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Abdominal aortic aneurysm (AAA) is a progressive dilatation of the aorta that can lead to aortic rupture. The pathophysiology of the disease is not well characterized but is known to be caused by the general breakdown of the extracellular matrix within the aortic wall. In this comprehensive literature review, all current research on proteins that have been investigated for their potential prognostic capabilities in patients with AAA was included. A total of 45 proteins were found to be potential prognostic biomarkers for AAA, predicting incidence of AAA, AAA rupture, AAA growth, endoleak, and post-surgical mortality. The 45 proteins fell into the following seven general categories based on their primary function: (1) cardiovascular health, (2) hemostasis, (3) transport proteins, (4) inflammation and immunity, (5) kidney function, (6) cellular structure, (7) and hormones and growth factors. This is the most up-to-date literature review on current prognostic markers for AAA and their functions. This review outlines the wide pathophysiological processes that are implicated in AAA disease progression. [ABSTRACT FROM AUTHOR]

Objective: To develop machine learning (ML) models that predict outcomes following endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA). Background: EVAR carries non-negligible perioperative risks; however, there are no widely used outcome prediction tools. Methods: The National Surgical Quality Improvement Program targeted database was used to identify patients who underwent EVAR for infrarenal AAA between 2011 and 2021. Input features included 36 preoperative variables. The primary outcome was 30-day major adverse cardiovascular event (composite of myocardial infarction, stroke, or death). Data were split into training (70%) and test (30%) sets. Using 10- fold cross-validation, 6 ML models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Subgroup analysis was performed to assess model performance based on age, sex, race, ethnicity, and prior AAA repair. Results: Overall, 16,282 patients were included. The primary outcome of 30-day major adverse cardiovascular event occurred in 390 (2.4%) patients. Our best-performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.95 (0.94-0.96) compared with logistic regression [0.72 [0.70-0.74)]. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.06. Model performance remained robust on all subgroup analyses. Conclusions: Our newer ML models accurately predict 30-day outcomes following EVAR using preoperative data and perform better than logistic regression. Our automated algorithms can guide risk mitigation strategies for patients being considered for EVAR. [ABSTRACT FROM AUTHOR]

Introduction: Genetic testing is used across medical disciplines leading to unprecedented demand for genetic services. This has resulted in excessive waitlists and unsustainable pressure on the standard model of genetic healthcare. Alternative models are needed; e-health tools represent scalable and evidence-based solution. We aim to evaluate the effectiveness of the Genetics Navigator, an interactive patient-centred digital platform that supports the collection of medical and family history, provision of pregenetic and postgenetic counselling and return of genetic testing results across paediatric and adult settings., Methods and Analysis: We will evaluate the effectiveness of the Genetics Navigator combined with usual care by a genetics clinician (physician or counsellor) to usual care alone in a randomised controlled trial. One hundred and thirty participants (adults patients or parents of paediatric patients) eligible for genetic testing through standard of care will be recruited across Ontario genetics clinics. Participants randomised into the intervention arm will use the Genetics Navigator for pretest and post-test genetic counselling and results disclosure in conjunction with their clinician. Participants randomised into the control arm will receive usual care, that is, clinician-delivered pretest and post-test genetic counselling, and results disclosure. The primary outcome is participant distress 2 weeks after test results disclosure. Secondary outcomes include knowledge, decisional conflict, anxiety, empowerment, quality of life, satisfaction, acceptability, digital health literacy and health resource use. Quantitative data will be analysed using statistical hypothesis tests and regression models. A subset of participants will be interviewed to explore user experience; data will be analysed using interpretive description. A cost-effectiveness analysis will examine the incremental cost of the Navigator compared with usual care per unit reduction in distress or unit improvement in quality of life from public payer and societal perspectives., Ethics and Dissemination: This study was approved by Clinical Trials Ontario. Results will be shared through stakeholder workshops, national and international conferences and peer-reviewed journals., Trial Registration Number: NCT06455384., Competing Interests: Competing interests: YB and MC are cofounders of Genetics Adviser., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke or death following TFCAS., Methods and Results: The VQI (Vascular Quality Initiative) database was used to identify patients who underwent TFCAS for carotid artery stenosis between 2005 and 2024. We identified 112 features from the index hospitalization (82 preoperative [demographic/clinical], 13 intraoperative [procedural], and 17 postoperative [in-hospital course/complications]). The primary outcome was 1-year postprocedural stroke or death. The data were divided into training (70%) and test (30%) sets. Six machine learning models were trained using preoperative features with 10-fold cross-validation. The primary model evaluation metric was area under the receiver operating characteristic curve. The algorithm with the best performance was further trained using intra- and postoperative features. Model robustness was assessed using calibration plots and Brier scores. Overall, 35 214 patients underwent TFCAS during the study period and 3257 (9.2%) developed 1-year stroke or death. The best preoperative prediction model was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.93-0.95). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67). The extreme gradient boosting model maintained excellent performance at the intra- and postoperative stages, with area under the receiver operating characteristic curve values of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted/observed event probabilities with Brier scores of 0.11 (preoperative), 0.11 (intraoperative), and 0.09 (postoperative)., Conclusions: Machine learning can accurately predict 1-year stroke or death following TFCAS, performing better than logistic regression.

Background: Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization., Methods and Results: The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure., Conclusions: Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

Machine learning (ML) solutions are increasingly entering healthcare. They are complex, sociotechnical systems that include data inputs, ML models, technical infrastructure and human interactions. They have promise for improving care across a wide range of clinical applications but if poorly implemented, they may disrupt clinical workflows, exacerbate inequities in care and harm patients. Many aspects of ML solutions are similar to other digital technologies, which have well-established approaches to implementation. However, ML applications present distinct implementation challenges, given that their predictions are often complex and difficult to understand, they can be influenced by biases in the data sets used to develop them, and their impacts on human behaviour are poorly understood. This manuscript summarises the current state of knowledge about implementing ML solutions in clinical care and offers practical guidance for implementation. We propose three overarching questions for potential users to consider when deploying ML solutions in clinical care: (1) Is a clinical or operational problem likely to be addressed by an ML solution? (2) How can an ML solution be evaluated to determine its readiness for deployment? (3) How can an ML solution be deployed and maintained optimally? The Quality Improvement community has an essential role to play in ensuring that ML solutions are translated into clinical practice safely, effectively, and ethically., Competing Interests: Competing interests: AAV and MM are co-inventors of CHARTwatch, an artificial intelligence early warning system for patient deterioration and have the potential to acquire minority interests in a start-up company, Signal1., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)