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A predictive model of hospitalization cost after cerebral aneurysm clipping.

ABSTRACT: 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.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.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 R(2) values very similar to the initial models. The predicted and observed values in the validation cohort demonstrated good correlation.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.

SUBMITTER: Bekelis K

PROVIDER: S-EPMC5224932 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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A predictive model of hospitalization cost after cerebral aneurysm clipping.

Bekelis Kimon K Missios Symeon S MacKenzie Todd A TA Labropoulos Nicos N Roberts David W DW

Journal of neurointerventional surgery 20150112 3

<h4>Background</h4>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.<h4>Methods</h4>We performed a retrospective study involving CAC patients who were registered in the Nationwid ...[more]

PMID: 25583532

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Project description:Aneurysm clipping requires the proficiency of several skills, yet the traditional way of practicing them has been recently challenged. The use of simulators could be an alternative educational tool. The aim of this data analysis is to provide further evaluation of a reusable low-cost 3D printed training model we developed for aneurysm clipping [1]. The simulator was designed to replicate the bone structure, arteries and targeted aneurysms. Thirty-two neurosurgery residents performed a craniotomy and aneurysm clipping using the model and then filled out a survey. The survey was designed in two parts: a 5-point Likert scale questionnaire and three questions requiring written responses [1]. Two dimensions of the model were evaluated by the questionnaire: the face validity, assessed by 5 questions about the realism of the model, and the content validity, assessed by 6 questions regarding the usefulness of the model during the different steps of the training procedure. The three questions requiring written responses referred to the strengths and weaknesses of the simulator and a global yes/no question as to whether or not they would repeat the experience. Demographic data, experience level and survey responses of the residents were grouped in a dataset [2]. A descriptive analysis was performed for each dimension. Then, the groups were compared according to their level of expertise (Junior and Senior groups) with an independent sample t-test. A Confirmatory Factor Analysis (CFA) was estimated, using a Weighted Least Squares Mean Variance adjusted (WLSMV) which works best for the ordinal data [3]. Fitness was calculated using chi-square (χ2) test, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and the Root Mean Square Error of Approximation (RMSEA). A non-significant χ2, CFI and TLI greater than 0.90 and RMSEA < 0.08 were considered an acceptable fit [4]. All data analysis was performed using IBM SPSS 23.0 statistical software. Data are reported as mean + standard deviation (SD). A probability p < 0.05 was considered significant. Exploratory Factor Analysis was done to explore the factorial structure of the 11-items scale in the sample, first we performed a principal components analysis. The Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis (KMO = 0.784; Bartlett's Test of Sphericity χ2 (55) = 243.44, p < .001), indicating correlation is adequate for factor analysis. Considering Eigen values greater than 1, a two-factor solution explained 73.1% of the variance but left one item in factor 2 (Q 11). The results of this factor analysis are presented in Table 1. Confirmatory Factor Analysis, considering only the 10 items in the first factor (removing question 11 of our model), was performed. This model reached the following fit: χ2 (35) = 38.821, p > .05; CFI = 0.997; TLI = 0.996; RMSEA 0.058, without any error terms to exhibit covariance. Regarding the reliability of the questionnaire, the internal consistency was explored in the 10 items selected in the confirmatory factor analysis with an alpha coefficient (α = 0.941).

Project description:ObjectivesWe aim to evaluate the diagnostic test accuracy (DTA) of intraoperative evoked potential (EP) monitoring to detect cerebral injury during clipping of cerebral aneurysms.DesignSystematic review.Data sourcesMajor electronic databases including MEDLINE, EMBASE, LILACS.Eligibility criteriaWe included studies that reported the DTA of intraoperative EP monitoring during intracranial aneurysm clipping procedures in adult patients.Data extraction and synthesisAfter quality assessment, we performed a meta-analysis using the bivariate random effects model, and calculated the possible range of DTA point estimates using a new best-case/worst-case scenario approach to quantify the impact of rescue intervention on DTA.ResultsA total of 35 studies involving 4011 patients were included. The quality of the primary studies was modest and the heterogeneity across studies was high. The pooled sensitivity and specificity for predicting postoperative neurological deficits for the somatosensory evoked potential (SSEP) monitoring was 59% (95% CI: 39% to 76%; I2: 76%) and 86% (95% CI: 77% to 92%; I2: 94%), for motor evoked potential (MEP) monitoring was 81% (95% CI: 58% to 93%; I2: 54%) and 90% (95% CI: 86% to 93%; I2: 81%), and for combined SSEP and MEP monitoring was 92% (95% CI: 62% to 100%) and 88% (95% CI: 83% to 93%). The best-case/worst-case range for the pooled point estimates for sensitivity and specificity for SSEP was 50%-63% and 81%-100%, and for MEP was 59%-74% and 93%-100%, and for combined SSEP and MEP was 89%-94% and 83%-100%.ConclusionsDue to the modest quality and high heterogeneity of the existing primary studies, it is not possible to confidently support or refute the diagnostic value of EP monitoring in cerebral aneurysm clipping surgery. However, combined SSEP and MEP appears to provide the best DTA for predicting postoperative stroke. Contrary to popular assertion, the modest sensitivity of SSEP monitoring is not explained by the use of rescue intervention.Prospero registration numberCRD42015016884.

Dataset Information

A predictive model of hospitalization cost after cerebral aneurysm clipping.

Publications

A predictive model of hospitalization cost after cerebral aneurysm clipping.

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