Artificial Intelligence Stress Echo (FINESSE) Project

Angina Pectoris, Chest Pain, Coronary Artery Disease, Myocardial Infarction (MI), Myocardial Ischemia

Dobutamine stress echocardiography, Stress echocardiography, Coronary artery disease, Myocardial ischemia, Chest pain, Risk stratification, Machine learning, Artificial intelligence, Cardiovascular risk prediction, Major adverse cardiovascular events, Stroke

The goal of this observational study is to learn whether combining stress echocardiography (stress echo) results with routine clinical information can better predict important heart outcomes in adults (18+) with chest pain who were assessed for suspected coronary artery disease. The main questions it aims to answer are: Can an artificial intelligence / machine learning model using stress echo findings plus clinical factors (such as blood pressure, diabetes, smoking, other health conditions, medications, and body measurements) predict major heart-related events (such as heart attack, stroke, death related to heart disease, or the need for coronary procedures) more accurately than stress echo results alone? Can the model help identify which patients are most likely to benefit from further invasive assessment and possible coronary revascularisation (for example, a stent or bypass surgery)? Which combination of stress echo measurements and clinical factors contributes most to risk prediction? Participants will: Not be asked to attend extra visits or have additional tests for this study. Have their existing stress echo reports and routinely collected hospital record data analysed (approximately 3,000 people who previously had dobutamine stress echo at Milton Keynes University Hospital). In some cases, if outcomes are not fully available from hospital records, the research team may check additional sources (such as GP records, or contacting the patient if appropriate) to confirm whether a major heart-related event occurred.

This is a single-centre, retrospective observational study using an existing dataset of pharmacological (dobutamine) stress echocardiography (SE) reports generated within Milton Keynes University Hospital over approximately 15 years, starting from 2002. The SE dataset comprises reports/letters produced by a single, experienced clinician, which reduces inter-observer variability and supports consistent interpretation across the cohort. Data sources and cohort construction SE reports (in document format) will be converted into a structured research database. A computer science team will develop a generalisable approach to extract structured variables from the clinical SE reports, building on prior proof-of-concept work demonstrating feasibility of converting these reports into a database. The dataset includes clinical variables (e.g., cardiovascular risk factors, comorbidities, prescribed medications, and anthropometrics) alongside SE-derived measures (including ischaemia detection and wall motion scoring at rest and peak stress). Stress echocardiography technique (context for imaging-derived variables) The study dataset reflects contemporary dobutamine SE practice at MKUH, with contrast-enhanced imaging used in the majority of cases (SonoVue contrast with rota pump infusion equipment). Studies were performed predominantly on Philips echocardiography systems, with image acquisition across standard stages (resting, intermediate, peak stress, and recovery) and standard views (apical 4-, 2-, and 3-chamber; parasternal long- and short-axis). Reporting used dedicated platforms enabling stage-by-stage comparison. Outcome ascertainment and linkage Following database completion, a research nurse will query the hospital Electronic Data Management system to ascertain major adverse cardiovascular events (MACE) for the cohort. Where outcomes cannot be confirmed from hospital systems (e.g., patients no longer served by the hospital), missing outcome information will be explored via primary care physician contact and/or patient contact as appropriate. Data processing, quality checks, and handling missingness Extracted data will undergo cleaning prior to analysis. Natural Language Processing (NLP) and feature engineering approaches will be used to transform extracted information into model-ready features. As part of preprocessing, data fields will be checked for completeness and consistency before modelling. Missing outcome data will be addressed through the external outcome checks described above. Statistical / machine learning approach and internal validation After preprocessing, subset feature selection methods will be applied to identify the most informative predictors for risk classification. Supervised learning will be used to discriminate between lower-risk cases and cases requiring further investigation, with additional modelling approaches (including regression techniques) planned to support quantification of disease stage in abnormal cases. Overfitting will be mitigated through use of techniques robust to overfitting (e.g., ensemble methods) and internal validation using k-fold cross-validation (five folds), ensuring separation of training and validation data. Sample size and additional analyses The study will utilise the available full dataset (approximately 3,000 patients) to maximise model development and internal validation. A cost analysis is also planned using the available data.

No related papers found yet.

United Kingdom