Explore the vast range of titles available.

IntroductionExercise Stress Testing (EST) is a well-established screening tool for CAD with reported mean sensitivity of 67- 68 % and specificity of 72- 77 %. In recent years, Computer Tomography Coronary Angiogram (CTCA) had largely replaced EST as preferred screening test due to better sensitivity(98–99%) and specificity(82–89%) with positive predictive value(PPV) and negative predictive value(NPV) of 85% and 92%, respectively. However, CTCA is less accessible due to cost and resources.MethodsWe performed a retrospective review of patients referred to Mercy University Hospital(MUH) for EST from Jan 2022-July 2024 who had either CTCA or Invasive Coronary Angiogram(ICA) to confirm the presence of significant CAD defined by stenosis>50%. 1107 patients completed EST comprising 723 Males and 384 Females aged 14–88 years old with a mean of 59.3 years old.Results 822/1107 (74.3%) patients had negative EST, 137/1107 (12.4%) had positive EST. 61/1107 (5.51%) patients had Equivocal results and 87/1107 (7.85%) patients had Inconclusive or Submaximal results. (figure 1) 62/822 (7.54%) with negative EST and 94/137 (68.6%) with positive EST had subsequent investigation for CAD by either CTCA or ICA. (figure 2) 53/94 (56.3%) patients with positive EST were true positives and 41/94 (43.6%) were false positive. 41/62 (66.1%) patients with negative EST who had available follow up were true negative and 21/62 (33.9%) were false negative. (table 1).Conclusions/ImplicationsOur experience in EST demonstrated 71.6 % sensitivity in detecting significant CAD. Our low specificity (50.0%) result is subjected to verification bias. In fact, 342/822 (41.6%) patients with negative results are discharged from Cardiology services if they have no high risk features.EST remains a reliable, low cost screening test for CAD. It is suitable for patients with low pretest probability and able to alleviate long waiting lists for CTCA.Abstract 90 Table 1Confusion matrix: EST results & presence of CAD[Image Omitted. See PDF.]Abstract 90 Figure 1EST results[Image Omitted. See PDF.]Abstract 90 Figure 2Referrals for further investigations[Image Omitted. See PDF.]

IntroductionRegadenoson is a highly selective adenosine A2 receptor blocker. It induces profound coronary vasodilation, without significant atrioventricular block or bronchoconstriction, enabling sustained uptake of radioisotopes for up to 30 minutes to enable myocardial perfusion imaging (MPI). However, its effect on the QTc interval remains uncertain with previous literature reporting mixed results.MethodsDuring the covid-19 pandemic, as the use of aerosol-generating procedures including exercise stress tests was restricted, an increase in MPI use was seen in our centre, and we thus undertook a prospective evaluation of the effect of Regadenoson on QTc. Consecutive patients were enrolled (n=34) between October 2021 and February 2022. Patients with QTc >460ms at baseline or a paced rhythm were excluded from the study. QTc was measured from a 12-lead ECG at baseline, immediately after Regadenoson and radioisotope were administered intravenously via a brachial vein (t=40secs), and then at 2-minute intervals from the start of Regadenoson administration until any QTc prolongation (if present) had resolved.ResultsMedian baseline QTc was 425 ms (range: 389 ms – 460 ms, figure 1). Median QTc at peak prolongation (observed at 40 secs in 32 patients and 4 mins in 2 patients) was 477 ms (range 434 ms - 545 ms) including, of note, 4 (11%) patients with QTc prolongation >500ms. The median time for QTc to return to ≤460ms was 4 mins (2 mins - 32 mins). No significant side effects or dysrhythmias were reported.Abstract 8 Figure 1Peak QTc prolongation at baseline vs post-regadenoson administrationConclusionRegadenoson administration was well tolerated by patients. As a result of the QTc prolongation of >500ms seen in some patients, albeit with the absence of dysrhythmia, regadenoson administration should be undertaken by experienced staff in an appropriate clinical setting. However, more research is needed to ascertain with greater accuracy the average QTc prolongation of Regadenoson, as it shows promise as a potential agent where exercise is either not possible or not permitted due to restrictions.

IntroductionThere are European guidelines on the management of coronary artery calcification (CAC)2 when seen on CT Coronary angiogram (CTCA) or CT calcium scoring. There is a paucity in guidelines on management of incidental CAC reported on non-cardiac gated CT Thorax. A finding of CAC in a patient without known atherosclerosis provides an opportunity to assess cardiac risk, promote risk factor optimisation and evaluate need for cardiac work-up.Aims We aim to explore the prevalence of incidental CAC reported on thoracic CTs of inpatients and evaluate current risk factor assessment and optimisation.MethodsSingle-centre retrospective observational study of consecutive general medical inpatients aged 40–75, who had undergone a non-cardiac CT thorax during their admission. Data were collected using local electronic health records. Exclusion criteria were patients with known ischaemic heart disease (IHD). Risk factors assessment was noted by documentation of smoking status, hypertension, diabetes and Low-density lipoprotein (LDL) values.ResultsA total of 186 patients with thoracic CT scans were identified. On review of all CT reports, 53 (28.4%) patients had CAC reported, of whom 17 had known IHD. Therefore 36 (19.4%) patients were identified for further analysis. Baseline demographics as per table 1. LDL and HBA1C were checked in 14 (39%) and 16 (44%) patients respectively as per Table 2. An exercise stress test was booked in none of the patients. A coronary angiogram was booked in 1 patient.ConclusionOne fifth of medical inpatients in our study had a new finding of CAC on thoracic imaging. Cardiovascular risk factors of LDL and HBA1C were checked in less than half of patients. There is a valuable opportunity to optimise cardiac risk factors in a subset of medically admitted patients with CAC reported on non-cardiac CTs. This can be facilitated by raising awareness and implementing a reference flowchart (figure 1).Abstract 25 Table 1[Image Omitted. See PDF.]Abstract 25 Table 2[Image Omitted. See PDF.]Abstract 25 Figure 1[Image Omitted. See PDF.]

ObjectiveCurrent evidence of the associations between cardiorespiratory fitness (CRF) and mortality is limited. We performed a meta-analysis to assess the dose–response association of CRF with mortality from all causes, cardiovascular disease (CVD) and cancer in healthy population.MethodsPubMed, EMBASE and Web of Science were searched up to 26 December 2019 for reports of cohort studies giving risk estimates for all-cause, CVD and cancer mortality by level of CRF. Cohort studies were included if CRF was assessed by an exercise stress test and reported as at least three levels or per incremental increase, and the association of CRF with all-cause, CVD and cancer mortality was evaluated. Generalised least-squares regression models were used to assess the quantitative relation of CRF with all-cause, CVD and cancer mortality.Results34 cohort studies were eligible for the meta-analysis. The pooled relative risks (RRs) for all-cause, CVD and cancer mortality per one-metabolic equivalent increase in CRF were 0.88 (95% CI 0.83 to 0.93), 0.87 (95% CI0.83 to 0.91) and 0.93 (95% CI 0.91 to 0.96), respectively. As compared with lowest CRF, with intermediate CRF, the summary RRs for all-cause, CVD and cancer mortality were 0.67 (95% CI 0.61 to 0.74), 0.60 (95% CI 0.51 to 0.69) and 0.76 (95% CI 0.69 to 0.84), respectively, and with highest CRF were 0.47 (95% CI 0.39 to 0.56), 0.49 (95% CI 0.42 to 0.56) and 0.57 (95% CI 0.46 to 0.70), respectively.ConclusionOur analysis showed inverse dose–response associations of CRF with all-cause, CVD and cancer mortality, which provides evidence for public health recommendations for preventing all-cause, CVD and cancer mortality.PROSPERO registration numberCRD42020208883.

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the “Stress-Predict Dataset”, created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.

•Functional connectivity (FC) patterns derived from fMRI tasks outperform resting-state FC at predicting individual differences in a measure of cognitive task performance and a task-derived behavioral inhibition measure.•The improvement in behavioral prediction afforded by fMRI tasks over resting-state is largely associated with the FC of the task model fit.•FC of the task model fit and task design model parameters contain shared and unique behavioral prediction power. Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns.

Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistently yield better predictions of fluid intelligence than others, and the task that generates the best-performing models varies by sex. By considering task-induced brain state and sex, the best-performing model explains over 20% of the variance in fluid intelligence scores, as compared to <6% of variance explained by rest-based models. This suggests that identifying and inducing the right brain state in a given group can better reveal brain-behavior relationships, motivating a paradigm shift from rest- to task-based functional connectivity analyses. Decoding or predicting cognitive traits from brain activity is an exciting prospect. Here, the authors show that task-based functional connectivity better predicts intelligence-related measures than rest-based connectivity, suggesting that cognitive tasks amplify individual differences in trait-relevant circuitry.