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Original research
Cerebral aneurysm coiling: a predictive model of hospitalization cost
  1. Kimon Bekelis1,
  2. Symeon Missios2,
  3. Nicos Labropoulos3,4
  1. 1Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
  2. 2Department of Neurosurgery, Cleveland Clinic, Cleveland, Ohio, USA
  3. 3Section of Vascular Surgery, Stony Brook University Medical Center, Stony Brook, New York, USA
  4. 4Department of Radiology, Stony Brook University Medical Center, Stony Brook, New York, USA
  1. Correspondence to Dr K Bekelis, Dartmouth-Hitchcock Medical Center, One Medical Center Dr, Lebanon, NH 03756, USA; kbekelis{at}


Background Several initiatives have been put in place to minimize healthcare expenditures. In new and evolving fields such as endovascular aneurysm treatment, there are limited data to support such measures. The objective of the present study was to develop and validate a predictive model of hospitalization cost after cerebral aneurysm coiling (CACo).

Methods We performed a retrospective study involving CACo patients who were registered in the Nationwide Inpatient Sample database from 2005 to 2010. The cohort underwent 1:1 randomization to create derivation and validation subsamples. Regression techniques were used for the creation of a parsimonious predictive model.

Results Of the 10 928 patients undergoing CACo, 6617 (60.5%) presented with unruptured and 4311 (39.5%) with ruptured aneurysms. Median hospitalization cost was US$35 446 (IQR $13 801–$57 091). Common drivers of cost identified in the multivariate analysis included: length of stay; number of admission diagnoses and procedures; hospital size and region; patient income; hydrocephalus; acute renal failure; and seizures. The model was validated in independent cohorts and demonstrated a final R2 value very similar to the initial model. The predicted and observed values in the validation cohort demonstrated good correlation.

Conclusions This national study identified significant drivers of hospitalization cost after CACo. The presented model can be utilized as an adjunct in the cost containment debate and the creation of data driven policies.

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