Iron deficiency anemia in the elderly: the diagnostic process.

OBJECTIVE: To determine the effectiveness of physician probability estimates calculated on the basis of findings from history-taking and physical examination in the diagnosis of iron deficiency anemia in elderly patients. DESIGN: Prospective study. SETTING: Two community hospitals offering secondary and tertiary care. PATIENTS: A total of 259 patients over 65 years of age found to have previously undiagnosed anemia. MEASURES: Physician estimates of the likelihood of iron deficiency before (pretest probability) and after (post-test probability) the laboratory test results were available. The hemogram was available to the physicians when they made their pretest probability estimates. Because the serum ferritin level proved to be the most powerful of the laboratory test results studied, the likelihood ratios associated with the post-test estimates were compared with the ratios associated with the serum ferritin level. MAIN RESULTS: The post-test probability estimates were influenced by the serum ferritin level and the pretest estimates. The post-test estimates derived from the findings obtained through history-taking and physical examination and the laboratory test results (including the serum ferritin level) were slightly less accurate in predicting iron deficiency than the serum ferritin level alone. Nevertheless, a model in which the pretest estimates were used in addition to the serum ferritin level to predict iron deficiency proved to be more powerful than the serum ferritin level alone (p = 0.006). This indicated that the limitations of the post-test estimates were due to a misinterpretation of the serum ferritin level and that the findings from history-taking and physical examination added important diagnostic information. CONCLUSIONS: Physicians must be aware of test properties to provide optimal care to their patients. If test results are properly interpreted, pretest probabilities derived from findings obtained through history-taking and physical examination can add useful information that will lead to more accurate diagnoses.     

Diagnostic testing revisited: pathways through uncertainty

To aid physicians who may be having difficulty applying the principles of decision analysis to diagnostic data according to the methods published in the past several years, the authors of this paper set out a few principles and schemes for using and interpreting diagnostic data obtained from dichotomous tests. They also present a simple BASIC program for calculating post-test probabilities from likelihood ratios and pretest probabilities that a particular disease is present in a particular patient; the program can be adapted for use on microcomputers.     

A Decision-Tree Strategy for Combining Diagnostic Tests for Prediction

In the context of medical screening, various diagnostic tests have been developed for determining whether a disease is present in an individual. Similarly, in the context of toxicological screening, a variety of short-term assays have been developed to predict whether a chemical would be carcinogenic if tested in a long-term bioassay. In both contexts, it is a challenge to combine the results of several predictive tests in a way that improves on the predictivity of the individual tests. Increases in positive predictivity can be accompanied by decreases in negative predictivity, and vice versa. This article presents a decision-tree classification model for combining results from several independent short-term or diagnostic tests to quantify the likelihood of a true positive result (patient has disease, or chemical is carcinogenic). The decision-tree strategy determines the most advantageous sequence for conducting the predictive tests. The classification model is based on statistical confidence limits on the predictive probability of disease (carcinogenicity) rather than on the central estimate of the predictive probability. This model is applied to the assessment of the abilities of four short-term tests in the prediction of chemical carcinogenicity under the assumption of independence among the four tests, and is used to demonstrate a testing strategy for the application of three pancreatic cancer diagnostic tests.     

Predictive value and efficiency of hematology data

Laboratory test results and procedures can be evaluated at four levels:1. Analytic analysis of laboratory test: precision, technical sensitivity, technical specificity; 2. Diagnostic analysis of laboratory test: diagnostic sensitivity, diagnostic specificity, Youden index, likelihood ratio, etc.; 3. Operational analysis of laboratory test: predictive value of positive result, predictive value of negative result, efficiency, discriminant function, etc.; 4. Medical decision-making analysis of laboratory test: threshold probability, cost-benefit analysis, solving the decision tree. Analysis of results or selection of tests can occur at any level, without knowledge of the test's evaluation or performance at the remaining levels. Alternatively, the development of new laboratory tests can proceed from level 1 to level 4, or vice versa. Unfortunately, the former is usually the case and most of the tests in use today have never been evaluated at the medical decision-making level (level 4). Recent efforts at developing automated WBC differential counters represent a disproportionate amount of time and energy expended at level 1, and typify our backward approach to laboratory medicine. In thinking about the development of new diagnostic tests, we should begin at level 4 to characterize the properties and specifications that the test must meet. As an example, an in vitro test for the diagnosis of pulmonary embolism could be characterized in this fashion with criteria specified at each of the lower levels. Returning to the question of "How good should a laboratory test be?", we can see that the answer must come from an analysis of the benefit-cost equation (level 4). Figure 2 is a plot of the net benefit and cost of treatment versus the threshold probability. Since the threshold probability defines how certain one must be of the diagnosis before proceeding with treatment, it serves as a minimum probability which should be exceeded by the predictive value of the test. When the benefit--cost ratio is low, a test with a very high predictive value is required to exceed the threshold probability. On the other hand, when the benefit--cost ratio is high, even a test with a low predictive value would be of use to the physician in making the decision to treat the patient. Within this framework, a number of clinical situations could be evaluated and problems requiring the development of highly predictive laboratory tests (low benefit--cost ratios) could be identified. Too much emphasis in laboratory medicine has been placed on the "laboratory" and not enough on the "medicine". How important is the coefficient of variation when the benefit--cost ratio is high? Tests can not be developed or selected appropriately in a therapeutic vacuum.     

Communicating accuracy of tests to general practitioners: a controlled study

ObjectiveTo assess the extent to which different forms of summarising diagnostic test information influence general practitioners'' ability to estimate disease probabilities.DesignControlled questionnaire study.SettingThree Swiss conferences in continuous medical education.Participants263 general practitioners.InterventionQuestionnaire with multiple choice questions about terms of test accuracy and a clinical vignette with the results of a diagnostic test described in three different ways (test result only, test result plus test sensitivity and specificity, test result plus the positive likelihood ratio presented in plain language).ResultsThe correct definitions for sensitivity and predictive value were chosen by 76% and 61% of the doctors respectively, but only 22% chose the correct answer for the post-test probability of a positive screening test. In the clinical vignette doctors given the test result only overestimated its diagnostic value (median attributed likelihood ratio (aLR)=9.0, against 2.54 reported in the literature). Providing the scan''s sensitivity and specificity reduced the overestimation (median aLR=6.0) but to a lesser extent than simple wording of the likelihood ratio (median aLR=3.0).ConclusionMost general practitioners recognised the correct definitions for sensitivity and positive predictive value but did not apply them correctly. Conveying test accuracy information in simple, non-technical language improved their ability to estimate disease probabilities accurately.

What is already known on this topic

Many doctors confuse the sensitivity of clinical tests and their positive predictive valueDoctors tend to overestimate information derived from such tests and underestimate information from a patient''s clinical historyMost primary research on diagnostic accuracy is reported using sensitivity and specificity or likelihood ratios

What this study adds

In a cohort of experienced Swiss general practitioners most were unable to interpret correctly numerical information on the diagnostic accuracy of a screening testWhen presented with a positive result alone they grossly overestimated its valueAdding information on the test''s sensitivity and specificity moderated these overestimates, and expressing the same numerical information as a positive likelihood ratio in simple, non-technical language brought the estimates still closer to their true values     

The Diagnostic Performance of Coronary Artery Angiography with 64-MSCT and Post 64-MSCT: Systematic Review and Meta-Analysis

Purpose

To comprehensively investigate the diagnostic performance of coronary artery angiography with 64-MDCT and post 64-MDCT.

Materials and Methods

PubMed was searched for all published studies that evaluated coronary arteries with 64-MDCT and post 64-MDCT. The clinical diagnostic role was evaluated by applying the likelihood ratios (LRs) to calculate the post-test probability based on Bayes'' theorem.

Results

91 studies that met our inclusion criteria were ultimately included in the analysis. The pooled positive and negative LRs at patient level were 8.91 (95% CI, 7.53, 10.54) and 0.02 (CI, 0.01, 0.03), respectively. For studies that did not claim that non-evaluable segments were included, the pooled positive and negative LRs were 11.16 (CI, 8.90, 14.00) and 0.01 (CI, 0.01, 0.03), respectively. For studies including uninterruptable results, the diagnostic performance decreased, with the pooled positive LR 7.40 (CI, 6.00, 9.13) and negative LR 0.02 (CI, 0.01, 0.03). The areas under the summary ROC curve were 0.98 (CI, 0.97 to 0.99) for 64-MDCT and 0.96 (CI, 0.94 to 0.98) for post 64-MDCT, respectively. For references explicitly stating that the non-assessable segments were included during analysis, a post-test probability of negative results >95% and a positive post-test probability <95% could be obtained for patients with a pre-test probability of <73% for coronary artery disease (CAD). On the other hand, when the pre-test probability of CAD was >73%, the diagnostic role was reversed, with a positive post-test probability of CAD >95% and a negative post-test probability of CAD <95%.

Conclusion

The diagnostic performance of post 64-MDCT does not increase as compared with 64-MDCT. CTA, overall, is a test of exclusion for patients with a pre-test probability of CAD<73%, while for patients with a pre-test probability of CAD>73%, CTA is a test used to confirm the presence of CAD.     

Clinical versus laboratory detection of alcohol abuse: the alcohol clinical index.

To determine reliable indicators of alcohol abuse a comprehensive set of clinical and laboratory information was acquired from three groups of subjects with a wide range of drinking histories: 131 outpatients with alcohol problems, 131 social drinkers, and 52 patients from family practice. Findings from clinical examination provided greater diagnostic accuracy than laboratory tests for detecting alcohol abuse. Logistic regression analysis produced an overall accuracy of 85-91% for clinical signs, 84-88% for items from the medical history, and 71-83% for laboratory tests in differentiating the three groups. Further analyses showed 17 clinical signs and 13 medical history items that formed a highly diagnostic instrument (alcohol clinical index) that could be used in clinical practice. A probability of alcohol abuse exceeding 0.90 was found if four or more clinical signs or four or more medical history items from the index were present. Despite recent emphasis on the laboratory diagnosis of alcohol abuse simple clinical measures seem to provide better diagnostic accuracy.     

Subjective interpretation,laboratory error and the value of forensic DNA evidence: Three case studies

This article discusses two factors that may profoundly affect the value of DNA evidence for proving that two samples have a common source: uncertainty about the interpretation of test results and the possibility of laboratory error. Three case studies are presented to illustrate the importance of the analyst's subjective judgments in interpreting some RFLP-based forensic DNA tests. In each case, the likelihood ratio describing the value of DNA evidence is shown to be dramatically reduced by uncertainty about the scoring of bands and the possibility of laboratory error. The article concludes that statistical estimates of the frequency of matching genotypes can be a misleading index of the value of DNA evidence, and that more adequate indices are needed. It also argues that forensic laboratoires should comply with the National Research Council's recommendation that forensic test results be scored in a blind or objective manner.Editor's commentsThe author treats the timely and important issue of laboratory error. Readers will need to read the paper by Lempert in this volume for an alternative interpretation of the 1989 proficiency testing of Cellmark diagnostics.     

Interpretation of diagnostic data: 3. How to do it with a simple table (part B).

The following guidelines are useful if you want to "do it with a simple table" (Table IV): First, identify the sensitivity and specificity of the sign, symptom or diagnostic test you plan to use. Many are already in the literature, and subspecialists should either know them for their field or be able to track them down for you. Depending on whether you are considering a sign, a symptom or a diagnostic laboratory test, you will want to track down a clinical subspecialist, a radiologist, a pathologist and so on. Start your table with a total of 1000 patients, as shown in location (a + b + c + d) of panel A. Using the information you have about the patient before you apply the diagnostic test, estimate the patient''s pretest likelihood (prevalence or prior probability) of the target disorder -- let''s say 10%. Take this proportion of the total (100) and place it in location (a + c); the remaining 900 patients go in location (b + d) (panel B). Multiply (a + c) (100) by the sensitivity of the diagnostic test (let''s say 83%) and place the result (83) in cell a and the difference (17) in cell c; similarly, multiply (b + d) (900) by the specificity of the diagnostic test (let''s say 91%) and place the result (819) in cell d and the difference (81) in cell b (panel C). If (a + b) and (c + d) do not add up to 1000, you will know you have made a mistake. You can now calculate the positive predictive value, a/(a + b), and the negative predictive value, d/(c + d), as shown in panel D. You have now reached a level of understanding a fair bit beyond the rule-in/rule-out strategy discussed in part 1 of our series. Furthermore, you can already do more than most clinicians, so you may want to stop here, at least for a while. On the other hand, you may want to go further and learn how to handle slightly more complex tables with multiple cut-off points. In the next article you will find more powerful ways to take advantage of the degree of positivity and negativity of diagnostic test results.     

Deep vein thrombosis: Current status and nanotechnology advances

Deep vein thrombosis (DVT) affects up to 2 million people in the United States, and worldwide incidence is 70 to 113 cases per 100,000 per year. Mortality from DVT is often due to subsequent pulmonary embolism (PE). Precise diagnosis and treatment is thereby essential for the management of DVT. DVT is diagnosed by a thorough history and physical examination followed by laboratory and diagnostic tests. The choice of laboratory and diagnostic test is dependent on clinical pretest probability. Available laboratory and diagnostic techniques mainly involve D-dimer test, ultrasound, venography, and magnetic resonance imaging. The latter two diagnostic tools require high doses of contrast agents including either radioactive or toxic materials. The available treatment options include lifestyle modifications, mechanical compression, anticoagulant therapy, inferior vena cava filter, and thrombolysis/thrombolectomy. All of these medical and surgical treatments have serious side effects including improper clot clearance and increased risk of hemorrhage occurrence. Therefore, research in this field has recently focused on the development of non-invasive and accurate diagnostics, such as ultrasound enhanced techniques and molecular imaging methods, to assess thrombus location and its treatment course. The frontier of nanomedicine also shows high prospects in tackling DVT with efficient targeted drug delivery. This review describes the pathology of DVT along with successive medical problems such as PE and features a detailed listing of various diagnostic and therapeutic modalities that have been in use and are under development.     

Immunochromatographic monoclonal test for detection of Helicobacter pylori antigen in stool is useful in children from high-prevalence developing country

BACKGROUND: Tests to detect Helicobacter pylori antigens in feces for diagnosis of infection in children demonstrate controversial results. One novel and fast monoclonal test improves diagnostic accuracy in adults, but clinical evidence of its usefulness at pediatric age is insufficient to date. The objective of this work was to evaluate the diagnostic accuracy of this test in a sample of Mexican children. METHODS: We conducted a transversal study in 150 selected children with digestive symptoms suggestive of organic disease in whom a clinical history was conducted in addition to a fast monoclonal test (ImmunoCardSTAT HpSA, Meridian Diagnostics) performed by immunochromatography. Patients were submitted to endoscopy and histopathologic study. RESULTS: Of the 150 children (mean age 7.8 +/- 4.7 years), 107 (71.3%) were positive for the test, and presence of H. pylori was confirmed histologically in 109 (72.7%) children, with sensitivity of 96.3% (95% CI = 95.8-96.8), specificity of 95.1% (95% CI = 93.9-96.4), and accuracy of 96.0% (95% CI, -95.6 to -96.3); pretest probability was 0.73, while post-test probability was 0.98. Infection rate and test accuracy increased with age. CONCLUSIONS: This test is useful for detecting H. pylori infection in children of all ages, and is a good alternative for screening studies in developing countries with elevated prevalence, due to its being fast, noninvasive, inexpensive, and easy to carry out.     

Interpretation of diagnostic data: 4. How to do it with a more complex table.

A more complex table is especially useful when a diagnostic test produces a wide range of results and your patient''s levels are near one of the extremes. The following guidelines will be useful: Identify the several cut-off points that could be used. Fill in a complex table along the lines of Table I, showing the numbers of patients at each level who have and do not have the target disorder. Generate a simple table for each cut-off point, as in Table II, and determine the sensitivity (TP rate) and specificity (TN rate) at each of them. Select the cut-off point that makes the most sense for your patient''s test result and proceed as in parts 2 and 3 of our series. Alternatively, construct an ROC curve by plotting the TP and FP rates that attend each cut-off point. If you keep your tables and ROC curves close at hand, you will gradually accumulate a set of very useful guides. However, if you looked very hard at what was happening, you will probably have noticed that they are not very useful for patients whose test results fall in the middle zones, or for those with just one positive result of two tests; the post-test likelihood of disease in these patients lurches back and forth past 50%, depending on where the cut-off point is. We will show you how to tackle this problem in part 5 of our series. It involves some maths, but you will find that its very powerful clinical application can be achieved with a simple nomogram or with some simple calculations.     

Statistical Inference For Risk Difference in an Incomplete Correlated 2 × 2 Table

In some infectious disease studies and 2‐step treatment studies, 2 × 2 table with structural zero could arise in situations where it is theoretically impossible for a particular cell to contain observations or structural void is introduced by design. In this article, we propose a score test of hypotheses pertaining to the marginal and conditional probabilities in a 2 × 2 table with structural zero via the risk/rate difference measure. Score test‐based confidence interval will also be outlined. We evaluate the performance of the score test and the existing likelihood ratio test. Our empirical results evince the similar and satisfactory performance of the two tests (with appropriate adjustments) in terms of coverage probability and expected interval width. Both tests consistently perform well from small‐ to moderate‐sample designs. The score test however has the advantage that it is only undefined in one scenario while the likelihood ratio test can be undefined in many scenarios. We illustrate our method by a real example from a two‐step tuberculosis skin test study.     

Assessment of the required performance and the development of corresponding program decision rules for neglected tropical diseases diagnostic tests: Monitoring and evaluation of soil-transmitted helminthiasis control programs as a case study

Recently, the World Health Organization established the Diagnostic Technical Advisory Group to identify and prioritize diagnostic needs for neglected tropical diseases, and to ultimately describe the minimal and ideal characteristics for new diagnostic tests (the so-called target product profiles (TPPs)). We developed two generic frameworks: one to explore and determine the required sensitivity (probability to correctly detect diseased persons) and specificity (probability to correctly detect persons free of disease), and another one to determine the corresponding samples sizes and the decision rules based on a multi-category lot quality assurance sampling (MC-LQAS) approach that accounts for imperfect tests. We applied both frameworks for monitoring and evaluation of soil-transmitted helminthiasis control programs. Our study indicates that specificity rather than sensitivity will become more important when the program approaches the endgame of elimination and that the requirements for both parameters are inversely correlated, resulting in multiple combinations of sensitivity and specificity that allow for reliable decision making. The MC-LQAS framework highlighted that improving diagnostic performance results in a smaller sample size for the same level of program decision making. In other words, the additional costs per diagnostic tests with improved diagnostic performance may be compensated by lower operational costs in the field. Based on our results we proposed the required minimal and ideal diagnostic sensitivity and specificity for diagnostic tests applied in monitoring and evaluating of soil-transmitted helminthiasis control programs.     

Interleukin-8 for Diagnosis of Neonatal Sepsis: A Meta-Analysis

BackgroundNeonatal sepsis (NS) is a life-threatening disorder and an important cause of morbidity and mortality in neonates. Previous studies showed that interleukin 8 (IL-8) may effectively and rapidly diagnose NS.ObjectiveWe conducted the systematic review and meta-analysis to investigate the diagnostic value of the IL-8 in NS.MethodsThe literature was searched in PUBMED, EMBASE, Cochrane Library, CNKI, VIP and other Chinese Medical Databases during October 1998 to January 2014 using set search criteria. Each included study was evaluated by quality assessment of diagnostic accuracy studies tool. Two investigators independently extracted the data and study characteristics, and disagreements, if any, were resolved by consensus. Meta-disc software was used to calculate the pooled sensitivity, specificity and summary diagnostic odds ratio (SDOR), I² or Cochrane Q to test heterogeneity, and meta-regression to investigate the source of heterogeneity. Funnel plots were used to test the potential presence of publication bias. False-positive report probability (FPRP) was calculated to confirm the significance of the results.ResultsEight studies (548 neonates) were included in this meta-analysis. The pooled sensitivity and specificity of IL-8 were 0.78 and 0.84, respectively, which had moderate accuracy in the diagnosis of NS. The pooled diagnostic odds ratio (DOR) and area under curve (AUC) was 21.64 and 0.8908 (Q*=0.8215), respectively. The diagnostic threshold analysis showed that there was no threshold effect. The meta-regression analysis showed the cut-off, QUADAS and onset time have no effect on the heterogeneity. The funnel plots showed the existence of publication bias.ConclusionMeta-analysis showed IL-8 had a moderate accuracy (AUC=0.8908) for the diagnosis of NS. IL-8 is a helpful biomarker for early diagnosis of NS. However, we should combine the results with clinical symptoms and signs, laboratory and microbial results.     

Testing tumors from different anatomic sites for clonal relatedness using somatic mutation data

A common task for the cancer pathologist is to determine, in a patient suffering from cancer, whether a new tumor in a distinct anatomic site from the primary is an independent occurrence of cancer or a metastasis. As mutational profiling of tumors becomes more widespread in routine clinical practice, this diagnostic task can be greatly enhanced by comparing mutational profiles of the tumors to determine if they are sufficiently similar to conclude that the tumors are clonally related, that is, one is a metastasis of the other. We present here a likelihood ratio test for clonal relatedness in this setting and provide evidence of its validity. The test is unusual in that there are two possible alternative hypotheses, representing the two anatomic sites from which the single clonal cell could have initially emerged. Although evidence for clonal relatedness is largely provided by the presence of exact mutational matches in the two tumors, we show that it is possible to observe data where the test is statistically significant even when no matches are observed. This can occur when the mutational profile of one of the tumors is closely aligned with the anatomic site of the other tumor, suggesting indirectly that the tumor originated in that other site. We exhibit examples of this phenomenon and recommend a strategy for interpreting the results of these tests in practice.     

The use of score tests for inference on variance components

Whenever inference for variance components is required, the choice between one-sided and two-sided tests is crucial. This choice is usually driven by whether or not negative variance components are permitted. For two-sided tests, classical inferential procedures can be followed, based on likelihood ratios, score statistics, or Wald statistics. For one-sided tests, however, one-sided test statistics need to be developed, and their null distribution derived. While this has received considerable attention in the context of the likelihood ratio test, there appears to be much confusion about the related problem for the score test. The aim of this paper is to illustrate that classical (two-sided) score test statistics, frequently advocated in practice, cannot be used in this context, but that well-chosen one-sided counterparts could be used instead. The relation with likelihood ratio tests will be established, and all results are illustrated in an analysis of continuous longitudinal data using linear mixed models.     

Liver enzyme alteration: a guide for clinicians

ISOLATED ALTERATIONS OF BIOCHEMICAL MARKERS OF LIVER DAMAGE in a seemingly healthy patient can present a challenge for the clinician. In this review we provide a guide to interpreting alterations to liver enzyme levels. The functional anatomy of the liver and pathophysiology of liver enzyme alteration are briefly reviewed. Using a schematic approach that classifies enzyme alterations as predominantly hepatocellular or predominantly cholestatic, we review abnormal enzymatic activity within the 2 subgroups, the most common causes of enzyme alteration and suggested initial investigations.Abnormal liver enzyme levels may signal liver damage or alteration in bile flow. Liver enzyme alteration may be either the accompanying biochemical picture in a patient with symptoms or signs suggestive of liver disease or an isolated, unexpected finding in a patient who has undergone a wide range of laboratory tests for a nonhepatic disease or for minor, vague complaints. The latter situation is a common clinical scenario today because of the routine incorporation of hepatic tests in automated blood chemistry panels. Isolated alterations of biochemical markers of liver damage in a seemingly healthy patient often represent a challenge even for the experienced clinician and usually set off a battery of further, costly tests¹ and consultations that may ultimately prove unnecessary. The aim of this review is to provide physicians in general practice with a guide to interpreting liver enzyme alterations.