Combining clinician skills, patient empowerment and AI tools to predict and prevent Cardiovascular risk factors and events: A case study.

Introduction: Cardiovascular disease (CVD) accounted for almost 33% of global deaths in 2019 and remains a leading cause of acute emergency department (ED) attendance (1). Ischaemic heart disease represented more than 40% of these deaths, 80% of which are preventable (1) emphasizing the need for early identification, intervention, and education on risk factor prevention. This case study explores the effectiveness of a proactive cardiovascular screening service, incorporating AI-enabled technology and point of care (POC) blood testing, for timely and accurate predicting cardiovascular issues. The methodology involved data collection through questionnaires, established screening protocols, digital tools with advanced AI algorithms, and adherence to ethical considerations. Case Study: An active 60 year old male, history of hypertension and pre-diabetes, no history of chest pain or palpitations, received a proactive cardiovascular risk assessment incorporating: POC HBA1C/NTproBNP (LumiraDX), a non-fasting lipid profile, Heartscience MyoVista wavECG and cardiorespiratory auscultation. Findings: Elevated HBA1C, LDL and NON-HDL levels. Conventional ECG Glasgow Analysis suggested borderline prolonged QT and left ventricular hypertrophy. Positive MyoVista report for Left Ventricular (LV) repolarisation abnormality, indicative of diastolic dysfunction. Interventions: 24 hour blood pressure monitor revealed sub-optimal control: average 140/90mmHg with 4% nocturnal dipping. 3 day Holter monitor identified a supraventricular ectopic run at 149bpm, associated with a future risk of Atrial Fibrillation (afib). Lifestyle modification education provided. Advised the individual to attend his GP regarding blood pressure control and statin therapy, and referral to Cardiology for echo examination. Before the echo was planned, an episode of chest pain led to an emergency department (ED) presentation which identified paroxysmal afib and an acute coronary syndrome requiring emergency coronary angiogram. The angiogram revealed triple vessel disease potentially requiring Coronary Artery Bypass Grafting (CABG). However, a strong case was presented by the patient, for Percutaneous Coronary Intervention (PCI) over CABG, and a staged PCI strategy was decided. The first PCI stage was successful, however, numerous delays lead to an inpatient stay of 13 days and a transfer to another hospital for PCI intervention. Conclusion: If proactive screening were undertaken, this ED attendance and extended hospital admission could likely have been avoided. Elevated blood pressure, lipids and HBA1C would be identified earlier and treatment started sooner, thus reducing risk factors. The HeartSciences wavECG indicated a repolarisation abnormality and an abnormal ECG, confirming a cardiology referral was required for an ECHO examination and which likely would lead to a CT Angio/Angio or cardiac MRI and if required PCI interventions managed electively as 1-3 days in hospital. Combining clinician skill, POC testing, patient education and AI analysis help predict cardiovascular risks, leading to opportunities for early intervention, and subsequently reducing the number of acute emergency admissions. The implications/benefits of incorporating this proactive screening service include potential reductions in healthcare costs, improved patient outcomes, and enhanced awareness of cardiovascular health. While limitations, such as potential biases and technological constraints, were acknowledged, the case study ultimately emphasizes the crucial role of integrating AI-enabled technology into proactive cardiovascular screening services to predict and prevent acute emergency admissions effectively.