Acoustic-based rule out of stable coronary artery disease: the FILTER-SCAD trial.

BACKGROUND AND AIMS: Overtesting of low-risk patients with suspect chronic coronary syndrome (CCS) is widespread. The acoustic-based coronary artery disease (CAD) score has superior rule-out capabilities when added to pre-test probability (PTP). FILTER-SCAD tested whether providing a CAD score and PTP to cardiologists was superior to PTP alone in limiting testing. METHODS: At six Danish and Swedish outpatient clinics, patients with suspected new-onset CCS were randomised to either standard diagnostic examination (SDE) with PTP, or SDE plus CAD score, and cardiologists provided with corresponding recommended diagnostic flowcharts. The primary endpoint was cumulative number of diagnostic tests at one year and key safety endpoint major adverse cardiac events (MACE). RESULTS: In total 2008 patients (46% male, median age 63 years) were randomised from October 2019 to September 2022. When randomised to CAD score (n=1002), it was successfully measured in 94.5%. Overall, 13.5% had PTP ≤5%, and 39.5% had CAD score ≤20. Testing was deferred in 22% with no differences in diagnostic tests between groups (p for superiority =0.56). In the PTP ≤5% subgroup, the proportion with deferred testing increased from 28% to 52% (p<0.001). Overall MACE was 2.4 per 100 person-years. Non-inferiority regarding safety was established, absolute risk difference 0.4% (95% CI -1.85 to 1.06) (p for non-inferiority = 0.005). No differences were seen in angina-related health status or quality of life. CONCLUSIONS: The implementation strategy of providing cardiologists with a CAD score alongside SDE did not reduce testing overall but indicated a possible role in patients with low CCS likelihood. Further strategies are warranted to address resistance to modifying diagnostic pathways in this patient population.

Predicting Drug-Target Interactions by Measuring Confidence with Consistent Causal Neighborhood Interventions.

Predicting drug-target interactions (DTI) is a crucial stage in drug discovery and development. Understanding the interaction between drugs and targets is essential for pinpointing the specific relationship between drug molecules and targets, akin to solving a link prediction problem using information technology. While knowledge graph (KG) and knowledge graph embedding (KGE) methods have been rapid advancements and demonstrated impressive performance in drug discovery, they often lack authenticity and accuracy in identifying DTI. This leads to increased misjudgment rates and reduced efficiency in drug development. To address these challenges, our focus lies in refining the accuracy of DTI prediction models through KGE, with a specific emphasis on causal intervention confidence measures (CI). These measures aim to assess triplet scores, enhancing the precision of the predictions. Comparative experiments conducted on three datasets and utilizing 9 KGE models reveal that our proposed confidence measure approach via causal intervention, significantly improves the accuracy of DTI link prediction compared to traditional approaches. Furthermore, our experimental analysis delves deeper into the embedding of intervention values, offering valuable insights for guiding the design and development of subsequent drug development experiments. As a result, our predicted outcomes serve as valuable guidance in the pursuit of more efficient drug development processes.

The glycemic gap as a prognostic indicator in cardiogenic shock: a retrospective cohort study.

BACKGROUND: Stress-induced hyperglycemia (SIH) is associated with poor outcomes in cardiogenic shock (CS), and there have been inconsistent results among patients with or without diabetes mellitus (DM). The glycemic gap (GG) is derived by subtracting A1c-derived average glucose from blood glucose levels; it is a superior indicator of SIH. We aimed to explore the role of GG in the outcomes of patients with CS. METHODS: Data on patients diagnosed with CS were extracted from the MIMIC-IV v2.0 database to investigate the relationship between GG and 30-day mortality (Number of absolute GG subjects = 359; Number of relative GG subjects = 357). CS patients from the Second Affiliated Hospital of Wenzhou Medical University were enrolled to explore the correlation between GG and lactic acid (Number of absolute GG subjects = 252; Number of relative GG subjects = 251). Multivariate analysis, propensity score-matched (PSM) analysis, inverse probability treatment weighting (IPTW), and Pearson correlation analysis were applied. RESULTS: Absolute GG was associated with 30-day all-cause mortality in CS patients (HRadjusted: 1.779 95% CI: 1.137-2.783; HRPSM: 1.954 95% CI: 1.186-3.220; HRIPTW: 1.634 95% CI: 1.213-2.202). The higher the absolute GG level, the higher the lactic acid level (ßadjusted: 1.448 95% CI: 0.474-2.423). A similar trend existed in relative GG (HRadjusted: 1.562 95% CI: 1.003-2.432; HRPSM: 1.790 95% CI: 1.127-2.845; HRIPTW: 1.740 95% CI: 1.287-2.352; ßadjusted:1.294 95% CI: 0.369-2.219). Subgroup analysis showed that the relationship existed irrespective of DM. The area under the curve of GG combined with the Glasgow Coma Scale (GCS) for 30-day all-cause mortality was higher than that of GCS (absolute GG: 0.689 vs. 0.637; relative GG: 0.688 vs. 0.633). GG was positively related to the triglyceride-glucose index. Kaplan-Meier curves revealed that groups of higher GG with DM had the worst outcomes. The outcomes differed among races and GG levels (all P < 0.05). CONCLUSIONS: Among patients with CS, absolute and relative GGs were associated with increased 30-day all-cause mortality, regardless of DM. The relationship was stable after multivariate Cox regression analysis, PSM, and IPTW analysis. Furthermore, they reflect the severity of CS to some extent. Hyperlactatemia and insulin resistance may underlie the relationship between stress-induced hyperglycemia and poor outcomes in CS patients. They both improve the predictive efficacy of the GCS.

Electricity distribution networks resilience in area exposed to salt dust: Fragility curve modeling of insulators, Monte Carlo-based resilience assessment, and enhancement measures.

Resilience of the power system against natural disasters is a vital need for sustainable energy supply. As a result of global warming, lakes and rivers have dried out, resulting in dust hubs that threaten the normal operation of outdoor power system equipment. Unlike other events like hurricanes and blizzards, the impact of extreme salt dust on power system insulator failures and network resilience in affected areas remains unexamined. In this paper, to avoid power curtailment caused by insulators breakdown in electricity distribution networks, the resiliency assessment and enhancement of these networks against salt dust is investigated. Failure mechanism analysis of insulators and fragility curves extraction of them in face of salt pollution and relative humidity are done using mathematical modelling and experimental tests to extract the breakdown probability; Experimental tests are conducted in the High Voltage Laboratory, University of Tehran (HVLUT) and a novel method is proposed to extract 3-dimensional fragility curves of insulators. A Monte Carlo-based resiliency assessment method is then employed to obtain resiliency curve against the salt dust. Some suitable indicators are introduced for this purpose. In addition, several resiliency enhancement measures are proposed and ranked using a benefit to cost ratio (BCR) index. Numerical simulations are conducted on two real distribution feeders in a distribution grid around Urmia Salt Lake, Iran. Numerical results confirm the effectiveness and applicability of the proposed method.

Performance Evaluation of Structural Health Monitoring System Applied to Full-Size Composite Wing Spar via Probability of Detection Techniques.

Probability of detection (POD) is an acknowledged mean of evaluation for many investigations aiming at detecting some specific property of a subject of interest. For instance, it has had many applications for Non-Destructive Evaluation (NDE), aimed at identifying defects within structural architectures, and can easily be used for structural health monitoring (SHM) systems, meant as a compact and more integrated evolution of the former technology. In this paper, a probability of detection analysis is performed to estimate the reliability of an SHM system, applied to a wing box composite spar for bonding line quality assessment. Such a system is based on distributed fiber optics deployed on the reference component at specific locations for detecting strains; the attained data are then processed by a proprietary algorithm whose capability was already tested and reported in previous works, even at full-scale level. A finite element (FE) model, previously validated by experimental results, is used to simulate the presence of damage areas, whose effect is to modify strain transfer between adjacent parts. Numerical data are used to verify the capability of the SHM system in revealing the presence of the modeled physical discontinuities with respect to a specific set of loads, running along the beam up to cover its complete extension. The POD is then estimated through the analysis of the collected data sets, wide enough to assess the global SHM system performance. The results of this study eventually aim at improving the current strategies adopted for SHM for bonding analysis by identifying the intimate behavior of the system assessed at the date. The activities herein reported have been carried out within the RESUME project.

Performance Evaluation of UOWC Systems from an Empirical Channel Model Approach for Air Bubble-Induced Scattering.

Underwater optical wireless communication (UOWC) systems provide the potential to establish secure high-data-rate communication links in underwater environments. The uniqueness of oceanic impairments, such as absorption, scattering, oceanic turbulence, and air bubbles demands accurate statistical channel models based on empirical measurements for the development of UOWC systems adapted to different types of water and link conditions. Recently, generalized Gamma and a mixture of two generalized Gamma probability density functions (PDF) were proposed to describe the statistical behavior of small and large air bubbles, respectively, when considering several levels of particle-induced scattering. In this paper, we derive novel closed-form analytic expressions to compute the bit error rate (BER) and outage performance using both proposed PDFs for various scattering conditions. Furthermore, simple asymptotic expressions are obtained to determine the diversity order of each scenario. Monte Carlo simulation results verify the obtained theoretical expressions. Our results also reveal that UOWC systems present lower BER and outage performance under more turbid water cases with respect to the tap water case due to the higher diversity order and despite the significant increases in pathloss at short link distances. Particle-induced scattering provides an inherent mechanism of turbid waters to mitigate air bubble-induced fluctuations and light blockages.

Artificial intelligence for response prediction and personalisation in radiation oncology.

Artificial intelligence (AI) systems may personalise radiotherapy by assessing complex and multifaceted patient data and predicting tumour and normal tissue responses to radiotherapy. Here we describe three distinct generations of AI systems, namely personalised radiotherapy based on pretreatment data, response-driven radiotherapy and dynamically optimised radiotherapy. Finally, we discuss the main challenges in clinical translation of AI systems for radiotherapy personalisation.

Estimation of time-varying kernel densities and chronology of the impact of COVID-19 on financial markets.

The time-varying kernel density estimation relies on two free parameters: the bandwidth and the discount factor. We propose to select these parameters so as to minimize a criterion consistent with the traditional requirements of the validation of a probability density forecast. These requirements are both the uniformity and the independence of the so-called probability integral transforms, which are the forecast time-varying cumulated distributions applied to the observations. We thus build a new numerical criterion incorporating both the uniformity and independence properties by the mean of an adapted Kolmogorov-Smirnov statistic. We apply this method to financial markets during the onset of the COVID-19 crisis. We determine the time-varying density of daily price returns of several stock indices and, using various divergence statistics, we are able to describe the chronology of the crisis as well as regional disparities. For instance, we observe a more limited impact of COVID-19 on financial markets in China, a strong impact in the US, and a slow recovery in Europe.

Reduction scenarios of plastic waste emission guided by the probability distribution model to avoid additional ocean plastic pollution by 2050s.

Marine plastic pollution is progressing worldwide and will become increasingly serious if plastic waste emissions continue at the current rate or increase with economic growth. Here, we report a particle tracking-based probability distribution model for predicting the abundances of marine macroplastics and microplastics, which undergo generation, transport, and removal processes in the world's upper ocean, under various scenarios of future land-to-sea plastic waste emissions. To achieve the Osaka Blue Ocean Vision, which aims to reduce additional pollution by marine plastic litter to zero by 2050, plastic waste emission in ∼2035 should be reduced by at least 32 % relative to 2019. It is necessary to take stringent measures such as 'system change scenario' or 'improve waste management scenario' proposed in previous studies to reduce the marine plastic pollution by 2050.

Bayesian Predictive Probability Based on a Bivariate Index Vector for Single-Arm Phase II Study With Binary Efficacy and Safety Endpoints.

In oncology, Phase II studies are crucial for clinical development plans as such studies identify potent agents with sufficient activity to continue development in the subsequent Phase III trials. Traditionally, Phase II studies are single-arm studies, with the primary endpoint being short-term treatment efficacy. However, drug safety is also an important consideration. In the context of such multiple-outcome designs, predictive probability-based Bayesian monitoring strategies have been developed to assess whether a clinical trial will provide enough evidence to continue with a Phase III study at the scheduled end of the trial. Therefore, we propose a new simple index vector to summarize the results that cannot be captured by existing strategies. Specifically, we define the worst and most promising situations for the potential effect of a treatment, then use the proposed index vector to measure the deviation between the two situations. Finally, simulation studies are performed to evaluate the operating characteristics of the design. The obtained results demonstrate that the proposed method makes appropriate interim go/no-go decisions.

CT Pulmonary Angiogram Clinical Pretest Probability Tool: Impact on Emergency Department Utilization.

OBJECTIVE: Currently, computed tomographic pulmonary angiogram (CTPA) for evaluating acute pulmonary embolism (PE) in Emergency Departments (EDs) is overused and with low yields. The goal of this study is to assess the impact of an evidence-based clinical decision support (CDS) tool, aimed at optimizing appropriate use of CTPA for evaluating PE. METHODS: The study was performed at EDs in a large healthcare system and included 9 academic and community hospitals. The primary outcome was the percent difference in utilization (number of CTPA performed/number of ED visits) and secondary outcome was yield (percentage of CTPA positive for acute PE), comparing 12 months before (6/1/2021-5/31/2022) vs. 12 months after (6/1/2022-5/31/2023) a system-wide implementation of the CDS. Univariate and multivariable analyses using logistic regression were performed to assess factors associated with diagnosis of acute PE. Statistical process control (SPC) charts were used to assess monthly trends in utilization and yield. RESULTS: Among 931,677 visits to Emergency Departments, 28,101 CTPAs were performed on 24,675 patients. 14,825 CTPAs were performed among 455,038 visits (3.26%) pre-intervention; 13,276 among 476,639 visits (2.79%) post-intervention, a 14.51% relative decrease in CTPA utilization (chi-square, p<0.001). CTPA yield remained unchanged (1371/14825=9.25% pre- vs. 1184/13276=8.92% post-intervention; chi-square, p=0.34). Patients with COVID diagnosis prior to CTPA had higher probability of acute PE. SPC charts demonstrated seasonal variation in utilization (Friedman test, p=0.047). DISCUSSION: Implementing a CDS based on validated decision rules was associated with a significant reduction in CTPA utilization. The change was immediate and sustained for 12 months post-intervention.

The effect of volatility in linguistic input on prediction behavior in autistic toddlers.

Domain-general prediction differences have been posited as underlying many aspects of the cognitive-behavioral profile in autism. An interesting potential implication of such differences is hyperplasticity of learning-the idea that autistic individuals may privilege more recent input over the accumulation of prior learning. Because real world language input is highly variable, hyperplasticity could have serious ramifications for language learning. To investigate potential hyperplasticity during a language processing task, we administered an experimental anticipatory eye movement (AEM) task to 2- to 3-year-old autistic children and neurotypical (NT) peers. Autistic children's change in anticipation from before to after a switch in contingencies did not significantly differ from NT counterparts, failing to support claims of hyperplasticity in the linguistic domain. Analysis of individual differences among autistic children revealed that cognitive ability was associated with prediction of the initial, stable contingencies, but neither age nor receptive language related to task performance. Results are discussed in terms of clinical implications and the broader context of research investigating prediction differences in autism.

Survival Outcome Superiority of Total Pharyngolaryngectomy Compared with Chemoradiotherapy for T4aM0 Hypopharyngeal Squamous Cell Carcinoma: A Nationwide Database Study of Japan.

BACKGROUND: The purpose of this study is to elucidate whether total pharyngolaryngectomy (TPL) or chemoradiotherapy (CRT) provides a better prognostic outcome in patients with T4aM0 hypopharyngeal carcinoma (HPSCC) using a nationwide database. METHODS: All data were obtained from the Head and Neck Cancer Registry of Japan, and information from patients who were newly diagnosed with T4aM0 HPSCC between 2011 and 2015 was extracted. The primary endpoint was disease-specific survival (DSS), and the secondary endpoint was overall survival (OS). The inverse probability of treatment weighting (IPTW) adjustments was used for survival analyses. RESULTS: Our cohort included 1143 patients. The TPL and CRT groups included 724 and 419 patients, respectively. Following IPTW adjustments, both the OS and DSS of the TPL group were significantly longer than those of the CRT group (P = .02 and P = .002, respectively). CONCLUSIONS: Survival superiority was demonstrated for patients with T4aM0 HPSCC treated with TPL compared with those treated with CRT.

Range aware message drop policy for delay tolerant networks.

In delay tolerant networks (DTNs) the messages are often not delivered to the destination due to a lack of end-to-end connectivity. In such cases, the messages are stored in the buffer for a long time and are transmitted when the nodes come into the range of each other. The buffer size of each node has a limited capacity, and it cannot accommodate the new incoming message when the buffer memory is full, and as a result network congestion occurs. This leads to a low delivery probability and thus increases the overhead ratio. In this research work, a new buffer management scheme called Range Aware Drop (RAD) is proposed which considers metrics such as message size and time to live (TTL). RAD utilizes TTL as an important metric and as a result, reduces the unnecessary message drop. Simulation results reveal that RAD performs significantly better than drop oldest (DOA) and size aware drop (SAD) in terms of delivery probability and overhead ratio. The obtained results also revealed that the hop-count average of SAD is 3.9 and DOA is 3.4 while the hop-count average of RAD is just 1.7. Also, the message drop ratio of the RAD is 36.2% while SAD and DOA have message drop ratios of 73.3% and 84.9% respectively.

Adult mortality from non-communicable diseases in Fiji's major ethnic groups 2013-17.

Background: Sustainable Development Goal 3.4.1 (SDG3.4.1) targets a one-third reduction in non-communicable disease (NCD) mortality in ages 30-69-years by 2030 (relative to 2015). Directing interventions to achieve this aim requires reliable estimates of underlying cause of death (UCoD). This may be problematic when both cardiovascular diseases (CVD) and diabetes are present due to a lack of consistency in certification of such deaths. We estimate empirically 2013-17 NCD mortality in Fiji, by sex and ethnicity, from CVD, diabetes, cancer, and chronic lower respiratory diseases (CRD), and aggregated as NCD4. Methods: UCoD was determined from Medical Certificates of Cause-of-Death (MCCD) from the Fiji Ministry of Health after pre-processing of mortality data where diabetes and/or hypertension were present in order to generate internationally comparable UCoD. If no potentially fatal complications from diabetes or hypertension accompanied these causes in Part I (direct cause) of the MCCD, these conditions were re-assigned to Part II (contributory cause). The probability of a 30-year-old dying before reaching age 70-years (PoD30-70), by cause, was calculated. Findings: The PoD30-70 from NCD4 over 2013-17 differed by sex and ethnicity: in women, it was 36% (95%CI 35-37%) in i-Taukei and 27% (26-28%) in Fijians of Indian descent (FID); in men, it was 41% (40-42%) in both i-Taukei and FID.PoD30-70 from CVD, diabetes, cancer and CRD in women was: 18%, 10%, 13% and 1·0% in i-Taukei; 13%, 10%, 5·6% and 1·1% in FID; in men was: 28%, 8.4%, 7·6% and 2·2% in i-Taukei; 31%, 8.3%, 3.5% and 3·1% in FID. Interpretation: To achieve SDG3.4.1 goals in Fiji by 2030, effective population wide and ethnic-specific interventions targeting multiple NCDs are required to reduce PoD30-70 from NCD4: from 36% to 24% in i-Taukei, and 27% to 18% in FID women; and from 41% to 27% in i-Taukei and FID men. Funding: Not applicable.

External validation of and improvement upon a model for the prediction of placenta accreta spectrum severity using prospectively collected multicenter ultrasound data.

INTRODUCTION: This study aimed to validate the Sargent risk stratification algorithm for the prediction of placenta accreta spectrum (PAS) severity using data collected from multiple centers and using the multicenter data to improve the model. MATERIAL AND METHODS: We conducted a multicenter analysis using data collected for the IS-PAS database. The Sargent model's effectiveness in distinguishing between abnormally adherent placenta (FIGO grade 1) and abnormally invasive placenta (FIGO grades 2 and 3) was evaluated. A new model was developed using multicenter data from the IS-PAS database. RESULTS: The database included 315 cases of suspected PAS, of which 226 had fully documented standardized ultrasound signs. The final diagnosis was normal placentation in 5, abnormally adherent placenta/FIGO grade 1 in 43, and abnormally invasive placenta/FIGO grades 2 and 3 in 178. The external validation of the Sargent model revealed moderate predictive accuracy in a multicenter setting (C-index 0.68), compared to its higher accuracy in a single-center context (C-index 0.90). The newly developed model achieved a C-index of 0.74. CONCLUSIONS: The study underscores the difficulty in developing universally applicable PAS prediction models. While models like that of Sargent et al. show promise, their reproducibility varies across settings, likely due to the interpretation of the ultrasound signs. The findings support the need for updating the current ultrasound descriptors and for the development of any new predictive models to use data collected by different operators in multiple clinical settings.

Group sequential designs for clinical trials when the maximum sample size is uncertain.

Motivated by the experience of COVID-19 trials, we consider clinical trials in the setting of an emerging disease in which the uncertainty of natural disease course and potential treatment effects makes advance specification of a sample size challenging. One approach to such a challenge is to use a group sequential design to allow the trial to stop on the basis of interim analysis results as soon as a conclusion regarding the effectiveness of the treatment under investigation can be reached. As such a trial may be halted before a formal stopping boundary is reached, we consider the final analysis under such a scenario, proposing alternative methods for when the decision to halt the trial is made with or without knowledge of interim analysis results. We address the problems of ensuring that the type I error rate neither exceeds nor falls unnecessarily far below the nominal level. We also propose methods in which there is no maximum sample size, the trial continuing either until the stopping boundary is reached or it is decided to halt the trial.

A Bayesian Analysis of the Probability of the Origin of Life Per Site Conducive to Abiogenesis.

The emergence of life from nonlife, or abiogenesis, remains a fundamental question in scientific inquiry. In this article, we investigate the probability of the origin of life (per conducive site) by leveraging insights from Earth's environments. If life originated endogenously on Earth, its existence is indeed endowed with informative value, although the interpretation of the attendant significance hinges critically upon prior assumptions. By adopting a Bayesian framework, for an agnostic prior, we establish a direct connection between the number of potential locations for abiogenesis on Earth and the probability of life's emergence per site. Our findings suggest that constraints on the availability of suitable environments for the origin(s) of life on Earth may offer valuable insights into the probability of abiogenesis and the frequency of life in the universe.

Sex-specific associations between habitual snoring and cancer prevalence: insights from a US Cohort Study.

Study Objectives: To investigate the sex-specific association between habitual snoring and overall cancer prevalence and subtypes, and to examine the influence of age, body mass index (BMI), and sleep duration on this association. Methods: This study utilized data from the National Health and Nutrition Examination Survey cycles between 2005 and 2020 and included 15 892 participants aged 18 and over. We employed inverse probability of treatment weighting based on propensity scores to adjust for confounders when comparing the prevalence of cancer between habitual snorers and non-habitual snorers for each sex and cancer type. Subgroup analyses were conducted based on sleep duration, age, and BMI categories. Results: The cohort (mean age 48.2 years, 50.4% female, and 30.5% habitual snorers) reported 1385 cancer cases. In men, habitual snoring was linked to 26% lower odds of any cancer (OR 0.74, 95% CI: 0.66 to 0.83), while in women, it showed no significant difference except lower odds of breast cancer (OR 0.77, 95% CI: 0.63 to 0.94) and higher odds of cervix cancer (OR 1.54, 95% CI: 1.18 to 2.01). Age and sleep duration significantly influenced the snoring-cancer relationship, with notable variations by cancer type and sex. Conclusions: Habitual snoring exhibits sex-specific associations with cancer prevalence, showing lower prevalence in men and varied results in women. These findings emphasize the critical need for further research to uncover the biological mechanisms involved. Future investigations should consider integrating sleep characteristics with cancer prevention and screening strategies, focusing on longitudinal research and the integration of genetic and biomarker analyses to fully understand these complex relationships.