Absolute risk assessment for guiding cardiovascular risk management in a chest pain clinic

Blazak, P.; Greaves, Kim

doi:10.5694/mja2.51313

Cited by 3 publications

(2 citation statements)

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“…Similarly, in a recently published study that randomised patients without CVD attending a RACC to either ACVDR‐guided management versus control (i.e. management at the discretion of the treating physician), those receiving ACVDR‐guided management had a significant improvement in risk scores (between‐group difference 2.7%; 95% CI 1.3–4.1%), total cholesterol, low‐density lipoprotein cholesterol, systolic BP and smoking at 12‐month follow up 24 . Thus, basing management decisions on calculation of absolute CVD risk may improve health outcomes.…”

Section: Discussionmentioning

confidence: 99%

Can existing electronic medical records be used to quantify cardiovascular risk at point of care?

Klimis

Shaw

Huben

et al. 2022

Internal Medicine Journal

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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 AE 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.

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Section: Discussionmentioning

confidence: 99%

Can existing electronic medical records be used to quantify cardiovascular risk at point of care?

Klimis

Shaw

Huben

et al. 2022

Internal Medicine Journal

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“…12 Quantitative absolute risk assessments in the form of risk scores are a prediction rule that has proven to be of value for physicians as well as for patients. 13 Risk scores rely on prognostic or predictive models and usually serve medical staff to assess the outcome of certain clinical procedures or identify patients at risk. 14 Quantitative risk assessment can be both part of a larger decision support system and a standalone application.…”

Section: Background and Significancementioning

confidence: 99%

Integration of Risk Scores and Integration Capability in Electronic Patient Records

Heider

Mang

2022

Appl Clin Inform

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Background Digital availability of patient data is continuously improving with the increasing implementation of electronic patient records in physician practices. The emergence of digital health data defines new fields of application for data analytics applications, which in turn offer extensive options of using data. Common areas of data analytics applications include decision support, administration, and fraud detection. Risk scores play an important role in compiling algorithms that underlay tools for decision support. Objectives This study aims to identify the current state of risk score integration and integration capability in electronic patient records for cardiovascular disease and diabetes in German primary care practices. Methods We developed an evaluation framework to determine the current state of risk score integration and future integration options for four cardiovascular disease risk scores (arriba, Pooled Cohort Equations, QRISK3, and Systematic Coronary Risk Evaluation) and two diabetes risk scores (Finnish Diabetes Risk Score and German Diabetes Risk Score). We then used this framework to evaluate the integration of risk scores in common practice software solutions by examining the software and inquiring the respective software contact person. Results Our evaluation showed that the most widely integrated risk score is arriba, as recommended by German medical guidelines. Every software version in our sample provided either an interface to arriba or the option to implement one. Our assessment of integration capability revealed a more nuanced picture. Results on data availability were mixed. Each score contains at least one variable, which requires laboratory diagnostics. Our analysis of data standardization showed that only one score documented all variables in a standardized way. Conclusion Our assessment revealed that the current state of risk score integration in physician practice software is rather low. Integration capability currently faces some obstacles. Future research should develop a comprehensive framework that considers the reasonable integration of risk scores into practice workflows, disease prevention programs, and the awareness of physicians and patients.

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