Cardiovascular Prediction Tools Research Articles

P081 Out-patient management of gout in secondary care: Results from the North West regional audit

Abstract Background/Aims Recommendations for gout management have significantly evolved as outlined by the latest National Institute for Health and Care Excellence (NICE), European League Against Rheumatism (EULAR), British Society for Rheumatology (BSR) and the Medicines and Healthcare products Regulatory Agency (MHRA) guidelines, with increased emphasis being placed on optimising cardiovascular risk. This regional audit aimed to assess concordance with these guidelines within rheumatology outpatient settings in the North West region of England. Methods New or follow-up patients with gout, presenting to a rheumatology outpatient setting within the Greater Manchester and Lancashire area over a five-week period between 1/3/23 and 7/4/23 were included. We collated data on demographics, co-morbidity assessment including cardiovascular risk, prescribing of febuxostat in major cardiovascular disease, diuretic use and advice, setting of treat-to-target levels, use of cardiovascular risk prediction tools and subsequent follow up arrangements. For new patients, collected data also included referral reason, and prescription recommendations for first-line urate-lowering therapy (ULT) and co-prescriptions for flare prophylaxis. Results Ninety-two patients (mean age 60 years, 80% male) were included from 13 rheumatology departments. Twenty-three patients were new referrals. Sixty-seven patients (73%) had ≥ 1 relevant comorbidity; hypertension being the most common (55%). Twenty (22%) patients were on diuretics. Twelve (55%) of these patients had a diagnosis of hypertension without heart failure; none received recommendations to reduce or stop diuretics. In line with current guidelines, no patient with major cardiovascular disease was treated with febuxostat. Treat-to-target levels were agreed in 49 (57%) applicable patients. Cardiovascular prediction tools were only calculated or known in three (3%) patients. However, clinic data collected would have allowed a risk assessment calculation in 13 (14%) patients. Sixteen (17%) patients audited had no planned follow-up. Compared to NICE guidelines, 18 (78%) new patients reviewed in clinic were referred appropriately to rheumatology services. Twenty-two (96%) new patients referred were eligible to start ULT, of which 15 (75%) were started on a NICE/ EULAR recommended first-line ULT treatment (14 patients on allopurinol including 11 patients with no major cardiovascular disease, one patient on febuxostat). Eleven (79%) patients started on allopurinol were initiated on a low dose (50-100mg), as recommended by NICE and the BSR. Ten (67%) patients of those initiated on ULT were also prescribed prophylactic colchicine. Two additional patients were prescribed NSAIDs with PPI cover and one patient was prescribed oral steroids without PPI cover. Conclusion Most new patients were referred and prescribed first-line ULT treatment appropriately, often with colchicine prophylaxis. Uptake of febuxostat as first-line therapy was limited. Cardiovascular risk assessment was rarely done; further work exploring barriers to undertaking these assessments in rheumatology clinics is likely to improve both patient care and guideline compliance. Disclosure L. Gould: Other; L.G. has received conference fees from Eli Lilly. A. Richards: None. L. Gray: Other; L.G. has received travel and conference fees from Eli Lilly. M. Badarulla: Other; M.B. has received conference fees from Eli Lilly. L. Mercer: Other; L.M. has received conference fees from Nordic Pharma and UCB. R. Farid: None. R. Heaton: Consultancies; R.H. has received consultancy fees from Galapagos. Honoraria; R.H. has received speaker honorarium from Abbvie and Nordic. Other; R.H. has received conference fees from UCB. M. Al-Attar: None. M. Whitehead: None. S. Kamath: None. L. Teh: None. A. Odekunle: None. C.A. Sharp: None. A. Babiker: None. S. Hopping: None. A. Oldroyd: None. K. Lazarewicz: None. C. Saleh: None. S. Horton: None. D. Roy: Honoraria; D.R. has an honorary University of Manchester research assistant contract. A. Opeseyitan: None. D. Das: None. S. Varughese: None. D. Yidana: None. S. Wig: None. J. Bluett: Grants/research support; J.B. has received a research grant award from Pfizer. Other; J.B. has received travel/conference fees from UCB, Fresenius Kabi, Pfizer and Eli Lilly.

Predictive performance of a competing risk cardiovascular prediction tool CRISK compared to QRISK3 in older people and those with comorbidity: population cohort study

BackgroundRecommended cardiovascular disease (CVD) prediction tools do not account for competing mortality risk and over-predict incident CVD in older and multimorbid people. The aim of this study was to derive and validate a competing risk model (CRISK) to predict incident CVD and compare its performance to that of QRISK3 in UK primary care.MethodsWe used UK linked primary care data from the Clinical Practice Research Datalink (CPRD) GOLD to identify people aged 25–84 years with no previous CVD or statin treatment split into derivation and validation cohorts. In the derivation cohort, we derived models using the same covariates as QRISK3 with Fine-Gray competing risk modelling alone (CRISK) and with Charlson Comorbidity score (CRISK-CCI) as an additional predictor of non-CVD death. In a separate validation cohort, we examined discrimination and calibration compared to QRISK3. Reclassification analysis examined the number of patients recommended for treatment and the estimated number needed to treat (NNT) to prevent a new CVD event.ResultsThe derivation and validation cohorts included 989,732 and 494,865 women and 946,784 and 473,392 men respectively. Overall discrimination of CRISK and CRISK-CCI were excellent and similar to QRISK3 (for women, C-statistic = 0.863/0.864/0.863 respectively; for men 0.833/0.819/0.832 respectively). CRISK and CRISK-CCI calibration overall and in younger people was excellent. CRISK over-predicted in older and multimorbid people although performed better than QRISK3, whilst CRISK-CCI performed the best. The proportion of people reclassified by CRISK-CCI varied by QRISK3 risk score category, with 0.7–9.7% of women and 2.8–25.2% of men reclassified as higher risk and 21.0–69.1% of women and 27.1–57.4% of men reclassified as lower risk. Overall, CRISK-CCI recommended fewer people for treatment and had a lower estimated NNT at 10% risk threshold. Patients reclassified as higher risk were younger, had lower SBP and higher BMI, and were more likely to smoke.ConclusionsCRISK and CRISK-CCI performed better than QRISK3. CRISK-CCI recommends fewer people for treatment and has a lower NNT to prevent a new CVD event compared to QRISK3. Competing risk models should be recommended for CVD primary prevention treatment recommendations.

Abstract P096: Developing A Lifestyle-Based Cardiovascular Health Tool For Teens

Purpose: Previous studies have shown that young women have low awareness of heart disease, despite cardiovascular (CV) disease being the number one cause of death in women. While CV risk assessment tools exist for adults, there are no similar tools specifically for adolescents. This project focuses on developing a lifestyle-based CV health prediction tool and behavior intervention for young women. Methods: We report a qualitative case study of four hour-long semi-structured interviews with young women aged 13-21 years participating in a larger study to develop the new CV health tool. Patients were recruited from a primary care practice in Atlanta, GA. Participants completed the Healthy Heart Score survey, an existing web-based CV prediction tool developed for adults, while providing feedback on how to adapt the tool for young women. Topics included conditions to engage with the tool (e.g. at school or doctor’s office), app architecture (including layout, word choice, and use of visuals), and suggestions for improvement of the tool. We used qualitative coding and thematic analyses to synthesize data. Results: Prior to engaging with the Heart Healthy Score tool, participants initially demonstrated minimal knowledge of heart attacks/strokes (“I don’t know about preventing” [heart attacks and strokes]) and of their own risk (“I’ve never associated… young women with [heart disease]”). As participants used the tool, their knowledge of heart healthy choices and awareness of their own choices increased, cueing action (“I definitely need to get my health… together now before it’s too late”). Throughout the survey, teens had varied emotional reactions, from shame/embarrassment about health (“I… got upset because… I shouldn’t be eating this much”), to surprise/shock (“I thought I ate way more fruits and vegetables''), to enthusiasm to engage with app in the future (“I might like to use this tool because it’ll … help me [have] a better way of understanding … what to eat, what is healthy”). Participant recommendations to improve the tool for future use with teens focused on the importance of using examples and visual representations (“All the examples … really help me … figure out … how much I’m eating”, “I don’t really have a good idea of… what ounces really look like… a little visual would help [figure] that out”), and the importance of goal-setting and motivational language to incentivize teens to engage with the tool (“Instead of just telling you what you need to do to become better, they give you… an actual goal”). Conclusions: Through this case study, young women demonstrate a knowledge gap about heart disease risk and healthy choices. Adapting a CV health assessment tool for teens, with added examples, visuals, and motivational language, will allow teens to reflect on their own lifestyle choices, increase awareness of their risk for future heart disease, and ultimately serve as an impetus for behavior change.