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Background: Typical signs of heart failure (HF), like increased cardiothoracic ratio (CTR) and pleural effusion, can be seen on X-ray. Artificial Intelligence (AI) can help in the early and quicker diagnosis of HF., Objectives: The study's goal was to demonstrate that the AI interpretation of chest X-rays can assist the clinician in diagnosing HF., Methods: Patients older than 45 years were included in the study. The study analyzed 10 100 deidentified outpatient chest X-rays by AI algorithm. The AI-generated report was later verified by an independent radiologist. Patients with CTR > 0.5 and pleural effusion were marked as potential HF. Flagged patients underwent confirmatory tests, and those labeled as negative also underwent further investigations to rule out HF., Results: Out of 10 100, the AI algorithm detected 183 (1.8%) patients with increased CTR and pleural effusion on chest X-rays. One hundred and six out of 183 underwent diagnostic tests. Eighty-two (77%) out of 106 were diagnosed with HF according to current guidelines. From the remaining 9917 patients, 106 patients were randomly selected. Nine (8%) out of them were diagnosed with HF. The positive predictive value of AI for diagnosing HF is 77%, and the negative predictive value is 91%. More than half (54.9%) of newly diagnosed patients had HF with preserved ejection fraction., Conclusion: HF is a risky condition with nonspecific symptoms that are difficult to diagnose, especially in the early stages. Using AI assistance for X-ray interpretation can be helpful for early diagnosis of HF especially HF with preserved ejection fraction., (© 2023 The Authors. Clinical Cardiology published by Wiley Periodicals LLC.)

Background: Dipeptidyl peptidase 3 (DPP-3) is an intracellular enzyme that causes hemodynamic instability and cardiac depression in several cases such as cardiogenic shock, sepsis and burns where DPP-3 is released into the blood due to cell death. Data on the effect of higher DPP-3 levels on acute coronary syndrome (ACS) patients are currently lacking. The aim of this study was to evaluate the effect of DPP-3 levels on ACS patients., Methods: In this prospective study, we included ACS patients including STEMI and non-STEMI groups and a control group to compare various demographic, echocardiographic and laboratory parameters including DPP-3. DPP-3 levels were measured at 24 th , 48 th , and 72 nd h from the onset of symptoms in ACS patients and then compared with left ventricle ejection fraction (LVEF) for the assessment of left ventricle systolic function., Results: A total of 70 ACS patients (age 62.5 ± 11 years, 68.6% male) were recruited and 48 normal individuals were included as control group (age 61.1 ± 10 years, 66.7% male). It has been demonstrated that DPP-3 levels are significantly higher in the ACS group than the control group like troponin I levels. DPP-3 levels were found to be one of the independent predictors of LVEF similar to NT-proBNP and troponin I., Conclusions: This study suggests that DPP-3 could be an important indicator of myocardial depression predicting left ventricle systolic function in ACS.