Impact of Preexisting Heart Failure on Treatment and Outcomes in Older Patients With Hodgkin Lymphoma.

Background: Older patients with Hodgkin lymphoma (HL) often have comorbid cardiovascular disease; however, the impact of pre-existing heart failure (HF) on the management and outcomes of HL is unknown. Objectives: The aim of this study was to assess the prevalence of pre-existing HF in older patients with HL and its impact on treatment and outcomes. Methods: Linked Surveillance, Epidemiology, and End Results (SEER) and Medicare data from 1999 to 2016 were used to identify patients 65 years and older with newly diagnosed HL. Pre-existing HF, comorbidities, and cancer treatment were ascertained from billing codes and cause-specific mortality from SEER. The associations between pre-existing HF and cancer treatment were estimated using multivariable logistic regression. Cause-specific Cox proportional hazards models adjusted for comorbidities and cancer treatment were used to estimate the association between pre-existing HF and cause-specific mortality. Results: Among 3,348 patients (mean age 76 ± 7 years, 48.6% women) with newly diagnosed HL, pre-existing HF was present in 437 (13.1%). Pre-existing HF was associated with a lower likelihood of using anthracycline-based chemotherapy regimens (OR: 0.42; 95% CI: 0.29-0.60) and a higher likelihood of lymphoma mortality (HR: 1.25; 95% CI: 1.06-1.46) and cardiovascular mortality (HR: 2.57; 95% CI: 1.96-3.36) in models adjusted for comorbidities. One-year lymphoma mortality cumulative incidence was 37.4% (95% CI: 35.5%-39.5%) with pre-existing HF and 26.3% (95% CI: 25.0%-27.6%) without pre-existing HF. The cardioprotective medications dexrazoxane and liposomal doxorubicin were used in only 4.2% of patients. Conclusions: Pre-existing HF in older patients with newly diagnosed HL is common and associated with higher 1-year mortality. Strategies are needed to improve lymphoma and cardiovascular outcomes in this high-risk population.

Automated Detection of Aortic Stenosis Using Machine Learning.

BACKGROUND: Aortic stenosis (AS) is a degenerative valve condition that is underdiagnosed and undertreated. Detection of AS using limited two-dimensional echocardiography could enable screening and improve appropriate referral and treatment of this condition. The aim of this study was to develop methods for automated detection of AS from limited imaging data sets. METHODS: Convolutional neural networks were trained, validated, and tested using limited two-dimensional transthoracic echocardiographic data sets. Networks were developed to accomplish two sequential tasks: (1) view identification and (2) study-level grade of AS. Balanced accuracy and area under the receiver operator curve (AUROC) were the performance metrics used. RESULTS: Annotated images from 577 patients were included. Neural networks were trained on data from 338 patients (average n = 10,253 labeled images), validated on 119 patients (average n = 3,505 labeled images), and performance was assessed on a test set of 120 patients (average n = 3,511 labeled images). Fully automated screening for AS was achieved with an AUROC of 0.96. Networks can distinguish no significant (no, mild, mild to moderate) AS from significant (moderate or severe) AS with an AUROC of 0.86 and between early (mild or mild to moderate AS) and significant (moderate or severe) AS with an AUROC of 0.75. External validation of these networks in a cohort of 8,502 outpatient transthoracic echocardiograms showed that screening for AS can be achieved using parasternal long-axis imaging only with an AUROC of 0.91. CONCLUSION: Fully automated detection of AS using limited two-dimensional data sets is achievable using modern neural networks. These methods lay the groundwork for a novel method for screening for AS.

How often do prescribers include indications in drug orders? Analysis of 4 million outpatient prescriptions.

PURPOSE: To examine the extent to which outpatient clinicians currently document drug indications in prescription instructions. METHODS: Free-text sigs were extracted from all outpatient prescriptions generated by the computerized prescriber order entry system of a major academic institution during a 5-year period. Natural language processing was used to identify drug indications. The data set was analyzed to determine the rates at which prescribers included indications. It was stratified by provider specialty, drug class, and specific medications, to determine how often these indications were in prescriptions for as-needed (PRN) versus non-PRN medications. RESULTS: During the study period, 4,356,086 prescriptions were ordered. Indications were included in 322,961 orders (7.41%). From these orders, 249,262 indications (77.18%) were written for PRN orders. Although internal medicine prescribers generated the highest number of medication orders, they included indications in only 6.26% of their prescriptions, whereas orthopedic surgery providers had the highest rate of documenting indications (33.41%). Pain was the most common indication, accounting for 30.35% of all documented indications. The drug class with the highest number of sigs-containing indications was narcotic analgesics. Non-PRN chronic medication prescriptions rarely included the indication. CONCLUSION: Prescribers rarely included drug indications in electronic free-text prescription instructions, and, when they did, it was mostly for PRN uses such as pain.

From bedside to bench and back again: translational studies of mechanical unloading of the left ventricle to promote recovery after acute myocardial infarction.

Heart failure is a major cause of global morbidity and mortality. Acute myocardial infarction (AMI) is a primary cause of heart failure due in large part to residual myocardial damage despite timely reperfusion therapy. Since the 1970's, multiple preclinical laboratories have tested whether reducing myocardial oxygen demand with a mechanical support pump can reduce infarct size in AMI. In the past decade, this hypothesis has been studied using contemporary circulatory support pumps. We will review the most recent series of preclinical studies in the field which led to the recently completed Door to Unload ST-segment Elevation Myocardial Infarction (DTU-STEMI) safety and feasibility pilot trial.