Clinical applications of microarray-based diagnostic tests

Nearly 15 years have passed since the possibility of analyzing nucleic acid analytes in a massively parallel fashion was proposed using the then new concept of microarrays. A decade ago, proof of principle demonstration projects established the use of high density microarrays to genotype multiple polymorphisms within a large gene [cystic fibrosis transmembrance regulator (CFTR)], to rapidly analyze DNA sequences by hybridization and to ascertain differential gene expression of the entire genome of an organism. The use of microarrays has had an explosive influence on the rate at which new biological information can be learned, including in a nonhypothesis driven manner. The past decade has also seen these research tools applied increasingly to questions of clinical and medical relevance. Genotyping drug metabolizing enzyme genes, resequencing important tumor suppressor genes, and classifying neoplastic disease by differential gene expression profiles are but a few of the many possibilities to provide clinically useful information using microarray-based diagnostic tests.     

Assessing the diagnostic accuracy of a sequence of tests

We consider the assessment of the overall diagnostic accuracy of a sequence of tests (e.g. repeated screening tests). The complexity of diagnostic choices when two or more continuous tests are used in sequence is illustrated, and different approaches to reducing the dimensionality are presented and evaluated. For instance, in practice, when a single test is used repeatedly in routine screening, the same screening threshold is typically used at each screening visit. One possible alternative is to adjust the threshold at successive visits according to individual-specific characteristics. Such possibilities represent a particular slice of a receiver operating characteristic surface, corresponding to all possible combinations of test thresholds. We focus in the development and examples on the setting where an overall test is defined to be positive if any of the individual tests are positive ('believe the positive'). The ideas developed are illustrated by an example of application to screening for prostate cancer using prostate-specific antigen.     

Simple and inexpensive immunoassay-based diagnostic tests

Simple and inexpensive yet sensitive and robust diagnostic tests are critically needed for resource-poor settings to enable timely diagnosis and effective use of limited health care resources. Current tests are often too expensive, too slow, or have compromised clinical performance, and they often require health care professional to perform the test. In addition, most assays are not intended to be used in extreme environmental conditions, but their performance may be affected by high temperatures and humidity often encountered in resource-poor settings. This review provides an overview of current immunoassay technologies and their advantages and limitations with respect to their feasibility to resource-poor settings. Future trends of immunoassay development for decentralized testing are also discussed. Homogeneous assays as such are out of the scope of this article because they are generally not yet sensitive enough or otherwise less feasible for inexpensive rapid diagnostic tests.     

Detection sensitivity of influenza rapid diagnostic tests

The sensitivity of influenza rapid diagnostic tests (IRDTs) currently available in Japan for various influenza virus strains, including human H7N9 and H5N1 isolates, were compared and it was found that all of the IRDTs examined detected these viruses; however, their detection sensitivities differed.     

Assessing strength of evidence in diagnostic tests

Clinical encounters between clinicians and patients begin with an attempt at diagnosis, a foundational element in determining a patient's ultimate outcome. Diagnosis that is expedient and accurate will result in a treatment that is expedient, appropriate, and cost-effective. In essence, evidence-based diagnosis is as vital as evidence-based intervention and treatment. If surgeons are committed to making expedient and accurate diagnoses, they must strive to apply diagnostic tests not just on the basis of ease, novelty, or availability but for the soundness of evidence behind them. In the scopes of both aesthetic and reconstructive surgery, advocating evidence-driven diagnostic test use is relevant. A pertinent example of how this relates to plastic surgery is the U.S. Food and Drug Administration recommendation to screen asymptomatic women with silicone breast implants with magnetic resonance imaging. For an important recommendation such as this that has tremendous cost implications to patients, sound study design and rigorous evaluation of the accuracy of magnetic resonance imaging as a screening tool has important health policy implications. The authors demonstrate how to determine the accuracy of diagnostic tests and, more importantly, illustrate the essential qualities of any study to establish the accuracy of a diagnostic test.     

A policy approach to the development of molecular diagnostic tests

Efficiently generating evidence of clinical utility is a major challenge for ensuring clinical adoption of valuable diagnostics. A new approach to reimbursement in the United States offers a balance between evidence and incentives for molecular diagnostic tests.     

Profile-likelihood inference for highly accurate diagnostic tests

We consider profile-likelihood inference based on the multinomial distribution for assessing the accuracy of a diagnostic test. The methods apply to ordinal rating data when accuracy is assessed using the area under the receiver operating characteristic (ROC) curve. Simulation results suggest that the derived confidence intervals have acceptable coverage probabilities, even when sample sizes are small and the diagnostic tests have high accuracies. The methods extend to stratified settings and situations in which the ratings are correlated. We illustrate the methods using data from a clinical trial on the detection of ovarian cancer.     

Cost of carrying out clinical diagnostic tests.

The total cost of performing diagnostic tests in a hospital laboratory during one year was assessed. The largest single item of expenditure was the cost of the salaries of the technical staff, while the cost of reagents (including radiopharmaceuticals) was relatively small. The total costs of carrying out diagnostic tests are much higher than is often recognised by those who request them. The use of relatively expensive, commercially available assay kits saves time and gives good value for money. It may be worth taking this into account when planning hospital budgets.     

Clinical utility of prostate carcinoma molecular diagnostic tests

Instead of relying on serum prostate-specific antigen (PSA) to identify patients for prostate biopsy, new laboratory tests are needed that have improved specificity for prostate carcinoma (CaP), allow accurate classification of clinically insignificant CaPs, allow for detection of clinically significant CaP in patients without elevated serum PSA, and allow for identification of aggressive forms of CaP, which may warrant adjunctive or even molecularly targeted therapy in the future. Over the last several years, high-throughput gene expression profiling and proteinomics have led to the identification of genes and proteins that are specifically overexpressed in CaP. Molecular diagnostic techniques readily translated to the clinical laboratory have been incorporated into the development of new tests based on these novel molecular alterations in CaP. Some of these tests already have well-documented clinical utility, such as in facilitating prostate biopsy decisions, and are routinely available. The current review focuses on the biological, clinical, and laboratory aspects of the most promising of these current and near-future molecular CaP tests.     

The effect of conditional dependence on the evaluation of diagnostic tests

The accuracy of a new diagnostic test is often determined by comparison with a reference test which also has unknown error rates. Maximum likelihood estimation of the error rates of both tests is possible if they are simultaneously applied to two populations with different disease prevalences. The estimation procedure assumes that the two tests are independent, conditional on a subject's true diagnostic status. If the tests are conditionally dependent, error rates for both tests can be substantially underestimated. Estimators for the prevalence rates in the two populations can be positively or negatively biased, depending on the relative magnitude of the two conditional covariances and the value of the prevalence parameter.