A prognostic model to personalize monitoring regimes for patients with incidental asymptomatic meningiomas
| Autor: | Brian Haylock, Nitika Rathi, Richard D C Moon, Midhun Mohan, Ruwanthi Kolamunnage-Dona, Andrew Brodbelt, Anna Crofton, Samantha J Mills, Michael D. Jenkinson, Abdurrahman I. Islim |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Cancer Research medicine.medical_specialty Clinical Decision-Making Clinical Investigations risk score Asymptomatic Meningioma Cohort Studies Interquartile range Internal medicine Meningeal Neoplasms asymptomatic Medicine Humans Precision Medicine Aged Retrospective Studies Aged 80 and over Incidental Findings Framingham Risk Score business.industry Hazard ratio Age Factors Editorials Retrospective cohort study incidental Middle Aged medicine.disease Decision Support Systems Clinical Prognosis Comorbidity Oncology Disease Progression Female Neurology (clinical) medicine.symptom business Kidney disease |
| Zdroj: | Neuro Oncol Neuro-Oncology |
| ISSN: | 1523-5866 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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