 while the “benefit” factors are graded from 0 to +2. A retrospective analysis was performed on 300 consecutive patients who underwent surgery for intracranial meningiomas performed by the senior author between January 2000 and December 2004. For all patients, the total CLASS score was calculated and divided into three groups: Group I = a score of +1 or above, Group II = 0 or −1, and Group III = −2 or below. Early outcome measured at 6 weeks using the Glasgow Outcome Scale (GOS) and postoperative complications were analyzed by using a logistic regression model.

Results: Poor early outcome (GOS 1–3) was seen in 1.9% (2/108) in Group I, 4% (6/152) in Group II, and 15% (6/40) in Group III patients. The odds of poor outcome were 936% higher for Group III than for Group I patients [OR: 10.36; 95% CI: 1.99–53.89]. Neurological and medical complications were 7% and 3% for Group I; 15% and 7% for Group II; 25% and 15% for Group III, respectively. Logistic regression model showed significantly higher odds of having neurological and medical complications for Groups II and III as compared with Group I.

Conclusion: A new simple algorithm (CLASS) is proposed to help in the patient selection process for meningioma surgery based on the above results: surgery is recommended for Group I and no surgery for Group III patients. For Group II patients, surgery may be considered with caution.