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Re: Procalcitonin is not necessarily better than C-reactive protein in febrile infants under 3 months of

To the editor:

Olaciregui et al. reported an interesting article and concluded that the diagnostic value of procalcitonin (PCT) is greater than C reactive protein (CRP) in predicting infants with more invasive bacterial diseases (sepsis, bacteraemia). However, due to the following reasons, the conclusion should be more conservative.

First, the authors claimed that the area under curve (AUC) is greater when comparing PCT using a cut-off point of 0.5 ng/ml and CRP using a cut- off point of 30 mg/l (Table 2). I think it is a misuse of receiver operating characteristic (ROC) curves. Because ROC curves are plotted by sensitivity and 1-specificity using different cut-off points, AUC will not change when shifting cut-off points.(1) Obviously, it is not sufficient to support any specific cut-off point by comparing AUC of ROC curves.

Second, the authors strengthened the conclusion by larger odds ratio of PCT in the multivariate logistic regression model in subgroup analysis (Table 3 and Table 4). However, because CRP and PCT are both good predictors of serious bacterial infections, they must be highly correlated with each other. It brings a very serious problem of collinearity.(2) Thus, I am afraid that the model is an unstable model and the result might greatly change when adding some more cases. It should be very conservative when explaining the results of this model.

Finally, because CRP is almost routinely performed for febrile infants under 3 months of age in clinical practice, it is meaningless to argue PCT is better than CRP or not. It will be more interesting to see how much the increment in AUC is between CRP alone and PCT plus CRP. Some new methods of measuring quantify the improvement such as net reclassification improvement and integrated discrimination improvement can be applied for this purpose.(3)

References:

1.Bewick V, Cheek L, Ball J. Statistics review 13: receiver operating characteristic curves. Crit Care 2004;8(6):508-12.

2.Nathanson BH, Higgins TL. An introduction to statistical methods used in binary outcome modeling. Semin Cardiothorac Vasc Anesth 2008;12(3):153-66.