Sacubitril/Valsartan Off-Label Uses for Heart Failure

Sacubitril/valsartan is an angiotensin receptor/neprilysin inhibitor that the Food and Drug Administration has indicated to reduce the risk of cardiovascular hospitalization and death in patients with left ventricular ejection fraction below normal and with no specified ejection-fraction cut-off. However, clinically significant patient groups were excluded or minimally represented in sacubitril/valsartan's pivotal clinical trials. Clinicians often encounter scenarios in which a sacubitril/valsartan off-label use may be beneficial, but limited resources are available to evaluate the efficacy and safety in these patients. This state-of-the-art review describes contemporary literature for sacubitril/valsartan Food and Drug Administration off-label indications to help clinicians assess its appropriateness in these selected, clinically important groups of patients: those with acute decompensated heart failure, acute coronary syndrome, peripartum cardiomyopathy, chemotherapy-induced cardiomyopathy, adult congenital heart disease, cardiomyopathy in dialysis patients, right ventricular failure, or durable left ventricular assist device.     

Development of a Semiautomated Database for Patients With Adult Congenital Heart Disease

BackgroundDatabases for Congenital Heart Disease (CHD) are effective in delivering accessible datasets ready for statistical inference. Data collection hitherto has, however, been labour and time intensive and has required substantial financial support to ensure sustainability. We propose here creation and piloting of a semiautomated technique for data extraction from clinic letters to populate a clinical database.MethodsPDF formatted clinic letters stored in a local folder, through a series of algorithms, underwent data extraction, preprocessing, and analysis. Specific patient information (diagnoses, diagnostic complexity, interventions, arrhythmia, medications, and demographic data) was processed into text files and structured data tables, used to populate a database. A specific data validation schema was predefined to verify and accommodate the information populating the database. Unsupervised learning in the form of a dimensionality reduction technique was used to project data into 2 dimensions and visualize their intrinsic structure in relation to the diagnosis, medication, intervention, and European Society of Cardiology classification lists of disease complexity. Ninety-three randomly selected letters were reviewed manually for accuracy.ResultsThere were 1409 consecutive outpatient clinic letters used to populate the Scottish Adult Congenital Cardiac Database. Mean patient age was 35.4 years; 47.6% female; with 698 (49.5%) having moderately complex, 369 (26.1%) greatly complex, and 284 (20.1%) mildly complex lesions. Individual diagnoses were successfully extracted in 96.95%, and demographic data were extracted in 100% of letters. Data extraction, database upload, data analysis and visualization took 571 seconds (9.51 minutes). Manual data extraction in the categories of diagnoses, intervention, and medications yielded accuracy of the computer algorithm in 94%, 93%, and 93%, respectively.ConclusionsSemiautomated data extraction from clinic letters into a database can be achieved successfully with a high degree of accuracy and efficiency.