Citations

Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.

AbstractBackgroundAccurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs.ObjectiveDevelop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG.MethodsWe developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model).ResultsOf the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording.ConclusionAccurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.

Abstract Background Accurate automated wide QRS complex tachycardia (WCT) discrimination between ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using novel calculations derived from computerized electrocardiogram (ECG) data from paired WCT and baseline ECGs. Purpose Our aim was to develop and trial novel WCT discrimination approaches for WCT patients with and without a corresponding baseline ECG. Central to this analysis was the creation and use of a novel parameter (i.e., percent monophasic time-voltage area [PMonoTVA] [%]) that may be derived from computerized ECG measurements present on the WCT ECG alone. Methods In a two-part study, we derived and tested WCT differentiation models comprised of novel and previously established parameters formulated from computerized data of paired WCT and baseline ECGs. In Part 1, novel and established parameters generated from WCT and baseline ECG data were used to derive, validate, and compare five different binary classification models: (i) logistic regression [LR], (ii) artificial neural network [ANN], (iii) Random Forests [RF], (iv) support vector machine [SVM], and (v) ensemble learning (EL). In Part 2, two unique LR models were derived, validated, and compared using parameters generated from computerized data of the (i) WCT ECG alone (i.e., Solo Model) and (ii) paired WCT and baseline ECGs (i.e., Paired Model). Results In Part 1, among 103 patients with VT or SWCT diagnoses established from corroborating electrophysiology studies or intra-cardiac device recordings, favorable diagnostic performance was achieved by all modeling technique subtypes: LR (area under the receiver operating characteristic curve [AUC] 0.95), ANN (AUC 0.91), RF (AUC 0.97), SVM (AUC 0.98), and EL (AUC 0.97). In Part 2, among 235 patients with a VT or SWCT diagnosis established with (Gold Standard cohort) or without (Non-Gold Standard cohort) a corroborating electrophysiology procedure or intra-cardiac device recording, favorable diagnostic performance was achieved by the Solo Model (AUC 0.86) and Paired Model (AUC 0.95) (Table). Conclusion Accurate WCT discrimination may be accomplished using novel parameters derived from computerized data of the WCT ECG alone and paired WCT and baseline ECGs. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health

AbstractIncidentally discovered intracardiac masses often represent diagnostic dilemmas. No guideline‐directed algorithm exists for evaluation and management in these cases. Understanding the utility and limitations of different imaging modalities expedites evaluation of differential diagnoses and management, particularly when there are discordant imaging findings. This case further demonstrates that benign cardiac tumors may grow rapidly, and that new and rapid emergence of an intracardiac mass does not necessarily correlate with a diagnosis of thrombus or malignancy. It also highlights the importance of a broad differential diagnosis and a systematic management approach in patients with intracardiac masses.

Abstract Background and Aims Immunotherapy has emerged as an effective treatment for patients with advanced‐stage HCC. We aimed to investigate the efficacy of immunotherapy for advanced HCC in a nationwide cohort and racial and ethnic disparities in access to immunotherapy. Approach and Results We used the US National Cancer Database to identify patients with tumor‐node‐metastasis stage 3 or 4 HCC between 2017 and 2018. We performed multivariable Cox regression to identify factors associated with overall survival (OS) and logistic regression to identify factors associated with receipt of immunotherapy. Of the 3,990 patients treated for advanced HCC, 3,248 (81.4%) patients received chemotherapy and 742 (18.6%) patients received immunotherapy as a first‐line treatment. Immunotherapy was associated with improved OS compared with chemotherapy (adjusted HR: 0.76, 95% CI: 0.65–0.88) after adjusting for covariates. There were racial and ethnic disparities in access to immunotherapy, with Hispanic (adjusted OR [aOR]: 0.63, 95% CI: 0.46–0.83) and Black patients (aOR: 0.71, 95% CI: 0.54–0.89) less likely to receive immunotherapy compared with White patients. There was a significant interaction between race‐ethnicity and facility type, with higher disparity observed in nonacademic centers (interaction p = 0.004). Conclusions Immunotherapy was associated with improved OS compared with chemotherapy in advanced HCC. There are significant disparities in early access to immunotherapy, likely due to differential access to clinical trials and experimental therapies. A comprehensive approach to monitoring and eliminating racial‐ethnic disparities in the management of advanced HCC is urgently needed.

Key Points A computable phenotype combines routinely collected data elements from the EHR with logic elements to identify a condition of interest. This validated computable phenotype has strong classification characteristics to identify individuals with primary nephrotic syndrome. This computable phenotype for primary nephrotic syndrome can facilitate future research of these rare diseases. Background Primary nephrotic syndromes are rare diseases which can impede adequate sample size for observational patient-oriented research and clinical trial enrollment. A computable phenotype may be powerful in identifying patients with these diseases for research across multiple institutions. Methods A comprehensive algorithm of inclusion and exclusion ICD-9 and ICD-10 codes to identify patients with primary nephrotic syndrome was developed. The algorithm was executed against the PCORnet CDM at three institutions from January 1, 2009 to January 1, 2018, where a random selection of 50 cases and 50 noncases (individuals not meeting case criteria seen within the same calendar year and within 5 years of age of a case) were reviewed by a nephrologist, for a total of 150 cases and 150 noncases reviewed. The classification accuracy (sensitivity, specificity, positive and negative predictive value, F1 score) of the computable phenotype was determined. Results The algorithm identified a total of 2708 patients with nephrotic syndrome from 4,305,092 distinct patients in the CDM at all sites from 2009 to 2018. For all sites, the sensitivity, specificity, and area under the curve of the algorithm were 99% (95% CI, 97% to 99%), 79% (95% CI, 74% to 85%), and 0.9 (0.84 to 0.97), respectively. The most common causes of false positive classification were secondary FSGS (nine out of 39) and lupus nephritis (nine out of 39). Conclusion This computable phenotype had good classification in identifying both children and adults with primary nephrotic syndrome utilizing only ICD-9 and ICD-10 codes, which are available across institutions in the United States. This may facilitate future screening and enrollment for research studies and enable comparative effectiveness research. Further refinements to the algorithm including use of laboratory data or addition of natural language processing may help better distinguish primary and secondary causes of nephrotic syndrome.

INTRODUCTION: The purpose of this study was to find out whether dissemination of gastroenterology and hepatology (GI) research on social media networks correlates with citation count at 5 years. METHODS: We correlated the Altmetric Attention Score with Web of Science citation counts at 5 years for scholarly work published in the 10 highest impact factor GI journals in 2014. RESULTS: In 4,026 analyzed items, the correlation ( r ) between Altmetric Attention Score and citations at 5 years was 0.62 ( P < 0.001), representing strong correlation. Twitter was the platform with the strongest correlation with citations. DISCUSSION: Social media attention garnered by GI scholarly work strongly correlates with the number of citations at 5 years.

Abstract Background and Aims: For hepatocellular carcinoma (HCC) with vascular invasion, systemic treatment is recommended but transarterial radioembolization (TARE) is often used in clinical practice. The aim of this study was to investigate the current usage of TARE vs. systemic therapy as a first-line treatment for HCC with vascular invasion in the United States (US). Method: We performed a retrospective cohort study using the National Cancer Database (NCDB), which represents more than 70 percent of newly diagnosed cancer cases nationwide. All HCC patients diagnosed between 2010 and 2015 with the tumor stage of T3BN0M0 according to the 7th edition of the American Joint Committee on Cancer TNM staging system (tumor involving a major branch of a large vein of the liver without lymph nodes or extrahepatic metastasis) and received TARE or systemic treatment as a first-line treatment were included. Multivariable logistic regression was used to estimate factors associated with the utilization of TARE vs. systemic therapy. Results: A total of 1979 patients who received systemic treatment and 524 patients who received TARE were included. The proportion of patients receiving TARE increased from 15.7% in 2010 to 31.4% in 2015 (P&lt;0.01). The median tumor size of the largest lesion was comparable (7.0cm for TARE vs. 7.1 cm for systemic treatment, P=0.43). Factors independently associated with increased likelihood of receiving TARE over systemic treatment were: advanced age (Odds Ratio [OR]: 1.02, 95% confidence interval [CI]: 1.01 - 1.03, p=0.005), insurance status: private (OR: 3.06, 95% CI: 1.61 - 5.65, p&lt;0.001), Medicaid/Medicare (OR: 2.91, 95% CI: 1.52 - 5.31, p&lt;0.001), others (OR: 4.85, 95% CI: 1.90 - 11.62, p&lt;0.001) vs. no insurance, and receiving care in the Midwest vs. Northeast region of the US (OR: 1.89, 95% CI: 1.38 - 2.53, p&lt;0.001). Patients were significantly less likely to receive TARE if receiving treatment at community cancer program (OR: 0.24, 95% CI: 0.098 - 0.49, p&lt;0.001) or comprehensive community cancer program (OR: 0.62, 95% CI: 0.48 - 0.82, p&lt;0.001) vs. academic cancer program. Larger tumor size (OR per 1cm: 0.97, 95% CI: 0.95 - 1.00, p=0.03) and higher Charlson comorbidity index of ≥3 vs. 0 (OR: 0.53, 95% CI: 0.38 - 0.73, p&lt;0.001) were inversely associated with receiving TARE. Recent HCC diagnosis year was associated with an increased likelihood of receiving TARE (OR: 1.23, 95% CI:1.16 - 1.32, p&lt;0.001). Conclusion: The proportion of patients receiving TARE as a first-line treatment for HCC with macrovascular invasion has been increasing in the US. Age, comorbidities, insurance status, tumor size, and the type and location of cancer centers are associated with the usage of TARE vs. systemic treatment in HCC patients with vascular invasion. Citation Format: Joseph C. Ahn, Michael Luu, Walid Ayoub, Alexander Kuo, Andrew Hendifar, Jun Gong, Kambiz Kosari, Shelly Lu, Nicholas Nissen, Ju Dong Yang. Transarterial radioembolization versus systemic treatment for the management of hepatocellular carcinoma with vascular invasion: Analysis of the US National Cancer Database [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2350.