Background: Acute pulmonary embolism (PE) is a critical condition where the timely and accurate assessment of right ventricular (RV) dysfunction is important for patient management. Given the limited availability of echocardiography in emergency departments (EDs), an artificial intelligence (AI) application that can identify RV dysfunction from electrocardiograms (ECGs) could improve the treatment of acute PE. Methods: This retrospective study analyzed adult acute PE patients in an ED from January 2021 to December 2023. We evaluated a smartphone application which analyzes printed ECGs to generate digital biomarkers for various conditions, including RV dysfunction (QCG-RVDys). The biomarker’s performance was compared with that of cardiologists and emergency physicians. Results: Among 116 included patients, 35 (30.2%) were diagnosed with RV dysfunction. The QCG-RVDys score demonstrated significant effectiveness in identifying RV dysfunction, with a receiver operating characteristic–area under the curve (AUC) of 0.895 (95% CI, 0.829–0.960), surpassing traditional biomarkers such as Troponin I (AUC: 0.692, 95% CI: 0.536–0.847) and ProBNP (AUC: 0.655, 95% CI: 0.532–0.778). Binarized based on the Youden Index, QCG-RVDys achieved an AUC of 0.845 (95% CI: 0.778–0.911), with a sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 91.2% (95% CI: 82.4–100%), 77.8% (95% CI: 69.1–86.4%), 63.3% (95% CI: 54.4–73.9%), and 95.5% (95% CI: 90.8–100%), respectively, significantly outperforming all the expert clinicians, with their AUCs ranging from 0.628 to 0.683. Conclusions: The application demonstrates promise in rapidly assessing RV dysfunction in acute PE patients. Its high NPV could streamline patient management, potentially reducing the reliance on echocardiography in emergency settings.

Elevated visit-to-visit blood pressure variability (BPV), independent of mean BP, has been associated with cardiovascular events. However, its impact after ST-elevation myocardial infarction (STEMI) has not been established. This study aimed to investigate the prognostic impact of BPV on patients after STEMI. We analyzed the data and clinical outcomes of STEMI survivors who underwent successful primary coronary intervention from 2003 to 2009. BP was measured at discharge and at 1, 3, 6, 12, 24, and 36 months, and we calculated BPV as the intra-individual standard deviations (SDs) of systolic BP (SBP) across these measurements. We classified the patients as high and low-BPV group, and evaluated the outcomes: occurrence of major adverse cardiovascular events (MACEs), death, recurrent myocardial infarction, and target vessel revascularization within 60 months. We enrolled 343 patients, and mean follow-up duration was 68 ± 34 months (median: 76 months). Mean and median SBP SDs were 13.2 and 12.3 mmHg, and patients were divided into one of the two groups based on the median (high-BPV group = SD ≥ 12.3 mmHg; low-BPV group = SD < 12.3 mmHg). The MACE-free survival in the high-BPV group was significantly worse than that in low-BPV group (log-rank p = 0.035). For the high-BPV group, the risk of a MACE significantly increased by 57% (95% confidence interval: 1.03–2.39; p = 0.038). Visit-to-visit systolic BPV was associated with increased rates of adverse clinical outcomes in patients after STEMI. Careful assessment of BP and attempts to reduce BPV might be also important in STEMI survivors.

Background: During fractional flow reserve (FFR) measurements, distal coronary pressure (Pd) can be influenced by hydrostatic pressure changes resulting from the height difference (HD) between the coronary ostium and the location of the distal pressure sensor. Aims: We investigated the effect of aortocoronary HD on the FFR measurements in each coronary artery. Methods: In this retrospective cohort study, we analyzed 257 patients who underwent FFR measurements and coronary computed tomography (CCTA) within a year. Using CCTA, we measured HD as the vertical distance between the coronary ostium and a matched point of the distal coronary pressure sensor identified on coronary angiography. Results: The location of the Pd sensor was higher than the coronary ostium in the left anterior descending artery (LAD) (-4.64 ± 1.15 cm) and lower than the coronary ostium in the left circumflex artery (LCX) (2.54 ± 1.05 cm) and right coronary artery (RCA) (2.03 ± 1.28 cm). The corrected FFR values by HD were higher in the LAD (0.78 ± 0.09 to 0.82 ± 0.09, P<0.01) and lower in the LCX and RCA than the original FFR values (0.87 ± 0.07 to 0.85 ± 0.08, P<0.01; 0.87 ± 0.10 to 0.86 ± 0.10, P<0.01, respectively). Using an FFR cut-off value of 0.8, the concordance rates between the FFR and corrected FFR values were 77.8%, 95.2%, and 100% in the LAD, LCX, and RCA, respectively. Conclusion: HD between the coronary ostium and the distal coronary pressure sensor may affect FFR measurements and FFR-guided treatment decisions for coronary artery disease.

Soh MS. Int J Heart Fail. 2026 Jul;8(3):253-255. https://doi.org/10.36628/ijhf.2026.0089

Soh MS. Int J Heart Fail. 2025 Jul;7(3):184-186. https://doi.org/10.36628/ijhf.2025.0055

Background and Objectives: The diagnostic accuracy of an artificial intelligence (AI)-derived initial 12-lead electrocardiogram (ECG) analysis was evaluated for early carbon monoxide-induced cardiomyopathy (CO-CMP) risk detection. Materials and Methods: Retrospective medical data of carbon monoxide poisoning (COP) cases between 1 January 2015 and 31 December 2024 were screened for the primary outcome: odds ratio (OR) for echocardiographically confirmed CO-CMP among those with high-risk probability score per the AI-derived model. Secondary outcomes included left ventricular ejection fraction (LVEF) and AI-derived probability score, critical care requirements, including intubation and intensive care unit (ICU) admission, and cardiac arrest events. Results: A total of 51 patients with acute COP were included in the final analysis, with 13 (25.5%) being diagnosed with CO-CMP. The LVEF in the CO-CMP group was lower than that in the non-CO-CMP group (40.00 ± 13.80% vs. 63.76 ± 6.24%, p < 0.001). The AI-derived probability score was higher in the CO-CMP group (11.3 [3.8–32.7] vs. 0.5 [0.2–2.2], p < 0.001). Among cardiac biomarkers, troponin I (2.37 [0.32–7.88] vs. 0.06 [0.06–0.95] ng/mL, p = 0.002) was higher in the CO-CMP group. Patients with CO-CMP required recurrent ventilator support (76.9% vs. 21.1%, p < 0.001) and ICU admission (92.3% vs. 42.1%, p = 0.003). In multivariable regression analysis, the AI-derived prediction model was independently associated with CO-CMP (OR 1.14; 95% confidence interval (CI) 1.02–1.27; p = 0.017; Firth-penalized OR 1.11; 95% CI 1.03–1.25; p < 0.001). Receiver operating characteristic analysis of the AI-derived model showed an area under the curve of 0.85 (95% CI 0.70–0.96) for the AI score alone and 0.92 (95% CI 0.83–0.99) for the Combined AI–cardiac marker model, with a sensitivity of 92.3% and specificity of 81.6%. Pairwise DeLong comparisons between the Combined AI model and comparator models did not reach statistical significance (Combined vs. AI-only, p = 0.092; Combined vs. cardiac markers, p = 0.052); however, the likelihood-ratio test for adding the AI probability score to the cardiac marker-only model demonstrated significant incremental information (χ² = 13.68, p < 0.001). Conclusions: AI-based ECG analysis showed exploratory diagnostic association with LV systolic dysfunction observed in suspected CO-CMP patients. Given the limited sample size, low events-per-variable ratio, and lack of external validation, these findings suggest that AI-ECG analysis may provide incremental information for early cardiac risk stratification in selected patients.