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Research · 02 of 05

A model predicts two-year relapse in ANCA vasculitis with severe kidney involvement

A PEXIVAS-derived model estimated two-year relapse risk in severe ANCA vasculitis moderately well; it needs external validation.

Design
Post hoc risk-model derivation from a randomised trial cohort
Population
649 PEXIVAS participants with severe ANCA-associated vasculitis in remission
Primary outcome
First relapse within 2 years of remission
Effect
Optimism-corrected C statistic 0.709; calibration slope 0.99

Using 649 PEXIVAS participants with ANCA-associated vasculitis and an eGFR below 50 or diffuse alveolar haemorrhage who achieved remission, investigators built a model to estimate the risk of first relapse within two years. One hundred relapsed.

The final model included sex, induction with oral cyclophosphamide or rituximab, ANCA subtype, non-haemorrhagic respiratory or mucous membrane and eye involvement at presentation, and eGFR at remission. It discriminated moderately well (C statistic 0.71) and was well calibrated internally.

Duration and intensity of maintenance therapy in vasculitis is often a judgement call, balancing relapse against infection. A tool that quantifies relapse risk could support those decisions. It has not been validated externally and was built in a trial population with severe disease.

  • Record ANCA subtype, induction agent and organ involvement at presentation, as these predicted relapse.
  • Use relapse risk factors to inform the intensity of monitoring after remission.
  • Do not base maintenance decisions on this model alone until it is externally validated.

Why it matters

It moves maintenance decisions in vasculitis towards measured rather than intuitive risk.

Don't overread it

Internally validated only, in a trial population; performance elsewhere is unknown.

The statistics, in plain English

A C statistic of 0.71 means the model correctly ranks who relapses first about 71% of the time — useful but far from certain. A calibration slope near 1 means predicted and observed risks matched in the development data.

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