Simple SBI Prediction – Hopeless

It remains a noble endeavour to attempt to identify the risk of serious bacteria infections in children.  That said, many have tried, and many have failed.

These authors from the Netherlands and the United Kingdom try, yet again.  They note the best performing decision instrument incorporates 26 variables – which they feel is unworkably unwieldy in a clinical setting – and attempt to derive their own, tighter instrument.  Unfortunately, the clinical variables that shake out of their prediction methodology all have odds ratios less than 6 – leading to a prediction model that can be calibrated only either for horrible sensitivity or horrible specificity.  The sensitive model will lead to over-testing of an otherwise well population, and the specific model will essentially pick up only the cases that were clinically obvious.

It’s becoming pretty clear over the years that attempting to reduce the number of discrete clinical variables in the febrile SBI decision-instrument is a dead-end strategy.  Complex clinical problems simply defy dimension reduction.  Furthermore, the true test of a decision instrument also ought not just be statistical evaluation in a vacuum, but comparison with clinical judgement.

“Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: diagnostic study”
www.bmj.com/content/346/bmj.f1706