Using electronic medical records to identify patients at risk for underlying cardiac amyloidosis.

Abstract

Background: Identification of transthyretin cardiac amyloidosis (ATTR-CA) patients is largely based on pattern recognition by providers, and this can be automated through electronic medical systems (EMR).

Methods: All patients in a large academic hospital with age > 60, ICD-10 code for chronic diastolic heart failure and no previous diagnosis of any amyloidosis were included. An Epic EMR scoring logic assigned risk scores to patients for ICD-10 and CPT codes associated with ATTR-CA, as follows: carpal tunnel syndrome (score 5), aortic stenosis/TAVR (5), neuropathy (4), bundle branch block (4), etc. The individual patients' scores were added, and patients were arranged in descending order of total scores- ranging from 50 to 0. Data is reported as median (interquartile range) and analyzed with non-parametric tests.

Results: Of the total 11,648 patients identified, 132 consecutive patients with highest risk scores (score ≥ 30) were enrolled as cases, while 132 patients with scores between 10 and 19 with available echocardiography data served as age-matched controls. Strain echocardiography is not routinely performed. Patients with high scores were more likely to have CA associated findings- African-American race, higher left ventricular (LV) mass index and left atrial volume and lower LV ejection fraction. High score patients had higher troponin and a trend towards high NT-proBNP.

Conclusion: The modern EMR can be used to flag patients with high risk for ATTR-CA (score ≥ 30 using the proposed logic) through best practice advisory. This could encourage screening during echocardiography using strain or during unsuspected clinic visits.