Dual antiplatelet therapy (DAPT) combines two antiplatelet agents to decrease the risk of thrombotic complications associated with atherosclerotic cardiovascular diseases. Emerging data about the duration of DAPT is being published continuously. New approaches are trying to balance the time, benefits, and risks for patients taking DAPT for established cardiovascular diseases. Short-term dual DAPT of 3–6 months, or even 1 month in high-bleeding risk patients, is equivalent in terms of efficacy and effectiveness compared to long-term DAPT for patients who experienced percutaneous coronary intervention in an acute coronary syndrome setting. Prolonged DAPT beyond 12 months reduces stent thrombosis, major adverse cardiovascular events, and myocardial infarction rates but increases bleeding risk. Extended DAPT does not significantly benefit stable coronary artery disease patients in reducing stroke, myocardial infarction, or cardiovascular death. Ticagrelor and aspirin reduce cardiovascular events in stable coronary artery disease with diabetes but carry a higher bleeding risk. Antiplatelet therapy duration in atrial fibrillation patients after percutaneous coronary intervention depends on individual characteristics and bleeding risk. Antiplatelet therapy is crucial for post-coronary artery bypass graft and transcatheter aortic valve implantation; Aspirin (ASA) monotherapy is preferred. Antiplatelet therapy duration in peripheral artery disease depends on the scenario. Adding vorapaxar and cilostazol may benefit secondary prevention and claudication, respectively. Carotid artery disease patients with transient ischemic attack or stroke benefit from antiplatelet therapy and combining ASA and clopidogrel is more effective than ASA alone. The optimal duration of DAPT after carotid artery stenting is uncertain. Resistance to ASA and clopidogrel poses an incremental risk of deleterious cardiovascular events and stroke. The selection and duration of antiplatelet therapy in patients with cardiovascular disease requires careful consideration of both efficacy and safety outcomes. The use of combination therapies may provide added benefits but should be weighed against the risk of bleeding. Further research and clinical trials are needed to optimize antiplatelet treatment in different patient populations and clinical scenarios.

In recent decades, significant advancements in pharmacological, catheter-based, and surgical reperfusion technologies have markedly improved outcomes for individuals undergoing acute myocardial infarction. Despite these remarkable progressions, a segment of patients, particularly those with extensive infarctions or delays in revascularization, remains vulnerable to the onset of mechanical complications associated with myocardial infarction. These complications, spanning mechanical, electrical, ischemic, inflammatory, and thromboembolic events, pose substantial risks of morbidity, mortality, and increased utilization of hospital resources. The management of patients experiencing these complications is intricate, necessitating collaborative efforts among various specialties. Timely identification, accurate diagnosis, hemodynamic stabilization, and decision-making support are crucial for guiding patients and their families in choosing between definitive treatments or palliative care. This review underscores the critical importance of promptly identifying and initiating therapy to reduce prolonged periods of cardiogenic shock and the potential for fatality. By presenting key clinical and diagnostic insights, this review aims to further improve early diagnosis and offer an updated perspective on current management strategies for the diverse range of complications associated with acute myocardial infarction.

Primary care has the potential to be transformed by artificial intelligence (AI) and, in particular, machine learning (ML). This review summarizes the potential of ML and its subsets in influencing two domains of primary care: pre-operative care and screening. ML can be utilized in preoperative treatment to forecast postoperative results and assist physicians in selecting surgical interventions. Clinicians can modify their strategy to reduce risk and enhance outcomes using ML algorithms to examine patient data and discover factors that increase the risk of worsened health outcomes. ML can also enhance the precision and effectiveness of screening tests. Healthcare professionals can identify diseases at an early and curable stage by using ML models to examine medical pictures, diagnostic modalities, and spot patterns that may suggest disease or anomalies. Before the onset of symptoms, ML can be used to identify people at an increased risk of developing specific disorders or diseases. ML algorithms can assess patient data such as medical history, genetics, and lifestyle factors to identify those at higher risk. This enables targeted interventions such as lifestyle adjustments or early screening. In general, using ML in primary care offers the potential to enhance patient outcomes, reduce healthcare costs, and boost productivity.