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Updated: Jan 30, 2026

In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Performance and calibration of the algorithm ASSIGN in predicting cardiovascular disease in Italian patients with
Luca Navarini1, Domenico Paolo Emanuele Margiotta2, Luisa Costa3
1Unit of Allergology, Clinical Immunology and Rheumatology, Università Campus Bio-Medico di Roma, Rome, Italy.
Insights
Cardiovascular risk algorithms like ASSIGN show good performance in psoriatic arthritis (PsA) patients. Adaptations for rheumatoid arthritis (RA) or EULAR guidelines did not improve prediction accuracy for these PsA patients.
Area of Science:
- Cardiology
- Rheumatology
- Epidemiology
Background:
- Psoriatic arthritis (PsA) patients face elevated cardiovascular (CV) risk.
- Existing CV risk algorithms may require adaptation for PsA management.
- European Alliance of Associations for Rheumatology (EULAR) suggested adaptations for rheumatoid arthritis (RA) that may apply to PsA.
Purpose of the Study:
- To evaluate the performance and calibration of the ASSIGN CV risk algorithm and its adaptations (ASSIGN-RA, ASSIGN*1.5) in a PsA cohort.
- To assess if EULAR-recommended adaptations improve CV risk prediction in PsA.
- To compare the performance of ASSIGN with other algorithms like Progetto CUORE and QRISK2.
Main Methods:
- Prospective data from two Italian PsA cohorts were analyzed.
- Area Under the ROC Curve (AUC) assessed discriminatory ability.
- Hosmer-Lemeshow (HL) test and calibration plots evaluated model fit.
- Sensitivity and specificity were calculated at a 20% risk threshold.
Main Results:
- The ASSIGN algorithm demonstrated good discriminatory ability (AUC ~0.818) in PsA patients.
- No significant improvement in performance or calibration was observed with ASSIGN-RA or ASSIGN*1.5 adaptations.
- The ASSIGN algorithm performed comparably to Progetto CUORE and QRISK2.
- A notable proportion (20%) of CV events occurred in patients categorized as "low risk" by the algorithms.
Conclusions:
- The standard ASSIGN algorithm is a potentially useful tool for predicting CV risk in PsA patients.
- Current EULAR-based adaptations for RA do not enhance CV risk prediction accuracy in PsA.
- Further research may be needed to refine CV risk assessment in PsA, particularly for "low-risk" individuals.
Abstract:
The increased cardiovascular (CV) risk is one of the major challenges in the management of patients with psoriatic arthritis (PsA). Recently, EULAR suggested to adapt the already available CV risk algorithms with a 1.5 multiplication factor in all the patients with rheumatoid arthritis (RA), but it is still uncertain if this adaptation could also be applied to patients with PsA. This study aims to evaluate the performance and calibration of the CV risk algorithm ASSIGN and its adaptations for RA (ASSIGN-RA) and according to EULAR recommendations in a cohort of patients with PsA (ASSIGN*1.5). Prospectively, collected data from two Italian cohorts has been analyzed. The discriminatory ability for CV risk prediction was assessed using the areas under the ROC curves. Calibration between predicted and observed events was assessed by Hosmer-Lemeshow (HL) test and calibration plots. For each algorithm, sensitivity and specificity were calculated for low- to high-risk cut-off (20%). One hundred fifty-five patients were enrolled with an observation of 1550 patient/years. Area under the ROC were 0.8179 (95% CI 0.72014 to 0.91558) for ASSIGN, 0.8160 (95% CI 0.71661 to 0.91529) for ASSIGN-RA, and 0.8179 (95% CI 0.72014 to 0.91558) for ASSIGN*1.5. HL tests did not demonstrate poor model fit for none of the algorithms. Discriminative ability and calibration were not improved by adaptation of the algorithms according to EULAR recommendations. Up to 20% of CV events occurred in patients at "low risk". No difference in performance has been observed between ASSIGN, Progetto CUORE, and QRISK2. ASSIGN could represent a useful tool in predicting CV risk in patients with PsA. Adaptation for RA or according to EULAR recommendations did not show any further improvement in performance and calibration.
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