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Published on: August 11, 2015
Proposing a validated clinical app predicting hospitalization cost for extracranial-intracranial bypass surgery
Hai Sun1, Piyush Kalakoti1, Kanika Sharma1
1Neurosurgery, Louisiana State University Health Sciences Center, Shreveport, Louisiana, United States of America.
Insights
This study identifies key factors influencing extracranial-intracranial (ECIC) bypass costs, aiming to inform healthcare policies and reduce expenditures in neurosurgery. Understanding these drivers can aid in cost containment strategies.
Area of Science:
- Neurosurgery
- Health Economics
- Healthcare Policy
Background:
- Healthcare reforms in the US aim to control rising costs.
- Limited data exists on modifiable cost drivers for cerebrovascular procedures like extracranial-intracranial (ECIC) bypass.
- High hospitalization costs necessitate identification of predictive factors.
Purpose of the Study:
- To develop a predictive model for initial hospitalization costs in patients undergoing ECIC bypass surgery.
- To identify modifiable targets associated with high costs in neurosurgical procedures.
Main Methods:
- Observational cohort study using the Nationwide Inpatient Sample (2002-2011).
- Analysis of 1533 patients undergoing ECIC bypass.
- Ordinary least square modeling to identify cost drivers; model validation performed.
Main Results:
- Median hospitalization cost was $37,525.
- Key cost drivers identified: Asian race, private payer, elective admission, hyponatremia, complications (neurological, respiratory, renal), specific procedures (moyamoya disease, COD without infarction), center volume, hospital region, procedure complexity, and length of stay.
- Model validated in an independent cohort.
Conclusions:
- Identified drivers can inform data-driven healthcare policies.
- Findings may impact reimbursement criteria and hospital auditing.
- Results contribute to the debate on cost containment in neurosurgery.
Object:
United States healthcare reforms are focused on curtailing rising expenditures. In neurosurgical domain, limited or no data exists identifying potential modifiable targets associated with high-hospitalization cost for cerebrovascular procedures such as extracranial-intracranial (ECIC) bypass. Our study objective was to develop a predictive model of initial cost for patients undergoing bypass surgery.
Methods:
In an observational cohort study, we analyzed patients registered in the Nationwide Inpatient Sample (2002-2011) that underwent ECIC bypass. Split-sample 1:1 randomization of the study cohort was performed. Hospital cost data was modelled using ordinary least square to identity potential drivers impacting initial hospitalization cost. Subsequently, a validated clinical app for estimated hospitalization cost is proposed (https://www.neurosurgerycost.com/calc/ec-ic-by-pass).
Results:
Overall, 1533 patients [mean age: 45.18 ± 19.51 years; 58% female] underwent ECIC bypass for moyamoya disease [45.1%], cerebro-occlusive disease (COD) [23% without infarction; 12% with infarction], unruptured [12%] and ruptured [4%] aneurysms. Median hospitalization cost was $37,525 (IQR: $16,225-$58,825). Common drivers impacting cost include Asian race, private payer, elective admission, hyponatremia, neurological and respiratory complications, acute renal failure, bypass for moyamoya disease, COD without infarction, medium and high volume centers, hospitals located in Midwest, Northeast, and West region, total number of diagnosis and procedures, days to bypass and post-procedural LOS. Our model was validated in an independent cohort and using 1000-bootstrapped replacement samples.
Conclusions:
Identified drivers of hospital cost after ECIC bypass could potentially be used as an adjunct for creation of data driven policies, impact reimbursement criteria, aid in-hospital auditing, and in the cost containment debate.
