A prognostic model to personalize monitoring regimes for patients with incidental asymptomatic meningiomas.

ABSTRACT:

Background

Asymptomatic meningioma is a common incidental finding with no consensus on the optimal management strategy. We aimed to develop a prognostic model to guide personalized monitoring of incidental meningioma patients.

Methods

A prognostic model of disease progression was developed in a retrospective cohort (2007-2015), defined as: symptom development, meningioma-specific mortality, meningioma growth or loss of window of curability. Secondary endpoints included non-meningioma-specific mortality and intervention.

Results

Included were 441 patients (459 meningiomas). Over a median of 55 months (interquartile range, 37-80), 44 patients had meningioma progression and 57 died (non-meningioma-specific). Forty-four had intervention (at presentation, n = 6; progression, n = 20; nonprogression, n = 18). Model parameters were based on statistical and clinical considerations and included: increasing meningioma volume (hazard ratio [HR] 2.17; 95% CI: 1.53-3.09), meningioma hyperintensity (HR 10.6; 95% CI: 5.39-21.0), peritumoral signal change (HR 1.58; 95% CI: 0.65-3.85), and proximity to critical neurovascular structures (HR 1.38; 95% CI: 0.74-2.56). Patients were stratified based on these imaging parameters into low-, medium- and high-risk groups and 5-year disease progression rates were 3%, 28%, and 75%, respectively. After 5 years of follow-up, the risk of disease progression plateaued in all groups. Patients with an age-adjusted Charlson comorbidity index ?6 (eg, an 80-year-old with chronic kidney disease) were 15 times more likely to die of other causes than to receive intervention at 5 years following diagnosis, regardless of risk group.

Conclusions

The model shows that there is little benefit to rigorous monitoring in low-risk and older patients with comorbidities. Risk-stratified follow-up has the potential to reduce patient anxiety and associated health care costs.

SUBMITTER: Islim AI 

PROVIDER: S-EPMC7032634 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature