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Comments on: A Model to Predict Central-Line-Associated Bloodstream Infection Among Patients With Peripherally Inserted Central Catheters: The MPC Score

Published online by Cambridge University Press:  05 February 2018

Saeid Safiri
Affiliation:
Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran
Erfan Ayubi*
Affiliation:
Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
*
Address correspondence to Erfan Ayubi, MSc, PhD, Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran (aubi65@gmail.com).
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Abstract

Type
Letters to the Editor
Copyright
© 2018 by The Society for Healthcare Epidemiology of America. All rights reserved 

To the Editor—We read the article by Herc et alReference Herc, Patel, Washer, Conlon, Flanders and Chopra 1 with great interest. Although the methodology and results of the study were very interesting, we think some methodological issues should be noted.

The results demonstrate that area under the curve (AUC) for peripherally inserted central catheter (PICC) dwell times at 6, 10, 14 and 21 days were 0.70, 0.75, 0.77, and 0.80, respectively.Reference Herc, Patel, Washer, Conlon, Flanders and Chopra 1 The authors point out that the central-line-associated bloodstream infections (CLABSI) risk model at dwell time of 21 days has good prediction performance because the AUC value at 21 days wasat its maximum.Reference Herc, Patel, Washer, Conlon, Flanders and Chopra 1 To us the most important concern is that the difference between the AUC at 14 and 21 days is negligible (0.77 vs 0.80). In other words, the CLABSI risk model at dwell times of 14 and 21 days may have the same prediction performance. We recommend that the authors try to test the statistical comparison of AUCs with available statistical methodsReference DeLong, DeLong and Clarke-Pearson 2 , Reference Hanley and McNeil 3 because empirical comparisons of AUCs may be misleading.

Although AUC analysis can produce all possible discriminative thresholds, the results of AUC analyses can be hardly translated into clinical practice.Reference Halligan, Altman and Mallett 4 Net benefit methods are alternative approaches of receiver operating characteristic curve (ROC) analysis; these methods can better clarify the prediction performance of a PICC-CLABSI risk-prediction tool.

ACKNOWLEDGMENT

Financial support: No financial support was provided relevant to this article.

Potential conflicts of interest: All authors report no conflicts of interest relevant to this article.

References

REFERENCES

1. Herc, E, Patel, P, Washer, LL, Conlon, A, Flanders, SA, Chopra, V. A model to predict central-line-associated bloodstream infection among patients with peripherally inserted central catheters: the MPC score. Infect Control Hosp Epidemiol 2017;38:11551166.Google Scholar
2. DeLong, ER, DeLong, DM, Clarke-Pearson, DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837845.Google Scholar
3. Hanley, JA, McNeil, BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983;148:839843.Google Scholar
4. Halligan, S, Altman, DG, Mallett, S. Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: a discussion and proposal for an alternative approach. Eur Radio 2015;25:932939.Google Scholar