Clinical Features and Long-Term Outcomes in Very Young Patients with Myocardial Infarction with Non-Obstructive Coronary Arteries.

Background: The main cause of acute coronary syndrome (ACS) is coronary artery obstruction due to atherosclerotic plaque growth or thrombus formation secondary to plaque rupture or erosion. However, there is a subgroup of patients with signs and symptoms suggestive of ACS but without relevant coronary artery obstruction on coronary angiography. This population is defined as myocardial infarction with non-obstructive coronary arteries (MINOCA). The present study analyzes the clinical features and outcomes of very young patients with a diagnosis of MINOCA. Method: Nested case-control study of ≤40-year-old patients referred for coronary angiography due to clinical suspicion of ACS. Patients were divided into three groups: patients with obstructive coronary artery disease (CAD), patients diagnosed with MINOCA, and controls with non-coronary artery disease. Results: Of 19,321 coronary angiographies performed in our center in a period of 10 years, 408 (2.1%) were in patients ≤40 years old, and MINOCA was identified in 32 (21%) patients. The cardiovascular risk factors for obstructive CAD and MINOCA were very similar. The incidence of major adverse cardiovascular events (MACE) at follow-up was significantly higher in the MINOCA (HR 4.13 (95%CI 1.22-13.89) and obstructive CAD (HR 4.59 (95%CI 1.90-10.99) patients compared to controls. Cocaine use HR 14.58 (95%CI 3.08-69.02), family history of CAD HR 6.20 (95%CI 1.40-27.43), and depression HR 5.16 (95%CI 1.06-25.24) were associated with a poor outcome in the MINOCA population. Conclusion: Very young patients with MINOCA had a poor prognosis at long-term follow-up, similar to patients with obstructive CAD. Focusing efforts on secondary prevention is essential in this population.

Coronary Artery Disease in Very Young Patients: Analysis of Risk Factors and Long-Term Follow-Up.

Coronary artery disease (CAD) is a common chronic condition in the elderly. However, the earlier CAD begins, the stronger its impact on lifestyle and costs of health and social care. The present study analyzes clinical and angiographic features and the outcome of very young patients undergoing coronary angiography due to suspected CAD, including a nested case-control study of ≤40-year-old patients referred for coronary angiography. Patients were divided into two groups: cases with significant angiographic stenosis, and controls with non-significant stenosis. Of the 19,321 coronary angiographies performed in our center in a period of 10 years, 504 (2.6%) were in patients ≤40 years. The most common cardiovascular risk factors for significant CAD were smoking (OR 2.96; 95% CI 1.65-5.37), dyslipidemia (OR 2.18; 95% CI 1.27-3.82), and family history of CAD (OR 1.95; 95% CI 1.05-3.75). The incidence of major adverse cardiovascular events (MACE) at follow-up was significantly higher in the cases compared to controls (HR 2.71; 95% CI 1.44-5.11). Three conventional coronary risk factors were directly related to the early signs of CAD. MACE in the long-term follow-up is associated to dyslipidaemia and hypertriglyceridemia. Focusing efforts for the adequate control of CAD in young patients is a priority given the high socio-medical cost that this disease entails to society.

Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients.

Coronary artery disease is a chronic disease with an increased expression in the elderly. However, different studies have shown an increased incidence in young subjects over the last decades. The prediction of major adverse cardiac events (MACE) in very young patients has a significant impact on medical decision-making following coronary angiography and the selection of treatment. Different approaches have been developed to identify patients at a higher risk of adverse outcomes after their coronary anatomy is known. This is a prognostic study of combined data from patients ≤40 years old undergoing coronary angiography (n = 492). We evaluated whether different machine learning (ML) approaches could predict MACE more effectively than traditional statistical methods using logistic regression (LR). Our most effective model for long-term follow-up (60 ± 27 months) was random forest (RF), obtaining an area under the curve (AUC) = 0.79 (95%CI 0.69-0.88), in contrast with LR, obtaining AUC = 0.66 (95%CI 0.53-0.78, p = 0.021). At 1-year follow-up, the RF test found AUC 0.80 (95%CI 0.71-0.89) vs. LR 0.50 (95%CI 0.33-0.66, p < 0.001). The results of our study support the hypothesis that ML methods can improve both the identification of MACE risk patients and the prediction vs. traditional statistical techniques even in a small sample size. The application of ML techniques to focus the efforts on the detection of MACE in very young patients after coronary angiography could help tailor upfront follow-up strategies in such young patients according to their risk of MACE and to be used for proper assignment of health resources.