INTRODUCTION:Shortness of breath while bending and its more objective version, bending oxygen saturation index (BOSI), are the latest additions to pulmonary arterial hypertension (PAH) symptom and signs armamentarium. In this study, we aimed to evaluate the association between BOSI and clinical outcomes in patients with PAH and to explore its potential to complement current risk estimation schemes. METHODS:In this single-center, prospective, observational study, we enrolled patients with PAH who are under stable treatment. Baseline mortality risk was estimated using established risk schemes. Primary endpoint was defined as the combination of all-cause hospitalization and all-cause mortality at 1 year. The discriminative performance of BOSI was evaluated using ROC curve analysis. RESULTS:A total of 102 patients were enrolled into the study. BOSI was equal to or more than 3 in 33 patients (32.4%). Primary endpoint occurred in 20 (60.6%) in BOSI ≥3 group and 16 (23.2%) in BOSI <3 group (p < 0.001). ROC analysis showed that BOSI had a significant discriminative ability (AUC 0.687, p = 0.002). Cox regression analysis showed that a BOSI ≥3 was significantly associated with adverse events, even after adjustment for baseline risk estimated by the four most used risk schemes (REVEAL, REVEAL Lite, COMPERA, and European Society of Cardiology/European Respiratory Society risk scores). CONCLUSION:BOSI is independently associated with adverse events in patients with PAH and its addition to current risk scores may improve baseline risk estimation.

In the evolving landscape of ECG signal analysis, the challenge of limited transparency in machine learning models remains a significant barrier to their effective integration into clinical practice. This study addresses this issue by investigating the use of counterfactual explanations to improve model interpretability for clinicians, particularly in differentiating healthy subjects from myocardial infarction patients. Utilizing the PTB-XL dataset, we developed a methodology for systematic feature extraction and refinement to prepare for counterfactual analysis. This led to the creation of the Visualizing Counterfactual Clues on Electrocardiograms (VCCE) method, designed to improve the practicality of counterfactual explanations in a clinical setting. The validity of our approach was assessed using custom metrics that reflect the diagnostic relevance of counterfactuals, evaluated with the help of two cardiologists. Our findings suggest that this approach could support future efforts in using ECGs to predict patient outcomes for cardiac conditions, achieving interpretation validity scores of 23.29 +/- 1.04 and 20.28 +/- 0.99 out of 25 for high and moderate-quality interpretations, respectively. Clinical alignment scores of 0.83 +/- 0.12 for high-quality and 0.57 +/- 0.10 for moderate-quality interpretations underscore the potential clinical applicability of our method. The methodology and findings of this study contribute to the ongoing discussion on enhancing the interpretability of machine learning models in cardiology, offering a concept that bridges the gap between advanced data analysis techniques and clinical decision-making. The source code for this study is available at https://github.com/tanyelai/vcce.

Background: Although high left ventricular filling pressures [left ventricular (LV) enddiastolic pressure or pulmonary capillary wedge pressure (PCWP)] are widely taken as surrogates for LV diastolic dysfunction, the actual distending pressure that governs LV diastolic stretch is transmural pressure difference (∆P TM ).Clinically, preferring ∆P TM over PCWP may improve diagnostic and therapeutic decision-making.We aimed to compare the clinical implications of diastolic function characterization based on PCWP or ∆P TM . Methods:We retrospectively screened our hospital database for adult patients with a clinical diagnosis of heart failure who underwent right heart catheterization.Echocardiographic diastolic dysfunction was graded according to the current guidelines.LV end-diastolic properties were assessed with construction of complete end-diastolic pressure-volume relationship (EDPVR) curves using the single-beat method.Survival status was checked via the electronic national health-care system.Results: A total of 693 cases were identified in our database; the final study population comprised 621 cases.∆P TM -based, but not PCWP-based, EDPVR diastolic stiffness constants were significantly predictive of advanced diastolic dysfunction.PCWP-based diastolic stiffness constants were not able to predict 5-year mortality, whereas ∆P TM -based EDPVR stiffness constants and volumes all turned out to have significant predictive power for 5-year mortality. Conclusion:Left ventricular diastolic function assessment can be improved using ∆P TM instead of PCWP.As ∆P TM ultimately linked to right-sided functions, this approach emphasizes the limitations of taking LV diastolic function as an isolated phenomenon and underlines the need for a complete hemodynamic assessment involving the right heart in therapeutic and prognostic decision-making processes.

Aims A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery [occlusion myocardial infarction (OMI)], leading to poor outcomes due to delayed identification and invasive management. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria. Methods and results An AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 +/- 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (CI): 0.924-0.951] in identifying the primary OMI outcome, with superior performance [accuracy 90.9% (95% CI: 89.7-92.0), sensitivity 80.6% (95% CI: 76.8-84.0), and specificity 93.7 (95% CI: 92.6-94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1-85.1), sensitivity 32.5% (95% CI: 28.4-36.6), and specificity 97.7% (95% CI: 97.0-98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5-91.9), sensitivity 73.0% (95% CI: 68.7-77.0), and specificity 95.7% (95% CI: 94.7-96.6)]. Conclusion The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.

A generation ago thrombolytic therapy led to a paradigm shift in myocardial infarction (MI), from Q-wave/non-Q-wave to ST-segment elevation MI (STEMI) vs non-STEMI. Using STE on the electrocardiogram (ECG) as a surrogate marker for acute coronary occlusion (ACO) allowed for rapid diagnosis and treatment. But the vast research catalyzed by the STEMI paradigm has revealed increasing anomalies: 25% of "non-STEMI" have ACO with delayed reperfusion and higher mortality. Studying these limitations has given rise to the occlusion MI (OMI) paradigm, based on the presence or absence of ACO in the patient rather than STE on ECG. The OMI paradigm shift harnesses advanced ECG interpretation aided by artificial intelligence, complementary bedside echocardiography and advanced imaging, and clinical signs of refractory ischemia, and offers the next opportunity to transform emergency cardiology and improve patient care. This State-of-the-Art Review examines the paradigm shifts from Q wave to STEMI to OMI.

Although current pulmonary hypertension (PH) guidelines recommend a pulmonary capillary wedge pressure (PCWP) >15 mm Hg for the detection of a postcapillary component, the rationale of this recommendation may not be quite compatible with the peculiar hemodynamics of PH. We hypothesize that a high PCWP alone does not necessarily indicate left-sided disease, and this diagnosis can be improved using left ventricle transmural pressure difference (∆ PTM). In this 2-center, retrospective, observational study, we enrolled 1,070 patients with PH who underwent heart catheterization, with the final study population comprising 961 cases. ∆ PTM was calculated as PCWP minus right atrial pressure. The patients with group II PH had significantly higher ∆ PTM values (12.6 ± 6.6 mm Hg) compared with the other groups (1.1 ± 4.8 in group I, 12.4 ± 6.6 in group II, 2.5 ± 6.4 in group III, and 0.8 ± 8.0 in group IV, p <0.001) despite overlapping PCWP values. A ∆ PTM cutoff of 7 mm Hg identifies left heart disease when PCWP is >15 (area under curve 0.825, 95% confidence interval 0.784 to 0.866, p <0.001). Five-year mortality was significantly higher in patients with high ∆ PTM and PCWP subgroups compared with low ∆ PTM plus high PCWP (26.1% vs 18.5%, p = 0.027) and low ∆ PTM and PCWP subgroups (26.1% vs 15.6%, p <0.001). ∆ PTM has supplementary discriminatory power in distinguishing patients with and without postcapillary PH. In conclusion, a new approach utilizing ∆ PTM may improve our understanding of PH pathophysiology and may identify a subpopulation that may potentially benefit from PH-specific treatments.

Objective: We aimed to investigate the effect of right ventricular energy failure (RVEF) on hemodynamic and clinical outcomes in patients diagnosed with chronic thromboembolic pulmonary hypertension (CTEPH) undergoing pulmonary endarterectomy (PEA) surgery or balloon pulmonary angioplasty (BPA). Patients and Methods: A total of 100 CTEPH patients planned for PEA or BPA were included in the study. Based on the presence of RVEF during diagnosis, patients divided into two groups. Hemodynamic data from right heart catheterization (RHC) were compared before and after procedures in 3-6 months follow up period. Results: Patients with RVEF revealed a decrease in mean pulmonary artery pressure (mPAP) from 54.67 +/- 12.27 mmHg to 36.12 +/- 11.76 mmHg (p:<0.001), mean right atrial pressure (mRAP) from 13.40 +/- 4.08 mmHg to 9.76 +/- 4.56 mmHg (p:0.003), and pulmonary vascular resistance (PVR) from 11.36 +/- 5.15 Wood Units (WU) to 5.46 +/- 3.30 WU (p <0.001). In the non-RVEF group, mPAP decreased from 38.82 +/- 12.61 mmHg to 30.81 +/- 10.57 mmHg (p:<0.001), mRAP from 7.09 +/- 3.02mmHg to 7.15 +/- 3.07mmHg (p: 0.917), and PVR from 6.33 +/- 3.65 WU to 4.09 +/- 2.31 WU (p:<0.001). Conclusion:The presence of RVEF at the time of diagnosis in CTEPH patients does not have a negative impact on early perioperative and 3-month postoperative outcomes following PEA or BPA. This high-risk patient group demonstrated significant hemodynamic and clinical benefits from both PEA and BPA.

Right ventricular (RV) failure has a significant adverse impact on pulmonary hyperten-sion (PH) prognosis. None of the currently used parameters directly assess whether RV fails to provide enough energy output to propel the blood through diseased pulmonary vascular system. Furthermore, most of the current parameters are affected by the volume status of the patient. We aimed to explore whether RV energy failure has a predictive power for mortality on top of the established prognostic risk parameters in patients with PH. We screened 723 cases from our database. A total of 3 sets of binary regression analy-ses were executed to determine the hazard ratios (HRs) of RV energy failure for 5-year mortality in clinical, echocardiographic, and hemodynamic context, using adjustment var-iables chosen according to previous studies. The final study population encompassed 549 cases. A total of 77 patients died during the 5-year follow-up (14%). RV energy failure was observed in 146 of 549 patients (26.6%). In the univariate model, RV energy failure strongly associated with increased long-term mortality (HR 4.25, 95% confidence interval [CI] 2.58 to 7.00, p <0.001). It also emerged as a significant predictor of long-term mortal-ity in clinical and hemodynamic multivariate models (HR 2.59, 95% CI 1.43 to 4.67, p = 0.002 and HR 2.05, 95% CI 1.15 to 3.63, p = 0.015, respectively). In conclusion, our study indicates that the presence of RV energy failure independently predicts long-term mortal-ity in PH. (c) 2023 Elsevier Inc. All rights reserved. (Am J Cardiol 2023;193:19-27)

Cor triatriatum sinister (CTS) is a rare adult congenital heart disease. The usual presentation may vary according to the size of the hole in the membrane in the left atrium and the pressure gradient. In addition to acute clinical presentations including acute pulmonary edema and sudden cardiac death, patients may present with chronic findings such as right heart failure due to pulmonary hypertension. The development of pulmonary hypertension is an important indicator of mortality. In cases where non-invasive methods are not sufficient for the diagnosis of pulmonary hypertension, exercise right heart catheterization may also be used. We present a patient with CTS, in whom the final decision was made with the help of an exercise right heart catheterization.

HomeCirculation: Heart FailureVol. 16, No. 11Pulse of Atrioventricular Dissociation No AccessResearch ArticleRequest AccessFull TextAboutView Full TextView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toNo AccessResearch ArticleRequest AccessFull TextPulse of Atrioventricular Dissociation Emre Aslanger, Berk Erdinç and Taylan Akgün Emre AslangerEmre Aslanger Correspondence to: Emre Aslanger, MD, Department of Cardiology, Başakşehir Pine and Sakura City Hospital, Olimpiyat Bulvari Yolu, 34480 Başakşehir/Istanbul, Turkey. Email E-mail Address: [email protected] https://orcid.org/0000-0002-9346-4992 Department of Cardiology, Başakşehir Pine and Sakura City Hospital, Turkey. , Berk ErdinçBerk Erdinç https://orcid.org/0000-0001-7422-0610 Department of Cardiology, Başakşehir Pine and Sakura City Hospital, Turkey. and Taylan AkgünTaylan Akgün Department of Cardiology, Başakşehir Pine and Sakura City Hospital, Turkey. Originally published27 Jul 2023https://doi.org/10.1161/CIRCHEARTFAILURE.123.010938Circulation: Heart Failure. 2023;16"Pulse of Atrioventricular Dissociation." Circulation: Heart Failure, 16(11), pp. e010938FootnotesFor Sources of Funding and Disclosures, see page 1010.Correspondence to: Emre Aslanger, MD, Department of Cardiology, Başakşehir Pine and Sakura City Hospital, Olimpiyat Bulvari Yolu, 34480 Başakşehir/Istanbul, Turkey. Email mr_aslanger@hotmail.com Previous Back to top Next FiguresReferencesRelatedDetails November 2023Vol 16, Issue 11 Advertisement Article Information Metrics © 2023 American Heart Association, Inc.https://doi.org/10.1161/CIRCHEARTFAILURE.123.010938PMID: 37497649 Originally publishedJuly 27, 2023 Keywordsheart blockmiddle agedphysical examinationrespirationtachycardiaventricularPDF download Advertisement Subjects Arrhythmias

Background: Pulmonary hypertension is a complex syndrome that encompasses a diverse group of pathophysiologies predisposed by different environmental and genetic factors. It is not clear to which extent the universal risk classification schemes can be applied to cohorts in individual pulmonary hypertension centers with differing environmental backgrounds, genetic pools, referral networks. Aims: To explore whether the recommended risk classification schemes could reliably be used for mortality prediction in an unselected pulmonary hypertension population of a tertiary pulmonary hypertension center. Study Design: A retrospective cross-sectional study. Methods: We retrospectively screened our hospital database for the patients with pulmonary hypertension between 2015 and 2022. The grouping of pulmonary hypertension was made as follows in accordance with current guidelines: Group 1: patients with pulmonary arterial hypertension, Group 2: patients with pulmonary hypertension associated with left heart disease, Group 3: patients with pulmonary hypertension associated with lung disease and/or hypoxia, and Group 4: patients with pulmonary hypertension associated with pulmonary artery obstructions. Then, we compared the predicted and observed mortality rates of four different risk classification schemes (REVEAL, REVEAL-Lite, ESC/ERS and COMPERA). Results: We identified 723 cases in our pulmonary hypertension database, the final study population consisted of 549 patients. The REVEAL, REVEAL-Lite and European Society of Cardiology/European Respiratory Society risk scores significantly underestimated the mortality risk in the low-risk stratum (5.3% vs. 1.9%, P < 0.001; 5.3% vs. 2.9%, P = 0.015 and 6.3% vs. 1%, P < 0.001, respectively) and overestimated the mortality risk in the high-risk stratum (11.8% vs. 25.8%, P < 0.001; 10.4% vs. 25.1%, P < 0.001 and 13.2% vs. 30%, P < 0.001, respectively). Although the COMPERA 4-strata model significantly underestimated the risk in low- and intermediate-low risk strata (4.9% vs. 1.5%, P < 0.001 and 6.8% vs. 2.8%, P = 0.001, respectively), it was accurate in intermediate-high and high-risk groups (10.1% vs. 8.7%, P = 0.592 and 15.6% vs. 22%, P = 0.384, respectively). The analyses limited only to group 1 pulmonary hypertension patients gave similar results. Conclusion: The established risk classification schemes may not perform as good as expected in unselected pulmonary hypertension populations and this may have important implications on management decisions. Tertiary centers should not uncritically accept the published risk prediction models and consider modifying current risk scores according to their own patient characteristics.

ABSTRACT Background One third of Non-ST-elevation myocardial infarction (NSTEMI) patients present with an acutely occluded culprit coronary artery (occlusion myocardial infarction [OMI]), which is associated with poor short and long-term outcomes due to delayed identification and consequent delayed invasive management. We sought to develop and validate a versatile artificial intelligence (AI)-model detecting OMI on single standard 12-lead electrocardiograms (ECGs) and compare its performance to existing state-of-the-art diagnostic criteria. Methods An AI model was developed using 18,616 ECGs from 10,692 unique contacts (22.9% OMI) of 10,543 patients (age 66±14 years, 65.9% males) with acute coronary syndrome (ACS) originating from an international online database and a tertiary care center. This AI model was tested on an international test set of 3,254 ECGs from 2,263 unique contacts (20% OMI) of 2,222 patients (age 62±14 years, 67% males) and compared with STEMI criteria and annotations of ECG experts in detecting OMI on 12-lead ECGs using sensitivity, specificity, predictive values and time to OMI diagnosis. OMI was based on a combination of angiographic and biomarker outcomes. Results The AI model achieved an area under the curve (AUC) of 0.941 (95% CI: 0.926-0.954) in identifying the primary outcome of OMI, with superior performance (accuracy 90.7% [95% CI: 89.5-91.9], sensitivity 82.6% [95% CI: 78.9-86.1], specificity 92.8 [95% CI: 91.5-93.9]) compared to STEMI criteria (accuracy 84.9% [95% CI: 83.5-86.3], sensitivity 34.4% [95% CI: 30.0-38.8], specificity 97.6% [95% CI: 96.8-98.2]) and similar performance compared to ECG experts (accuracy 91.2% [95% CI: 90.0-92.4], sensitivity 75.9% [95% CI: 71.9-80.0], specificity 95.0 [95% CI: 94.0-96.0]). The average time from presentation to a correct diagnosis of OMI was significantly shorter when relying on the AI model compared to STEMI criteria (2.0 vs. 4.9 hours, p<0.001). Conclusions The present novel ECG AI model demonstrates superior accuracy and earlier diagnosis of AI to detect acute OMI when compared to the STEMI criteria. Its external and international validation suggests its potential to improve ACS patient triage with timely referral for immediate revascularization. CLINICAL PERSPECTIVE What is new? A novel artificial intelligence (AI) model detecting acute occluded coronary artery (OMI) using standard 12-lead electrocardiograms (ECGs) was developed from an international cohort. The OMI AI model is the first of its kind to be validated in an external international cohort of patients using an objective angiographically confirmed endpoint of OMI. Our study demonstrated the OMI AI models superior accuracy in identifying OMI and shorter time to correct diagnosis compared to standard of care STEMI criteria. What are the clinical implications? The OMI AI model has the potential to improve ACS triage and clinical decision-making by enabling timely and accurate detection of OMI in NSTEMI patients. The robustness and versatility of the OMI AI model indicate its potential for real-world clinical implementation in ECG devices from multiple vendors. Prospective studies are essential to evaluate the efficacy of the OMI AI model and its impact on patient outcomes in real-world settings.

Acute heart attacks such as myocardial infarction (MI) are the main reasons for global deaths. Additionally, approximately half of the deaths occur before the treatment. Hence, it is crucial to diagnose MI fast and cheaply. 12-lead electrocardiogram (ECG) is noninvasive and fast compared to alternative devices. In this work, we aimed to train and validate a residual network model that can distinguish MI and healthy 12-lead ECG records. Moreover, we investigated the contribution of patient information such as age and sex to the decision. Additionally, we compared the performances of models trained with two different loss functions which are binary cross-entropy and pinball loss. We observed the highest accuracy, recall, and F1 score which are 97.86%, 98.73%, and 98.66%, respectively. Furthermore, since we used a convolutional neural network-based architecture, we obtained explainable results using gradient class activation maps by highlighting the ECG segments that contribute the most to the decision.

Objective: The development of right ventricular failure has a significant adverse prognostic impact on the course of pulmonary hypertension. Right ventricular energy failure has been shown to double the mortality of pulmonary hypertension even after correction for many established risk predictors. We hypothesize that bendopnea may indicate right ventricular energy failure in patients with pulmonary hypertension.Methods: We prospectively enrolled patients with pulmonary hypertension who were admitted to our pulmonary hypertension outpatient clinic between January 2021 and June 2021. Bendopnea was assessed by asking patients to bend forward and report any shortness of breath within 30 seconds. Routine physical examination, laboratory tests, echocardiography, and right heart catheterization parameters were collected.Results: A total of 167 patients were enrolled into the study. Bendopnea and right ventricular energy failure was present in 79 (47.3%) and 43 (25.7%) patients, respectively. Bendopnea accurately predicted the presence of right ventricular energy failure (area under the curve, 0.667; 95% CI, 0.574-0.760; P < 0.001) and had a significantly superior diagnostic power compared with many other symptoms and signs.Conclusions: Our study shows that bendopnea predicts right ventricular energy failure in patients with pulmonary hypertension and can be added to our physical examination armamentarium as an easy, rapid, and noninvasive prognostic tool.