Acceptance Probability Research Articles

The Monte Carlo Model and Its Application

This research mainly investigated the Monte Carlo model through literature review and case analysis. This research provides an example of the area of a circle to quickly understand what a Monte Carlo model is. Through Monte Carlo, this paper introduced some other algorithms based on it, such as Markov chain, Monte Carlo Markov chain algorithm and Metropolis algorithm. In addition, the Ising model was introduced and it was found that the model can calculate the energy change corresponding to different probabilities of spin acceptance or rejection flip using the Boltzmann distribution at the same temperature. The main objective of this research is that most Ising models occur in high-dimensional states, changing the spin will affect the adjacent spins, and the difficulty of calculating the energy changes increases dramatically with increasing dimensions. Research has found that Monte Carlo methods enable Ising model problems in high-dimensional or complex interactions to be effectively solved through Monte Carlo simulations. Through extensive random sampling and statistical processing, Monte Carlo simulations can estimate the transition behavior of each spin

Conflict of Interests Regarding Peer Review: Bias in Manuscript Rejection

The majority of submitted manuscripts, across all article-journal types, may be rejected for one or more reasons... While the reasons for rejection vary between desk, post-peer-review, and post-editorial-review rejections, many of the issues raised could be easily avoided by devoting enough time to planning and conceptualizing the work under consideration. Indeed, Aristotle (384–322 BC) in the Rhetoric (the art of persuasion), which was a theory of - civic discourse - stated: “The mistake lies in the beginning; as the proverb says, ‘Well begun is half done;’ so an error at the beginning, though quite small, bears the same ratio to the errors in the other parts.” This quote seems especially appropriate in this case. This paper examines the various reasons for which an article may not be accepted or considered for publication and then the paper moves into providing an explanation of the different reasons by which an article may not be considered for publication based on the reviewer’s belief, faith, professional discrepancies or methodological outcomes. Well organised and sufficient preparation is the key to success. The authors hope that people across academia and other professional experts can use this discussion when leading training-building research workshops to create awareness and assist researchers in planning and writing research papers that have better probabilities of acceptance, yet are easy to assimilate for the average reader and truly advance knowledge. Keywords: bias, manuscript, prejudice, rejection, review

Reducing Healthcare Maintenance Costs: A Machine Learning Model to Improve Seasonal Vaccine Accessibility and Acceptance Using Vaccination History and Social Determinants of Health

Purpose: This paper will explain how machine learning models using vaccination history and social determinants of health can predict vaccine acceptancy. With this approach, a health care system can benefit significantly due to enhanced vaccine coverage and avoiding a considerable number of hospitalizations. It especially targets persons with probabilities of vaccine acceptance ranging from 40% to 75%. Methodology: The proposed study will develop predictive ML models using integrated datasets that combine vaccination history with SDOH variables. Multiple ML algorithms will be trained and tested, which will be evaluated on metrics such as accuracy, precision, recall, F1-score, and AUC. A comparative analysis has been performed showing the strengths and weaknesses of each approach. Findings: The article has exemplified that machine learning models are capable of predicating vaccine acceptancy by comparing patterns in historic data and with SDOH factors, hence finding health care-eligible populations. This can be duly utilized by health care systems while seeking to find those populations wherein the probabilities of vaccine acceptances are average, making outreach most effective with optimum efficiency at reduced cost using more effective vaccines. The findings bring front and center the need for ethics practice, robust privacy mechanisms, and cloud-based deployments that can assure scalability and reliability. Unique Contribution to Theory, Policy, and Practice: This current research contributes to the theoretical understanding of vaccine hesitancy through complex modeling interactions that involve vaccination history and SDOH. It could use this to inform health policy on evidence-based strategies for increasing vaccine uptake and efficiently allocating resources. More practically, it lays out the basic technical and ethical structure needed to deploy predictive models in real-world health care settings by incorporating SRE into such initiatives to make them reliable and scalable. It helps bridge the gap through a holistic approach-from data science, to public health policy, to operational implementation.

A New Hyperparameter Tuning Framework for Regression Tasks in Deep Neural Network: Combined-Sampling Algorithm to Search the Optimized Hyperparameters

This paper introduces a novel hyperparameter optimization framework for regression tasks called the Combined-Sampling Algorithm to Search the Optimized Hyperparameters (CASOH). Our approach enables hyperparameter tuning for deep learning models with two hidden layers and multiple types of hyperparameters, enhancing the model’s capacity to work with complex optimization problems. The primary goal is to improve hyperparameter tuning performance in deep learning models compared to conventional methods such as Bayesian Optimization and Random Search. Furthermore, CASOH is evaluated alongside the state-of-the-art hyperparameter reinforcement learning (Hyp-RL) framework to ensure a comprehensive assessment. The CASOH framework integrates the Metropolis-Hastings algorithm with a uniform random sampling approach, increasing the likelihood of identifying promising hyperparameter configurations. Specifically, we developed a correlation between the objective function and samples, allowing subsequent samples to be strongly correlated with the current sample by applying an acceptance probability in our sampling algorithm. The effectiveness of our proposed method was examined using regression datasets such as Boston Housing, Critical heat flux (CHF), Concrete compressive strength, Combined Cycle Power Plant, Gas Turbine CO, and NOx Emission, as well as an ‘in-house’ dataset of lattice-physics parameters generated from a Monte Carlo code for nuclear fuel assembly simulation. One of the primary goals of this study is to construct an optimized deep-learning model capable of accurately predicting lattice-physics parameters for future applications of machine learning in nuclear reactor analysis. Our results indicate that this framework achieves competitive accuracy compared to conventional random search and Bayesian optimization methods. The most significant enhancement was observed in the lattice-physics dataset, achieving a 56.6% improvement in prediction accuracy, compared to improvements of 53.2% by Hyp-RL, 44.9% by Bayesian optimization, and 38.8% by random search relative to the nominal prediction. While the results are promising, further empirical validation across a broader range of datasets would be helpful to better assess the framework’s suitability for optimizing hyperparameters in complex problems involving high-dimensional parameters, highly non-linear systems, and multi-objective optimization tasks.

Прогнозирование помехоустойчивости спутниковой системы Коспас-Сарсат на основе результатов GPS-мониторинга мелкомасштабных возмущений ионосферы

A method for predicting the interference immunity of the satellite system Cospas-Sarsat under conditions of small-scale ionospheric disturbances has been developed based on the results of GPS-monitoring of small-scale fluctuations in the total electron content of the ionosphere. Within this method, the problem of developing a technique for determining the dependence of the probability of erroneous signal reception in satellite communication systems on the choice of the carrier frequency and the signal-to-noise ratio at the receiver input, as well as fluctuations in the total electron content of the ionosphere determined by GPS-monitoring methods on the navigation path of radio wave propagation, has been solved. Based on the obtained dependence, the features of the structure and algorithm of the interference immunity prediction complex of the satellite system Cospas-Sarsat based on GPS-monitoring of small-scale fluctuations in the total electron content are justified. These features include that the inclination angle of the radio wave propagation path in the satellite radio line is initially unknown but can be calculated after solving the problem of determining the coordinates of the radio buoy at the receiver of the spacecraft. Furthermore, a hardware and software implementation of the interference immunity prediction complex of the satellite system Cospas-Sarsat based on the results of GPS ionospheric monitoring has been developed. Experimental results have been obtained regarding the change in the probability of erroneous signal reception in the Cospas-Sarsat satellite system during specified signal-to-noise ratios under conditions of small-scale ionospheric disturbances and increasing levels of ionospheric scintillation. Based on this, estimates of the energy reserve have been obtained to ensure an acceptable error probability in the Cospas-Sarsat satellite system for various values of fluctuations in the total electron content of the ionosphere (0.01 TECU; 0.015 TECU; 0.03 TECU) and levels of scintillation index (0.35; 0.55; 0.85). It has been established that to maintain an acceptable level of signal reception error probability during the formation of small-scale ionospheric disturbances over a 4-minute period, accompanied by an increase in the scintillation index to a moderate level (0.55), an increase in the energy reserve of 4.6 dB will be required. In the event of strong scintillations lasting for 20 seconds (at a level of 0.85), it will be necessary to raise the energy reserve of the Cospas-Sarsat satellite system by 13 dB.

Stochastic Considerations on the Upper and Lower Limits of Colony Counts on Agar Plate for Microbial Count Calculation

The upper and lower limits of microbial colony count on agar plate are essential for microbial cell calculation of food samples. In the present study, the limits presented by the three standards of Ministry of Health, Labor, and Welfare of Japan, Food and Drug Administration of US (FDA), and Japanese Industrial Standards (JIS) were studied from the stochastic point of view. Here colony counts per plate were assumed to follow the Poisson distribution from our previous studies. The probability of acceptance of a pair of colony counts, PA for cell calculation by each standard was then calculated for various values of the mean of the Poisson distribution theoretically and by simulation with random sampling numbers. The values of PA at the lower and upper limits of the standards were low. For example, PA at the mean of 25 which is the lower limit of the FDA standard was 0.278. The values of PA of colony count pairs by the three standards were demonstrated in a wide range of the mean colony count. The largest range of the mean at PA>0.5 was observed in the JIS standard among the three. A new method to determine the limits for colony counts was developed with the risk of not acceptance. These findings would be helpful to consider the limits of colony counts for cell calculation.

88 Evaluating the impact of ChatGPT-4 on medical abstracts

Abstract Background Since its launch in November 2022, ChatGPT has received recognition for its use in medical writing. Some even suggested it could write scientific abstracts. However, its real-life effectiveness to help researchers improve conference abstracts remains unexplored. Objectives To evaluate whether ChatGPT 4.0 could improve the quality of abstracts submitted to a medical conference by clinical researchers with various experiences. Design/Methods In October 2023, we conducted an experimental study involving 24 international researchers who provided one “original” abstract intended for submission at the 2024 Pediatric Academic Society (PAS) conference. Our research team created a prompt asking ChatGPT-4 to improve the quality of the abstract while adhering PAS submission guidelines. Researchers received the revised version and were tasked with creating a “final” abstract. The quality of each version (original, ChatGPT and final) was evaluated by the researchers themselves using a numeric scale (0-100). Additionally, three co-investigators assessed abstracts blinded to the version. The primary analysis focused on the mean difference in scores between the final and original abstracts. We also compared scores of the three versions and determined the proportion of researchers reporting ChatGPT's impact on their abstract and its acceptance probability. For an effect size of 0.7, it was estimated that we needed at least 20 pairs of abstracts. Results Abstract quality varied between the three versions with mean scores of 82, 65 and 90 for the original, ChatGPT and final versions, respectively. Overall, the final version displayed significantly improved quality compared to the original (mean difference 8.0 points; 95% CI: 5.6-10.3). Of note, 3 (14%) abstracts remained unchanged between final and original versions. Independent ratings by the co-investigator confirmed statistical improvements (mean difference 0.94 points; 95% CI: 0.44-1.44). Researchers identified minor (n=10) and major (n=3) factual errors in ChatGPT’s abstracts. However, 18 (75%) participants reported that ChatGPT contributed to the enhancement of their final abstract. Ten (42%) participants believed it improved their abstract's chances of acceptance. Eighteen (75%) researchers reported being uncomfortable using ChatGPT version for submission. Conclusion While ChatGPT 4.0 does not produce abstracts of better quality then the one crafted by researchers, it serves as a valuable tool for researchers to enhance the quality of their own abstracts. The utilization of such tools is a potential strategy for researchers seeking to improve their abstracts.

Parallel multiple-chain DRAM probabilistic model for ultrasonic pulse velocity of concrete performance in the pipe of arch bridge

The existing models for predicting the compaction state of concrete inside steel tubes based on ultrasonic wave velocity have a major limitation in that they cannot comprehensively consider concrete compressive strength, instar, and various sources of uncertainties, both subjective and objective. To overcome this limitation, the study introduced a probability model for ultrasonic wave velocity in the concrete performance inside arch bridge steel tubes, based on the parallel multiple-chain Delayed Rejection Adaptive Metropolis (DRAM) algorithm. Initially, the study considers the influence of concrete compressive strength and instar, establishing a simplified deterministic model for predicting concrete compactness inside the tubes. Building on this, the study incorporates material stochastic uncertainty and cognitive uncertainty, deriving analytical expressions for a probabilistic prediction model of concrete compactness. Furthermore, using parallel processing alongside the DRAM algorithm, the study explores how the number of parallel chains and the order of acceptance probabilities influence the probabilistic model parameters. The study selected and updated the posterior distribution information of the probabilistic model parameters. Finally, the effectiveness of the model was validated using experimental data. As shown in the analysis, with better prediction accuracy and lower dispersion, the model not only selects the optimal Markov chain iteration path in a short time, but also corrects the parameters affecting the prediction accuracy of the existing model, and also provides a probabilistic method for correcting the confidence intervals of the existing model.

Using Rejection Sampling Probability of Acceptance as a Measure of Independence

This article proposes a new association statistic for determining whether random variables are statistically independent. The proposed association statistic can also be used to examine the strength of both linear and nonlinear dependency between variables. This statistic is derived by examining how the conditional probabilities of events differ from their corresponding marginal probabilities. The new statistic is defined in terms of the probability of acceptance commonly associated with the Accept–Reject algorithm and has a very simple formula. The simulated density of the probability of acceptance can be used to measure the degree of uncertainty of the estimated values of the new association statistic. The results from simulations as well as examples that employ real data indicate that this new association statistic is very powerful in detecting linear and nonlinear associations between two random variables. Supplementary materials for this article are available online.

External Validation of a Model for Persistent Perfusion Deficit in Patients With Incomplete Reperfusion After Thrombectomy: EXTEND-PROCEED.

We recently developed a model (PROCEED) that predicts the occurrence of persistent perfusion deficit (PPD) at 24 hours in patients with incomplete angiographic reperfusion after thrombectomy. This study aims to externally validate the PROCEED model using prospectively acquired multicenter data. Individual patient data for external validation were obtained from the Endovascular Therapy for Ischemic Stroke with Perfusion-Imaging Selection, Tenecteplase versus Alteplase Before Endovascular Therapy for Ischemic Stroke part 1 and 2 trials, and a prospective cohort of the Medical University of Graz. The model's primary outcome was the occurrence of PPD, defined as a focal, wedge-shaped perfusion delay on 24-hour follow-up perfusion imaging that corresponds to the capillary phase deficit on last angiographic series in patients with <Thrombolysis in Cerebral Infarction 3 reperfusion after thrombectomy. The model's performance was evaluated with discrimination, calibration accuracy, and clinical decision curves. We included 371 patients (38% with PPD). The externally validated model had good discrimination (C-statistic 0.81, 95% CI 0.77-0.86) and adequate calibration (intercept 0.25, 95% CI 0.21-0.29 and slope 0.98, 95% CI 0.90-1.12). Across a wide range of probability thresholds (i.e., depending on the physicians' preferences on how the model should be used), the model shows net benefit on clinical decision curves, informing physicians on the likelihood of PPD. If a physician's attitude toward false-positive and false-negative ratings is equal, the model would reduce 13 in 100 unnecessary interventions by correctly predicting complete delayed reperfusion, without missing a patient with PPD. The externally validated model had adequate predictive accuracy and discrimination. Depending on the acceptable threshold probability, the model accurately predicts persistent incomplete reperfusion and may advise physicians whether additional reperfusion attempts should be performed.

Simulation based Ecological Risk Assessment of the Black Sea Ecosystem

The article presents results of the conducted simulation research in ecological risk for three shelf areas of the Black Sea exposed to different combinations of the external stressors. Simulated risk calculations were performed for the aggregated phytoplankton taking into account the intra-annual variations in biomass values obtained from the observation data. The results revealed significant dependence of the ecological risk on the intra-annual state of phytoplankton, and also made it possible to calculate dependence of the acceptable probability of impacts on the ecological risk. The latter identified areas of probable errors of the second kind, which is of practical importance for ensuring ecological safety in the shelf resources development.

A Bayesian approach with double group sampling plan to estimate quality regions for proportion of nonconforming products in industry based on beta prior

To succeed in the global market, firms must prioritize quality over individual goals and preferences. One of the two primary approaches for ensuring quality is acceptance sampling, which is employed in statistical process control to inspect attributes of the product for acceptance. In acceptance sampling, the lot is either accepted or not-accepted based on predetermined acceptance criteria for inspection. This paper presents a proposed Bayesian double group sampling plan (BDGSP) for estimating quality regions. The binomial distribution is used to construct the likelihood function for both nonconforming and conforming products based on acceptance criteria. To calculate the average probability of acceptance, the beta distribution is used as a suitable prior distribution of the binomial distribution. Four distinct quality regions are predicted for various indicated producer’s and consumer’s risk levels. Based on various combinations of values for design parameters, the suggested plan generates variation point values. Risks for producers and consumers are related to acceptable quality level and limiting quality level. To track the effects of changes in the values of the specified parameters, operating characteristic curves are utilized. The applicability of the proposed plan for current industrial strategies is demonstrated using a real dataset.

Estimating Tail Probability in MMPP/D/1 Queue with Importance Sampling by Service Rate Adjustments

The Asynchronous Transfer Mode (ATM) is an efficient technology for call relays, and it transmits information from multiple services including data, video, or voice. This information is conveyed at ATM multiplexers in small fixed-size packets called cells. The acceptable cell loss probability at ATM multiplexers is about 10−12. Important Sampling (IS) is an efficient method for estimating tiny probabilities that cannot be achieved by traditional Monte Carlo (MC) methods. This research presents a novel approach for evaluating the tail probability in the MMPP/D/1 queue system utilizing importance sampling simulation in the ATM network. To generate more rare events, a virtual queue is implemented in the dequeue process by decreasing the processing rate in the queue. In this way, the tail probability can be estimated on a real-time network.

Performance vs. lottery: department transfer system desired by employees

Purpose Among the nonfinancial rewards for employees are career development opportunities, for which an important measure is department transfers. This is because departments are unevenly popular, and assigning everyone to their desired department is difficult. However, research on department transfers is limited to improving the efficiency of human resources utilization from a management perspective, with employee perspectives being lacking. This study aims to fill this gap in the literature. Design/methodology/approach The authors conducted a randomized controlled trial in an online survey environment with 2,000 full-time employees in Japan. Through the lens of self-determination theory, the authors hypothesized that employees seek department transfers based on performance rather than department transfers based on luck (lottery). Findings The results supported the hypothesis. For performance-based department transfers, an acceptance probability of 30% was found to be appropriate. Furthermore, mid-career, male, staff-level employees, those with experience of changing jobs and those who are more satisfied with their current job were more likely to seek this system. Practical implications Corporate managers should note that employees emphasize self-determination regarding their careers, dislike relying based on luck and seek department transfers relying on merit-based performance. Originality/value To the best of the authors’ knowledge, this is the first study to determine the desirable system design for department transfers from the employee’s perspective and extend self-determination theory.

Multi-resource dynamic coordinated planning of flexible distribution network

The flexible distribution network presents a promising architecture to accommodate highly integrated distributed generators and increasing loads in an efficient and cost-effective way. The distribution network is characterised by flexible interconnections and expansions based on soft open points, which enables it to dispatch power flow over the entire system with enhanced controllability and compatibility. Herein, we propose a multi-resource dynamic coordinated planning method of flexible distribution network that allows allocation strategies to be determined over a long-term planning period. Additionally, we establish a probabilistic framework to address source-load uncertainties, which mitigates the security risks of voltage violations and line overloads. A practical distribution network is adopted for flexible upgrading based on soft open points, and its cost benefits are evaluated and compared with that of traditional planning approaches. By adjusting the acceptable violation probability in chance constraints, a trade-off between investment efficiency and operational security can be realised.

Optimal designing of skip-lot sampling plan with a provision for reducing the normal inspection using the Taguchi process capability index

A product with a strong track record of quality might have less inspection according to the skip-lot sampling plan. As a result, skip-lot sampling plans are created to cut down the inspection cost with desired protection. The skip-lot sampling method is also reliable, practical, and favorable from an economic standpoint. As a result, this work develops the SkSP-V plan, a novel system of skip-lot sampling plan, based on the Taguchi process capability index. The proposed plan stipulates that any lot is rejected during sampling inspection, normal inspection must resume, however, it also allows for a reduced level of normal inspection in the event that higher product quality is demonstrated. The exact sampling distribution of the estimated Taguchi capability index is used to derive the operating characteristic function of the proposed plan. An optimization model is employed to establish the optimal plan parameters, guaranteeing adherence to the specified quality levels and risk criteria. The performance measures such as probability of acceptance and average sample number of the proposed plan are examined and compared with existing plans. This article also includes real-world example to effectively demonstrate the practical implementation of the proposed SkSP-V plan based on the Taguchi process capability index.

Decentralized Online Order Fulfillment in Omni-Channel Retailers

We consider an order fulfillment problem of an omni-channel retailer that ships online orders from its distribution center (DC) and brick-and-mortar stores. Stores use their local information, not observed by the retailer, that can lead them to accept or reject fulfillment requests of items in an online order. We investigate the problem of sequencing requests to stores and inventory rationing decisions at the DC to minimize expected costs under uncertain store acceptance behavior and when items are indistinguishable in terms of shipping. First, under the scenario that stores are used only when the DC has insufficient inventory, we propose a Markov Decision Process formulation and analyze the performance of myopic policies that are preferable because of their interpretability. We show that the performance rate of a myopic approach that orders stores by cost only depends on the number of items in an order, which is small in practice. We also determine conditions for the range of acceptance probabilities for the myopic policy to be optimal for small-sized orders. Using optimality conditions for a special case of the problem, we develop an adaptive variant of the myopic policy, and propose a new degree-based strategy that balances shipping costs and acceptance probabilities. Numerical testing suggests that the best-performing sequencing policy is within 1% of optimality on average. Moreover, using two years of data from a large omni-channel retailer in North America, we observe that adaptive policies, albeit more complex, are beneficial in reducing costs and split deliveries if acceptance rates can be estimated accurately. Second, we determine when the retailer should ship from stores or ration the inventory at the DC. We show that for single-item orders, the optimal policy has a threshold structure, where, remarkably, the highest priority region is also subject to rationing. We then consider the novel multi-unit-single-item rationing problem, and leverage the structure of the single-unit model to develop a heuristic. We numerically establish the efficacy of rationing models and our heuristic.

Thermodynamics of Computations with Absolute Irreversibility, Unidirectional Transitions, and Stochastic Computation Times

Developing a thermodynamic theory of computation is a challenging task at the interface of nonequilibrium thermodynamics and computer science. In particular, this task requires dealing with difficulties such as stochastic halting times, unidirectional (possibly deterministic) transitions, and restricted initial conditions, features common in real-world computers. Here, we present a framework which tackles all such difficulties by extending the martingale theory of nonequilibrium thermodynamics to generic nonstationary Markovian processes, including those with broken detailed balance and/or absolute irreversibility. We derive several universal fluctuation relations and second-law-like inequalities that provide both lower and upper bounds on the intrinsic dissipation (mismatch cost) associated with any periodic process—in particular, the periodic processes underlying all current digital computation. Crucially, these bounds apply even if the process has stochastic stopping times, as it does in many computational machines. We illustrate our results with exhaustive numerical simulations of deterministic finite automata processing bit strings, one of the fundamental models of computation from theoretical computer science. We also provide universal equalities and inequalities for the acceptance probability of words of a given length by a deterministic finite automaton in terms of thermodynamic quantities, and outline connections between computer science and stochastic resetting. Our results, while motivated from the computational context, are applicable far more broadly. Published by the American Physical Society 2024

An appraisal of the LQI as an approach to setting target reliabilities in ISO 2394:2015

The idea of coupling economics and safety into one optimization exercise, and hence deriving target reliabilities explicitly from socio-economic consequences of limit state violation, appeared in the JCSS Probabilistic Model Code around the turn of the century; the effort culminated in the form of a Life Quality Index (LQI)-based marginal lifesaving costs (MLSC) methodology in ISO 2394:2015 for determining maximum acceptable failure probabilities, pT, in life safety limit states for civil engineering structures across all nations. Unfortunately, while the methodology does yield adequate levels of safety when applied to structures in the developed countries, the recommended values of pT turn out to be one to two orders of magnitude higher, and unacceptable to every known standard in the world including its own earlier edition (ISO 2394:1998) when applied to structures in the developing world (exemplified by India) with identical functions and expected fatalities. This arises from two shortcomings: (1) an MLSC approach is generally unable to provide an independent constraint on monetary optimization, and (2) the LQI-based measure of MLSC constraint is strongly dependent on a country’s per capita GDP (g) and the resultant pT is effectively governed by the reciprocal of g. This paper recommends continuing with the absolute − not marginal − consequences of violating life safety limit states, and setting constraints on monetary optimization that are consistent with fatality risks from engineering activities more universally accepted to be safe.

Cost-effectiveness of extracorporeal cardiopulmonary resuscitation vs. conventional cardiopulmonary resuscitation in out-of-hospital cardiac arrest: a pre-planned, trial-based economic evaluation.

When out-of-hospital cardiac arrest (OHCA) becomes refractory, extracorporeal cardiopulmonary resuscitation (ECPR) is a potential option to restore circulation and improve the patient's outcome. However, ECPR requires specific materials and highly skilled personnel, and it is unclear whether increased survival and health-related quality of life (HRQOL) justify these costs. This cost-effectiveness study was part of the INCEPTION study, a multi-centre, pragmatic randomized trial comparing hospital-based ECPR to conventional CPR (CCPR) in patients with refractory OHCA in 10 cardiosurgical centres in the Netherlands. We analysed healthcare costs in the first year and measured HRQOL using the EQ-5D-5L at 1, 3, 6, and 12 months. Incremental cost-effectiveness ratios (ICERs), cost-effectiveness planes, and acceptability curves were calculated. Sensitivity analyses were performed for per-protocol and as-treated subgroups as well as imputed productivity loss in deceased patients. In total, 132 patients were enrolled: 62 in the CCPR and 70 in the ECPR group. The difference in mean costs after 1 year was €5109 (95% confidence interval -7264 to 15 764). Mean quality-adjusted life year (QALY) after 1 year was 0.15 in the ECPR group and 0.11 in the CCPR group, resulting in an ICER of €121 643 per additional QALY gained. The acceptability curve shows that at a willingness-to-pay threshold of €80.000, the probability of ECPR being cost-effective compared with CCPR is 36%. Sensitivity analysis showed increasing ICER in the per-protocol and as-treated groups and lower probabilities of acceptance. Hospital-based ECPR in refractory OHCA has a low probability of being cost-effective in a trial-based economic evaluation.