Low-risk Individuals Research Articles

Predicting vulnerable coronary arteries: a combined radiomics-biomechanics approach

Background and ObjectiveNowadays, vulnerable coronary plaque detection from coronary computed tomography angiography (CCTA) is suboptimal, although being crucial for preventing major adverse cardiac events. Moreover, despite the suggestion of various vulnerability biomarkers, encompassing image and biomechanical factors, accurate patient stratification remains elusive, and a comprehensive approach integrating multiple markers is lacking. To this aim, this study introduces an innovative approach for assessing vulnerable coronary arteries and patients by integrating radiomics and biomechanical markers through machine learning methods. MethodsThe study included 40 patients (7 high-risk and 33 low-risk) who underwent both CCTA and coronary optical coherence tomography (OCT). The dataset comprised 49 arteries (with 167 plaques), 7 of which (with 28 plaques) identified as vulnerable by OCT. Following image preprocessing and segmentation, CCTA-based radiomic features were extracted and a finite element analysis was performed to compute the biomechanical features. A novel machine learning pipeline was implemented to stratify coronary arteries and patients. For each stratification task, three independent predictive models were developed: a radiomic, a biomechanical and a combined radiomic-biomechanical model. Both k-nearest neighbors (KNN) and decision tree (DT) classifiers were considered. ResultsThe best radiomic model (KNN) detected all 7 vulnerable arteries and patients and was associated with a balanced accuracy of 0.86 (sensitivity=1, specificity=0.71) for the artery model and of 0.83 (sensitivity=1, specificity=0.67) for the patient model. The best biomechanical model (DT) detected 6 over 7 vulnerable arteries and patients and remarkably increased the specificity, resulting in a balanced accuracy of 0.89 (sensitivity=0.86, specificity=0.93) for the artery model and of 0.88 (sensitivity=0.86, specificity=0.91) for the patient model. Notably, the combined approach optimized the performance, with an increase in the balance accuracy up to 0.94 for the artery model and up to 0.92 for the patient model, being associated with sensitivity=1 and high specificity (0.88 and 0.85 for artery and patient models, respectively). ConclusionThis investigation highlights the promise of radio-mechanical coronary artery phenotyping for patient stratification. If confirmed from larger studies, our approach enables a more personalized management of the disease, with the early identification of high-risk individuals and the reduction of unnecessary interventions for low-risk individuals.

Foreign body aspiration through the eyes of a pediatric pulmonologist: Is it possible to reduce the rate of negative rigid bronchoscopies?

Identifying a foreign body aspiration (FBA) still remains a diagnostic difficulty. Moreover, the indications for bronchoscopy in subjects of suspected foreign bodies are not clear. The aim of this study was to evaluate the effectiveness of pediatric pulmonologists in diagnosing FBA. This was a retrospective, single-center study on children who underwent rigid bronchoscopy for suspected FBA. Data on the patients were obtained from the medical records. Patients who had foreign bodies (FB) identified during rigid bronchoscopy were classified as FB positive, and those in whom rigid bronchoscopy did not detect FB were defined as FB negative. Demographic data as well as consultation status with a pediatric pulmonologist were compared between these two groups. Furthermore, the patients were categorized into three groups based on their clinical scores that assessed the likelihood of the presence of FB: low risk, moderate risk, and high risk. Out of 474 rigid bronchoscopies, 232 (48.9%) detected FB. Consultation by a pediatric pulmonologist was not requested in 388 (81.8%). Out of these 388 patients, 206 (53%) were negative for FB. In terms of FB detection success, there was no difference between individuals who sought pulmonology consultation and those who did not (58.1% vs. 53.1% respectively, p=0.059). However, when the children were categorized based on their risk levels, the incidence of detecting FB among children in low-risk group was 42% when they received consultation from the pulmonology department, whereas this incidence dropped to 5.6% when pulmonology consultation was not sought (p<0.001). Consulting a pediatric pulmonologist, particularly for low-risk individuals, might reduce the likelihood of performing unnecessary bronchoscopies. Given that rigid bronchoscopy is an intrusive technique, it is crucial to reduce the number of negative bronchoscopies in order to mitigate complications associated with it.

MS O08 - HPB Biomarkers for risk stratification of Intraductal Papillary Mucinous Neoplasm of the pancreas: A systematic review

Abstract Background Intraductal Papillary Mucinous Neoplasm (IPMN) of the pancreas is the precursor lesion from which pancreatic ductal adenocarcinoma (PDAC) arises in 10% of individuals. IPMNs may be associated with low-grade or high-grade dysplasia or an associated adenocarcinoma. Accurately determining low-risk from high-risk IPMN is crucial for early detection initiatives for PDAC, so treatment can be offered before invasive cancer in high-risk individuals but without causing harm to low-risk individuals. Currently, imaging forms the basis for stratification but this is costly, resource intense and is limited in accuracy. There is a need to develop biomarkers to improve the risk stratification of IPMN. Method A systematic review was conducted according to PRISMA-DTA guidelines by screening EMBASE, MEDLINE, CENTRAL and bibliographic reference lists for comparative studies. Studies were excluded if they were based on non-human subjects, not specific to IPMN, or determined IPMN from PDAC rather than low-grade from high-grade dysplasia in IPMN. Data extracted included study design, cohort demographics, specimen type, biomarker type, methodology for biomarker discovery, positive predictive value, negative predictive value, sensitivity, specificity or AUC value and were evaluated against the STARD checklist. Study quality was evaluated using the QUADAS-2 tool. Results We identified 1761 studies of which 88 were selected for analysis. Twenty-six studies identified biomarkers derived from serum/plasma, 25 from biopsy specimens, 18 from cystic fluid, 13 from radiological findings, 4 from pancreatic juice, 3 from clinical risk stratification and 1 urinary biomarker. Markers were based on proteins (43%), mRNA (1%), miRNA (14%), lncRNA (2%), DNA (16%), metabolites (1%), radiomics (19%), glycans (5%), inflammatory markers (5%) and clinical history (5%). We observed discriminatory values ranging from AUC 0.58-1, specificity 64-100% and sensitivity 56-90.9%. Conclusion This systematic review outlines the current evidence-base for potential biomarkers to determine low-risk from high-risk IPMNs of the pancreas. Current studies are limited by retrospective sample collection, limited cohort sizes and lack of external validation of candidate biomarkers. There is a need to develop and validate biomarkers in a longitudinal prospective cohort study of individuals undergoing IPMN screening with the ability to characterise low-risk to high-risk transformation which after external validation would be suitable for adoption into clinical practice.

Abstract 4134977: Utility of Coronary Artery Calcium Scoring in Low-Risk Patients: the Multi-Ethnic Study of Atherosclerosis (MESA)

Introduction: Coronary artery calcium (CAC) scoring is a powerful tool for atherosclerotic cardiovascular disease (ASCVD) risk assessment. Guidelines recommend consideration of CAC scoring in intermediate risk patients, but it’s utility in low and borderline risk patients is less clear. Methods: We used data from 2,894 participants from MESA with low (&lt;5%) or borderline (5-&lt;7.5%) 10-year ASCVD risk defined by the pooled cohort equations (PCE). We evaluated the association between CAC score and coronary heart disease (CHD) and ASCVD risk using multivariable adjusted Cox proportional hazard models. We also evaluated if the addition of CAC to the pooled cohort equations (PCE) improved risk prediction using Harrell’s C-index and net reclassification improvement (NRI). Results/Data: Mean age was 54.1 ± 5.8 years with 66.9% women. Among low-risk individuals, the ASCVD event rate was 3.3% with CAC=0, and 7.8% with CAC&gt;0, and 4.7% vs. 11.4% among borderline-risk individuals. Natural-log transformed CAC score was associated with increased ASCVD risk in low-risk (HR 1.35, 95% CI 1.22-1.50) and borderline-risk (HR 1.29, 95% CI 1.15-1.45) individuals. Similar results were seen for CHD with higher HRs ( Table 1 ). The addition of CAC to the PCE improved the C-index (SE) among low/borderline risk individuals from 0.620 (0.023) to 0.671 (0.024). Continuous NRI was also significant with the addition of CAC to the PCE in low/borderline risk (0.422, 95% CI 0.248-0.611). Greater improvement was seen for CHD ( Table 2 ). Conclusions: The presence of CAC in low and borderline risk individuals is associated with increased ASCVD and CHD risk. The addition of CAC scoring to the PCE improved risk prediction in low and borderline risk individuals, suggesting potential clinical utility in this population.

Prediction of high-grade cervical precancerous abnormalities: The role of personal factors, vaginal microflora, sexually transmitted infections, and high-risk human papillomavirus.

High-risk human papillomavirus infection (HR-HPV) is necessary but not the only factor needed to develop cervical cancer. It is essential to estimate cervical cancer development risk in the population of high-risk HPV-positive women and to avoid unnecessary examinations and treatment in low-risk individuals. The study aimed to identify associations between different personal factors, vaginal microflora, sexually transmitted, high-risk HPV infection, and various degrees of cervical precancerous lesions. A study was performed in 2016-2020. The study group consisted of 112 patients with abnormal cervical cytology results referred for colposcopic examination. 120 women who came for a routine gynecological check-up were included in the control group. Material from the cervix and upper vaginal fornix was taken for pH measurement, wet mount microscopy, testing the six most common high-risk HPV DNA types (16/18, 31, 33, 45, 58), HPV E6/E7 mRNA, and 7 genital infections-C. trachomatis, N. gonorrhea, T. vaginalis, M. hominis, M. genitalium, U. urealyticum, U. parvum. Results showed that women with all grades of cervical intraepithelial neoplasia (CIN) more often were smokers, had increased vaginal pH levels, and had positive HR-HPV DNA and HR HPV E6/E7 mRNA expression. Abnormal vaginal microflora, especially types associated with aerobic vaginitis, and M. hominis were significantly more often found in women with CIN2+. The presence of C.trachomatis, U. parvum, and U.urealyticum did not differ between the groups. The most important factors independently associated with CIN2+ were positive high-risk HPV E6/E7 mRNA expression (OR 59.4, 95% CI 14.84-237.51), and positive high-risk HPV DNA (OR 3.9, 95% CI 1.16-13.23). Higher education level was associated with reduced risk of CIN2+ (OR 0.2, 95% CI 0.07-0.71). In conclusion, this study reports HR-HPV DNA of the most common six types and E6/E7 mRNA positivity as the most significant factors associated with CIN2+ lesions and higher education related to lower risk of high-grade cervical lesions.

Low-Risk Individuals Should Not Currently Be Screened for Lung Cancer

Low-Risk Individuals Should Not Currently Be Screened for Lung Cancer

Development and Validation of an Individualized Nomogram for Predicting Patent Foramen Ovale-Associated Stroke: Patent Foramen Ovale Morphology-Based Analysis.

We previously reported four patent foramen ovale (PFO) morphological types that influenced right-to-left shunt (RLS) grades. Herein, we aimed to study the relationship between PFO morphology and cryptogenic stroke (CS). We further developed a nomogram based on four PFO morphological types and functional parameters to guide clinicians in judging the risk of PFO-associated stroke. This was a retrospective observational study involving adult patients with PFO between January 2020 and November 2022. Patients were divided into a PFO-associated stroke group (CS group) and a group without cryptogenic stroke (non-CS group). Four types of PFO and RLS grades were analyzed. Nomograms were made to predict PFO-associated stroke using multivariable logistic regression analysis. The discrimination performance of the model was internally validated and assessed using the receiver operating characteristic. We enrolled 389 patients (male, 182 patients; female, 207 patients) with PFO, the mean age was 43.3±8.1 years. The derivation cohort comprised 293 patients (CS group, 186 patients; non-CS group, 107 patients). The predictive nomogram comprised PFO morphological types, interatrial septum (IAS) mobility distance, septum primum thickness, PFO channel length at rest, and contrast-transthoracic echocardiography (c-TTE) RLS grade during the Valsalva maneuver. A validation cohort was established (CS group, 61 patients; non-CS group, 35 patients). The model area under the curve (AUC) was 0.891 (95% confidence interval = 0.855-0.928) in the derivation cohort and 0.935 (95% confidence interval = 0.885-0.986) in the validation cohort. Calibration curve analysis showed that the nomogram had a C-index of 0.891 in the derivation cohort and 0.935 in the validation cohort. The decision curve analysis (DCA) indicated that the nomogram had clinical applicability. Adding four PFO morphological types improved the risk stratification capability for PFO-associated stroke. The nomogram can identify high or low-risk PFO individuals and select patients who will likely benefit from interventional device closure.

Hospital acquired pressure injuries prevalence and preventive measures in Omani critical care units: A multicenter cross-sectional study

BackgroundPressure Injuries are a prevalent and concerning issue in critical care settings, impacting patient well-being and healthcare systems. PurposeMeasure the point prevalence of acquired pressure injuries in Omani critical care units and assess the adequacy of preventive measures. MethodsA multicenter cross-sectional survey was conducted in four major Omani hospitals, encompassing various geographical regions and health sectors. A convenient sample of 156 adult patients in critical care units was examined using the European Pressure Ulcer Advisory Panel methodology. Data on prevalence, risk assessment, and prevention measures were collected and analyzed. ResultsThe study revealed a prevalence rate of 21.8 % for hospital-acquired pressure injuries (including stage I) and 19.2 % (excluding stage I) in Omani critical care units. The sacrum was the most affected area, with stage II being predominant. Alarmingly, some at-risk patients did not receive appropriate preventive equipment, while low-risk patients received unnecessary measures. Approximately 73.3 % of at-risk patients received adequate prevention. ConclusionThis pioneering study in Oman addresses the prevalence of Pressure Injuries, revealing rates that are higher than international norms. Additionally, the research underscores deficiencies in preventive measures, such as inadequate prevention for at-risk patients and unnecessary measures for low-risk individuals.

Cardiovascular disease risk reduction for all-cause mortality benefit in low-risk individuals

Background: Current recommendations for the primary prevention of cardiovascular disease (CVD) are based solely on 10-year CVD risk assessment. However, CVD mortality is not compared with all-cause mortality, which could provide insights into the potential benefits of CVD risk interventions on overall mortality. Methods: CVD risk for average American white and black males and females is calculated using the 2013 American Heart Association equations. CVD death risk is determined by combining CVD risk with the death rate following a CVD diagnosis. This CVD death risk is then compared with all-cause mortality data from National Vital Statistics Reports. A larger ratio indicates that most deaths are attributed to CVD, suggesting that intervention may be more beneficial. Results: The CVD mortality ratio is highest for white men and lowest for black men, indicating that white male likely to benefit most from CVD risk modification, while black males will benefit the least. Additionally, the ratio, and the potential benefit, decreases with age for all groups, especially after the age of 70. Conclusion: For the average U.S. population without a CVD diagnosis, the potential mortality benefit from interventions targeting CVD risk factors varies significantly based on patient characteristics such as race, age, and gender. The potential benefit is greatest for white males and decreases with age for all groups.

Left ventricular pressure-strain loops for the assessment of myocardial work in a low-risk community-based cohort: associations with long-term prognosis

Abstract Background Non-invasive left ventricular (LV) pressure-strain loop analysis is a novel technique for the evaluation of global myocardial work (GMW) that provides additional insights on LV contractility and energetics beyond traditional metrics, such as ejection fraction (EF) and global longitudinal strain (GLS). Despite its established value in numerous cardiac diseases, there remains limited data on its predictive power in a low-risk population. Purpose Our objective was to determine the long-term prognostic value of GMW in a population-based screening sample comprising low-risk adult individuals. Methods We retrospectively identified 1562 volunteers from a population-based screening program (mean age 54±15 years, 59% female), with a median follow-up time of 11 years. All subjects underwent 2D echocardiography to measure LV EF, GLS, peak atrial longitudinal strain (PALS), GMW index (GMWI), global wasted work (GWW), and GMW efficiency (GMWE). The primary endpoint was all-cause mortality. Results 191 subjects (12,2%) met the primary endpoint. Subjects who experienced adverse outcomes exhibited significantly lower LV EF (dead vs. alive; 49.7±8.3 vs. 52.7 ±6.4 %, p&amp;lt;0.001), GLS (-18.7±4.1 vs. -20.4±3.6 %, p&amp;lt;0.001), PALS (33.6±15.7 vs. 44.2±15.2 %, p&amp;lt;0.001), and GMWI (2181.7±591.3 vs. 2286.8±482.5 mmHg%, p=0.008). Furthermore, subjects with adverse outcomes had significantly higher GWW (132.6±249.4 vs 95.1±113.4 mmHg%, p&amp;lt;0.001) and, consequently, lower GMWE (0.95±0.05 vs. 0.96±0.03, p&amp;lt;0.001). Univariate Cox regression analysis revealed GMWI to be a significant predictor of mortality (hazard ratio for 100 units 0.957 [95% CI: 0.929-0.986], p=0.004), alongside GMWE (hazard ratio for 1 percent 0.940 [95% CI: 0.912-0.968], p&amp;lt;0.001), and GWW (hazard ratio for 100 units 1.124 [95% CI: 1.063-1.188], p&amp;lt;0.001). By multivariable Cox regression encompassing age, sex, GMWI, LVEF, GLS, PALS, and systolic blood pressure, GMWI remained an independent predictor of all-cause mortality (hazard ratio 0.932 [95% CI: 0.874-0.995], p=0.035), while GLS was not an independent predictor. Moreover, integrating the Atherosclerosis Risk in Communities (ARIC)-HF risk factors (age, sex, race, coronary artery disease, antihypertensive medication, blood pressure, diabetes mellitus, body mass index, heart rate, and smoking status), we constructed a multivariate model with GMWI. GMWI was still an independent predictor of mortality (hazard ratio for 100 units 0.941 [95% CI: 0.903-0.980], p=0.004). Using the GMWI cut-off value of 1576 mm Hg% (based on a large-scale healthy population study), the population was divided into 2 groups. Using Kaplan-Meier curves (Figure), the outcomes of the groups differed significantly (log-rank p&amp;lt;0.001). Conclusions GMW provides added value in clinical outcome prediction, even in low-risk individuals. Myocardial work analysis is a promising approach for a comprehensive evaluation of cardiac function and for cardiovascular risk stratification.

Performance of the 2020 ACC/AHA and 2023 ESC risk stratification scales for primary prevention of sudden cardiac death in a Latin American population with hypertrophic cardiomyopathy

Abstract Background Identifying patients with hypertrophic cardiomyopathy (HCM) who are at high risk of sudden cardiac death (SCD) is essential for managing this condition. The American Heart Association/American College of Cardiology (AHA/ACC) and European Society of Cardiology (ESC) guidelines help assess and stratify the risk of determining which patients would benefit from primary prevention with an implantable cardioverter-defibrillator (ICD). Objective The study aims to evaluate the performance of the 2020 AHA/ACC and 2023 ESC risk stratification scales (AHA/ACC-rss; ESC-rss) for the primary prevention of SCD in Latin-American patients diagnosed with HCM. Methods A retrospective cohort of HCM from a single-center was analyzed. The inclusion criteria were individuals aged 16 years or older, without a history of SCD, and who had not undergone any treatment such as myomectomy or septal ablation with alcohol (Figure 1). The primary outcome was the identification of SCD or an equivalent event (successful resuscitation after cardiac arrest or appropriate therapy from the ICD). Results One hundred twenty patients were included (mean age 46.2 years old) and the mean follow-up duration was 61.1 months. From the total sample, 10 patients (8.3%) experienced an equivalent SCD event; 7 patients (5.8%) were ICD-protected and 3 (2.5%) required external defibrillation. The ESC-rss showed a greater area under the ROC curve (AUC, 0.79; 95% CI 0.62-0.94 vs AUC, 0.63; 95% CI 0.49-0.78) and specificity (85%; 95% CI 63-92 vs 51%; 95% CI 37.3-64.5). Sensitivity was similar between both scales (AHA/ACC-rss: 77%; 95% CI 50-100 vs ESC-rss: 73%; 95% CI 43-100). Remarkably, 2.5% of the patients who experienced an equivalent of SCD were incorrectly identified as low-risk individuals by either the AHA/ACC-rss, ESC-rss, or both (Table 1). Conclusion The SCD risk stratification recommended by the ESC-rss demonstrated a more effective identification of Latin American patients with HCM for ICD placement because the area under the curve was more significant than those of the AHA/ACC-rss. This single-center study highlights the need for a multicenter prospective study in Latin America.

Visual coronary artery calcium assessment on routine chest CT can readily and cheaply identify liver transplant recipients with a low cardiac risk

Abstract Background Peri- and postoperative cardiac complications play an important role in survival of patients after liver transplantation (LT). However, the optimal approach for cardiac risk prediction in these patients has been debated on. LT recipients routinely receive non-gated computed tomography (CT) of the abdomen and chest to follow-up on their liver diseases. This study analyzed whether performance of coronary artery calcium (CAC) assessment on freely available scans can enhance cardiac risk stratification of LT recipients. Methods Consecutive LT recipients from 2008-2023 were included and analyzed retrospectively. CAC was scored visually on non-gated CT on a semi-quantitative ordinal scale and stratified in two groups according to CAC severity. Peri-operative (≤90 days) and post-operative all-cause mortality and cardiac events were analyzed. Furthermore, all-cause mortality and cardiac events were analyzed and compared between the groups with a Kaplan Meier curve and Cox proportional hazard regression. Results A total of 149 patients were included with a median age of 58 years. The median follow-up time was 5 years. The majority (74%) of patients had no or mild CAC. None of these patients died perioperatively due to cardiac causes, or experienced peri-operative cardiac events. The post-operative 10-year survival of LT recipients with no or mild calcifications was significantly better (p&amp;lt;0.01) than patients with severe CAC. The 10-year cardiac event-free survival was significantly better in patients with no or mild CAC (p&amp;lt;0.01) and the incidence increased &amp;gt;4-fold from only 4% in patients with no-mild CAC to 18% in patients with severe CAC. Of interest, severe CAC was a significant predictor of cardiac events (HR 4.6, p=0.02), while classical cardiovascular risk factors e.g. diabetes were not. Conclusion CAC assessment on routinely available non-gated CT can readily enhance peri- and postoperative cardiac risk stratification of LT recipients, as it identifies a selection of low-risk individuals for having peri- and post-operative cardiac events and all-cause mortality. Thereby, reducing test burden for the patients and saving healthcare expenses.

Prediction of family inheritance in non-sarcomeric hypertrophic cardiomyopathy using clinical variables and 12-lead electrocardiogram artificial intelligence models

Abstract Background Hypertrophic cardiomyopathy (HCM) can be familial even in the absence of a causative sarcomeric variant, justifying periodical screening of first-degree relatives. However, indiscriminate screening may burden low-risk individuals unnecessarily. The prevalence and predictors of familial disease in non-sarcomeric HCM remain inadequately characterized. Purpose Our study aims to develop a tailored approach to family screening based on index cases’ clinical profiles. By leveraging artificial intelligence (AI) models, we aim to predict the likelihood of family disease using pertinent clinical variables and electrocardiograms (ECGs) from the proband. Methods From January 2008 to June 2022, all probands diagnosed with HCM at our Heart Institute genetics clinic were included if they had i) negative genetic testing for a (likely) pathogenic variant in a sarcomeric gene and ii) ≥1 first- or second-degree relative screened for HCM. The outcome was the presence of family disease defined as left ventricular wall thickness ≥13mm or verified autopsy report confirming HCM. Predictors of family disease were assessed at the first genetic clinic visit and considered age, sex, hypertension, and echocardiography metrics (maximal wall thickness, septal morphology, outflow tract obstruction at rest or Valsava). Using 12-lead ECGs and clinical variables, we used machine learning to train two distinct AI models. Model A utilized ECG signals only to train a ResNet model, while Model B used both ECGs and selected clinical variables (below 0.2 alpha level in logistic regressions) to train a random forest model to predict family disease. We performed evaluations at both ECG-level (one ECG used per patient) and patient-level (all available ECGs used in a patient) and we present individual and ensemble model performance. Results Among 356 families with sarcomere-negative HCM, 106 (30%) were familial. Probands were aged 53 ± 16 years, 30% female, 36% with a diagnosis of hypertension and 43% with peak outflow tract obstruction ≥30mmHg. In univariable logistic regressions, family disease was associated with age at diagnosis (OR 0.89 per 5-year, P=0.003), hypertension (OR 0.60, P=0.046) and obstruction (OR 0.52, P=0.013). The ensemble AI model, which integrated clinical and ECG data, achieved an AUC of 0.82 (95% CI: [0.795, 0.848]). It demonstrated a specificity of 0.891 (95% CI: [0.867, 0.913]) and a negative predictive value of 0.798 (95% CI: [0.772, 0.823]). When comparing ECG-level and patient-level data (1 to 10 ECGs per patient), the models consistently achieved AUC values between 0.78 and 0.81, indicating stable performance across various data subsets. Conclusion The prevalence of family inheritance in sarcomere-negative HCM can be accurately predicted using AI models that integrate clinical variables and 12-lead ECG data. This approach could enable tailored screening strategies based on the likelihood of family disease.

Background predation risk induces anxiety-like behaviour and predator neophobia in zebrafish

Prey face a major challenge in balancing predator avoidance with other essential activities. In environments with high risk, prey may exhibit neophobia (fear of novelty) due to the increased likelihood of novel stimuli being dangerous. The zebrafish, Danio rerio, is an established model organism for many scientific studies. Although spatial and object neophobia in zebrafish have received previous attention, little is known about the role of background risk in inducing neophobia in zebrafish. Here, we present two experiments using zebrafish to explore whether background predation risk can induce anxiety-like behaviour in a novel environment and neophobic responses when exposed to a novel odour. Over five days, we repeatedly exposed zebrafish to either high background risk in the form of chemical alarm cues (i.e., injured conspecific cues that indicate a predator attack) or a low-risk water control stimulus. In Experiment 1, when tested in a novel tank, zebrafish exposed to high predation risk displayed anxiety-like responses (reduced activity and increased bottom time spent) compared to their low-risk counterparts. Moreover, high-risk individuals showed reduced intra-session habituation to the novel tank compared to low-risk individuals. In Experiment 2, high-risk individuals exhibited fear responses toward a novel odour, unlike low-risk individuals. These results reveal that short-term repeated exposures to high risk can induce anxiety-like behaviour and predator odour neophobia in zebrafish.

Data-driven Machine Learning Models for Risk Stratification and Prediction of Emergence Delirium in Pediatric Patients Underwent Tonsillectomy/Adenotonsillectomy.

In the pediatric surgical population, Emergence Delirium (ED) poses a significant challenge. This study aims to develop and validate machine learning (ML) models to identify key features associated with ED and predict its occurrence in children undergoing tonsillectomy or adenotonsillectomy. The analysis involved data cleaning, exploratory data analysis (EDA), supervised predictive modeling, and unsupervised learning on a medical dataset (n = 423). After preliminary data cleaning, EDA encompassed plotting histograms, boxplots, pairplots, and correlation heatmaps to understand variable distributions and relationships. Four predictive models were trained including logistic regression (LR), random forest (RF), Support Vector Machine (SVM), and Gradient Boosting (XGBoost). The models were evaluated and compared using Receiver Operating Characteristic (ROC) Area Under the Curve (AUC), precision, recall, and feature importance. The RF model showed better performance and was used for the test (AUC-ROC 0.96, precision 1.00, and recall 0.92 on the validation set). K-means clustering was applied to find groups within the data. Elbow method and silhouette scores were used to determine the optimal number of clusters. The formed clusters were analyzed by aggregating features to understand the characteristics of each cluster. EDA revealed significant positive correlations between age, weight, American Society of Anesthesiologists (ASA) health score, and surgery duration with the risk of developing ED. Among the ML models, RF achieved the highest performance. Key predictive variables, based on the model's feature importance, included delirium screening scales, extubation time, and time to regain consciousness. Unsupervised K-means clustering identified 2-3 optimal clusters, which represented distinct patient subgroups: younger, healthier, low-risk individuals (cluster 0), and older patients with increasing chronic disease burden, higher delirium screening scores, and consequently higher post-operative delirium risk (clusters 1 and 2). ML techniques are valuable tools for extracting insights and making accurate predictions from healthcare data. High-performing algorithm-based models can be implemented for clinical decision support systems, facilitating early identification and intervention for ED in pediatric patients. By investigating various variables, it is possible to assess risk and implement preventive measures effectively. Furthermore, unsupervised clustering reveals distinct patient subgroups, enabling personalized perioperative management strategies and enhancing overall patient care.

Separating Risk Prediction: Myocardial Infarction vs. Ischemic Stroke in 6.2M Screenings.

Traditional cardiovascular disease risk prediction models generate a combined risk assessment for myocardial infarction (MI) and ischemic stroke (IS), which may inadequately reflect the distinct etiologies and disparate risk factors of MI and IS. We aim to develop prediction models that separately estimate the risks of MI and IS. Our analysis included 6,242,404 individuals over 40 years old who participated in a cardiovascular health screening examination in 2009. Potential predictors were selected based on a literature review and the available data. Cox proportional hazards models were used to construct 5-year risk prediction models for MI, and IS. Model performance was assessed through discrimination and calibration. During a follow-up of 39,322,434.39 person-years, 89,140 individuals were diagnosed with MI and 116,259 with IS. Both models included age, sex, body mass index, smoking, alcohol consumption, physical activity, diabetes, hypertension, dyslipidemia, chronic kidney disease, and family history. Statin use was factored into the classification of dyslipidemia. The c-indices for the prediction models were 0.709 (0.707-0.712) for MI, and 0.770 (0.768-0.772) for IS. Age and hypertension exhibited a more pronounced effect on IS risk prediction than MI, whereas smoking, body mass index, dyslipidemia, and chronic kidney disease showed the opposite effect. The models calibrated well for low-risk individuals. Our findings underscore the necessity of tailored risk assessments for MI and IS to facilitate the early detection and accurate identification of heterogeneous at-risk populations for atherosclerotic cardiovascular disease.

Protocol for a systematic review and meta-analysis of interventions aimed at delabeling low-risk penicillin allergies with consideration for sex and gender

BackgroundApproximately, 10% of people report a penicillin allergy; however, more than 90% can safely undergo delabeling after a detailed history, oral challenge, or other investigations such as penicillin skin testing (PST). Although PST is the gold standard, the results can be heterogeneous, and awaiting specialist assessment may take an inordinate amount of time. Therefore, oral provocation challenge has become acceptable for individuals with low-risk penicillin allergy histories. There also appears to be an association with increased prevalence of adverse drug reaction reporting in female individuals, which may translate to penicillin allergy prevalence; however, the evidence has not been assessed through a sex and gender lens. This systematic review will identify and synthesize the findings from studies that report measures of effectiveness and safety of interventions aimed at delabeling penicillin allergies in low-risk individuals. Information related to sex and gender will be extracted, where available, to understand potential differences in allergy reporting and patient outcomes.MethodsThe Cochrane Handbook for Systematic Reviews of Interventions and the Centre for Review and Dissemination’s Guidance for Undertaking Reviews in Health Care will be used as frameworks for conducting this systematic review. The literature search will be conducted by a medical librarian (B. M. M.) and will consist of a search strategy to identify and retrieve published studies that meet our inclusion criteria. Studies that require penicillin skin testing (PST) as a step prior to other interventions will be excluded. Integrated knowledge translation involving co-design was carried out for this systematic review protocol creation. Data extraction will be conducted at four levels: (1) study level, (2) patient level, (3) intervention level, and (4) outcome level. A narrative descriptive synthesis of results and risk of bias of all included studies will be provided, and, if relevant, a meta-analysis will be performed.DiscussionThe dissemination of findings from this knowledge synthesis to various stakeholders is intended to inform on options for evidence-based interventions to aid in delabeling penicillin allergies in individuals with a low risk of experiencing a hypersensitivity reaction. Detailed reporting on the characteristics of delabeling interventions as well as the effectiveness of similar interventions will benefit policy makers considering the implementation of a penicillin allergy delabeling protocol. Additionally, findings from this systematic review will report on the current evidence regarding the role of sex and gender in both the prevalence and outcomes associated with the presence of penicillin allergies.Systematic review registrationPROSPERO CRD42022336457.

Who Lives Longer, the Valve or the Patient? The Dilemma of TAVI Durability and How to Optimize Patient Outcomes

Over the past few years, transcatheter aortic valve implantation (TAVI) imposed itself as the first-choice therapy for symptomatic aortic stenosis (AS) in elderly patients at surgical risk. There have been continuous technological advancements in the latest iterations of TAVI devices and implantation techniques, which have bolstered their adoption. Moreover, the favorable outcomes coming out from clinical trials represent an indisputable point of strength for TAVI. As indications for transcatheter therapies now include a low surgical risk and younger individuals, new challenges are emerging. In this context, the matter of prosthesis durability is noteworthy. Initial evidence is beginning to emerge from the studies in the field, but they are still limited and compromised by multiple biases. Additionally, the physiopathological mechanisms behind the valve’s deterioration are nowadays somewhat clearer and classified. So, who outlasts who—the valve or the patient? This review aims to explore the available evidence surrounding this intriguing question, examining the various factors affecting prosthesis durability and discussing its potential implications for clinical management and current interventional practice.

Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts.

Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal-external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors.

GAN-Augmented Naïve Bayes for identifying high-risk coronary artery disease patients using CT angiography data

Coronary artery disease (CAD) is one of the most common cardiovascular disorders affecting millions of individuals globally. It is the leading cause of mortality in both the wealthy and impoverished nations. CAD patients exhibit a wide range of symptoms, some of which are not evident until a major incident occurs. The development of techniques for early detection and precise diagnosis is heavily dependent on research. The proposed system introduces a novel approach, Generative Adversarial Networks Augmented Naïve Bayes (GAN-ANB), to classify high-risk CAD patients using Coronary Computed Tomography Angiography (CCTA) imaging data. The database included images from Coronary Computed Tomography Angiography (CCTA) records of 5,000 individuals. The developed GAN framework consists of a generator to generate synthetic patient profiles, and a discriminator to distinguish between genuine and synthetic profiles to improve the identification of high-risk CAD patients. Adding synthetic data to the training process allowed the discriminator to be utilized further to improve predictive modeling. The performance of the GAN-enhanced prediction model was assessed using accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve (ROC). The model exhibited an outstanding Dice Similarity Coefficient (0.91), Mean Intersection Over Union (0.90), recall (0.96), and precision (0.98) in differentiating between high-risk and low-risk individuals. The identification of high-risk patients with CAD is greatly enhanced by the integration of GANs with clinical and imaging data. ROC of 0.99 was achieved by the GAN-ANB model, which outperformed conventional machine learning models, was achieved using the GAN-ANB model. High cholesterol level, diabetes, and some CCTA-derived imaging characteristics, including plaque load and luminal stenosis, were among the major predictors. This method offers a powerful tool for early diagnosis and intervention, potentially leading to improved patient outcomes and lower healthcare expenditure.