Can existing electronic medical records be used to quantify cardiovascular risk at point of care?. Issue 11 (17th June 2022)

• Record Type: Journal Article
• Authors: Klimis, Harry; Shaw, Tim; Von Huben, Amy; Charlston, Emma; Usherwood, Tim; Jennings, Garry; Messom, Ray; Thiagalingam, Aravinda; Gunja, Naren; Shetty, Amith; Chow, Clara K.
• Is Part Of: Internal medicine journal. Volume 52:Issue 11(2022)
• Page Start: 1934
• Page End: 1942
• Publication Date: 2022-06-17
• Subjects: cardiovascular risk -- risk score -- rapid access -- medical records -- electronic data; Medicine -- Periodicals; 616
• DOI: 10.1111/imj.15439
• Languages: English
• ISSNs: 1444-0903

Abstract

Abstract: Background: Using electronic data for cardiovascular risk stratification could help in prioritising healthcare access and optimise cardiovascular prevention. Aims: To determine whether assessment of absolute cardiovascular risk (Australian absolute cardiovascular disease risk (ACVDR)) and short‐term ischaemic risk (History, ECG, Age, Risk factors, and Troponin (HEART) score) is possible from available data in Electronic Medical Record (EMR) and My Health Record (MHR) of patients presenting with acute cardiac symptoms to a Rapid Access Cardiology Clinic (RACC). Methods: Audit of EMR and MHR on 200 randomly selected adults who presented to RACC between 1 March 2017 and 4 February 2020. The main outcomes were the proportion of patients for which ACVDR score and HEART score could be calculated. Results: Mean age was 55.2 ± 17.8 years and 43% were female. Most (85%) were referred from emergency for chest pain (52%). Forty‐six percent had hypertension, 35% obesity, 20% diabetes mellitus, 17% ischaemic heart disease and 18% were current smokers. There was no significant difference in MHR accessibility with age, gender and number of comorbidities. An ACVDR score could be estimated for 17.5% (EMR) and 0% (MHR) of patients. None had complete data to estimate HEART score in either EMR or MHR. Most commonly missing variables for ACVDR score were blood pressure (MHR) and high‐density lipoprotein cholesterol (EMR), and for HEART score the missing variables were body mass index and comorbidities (MHR and EMR). Conclusions: Significant gaps are apparent in electronic medical data capture of key variables to perform cardiovascular risk assessment. Medical data capture should prioritise the collection of clinically important data to help address gaps in cardiovascular management.