Systematic review and meta-analysis of galactomannan antigen testing in serum and bronchoalveolar lavage for the diagnosis of chronic pulmonary aspergillosis: defining a cutoff

BACKGROUND

A clear cutoff value of galactomannan (GM) has not been established for chronic pulmonary aspergillosis (CPA) and is frequently extrapolated from invasive pulmonary aspergillosis. We performed a systematic review and meta-analysis to evaluate the diagnostic performance of serum and bronchoalveolar lavage (BAL) GM, and to propose a cutoff.

METHODS

We extracted from the studies the cutoff of serum or/and BAL GM associated with true positives, false positives, true negatives, and false negatives. We performed a multi-cutoff model and a non-parametric random effect model. We estimated the optimal cutoff and the area under the curve (AUC) for GM in serum and BAL samples.

RESULTS

Nine studies from 1999 to 2021 were included. Overall, the optimal cutoff of serum GM was 0.96 with a sensitivity of 0.29 (95%CI: 0.14-0.51); specificity of 0.88 (95%CI: 0.73-0.95); and AUC of 0.529 (with a CI: [0.415-0.682] [0.307-0.713]). The AUC for the non-parametric ROC model was 0.631. For BAL GM the cutoff was 0.67 with a sensitivity of 0.68 (95%CI: 0.51-0.82), specificity of 0.84 (95%CI: 0.70-0.92), and AUC of 0.814 (with a CI: [0.696-0.895] [0.733-0.881]). The AUC for the non-parametric model was 0.789.

CONCLUSION

The diagnosis of CPA requires the assessment of a combination of mycological and serological factors, as no single serum and/or BAL GM antigen test is adequate. BAL GM performed better than serum, with better sensitivity and excellent accuracy.

A robust hazard ratio for general modeling of survival-times

Hazard ratios (HR) associated with the well-known proportional hazard Cox regression models are routinely used for measuring the impact of one factor of interest on a time-to-event outcome. However, if the underlying real model does not fit with the theoretical requirements, the interpretation of those HRs is not clear. We propose a new index, gHR, which generalizes the HR beyond the underlying survival model. We consider the case in which the study factor is a binary variable and we are interested in both the unadjusted and adjusted effect of this factor on a time-to-event variable, potentially, observed in a right-censored scenario. We propose non-parametric estimations for unadjusted gHR and semi-parametric regression-induced techniques for the adjusted case. The behavior of those estimators is studied in both large and finite sample situations. Monte Carlo simulations reveal that both estimators provide good approximations of their respective inferential targets. Data from the Health and Lifestyle Study are used for studying the relationship of the tobacco use and the age of death and illustrate the practical application of the proposed technique. gHR is a promising index which can help facilitate better understanding of the association of one study factor on a time-dependent outcome.

Meta-analysis of diagnostic test accuracy studies with multiple thresholds for data integration

OBJECTIVES

The objective of this study is to introduce methods to use all of the information without omission when individual studies provide multiple effect sizes according to multiple cut-off values (thresholds) during diagnostic test accuracy (DTA) for data integration. For diagnostic test meta-analysis, a general performance method for synthesizing data according to one cut value in one study and a performance method for synthesizing data according to two or more cut values in one study were compared and analyzed.

METHODS

As sample data for meta-analysis of DTA studies, 13 DTA studies on prostate cancer (34 effect sizes including total cut-offs) were collected. The summary statistics were calculated and the summary line was analyzed using the "meta", "mada", and "diagmeta" packagesof the R software.

RESULTS

The summary statistics of the random effect model univariate analysis of the "meta" package with a single cut-off corresponding to the highest Youden index in a single study and those of the bivariate analysis of the "mada" package were highly similar. However, in the bivariate analysis of the "diagmeta" package including all cut-off values, the sensitivity decreased and the specificity increased as the amount of data increased.

CONCLUSIONS

Considering the heterogeneity of the summary receiver op erating characteristic curve and the use of all given cut-offs, the use of the bivariate analysis model of the "diagmeta" package is recommended. This study focused on practical methods of DTA rather than theoretical concepts for use by researchers whose fields of study are non-statistics related. By performing this study, we hope that many researchers will use R software to determine the DTA more easily, and that there will be greater interest in related research.

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab

Assessment of programmed death ligand 1 (PD-L1) expression by immunohistochemistry (IHC) has emerged as an important predictive biomarker across multiple tumor types. However, manual quantitation of PD-L1 positivity can be difficult and leads to substantial inter-observer variability. Although the development of artificial intelligence (AI) algorithms may mitigate some of the challenges associated with manual assessment and improve the accuracy of PD-L1 expression scoring, use of AI-based approaches to oncology biomarker scoring and drug development has been sparse, primarily due to the lack of large-scale clinical validation studies across multiple cohorts and tumor types. We developed AI-powered algorithms to evaluate PD-L1 expression on tumor cells by IHC and compared it with manual IHC scoring in urothelial carcinoma, non-small cell lung cancer, melanoma, and squamous cell carcinoma of the head and neck (prospectively determined during the phase II and III CheckMate clinical trials). 1,746 slides were retrospectively analyzed, the largest investigation of digital pathology algorithms on clinical trial datasets performed to date. AI-powered quantification of PD-L1 expression on tumor cells identified more PD-L1-positive samples compared with manual scoring at cutoffs of ≥1% and ≥5% in most tumor types. Additionally, similar improvements in response and survival were observed in patients identified as PD-L1-positive compared with PD-L1-negative using both AI-powered and manual methods, while improved associations with survival were observed in patients with certain tumor types identified as PD-L1-positive using AI-powered scoring only. Our study demonstrates the potential for implementation of digital pathology-based methods in future clinical practice to identify more patients who would benefit from treatment with immuno-oncology therapy compared with current guidelines using manual assessment.

Systematic Review and Meta-Analysis of Diagnostic Biomarkers for Pediatric Pneumonia

BACKGROUND

Pneumonia causes significant morbidity and mortality in children worldwide, especially in resource-poor settings. Accurate identification of bacterial etiology leads to timely antibiotic initiation, minimizing overuse, and development of resistance. Host biomarkers may improve diagnostic sensitivity and specificity. We assessed the ability of biomarkers to correctly identify bacterial pneumonia in children who present with respiratory distress.

METHODS

A librarian-directed search was conducted of MEDLINE, EMBASE, CENTRAL, Global Health, the World Health Organization International Clinical Trials Registry Platform, and ClinicalTrials.gov to May 2020 with no language restriction. Included studies compared a diagnostic biomarker in children with bacterial pneumonia to those with nonbacterial respiratory distress.

RESULTS

There were 31 observational studies of 23 different biomarkers. C-reactive protein (CRP), procalcitonin (PCT), white blood cell (WBC) count, and erythrocyte sedimentation rate (ESR) were the biomarkers with sufficient data for meta-analysis. Meta-analysis revealed that CRP and PCT best differentiated bacterial from viral pneumonia with CRP summary AUROC (area under the receiver operating characteristic curve) 0.71 (0.69-0.73), Youden index 53 mg/L, sensitivity 0.70 (0.68-0.78), and specificity 0.64 (0.58-0.68) and PCT summary AUROC 0.70 (0.67-0.74), Youden index 0.59 ng/mL, sensitivity 0.69 (0.65-0.77), and specificity 0.64 (0.60-0.68). WBC and ESR did not perform as well. Nineteen other inflammatory and immunologic biomarkers were identified including CRP/mean platelet value, neutrophil/leukocyte ratio, interleukin 6, and interferon-alpha, with sensitivities from 60% to 85% and specificities from 76% to 83%.

CONCLUSION

CRP and PCT performed better than WBC and ESR but had suboptimal sensitivity. Some less well-studied novel biomarkers appear to have promise particularly in combination.

Neutrophil to Lymphocyte Ratio and Gastrointestinal Involvement Among Henoch Schonlein Purpura Patients: A Systematic Review and Meta-Analysis

Neutrophil to lymphocyte ratio has been examined among several studies regarding Henoch Schonlein purpura patients with gastrointestinal involvement. The current meta-analysis aims to evaluate neutrophil to lymphocyte ratio differences between Henoch Schonlein purpura patients with and without gastrointestinal manifestations.An extensive search of the available literature was done using MEDLINE (1966-2020), Clinicaltrials.gov (2008-2020), Cochrane Central Register of Controlled Trials (CENTRAL) (1999-2020) and Google Scholar (2004-2020) databases and statistical analysis was mainly conducted using RevMan 5.3 software.The current meta-analysis is based on data from six studies. Neutrophil to lymphocyte ratio was higher in Henoch Schonlein patients with gastrointestinal involvement than those without with a mean difference of 0.88 (95% confidence interval [CI]: 0.55, 1.22 [P < 10-5, I2 = 8%]). Low heterogeneity (I2) was observed between the included studies. Subgroup analysis, leave one out meta-analysis and meta-regression analysis were performed which led to the lowering of I2 to 0.The findings of our meta-analysis suggest that the neutrophil to lymphocyte ratio could serve as a marker of gastrointestinal involvement in patients with Henoch Schonlein purpura; however, most of the included studies are case-control, therefore future cohort studies should clarify the value of this marker.

International HIV Dementia Scale for HIV-Associated Neurocognitive Disorders: A Systematic Review and Meta-Analysis

The present study aims to systematically review the evidence on the accuracy of the International HIV Dementia Scale (IHDS) test for diagnosing human immunodeficiency virus (HIV)-associated neurocognitive disorders (HAND) and outline the quality and quantity of research evidence available on the accuracy of IHDS in people living with HIV. We conducted a systematic literature review, searching five databases from inception until July 2020. We extracted dichotomized positive and negative test results at various thresholds and calculated the sensitivity and specificity of IHDS. Quality assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Fifteen cross-sectional studies, published between 2011 and 2018, met the inclusion criteria for meta-analysis. Overall, 3760 patients were included, but most studies recruited small samples. We assessed most studies as being applicable to the review question, though we had concerns about the selection of participants in three studies. The accuracy of IHDS was investigated at thirteen cut-off points (scores 6-12). The threshold of 10 is the most useful for optimal HAND screening (including asymptomatic neurocognitive disorder, symptomatic HAND, and HIV-associated dementia) with fair diagnostic accuracy.

Meta-analysis of diagnostic accuracy studies with multiple thresholds: Comparison of different approaches

Methods for standard meta-analysis of diagnostic test accuracy studies are well established and understood. For the more complex case in which studies report test accuracy across multiple thresholds, several approaches have recently been proposed. These are based on similar ideas, but make different assumptions. In this article, we apply four different approaches to data from a recent systematic review in the area of nephrology and compare the results. The four approaches use: a linear mixed effects model, a Bayesian multinomial random effects model, a time-to-event model and a nonparametric model, respectively. In the case study data, the accuracy of neutrophil gelatinase-associated lipocalin for the diagnosis of acute kidney injury was assessed in different scenarios, with sensitivity and specificity estimates available for three thresholds in each primary study. All approaches led to plausible and mostly similar summary results. However, we found considerable differences in results for some scenarios, for example, differences in the area under the receiver operating characteristic curve (AUC) of up to 0.13. The Bayesian approach tended to lead to the highest values of the AUC, and the nonparametric approach tended to produce the lowest values across the different scenarios. Though we recommend using these approaches, our findings motivate the need for a simulation study to explore optimal choice of method in various scenarios.

Meta-analysis of full ROC curves with flexible parametric distributions of diagnostic test values

Diagnostic accuracy studies often evaluate diagnostic tests at several threshold values, aiming to make recommendations on optimal thresholds for use in practice. Methods for meta-analysis of full receiver operating characteristic (ROC) curves have been proposed but still have deficiencies. We recently proposed a parametric approach that is based on bivariate time-to-event models for interval-censored data to this task. To increase the flexibility of that approach, to cover a wide range of distributions of diagnostic test values and to address the open point of model selection, we here suggest to use the generalized F family of distributions that includes previously used distributions for the bivariate time-to-event model as special cases. The results of a simulation study are given as well as an illustration by an example of population-based screening for type 2 diabetes mellitus.

Retinal Changes in Schizophrenia: A Systematic Review and Meta-analysis Based on Individual Participant Data

BACKGROUND

Retinal assessment has indicated the presence of neuronal loss in neurodegenerative disorders, but its role in schizophrenia remains unclear. We sought to synthesize the available evidence considering 3 noninvasive modalities: optical coherence tomography, electroretinography, and fundus photography, and examine their diagnostic accuracy based on unpublished individual participant data, when provided by the primary study authors.

METHODS

We searched MEDLINE, SCOPUS, clinicaltrials.gov, PSYNDEX, Cochrane Controlled Register of Trials (CENTRAL), WHO International Clinical Trials Registry Platform, and Google Scholar, up to October 30, 2018. Authors were contacted and invited to share anonymized participant-level data. Aggregate data were pooled using random effects models. Diagnostic accuracy meta-analysis was based on multiple cutoffs logistic generalized linear mixed modeling. This study was registered with PROSPERO, number CRD42018109344.

RESULTS

Pooled mean differences of peripapillary retinal nerve fiber layer thickness in micrometer between 694 eyes of 432 schizophrenia patients and 609 eyes of 358 controls, from 11 case-control studies, with corresponding 95% confidence intervals (CIs) by quadrant were the following: -4.55, 95% CI: -8.28, -0.82 (superior); -6.25, 95% CI: -9.46, -3.04 (inferior); -3.18, 95% CI: -5.04, -1.31 (nasal); and -2.7, 95% CI: -4.35, -1.04 (temporal). Diagnostic accuracy, based on 4 studies, was fair to poor, unaffected by age and sex; macular area measurements performed slightly better.

CONCLUSION

The notion of structural and functional changes in retinal integrity of patients with schizophrenia is supported with current evidence, but diagnostic accuracy is limited. The potential prognostic, theranostic, and preventive role of retinal evaluation remains to be examined.

Purity Independent Subtyping of Tumors (PurIST), A Clinically Robust, Single-sample Classifier for Tumor Subtyping in Pancreatic Cancer

PURPOSE

Molecular subtyping for pancreatic cancer has made substantial progress in recent years, facilitating the optimization of existing therapeutic approaches to improve clinical outcomes in pancreatic cancer. With advances in treatment combinations and choices, it is becoming increasingly important to determine ways to place patients on the best therapies upfront. Although various molecular subtyping systems for pancreatic cancer have been proposed, consensus regarding proposed subtypes, as well as their relative clinical utility, remains largely unknown and presents a natural barrier to wider clinical adoption.

EXPERIMENTAL DESIGN

We assess three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a single-sample classifier (SSC) using penalized logistic regression based on the most robust and replicable schema.

RESULTS

We demonstrate that a tumor-intrinsic two-subtype schema is most robust, replicable, and clinically relevant. We developed Purity Independent Subtyping of Tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX.

CONCLUSIONS

The flexibility and utility of PurIST on low-input samples such as tumor biopsies allows it to be used at the time of diagnosis to facilitate the choice of effective therapies for patients with pancreatic ductal adenocarcinoma and should be considered in the context of future clinical trials.

Quantifying how diagnostic test accuracy depends on threshold in a meta-analysis

Tests for disease often produce a continuous measure, such as the concentration of some biomarker in a blood sample. In clinical practice, a threshold C is selected such that results, say, greater than C are declared positive and those less than C negative. Measures of test accuracy such as sensitivity and specificity depend crucially on C, and the optimal value of this threshold is usually a key question for clinical practice. Standard methods for meta‐analysis of test accuracy (i) do not provide summary estimates of accuracy at each threshold, precluding selection of the optimal threshold, and furthermore, (ii) do not make use of all available data. We describe a multinomial meta‐analysis model that can take any number of pairs of sensitivity and specificity from each study and explicitly quantifies how accuracy depends on C. Our model assumes that some prespecified or Box‐Cox transformation of test results in the diseased and disease‐free populations has a logistic distribution. The Box‐Cox transformation parameter can be estimated from the data, allowing for a flexible range of underlying distributions. We parameterise in terms of the means and scale parameters of the two logistic distributions. In addition to credible intervals for the pooled sensitivity and specificity across all thresholds, we produce prediction intervals, allowing for between‐study heterogeneity in all parameters. We demonstrate the model using two case study meta‐analyses, examining the accuracy of tests for acute heart failure and preeclampsia. We show how the model can be extended to explore reasons for heterogeneity using study‐level covariates.

Red cell distribution width to platelet ratio for liver fibrosis: a systematic review and meta-analysis of diagnostic accuracy

Introduction: Red cell distribution width to platelet ratio (RPR) may be a useful marker for the evaluation of liver fibrosis in chronic liver disease (CLD). We sought to investigate its value in fibrosis-related outcomes in a meta-analysis of diagnostic accuracy. Areas covered: We searched MEDLINE (1966-2019), Clinicaltrials.gov (2008-2019), Cochrane Central Register of Controlled Trials (CENTRAL) (1999-2019), Google Scholar (2004-2019) and WHO (International Clinical Trials Register Platform) databases using a structured algorithm. The articles were assessed by Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2). In over 1,800 patients for each outcome, pooled sensitivity and specificity for a) significant fibrosis, b) advanced fibrosis and c) cirrhosis were: a) 0.635 and 0.769 with an AUC of 0.747, b) 0.607 and 0.783 with an AUC of 0.773, c) 0.739 and 0.768 with an AUC of 0.818 respectively. Similar results were found for chronic hepatitis B in all outcomes. Subgroup analysis indicated a high specificity for advanced fibrosis detection in primary biliary cirrhosis. Sensitivity analysis did not alter the results. Expert opinion: RPR is a good predictor of fibrosis, especially as severity of chronic liver disease progresses. Future research should elucidate its value in specific etiologies of chronic liver disease.

Meta-analysis of full ROC curves: Additional flexibility by using semiparametric distributions of diagnostic test values

Diagnostic test accuracy studies frequently report on sensitivities and specificities for more than one threshold of the diagnostic test under study. Although it is obvious that the information from all thresholds should be used for a meta-analysis, in practice, frequently, only a single pair of sensitivity and specificity is selected. To overcome this disadvantage, we recently proposed a statistical model for the meta-analysis of such full receiver operating characteristic (ROC) curves that uses the relationship between a ROC curve and a bivariate model for interval-censored data. In this model, diagnostic tests values reported by the single studies were assumed to follow a parametric distribution. We propose a generalization of this model that allows for a flexible semiparametric modelling of the underlying distribution of the diagnostic test values by using the idea of piecewise constant hazard modelling. We show the results of a simulation study that indicates that the approach works reasonably well in practice. Finally, we illustrate the model by the example of population-based screening for type 2 diabetes mellitus.

Meta-analysis of full ROC curves using bivariate time-to-event models for interval-censored data

Systematic reviews and meta-analyses are the cornerstones of evidence-based medicine and inform treatment, diagnosis, or prevention of individual patients as well as policy decisions in health care. Statistical methods for the meta-analysis of intervention studies are well established today. Meta-analysis for diagnostic accuracy trials has also been a vivid research area in recent years, which is especially due to the increased complexity of their bivariate outcome of sensitivity and specificity. The situation is even more challenging when single studies report a full ROC curve with several pairs of sensitivity and specificity, each pair for a different threshold. Researchers frequently ignore this information and use only 1 pair of sensitivity and specificity from each study to arrive at meta-analytic estimates. Although methods to deal with the full information have been proposed, they have some disadvantages, eg, the numbers or values of thresholds have to be identical across studies, or the precise values of thresholds are ignored. We propose an approach for the meta-analysis of full ROC curves including the information from all thresholds by using bivariate time-to-event models for interval-censored data with random effects. This approach avoids the problems of previous methods and comes with the additional advantage that it allows for various distributions of the underlying continuous test values. The results from a small simulation study are given, which show that the approach works well in practice. Furthermore, we illustrate our new model using an example based on the population-based screening for type 2 diabetes mellitus.

Meta-analysis of test accuracy studies using imputation for partial reporting of multiple thresholds

Introduction

For tests reporting continuous results, primary studies usually provide test performance at multiple but often different thresholds. This creates missing data when performing a meta‐analysis at each threshold. A standard meta‐analysis (no imputation [NI]) ignores such missing data. A single imputation (SI) approach was recently proposed to recover missing threshold results. Here, we propose a new method that performs multiple imputation of the missing threshold results using discrete combinations (MIDC).

Methods

The new MIDC method imputes missing threshold results by randomly selecting from the set of all possible discrete combinations which lie between the results for 2 known bounding thresholds. Imputed and observed results are then synthesised at each threshold. This is repeated multiple times, and the multiple pooled results at each threshold are combined using Rubin's rules to give final estimates. We compared the NI, SI, and MIDC approaches via simulation.

Results

Both imputation methods outperform the NI method in simulations. There was generally little difference in the SI and MIDC methods, but the latter was noticeably better in terms of estimating the between‐study variances and generally gave better coverage, due to slightly larger standard errors of pooled estimates. Given selective reporting of thresholds, the imputation methods also reduced bias in the summary receiver operating characteristic curve. Simulations demonstrate the imputation methods rely on an equal threshold spacing assumption. A real example is presented.

Conclusions

The SI and, in particular, MIDC methods can be used to examine the impact of missing threshold results in meta‐analysis of test accuracy studies.

A novel statistical model for analyzing data of a systematic review generates optimal cutoff values for fractional exhaled nitric oxide for asthma diagnosis

OBJECTIVES

Measurement of fractional exhaled nitric oxide (FE_NO) might substitute bronchial provocation for diagnosing asthma. However, optimal FE_NO thresholds for diagnosing asthma remain unclear. We reanalyzed data collected for a systematic review investigating the diagnostic accuracy of FE_NO measurement to exploit all available thresholds under consideration of pretest probabilities using a newly developed statistical model.

STUDY DESIGN AND SETTING

One hundred and fifty data sets for a total of 53 different cutoffs extracted from 26 studies with 4,518 participants were analyzed with the multiple thresholds model. This model allows identifying thresholds at which the test is likely to perform best.

RESULTS

Diagnosing asthma might only be possible in a meaningful manner when the pretest probability of asthma is at least 30%. In that case, FE_NO > 50 ppb leads to a positive predictive value of 0.76 [95% confidence interval (CI): 0.29-0.96]. Excluding asthma might only be possible, when the pretest probability of asthma is 30% at maximum. Then, FE_NO < 20 ppb leads to a negative predictive value of 0.86 (95% CI 0.66-0.95).

CONCLUSION

The multiple thresholds model generates a more comprehensive and more clinically useful picture of the effects of different thresholds, which facilitates the determination of optimal thresholds for diagnosing or excluding asthma with FE_NO measurement.

Sentinel lymph node biopsy plus wide local excision vs. wide location excision alone for primary cutaneous melanoma: a systematic review and meta-analysis

BACKGROUND

Sentinel lymph node biopsy and wide local excision of the primary melanoma (SLNB) is now a standard staging procedure for patients with melanomas 1 mm or more in thickness, but its therapeutic benefit is not clear.

OBJECTIVE

To determine whether there is an association between performance of SLNB and patient prognosis.

METHODS

Studies assessing the association between performance of SLNB and patient prognosis were pooled from MEDLINE, EMBASE, PubMed, Cochrane Database of Systematic Reviews and Google Scholar. From each study, first author's last name, publication year, origin country, type of study design, characteristics of participants and the Hazard risk (HR) for melanoma specific survival (MSS) with the corresponding 95% confidence interval (95% CI) were collected. Methodological assessment of the studies was evaluated using the Newcastle-Ottawa scale (NOS) and the 'Risk of bias' tool detailed in the Cochrane Handbook for Systematic Reviews of Interventions. Meta-analyses for the global HR were performed. In addition, in order to explore the sources of heterogeneity among the studies, sensitivity analyses are also provided.

RESULTS

A total of six studies with 8764 patients who had undergone SLNB and 11054 patients who had undergone wide location excision alone (WLEA) were identified for the analysis. The indicators suggest that the heterogeneity is low: τ²  = 0; H = 1 [1; 1.74]; I²  = 0% [0%; 66.5%]. Evidence for publication bias was not found (Egger's test P = 0.4654). The pooled MSS HR from fixed effects analysis was determined to be 0.88 (95% CI = 0.80-0.96).

CONCLUSIONS

Although no significant survival difference was observed in four of the six series, the pooling summary data from all the studies that deal with this issue suggested that SLNB is associated with a significantly better outcome compared with WLEA for localized melanoma.

Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies

BACKGROUND

In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known.

METHODS

We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented.

RESULTS

We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis.

CONCLUSIONS

Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected.