External validation of a refined 4-strata risk assessment score from the French pulmonary hypertension Registry.

2021 
Introduction Contemporary risk assessment tools categorise patients with pulmonary arterial hypertension (PAH) as low, intermediate, or high-risk. A minority of patients achieve low-risk status with most remaining intermediate-risk. Our aim was to validate a 4-strata risk assessment approach categorising patients as low, intermediate-low, intermediate-high, or high risk, as proposed by the COMPERA Registry investigators. Methods We evaluated incident patients from the French PAH Registry and applied a 4-strata risk method at baseline and at first reassessment. We applied refined cut-points for 3 variables: World Health Organization functional class, 6-minute walk distance, and N-terminal pro-brain natriuretic peptide. We used Kaplan-Meier survival analyses and Cox proportional hazards regression to assess survival according to a 3-strata and 4-strata risk approach. Results At baseline (n=2879), the 4-strata approach identified 4 distinct risk groups and performed better than a 3-strata method for predicting mortality. The 4-strata model discrimination was higher than the 3-strata method when applied during follow-up and refined risk categories among subgroups with idiopathic PAH, connective tissue disease-associated PAH, congenital heart disease, and portopulmonary hypertension. Using the 4-strata approach, 53% of patients changed risk category from baseline compared to 39% of patients when applying the 3-strata approach. Those who achieved or maintained a low-risk status had the best survival, whereas there were more nuanced differences in survival for patients who were intermediate-low and intermediate-high. Conclusions The 4-strata risk assessment method refined risk prediction, especially within the intermediate risk category of patients, performed better at predicting survival and was more sensitive to change than the 3-strata approach.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    0
    References
    0
    Citations
    NaN
    KQI
    []