High Cost Research Articles

National Trends of Inpatient Radiation Cystitis: 2016-2019.

Radiation cystitis with hematuria (RCH) is a potentially devastating complication after pelvic radiation. The cumulative incidence of RCH is debated, and certain severe manifestations may require hospital admission. We aimed to evaluate demographics and outcomes of patients hospitalized for RCH. We performed a retrospective review of hospitalized patients with a primary or secondary diagnosis of RCH from 2016 to 2019 using the National Inpatient Sample. Our unit of analysis was inpatient encounters. Our primary outcome was inpatient mortality. Secondary outcomes included need for inpatient procedures, transfusion, length of stay (LOS), and cost of admission. We then performed multivariate analysis using either a logistic or linear regression to identify predictors of mortality and LOS. Cost was analyzed using a generalized linear model controlling for LOS. We identified 21,320 weighted cases of hospitalized patients with RCH. The average patient age was 75.4 years, with 84.7% male and 69.3% White. The median LOS was 4 days, and the median cost was $8767. The inpatient mortality rate was 1.3%. The only significant predictor for mortality was older age. The only significant predictor of both higher cost and longer LOS was an Elixhauser Comorbidity Score ≥ 3. RCH represents a significant burden to patients and the health care system, and we observed an increasing number of hospitalized patients over time. Additional research is needed to identify underlying causes of RCH and effective treatments for this sometimes-severe complication of pelvic radiation.

Perioperative albumin among adults undergoing Thoracic Surgery in the United States: utilization, associations with clinical outcomes, and contribution to hospital costs

Objectives: To estimate the use of albumin among adults undergoing thoracic surgery in the United States, compare baseline characteristics, clinical and cost outcomes of recipients versus non-recipients, and determine albumin's contribution to total hospital costs. Design: Retrospective cohort study. Setting: Nationwide sample of US hospitals. Participants: Adults undergoing open and minimally invasive thoracic surgery between 2011 and 2017. Interventions: Albumin on the day of surgery (identified using itemized hospital billing logs). Measurements and Main Results: Albumin was used in 170 of 342 US hospitals, among 13% and 7% of 14672 and 22532 patients who respectively underwent open and minimally invasive thoracic surgery (median volume 500cc). Baseline comorbidities and organ supportive treatments were several-fold more prevalent among recipients (particularly vasopressors, mechanical ventilation, red cell transfusions). In standardized mortality ratio propensity score weighted analysis, albumin use was not associated with in-hospital mortality (adjusted RR 1.17 [0.72, 1.92] and 1.51 [0.97, 2.34], with open and minimally procedures), but was associated with morbidity and higher costs, more so with minimally invasive than open surgery. Total costs among recipients were higher by $4,744 [$3,591, $5,897] and $5,088 [$4,075, $6,100] for open and minimally invasive procedures respectively. Albumin accounted for 2.6% of this difference (median $124 [$83-$189] per patient). Conclusions: Albumin use varies widely across hospitals, and 9% of patients receive it (median 500cc). Use was not associated with in-hospital mortality and was associated with more morbidity and cost. The cost of albumin accounted for a trivial portion of hospital costs. Clinical trials must examine effects of albumin on complications and costs after thoracic surgery.

Direct healthcare costs in the first 2 years of life: A comparison of screened and clinically diagnosed children with cystic fibrosis – The Irish comparative outcomes study of CF (ICOS)

BackgroundIn July 2011, Cystic Fibrosis (CF) was added to the Newborn Bloodspot Screening Programme in Ireland. The Irish Comparative Outcomes Study (ICOS) is a historical cohort study established to compare outcomes between clinically-detected and screen-detected children with CF. Here we present the results of economic analysis comparing direct healthcare costs in the first 2 years of life of children born between mid-2008 and mid-2016, in the pre-CF transmembrane conductance regulator modulator era. MethodsHealthcare resource use information was obtained from Cystic Fibrosis Registry of Ireland (CFRI), medical records and parental questionnaire. Hospital admissions, emergency department visits, outpatient appointments, antibiotics and maintenance medications were included. Costs were estimated using the Health Service Executive Casemix, Irish Medicines Formulary and hospital pharmacy data, adjusted for inflation using Consumer Price Index data from the Central Statistics Office. A Negative Binomial regression was used, with time in the study as an offset. ResultsOverall participation was 93 %. After exclusion of those with meconium ileus, data from 139 patients, with follow-up to 2 years of age, were available. 72 (51.8 %) were from the clinically diagnosed cohort. In the final model (n=105), clinically diagnosed children had 2.62-fold higher costs per annum (p<0.0001), when adjusted for confounders, including homozygous ΔF508 or G511D mutation, socio-demographic factors and time between diagnosis and first CFRI interaction. ConclusionsThere are few studies evaluating economic aspects of newborn screening for CF using routine care data. These results imply that the benefits of newborn screening extend to direct healthcare costs borne by the State.

A few-shot identification method for stochastic dynamical systems based on residual multipeaks adaptive sampling.

Neural networks are popular data-driven modeling tools that come with high data collection costs. This paper proposes a residual-based multipeaks adaptive sampling (RMAS) algorithm, which can reduce the demand for a large number of samples in the identification of stochastic dynamical systems. Compared to classical residual-based sampling algorithms, the RMAS algorithm achieves higher system identification accuracy without relying on any hyperparameters. Subsequently, combining the RMAS algorithm and neural network, a few-shot identification (FSI) method for stochastic dynamical systems is proposed, which is applied to the identification of a vegetation biomass change model and the Rayleigh-Van der Pol impact vibration model. We show that the RMAS algorithm modifies residual-based sampling algorithms and, in particular, reduces the system identification error by 76% with the same sample sizes. Moreover, the surrogate model accurately predicts the first escape probability density function and the P bifurcation behavior in the systems, with the error of less than 1.59×10-2. Finally, the robustness of the FSI method is validated.

A fast and efficient pseudo-direct identification method for orthotropic elastic properties of multi-layered composite plates

New types of laminated composites are continuously being developed to meet various requirements involved in engineering applications. Before mass production of these new composite materials can take place, it is necessary to identify their elastic properties. Although current identification methods based on vibration tests and optimization algorithms are highly accurate, their high computation cost is a drawback. To address this issue, this paper proposes a new method to identify orthotropic elastic properties of multi-layered composite plates. The proposed method is based on the Modal Stability Procedure, using the experimental vibration frequencies and corresponding numerical mode shapes of structures. It converges after only two or three iterations without requiring an optimization algorithm. Therefore, the computational cost of the proposed method is significantly lower than that of optimization-based methods. The effectiveness and robustness of this method are demonstrated through three examples with different material, stacking sequences, and geometries including curved plate.

Ischaemia with no obstructive coronary arteries: a review with focus on the Asian population.

Ischaemia with no obstructive coronary arteries (INOCA) has been a diagnostic and therapeutic challenge for decades. Several studies have demonstrated that INOCA is associated with an increased risk of death, adverse cardiovascular events, poor quality of life and high healthcare cost. Although there is increasing recognition of this entity in the Western population, in the Asian population, INOCA remains elusive and its prevalence uncertain. Despite its prognostic significance, diagnosis of INOCA is often delayed. In this review, we identified the multiple barriers to its diagnosis and management, and proposed strategies to overcome them.

Double-network gel electrolyte with high electrolyte retention for flexible high-voltage Zn-air batteries

The zinc-air battery (ZAB) exhibits promising potential for application in wearable devices due to its high energy density, low cost, and safety features. However, the semi-open structure of ZAB accelerates the evaporation of battery electrolytes and poses a potential risk of leakage. Herein, a novel polyacrylamide-co-sodium p-benzenesulfonate and carboxymethyl cellulose (PAS-CMC) double-network gel electrolyte was synthesized and utilized in a flexible ZAB. The prepared PAS-CMC exhibits exceptional electrical conductivity (315.53 mS cm−1), mechanical properties, as well as electrolyte retention capacity. The ZAB assembled by alkaline PAS-CMC exhibits remarkable stability during circulation for over 120 h. Compared with the traditional alkaline PAS-CMC electrolyte, the open-circuit voltage (2.03 V), operating voltage (1.90 V) and power density (164.3 mW cm−2) of the ZAB assembled by the acid-alkaline PAS-CMC prepared by the “sandwich” strategy have been significantly improved, showing excellent flexibility under various bending conditions without any degradation in performance. Furthermore, the interaction mechanisms between PAS-CMC/H2O and PAS-CMC/Zn were further elucidated by DFT calculation, thereby establishing a theoretical foundation for high-voltage flexible ZAB.

Study on the Optimal Configuration of Battery Energy Storage System in Distribution Networks Considering Carbon Capture Units

Background: The implementation of Battery Energy Storage Systems (BESSs) and carbon capture units can effectively reduce the total carbon emissions of distribution networks. However, their widespread adoption has been hindered by the high investment costs associated with the BESSs and power generation costs of carbon capture units. Objective: The objective of this paper is to optimize the location and sizing of BESSs in distribution networks that comprise renewable power plants and coal-fired power units with carbon capture systems. The optimization process aims to minimize the grid’s impact from the configuration while maximizing economic cost savings and the benefits of reducing carbon emissions. Methods: A bi-layer optimization model is proposed to determine the configuration of BESSs. The upper layer of the model optimizes the size and operation strategy of the BESSs to minimize the configuration and power generation costs, using YALMIP and CPLEX optimization tools. Carbon emission reduction benefits are considered through deep peak-shaving and carbon tax. The lower layer of the model aims to optimizes the placement of the BESSs to minimize voltage fluctuation and network loss in the power grid. To achieve this, we improved the efficiency of the Nondominated Sorting Genetic Algorithm II (NSGA-II) to update the BESS’s placement. Results: The IEEE33-bus and IEEE118-bus systems were utilized for simulation and comparison in various scenarios. The findings demonstrate that the proposed configuration method can decrease the cost of investment and power generation. Furthermore, it reduces the degree of node voltage fluctuation and network loss in the distribution network. Conclusion: The study reveals that determining the optimal scale of BESSs can mitigate high energy consumption in carbon capture systems and improve the overall performance of power systems that integrate carbon capture technology and renewable power plants.

A comprehensive review of current progress in biofuel production using marine algae biomass

The world's fossil fuel resources are rapidly depleting as the global population grows and energy demands rise. Current fossil fuel supplies are insufficient to meet rising demand and are nearing depletion. Careless exploitation of fossil fuels increases greenhouse gas emissions, environmental pollution, and global warming, resulting in ecological imbalances and health risks. Consequently, contamination, rising temperatures, and high oil costs have fueled the search for renewable and alternative energy sources. This study seeks to explore renewable energy alternatives that are critical to meeting future global energy demands. Marine algae such as red algae, green algae, and brown algae are gaining popularity as economically viable and environmentally friendly sources of renewable biofuel. Several studies are currently underway to assess the ability of marine algae to produce a variety of bioproducts, including biofuels. Macroalgae, in particular, emerges as an ideal resource for biofuel production due to their lipid and carbohydrate content, coupled with low or absent lignin content. The article investigates the potential of marine algal species to promote biofuel production and discusses novel methods for improving biofuel yields. It also emphasizes the economic factors and challenges associated with sustainable biofuel production.

Language-aware multiple datasets detection pretraining for DETRs

Pretraining on large-scale datasets can boost the performance of object detectors while the annotated datasets for object detection are hard to scale up due to the high labor cost. What we possess are numerous isolated filed-specific datasets, thus, it is appealing to jointly pretrain models across aggregation of datasets to enhance data volume and diversity. In this paper, we propose a strong framework for utilizing Multiple datasets to pretrain DETR-like detectors, termed METR, without the need for manual label spaces integration. It converts the typical multi-classification in object detection into binary classification by introducing a pre-trained language model. Specifically, we design a category extraction module for extracting potential categories involved in an image and assign these categories into different queries by language embeddings. Each query is only responsible for predicting a class-specific object. Besides, to adapt our novel detection paradigm, we propose a Class-wise Bipartite Matching strategy that limits the ground truths to match queries assigned to the same category. Extensive experiments demonstrate that METR achieves extraordinary results on either multi-task joint training or the pretrain & finetune paradigm. Notably, our pre-trained models have high flexible transferability and increase the performance upon various DETR-like detectors on COCO val2017 benchmark. Our code is publicly available at: https://github.com/isbrycee/METR.

Dynamic scheduling of electricity demand for decentralized EV charging systems

The rapid growth of electric vehicles (EVs) has brought forth new challenges to the power grid. Further, the simultaneous charging of EVs could lead to peak demand, potentially causing overloading, voltage swings, and other grid-related problems. To address these issues and lower the high energy costs faced by EV owners and grid operators, EV charging must be optimized. The study proposes an innovative strategy that utilizes decentralized charging systems to lessen the impact of EVs on the grid. A decentralized EV scheduling strategy offers scalability. Thus, making it suitable for a large EV population and it remains resilient to the dynamic arrivals of the EVs. The approach aims to balance the load on the grid and improve the effectiveness of charging operations. To achieve this, a convex optimization problem has been developed to effectively regulate the charging procedure, taking into account the distinct attributes of each EV. The mechanism operates by dividing time into several intervals. Each electric vehicle in the system autonomously adjusts its charging rate during the assigned time slots, with the goal of minimizing individual charging expenses. Moreover, the system demonstrates flexibility in deciding when to charge and discharge, allowing prioritization based on individual EV battery levels and power grid conditions. As a result, the cost analysis was conducted using the number of EVs and the average group size. A comparison of computational times between centralized and decentralized systems was undertaken to demonstrate the efficacy of the system.

DACBN: Dual attention convolutional broad network for fine-grained visual recognition

Fine-grained visual classification (FGVC) is a challenging task due to its small inter-class differences and large intra-class differences. Most existing methods rely on manual labeling of key identification areas, which requires high labor costs. In addition, existing methods also tend to ignore the differences effect of different feature channels in the feature map, which has a certain impact on the model classification accuracy. To solve the above problems, this paper proposes a dual attention convolutional broad network. Firstly, a new dual attention mechanism is designed to suppress the background noise of fine-grained images and give greater weight to the discriminative feature regions and channels. Secondly, the ensemble broad learning system framework is used to further enhance the dual attention features, so that the discriminative features can further improve the recognition ability of the model. Finally, by multiple comparative experiments, it is reported that the method proposed in this article has achieved excellent recognition results on three commonly used datasets.

Fast approximation of fiber reinforced injection molding processes using eikonal equations and machine learning

Injection molding is a popular production process for short fiber reinforced components. The mechanical properties of such components depend on process-induced fiber orientations which are commonly predicted via numerical simulations. However, high computational costs prevent process simulations from being used in iterative procedures, such as topology optimization or finding optimal injection locations. We propose a fast approximation method that extracts nodal features and train a regression model to predict fill states, cooling times, volumetric shrinkage, and fiber orientations. The features are determined by solving eikonal equations with a fast iterative method and computing spatial moments to characterize node-adjacent material distributions. Subsequently, we use these features to train feed forward neural networks and gradient boosted regression trees with simulation data of a large dataset of geometries. This approach is significantly faster than conventional methods, providing 20x speed-up for single simulations and more than 200x speed-up in gate location optimization. It generalizes to arbitrary geometries and injection locations.

Efficient Energy Management System using Honey Badger Algorithm for Smart Agriculture

Today, optimization is crucial to solving energy crises, especially in smart homes. However, the optimization-based methods for energy management in smart agriculture available globally need further improvement, which motivates this study. To resolve the problem, an efficient scheduling farm energy management system is required. Therefore, this study proposes a Farm Energy Management System (FEMS) for smart agriculture by adopting a honey-badger optimization algorithm. In the proposed system, a multi-objective optimization problem is formulated to find the best solutions for achieving the set of objectives, such as electricity cost, load minimization and peak-to-average ratio minimization, while considering the farmers' comfort. The proposed system considers commercialized agriculture with the integration of Renewable Energy Resources (RES). Also, the proposed system minimizes both load consumption and electricity costs via the scheduling of farm appliances in response to Real-Time Pricing (RTP) and Time-of-Use (ToU) pricing schemes in the electricity market. Extensive experiments are carried out in MATLAB 2018A to determine the efficacy of the proposed system. The proposed FEMS consists of sixteen farm appliances with their respective power ratings, inclusive of RES. The simulation results showed that a system without FEMS has a high electricity cost of 50.69% as compared to 43.04% for FEMS without RES and 6.27% for FEMS with RES when considering the ToU market price. For RTP market price, a system without FEMS has an electricity cost of 42.30%, as compared to 30.64% for FEMS without RES and 27.24% for FEMS with RES. Besides, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 151.40 kW for FEMS without RES and 18.85 kW for FEMS with RES when considering the ToU market price. Also, for the RTP market price, the maximum load consumption for a system without FEMS is 246.80 kW, as compared to 186.40 kW for FEMS without RES and 90.68 kW for FEMS with RES. The significance of the study is to propose a conceptualized FEMS based on the honey badger optimization algorithm. The proposed system provides scheduling of farm appliances that alleviates the burden of the electricity grid and is cost-effective for large and small-scale farmers.

Toward a more resilient Thailand: Developing a machine learning-powered forest fire warning system

Forest fires in Thailand are a recurring and formidable challenge, inflicting widespread damage and ranking among the nation's most devastating natural disasters. Most detection methods are labor-intensive, lack speed for early detection, or result in high infrastructure costs. An essential approach to mitigating this issue involves establishing an efficient forest fire warning system based on amalgamating diverse available data sources and optimized algorithms. This research endeavors to develop a binary machine-learning classifier based on Thailand's forest fire occurrences from January 2019 to October 2022 using data acquired from satellite resources, including the Google Earth engine. We use four gas variables including carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone. The study explores a range of classification models, encompassing linear classifiers, gradient-boosting classifiers, and artificial neural networks. The XGBoost model is the top-performing option across various classification evaluation metrics. The model provides the accuracy of 99.6 % and ROC-AUC score of 0.939. These findings underscore the necessity for a comprehensive forest fire warning system that integrates gas measurement sensor devices and geospatial data. A feedback mechanism is also imperative to enable model retraining post-deployment, thereby diminishing reliance on geospatial attributes. Moreover, given that decision-tree-based algorithms consistently yield superior results, future research in machine learning for forest fire prediction should prioritize these approaches.

The enhanced oxidase-like activity of modified nanoceria/ZIF-67 for fluorescence and smartphone-assisted visual detection of tannic acid

As a substitute for natural enzymes, nanozymes have unique characteristics of high catalytic activity, excellent stability and low cost, making them pivotal in sensing applications. Among them, nanozymes with oxidase-like activity garner significant attention for antioxidant detection, as they efficiently catalyze the oxidation of antioxidants in the presence of oxygen. Herein, a nanozyme with an enhanced oxidase-like activity was synthesized through a 3-step strategy, that is, ceria nanoparticles were firstly modified with amino acid and oligopeptides, then combined with zeolitic imidazolate framework-67 (ZIF-67), and finally the composite was etched in neutral water. The as-synthesized nanozyme can oxidize the substrate 3,3′,5,5′-tetramethylbenzidine (TMB) or Amplex Red (AR) in the presence of oxygen, inducing a change in their color or fluorescent signals, verifying its oxidase-like activity. Tannic acid (TA), an antioxidant, competes with TMB/AR for oxidation by the nanozyme. Leveraging this competitive relationship, a fluorescent and portable trichromatic sensor was developed for the detection of tannic acid (TA) in food samples. The fluorescent sensor shows high sensitivity with detection limits (S/N = 3) of 0.011 µM, and the trichromatic sensor presents comparable performance to colorimetric sensor, exhibiting a linear range of 30–270 µM and detection limit of 1.03 µM. By using a smartphone platform, the trichromatic sensor enables convenient, rapid, inexpensive, and quantitative assessment of TA. Both two sensors were successfully applied to TA detection in 9 different foods. This study not only provides an effective strategy to enhance enzyme performance but also paves the way for their applications in food testing.

Intensive therapeutic education strategy for patients with acute heart failure (EduStra-HF): Design of a randomized controlled trial

Background: Heart failure is associated with reduced quality of life, hospitalizations, death and high healthcare costs. Despite care improvements, the rehospitalization rate after an acute heart failure episode, especially for acute heart failure, remains high.Methods: The Education Strategy for patients with acute Heart Failure (EduStra-HF; ClinicalTrials.gov Identifier NCT03035123) study will randomize patients admitted for acute heart failure in six French hospitals to usual care (control) or therapeutic education (intervention). All patients will be evaluated at baseline and will meet with a therapeutic education nurse before discharge. Those in the usual care arm will have standard appointments with their cardiologist and general practitioner. Those in the intervention arm will have an intensive follow-up schedule of phone calls, home visits and text messages from the therapeutic education nurses, plus cardiologist visits. Patients will be stratified by discharge location (home or cardiac rehabilitation centre) before randomization, and will be followed up for 1 year. The primary outcome will be the readmission rates for acute heart failure during 1 year in the two groups. Secondary outcomes will include: quality of life; time from inclusion to first readmission for acute heart failure; non-heart failure cardiovascular rehospitalization rates; length of stay for heart failure; cardiovascular and all-cause death; rates of patients receiving optimal medical therapies; evolution of knowledge about heart failure; and cost-effectiveness.Conclusions: This study will assess the efficacy and feasibility of a standardized management strategy for the care and follow-up of patients discharged after hospitalization for acute heart failure. The EduStra-HF strategy will combine various nurse care methods to help prevent rehospitalization.

Synergistic effect between S and Te enhancing the electrochemical behavior of heteroatomic TeS-x cathodes in aqueous Zn–TeS batteries

Aqueous Zn–S batteries (AZSBs) have garnered increasing attention in the energy storage field owing to their high capacity, energy density, and cost effectiveness. Nevertheless, sulfur (S) cathodes face challenges, primarily stemming from sluggish reaction kinetics and the formation of an irreversible byproduct (SO42−) during the charge, hindering the progress of AZSBs. Herein, Te–S bonds within S-based cathodes were introduced to enhance electron and ion transport and facilitate the conversion reaction from zinc sulfide (ZnS) to S. This was achieved by constructing heteroatomic TeS-x@Ketjen black composite cathodes (HM-TeS-x@KB, where x = 36, 9, and 4). The HM-TeS-9@KB electrode exhibits long-term cycling stability, maintaining a capacity decay rate of 0.1 % per cycle over 450 cycles at a current density of 10 A/g. Crucially, through a combination of experimental data analysis and theoretical calculations, the impact mechanism of Te on the charge and discharge of S active materials within the HM-TeS-9@KB cathode in AZSBs was investigated. The presence of Te–S bonds boost the intrinsic conductivity and wettability of the HM-TeS-9@KB cathode. Furthermore, during the charge, the interaction of preferentially oxidized Te with S atoms within ZnS promotes the oxidation reaction from ZnS to S and suppresses the irreversible side reaction between ZnS and H2O. These findings indicate that the heteroatomization of chalcogen S molecules represents a promising approach for enhancing the electrochemical performance of S cathodes in AZSBs.

Estimating cost function of expert players in differential games: A model-based method and its data-driven extension

In this paper, we introduce two algorithms for estimating the cost function of expert players engaged in optimal performance within linear continuous-time differential games. Initially, we propose a model-based algorithm, followed by its data-driven model-free extension. Both methods rely on optimal policy gains, obtained by observing Nash equilibrium trajectories of the expert players. The model-free method also utilizes the trajectories of the learner system. This method addresses the limitations found in existing model-free approaches, which may suffer from either high computational costs, limited applicability to specific systems, or both. The effectiveness of the proposed methods is demonstrated through numerical simulations.

Incomplete small bowel capsule endoscopy: Risk factors and cost-effectiveness of real-time viewing.

Background and study aims International guidelines recommend real-time viewing (RTV) in capsule endoscopy for gastric emptying monitoring, yet it is often overlooked in clinical practice. We aimed to assess risk factors for incomplete small bowel capsule endoscopy (SBCE) and evaluate the clinical relevance and cost-effectiveness of RTV implementation. Methods We included consecutive SBCEs from 2013 to 2020. RTV was not applied per local protocol. We used multivariate logistic regression to identify risk factors for incomplete SBCE, including prolonged gastric transit time (GTT) and prolonged small bowel transit time (SBTT). Results Analyzing 858 SBCEs, we observed a completion rate of 94.6%. Prolonged GTT and SBTT were present in 4.9% and 18.2% of complete SBCEs, and in 13% ( P =0.03) and 10.8% ( P =0.24) of incomplete SBCEs, respectively. Only 0.7% (6 of 858) had incomplete SBCE with prolonged GTT. In both univariate and multivariate analysis, a modifiable (prolonged GTT odds ratio [OR] 2.9; 95% confidence interval [CI] 1.1-7.5) and two unmodifiable risk factors (inpatient status OR 2.3; 95% CI 1.1-4.5) and history of incomplete SBCE (OR 4.2; 95% CI 1.3-13.7) were independently linked to higher incomplete SBCE rates. The pretest completion probability was 90.5% and 95.8% in patients with and without unmodifiable risk factors, respectively ( P <0.01). The direct cost of systematic RTV adoption and prokinetics administration would be €5059, aiming to identify and treat each case of prolonged GTT associated with incomplete SBCE. Conclusions Modern devices make incomplete SBCE rare, usually not tied to prolonged GTT. In a low-incidence scenario, widespread RTV use brings high costs and uncertain effectiveness.