Introduction: Several initiatives have been put in place to minimize healthcare expenditures. In new and evolving fields such as endovascular aneurysm treatment, there is 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 (NIS) database from 2005-2010. 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, 6,617 (60.5%) presented with unruptured, and 4,311 (39.5%) with ruptured aneurysms. The median hospitalization cost was $35,446 (Interquartile Range (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 final R2 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.
Patient Care: Recognizing that the initial hospitalization cost is a major component of the overall economic burden of healthcare, several policies have been put in place in an attempt to limit these expenditures. Bundled payment methods and benchmarking of several quality and utilization metrics are part of this drive. The applicability of these national performance-based policies in novel and constantly evolving areas, such as CACo is still vague, given the limited literature on identifiable targets. In this context, we identified drivers of cost for coiling of cerebral aneurysms and created a predictive model of hospitalization cost. This model can be utilized as an adjunct in the cost containment debate and the creation of data-driven policies.
Learning Objectives: By the conclusion of this session, participants should be able to:
1) Describe the drivers of cost after cerebral aneurysm coiling
2) Utilize a model for cost prediction after cerebral aneurysm coiling
3) Identify modifiable factors for cost containment
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