Citations

Abstract 3D printing, also known as additive manufacturing, holds immense potential for rapid prototyping and customized production of functional health‐related devices. With advancements in polymer chemistry and biomedical engineering, polymeric biomaterials have become integral to 3D‐printed biomedical applications. However, there still exists a bottleneck in the compatibility of polymeric biomaterials with different 3D printing methods, as well as intrinsic challenges such as limited printing resolution and rates. Therefore, this review aims to introduce the current state‐of‐the‐art in 3D‐printed functional polymeric health‐related devices. It begins with an overview of the landscape of 3D printing techniques, followed by an examination of commonly used polymeric biomaterials. Subsequently, examples of 3D‐printed biomedical devices are provided and classified into categories such as biosensors, bioactuators, soft robotics, energy storage systems, self‐powered devices, and data science in bioplotting. The emphasis is on exploring the current capabilities of 3D printing in manufacturing polymeric biomaterials into desired geometries that facilitate device functionality and studying the reasons for material choice. Finally, an outlook with challenges and possible improvements in the near future is presented, projecting the contribution of general 3D printing and polymeric biomaterials in the field of healthcare.

Abstract Background Ventricular tachycardia (VT) is a life-threatening arrhythmia that can complicate structural heart disease. While catheter ablation is effective in treating this condition, the recurrence of VT after the procedure continues to be a concern. Consequently, there is a need for a reliable system to evaluate the likelihood of arrhythmia recurrence following an ablation. Objective To predict recurrence or repeat ablation after a VT catheter ablation procedure using machine learning (ML) models. Methods We conducted a single-center, retrospective study of all patients undergoing catheter ablation for scar-related VT between 2012 and 2022. Data collected included demographics, comorbidities, medications, relevant laboratory abnormalities, electrocardiograms, echocardiograms, detailed procedural characteristics, and outcomes. Python version 3.8.5 for exploratory data analysis (EDA) and visualization. Using six different ML models from the training set (90:10 split), we used 34 variables to predict the primary outcome, including VT recurrence or repeat catheter ablation. The accuracy of these models was compared using ROC curves based on their performance on the test set. Results Out of 508 VT ablation procedures, 261 experienced a recurrence. Support Vector Classifier (SVM) performed the best, having an AUC of 0.73, followed by logistic regression at 0.69 (Figure A). SVM yielded sensitivity, specificity, positive and negative predictive values of 0.73, 0.73, 0.79, and 0.67, respectively. The F1 score was 0.70. Among the factors with the highest feature importance in the model, the most influential variable was the proceduralist’s experience level, followed by QRS width at baseline, LVEF, and patient’s age (Figure B). Conclusion Our model can predict recurrent VT after ablation with reasonable accuracy, outperforming the existing clinical risk scores significantly. Larger training sample sizes will help achieve more robust ML models and improve predictive accuracy further.Figure A: ROC curve depicting the AUROCB. Feature importance graph

Abstract Background Congenital Heart Disease (CHD) is associated with development of atrial fibrillation (AF) at an early age. With increased survival of this cohort into adulthood, the prevalence of concurrent CHD and AF is expected to increase over the next several decades. Little is known about the utility of clinical risk factors or prediction models in determining bleeding or stroke rates in adults with CHD and AF. Purpose This study aims to measure the performance of the CHADSVASC and HASBLED scores in a cohort of people with CHD. The association between traditional stroke and bleeding risk factors, as well as CHD-specific factors, will be quantified. Methods Patients age 18 or older attending one of three centers in the USA were included in this study if they had a history of AF on ECG, holter monitor or device interrogation, in addition to a diagnosis of Tetralogy of Fallot (TOF), Double Outlet Right Ventricle (DORV), or Pulmonary Atresia with Intact Ventricular Septum (PA-IVS). Outcomes were major bleeding event, and stroke rate. Analysis was conducted using cox univariable regression controlling for current age. Receiver operator curve and c-statistics were produced for the CHADSVASC and HASBLED scores respectively. Results 326 people were included in this study, of whom 161 (49.5%) had TOF secondary to Pulmonic Stenosis, 126 (38.8%) had TOF secondary to Pulmonary Atresia, 25 (7.7%) had DORV, and 155 (47.7%) had PA-IVS. 227 people used warfarin (79.4%), 28 (9.8%) used apixaban, 9 (3.1%) used Rivaroxaban, 3 (1%) used Edoxaban, and 5 (1.7%) used Enoxaparin for a period of at least 30 days during the study. 13 (4.5%) people didn’t receive anticoagulation during the study. 26 people had a stroke (8.8%) while 85 people (28.6%) had a major bleeding event. Only the CHADSVASC score was significantly associated with stroke rates (HR 2.103, 95% CI 1.555 to 2.844, p<0.001). Clinical variables, such as subtype of CHD, prior conduit placement or unifocalization were not associated with stroke or major bleeding events. C-statistic for the CHADSVASC score in predicting stroke events was 0.755 (Figure 1). C-statistic for the HASBLED score in predicting major bleeding was 0.442 (Figure 2). Conclusions Among people with CHD and AF, the CHADSVASC score can robustly predict stroke events. Other clinical variables are less valuable in predicting either bleeding or strokes. The HASBLED score likely doesn’t have a role in predicting major bleeding episodes in this cohort.

As a treatment for the widely spread cardiovascular diseases (CVD), bypass vascular grafts have room for improvement in terms of mechanical property match with native arteries. A 3D‐printed nozzle is presented, featuring unique internal structures, to extrude artificial vascular grafts with a flower‐mimicking geometry. The multilayer‐structured graft wall allows the inner and outer layers to interfere sequentially during lateral expansion, replicating the nonlinear elasticity of native vessels. Both experiment and simulation results verify the necessity and benefit of the flower‐mimicking structure in obtaining the self‐toughening behavior. The gelation study of natural polymers and the utilization of sacrificial phase enables the smooth extrusion of the multiphase conduit, where computer‐assisted image analysis is employed to quantify manufacturing fidelity. The cell viability tests demonstrate the cytocompatibility of the gelatin methacryloyl (GelMA)/sodium alginate grafts, suggesting potential for further clinical research with further developments. This study presents a feasible approach for fabricating bypass vascular grafts and inspires future treatments for CVD.

Abstract Aims : Direct oral anticoagulants (DOACs) are widely used for stroke prevention in people with non-valvular atrial fibrillation/flutter (NVAF). Anticoagulants have different bleeding profiles. Apixaban is associated with the lowest bleeding risk. This study evaluates the interaction between DOAC used and right ventricular systolic pressure (RVSP) on echocardiogram and bleeding rates. It was hypothesized that pulmonary hypertension may be associated with excess bleeding based on anticoagulant-specific pharmacologic profiles. Methods & Results : A retrospective analysis of a database was conducted. Multivariable regression models for bleeding rates were developed using a causative analysis approach among participants who had undergone an echocardiogram, and interaction terms for DOAC choice were incorporated. Stratum specific estimates were calculated using linear computation. Patients taking Apixaban had lower bleeding rates than those taking Rivaroxaban and Dabigatran. Among those without pulmonary hypertension, there was no difference in bleeding rates. Where RVSP was ≥45mmHg, there was a 90% increase in bleeding rates with use of Dabigatran (HR 1.99, p=0.024) and Rivaroxaban (HR 1.86, p=0.042) compared to Apixaban (Figure 1). Conclusions : Elevated RVSP is associated with excess bleeding in patients taking Rivaroxaban and Dabigatran, but not Apixaban, after controlling for confounding variables.

Cardiac sarcoidosis (CS) is characterized by various arrhythmic manifestations ranging from catastrophic sudden cardiac death secondary to ventricular arrhythmia, severe conduction disease, sinus node dysfunction, and atrial fibrillation. The management of CS is complex and includes not only addressing the arrhythmia but also controlling the myocardial inflammation resultant from the autoimmune reaction. Arrhythmic manifestations of CS carry significant prognostic implications and invariably affect long-term survival in these patients. In this review, we focus on management of arrhythmic manifestation of cardiac sarcoidosis as well as risk stratification for sudden cardiac death in these patients.

Background: cardiovascular diseases, including acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC), are significant causes of morbidity and mortality worldwide. Timely differentiation of these conditions is essential for effective patient management and improved outcomes. Methods: We conducted a review focusing on studies that applied artificial intelligence (AI) techniques to differentiate between acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC). Inclusion criteria comprised studies utilizing various AI modalities, such as deep learning, ensemble methods, or other machine learning techniques, for discrimination between AMI and TTC. Additionally, studies employing imaging techniques, including echocardiography, cardiac magnetic resonance imaging, and coronary angiography, for cardiac disease diagnosis were considered. Publications included were limited to those available in peer-reviewed journals. Exclusion criteria were applied to studies not relevant to the discrimination between AMI and TTC, lacking detailed methodology or results pertinent to the AI application in cardiac disease diagnosis, not utilizing AI modalities or relying solely on invasive techniques for differentiation between AMI and TTC, and non-English publications. Results: The strengths and limitations of AI-based approaches are critically evaluated, including factors affecting performance, such as reliability and generalizability. The review delves into challenges associated with model interpretability, ethical implications, patient perspectives, and inconsistent image quality due to manual dependency, highlighting the need for further research. Conclusions: This review article highlights the promising advantages of AI technologies in distinguishing AMI from TTC, enabling early diagnosis and personalized treatments. However, extensive validation and real-world implementation are necessary before integrating AI tools into routine clinical practice. It is vital to emphasize that while AI can efficiently assist, it cannot entirely replace physicians. Collaborative efforts among clinicians, researchers, and AI experts are essential to unlock the potential of these transformative technologies fully.

INTRODUCTION: Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction. METHODS: Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool. RESULTS: Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. DISCUSSION: AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.

Abstract Abstract Title: Real-world analysis comparing Black and White patients with triple-negative breast cancer receiving therapy per KEYNOTE-522 Background: KEYNOTE-522 was a randomized, double-blind, placebo-controlled phase 3 trial which resulted in the FDA approval of pembrolizumab with neoadjuvant chemotherapy for patients with newly diagnosed, high-risk, early-stage triple-negative breast cancer (TNBC). Despite the significant improvement in pathological complete response (pCR) and event-free survival rates across all patients, the landmark trial included only 4.5% Black patients. It is essential to assess outcomes in representative treatment populations. We assessed real-world toxicity and treatment outcomes across Black and White patients who received standard-of-care treatment per KEYNOTE-522. Methods: In this retrospective, multicenter study, we examined patients with early-stage TNBC who received planned treatment per KEYNOTE-522 as standard-of-care (SOC) therapy. 16 sites were included in the analysis. IRB approval was obtained from each participating site. Number and length of treatment delays, treatment-related toxicities (both chemotherapy and immune-related) of all grades, and pCR rate were collected from the electronic medical record of each participating site, and deidentified data were shared for central analysis. Results: Of the 577 patients who initiated treatment with chemotherapy and immunotherapy, 534 patients were included in this analysis with 105 patients who self-identified as Black (19.7%) and 429 who self-identified as White (80.3%). There were no statistically significant differences in clinical and pathological characteristics between the two groups. White women were more likely to have grade 3+ immune-related adverse events (irAEs) compared to Black women (33.8% vs 20.9%, P=0.011). There was no significant difference across Black and White patients with respect to grade 3+ chemotherapy-related adverse events. Out of the 444 patients who have completed surgery, no difference in pCR rate was observed between Black and White women (45/86 52.3% vs 200/358 55.9%) (p = 0.6). The authors also saw no difference in the rates of hospitalizations between Black and White women (39% vs 36% p = 0.5), and rates of acute care utilization (38% vs 38% p = 0.9). Conclusions: We report safety and efficacy across Black and White women in a real-world analysis of patients who received treatment per KEYNOTE-522. Notably, White patients had a significantly higher frequency of grade 3+ irAEs, although the reason for this finding is unclear based on this analysis. Black patients had similar pCR rates and rates of treatment-related hospitalizations compared to White patients. Assessment of outcomes and toxicity by race in clinical trials and real-world analyses are critical in drug development. Citation Format: Mara Hofherr, Andrew Davis, Spenser January, Emily Owen, Farah Raheem, Lida A. Mina, Suganya Arunachalam Karikalan, Lauren Lyons, Meredith Watson Rose, Katherine Madden, Jerline Hsin, Aimee Keegan, Wai Yu, Shawna Kraft, Allison Schepers, Emily Armgardt, Douglas Mazewski, Alison Svoboda, Kayla Harwood, Jodi Taraba, Yonatan Resnick, Shelly Hummert, Lisa Grate, Sidney Keisner, Jacob Hobbs, Todd Davis, Kristin Bastian, Dawn Minikel, Wiliam Adler, Traci White, Avneek Singh Sandu, Fouad Boulbol, Kelsey Finch, Katherine Clifton. Real-world analysis comparing Black and White patients with triple-negative breast cancer receiving therapy per KEYNOTE-522 [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS14-02.