Impact of Latency Variations on the Predictive Value of Facial Nerve Proximal-to-Distal Amplitude Ratio during Vestibular Schwannoma Surgery.

Introduction This study highlights the relation between compound muscle action potential (CMAP) latency variations and the predictive value of facial nerve (FN) proximal-to-distal (P/D) amplitude ratio measured at the end of vestibular schwannoma resection. Methods Forty-eight patients underwent FN stimulation at the brainstem (proximal) and internal acoustic meatus (distal) using a current intensity of 2 mA. The proximal latency and the P/D amplitude ratio were assessed. House-Brackmann grades I & II indicated good FN function, and grades III to VI were considered fair/poor function. A P/D amplitude ratio > 0.6 was used as a cutoff to indicate a good FN function, while a ratio of ≤ 0.6 indicated a fair/poor FN function. Results The P/D amplitude ratio was measured for all patients, and the calculated sensitivity (SE), specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) were 85.2, 85.7, 88.5, and 81.8%, respectively. The CMAPs from the mentalis muscle were then classified based on their proximal latency into group I (< 6 ms), group II (6-8 ms), and group III (> 8 ms). The SE, SP, PPV, and NPV became 90.5, 90.9, 95, and 83.3%, respectively, in group II. In group I, SE and NPV increased, whereas SP and PPV decreased. While in group III, SP and PPV increased, whereas SE and NPV decreased. Conclusion At a latency between 6 and 8 ms, the P/D amplitude ratio was predictive of outcomes with high SE and SP. When latency was < 6 ms or > 8 ms, the same predictive ability was not observed. Knowing the strengths and limitations is important for understanding the predictive value of the P/D amplitude ratio.

Preparing for Value: Evaluating Organizational Culture in Health Care Transformation.

The health care industry is experiencing a transformative shift from traditional fee-for-service models to value-based care (VBC), emphasizing improved patient outcomes, enhanced quality, and reduced costs. While Centers for Medicare & Medicaid Services Innovation Center models focus on financial and quality outcomes, a critical opportunity for reform lies in organizational culture. VBC signifies a cultural and systemic evolution aligned with the quintuple aim of enhancing equitable patient outcomes, improving quality, reducing costs, and prioritizing provider well-being. Cultural impacts play a pivotal role in this transformation.

Health Star Rating of Nonalcoholic, Packaged, and Ready-to-Drink Beverages in Türkiye: A Decision Tree Model Study.

This study aimed to compare the nutritional quality of beverages sold in Türkiye according to their labeling profiles. A total of 304 nonalcoholic beverages sold in supermarkets and online markets with the highest market capacity in Türkiye were included. Milk and dairy products, sports drinks, and beverages for children were excluded. The health star rating (HSR) was used to assess the nutritional quality of beverages. The nutritional quality of beverages was evaluated using a decision tree model according to the HSR score based on the variables presented on the beverage label. Moreover, confusion matrix tests were used to test the model's accuracy. The mean HSR score of beverages was 2.6±1.9, of which 30.2% were in the healthy category (HSR≥3.5). Fermented and 100% fruit juice beverages had the highest mean HSR scores. According to the decision tree model of the training set, the predictors of HSR quality score, in order of importance, were as follows: added sugar (46%), sweetener (28%), additives (19%), fructose-glucose syrup (4%), and caffeine (3%). In the test set, the accuracy rate and F1 score were 0.90 and 0.82, respectively, suggesting that the prediction performance of our model had the perfect fit. According to the HSR classification, most beverages were found to be unhealthy. Thus, they increase the risk of the development of obesity and other diseases because of their easy consumption. The decision tree learning algorithm could guide the population to choose healthy beverages based on their labeling information.

A Review on Camel Milk Composition, Techno-Functional Properties and Processing Constraints.

Camel milk plays a critical role in the diet of peoples belongs to the semi-arid and arid regions. Since prehistoric times, camel milk marketing was limited due to lacking the processing facilities in the camel-rearing areas, nomads practiced the self-consumption of raw and fermented camel milk. A better understanding of the techno-functional properties of camel milk is required for product improvement to address market and customer needs. Despite the superior nutraceutical and health promoting potential, limited camel dairy products are available compared to other bovines. It is a challenging impetus for the dairy industry to provide diversified camel dairy products to consumers with superior nutritional and functional qualities. The physicochemical behavior and characteristics of camel milk is different than the bovine milk, which poses processing and technological challenges. Traditionally camel milk is only processed into various fermented and non-fermented products; however, the production of commercially important dairy products (cheese, butter, yogurt, and milk powder) from camel milk still needs to be processed successfully. Therefore, the industrial processing and transformation of camel milk into various products, including fermented dairy products, pasteurized milk, milk powder, cheese, and other products, require the development of new technologies based on applied research. This review highlights camel milk's processing constraints and techno-functional properties while presenting the challenges associated with processing the milk into various dairy products. Future research directions to improve product quality have also been discussed.

What is the Value Proposition of Evidence-Based Guidelines? An Application of the Operative Value Index for Lumbar Fusions.

BACKGROUND: Elective lumbar fusions have received criticism for inappropriate utilization. Here, we use a novel Operative Value Index (OVI) to assess whether "indicated", evidence-based lumbar fusions are associated with increased value (outcomes per dollar spent). METHODS: This study is a retrospective analysis of a prospective observational cohort of 294 patients undergoing elective lumbar fusions at a single large academic institution. All patients were preoperatively evaluated by a panel of neurosurgeons for concordance with evidence-based medicine (EBM), determined through guidelines from the North American Spine Society. Oswestry Disability Index (ODI) scores were collected for all patients both preoperatively and at 6-months postoperatively. Time-driven activity-based costing (TDABC) was employed to determine both direct and indirect intraoperative costs. The OVI was defined as the percent improvement in ODI per $1,000 spent intraoperatively. Generalized linear mixed model (GLMM) regression, adjusting for confounders, was performed to assess whether EBM-concordant surgeries were associated with higher OVI. RESULTS: Of 294 elective lumbar fusions, 92.9% (n=273) were EBM-concordant. The average total cost of an EBM-concordant lumbar fusion was $17,932 (supplies: $13,020; personnel: $4,314), compared to $20,616 (supplies: $15,467; personnel: $4,758) for an EBM-discordant fusion. Average OVI was 2.27 for a concordant fusion, compared to 0.11 for a discordant fusion. GLMM analysis revealed that EBM-concordant cases were associated with significantly higher OVI (ß-coefficient 2.0, p<0.001). CONCLUSION: EBM-concordant fusions were associated with 2% greater improvement in ODI scores from baseline for every $1,000 spent intraoperatively. Systematic methods for increasing guideline adherence for lumbar fusions could therefore improve value at scale.

A tutorial on value-based adaptive designs: Could a value-based sequential two-arm design have created more health economic value for the Big CACTUS trial?

OBJECTIVES: Value-based trials aim to maximize the expected net benefit by balancing technology adoption decisions and clinical trial costs. Adaptive trials offer additional efficiency. This paper provides guidance on determining whether a value-based sequential design is the best option for an adaptive two-arm trial, illustrated through a case study. METHODS: We outline four steps for the value-based sequential approach. The case study re-evaluates the Big CACTUS trial design using pilot trial data and a model-based health economic analysis. Expected net benefit is computed for (a) original fixed design, (b) value-based design with fixed sample size, and (c) optimal value-based sequential design with adaptive stopping. We compare pre-trial modelling with actual Big CACTUS trial results. RESULTS: Over ten years, the adoption decision would impact approximately 215,378 patients. Pre-trial modelling shows that the expected net benefit minus costs is (a) £102m for the original fixed design, (b) £107m (+5.3% higher) for the value-based design with optimal fixed sample size, and (c) £109m (+6.7% higher) for the optimal value-based sequential design with maximum sample size of 435 per arm. Post hoc analysis using actual Big CACTUS trial data indicates that the value-adaptive trial with a maximum sample size of 95 participant pairs wouldn't have stopped early. Bootstrap simulations reveal a 9.76% probability of early completion with n=95, compared to 31.50% with n=435. CONCLUSIONS: The four-step approach to value-based sequential two-arm design with adaptive stopping was successfully implemented. Further application of value-based adaptive approaches could be useful to assess efficiency of alternative study designs.

Generating utilities for the CS-Base: a novel, generic, and patient-centered health-outcome measure.

OBJECTIVES: We have developed a new patient-centered, preference-based generic health-outcome measure, CS-Base, which is based on a novel multi-attribute preference response (MAPR) measurement framework. This study aimed to generate a first utility set for the CS-Base, making it suitable for use in health-economic evaluations. METHODS: CS-Base comprises 12 health attributes: mobility, vision, hearing, cognition, mood, anxiety, pain, fatigue, social functioning, daily activities, self-esteem, and independence, each with four levels. Our methodology to generate utilities for the CS-Base was twofold. First, we derived coefficients from patient MAPR data to calculate CS-Base values. Subsequently, these were normalized to a 0.0-1.0 utility scale, where 0.0 signifies "dead". The "dead" position was estimated using general population data from a discrete choice experiment (DCE+Dead), using a "division value" strategy which localize the position of states better or worse than dead. RESULTS: We analyzed MAPR data from 3,222 patients and DCE+Dead data from 1,995 respondents. All MAPR coefficients were negative, logically ordered, and significantly different from the reference level. The "dead" position was denoted by a division value of -148.385. Utility values spanned from -0.071 to 1.0, and only 53 of 16,777,216 states were deemed worse than dead. CONCLUSION: This study introduced the first CS-Base utility set, underlining a two-step utility derivation method. This method, blending societal and patient views, surpasses traditional preference-based approaches, yielding firmer results. However, improvement of the normalization procedure is expected. Estimating CS-Base utilities is an ongoing process that gains precision over time.

Valuation of multi-dimensional health states with a "bolt-on": is there a shortcut?

OBJECTIVES: In order to use the EQ-5D questionnaire with bolt-on dimensions in economic evaluation studies, new value sets are needed. In this study, we explored the feasibility of a new approach called scaling factor model, which estimates bolt-on value sets using estimated EQ-5D dimensional weights. METHODS: We designed a two-arm study, inviting university students to value health states with and without bolt-on items using the composite time trade-off (cTTO) method. We selected 25 health states from an orthogonal array and added 5 mildest EQ-5D states in the design. In arm 1, EQ-5D without self-care and standard EQ-5D states were valued, and in arm 2, standard EQ-5D states and EQ-5D with vision were valued. By arm, we compared the mean observed values of health states with and without bolt-on item. Next, by arm, we estimated value sets for the EQ-5D with bolt-on item states using both standard model and scaling factor model. Model performances were compared in terms of prediction accuracy and correlation with likelihood-based mean values. RESULTS: Adding a fifth-level bolt-on to EQ-5D resulted in statistically lower values. This effect is consistent across two arms and bolt-on items. The scaling factor models outperformed the standard models in all statistics. CONCLUSIONS: The scaling factor model offers a methodologically viable and low-cost option for producing value sets for EQ-5D, supplemented with bolt-on items. Future studies should further test this method using other bolt-on items and relevant study populations.

Use and Cost of Low-Value Services Among Veterans Dually Enrolled in VA and Medicare.

BACKGROUND: Over half of veterans enrolled in the Veterans Health Administration (VA) are also enrolled in Medicare, potentially increasing their opportunity to receive low-value health services within and outside VA. OBJECTIVES: To characterize the use and cost of low-value services delivered to dually enrolled veterans from VA and Medicare. DESIGN: Retrospective cross-sectional. PARTICIPANTS: Veterans enrolled in VA and fee-for-service Medicare (FY 2017-2018). MAIN MEASURES: We used VA and Medicare administrative data to identify 29 low-value services across 6 established domains: cancer screening, diagnostic/preventive testing, preoperative testing, imaging, cardiovascular testing, and surgery. We determined the count of low-value services per 100 veterans delivered in VA and Medicare in FY 2018 overall, by domain, and by individual service. We applied standardized estimates to determine each service's cost. KEY RESULTS: Among 1.6 million dually enrolled veterans, the mean age was 73, 97% were men, and 77% were non-Hispanic White. Overall, 63.2 low-value services per 100 veterans were delivered, affecting 32% of veterans; 22.9 services per 100 veterans were delivered in VA and 40.3 services per 100 veterans were delivered in Medicare. The total cost was $226.3 million (M), of which $62.6 M was spent in VA and $163.7 M in Medicare. The most common low-value service was prostate-specific antigen testing at 17.3 per 100 veterans (VA 55.9%, Medicare 44.1%). The costliest low-value service was percutaneous coronary intervention (VA $10.1 M, Medicare $32.8 M). CONCLUSIONS: Nearly 1 in 3 dually enrolled veterans received a low-value service in FY18, with twice as many low-value services delivered in Medicare vs VA. Interventions to reduce low-value services for veterans should consider their substantial use of such services in Medicare.

Short-term high-light intensity and low temperature improve the quality and flavor of lettuce grown in plant factory.

BACKGROUND: Lettuce holds a prominent position in the year-round supply of vegetables, offering a rich array of health-beneficial substances, such as dietary fiber, phenolic compounds, lactucopicrin and lactucin. As such, its flavor has garnered increasing attention. Balancing the enhancement of beneficial compounds with the reduction of undesirable taste is a key focus of scientific research. To investigate short-term management to improve the nutritional quality and flavor of lettuce, combinations of different light intensities (200, 500 and 800 µm ol m-2 s-1) and temperatures (10 and 22 °C) were applied separately to 'Lollo Rosso' and 'Little Butter Lettuce' for 7 days before harvest. RESULTS: The results obtained showed that increasing light intensity at low temperatures decreased nitrate content and increased soluble sugar, soluble protein, anthocyanin and phenolic compound content. In the case of lettuce flavor, the bitterness-related metabolites such as lactucin and lactucopicrin were reduced with high light intensity at a low temperature of 10 °C. With this combination, the fructose and glucose contents increased, significantly improving lettuce flavor. CONCLUSION: Higher light intensity combined with low temperature for 7 days before harvest effectively improved the nutritional quality and flavor of lettuce, suggesting its great potential for use in horticultural practices. © 2024 Society of Chemical Industry.

Serum amino acid profiles in clinically normal Noma horses.

Plasma or serum amino acids are used to evaluate nutritional status and metabolic disorders. In this study, we aimed to set reference values of serum amino acid concentrations in the Noma horse, a Japanese native horse. Thirty-one horses were classified into six age groups: neonatal foal (0-4 days), foal (0.5-1 years), youth (5 years), middle age (10 years), old (15 years), and extra-old (>20 years). Horses >5 years of age were analyzed together as the adult group. In the adult horses, there were no significant differences among the serum amino acid concentrations of each age group. The foal group had higher concentrations of alanine, aspartic acid, glutamic acid, α-aminoadipic acid, and 3-methyl-histidine than the adult group. The neonatal foal group had higher serum concentrations of phenylalanine, lysine, alanine, proline, aspartic acid, glutamic acid, ß-alanine, and ß-amino-iso-butyric acid and lower tryptophan concentrations and Fischer's ratios than the adult group. The neonatal foal group had higher ß-amino-iso-butyric acid concentrations and lower tryptophan and 3-methyl-histidine concentrations than the foal group. Therefore, reference values might be set separately in neonatal foals, foals, and adult horses. The data for the serum amino acid concentrations can be used for health care through physiological and pathological evaluations in Noma horses.

Diagnostic meta-analysis of the efficacy of ultrasonography for diagnosing carpal tunnel syndrome: A comparison between Asian and non-Asian populations.

BACKGROUND: Ultrasonography is used to diagnose carpal tunnel syndrome (CTS) according to various criteria. This diagnostic meta-analysis aimed to evaluate the efficacy of ultrasonography for diagnosing CTS, focusing on the cross-sectional area (CSA) of the median nerve (MN) at the inlet of the carpal tunnel and regional variations in diagnostic thresholds between Asian and non-Asian populations. METHODS: A comprehensive literature search was conducted using PubMed, Embase, and the Cochrane Library. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Patient demographic data, diagnostic "gold standards", CSA cutoff values, and diagnostic results were extracted. Meta-analysis was performed to determine the sensitivity, specificity, and optimal CSA cutoff values. RESULTS: For the 25 included studies, a combined sensitivity of 88% and specificity of 84% for CSA measurements at the carpal tunnel inlet were obtained. The Asian group had a sensitivity of 84% and specificity of 86%, while the non-Asian group had a sensitivity of 91% and specificity of 82%. The mean CSA in the Asian group was significantly lower than that in the non-Asian group (12.93 mm2 and 14.77 mm2, respectively; p = 0.042). For the Asian group, the summary receiver operating characteristic curve had an area under the curve (AUC) of 0.92 with an optimal cutoff of 10.5 mm2; for the non-Asian group, an AUC of 0.94 was obtained with a cutoff of 11.5 mm2. CONCLUSION: Ultrasonography is a reliable diagnostic method for CTS, with distinct optimal cutoff values observed between Asian and non-Asian populations. Therefore, population-specific diagnostic criteria for CTS are recommended.

Analysis and Modelling of the Predictive Value of PCT, PLR and NLR for Ureteric Sepsis after Ureteral Stone Surgery: A Retrospective Cohort Study.

OBJECTIVE: This study aimed to analyse the value of procalcitonin (PCT), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in predicting postoperative ureteral stone complications of urogenic sepsis. The production of a clinical prediction model could provide additional direction to reduce the likelihood of postoperative urogenital sepsis. METHODS: The clinical data of 520 patients with ureteral stones who underwent surgical treatment from January 2022, to September 2023, in the hospital were retrospectively analysed. The patients were divided into urogenic sepsis group (n = 42) and non-urogenic sepsis group (n = 478) in accordance with the occurrence of urogenic sepsis in the postoperative period. The peripheral blood PCT, PLR and NLR levels were collected within 24 h postoperatively in the two groups. The receiver operating characteristic (ROC) curve was employed to evaluate the predictive value of PCT, PLR and NLR levels for postoperative urogenital sepsis in patients with ureteral stones. RESULTS: Logistic regression analysis showed that PCT (odds ratio (OR) = 4.25, 95% CI: 1.85-9.78), PLR (OR = 4.00, 95% CI: 1.78-9.05) and NLR (OR = 2.29, 95% CI: 1.05-5.01) were risk factors for postoperative complication sepsis in patients with ureteral stones (p < 0.05). The ROC curves showed that the areas under the curve of PCT, PLR and NLR levels alone and in combination for predicting urogenic sepsis complications after emergency ureteral stone surgery were 0.683, 0.692, 0.611 and 0.799, respectively. CONCLUSIONS: Urogenic sepsis leads to increased serum PCT, NLR and PLR levels in patients undergoing surgical treatment for ureteral stones. Physicians should pay close attention to these indices to provide further theoretical support for reducing postoperative urogenic sepsis.

Prediction of Adolescents' Fluid Intelligence Scores based on Deep Learning with Reconstruction Regularization.

Objective: The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents. Methods: We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.0. A total of 148 ROIs were selected and 604 features were proposed by FreeSurfer segmentation. The training and testing sets were divided in a ratio of 7:3. To predict fluid intelligence scores, we used AE, MLP and classic machine learning models, and compared their performance on the test set. In addition, we explored their performance across gender subpopulations. Moreover, we evaluated the importance of features using the SHapley Additive Explain method. Results: The proposed model achieves optimal performance on the test set for predicting actual fluid intelligence scores (PCC = 0.209 ± 0.02, MSE = 105.212 ± 2.53). Results show that autoencoders with refactoring regularization are significantly more effective than MLPs and classical machine learning models. In addition, all models performed better on female adolescents than on male adolescents. Further analysis of relevant characteristics in different populations revealed that this may be related to gender differences in underlying fluid intelligence mechanisms. Conclusions: We construct a weak but stable correlation between brain structural features and raw fluid intelligence using autoencoders. Future research may need to explore ensemble regression strategies utilizing multiple machine learning algorithms on multimodal data in order to improve the predictive performance of fluid intelligence based on neuroimaging features.

Multi-instance learning attention model for amyloid quantification of brain sub regions in longitudinal cognitive decline.

Amyloid PET scans help in identifying the beta-amyloid deposition in different brain regions. The purpose of this study is to develop a deep learning model that can automate the task of finding amyloid deposition in different regions of the brain only by using PET scan and without the corresponding MRI scan. 2647 18F-Florbetapir PET scans are collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) from multiple centres taken over a period. A deep learning model based on multi-instance learning and attention is proposed which is trained and validated using 80% of the scans and the remaining 20% of the scans are used for testing the model. The performance of the model is validated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The proposed model is further tested upon an external dataset consisting of 1413 18F-Florbetapir PET scans from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study. The proposed model achieves MAE of 0.0243 and RMSE of 0.0320 for summary Standardized Uptake Value Ratio (SUVR) based on composite reference region for ADNI test set. When tested on the A4-study dataset, the proposed model achieves MAE of 0.038 and RMSE of 0.0495 for summary SUVR based on the composite region. The results show that the proposed model provides less MAE and RMSE when compared with existing models. A graphical user interface is developed based on the proposed model where the predictions are made by selecting the files of 18F-Florbetapir PET scans.

Divergent beliefs about food safety and affordability in Nigeria.

Access to safe, affordable diets is paramount for improved nutritional outcomes. Yet, how do stakeholders perceive the binding constraints and requisite policy actions to increase food safety and affordability? Focusing on Nigeria, this paper uses best-worst scaling techniques applied to a survey of 200 government and agrifood system stakeholders to examine their policy beliefs on safety and affordability vis-à-vis the vegetable and fish value chains. We find that divergence among stakeholders is greater for food safety than affordability. While antibiotics overuse and toxin exposure, lack of knowledge, and weak legislation were identified by different stakeholders as the binding constraints for food safety, high costs of inputs and infrastructure, as well as security threats, were seen as common challenges for affordability across most, though not all, stakeholders for both value chains. Overall, the paper highlights the importance of beliefs in the agrifood system policymaking process and emphasizes the need to explore not only the existence but also the source of divergent beliefs among policy actors in greater depth.

What defines the "value" of robotic surgery for patients with gastrointestinal cancers? Perspectives from a U.S. Cancer Center.

The use of robotic surgery has experienced rapid growth across diverse medical conditions, with a notable emphasis on gastrointestinal cancers. The advanced technologies incorporated into robotic surgery platforms have played a pivotal role in enabling the safe performance of complex procedures, including gastrectomy and pancreatectomy, through a minimally invasive approach. However, there exists a noteworthy gap in high-level evidence demonstrating that robotic surgery for gastric and pancreatic cancers has substantial benefits compared to traditional open or laparoscopic methods. The primary impediment hindering the broader implementation of robotic surgery is its cost. The escalating healthcare expenses in the United States have prompted healthcare providers and payors to explore patient-centered, value-based healthcare models and reimbursement systems that embrace cost-effectiveness. Thus, it is important to determine what defines the value of robotic surgery. It must either maintain or enhance oncological quality and improve complication rates compared to open procedures. Moreover, its true value should be apparent in patients' expedited recovery and improved quality of life. Another essential aspect of robotic surgery's value lies in minimizing or even eliminating opioid use, even after major operations, offering considerable benefits to the broader public health landscape. A quicker return to oncological therapy has the potential to improve overall oncological outcomes, while a speedier return to work not only alleviates individual financial distress but also positively impacts societal productivity. In this article, we comprehensively review and summarize the current landscape of health economics and value-based care, with a focus on robotic surgery for gastrointestinal cancers.

Verification of the termination of resuscitation rules in pediatric out-of-hospital cardiac arrest cases.

Aim: Pediatric out-of-hospital cardiac arrest has an unfavorable prognosis; therefore, making accurate predictions of outcomes is crucial for tailoring treatment plans. The termination of resuscitation rules must accurately predict unfavorable outcomes. In this study, we aimed to assess if the current termination of resuscitation rules for adults can predict factors associated with unfavorable outcomes in pediatric out-of-hospital cardiac arrest and examine the relationship between these factors and unfavorable outcomes. Methods: A retrospective nationwide cohort study of pediatric cases registered in the Japanese Association for Acute Medicine Multicenter Out-of-Hospital Cardiac Arrest Registry from June 1, 2014, to December 31, 2020, was conducted. The association between the current termination of resuscitation rules and outcomes, such as 30-day mortality and unfavorable 30-day neurological outcomes following out-of-hospital cardiac arrest, was evaluated. Results: A total of 1,216 participants were included. The positive predictive value for predicting 30-day mortality for each termination of resuscitation rule exceeded 0.9. The specificity and positive predictive value for predicting unfavorable 30-day neurological outcomes were 1.00, indicating that no rules identified favorable outcomes. Factors such as no bystander witness, no return of spontaneous circulation before hospital arrival, no automated external defibrillator or defibrillator use, and no bystander cardiopulmonary resuscitation were associated with poor 30-day mortality and neurological outcomes. Conclusion: Adult termination of resuscitation rules had a high positive predictive value for predicting pediatric out-of-hospital cardiac arrest. However, surviving cases make it challenging to use these rules for end-of-resuscitation decisions, indicating the need for identifying new rules to help predict neurological outcomes.

Factors influencing the reimbursement of cancer drugs in Europe: A scoping review.

RATIONALE: Reimbursement process of oncology drugs in Europe occurs within a complex decision-making process that varies between Member States. Distinctions between the States trigger societal debates since it is necessary to balance access to medicines and health systems sustainability. AIMS AND OBJECTIVES: We aimed to review the evidence concerning factors associated with the reimbursement decision or Health Technology Agency recommendation of oncology drugs in Europe. METHODS: A systematic literature search was performed in two databases from inception to august 2023. Screening and data extraction were performed by pairs. RESULTS: Thirteen articles were included and encompassed data from 11 nations. Seven articles showed that cost-effective (C-E) drugs and lower Incremental Cost-Effectiveness Ratios (ICERs) had higher likelihood of reimbursement. Disease severity might influence the reimbursement decision with financial agreements. Improvement in clinical outcomes, substantial clinical benefit (p < 0.01) or overall survival gains (p < 0.05) were positively associated. Orphan drug designation impact varies between countries but positive decisions are usually achieved under specific conditions. Clinical and C-E uncertainty frequently led to reimbursement with financial agreements or outcomes-based conditions. Sociodemographic factors as: social health insurance system, higher Gross Domestic Product and larger elderly population were positively associated with reimbursement (p < 0.01). CONCLUSION: There is a need for further research into key determinants of reimbursement decisions in Europe and the development of drug access models that can effectively address and overcome costs and effectiveness uncertainties.