Chapter 1: Introduction to the Methods Guide for Medical Test Reviews

Evaluation of medical tests presents challenges distinct from those involved in the evaluation of therapies; in particular, the very great importance of context and the dearth of comprehensive RCTs aimed at comparing the clinical outcomes of different tests and test strategies. Available guidance provides some suggestions: 1) Use of the PICOTS typology for clarifying the context relevant to the review, and 2) use of an organizing framework for classifying the types of medical test evaluation studies and their relationship to potential key questions. However, there is a diversity of recommendations for reviewers of medical tests and a proliferation of concepts, terms, and methods. As a contribution to the field, this Methods Guide for Medical Test Reviews seeks to provide practical guidance for achieving the goals of clarity, consistency, tractability, and usefulness

Optimizing the diagnostic work-up of acute uncomplicated urinary tract infections

<p>Abstract</p> <p>Background</p> <p>Most diagnostic tests for acute uncomplicated urinary tract infections (UTIs) have been previously studied in so-called single-test evaluations. In practice, however, clinicians use more than one test in the diagnostic work-up. Since test results carry overlapping information, results from single-test studies may be confounded. The primary objective of the Amsterdam Cystitis/Urinary Tract Infection Study (ACUTIS) is to determine the (additional) diagnostic value of relevant tests from patient history and laboratory investigations, taking into account their mutual dependencies. Consequently, after suitable validation, an easy to use, multivariable diagnostic rule (clinical index) will be derived.</p> <p>Methods</p> <p>Women who contact their GP with painful and/or frequent micturition undergo a series of possibly relevant tests, consisting of patient history questions and laboratory investigations. Using urine culture as the reference standard, two multivariable models (diagnostic indices) will be generated: a model which assumes that patients attend the GP surgery and a model based on telephone contact only. Models will be made more robust using the bootstrap. Discrimination will be visualized in high resolution histograms of the posterior UTI probabilities and summarized as 5^th, 10^th, 25^th50^th, 75^th, 90^th, and 95^thcentiles of these, Brier score and the area under the receiver operating characteristics curve (ROC) with 95% confidence intervals. Using the regression coefficients of the independent diagnostic indicators, a diagnostic rule will be derived, consisting of an efficient set of tests and their diagnostic values.</p> <p>The course of the presenting complaints is studied using 7-day patient diaries. To learn more about the natural history of UTIs, patients will be offered the opportunity to postpone the use of antibiotics.</p> <p>Discussion</p> <p>We expect that our diagnostic rule will allow efficient diagnosis of UTIs, necessitating the collection of diagnostic indicators with proven added value. GPs may use the rule (preferably after suitable validation) to estimate UTI probabilities for women with different combinations of test results. Finally, in a subcohort, an attempt is made to identify which indicators (including antibiotic treatment) are useful to prognosticate recovery from painful and/or frequent micturition.</p

A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology

Over time, methods for the development of clinical decision support (CDS) systems have evolved from interpretable and easy-to-use scoring systems to very complex and non-interpretable mathematical models. In order to accomplish effective decision support, CDS systems should provide information on how the model arrives at a certain decision. To address the issue of incompatibility between performance, interpretability and applicability of CDS systems, this paper proposes an innovative model structure, automatically leading to interpretable and easily applicable models. The resulting models can be used to guide clinicians when deciding upon the appropriate treatment, estimating patient-specific risks and to improve communication with patients.We propose the interval coded scoring (ICS) system, which imposes that the effect of each variable on the estimated risk is constant within consecutive intervals. The number and position of the intervals are automatically obtained by solving an optimization problem, which additionally performs variable selection. The resulting model can be visualised by means of appealing scoring tables and color bars. ICS models can be used within software packages, in smartphone applications, or on paper, which is particularly useful for bedside medicine and home-monitoring. The ICS approach is illustrated on two gynecological problems: diagnosis of malignancy of ovarian tumors using a dataset containing 3,511 patients, and prediction of first trimester viability of pregnancies using a dataset of 1,435 women. Comparison of the performance of the ICS approach with a range of prediction models proposed in the literature illustrates the ability of ICS to combine optimal performance with the interpretability of simple scoring systems.The ICS approach can improve patient-clinician communication and will provide additional insights in the importance and influence of available variables. Future challenges include extensions of the proposed methodology towards automated detection of interaction effects, multi-class decision support systems, prognosis and high-dimensional data

Diagnostic thinking and information used in clinical decision-making: a qualitative study of expert and student dental clinicians

<p>Abstract</p> <p>Background</p> <p>It is uncertain whether the range and frequency of Diagnostic Thinking Processes (DTP) and pieces of information (concepts) involved in dental restorative treatment planning are different between students and expert clinicians.</p> <p>Methods</p> <p>We video-recorded dental visits with one standardized patient. Clinicians were subsequently interviewed and their cognitive strategies explored using guide questions; interviews were also recorded. Both visit and interview were content-analyzed, following the Gale and Marsden model for clinical decision-making. Limited tests used to contrast data were t, χ², and Fisher's. Scott's π was used to determine inter-coder reliability.</p> <p>Results</p> <p>Fifteen dentists and 17 senior dental students participated in visits lasting 32.0 minutes (± 12.9) among experts, and 29.9 ± 7.1 among students; contact time with patient was 26.4 ± 13.9 minutes (experts), and 22.2 ± 7.5 (students). The time elapsed between the first and the last instances of the clinician looking in the mouth was similar between experts and students. Ninety eight types of pieces of information were used in combinations with 12 DTPs. The main differences found in DTP utilization had dentists conducting diagnostic interpretations of findings with sufficient certainty to be considered definitive twice as often as students. Students resorted more often to more general or clarifying enquiry in their search for information than dentists.</p> <p>Conclusions</p> <p>Differences in diagnostic strategies and concepts existed within clearly delimited types of cognitive processes; such processes were largely compatible with the analytic and (in particular) non-analytic approaches to clinical decision-making identified in the medical field. Because we were focused on a clinical presentation primarily made up of non-emergency treatment needs, use of other DTPs and concepts might occur when clinicians evaluate emergency treatment needs, complex rehabilitative cases, and/or medically compromised patients.</p