Performance of published scoring tools for predicting the risk of perioperative respiratory adverse events in children - An evaluation in a large paediatric cohort.

Anaesth Crit Care Pain MedFeb 17, 2026 (epub)
Clinical ResearchAnesthesiologyPediatrics

Daisy Evans, David Sommerfield, Neil Hauser et al.

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In 30 seconds

This post-hoc secondary analysis evaluated six published risk scores for predicting perioperative respiratory adverse events (PRAE) in 12,364 cases of paediatric general anaesthesia. Any PRAE occurred in 11.9% of cases and severe PRAE in 2.0%. All scores were associated with PRAE, but their discrimination was poor to moderate: AUCs were 0.562-0.649 for any PRAE and 0.509-0.614 for severe PRAE. The OLDS score performed best for any PRAE, with an AUC of 0.649. The authors conclude that existing tools require careful adjustment before use outside their development settings.

Key findings

  • Any PRAE occurred in 11.9% of cases; severe PRAE occurred in 2.0%.
  • AUCs for predicting any PRAE ranged from 0.562-0.649; for severe PRAE, they ranged from 0.509-0.614.
  • The OLDS score had the highest reported AUC for any PRAE (0.649), but overall discrimination remained limited.

Why it matters

Preoperative risk estimates may inform planning for paediatric anaesthesia, but these results indicate that available scores do not reliably distinguish children who will experience PRAE in this cohort. Clinicians and services should be cautious about relying on a published score without considering whether it has been validated and appropriately adapted for their clinical setting.

What to keep in mind

This was a post-hoc secondary analysis, and the scores were evaluated in a test group outside their development contexts; the abstract does not report external validation across multiple settings.

Source

Published in Anaesth Crit Care Pain Med. This summary was written by xxcode from the publication's abstract and metadata. It is not peer reviewed and is not a substitute for the original article. For clinical decisions, review the original publication.

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AI-generated summaries may contain errors or omissions. Verify clinically important information with the original publication.

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