Dataset Information

Innovation in unruptured intracranial aneurysm coiling: At which price or efficacy are new technologies cost-effective?

ABSTRACT:

Background

Unruptured intracranial aneurysms (UIA) are increasingly being treated by endovascular coiling as opposed to open surgical clipping. Unfortunately, endovascular coiling imparts an approximate 25% recanalization rate, leading to additional procedures and increased rupture risk. While a new health technology innovation (HTI) that reduces this recanalization rate would benefit patients, few advancements have been made. We aim to determine whether cost-effectiveness has been a barrier to HTI.

Methods

A probabilistic Markov model was constructed from the healthcare payer perspective to compare standard endovascular treatment of UIA to standard treatment plus the addition of a HTI adjunct. Costs were measured in 2018 USD and health outcomes were measured in quality-adjusted life-years (QALY). In the base case, the HTI was a theoretical mesenchymal stem cell therapy which reduced the aneurysm recanalization rate by 50% and cost $10,000 per procedure. All other model inputs were derived from the published scientific literature.

Results

Based on the model results, we found that for a given HTI price (y) and relative risk reduction of aneurysm recanalization (x), the HTI was always cost-effective if the following equation was satisfied: y ≤ 20268 ∙ x, using a willingness-to-pay threshold of $50,000 per QALY. The uncertainty surrounding whether an aneurysm would recanalize was a significant driver within the model. When the uncertainty around the risk of aneurysm recanalization was eliminated, the 10-year projected additional benefit to the United States healthcare system was calculated to be $113,336,994.

Conclusion

Cost-effectiveness does not appear to be a barrier to innovation in reducing the recanalization rate of UIA treated by endovascular coil embolization. Our model can now be utilized by academia and industry to accentuate economically feasible HTI and by healthcare payers to calculate their maximum willingness-to-pay for a new technology. Our results also indicate that predicting a patient's baseline risk of aneurysm recanalization is a critical area of future research.

SUBMITTER: Ben-Israel D

PROVIDER: S-EPMC8351982 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background and purposeStent-assisted coiling was initially invented for wide-neck aneurysms, but is now used for smaller berry aneurysms. The aim of this study was to compare the safety and efficiency of stent-assisted coiling with conventional coiling in treatment of intracranial aneurysms.MethodsA meta-analysis of studies that compared stent-assisted coiling with coiling only was conducted by searching English literatures via Pubmed, Medline and Cochrane Library databases without restricting the publication year. The primary outcomes in this study were immediate occlusion, progressive thrombosis rate, all-complication rate and angiographic recurrence. The secondary outcomes examined were packing density, mortality, permanent complication and thromboembolic complication rate.ResultsTen retrospective cohort studies were included. There is currently only one unfinished randomized study. Although the stent-assisted coiling group tended to show a lower initial occlusion rate than that of the coiling-only group (57.6% versus 68.7%; OR, 0.66; 95% CI, 0.30-1.44; P = 0.30), it achieved a significantly higher progressive thrombosis rate during follow up compared to that of the coiling only group (37.5% versus 19.4%; OR, 2.75; 95% CI, 1.95-3.86; P<0.00001) and a significantly lower recurrence rate (16.2% versus 34.4%; OR, 0.35; 95% CI, 0.25-0.49; P<0.00001). With respect to safety concerns, the all-complication rate (17.6% versus 15.9%; OR, 1.12; 95% CI, 0.77-1.62; P = 0.56), mortality rate (9.1% versus 2.6%; OR, 2.31; 95% CI, 0.68-7.82; P = 0.18), permanent complication rate (5.6% versus 3.9%; OR, 1.52; 95% CI, 0.96-2.41; P = 0.08) and thromboembolic complication rate (4.2% versus 4.9%; OR, 0.99; 95% CI, 0.41-2.38; P = 0.97) did not show significant difference between the two groups.ConclusionsStent-assisted coiling has a lower recurrence rate than conventional coiling. Analysis of complication events did not show any significant difference between the two methods. Despite the findings reported herein, further validation by well-designed prospective studies is needed.

Project description:BACKGROUND:Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. METHODS:Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n?=?94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n?=?40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. RESULTS:Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC)?=?0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. CONCLUSIONS:We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.