External validation of the accuracy of cardiovascular risk prediction tools in psoriatic disease: a UK Biobank study

Abstract

Introduction: Risk prediction is important for preventing and managing cardiovascular disease (CVD). CVD risk prediction tools designed for the general population may be inaccurate in people with inflammatory diseases.

Objectives: To investigate the performance of four cardiovascular risk prediction tools (QRISK3, Framingham Risk Score, Reynolds Risk Score and SCORE) in psoriatic arthritis (PsA) and psoriasis. We also compare performance in participants with no inflammatory conditions and in people with rheumatoid arthritis (RA).

Methods: This research utilised the UK Biobank Resource. We identified participants with PsA, psoriasis and RA and calculated their cardiovascular risk using each risk tool. We assessed model calibration by comparing observed and predicted outcomes. Discrimination of 10-year risk prediction was assessed using time-dependent area under ROC curve (AUC), sensitivity, specificity, positive and negative predictive values.

Results: We included 769 individuals with PsA, 8062 with psoriasis and 4772 with RA when assessing the QRISK3 tool. Predictions for individuals with psoriasis were roughly as accurate as those with no inflammatory conditions with time-dependent AUC of 0.74 (95%CI, 0.72, 0.76) and of 0.74 (95%CI, 0.72, 0.77) respectively. In contrast, individuals with PsA obtained the least accurate predictions with an AUC of 0.70 (95%CI, 0.64, 0.76). Individuals with RA also obtained less accurate predictions with AUC of 0.72 (0.69,0.74). For the Framingham risk score, AUCs varied between 0.61 (95%CI, 0.55, 0.68) for participants with PsA and 0.71 (95%CI, 0.68, 0.74) for individuals with no inflammatory condition.

Conclusions: In general, CVD risk prediction accuracy was similar for individuals with psoriasis or no inflammatory condition, but lower for individuals with PsA or RA.

Keywords: Cardiovascular risk prediction; Psoriasis; Psoriatic arthritis; Rheumatoid arthritis; UK Biobank; Validation.

Fig. 1

Flow chart showing inclusion/exclusion of individuals for the analysis cohort for each risk tool

Fig. 2

The Kaplan–Meier curves showing incidence of CVD events for each disease according to the four risk tool definitions

Fig. 3

Time-dependent area under ROC curve (AUC) for risk prediction tool and disease cohort. Point estimates and 95% confidence intervals are shown

Fig. 4

Calibration plots comparing observed CVD risk in deciles of predicted CVD risk using each of the four CVD risk tools, and for each disease cohort