10 Cac-rds and cad-rds as a potential tool to better characterise cad prevalence, severity and variation within uk nice cg95 compliant pathways. (15th May 2019) - Schmitt, Matthias; Ullah, Abid; Reid, Anna; Caswell, Craig; Wong, Chris; Freeman, Gavin; Greaves, Melanie; Nucifora, Gaetano
Abstract : Background: CAC-RADS and CAD-RADS were introduced to create a standardized method to communicate findings of coronary calcium scoring (CCS) and coronary CT angiography (cCTA) and to facilitate standardized clinical decision making regarding the potential need of further assessment and management. Yet an important and welcome 'by-product' may be the ability to respectively quantitate and semi-quantitate the extent and/or severity of disease biomarkers in large distinct patient groups and population. For example, characterisation of the UK NICE-CG95 compliant RACPAC populations, as underway in the National audit CA2017-18-191, has the potential to yield extremely valuable information on disease distribution and hot-spots allowing the creation of 'disease heat maps' which in turn can be used to inform public health and research strategies alike as well as informing and ultimately optimising down-stream resource utilisation. Methods: We sought to characterise our own RACPAC population and determine to what degree the variability in disease over time may be influenced by intra- and inter-observer variability. This is a basic yet important first step to understand fluctuations of disease prevalence and variation. We studied our RACPAC-CT data with respect to CAC-RDS and CAD-RDS reporting output over a 1-year period. We performed inter- and intra-observer variability for both CAC-RDS and CAD-RDS of the 2 Consultants with the highest reporting volume (>500 reports/annum). Below we show the completed data relating to the 2018 Calendar year submitted to the national audit. Results: Below Table 1 shows the 2018 CAD-RDS disease distribution of the Manchester Foundation Trust (Wythenshawe site) Cardiac CT service which is fed by the RACPAC clinics of Wythenshawe, Stepping Hill Hospital FT and Salford Royal FT. Cohen's Kappa analysis for CAC-RDS and CAD-RDS intra and inter-observer variability for 20 randomly selected cases within were as follows; CAC-RDS- intra-observer variability; Weighted Kappa 1.0 (95% CI 1.0–1.0) CAC=RDS – inter-observer variability; Weighted Kappa 0.96 (95% CI 0.90–1.0) CAD-RDS – intra-observer variability; Weighted Kappa 0.84 (95% CI 0.715–0.98) CAD-RDS – inter-observer variability; Weighted Kappa 0.9 (95% CI 0.80–0.99) Conclusion: This QC process of real-world data collected in a NICE CG95 compliant NHS environment demonstrates that the disease population presenting to GM RACPAC clinics is reasonable stable over time and broadly similar to the population studied in SCOTHEART. It provides sound data for commissioners and healthcare providers with respect to the disease distribution and severity presenting via NICE CG 95 compliant RACPAC clinics. Importantly, it demonstrates that temporal disease variability is unlikely the consequence of significant variability of CAC-RDS and CAD-RDS application and variation when applied by experienced practitioners and as such validates these tools to compare regional and national data.