{
  "abstract": "Background Flow diverters have gone from absent in the NIS in 2016 to nearly half of all treated intracranial aneurysm admissions by 2023. Whether this rapid, large-scale national adoption has occurred without a concurrent increase in inpatient mortality — across a real-world, all-comer population — represents a critical unanswered safety surveillance question. Whether treatment modality independently influences mortality — after accounting for the profound effect of rupture status — remains unresolved at the national level.Methods We analyzed the National Inpatient Sample (NIS, 2016-2023) to identify adults treated for intracranial aneurysm with clipping, coiling/embolization, or flow diversion. In-hospital mortality was the primary outcome. Survey-weighted logistic regression was performed for the overall cohort and separately for unruptured and ruptured subpopulations, adjusting for treatment group (clipping as reference), rupture status, calendar year, age, sex, race, insurance, income, hospital characteristics, Elixhauser comorbidity score, weekend admission, and elective status.Results On adjusted overall-cohort analysis, neither coiling (OR 1.08, 95% CI 0.81-1.44, p = 0.607) nor flow diversion (OR 0.70, 95% CI 0.37-1.34, p = 0.283) differed significantly from clipping in odds of in-hospital death. This null treatment-modality finding persisted in the unruptured subpopulation (coiling OR 1.22, p = 0.389; flow diverter OR 0.64, p = 0.310) and in the ruptured subpopulation (coiling OR 1.14, p = 0.488; flow diverter OR 0.97, p = 0.949). In striking contrast, ruptured aneurysm was the single most powerful independent predictor of in-hospital mortality (OR 3.52, 95% CI 2.62-4.74, p<0.001). Additional significant predictors included older age (OR 1.030 per year, p<0.001), higher comorbidity burden (Elixhauser OR 1.193 per unit, p<0.001), and elective admission (elective OR 0.172, p<0.001). Calendar year was not a significant mortality predictor in any model, indicating stable inpatient mortality rates despite the major shift in modality composition over the study period.Conclusions Despite a sevenfold per-year increase in flow diverter adoption nationally, in-hospital mortality did not differ significantly across clipping, coiling, and flow diversion — and remained stable over time — providing large-scale real-world safety validation for this rapid technology transition. Rupture status is the overwhelmingly dominant mortality driver, conferring 3.5-fold greater odds of death independent of treatment choice, stable across all subpopulations. These findings provide important real-world safety reassurance for the national transition toward flow diversion in unruptured aneurysms and highlight the primacy of subarachnoid hemorrhage severity over device selection in determining inpatient mortality outcomes.Disclosures A. Elbayomy: None. S. Hassan: None. S. Hassan: None. L. Mcguire: None. A. Ahmed: None.",
  "authors": [
    {
      "affiliations": [
        "Department of Neurological Surgery University of Wisconsin School of Medicine and Public Health, Madison, WI"
      ],
      "name": "A Elbayomy"
    },
    {
      "affiliations": [
        "Department of Neurological Surgery University of Wisconsin School of Medicine and Public Health, Madison, WI"
      ],
      "name": "S Hassan"
    },
    {
      "affiliations": [
        "Department of Neurological Surgery University of Wisconsin School of Medicine and Public Health, Madison, WI"
      ],
      "name": "S Hassan"
    },
    {
      "affiliations": [
        "Department of Neurological Surgery University of Wisconsin School of Medicine and Public Health, Madison, WI"
      ],
      "name": "L Mcguire"
    },
    {
      "affiliations": [
        "Department of Neurological Surgery University of Wisconsin School of Medicine and Public Health, Madison, WI"
      ],
      "name": "A Ahmed"
    }
  ],
  "title": "E-139 Real-world safety of the national shift to flow diversion: no increase in in-hospital mortality despite rapid adoption – a national inpatient sample analysis",
  "uid": "7f83c3f3-367c-54d5-a486-8f3d53f35859"
}