RE: "Selective Cutoff Reporting in Studies of Diagnostic Test Accuracy: A Comparison of Conventional and Individual-Patient-Data Meta-Analyses of the Patient Health Questionnaire - 9 Depression Screening Tool"

Predictive Value and Optimal Threshold of Follicle Size in IVF: Systematic Review and Multiple-Threshold Meta-Analysis

BACKGROUND

Follicle size was generally monitored during controlled ovarian stimulation, yet its predictive value for oocyte developmental potential and the discriminating threshold is debated.

OBJECTIVES

To explore the predictive value of follicle size for oocyte developmental competency and establish the corresponding optimal threshold.

SEARCH STRATEGY

We searched PubMed, Web of Science, EMBASE and Cochrane Library up to February 29th, 2024.

SELECTION CRITERIA

Included studies investigated the association between follicle size and oocyte developmental competency in IVF treatments.

DATA COLLECTION AND ANALYSIS

Data extraction followed the Cochrane Handbook. A multiple-threshold meta-analysis and standard bivariate meta-analysis were used.

MAIN RESULTS

This meta-analysis included 14 studies comprising 25 528 follicles. Results showed follicle size is predictive for oocyte developmental competence, including oocyte maturity (area under the receiver operation characteristic curve [AUC]: 0.72, 95% confidence interval 0.66-0.77; n = 24 116; follicle size ≥ 15 mm), normal fertilisation (0.62, 0.55-0.69; n = 25 321; follicle size ≥ 16 mm), blastocyst formation (0.61, 0.53-0.69; n = 12 859; follicle size ≥ 15 mm) and good-quality embryo (0.64, 0.54-0.71; n = 16 631; follicle size ≥ 16 mm) per oocyte. Based on per mature oocyte or two-pronuclei zygote, follicle size showed little predictive capacity for embryological parameters.

CONCLUSIONS

Follicle size could predict oocyte developmental competence with corresponding optimal thresholds identified. However, the benefits for embryological fate may plateau at follicle sizes ≥ 15-16 mm once the oocytes achieve maturation or fertilisation.

Diagnostic Accuracy of an Indirect Enzyme Linked Immunosorbent Assay (iELISA) for Screening of Babesia bovis in Cattle from West Africa

The epidemiology of corresponding tick-borne diseases has changed as a result of the recent introduction of Rhipicephalus (Boophilus) microplus to West Africa. The current study aimed to assess the diagnostic performance of an indirect ELISA for the detection of Babesia bovis infection in cattle. In a cross-section study, using a Bayesian Latent Class Model and iELISA diagnostic test for cattle babesiosis due to Babesia bovis, accuracy has been assessed with RT-PCR as an imperfect reference test. A total of 766 cattle were tested. The optimal diagnostic performances were obtained with 5% percentage of positivity. Sensitivity and specificity were, respectively, 0.94 [Cr. I.: 0.85−0.99] and 0.89 [Cr. I.: 0.87−0.92]. Additional diagnostic characteristics revealed that the Positive Predictive Value (PPV) and Negative Predictive Value (NPV) were 96.6% [Cr. I.: 92.7−100%] and 82.2% [Cr. I.: 72−93%]. Overall, this test well discriminates an infected status from an uninfected status considering the area under the ROC curve (AUC) which was 0.78 [Cr. I: 0.72−0.85] and a Diagnostic Odds Ratio (DOR) of 127.8 [Cr. I.: 10.43−1562.27]. The AUC was significantly higher than 0.5 (p < 10−5). In consequence, this serologic assay could be suitable in moderate to high prevalence assessments.

An empirical comparison of three methods for multiple cutoff diagnostic test meta-analysis of the Patient Health Questionnaire-9 (PHQ-9) depression screening tool using published data vs individual level data

Selective cutoff reporting in primary diagnostic accuracy studies with continuous or ordinal data may result in biased estimates when meta-analyzing studies. Collecting individual participant data (IPD) and estimating accuracy across all relevant cutoffs for all studies can overcome such bias but is labour intensive. We meta-analyzed the diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9) depression screening tool. We compared results for two statistical methods proposed by Steinhauser and by Jones to account for missing cutoffs, with results from a series of bivariate random effects models (BRM) estimated separately at each cutoff. We applied the methods to a dataset that contained information only on cutoffs that were reported in the primary publications and to the full IPD dataset that contained information for all cutoffs for every study. For each method, we estimated pooled sensitivity and specificity and associated 95% confidence intervals for each cutoff and area under the curve (AUC). The full IPD dataset comprised data from 45 studies, 15 020 subjects, and 1972 cases of major depression and included information on every possible cutoff. When using data available in publications, using statistical approaches outperformed the BRM applied to the same data. AUC was similar for all approaches when using the full IPD dataset, though pooled estimates were slightly different. Overall, using statistical methods to fill in missing cutoff data recovered the receiver operating characteristic (ROC) curve from the full IPD dataset well when using only the published subset. All methods performed similarly when applied to the full IPD dataset.