Meningiomas in the elderly, the surgical benefit and a new scoring system.

OBJECTIVE

The purpose of the study was to define and identify prognostic indicators within an elderly population of patients suffering from intracranial meningiomas. The clinical presentation of the patient with meningioma is diverse, manifesting as a different clinical entity in the elderly patient compared to a similar type of tumor in a young patient.

METHODS

Two hundred fifty patients aged over 65 years admitted to RAMBAM Medical Center with meningiomas from 1995-2005 were characterized. We report the presenting symptoms, chronic illnesses, perioperative and longterm follow-up results for a 5-year period.

RESULTS

Based on univariate and multivariate analysis,significant prognostic indicators were identified and were implemented into a new geriatric scoring system (GSS)including tumor size and location, peritumoral edema,neurological deficits, Karnofsky score (Clancey J Neurosci Nurs 27:220, 1995; Crooks et al. J Gerontol 46:M139-M144, 1991), and associated diabetes, hypertension or lung disease. Seven outcome parameters were retrospectively tested using the scoring system, namely mortality,Barthel Index score (Mahoney and Barthel Md State Med J 14:61-65, 1965), Karnofsky score and consciousness expressed by the Glasgow Coma Scale score (Jennett and Bond Lancet 1:480-484, 1975) 5 years after surgery, as well as recurrence within and beyond 12 months. Age proved to inversely correlate with outcome. Morbidity and mortality were significantly lower in women. The extent of surgical resection (Simpson J Neurol Neurosurg Psychiatry 20:22-39, 1957) had no influence on functional outcome, although radical resection was associated with significantly lower mortality. Generally, a GSS score higher than 14 was associated with a significantly more favorable outcome.

CONCLUSION

The present results suggest that common experience-based considerations may be optimized and implemented into a simple scoring system that in turn may allow for outcome prediction and evidence-based decision making