HPV genotype replacement: too early to tell Inge Verdenius and colleagues1 suggest that cross-protection with the human papillomavirus (HPV) vaccine Cervarix might prevent genotype replacement. Some points deserve further discussion. To our knowledge, only one study2 has reported an increase in prevalence of non-vaccine HPV types after vaccination. However, that such increase is induced by vaccination, and thus, that genotype replacement is taking place, is not certain. Nevertheless, we agree that genotype replacement could be happening, because that increase was significant for vaccinated, but not unvaccinated, participants. Yet, the conclusion that genotype replacement is happening in patients vaccinated with Gardasil on the basis of findings from only one study seems premature. Notably, Tabrizi and colleagues’2 findings for Gardasil were not in line with those of Kahn and colleagues,3 because they reported a slight (non-significant) reduction in the prevalence of non-vaccine HPV types in the postvaccination era in Australia (table). Awaited surveillance

Country Age range (years) Vaccine

Tabrizi et al, 20122

Kahn et al, 20123













≥1 vaccine doses

338 (84%)

242 (59%)


Recruitment Pre-vaccine period



Post-vaccine period



Prevalence of high-risk NVT* Pre-vaccine group



Post-vaccine vaccinated group



Difference (post–pre)



NVT=non-vaccine types. *Unadjusted.

Table: Surveillance studies comparing prevalence of HPV types pre-vaccination and post-vaccination


data for the two available vaccines from different settings with varying vaccination coverage should show whether genotype replacement is indeed at work. Verdenius and colleagues’ hypothesis1 followed Malagón and coworkers4 meta-analysis of vaccineinduced cross-protection. However, results of that meta-analysis should be interpreted with caution, because Malagón and coworkers reported significant differences between the two vaccines for persistent infections with HPV types 31 and 45, but not for type 33, for which CIs largely overlapped. Moreover, the two vaccines did not differ for either types 58 and 52, which are both related to type 16 and are largely present in cervical lesions in Asia. 5 The investigators noted that efficacy of Cervarix decreased during follow-up, suggesting waning cross-protection. Overall, if vaccine cross-protection lasts only less than a decade, its beneficial effects, potentially including prevention of genotype replacement, could merely be transitory. Should Verdenius and colleagues’ hypothesis prove true, that Cervarix cross-protection could counterbalance genotype replacement, then two events could happen: HPV types, for which some degree of Cervarix crossprotection has been reported,4 would correspond to those increasing in the population vaccinated with Gardasil;2 or those types would differ, but would somehow interact indirectly through other HPV types. Because Kahn and colleagues2 did not report prevalence data by genotype, whether such HPV-genotype matching happens is unknown. Available data are insufficient to infer that vaccine cross-protection might prevent genotype replacement. To assess vaccination effects on prevalence of non-vaccine HPV types, surveillance studies should provide data by genotype. Such information is essential for disease prevention in

the near future because oncogenic potentials of HPV types differ, and changes in incidence of HPV-related disease are preceded by changes in prevalence of HPV-type infection. We declare that we have no conflicts of interest.

*Margarita Pons-Salort, Anne C M Thiébaut, Didier Guillemot, Michel Favre, Elisabeth Delarocque-Astagneau [email protected] Institut Pasteur, Unité de Pharmacoépidémiologie et Maladies Infectieuses, Paris 75725, France (MP-S, ACMT, DG, ED-A); Pharmaco-épidémiologie et évaluation de l’impact des produits de santé sur les populations (INSERM U657), Paris, France (MP-S, ACMT, DG, ED-A); University Versailles–SaintQuentin-en-Yvelines, Faculté de Médecine Paris Île-de-France Ouest, Garches, France (MP-S, ACMT, DG, ED-A); Assistance publique—Hôpitaux de Paris, Hôpital Raymond-Poincaré, Garches, France (DG); and Institut Pasteur, Centre National de Référence des HPV, Paris, France (MF) 1





Verdenius I, Groner JA, Harper DM. Cross protection against HPV might prevent type replacement. Lancet Infect Dis 2013; 13: 195. Tabrizi SN, Brotherton JML, Kaldor JM, et al. Fall in human papillomavirus prevalence following a national vaccination program. J Infect Dis 2012; 206: 1645–51. Kahn JA, Brown DR, Ding L, et al. Vaccinetype human papillomavirus and evidence of herd protection after vaccine introduction. Pediatrics 2012; 130: e249–56. Malagon T, Drolet M, Boily MC, et al. Cross-protective efficacy of two human papillomavirus vaccines: a systematic review and meta-analysis. Lancet Infect Dis 2012; 12: 781–89. Bao YP, Li N, Smith JS, et al. Human papillomavirus type distribution in women from Asia: a meta-analysis. Int J Gynecol Cancer 2008; 18: 71–9.

Procalcitonin as a diagnostic marker for sepsis We read with interest Christina Wacker and colleagues’ systematic review1 of the diagnostic accuracy of procalcitonin as a marker for sepsis. As the investigators stated, the cutoffs for procalcitonin concentration used for discrimination in the primary studies differed substantially between studies. Notably, “some had a cutoff that led to the most favourable results Vol 13 December 2013


λ Se + (1 − λ )Sp

where λ (between 0 and 1) is the weight that study investigators attributed to the sensitivity. A λ of 0·5 attributes equal weights to sensitivity and specificity, equivalent to maximisation of the Youden index. Our method calculates a summary receiver operating characteristic (SROC) curve based on the assumption that all investigators have optimised their cutoffs with a common weighting parameter—λ— which is estimated. To apply this model to the given data, we used the R (version R-2.15.3) package meta-analysis of diagnostic accuracy (version 0.5.4)3 and reproduced figure 3 from Wacker and colleagues’ study, showing the bivariate estimate of the average pair of sensitivity and specificity (figure). The red SROC curve (following Rutter and Gatsonis’ approach4) corresponds to that shown in figure 3. Our method produced the green curve shown in the figure. As expected, that curve runs below the Rutter-Gatsonis curve, showing the ability of our approach to account for the best cutoff selection and to avoid bias caused by an overoptimistic SROC curve, by contrast with the standard approach. The estimate of λ was 0·491, which is remarkably close to 0·5. This finding supports the hypothesis that investigators tended to select the cutoffs that maximised the Youden index. Vol 13 December 2013

As for the role of procalcitonin as a potential diagnostic marker for sepsis, its ability to distinguish between patients with sepsis and those with a systemic inflammatory response syndrome of non-infectious origin might be even lower than that derived from the meta-analysis done by Wacker and colleagues.1 Our method suggests a pooled sensitivity of 0·72 (rather than 0·77) and a pooled specificity of 0·73 (rather than 0·79)—values that have already been questioned to be sufficiently large to justify the use of procalcitonin routinely in clinical practice.5 GR was funded by the German Research Foundation (DFG; grant number RU 1747/1-1). We declare that we have no conflicts of interest.

*Gerta Rücker, Martin Schumacher [email protected] Institute of Medical Biometry and Medical Statistics, University Medical Center Freiburg, Freiburg 79104, Germany 1





Wacker C, Prkno A, Brunkhorst FM, Schlattmann P. Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis. Lancet Infect Dis 2013; 13: 426–35. Rücker G, Schumacher M. Summary ROC curve based on the weighted Youden index for selecting an optimal cutpoint in meta-analysis of diagnostic accuracy. Stat Med 2010; 29: 3069–78. R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, 2012. Rutter CM, Gatsonis CA. A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med 2001; 20: 2865–84. Afshari A, Harbarth S. Procalcitonin as diagnostic biomarker of sepsis. Lancet Infect Dis 2013; 13: 382–84.

We read with interest the article by Christina Wacker and colleagues1 about the use of procalcitonin as a sepsis biomarker. Contrary to a previous metaanalysis,2 the investigators conclude that procalcitonin usefully differentiates sepsis from a systemic inflammatory response syndrome (SIRS) arising from non-infectious causes. Although several studies that used a sensitive assay were included, the sensitivity and specificity of procalcitonin to diagnose sepsis were only marginally higher than reported previously.



0·6 Sensitivity

for diagnostic accuracy”.1 As such, the investigators of the primary studies seemed to select cutoffs such that they maximised the sum of sensitivity and specificity. We want to call the reader’s attention to an approach for meta-analysis of data from studies of diagnostic test accuracy that we published previously to account for this occurrence. 2 We modelled the assumption that the investigators of the primary studies selected their cutoff such that it maximised a weighted sum of sensitivity (Se) and specificity (Sp):


Summary operating point 95% confidence region 95% prediction region SROC curve* SROC curve accounting for selection†


0 0






False-positive rate

Figure: Results of Wacker and colleagues’ procalcitonin meta-analysis1 SROC=summary receiver operating characteristic. *Following Rutter and Gatsonis‘ approach.4 †Weighted sum, λ=0·491.

Similar to D-Dimer testing in venous thromboembolism, procalcitonin is a valuable marker to rule out bacterial infection or sepsis in patients with a low pretest probability of disease.3 However, in individuals with a moderate to high suspicion of sepsis, we believe that one procalcitonin measurement and one cutoff for all patients, irrespective of their underlying disease (eg, medical vs surgical or trauma), should not be used to guide initial treatment decisions because of its insufficient ability to differentiate SIRS from sepsis in critically ill patients.4 The strength of procalcitonin lies in its favourable kinetics compared with traditional markers like C-reactive protein. Consistent evidence supports the use of serial procalcitonin measurements for early discontinuation of antibiotics in patients with pneumonia and in those admitted to intensive-care units (ICU). 3 Additionally, several studies have shown that a rise in procalcitonin can precede infection by 24 h, and that the change between procalcitonin measurements on day –1 and day 0 might be a better marker

For the R statistical programme see


Procalcitonin as a diagnostic marker for sepsis.

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