PT  - JOURNAL ARTICLE
AU  - Kimon Bekelis
AU  - Symeon Missios
AU  - Todd A MacKenzie
AU  - Nicos Labropoulos
AU  - David W Roberts
TI  - A predictive model of hospitalization cost after cerebral aneurysm clipping
AID  - 10.1136/neurintsurg-2014-011575
DP  - 2016 Mar 01
TA  - Journal of NeuroInterventional Surgery
PG  - 316--322
VI  - 8
IP  - 3
4099  - http://jnis.bmj.com/content/8/3/316.short
4100  - http://jnis.bmj.com/content/8/3/316.full
SO  - J NeuroIntervent Surg2016 Mar 01; 8
AB  - Background Cost containment is the cornerstone of the Affordable Care Act. Although studies have compared the cost of cerebral aneurysm clipping (CAC) and coiling, they have not focused on identification of drivers of cost after CAC, or prediction of its magnitude. The objective of the present study was to develop and validate a predictive model of hospitalization cost after CAC.Methods We performed a retrospective study involving CAC patients who were registered in the Nationwide Inpatient Sample (NIS) database from 2005 to 2010. The two cohorts of ruptured and unruptured aneurysms 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 7798 patients undergoing CAC, 4505 (58%) presented with unruptured and 3293 (42%) with ruptured aneurysms. Median hospitalization cost was US$24 398 (IQR $17 079 to $38 249) and $73 694 (IQR $46 270 to $115 128) for the two cohorts, respectively. Common drivers of cost identified in the multivariate analyses included the following: length of stay, number of admission diagnoses and procedures, hospital size and region, and patient income. The models were validated in independent cohorts and demonstrated final R2 values very similar to the initial models. The predicted and observed values in the validation cohort demonstrated good correlation.Conclusions This national study identified significant drivers of hospitalization cost after CAC. The presented model can be utilized as an adjunct in the cost containment debate and the creation of data driven policies.