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May 14, 2026Computers in Biology and Medicine0 citationsOpen Access

Disentangling determinants of one-year modified Rankin scale in patients with incidentally detected solitary intracranial aneurysms

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LBLea BührerLRLisa L. RichardAGAlexandre Genoud

Key Points

  • To investigate how management strategy and patient characteristics affect one-year outcomes measured by the modified Rankin Scale in patients with solitary unruptured intracranial aneurysms.
  • Prospective, non-randomized cohort analysis of 487 patients
  • Outcome modeled using Conway-Maxwell-Poisson regression and estimated treatment effect with inverse probability weighting
  • Explored dependencies among variables using additive Bayesian networks
  • Age significantly influenced one-year mRS with an incidence rate ratio (IRR) of 1.43 (95% CI 1.16-1.77, p=0.0009)
  • Average treatment effects were 0.77 (0.45-1.31) for endovascular vs observation and 1.28 (0.76-2.15) for microneurosurgery
  • ABN analysis indicated age and prior mRS directly affect one-year outcomes

Abstract

BACKGROUND: Unruptured intracranial aneurysms (UIAs) affect 3 %-5 % of the population and are increasingly detected incidentally. Although rupture risk is low, UIAs pose clinical challenges, as rupture can cause severe disability or death, and treatments carry complications. OBJECTIVE: To investigate how management strategy and patient characteristics affect longitudinal patient outcomes measured as modified Rankin Scale (mRS) in individuals with an asymptomatic, saccular, incidentally detected, unruptured, solitary intracranial aneurysm (ASIS). METHODS: We analysed a prospective, non-randomized ASIS cohort of 487 patients from Geneva University Hospitals at three time-points, including one-year follow-up. Conway-Maxwell-Poisson (CMP) regression was used for outcome modelling, inverse probability weighting (IPW) to estimate treatment effect expressed as an incidence rate ratio (IRR), and additive Bayesian networks (ABNs) to explore dependencies among variables, including patients' conditions. Missing baseline data were imputed using multiple imputation by chained equations (MICE) with covariate shift adjustment. RESULTS: Age significantly influenced one-year mRS (IRR 1.43, 95 % CI 1.16-1.77, p = 0.0009) and interacted with management strategy (p = 0.04). Average treatment effects (risk ratios) were estimated to be 0.77 (0.45-1.31) for endovascular vs observation and 1.28 (0.76-2.15) for microneurosurgery. ABN analysis reassured that age and prior mRS directly affect one-year outcomes. CONCLUSION: Age and baseline mRS are key determinants of one-year outcome. The absence of evidence for other variables' effects on treatment impact may reflect the limited sample size. The findings highlight the central role of patient-specific factors in guiding UIA management decisions. TRIAL REGISTRATION: ClinicalTrials.gov: NCT05526352, registered 31.08.2022.

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Cite This Study

Bührer et al. (2026) studied this question.

synapsesocial.com/papers/6a05659da550a87e60a1df89https://doi.org/10.1016/j.compbiomed.2026.111731
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