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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Predictive performance of a competing risk cardiovascular prediction tool CRISK compared to QRISK3 in older people
Shona J Livingstone1, Bruce Guthrie2, Peter T Donnan1
1Division of Population Health and Genomics, University of Dundee, Mackenzie Building, Kirsty Semple Way, Dundee, UK.
Insights
A new competing risk model (CRISK-CCI) improves cardiovascular disease (CVD) prediction in UK primary care, outperforming QRISK3 by better identifying individuals for primary prevention treatment.
Area of Science:
- Cardiovascular epidemiology
- Health informatics
- Biostatistics
Background:
- Current cardiovascular disease (CVD) prediction tools often neglect competing mortality risks.
- This leads to over-prediction of incident CVD, particularly in older and multimorbid populations.
- Existing models may not accurately guide primary prevention strategies.
Purpose of the Study:
- To develop and validate a competing risk model (CRISK) for predicting incident CVD in UK primary care.
- To compare the performance of CRISK and a variant (CRISK-CCI) against the established QRISK3 tool.
- To assess the impact on treatment recommendations and the number needed to treat (NNT).
Main Methods:
- Utilized UK Clinical Practice Research Datalink (CPRD) GOLD data for derivation and validation cohorts (ages 25-84).
- Developed competing risk models (CRISK, CRISK-CCI) using Fine-Gray modeling, incorporating covariates similar to QRISK3 and Charlson Comorbidity Score.
- Evaluated model discrimination, calibration, and reclassification compared to QRISK3.
Main Results:
- CRISK and CRISK-CCI demonstrated excellent discrimination, comparable to QRISK3.
- CRISK-CCI showed superior calibration, especially in older and multimorbid individuals.
- CRISK-CCI reclassified a significant proportion of patients, recommending fewer for treatment with a lower NNT.
Conclusions:
- Competing risk models, particularly CRISK-CCI, offer improved accuracy for CVD prediction over QRISK3.
- CRISK-CCI provides a more precise tool for guiding primary prevention treatment decisions.
- Implementing competing risk models is recommended for optimizing CVD primary prevention strategies.
Background:
Recommended 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.
Methods:
We 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.
Results:
The 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.
Conclusions:
CRISK 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.
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