A capture–recapture approach for screening using two diagnostic tests with availability of disease status for the test positives only

The article considers screening human populations with two screening tests. If any of the two tests is positive, then full evaluation of the disease status is undertaken; however, if both diagnostic tests are negative, then disease status remains unknown. This procedure leads to a data constellation in which, for each disease status, the 2 × 2 table associated with the two diagnostic tests used in screening has exactly one empty, unknown cell. To estimate the unobserved cell counts, previous approaches assume independence of the two diagnostic tests and use specific models, including the special mixture model of Walter or unconstrained capture–recapture estimates. Often, as is also demonstrated in this article by means of a simple test, the independence of the two screening tests is not supported by the data. Two new estimators are suggested that allow associations of the screening test, although the form of association must be assumed to be homogeneous over disease status. These estimators are modifications of the simple capture–recapture estimator and easy to construct. The estimators are investigated for several screening studies with fully evaluated disease status in which the superior behavior of the new estimators compared to the previous conventional ones can be shown. Finally, the performance of the new estimators is compared with maximum likelihood estimators, which are more difficult to obtain in these models. The results indicate the loss of efficiency as minor.

A capture-recapture approach for screening using two diagnostic tests with availability of disease status for the test positives only

The article considers screening human populations with two screening tests. If any of the two tests is positive, then full evaluation of the disease status is undertaken; however, if both diagnostic tests are negative, then disease status remains unknown. This procedure leads to a data constellation in which, for each disease status, the 2 x 2 table associated with the two diagnostic tests used in screening has exactly one empty, unknown cell. To estimate the unobserved cell counts, previous approaches assume independence of the two diagnostic tests and use specific models, including the special mixture model of Walter or unconstrained capture-recapture estimates. Often, as is also demonstrated in this article by means of a simple test, the independence of the two screening tests is not supported by the data. Two new estimators are suggested that allow associations of the screening test, although the form of association must be assumed to be homogeneous over disease status. These estimators are modifications of the simple capture-recapture estimator and easy to construct. The estimators are investigated for several screening studies with fully evaluated disease status in which the superior behavior of the new estimators compared to the previous conventional ones can be shown. Finally, the performance of the new estimators is compared with maximum likelihood estimators, which are more difficult to obtain in these models. The results indicate the loss of efficiency as minor.

Bayesian Estimation for Performance Measures of Two Diagnostic Tests in the Presence of Verification Bias

Sensitivity and specificity are measures that allow us to evaluate the performance of a diagnostic test. In practice, it is common to have situations where a proportion of selected individuals cannot have the real state of the disease verified, since the verification could be an invasive procedure, as occurs with biopsy. This happens, as a special case, in the diagnosis of prostate cancer, or in any other situation related to risks, that is, not practicable, nor ethical, or in situations with high cost. For this case, it is common to use diagnostic tests based only on the information of verified individuals. This procedure can lead to biased results or workup bias. In this paper, we introduce a Bayesian approach to estimate the sensitivity and the specificity for two diagnostic tests considering verified and unverified individuals, a result that generalizes the usual situation based on only one diagnostic test.

Acceptance and potential barriers to effective use of diagnostic tests for visceral leishmaniasis in an urban area in Brazil

Abstract: INTRODUCTION: Acceptance of the IT LEISH(r) and direct agglutination test- made in the Laboratório de Pesquisas Clínicas (DAT-LPC) by healthcare professionals and patients suspected of visceral leishmaniasis (VL) in Ribeirão das Neves was evaluated. METHODS: Ninety-two patients and 47 professionals completed three questionnaires. RESULTS: Eighty-eight (96%) patients considered fingertip blood collection a positive test feature, and 86% (37) and 91% of professionals considered the IT LEISH(r) easy to perform and interpret, respectively. All professionals classified the DAT-LPC as simple and easy. CONCLUSIONS: Patients and healthcare professionals in Ribeirão das Neves demonstrated a high degree of acceptance of the IT LEISH(r) and DAT-LPC.

Rapid diagnostic tests for the home-based management of malaria, in a high-transmission area.

Rapid diagnostic tests (RDT) are sometimes recommended to improve the home-based management of malaria. The accuracy of an RDT for the detection of clinical malaria and the presence of malarial parasites has recently been evaluated in a high-transmission area of southern Mali. During the same study, the cost-effectiveness of a 'test-and-treat' strategy for the home-based management of malaria (based on an artemisinin-combination therapy) was compared with that of a 'treat-all' strategy. Overall, 301 patients, of all ages, each of whom had been considered a presumptive case of uncomplicated malaria by a village healthworker, were checked with a commercial RDT (Paracheck-Pf). The sensitivity, specificity, and positive and negative predictive values of this test, compared with the results of microscopy and two different definitions of clinical malaria, were then determined. The RDT was found to be 82.9% sensitive (with a 95% confidence interval of 78.0%-87.1%) and 78.9% (63.9%-89.7%) specific compared with the detection of parasites by microscopy. In the detection of clinical malaria, it was 95.2% (91.3%-97.6%) sensitive and 57.4% (48.2%-66.2%) specific compared with a general practitioner's diagnosis of the disease, and 100.0% (94.5%-100.0%) sensitive but only 30.2% (24.8%-36.2%) specific when compared against the fulfillment of the World Health Organization's (2003) research criteria for uncomplicated malaria. Among children aged 0-5 years, the cost of the 'test-and-treat' strategy, per episode, was about twice that of the 'treat-all' (U.S.$1.0. v. U.S.$0.5). In older subjects, however, the two strategies were equally costly (approximately U.S.$2/episode). In conclusion, for children aged 0-5 years in a high-transmission area of sub-Saharan Africa, use of the RDT was not cost-effective compared with the presumptive treatment of malaria with an ACT. In older patients, use of the RDT did not reduce costs. The question remains whether either of the strategies investigated can be made affordable for the affected population.

Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.

Accuracy of routine diagnostic tests used in paracoccidioidomycosis patients at a university hospital

The identification of appropriate laboratory measures to confirm clinical hypotheses is important in routine paracoccidioidomycosis medical care. The clinical records and laboratory reports of 401 paracoccidioidomycosis patients attended at the Tropical Diseases Area, Faculdade de Medicina de Botucatu, from 1974 to 2008 were reviewed. Direct mycological (DM), cell block (CB), histopathological (HP), and double immunodiffusion (DID) tests were evaluated before treatment. Typical Paracoccidioides brasiliensis yeast forms were observed in clinical specimens of 86% of the patients, but 14% were detected only by serological test. DM of 51 different tissue specimens produced 74.5% sensitivity, and 62.5% sensitivity was observed in 112 sputum samples. CB in 483 sputum samples generated 55.3% sensitivity. HP performed in 239 samples from different tissues revealed 96.7% sensitivity. Serology carried out in 351 patients and 200 healthy controls provided 90.0% sensitivity, 100.0% specificity, 100.0% positive predictive value, 85.1% negative predictive value and 93.6% accuracy. Comparisons of laboratory measurements performed in the same patient showed that sensitivity decreases from HP to DID to CB and DM, with the last two assays providing similar sensitivities. This study demonstrated that P. brasiliensis identification by HP, CB, and/or DM associated with DID is sufficient to establish the laboratorial diagnosis of paracoccidioidomycosis in practically all cases. (C) 2011 Royal Society of Tropical Medicine and Hygiene. Published by Elsevier Ltd. All rights reserved.

A General Latent Class Model for Performance Evaluation of Diagnostic Tests in the Absence of a Gold Standard: An Application to Chagas Disease

We propose a new general Bayesian latent class model for evaluation of the performance of multiple diagnostic tests in situations in which no gold standard test exists based on a computationally intensive approach. The modeling represents an interesting and suitable alternative to models with complex structures that involve the general case of several conditionally independent diagnostic tests, covariates, and strata with different disease prevalences. The technique of stratifying the population according to different disease prevalence rates does not add further marked complexity to the modeling, but it makes the model more flexible and interpretable. To illustrate the general model proposed, we evaluate the performance of six diagnostic screening tests for Chagas disease considering some epidemiological variables. Serology at the time of donation (negative, positive, inconclusive) was considered as a factor of stratification in the model. The general model with stratification of the population performed better in comparison with its concurrents without stratification. The group formed by the testing laboratory Biomanguinhos FIOCRUZ-kit (c-ELISA and rec-ELISA) is the best option in the confirmation process by presenting false-negative rate of 0.0002% from the serial scheme. We are 100% sure that the donor is healthy when these two tests have negative results and he is chagasic when they have positive results.

RANDOM EFFECTS MODELS IN A META-ANALYSIS OF THE ACCURACY OF DIAGNOSTIC TESTS WITHIN A GOLD STANDARD IN THE PRESENCE OF MISSING DATA

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian hierarchical model, both of which account for the heterogeneity across studies through study-specific random effects. The methods can be used to estimate the accuracy of two imperfect diagnostic tests in other meta-analyses when the prevalence of disease, the sensitivities and/or the specificities of diagnostic tests are heterogeneous among studies. Furthermore, simulation studies have demonstrated the importance of carefully selecting appropriate random effects on the estimation of diagnostic accuracy measurements in this scenario.

Rapid diagnostic tests for the home-based management of malaria, in a high-transmission area

Rapid diagnostic tests (RDT) are sometimes recommended to improve the home-based management of malaria. The accuracy of an RDT for the detection of clinical malaria and the presence of malarial parasites has recently been evaluated in a high-transmission area of southern Mali. During the same study, the cost-effectiveness of a 'test-and-treat' strategy for the home-based management of malaria (based on an artemisinin-combination therapy) was compared with that of a 'treat-all' strategy. Overall, 301 patients, of all ages, each of whom had been considered a presumptive case of uncomplicated malaria by a village healthworker, were checked with a commercial RDT (Paracheck-Pf). The sensitivity, specificity, and positive and negative predictive values of this test, compared with the results of microscopy and two different definitions of clinical malaria, were then determined. The RDT was found to be 82.9% sensitive (with a 95% confidence interval of 78.0%-87.1%) and 78.9% (63.9%-89.7%) specific compared with the detection of parasites by microscopy. In the detection of clinical malaria, it was 95.2% (91.3%-97.6%) sensitive and 57.4% (48.2%-66.2%) specific compared with a general practitioner's diagnosis of the disease, and 100.0% (94.5%-100.0%) sensitive but only 30.2% (24.8%-36.2%) specific when compared against the fulfillment of the World Health Organization's (2003) research criteria for uncomplicated malaria. Among children aged 0-5 years, the cost of the 'test-and-treat' strategy, per episode, was about twice that of the 'treat-all' (U.S.$1.0. v. U.S.$0.5). In older subjects, however, the two strategies were equally costly (approximately U.S.$2/episode). In conclusion, for children aged 0-5 years in a high-transmission area of sub-Saharan Africa, use of the RDT was not cost-effective compared with the presumptive treatment of malaria with an ACT. In older patients, use of the RDT did not reduce costs. The question remains whether either of the strategies investigated can be made affordable for the affected population.

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

Utility and safety of invasive (fractional flow reserve) and non-invasive (cardiac magnetic resonance imaging) diagnostic tests in patients with NSTEMI

A prospective randomised controlled clinical trial of treatment decisions informed by invasive functional testing of coronary artery disease severity compared with standard angiography-guided management was implemented in 350 patients with a recent non-ST elevation myocardial infarction (NSTEMI) admitted to 6 hospitals in the National Health Service. The main aims of this study were to examine the utility of both invasive fractional flow reserve (FFR) and non-invasive cardiac magnetic resonance imaging (MRI) amongst patients with a recent diagnosis of NSTEMI. In summary, the findings of this thesis are: (1) the use of FFR combined with intravenous adenosine was feasible and safe amongst patients with NSTEMI and has clinical utility; (2) there was discordance between the visual, angiographic estimation of lesion significance and FFR; (3). The use of FFR led to changes in treatment strategy and an increase in prescription of medical therapy in the short term compared with an angiographically guided strategy; (4) in the incidence of major adverse cardiac events (MACE) at 12 months follow up was similar in the two groups. Cardiac MRI was used in a subset of patients enrolled in two hospitals in the West of Scotland. T1 and T2 mapping methods were used to delineate territories of acute myocardial injury. T1 and T2 mapping were superior when compared with conventional T2-weighted dark blood imaging for estimation of the ischaemic area-at-risk (AAR) with less artifact in NSTEMI. There was poor correlation between the angiographic AAR and MRI methods of AAR estimation in patients with NSTEMI. FFR had a high accuracy at predicting inducible perfusion defects demonstrated on stress perfusion MRI. This thesis describes the largest randomized trial published to date specifically looking at the clinical utility of FFR in the NSTEMI population. We have provided evidence of the diagnostic and clinical utility of FFR in this group of patients and provide evidence to inform larger studies. This thesis also describes the largest ever MRI cohort, including with myocardial stress perfusion assessments, specifically looking at the NSTEMI population. We have demonstrated the diagnostic accuracy of FFR to predict reversible ischaemia as referenced to a non-invasive gold standard with MRI. This thesis has also shown the futility of using dark blood oedema imaging amongst all comer NSTEMI patients when compared to novel T1 and T2 mapping methods.

Diagnostic tests for amoebic liver abscess: comparison of enzyme - linked immunosorbent assay (Elisa) and counterimmunoelectrophoresis (CIE)

The liver abscess is the most frequent extraintestinal complication of intestinal amoebiasis: its diagnosis is suggested by the clinical picture but it must be confirmed by paraclinic tests. Themost stringent diagnosis requires identification of E. histolytica. But this is possible only in a few cases. Serological tests greatly improve the diagnosis of this severe complication of amoebiasis. We compared the Enzyme Linfed Immunosorbent Assay and the Counterimmunoeletrophoresis techniques. Both techniques were used to detect amoebic antibodies in 50 control patients, 30 patients with liver abscess and 30 patients with intestinal amoebiasis. All the sera from control patients gave negative results iin both techniques. When analysing the sera from patients with intestinal amoebiasis, 10% of them were positive by ELISA but non by CIE. The sera of patients with liver abscess, we found that 90% were positive by the ELISA method and 66.6% by the CIE technique. In patients with amoebic liver abscess, the results showed that the ELISA was more sensitive than the CIE, as it presented a higher sensitivity (100%) than that of the CIE technique (66%).

Prevalence and monthly distribution of head lice using two diagnostic procedures in several age groups in Uberlândia, State of Minas Gerais, Southeastern Brazil

Some epidemiological characteristics of head lice, Pediculus capitis, were studied using two procedures: cut hair analysis and head inspection. Higher prevalence rates were observed in the middle and at the end of the school terms. Both procedures indicated that children were the main reservoir for this type of pediculosis in Uberlândia.