Cardiogenic shock complicating non-ST-segment elevation myocardial infarction: An 18-year study.

OBJECTIVE: To evaluate the epidemiology and outcomes of non-ST-segment-elevation myocardial infarction-cardiogenic shock (NSTEMI-CS) in the United States. METHODS: Adult (>18 years) NSTEMI-CS admissions were identified using the National Inpatient Sample (2000-2017) and classified by tertiles of admission year (2000-2005, 2006-2011 and 2012-2017). Outcomes of interest included temporal trends of prevalence and in-hospital mortality, use of cardiac procedures, in-hospital mortality, hospitalization costs, and length of stay. RESULTS: In over 7.3 million NSTEMI admissions, CS was noted in 189,155 (2.6%). NSTEMI-CS increased from 1.5% in 2000 to 3.6% in 2017 (adjusted odds ratio 2.03 [95% confidence interval 1.97-2.09]; P < .001). Rates of non-cardiac organ failure and cardiac arrest increased during the study period. Between 2000 and 2017, coronary angiography (43.9%-63.9%), early coronary angiography (13.6%-25.6%), percutaneous coronary intervention (14.8%-31.6%), and coronary artery bypass grafting use (19.0%-25.8%) increased (P < .001). Over the study period, the use of intra-aortic balloon pump remained stable (28.6%-28.8%), and both percutaneous left ventricular assist devices (0%-9.1%) and extra-corporeal membrane oxygenation (0.1%-1.6%) increased (all P < .001). In hospital mortality decreased from 50.2% in 2000 to 32.3% in 2017 (adjusted odds ratio 0.27 [95% confidence interval 0.25-0.29]; P < .001). During the 18-year period, hospital lengths of stay decreased, and hospitalization costs increased. CONCLUSIONS: In the United States, prevalence of CS in NSTEMI has increased 2-fold between 2000 and 2017, while in-hospital mortality has decreased during the study period. Use of coronary angiography and percutaneous coronary intervention increased during the study period.

Renin-Angiotensin System Blocker in COVID-19. A Single Center Study.

ABSTRACT: Early during the Coronavirus disease 2019 (Covid-19) pandemic, concerns were raised regarding potential adverse outcomes in patients taking angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs). These concerns were based on animal studies showing increased ACE-2 expression in mice treated with ACEI/ARB. This is a single-center, retrospective, cohort study of 289 patients diagnosed with 2019 Novel Coronavirus (SARS-CoV-2) hospitalized between March of 2020 and June of 2020. The study was intended to investigate the impact of ACEIs and/or ARBs on in-hospital mortality, intensive care unit (ICU) admission, postadmission hemodialysis requirement, and the need for mechanical ventilation in patients with COVID-19. This cohort of 289 patients included 139 of 289 women (48%) with a mean age of 61 ± 19 years. Patients using ACEIs/ARBs were older (69.68 vs. 57.9 years; P < 0.0001), more likely to have a history of hypertension (97% vs. 36%; P < 0.0001), diabetes mellitus (48% vs. 20.9%; P < 0.0001), chronic heart failure (11.39% vs. 4.29%; P < 0.0512), coronary artery disease (20.25% vs. 7.14%; P < 0.0025), stroke/Transient Ischemic Attack (7.59% vs. 2.38%; P < 0.0761), chronic kidney disease (11.39% vs. 3.33%; P < 0.0167), atrial fibrillation/flutter (18.99% vs. 7.14%; P < 0.0080), and dementia (22.7% vs. 11.4%; P < 0.0233) compared with the nonuser group. There was significantly higher in-hospital mortality in patients using ACEIs/ARBs than nonusers, respectively (32.9% vs. 15.2%; P < 0.0015). However, a multivariate logistics regression analysis performed to adjust for common confounders demonstrated no significant difference in all-cause in-patient mortality (P 0.7141). Admission to ICU, postadmission hemodialysis requirement, and mechanical ventilation showed no significant differences between the 2 groups (P = NS). This study suggests that the use of ACEIs and ARBs in patients with COVID-19 was not found to significantly increase all-cause in-hospital mortality, ICU admissions, and hemodialysis and mechanical ventilation requirements.

Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports.

BACKGROUND: Cardiac magnetic resonance (CMR) imaging is important for diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM) patients. However, collection of information from large numbers of CMR reports by manual review is time-consuming, error-prone and costly. Natural language processing (NLP) is an artificial intelligence method for automated extraction of information from narrative text including text in CMR reports in electronic health records (EHR). Our objective was to assess whether NLP can accurately extract diagnosis of HCM from CMR reports. METHODS: An NLP system with two tiers was developed for information extraction from narrative text in CMR reports; the first tier extracted information regarding HCM diagnosis while the second extracted categorical and numeric concepts for HCM classification. We randomly allocated 200 HCM patients with CMR reports from 2004 to 2018 into training (100 patients with 185 CMR reports) and testing sets (100 patients with 206 reports). RESULTS: NLP algorithms demonstrated very high performance compared to manual annotation. The algorithm to extract HCM diagnosis had accuracy of 0.99. The accuracy for categorical concepts included HCM morphologic subtype 0.99, systolic anterior motion of the mitral valve 0.96, mitral regurgitation 0.93, left ventricular (LV) obstruction 0.94, location of obstruction 0.92, apical pouch 0.98, LV delayed enhancement 0.93, left atrial enlargement 0.99 and right atrial enlargement 0.98. Accuracy for numeric concepts included maximal LV wall thickness 0.96, LV mass 0.99, LV mass index 0.98, LV ejection fraction 0.98 and right ventricular ejection fraction 0.99. CONCLUSIONS: NLP identified and classified HCM from CMR narrative text reports with very high performance.

Conversion of left atrial volume to diameter for automated estimation of sudden cardiac death risk in hypertrophic cardiomyopathy.

BACKGROUND: A subset of patients with hypertrophic cardiomyopathy (HCM) is at high risk of sudden cardiac death (SCD). Practice guidelines endorse use of a risk calculator, which requires entry of left atrial (LA) diameter. However, American Society of Echocardiography (ASE) guidelines recommend the use of LA volume index (LAVI) for routine quantification of LA size. The aims of this study were to (a) develop a model to estimate LA diameter from LAVI and (b) evaluate whether substitution of measured LA diameter by estimated LA diameter derived from LAVI reclassifies HCM-SCD risk. METHODS: The study cohort was comprised of 500 randomly selected HCM patients who underwent transthoracic echocardiography (TTE). LA diameter and LAVI were measured offline using digital clips from TTE. Linear regression models were developed to estimate LA diameter from LAVI. A European Society of Cardiology endorsed equation estimated SCD risk, which was measured using LA diameter and estimated LA diameter derived from LAVI. RESULTS: The mean LAVI was 48.5 ± 18.8 mL/m2 . The derived LA diameter was 45.1 mm (SD: 5.5 mm), similar to the measured LA diameter (45.1 mm, SD: 7.1 mm). Median SCD risk at 5 years estimated by measured LA diameter was 2.22% (interquartile range (IQR): 1.39, 3.56), while median risk calculated by estimated LA diameter was 2.18% (IQR: 1.44, 3.52). 476/500 (95%) patients maintained the same risk classification regardless of whether the measured or estimated LA diameter was used. CONCLUSIONS: Substitution of measured LA diameter by estimated LA diameter in the HCM-SCD calculator did not reclassify risk.

Apical Hypertrophic Cardiomyopathy With a Sub-aortic Membrane: A Case Report.

Hypertrophic obstructive cardiomyopathy (HOCM) and subaortic membrane (SAS) are distinct cardiac conditions, but their coexistence presents complex diagnostic challenges. We report the case of a 52-year-old male with HOCM and a concurrent subaortic membrane, highlighting the intricacies of diagnosis and management. The patient's presentation included symptoms of dyspnea and chest tightness, and diagnostic evaluations revealed a unique combination of dynamic left ventricular outflow tract (LVOT) obstruction from HOCM and fixed obstruction from the subaortic membrane. This case emphasizes the importance of a comprehensive diagnostic workup to guide appropriate treatment decisions when managing multiple cardiac abnormalities.

Sex differences in heart transplantation - analysis of the national inpatient sample 2012-2019.

INTRODUCTION: Advanced heart failure therapies and heart transplantation (HT) have been underutilized in women. Therefore, we aimed to explore the clinical characteristics and outcomes of HT by sex. METHODS: We conducted a retrospective analysis of adult discharges from the National Inpatient Sample (NIS) between 2012 and 2019. International Classification of Disease (ICD) procedure codes were used to identify those who underwent HT. RESULTS: A total of 20,180 HT hospitalizations were identified from 2012-2019. Among them, 28 % were female. Women undergoing HT were younger (mean age 51 vs. 54.5 years, p<0.001). HT hospitalizations among men were more likely to have atrial fibrillation, diabetes, hypertension, renal failure, dyslipidemia, smoking, and ischemic heart disease. HT hospitalizations among women were more likely to have hypothyroidism and valvular heart disease. HT hospitalizations in women were associated with no significant difference in risk of in-hospital mortality (adjusted odds ratio [OR] 0.82; 95 % confidence interval [CI] 0.58-1.16, p=0.271), no significant difference in length of stay or inflation-adjusted cost. Men were more likely to develop acute kidney injury during HT hospitalization (69.2 % vs. 59.7 %, adjusted OR 0.71, 95 % CI 0.61-0.83, p<0.001). CONCLUSIONS: HT utilization is lower in women. However, most major in-hospital outcomes for HT are similar between the sexes. Further studies are need to explore the causes of lower rates of HT in women.

Association of Non-Alcoholic Fatty Liver Disease With in-Hospital Outcomes in Primary Heart Failure Hospitalizations With Reduced or Preserved Ejection Fraction.

Recent studies focusing on the prevalence, characteristics, and outcomes of primary heart failure (HF) with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF) in non-alcoholic fatty liver disease (NAFLD) are sparse. We sought to assess these using a nationally-representative population. We used the 2016-2018 National Inpatient Sample database to study the prevalence, characteristics, clinical risk profiles, morbidity, mortality, cost, and resource utilization among primary HFpEF and HFrEF hospitalizations with and without NAFLD. In the period from January 1, 2016, to December 31, 2018, there were 3,522,459 admissions of patients aged ≥18 years with a diagnosis of primary HF. Of these, 82,585 (2.3%) hospitalizations had secondary diagnosis of NAFLD. Admissions with NAFLD and HFrEF were associated with higher rates of in-hospital mortality (aOR 1.84, CI 1.66-2.04, P < 0.001) compared to admissions of HFrEF without NAFLD. Similarly, hospitalizations with HFpEF-NAFLD were associated with higher rates of in hospital mortality (aOR 1.65 CI 1.43-1.9, P < 0.001) compared to HFpEF admissions without NAFLD. Pressors use, cardiogenic shock, AKI with or without dialysis use, cardiac arrest, LOS and hospitalization cost were higher in admissions of HFrEF and HFpEF with NAFLD compared to those without NAFLD. In-hospital mortality, was higher in primary HFrEF and HFpEF admissions with NAFLD compared to without NAFLD. Physicians must be aware of the worse clinical outcomes of HFrEF and HFpEF in patients with NAFLD. Further clinical research is needed to address the knowledge gap and treatment options available for the patients with HF and NAFLD.

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes.

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.

Association of Chronic Renal Insufficiency with Inhospital Outcomes in Primary Heart Failure Hospitalizations (Insights from the National Inpatient Sample 2004 to 2018).

Chronic kidney disease (CKD) is a major co-morbidity in patients with heart failure (HF). There are limited contemporary data characterizing the clinical profile, inhospital outcomes, and resource use in patients hospitalized for HF with co-morbid CKD. We utilized a nationally representative population to address the knowledge gap. We examined the National Inpatient Sample 2004 to 2018 database to study the co-morbid profile, in-hospital mortality, clinical resource utilization, healthcare cost, and length of stay (LOS) in primary adult HF hospitalizations stratified by presence versus absence of a diagnosis codes of CKD. There were a total of 16,050,301 adult hospitalizations with a primary HF diagnosis from January 1, 2004, to December 31, 2018. Of these, 428,175 (33.81%) had CKD; 1,110,778 (6.92%) had end-stage kidney disease (ESKD); and 9,511,348 (59.25%) had no diagnosis of CKD. Patients with hospitalizations for HF with ESKD were younger (mean age 65.4 years) compared with those without ESKD. In multivariable analysis, those with CKD had higher odds of inhospital mortality (2.82% vs 3.57%, adjusted odds ratio [aOR] 1.30, confidence interval [CI] 1.28 to 1.26, p <0.001), cardiogenic shock (1.01% vs 1.79% aOR 2.00, CI 1.95 to 2.05, p <0.001), and the need for mechanical circulatory support (0.4% vs 0.5%, aOR 1.51, 1.44 to 1.57, p <0.001) compared with those without CKD. In multivariable analysis, those with ESKD had higher odds of inhospital mortality (2.82% vs 3.84%, aOR 2.07, CI 2.01 to 2.12, p <0.001), need for invasive mechanical ventilation use (2.04% vs 3.94%, aOR 1.79, CI 1.75 to 1.84, p <0.001), cardiac arrest (0.72% vs 1.54%, aOR 2.09, CI 2.00 to 2.17, p <0.001), longer LOS (Adjusted mean difference 1.48, 1.44 to 1.53, p <0.001) and higher inflation-adjusted cost (Adjusted mean difference 3,411.63, CI 3,238.35 to 3,584.91, p <0.001) compared with those without CKD. CKD and ESKD affected about 40.7% of all primary HF hospitalizations from 2004 to 2018. The inhospital mortality, clinical complications, LOS, and inflation-adjusted cost were higher in hospitalized patients with ESKD compared with patients with and without CKD. In addition, compared with those without CKD, hospitalized patients with CKD had higher inhospital mortality, clinical complications, LOS, and inflation-adjusted cost compared with patients with no diagnosis of CKD.

Pathophysiology and Management of Heart Failure in the Elderly.

The population of elderly adults is increasing globally. It has been projected that the population of adults aged 65 years will increase by approximately 80% by 2050 in the United States. Similarly, the elderly population is rising in other countries; a notable example being Japan where approximately 30% of the population are aged above 65 years. The pathophysiology and management of heart failure (HF) in this age group tend to have more intricacies than in younger age groups owing to the presence of multiple comorbidities. The normal aging biology includes progressive disruption at cellular and genetic levels and changes in molecular signaling and mechanical activities that contribute to myocardial abnormalities. Older adults with HF secondary to ischemic or valvular heart disease may benefit from surgical therapy, valve replacement or repair for valvular heart disease and coronary artery bypass grafting for coronary artery disease. While referring these patients for surgery, patient and family expectations and life expectations should be taken into account. In this review, we will cover the pathophysiology and the management of HF in the elderly, specifically discussing important geriatric domains such as frailty, cognitive impairment, delirium, polypharmacy, and multimorbidity.

Predictors of Mortality for Patients with COVID-19 in the Rural Appalachian Region.

BACKGROUND: The prevalence and outcome of coronavirus disease 2019 (COVID-19) in rural areas is unknown. METHODS: This is a multi-center retrospective cohort study of hospitalized patients diagnosed with COVID-19 from April 5, 2020 to December 31, 2020. The data were extracted from 13 facilities in the Appalachian Regional Healthcare system that share the same electronic health record using ICD-10-CM codes. RESULTS: The number of patients diagnosed with COVID-19 per facility ranged from 5 to 535 with a median of 106 patients. Total mortality was 11.4% and ranged from 0% to 22.6% by facility (median: 9.0%). Non-survivors had a greater prevalence of congestive heart failure (CHF), hypertension, type 2 diabetes mellitus, stroke, transient ischemic attack (TIA), and pulmonary embolism. Patients who died were also more likely to have had chronic obstructive pulmonary disease (COPD), acute respiratory failure (ARF), liver cirrhosis, chronic kidney disease (CKD), dementia, cancer, anemia, and opiate dependence. CONCLUSION: The aging population, multiple co-morbidities, and health-related behaviors make rural patients vulnerable to COVID-19. A better understanding of the disease in rural areas is crucial, given its heightened vulnerability to adverse outcomes.

Cardiogenic Shock Complicating ST-Segment Elevation Myocardial Infarction: An 18-Year Analysis of Temporal Trends, Epidemiology, Management, and Outcomes.

BACKGROUND: There are limited data on the temporal trends, incidence, and outcomes of ST-segment-elevation myocardial infarction-cardiogenic shock (STEMI-CS). METHODS: Adult (>18 years) STEMI-CS admissions were identified using the National Inpatient Sample (2000-2017) and classified by tertiles of admission year (2000-2005, 2006-2011, 2012-2017). Outcomes of interest included temporal trends, acute organ failure, cardiac procedures, in-hospital mortality, hospitalization costs, and length of stay. RESULTS: In ∼4.3 million STEMI admissions, CS was noted in 368,820 (8.5%). STEMI-CS incidence increased from 5.8% in 2000 to 13.0% in 2017 (patient and hospital characteristics adjusted odds ratio [aOR] 2.45 [95% confidence interval {CI} 2.40-2.49]; P < 0.001). Multiorgan failure increased from 55.5% (2000-2005) to 74.3% (2012-2017). Between 2000 and 2017, coronary angiography and percutaneous coronary intervention use increased from 58.8% to 80.1% and 38.6% to 70.6%, whereas coronary artery bypass grafting decreased from 14.9% to 10.4% (all P < 0.001). Over the study period, the use of intra-aortic balloon pump (40.6%-37.6%) decreased, and both percutaneous left ventricular assist devices (0%-12.9%) and extra-corporeal membrane oxygenation (0%-2.8%) increased (all P < 0.001). In hospital mortality decreased from 49.6% in 2000 to 32.7% in 2017 (aOR 0.29 [95% CI 0.28-0.31]; P < 0.001). During the 18-year period, hospital lengths of stay decreased, hospitalization costs increased and use of durable left ventricular assist device /cardiac transplantation remained stable (P > 0.05). CONCLUSIONS: In the United States, incidence of CS in STEMI has increased 2.5-fold between 2000 and 2017, while in-hospital mortality has decreased during the study period. Use of coronary angiography and PCI increased during the study period.

High Prevalence of Cardiac Amyloidosis in Clinically Significant Aortic Stenosis: A Meta-Analysis.

Background: There is growing evidence of coexistence of aortic stenosis (AS) and transthyretin cardiac amyloidosis (CA). Not screening AS patients at the time of hospital/clinic visit for CA represents a lost opportunity. Methods: We surveyed studies that reported the prevalence of CA among AS patients. Studies that compared patients with aortic stenosis with cardiac amyloidosis (AS-CA) and AS alone were further analyzed, and meta-regression was performed. Results: We identified nine studies with 1,321 patients of AS, of which 131 patients had concomitant CA, with a prevalence of 11%. When compared to AS-alone, the patients with AS-CA were older, more likely to be males, had higher prevalence of carpal tunnel syndrome, right bundle branch block. On echocardiogram, patients with AS-CA had thicker interventricular septum, higher left ventricular mass index (LVMI), lower myocardial contraction fraction, and lower stroke volume index. Classical low-flow low-gradient (LFLG) physiology was more common among patients with AS-CA. Patients with AS-CA had higher all-cause mortality than patients with AS alone (33% vs. 22%, P = 0.02) in a follow-up period of at least 1 year. Conclusions: CA has a high prevalence in patients with AS and is associated with worse clinical, imaging, and biochemical parameters than patients with AS alone.

Utilization of Implantable Cardioverter-Defibrillators in Patients With Heart Transplant (from National Inpatient Sample Database).

Heart transplant (HT) recipients represent a unique and vulnerable population in whom medium and long-term outcomes are significantly affected by the risk of arrhythmias and sudden cardiac death. The use of implantable cardioverter-defibrillators (ICDs) in this population remains debated. A retrospective analysis of the National Inpatient Sample data between 2009 and 2018 was conducted. Hospitalization data on patients who underwent HT, or who had a preexisting HT, and who received a new ICD were included (excluding the preexisting ICD). Outcomes assessed included inpatient mortality, length of stay, and inflation-adjusted costs. We explored temporal trends in ICD placement and mean length of stay, and predictors of ICD placement. Between 2009 and 2018, 22,673 hospitalizations were recorded for HT, during which patients either received a concurrent new ICD placement (n = 70 [0.31%]) or no new ICD placement (n = 22,603 [99.7%]). During the same period, 146,555 admissions were recorded in patients with a history of HT. ICD placement in patients with a preexisting HT was associated with significantly higher inflation-adjusted costs ($55,680.7 vs $17,219.2; p <0.001). Predictors of ICD placement in preexisting patients with HT included cardiac arrest during hospitalization (odds ratio [OR]:14.3 [3.5 to 58.6]), drug abuse (OR:6.0 [1.3 to 27.1]), and previous PCI (OR:6.0 [2.1 to 17.3]). In conclusion, ICD placement in patients with HT history was associated with significantly higher inflation-adjusted costs. In patients with HT history, factors predicting ICD placement included cardiac arrest at hospitalization, previous PCI, and drug abuse.

Outcomes of Heart Failure in COVID-19 Patients: An Appalachian Experience.

Background: The Southeastern rural areas of the USA have a higher prevalence of heart failure (HF). Coronavirus disease 2019 (COVID-19) infection is associated with poor outcomes in patients with HF. Our study aimed to compare the outcomes of hospitalized HF patients with and without COVID-19 infection specifically in rural parts of the USA. Methods: We conducted a retrospective cohort study of HF patients with and without COVID-19 hospitalized in Southeastern rural parts of the USA by using the Appalachian Regional Healthcare System. Analyses were stratified by waves from April 1, 2020 to May 31, 2021, and from June 1, 2021 to October 19, 2021. Results: Of the 14,379 patients hospitalized with HF, 6% had concomitant COVID-19 infection. We found that HF patients with COVID-19 had higher mortality rate compared to those without COVID-19 (21.8% versus 3.8%, respectively, P < 0.01). Additionally, hospital resource utilization was significantly higher in HF patients with COVID-19 compared to HF patients without COVID-19 with intensive care unit (ICU) utilization of 21.6% versus 13.8%, P < 0.01, mechanical ventilation use of 17.3% versus 6.2%, P < 0.01, and vasopressor/inotrope use of 16.8% versus 7.9%, P < 0.01. A lower percentage of those with COVID-19 were discharged home compared to those without a COVID-19 diagnosis (63.4% versus 72.0%, respectively). There was a six-fold greater odds of dying in the first wave and seven-fold greater odds of dying in the second wave. Conclusions: Our study confirms previous findings of poor outcome in HF patients with COVID-19. There is a need for review of healthcare resources in rural hospitals which already face numerous healthcare challenges.

Deep Neural Network for Cardiac Magnetic Resonance Image Segmentation.

The analysis and interpretation of cardiac magnetic resonance (CMR) images are often time-consuming. The automated segmentation of cardiac structures can reduce the time required for image analysis. Spatial similarities between different CMR image types were leveraged to jointly segment multiple sequences using a segmentation model termed a multi-image type UNet (MI-UNet). This model was developed from 72 exams (46% female, mean age 63 ± 11 years) performed on patients with hypertrophic cardiomyopathy. The MI-UNet for steady-state free precession (SSFP) images achieved a superior Dice similarity coefficient (DSC) of 0.92 ± 0.06 compared to 0.87 ± 0.08 for a single-image type UNet (p < 0.001). The MI-UNet for late gadolinium enhancement (LGE) images also had a superior DSC of 0.86 ± 0.11 compared to 0.78 ± 0.11 for a single-image type UNet (p = 0.001). The difference across image types was most evident for the left ventricular myocardium in SSFP images and for both the left ventricular cavity and the left ventricular myocardium in LGE images. For the right ventricle, there were no differences in DCS when comparing the MI-UNet with single-image type UNets. The joint segmentation of multiple image types increases segmentation accuracy for CMR images of the left ventricle compared to single-image models. In clinical practice, the MI-UNet model may expedite the analysis and interpretation of CMR images of multiple types.

Refractory Torsades de Pointes Due to Dofetilide Overdose.

Dofetilide, a class III antiarrhythmic, is widely used in the treatment of cardiac arrhythmias. Antiarrhythmic drugs can have a long duration of action that prolongs the QT interval. This causes bradycardia that predisposes to R-on-T phenomenon subsequently leading to torsades de pointes (TdP). This necessitates constant monitoring to prevent or treat ventricular arrhythmias or bradycardia associated with cardiac medications. Although extremely rare, dofetilide overdose has been described in the literature. However, no evidence found in the current literature required prolonged intervention after the initial acute stabilization, leading to scarcity of data for treatment of ongoing dofetilide overdose. We present the case of an intentional dofetilide overdose in a 61-year-old Caucasian woman with a history of congestive heart failure, atrial fibrillation, stage IIIb chronic kidney disease, diabetes mellitus type II, hypothyroidism, morbid obesity, and hypertension that required extensive interventions for refractory TdP that lasted 4 days. Therapeutic as well as excess dosage of dofetilide can lead to TdP, which is usually controlled by decreasing the dose or terminating drug administration. If the arrhythmia is not resolved, guidelines recommend management with activated charcoal if ingestion is within 15 minutes, followed by administration of 2 g IV (intravenous) magnesium and addressing the electrolyte imbalance. However, if the arrhythmia is persistent due to ongoing dofetilide toxicity, isoproterenol is given as a bridge to overdrive pacing and dopamine is used as an alternative to isoproterenol.

A Rare Presentation of an Acute Type A Aortic Dissection Obscured Due to the Simultaneous Presence of Acute Pancreatitis.

Introduction: Acute pancreatitis (AP) and acute aortic dissection (AAD) are medical emergencies that must be promptly recognized to avoid the development of life-threatening complications. Both of these diseases can present with chest or epigastric pain which can radiate to the back, thus, early suspicion based on clinical presentation and risk factors is essential. We present the case of a 56-year-old patient initially diagnosed with AP who was later found to have an AAD. Clinical Findings: A 56-year-old man with a history of alcohol abuse presented with 1 day of diffuse abdominal pain, nausea and vomiting. His lipase was 3,909 U/L and creatinine was 2.19 mg/dL and he was diagnosed with acute alcoholic pancreatitis with acute kidney injury. A non-contrast computed tomography (NCCT) scan of the abdomen showed aortic calcifications. He received 3.8 liters of fluids after which he developed acute respiratory distress requiring intubation. A workup for extracorporeal membrane oxygenation (ECMO) was initiated, given the suspicion of acute respiratory distress syndrome (ARDS) due to pancreatitis. This revealed an AAD with severe aortic regurgitation on transthoracic echocardiography (TTE). CT angiogram showed type A AAD involving the aortic root, ascending aorta, descending aorta, suprarenal and infrarenal abdominal aorta. The celiac axis, superior mesenteric artery (SMA) and inferior mesenteric artery (IMA) were patent. Outcomes: The patient underwent type A AAD repair with mechanical aortic valve replacement and survived the acute event. His AP resolved and he was discharged home with appropriate follow up. Conclusion: We hypothesize that if our patient was not assessed for ECMO, the finding of AAD would have been a diagnostic challenge. AP secondary to AAD is rare but a high index of suspicion is required for diagnosis.

Natural language processing of implantable cardioverter-defibrillator reports in hypertrophic cardiomyopathy: A paradigm for longitudinal device follow-up.

BACKGROUND: The follow-up of implantable cardioverter-defibrillators (ICDs) generates large amounts of valuable structured and unstructured data embedded in device interrogation reports. OBJECTIVE: We aimed to build a natural language processing (NLP) model for automated capture of ICD-recorded events from device interrogation reports using a single-center cohort of patients with hypertrophic cardiomyopathy (HCM). METHODS: A total of 687 ICD interrogation reports from 247 HCM patients were included. Using a derivation set of 480 reports, we developed a rule-based NLP algorithm based on unstructured (free-text) data from the interpretation field of the ICD reports to identify sustained atrial and ventricular arrhythmias, and ICD therapies. A separate model based on structured numerical tabulated data was also developed. Both models were tested in a separate set of the 207 remaining ICD reports. Diagnostic performance was determined in reference to arrhythmia and ICD therapy annotations generated by expert manual review of the same reports. RESULTS: The NLP system achieved sensitivity 0.98 and 0.99, and F1-scores 0.98 and 0.92 for arrhythmia and ICD therapy events, respectively. In contrast, the performance of the structured data model was significantly lower with sensitivity 0.33 and 0.76, and F1-scores 0.45 and 0.78, for arrhythmia and ICD therapy events, respectively. CONCLUSION: An automated NLP system can capture arrhythmia events and ICD therapies from unstructured device interrogation reports with high accuracy in HCM. These findings demonstrate the feasibility of an NLP paradigm for the extraction of data for clinical care and research from ICD reports embedded in the electronic health record.