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e13559 Background: Recent advances in targeted therapies have increased the survival and quality of life for non-small cell lung cancer (NSCLC) patients. Understanding clinical characteristics and predicting HER2 status may facilitate efficient use of next-generation sequencing (NGS) testing and promote precision medicine/targeted therapies. Methods: NSCLC patients ≤65 years old, diagnosed between 01/2011 and 06/2021 who have NGS test results were identified in from two National Cancer Institute designated cancer centers in the Northeastern region of the United States. Demographics, behavioral, and clinical characteristics were extracted from electronic health record (EHR) data and EHR-linked cancer registry. Prevalence prediction modelling of HER2/ERBB2 mutation was performed using machine learning (ML) methods including logistic regression (LR), random forest (RF), naïve Bayes (NB), extreme gradient boosting (XGBoost) and multi-layer perceptron (MLP). 10- fold cross validation and k-nearest neighbor method were used to impute missing values. Under-sampling, oversampling and feature selection methods were tried. Results: Among 761 NSCLC patients with NGS reports available, 353 (46%) were ≤65 years old. Among 353 (mean age 57, 58% female, 88% non-Hispanic, 69% white, 57% had metastasis, 58% prescribed chemotherapy) 25 (7%) had a HER2 mutation. Based on univariate significance tests with LR on the complete data: cerebrovascular disease, BMI between 27.5-29.9, KRAS, EGFR and MET were significant with odds ratios (ORs) ranging from 0.2-3.9. In our prediction models, BMI, cerebrovascular disease, weight loss, hepatic metastases, diabetes, congestive heart failure, and KRAS, EGFR, MET and RET alteration status were significant predictors in multivariate analyses, suggesting the potential usefulness of these features in predicting HER2 mutation among patients with NSCLC. Area under the ROC curve (AUC) varied across different models from 70.9% (RF), 70% (XGB and MLP), 69.5% (LR) to 68.2% (Naïve Bayes). The best performing model with RF yielding an AUC ~71% can provide relative high specificity and acceptable sensitivity depending on the cut off. Conclusions: Our prediction models yielded AUC around 70% with the best performing model’s AUC being 71% in a subgroup aged ≤65. After further validation, our prediction model may be used to identify patients appropriate for repeat NGS testing who did not have HER2 testing in first molecular profiling. Future work is needed to predict status in repeat testing with previously test negative patients who relapse or have progression of disease. Random forest (RF) Model performance across different cutoff for the predicted probability. [Table: see text]

Abstract Funding Acknowledgements Type of funding sources: None. Background/Introduction Immune checkpoint inhibitor (ICI) myocarditis is a life-threatening condition characterized by lymphocytic myocardial infiltration[1]. Complete heart block (CHB) occurs in 15% of cases and is often fatal[2-4]. The nature and optimal management of this often-malignant condition is poorly understood[5]. Purpose This case series assesses prognostic factors associated with survival at 90 days among people with ICI myocarditis and CHB. Methods The electronic patient record system was used to identify patients admitted with ICI myocarditis. A two-physician review was used to assess the presence of ICI-associated CHB. Wilcoxon rank sum and Spearman’s rank correlation were used for categorical and quantitative variables respectively. Data was analysed using STATA/IC 16.1. The primary outcome was person-day survival 90 days after presenting symptom onset. Results Six patients were included in the final analysis. Mean survival was 47 person days. All had metastatic disease and presented 15 to 32 days after the first ICI infusion. All had muscle weakness. Other common self-symptoms were shortness of breath (4), blurred vision (4), double vision (3), collapse (3), and leg swelling (1). Proximal myopathy (2), fatigable myopathy (1), ptosis (3), and peripheral oedema (1) were present on examination. Raised troponin T and white blood cell count (WBC) were associated with 90-day survival (see Table 1). Nt-pro BNP, AST, and CK levels were raised in all patients. Two patients were tested for anti-striated muscle antibody titres, and both were strongly positive. Increase in QRS duration compared to baseline ECG was associated with lower survival at 90 days. Only one patient had CHB on admission. Steroid dose, IVIG, and plasmapheresis weren’t associated with survival. One patient declined device therapy. One declined permanent pacemaker (PPM) after insertion of transvenous pacemaker (TVP). Both died within 2 weeks. Three people underwent insertion of a transvenous pacemaker (TVP) followed by PPM insertion (table 2). All three eventually died within 100 days. One person received isoproterenol and immunosuppression as a bridge to PPM and survived. One patient was managed initially with TVP but subsequently declined PPM insertion. TVP removal resulted in CHB recurrence, and they died shortly after discharge. One person declined device therapy and died while receiving comfort care. Lead malfunction was frequent. One patient experienced bradycardia arrest with failure of a TVP lead to capture, while another experienced lead dislodgement. Conclusion CHB secondary to ICI is often fatal. It should be considered in patients presenting with symptoms of myositis, raised troponin and increased QRS duration. Despite a small sample size, this study identifies several prognostic factors which can guide both patients and physicians with decision-making. Larger studies are needed to better understand the role of device therapy in this population.

Background: Recognition of the causes of early mortality after atrial fibrillation (AF) catheter ablation is essential for the improvement of patient safety. This study sought to determine the causes of early mortality (≤90 days) after AF ablation. Methods: We performed a retrospective analysis of AF ablation from January 1, 2013, to December 1, 2021 at the Mayo Clinic (Rochester, Phoenix, and Jacksonville). Causes of death were identified through a comprehensive chart review of the electronic health record from within the Mayo Clinic system and outside records when available. Results: A total of 6723 patients were included in the study. The 90-day all-cause mortality rate was 0.22% (n=15). Among all 90-day deaths, majority of the deaths (73.3%) did not have a direct relationship with the procedure. Sudden death was the most common cause of early death (20%), followed by peri-procedural stroke (13%), respiratory failure (13%), atrioesophageal fistula (13%), infection (7%), heart failure (7%), and traumatic brain injury (7%). The 90-day mortality rate directly due to AF ablation procedural complications was 0.06% (n=4). Conclusions: AF ablation procedure has a 90-day mortality of 0.22%, and the most common cause of early mortality was sudden death. The majority (73.3%) of early mortality was not directly associated with a procedural complication, and the mortality rate due to complications associated with the AF ablation procedure was low at 0.06%. Further studies are required to investigate causes and risk factors associated with sudden death in this patient population.

Microwaves are non-ionizing electromagnetic radiation with waves of electrical and magnetic energy transmitted at different frequencies. They are widely used in various industries, including the food industry, telecommunications, weather forecasting, and in the field of medicine. Microwave applications in medicine are relatively a new field of growing interest, with a significant trend in healthcare research and development. The first application of microwaves in medicine dates to the 1980s in the treatment of cancer via ablation therapy; since then, their applications have been expanded. Significant advances have been made in reconstructing microwave data for imaging and sensing applications in the field of healthcare. Artificial intelligence (AI)-enabled microwave systems can be developed to augment healthcare, including clinical decision making, guiding treatment, and increasing resource-efficient facilities. An overview of recent developments in several areas of microwave applications in medicine, namely microwave imaging, dielectric spectroscopy for tissue classification, molecular diagnostics, telemetry, biohazard waste management, diagnostic pathology, biomedical sensor design, drug delivery, ablation treatment, and radiometry, are summarized. In this contribution, we outline the current literature regarding microwave applications and trends across the medical industry and how it sets a platform for creating AI-based microwave solutions for future advancements from both clinical and technical aspects to enhance patient care.

Production of bowel sounds, established in the 1900s, has limited application in existing patient-care regimes and diagnostic modalities. We review the physiology of bowel sound production, the developments in recording technologies and the clinical application in various scenarios, to understand the potential of a bowel sound recording and analysis device—the phonoenterogram in future gastroenterological practice. Bowel sound production depends on but is not entirely limited to the type of food consumed, amount of air ingested and the type of intestinal contractions. Recording technologies for extraction and analysis of these include the wavelet-based filtering, autoregressive moving average model, multivariate empirical mode decompression, radial basis function network, two-dimensional positional mapping, neural network model and acoustic biosensor technique. Prior studies evaluate the application of bowel sounds in conditions such as intestinal obstruction, acute appendicitis, large bowel disorders such as inflammatory bowel disease and bowel polyps, ascites, post-operative ileus, sepsis, irritable bowel syndrome, diabetes mellitus, neurodegenerative disorders such as Parkinson’s disease and neonatal conditions such as hypertrophic pyloric stenosis. Recording and analysis of bowel sounds using artificial intelligence is crucial for creating an accessible, inexpensive and safe device with a broad range of clinical applications. Microwave-based digital phonoenterography has huge potential for impacting GI practice and patient care.

Atrial fibrillation (AF) is the most persistent arrhythmia today, with its prevalence increasing exponentially with the rising age of the population. Particularly at elevated heart rates, a functional abnormality known as cardiac alternans can occur prior to the onset of lethal arrhythmias. Cardiac alternans are a beat-to-beat oscillation of electrical activity and the force of cardiac muscle contraction. Extensive evidence has demonstrated that microvolt T-wave alternans can predict ventricular fibrillation vulnerability and the risk of sudden cardiac death. The majority of our knowledge of the mechanisms of alternans stems from studies of ventricular electrophysiology, although recent studies offer promising evidence of the potential of atrial alternans in predicting the risk of AF. Exciting preclinical and clinical studies have demonstrated a link between atrial alternans and the onset of atrial tachyarrhythmias. Here, we provide a comprehensive review of the clinical utility of atrial alternans in identifying the risk and guiding treatment of AF.