Improving detection and management of atrial fibrillation after ischaemic stroke in Glasgow (IMPROVE-AF): A quality improvement project.

BACKGROUND: The use of cardiac monitoring to detect atrial fibrillation (AF) is routine after ischaemic stroke but is often delayed leaving patients at risk from undetected AF. We sought to improve the detection of AF by delivering early prolonged 'in-house' cardiac monitoring. METHODS: We collected 3-months of data of people with stroke/transient ischaemic attack (TIA), but without AF, who underwent cardiac monitoring (Phase 1, pre-quality improvement project (QIP)). We then implemented an 'in-house' 7-day cardiac monitoring service for 12 months (Phase 2, during QIP). RESULTS: We included 244 people in Phase 1 and 172 in Phase 2. In Phase 1, 232 (95%) people completed cardiac monitoring. Of these, new AF was detected in 10 (4%). Median time from stroke/TIA onset to availability of the monitoring report in Phase 1 was 50 (interquartile range (IQR): 24-123) days. In Phase 2, 166 (97%) of people completed 7-day cardiac monitoring, with new AF detected in 17 (10%). Median time from onset to availability of the report in Phase 2 was 12 (IQR: 9-15) days. In people with AF detected, 'in-house' monitoring reduced the time of stroke/TIA onset to anticoagulant commencement from 41 (Phase 1) to 14 (Phase 2) days. DISCUSSION: The QIP has improved AF detection, reduced delays associated with conventional cardiac monitoring and prompted early initiation of oral anticoagulation.

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

BACKGROUND: Databases 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. METHODS: PDF 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. RESULTS: There 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. CONCLUSIONS: Semiautomated data extraction from clinic letters into a database can be achieved successfully with a high degree of accuracy and efficiency.