Urinary HPV Testing for Presence of Cervical HPV
Urinary HPV Testing for Presence of Cervical HPV
A prospective protocol was registered on PROSPERO (identification number CRD42013006928). This review was performed using recommended methods and reported in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement.
We searched several electronic sources from inception to December 2013: Medline, Embase, the Cochrane Library, Web of Science, BIOSIS, DARE, and SIGLE. MeSH and free text combinations using Boolean logic of the following search terms were used: urin*, self, home, test*, detect*, screen*, diagnos*, DNA, deoxyribonucleic acid, polymerase chain reaction, NAAT, NAT, nucleic acid test, nucleic acid amplification test, HPV, human papillomavir*, cervical cancer, and cervical pre-cancer. We manually searched recent issues of relevant publications and the reference lists of included texts and relevant articles. Experts were contacted for additional studies and data. There were no language restrictions.
Eligibility criteria were any test accuracy study where the detection of HPV DNA in urine was compared with its detection in the cervix in any sexually active woman concerned about HPV infection or the development of cervical cancer. We excluded studies if a different or no reference standard was used. We included studies in the meta-analysis if 2Ă—2 tables could be constructed from published or requested data. Certain factors can overestimate the diagnostic value of a test. Therefore we excluded studies from the meta-analysis if they used case-control designs, tested only patients with cervical cancer, or the total number of non-infected participants was zero.
We screened all titles and abstracts for relevant studies. Two reviewers (NP and JD) independently reviewed full texts for final selection. They documented reasons for exclusion.
We developed a data extraction sheet, piloted it on randomly selected studies, and refined it appropriately. Two reviewers (NP and JD) extracted the following data independently: study characteristics (authors, year of publication, country, context and purpose of testing), patient characteristics (including mean age and range, HIV status, cytology and biopsy results), characteristics of the index test (urine sample type, sample volume, storage temperature, DNA extraction method, DNA amplification method, timing of test in relation to reference standard), and accuracy of results into 2Ă—2 tables of urine positivity versus cervical swab positivity for any HPV, high risk HPV, and HPV 16 and 18. We considered the following HPV strains to be high risk: 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68, 73 and 82. We emailed study authors for missing data.
We discussed all discrepancies and involved a third independent reviewer (KK) if the discrepancy could not be resolved.
We applied the QUADAS-2 tool to all studies. Quality assessment involved scrutinising patient selection, conduct of the index test, conduct of the reference standard, and patient flow. We considered a study to be high quality if it used an appropriate patient spectrum, it used consecutive or random recruitment of participants, all participants used the same reference standard, the index and reference standard were performed within two weeks, and the majority of recruited participants were included in analyses.
The following were considered to be inappropriate patient spectrums that introduced bias as a result of a higher prevalence of HPV: populations comprising only patients with HIV, cervical cancer, or high grade CIN, or whose age was below current screening recommendations. We did not consider lack of blinding to test results as posing a high risk of bias, as the HPV test is objective. We assessed publication bias by regressing log(DOR) on inverse root squared of the effective sample size. However, this result should be interpreted cautiously given the lack of statistical power of this test and the absence of consensus on adequate methods to detect publication bias.
Data synthesis was performed according to a priori hypotheses outlined in the protocol. We constructed 2Ă—2 tables of detection of any HPV, high risk HPV, and HPV 16 and 18. For these three groups we fitted bivariate mixed effects logistic regression analysis. From the estimates we derived a summary receiver operating characteristic curve and the following summary accuracy measures with 95% confidence intervals: sensitivity (true positive rate), test specificity (true negative rate), positive likelihood ratio, and negative likelihood ratio. Where studies used more than one method of urine HPV testing, we included the method that was most similar to that of other studies in this review.
To visually explore heterogeneity, we generated forest plots for test sensitivity (true positive rate) and test specificity (true negative rate) with 95% confidence intervals for individual studies. To investigate sources of heterogeneity for both sensitivity and specificity, we included in the bivariate mixed effects models the following planned covariates: purpose of testing (HPV surveillance versus cervical cancer screening and follow-up of CIN), mean age, HIV status (positive versus negative for antibodies to HIV), prevalence of low grade or worse intraepithelial lesions on cytology, prevalence of grade 2 or worse CIN on biopsy, urine sampling method (first void urine versus random and midstream urine), HPV detection method (real time polymerase chain reaction (PCR) and nested PCR versus conventional PCR), use of non-commercial versus commercial DNA extraction methods, use of non-commercial versus commercial DNA amplification methods, and low versus high risk of bias as a result of patient selection. Owing to the restricted number of studies, we entered only one covariate in each analysis. We also did a sensitivity analysis to investigate the effect of studies including a narrow patient spectrum.
Statistical analyses were performed using the metandi and midas functions in STATA (version 13.0), and using METADAS macro in SAS (version 9.3).
Methods
A prospective protocol was registered on PROSPERO (identification number CRD42013006928). This review was performed using recommended methods and reported in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement.
Search Strategy
We searched several electronic sources from inception to December 2013: Medline, Embase, the Cochrane Library, Web of Science, BIOSIS, DARE, and SIGLE. MeSH and free text combinations using Boolean logic of the following search terms were used: urin*, self, home, test*, detect*, screen*, diagnos*, DNA, deoxyribonucleic acid, polymerase chain reaction, NAAT, NAT, nucleic acid test, nucleic acid amplification test, HPV, human papillomavir*, cervical cancer, and cervical pre-cancer. We manually searched recent issues of relevant publications and the reference lists of included texts and relevant articles. Experts were contacted for additional studies and data. There were no language restrictions.
Eligibility Criteria
Eligibility criteria were any test accuracy study where the detection of HPV DNA in urine was compared with its detection in the cervix in any sexually active woman concerned about HPV infection or the development of cervical cancer. We excluded studies if a different or no reference standard was used. We included studies in the meta-analysis if 2Ă—2 tables could be constructed from published or requested data. Certain factors can overestimate the diagnostic value of a test. Therefore we excluded studies from the meta-analysis if they used case-control designs, tested only patients with cervical cancer, or the total number of non-infected participants was zero.
Study Selection and Data Extraction
We screened all titles and abstracts for relevant studies. Two reviewers (NP and JD) independently reviewed full texts for final selection. They documented reasons for exclusion.
We developed a data extraction sheet, piloted it on randomly selected studies, and refined it appropriately. Two reviewers (NP and JD) extracted the following data independently: study characteristics (authors, year of publication, country, context and purpose of testing), patient characteristics (including mean age and range, HIV status, cytology and biopsy results), characteristics of the index test (urine sample type, sample volume, storage temperature, DNA extraction method, DNA amplification method, timing of test in relation to reference standard), and accuracy of results into 2Ă—2 tables of urine positivity versus cervical swab positivity for any HPV, high risk HPV, and HPV 16 and 18. We considered the following HPV strains to be high risk: 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68, 73 and 82. We emailed study authors for missing data.
We discussed all discrepancies and involved a third independent reviewer (KK) if the discrepancy could not be resolved.
Assessment of Study Quality
We applied the QUADAS-2 tool to all studies. Quality assessment involved scrutinising patient selection, conduct of the index test, conduct of the reference standard, and patient flow. We considered a study to be high quality if it used an appropriate patient spectrum, it used consecutive or random recruitment of participants, all participants used the same reference standard, the index and reference standard were performed within two weeks, and the majority of recruited participants were included in analyses.
The following were considered to be inappropriate patient spectrums that introduced bias as a result of a higher prevalence of HPV: populations comprising only patients with HIV, cervical cancer, or high grade CIN, or whose age was below current screening recommendations. We did not consider lack of blinding to test results as posing a high risk of bias, as the HPV test is objective. We assessed publication bias by regressing log(DOR) on inverse root squared of the effective sample size. However, this result should be interpreted cautiously given the lack of statistical power of this test and the absence of consensus on adequate methods to detect publication bias.
Data Synthesis
Data synthesis was performed according to a priori hypotheses outlined in the protocol. We constructed 2Ă—2 tables of detection of any HPV, high risk HPV, and HPV 16 and 18. For these three groups we fitted bivariate mixed effects logistic regression analysis. From the estimates we derived a summary receiver operating characteristic curve and the following summary accuracy measures with 95% confidence intervals: sensitivity (true positive rate), test specificity (true negative rate), positive likelihood ratio, and negative likelihood ratio. Where studies used more than one method of urine HPV testing, we included the method that was most similar to that of other studies in this review.
To visually explore heterogeneity, we generated forest plots for test sensitivity (true positive rate) and test specificity (true negative rate) with 95% confidence intervals for individual studies. To investigate sources of heterogeneity for both sensitivity and specificity, we included in the bivariate mixed effects models the following planned covariates: purpose of testing (HPV surveillance versus cervical cancer screening and follow-up of CIN), mean age, HIV status (positive versus negative for antibodies to HIV), prevalence of low grade or worse intraepithelial lesions on cytology, prevalence of grade 2 or worse CIN on biopsy, urine sampling method (first void urine versus random and midstream urine), HPV detection method (real time polymerase chain reaction (PCR) and nested PCR versus conventional PCR), use of non-commercial versus commercial DNA extraction methods, use of non-commercial versus commercial DNA amplification methods, and low versus high risk of bias as a result of patient selection. Owing to the restricted number of studies, we entered only one covariate in each analysis. We also did a sensitivity analysis to investigate the effect of studies including a narrow patient spectrum.
Statistical analyses were performed using the metandi and midas functions in STATA (version 13.0), and using METADAS macro in SAS (version 9.3).
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