Understanding and using diagnostic tests

Blood Pressure Screening in Children: Do We Have This Right?

Noninvasive prenatal screening that uses cell-free DNA from the plasma of pregnant women offers tremendous potential as a screening method for fetal aneuploidy. A number of laboratories have validated different techniques for the use of cell-free DNA as a screening test for fetal aneuploidy. All tests have a high sensitivity and specificity for trisomy 18 and trisomy 21, regardless of which molecular technique is used. Women whose results are not reported, indeterminate, or uninterpretable (a "no call" test result) from cell-free DNA screening should receive further genetic counseling and be offered comprehensive ultrasound evaluation and diagnostic testing because of an increased risk of aneuploidy. Patients should be counseled that cell-free DNA screening does not replace the precision obtained with diagnostic tests, such as chorionic villus sampling or amniocentesis and, therefore, is limited in its ability to identify all chromosome abnormalities. Cell-free DNA screening does not assess risk of fetal anomalies such as neural tube defects or ventral wall defects. Patients who are undergoing cell-free DNA screening should be offered maternal serum alpha-fetoprotein screening or ultrasound evaluation for risk assessment. The cell-free DNA screening test should not be considered in isolation from other clinical findings and test results. Management decisions, including termination of the pregnancy, should not be based on the results of the cell-free DNA screening alone. Patients should be counseled that a negative cell-free DNA test result does not ensure an unaffected pregnancy. Given the performance of conventional screening methods, the limitations of cell-free DNA screening performance, and the limited data on cost-effectiveness in the low-risk obstetric population, conventional screening methods remain the most appropriate choice for first-line screening for most women in the general obstetric population.

Noninvasive prenatal screening that uses cell-free DNA from the plasma of pregnant women offers tremendous potential as a screening method for fetal aneuploidy. A number of laboratories have validated different techniques for the use of cell-free DNA as a screening test for fetal aneuploidy. All tests have a high sensitivity and specificity for trisomy 18 and trisomy 21, regardless of which molecular technique is used. Women whose results are not reported, indeterminate, or uninterpretable (a "no call" test result) from cell-free DNA screening should receive further genetic counseling and be offered comprehensive ultrasound evaluation and diagnostic testing because of an increased risk of aneuploidy. Patients should be counseled that cell-free DNA screening does not replace the precision obtained with diagnostic tests, such as chorionic villus sampling or amniocentesis and, therefore, is limited in its ability to identify all chromosome abnormalities. Cell-free DNA screening does not assess risk of fetal anomalies such as neural tube defects or ventral wall defects. Patients who are undergoing cell-free DNA screening should be offered maternal serum alpha-fetoprotein screening or ultrasound evaluation for risk assessment. The cell-free DNA screening test should not be considered in isolation from other clinical findings and test results. Management decisions, including termination of the pregnancy, should not be based on the results of the cell-free DNA screening alone. Patients should be counseled that a negative cell-free DNA test result does not ensure an unaffected pregnancy. Given the performance of conventional screening methods, the limitations of cell-free DNA screening performance, and the limited data on cost-effectiveness in the low-risk obstetric population, conventional screening methods remain the most appropriate choice for first-line screening for most women in the general obstetric population.

How Should Results from Clinical Tests Be Integrated into the Diagnostic Process?

Counseling for non-invasive prenatal testing (NIPT): what pregnant women may want to know

To assess the ability of health care professionals to evaluate the effect of clinical test results in different settings. Subjects were presented with a series of generic clinical scenarios in which information about the test performance and the pretest probability of disease was varied. The subject estimates of posttest probability were compared with those calculated on the basis of Bayes' theorem. Fifty health care professionals, including 31 physicians and 19 nonphysicians, associated with a university teaching hospital. Under a variety of testing conditions, both the physicians and the nonphysicians inaccurately estimated the posttest probability of disease. Based on a logarithmic transformation, the error in probability estimation was divided into a portion related to the pretest probability of disease and a portion related to the test performance. Most of the error in posttest probability estimation was associated with the incorrect use of pretest probabilities. The subjects consistently overestimated the posttest probability of disease expected under Bayes' theorem, with increasing error associated with decreasing pretest probability. Physician estimates of posttest probability increased with increasing likelihood ratios for each scenario. Nonphysician estimates of posttest probabilities increased with increasing likelihood ratios for a positive test, but the estimates associated with a negative test result were inconsistent. Physicians and nonphysicians overestimate posttest probabilities with increasing error associated with decreasing disease risk. Some nonphysicians may not fully understand the effect of test performance on risk estimation, particularly in the setting of a negative test. Health care professionals should receive training in the proper evaluation of test information, with particular emphasis on the influence of pretest disease risk on the posttest probability of disease.

We monitored a large-scale implementation of the Simple Amplification-Based Assay semi-quantitative viral load test for HIV-1 version I (SAMBA I Viral Load=SAMBA I VL) within Médecins Sans Frontières' HIV programmes in Malawi and Uganda, to assess its performance and operational feasibility. Descriptive analysis of routine programme data between August 2013 and December 2016. The dataset included samples collected for VL monitoring and tested using SAMBA I VL in five HIV clinics in Malawi (four peripheral health centres and one district hospital), and one HIV clinic in a regional referral hospital in Uganda. SAMBA I VL was used for VL testing in patients who had been receiving ART for between 6months and ten years, to determine whether plasma VL was above or below 1000 copies/mL of HIV-1, reflecting ART failure or efficacy. Randomly selected samples were quantified with commercial VL assays. SAMBA I instruments and test performance, site throughput, and delays in communicating results to clinicians and patients were monitored. Between August 2013 and December 2016 a total of 60889 patient samples were analysed with SAMBA I VL. Overall, 0.23% of initial SAMBA I VL results were invalid; this was reduced to 0.04% after repeating the test once. Global test failure, including instrument failure, was 1.34%. Concordance with reference quantitative testing of VL was 2620/2727, 96.0% (1338/1382, 96.8% in Malawi; 1282/1345, 95.3% in Uganda). For Chiradzulu peripheral health centres and Arua Hospital HIV clinic, where testing was performed on-site, same-day results were communicated to clinicians for between 91% and 97% of samples. Same-day clinical review was obtained for 84.7% across the whole set of samples tested. SAMBA I VL testing is feasible for monitoring cohorts of 1000 to 5000 ART-experienced patients. Same-day results can be used to inform rapid clinical decision-making at rural and remote health facilities, potentially reducing time available for development of resistance and conceivably helping to reduce morbidity and mortality.

Color vision research is not new for the Federal Aviation Administration (FAA); the Civil Aerospace Medical Institute has been conducting color vision research and publishing the results since 1967 ( 3 ). The FAA originally initiated color vision research because of the emerging use of color coding in the airport environment and the FAA has continued a line of color vision research because of the increasing use of color coding resulting from changing technology inside the cockpit, on air traffic control displays, and in the airport environment. Color can be used to convey meaning without supplemental signage such as the ubiquitous traffic signal that alerts drivers to proceed with caution via a yellow flashing light or to stop via a red flashing light. However, that meaning is only conveyed if the driver can distinguish between the yellow and the red colors. Approximately 8 to 10% of the male population ( 5 ) has a congenital color vision deficiency and, depending upon the type and severity of that deficiency, that task of interpreting the meaning of color coding may be difficult or impossible. Consequently, the FAA has long maintained a color vision standard for aero-medical screening to ensure that pilots and air traffic controllers can perform safety-related tasks without adverse consequences. Throughout the past few years, the FAA has explored a variety of color vision tests, searching for a valid screening test that has high sensitivity and specificity, meaning the ability to detect the presence or absence of the deficiency, respectively. Basically, color vision tests can be categorized as diagnostic, screening, or occupational tests. Diagnostic tests are designed to specifically diagnose the type and degree of deficiency, the screening tests focus on differentiating between normal or deficient color vision, and the occupational tests seek to separate those capable versus incapable of certain tasks such as identifying colors of wires or lights (e.g., the Farnsworth Lantern test that was developed to assess the ability of potential Navy signalmen for identifying red, green, and white lights). A few tests have been developed for the purpose of precisely diagnosing and classifying color vision; however, when color vision test scores are compared to performance on occupational tasks such as identifying or discriminating colors used in signal lights, precision approach path indicator (PAPI) lights, colored navigation lights, color coded map reading tasks, color coded air traffic control displays, and cockpit displays, a specific cut-point on those selection tests has not been found that can fully separate those who can from those who cannot accurately perform the color-coded pilot or air traffic control tasks. Some tests, including new computerized instruments, have been designed to differentiate defects involving the long wavelength sensitive cones (protan-type), middle wavelength sensitive cones (deutan-type), and short wavelength sensitive cones (tritan-type). Congenital protan and deutan deficiencies are, collectively, extremely common, affecting 1 in 12 men and 1 in 230 women; however, recent evidence indicates that tritan defects are virtually never present at birth (e.g., congenital) and the inherited forms involve S cone photoreceptor degeneration that develops later in life with the exact onset depending upon the specific mutation ( 1, 4 ). Thus, the exact frequency of inherited tritan defects is uncertain; however, it is probably less than 1 in 500. In part, because the underlying pathophysiology has not been well understood, few tests have been available that are capable of detecting tritan deficiencies. In the past, those tests included the single Farnsworth F2 pseudoisochromatic plate (PIP), the Moreland anomaloscope, the Hardy, Rand, Rittler PIP test, and, most recently, the Oculus anomaloscope. Consequently, the occupational color vision tests used by most agencies only screen for the most common (protan and deutan) types of defects. The newly developed computerized color vision tests, including the Colour Assessment and Diagnostic Test, the Cambridge Colour Test, the Cone Contrast Test, and the Computerized Color Vision Test, are all designed to detect tritan defects. However, tritan weaknesses have been noted in several of the FAA ‘ s recent studies in much higher than the traditionally expected numbers and diagnostic agreement is low among those tests when tritan deficiencies are involved. In the past, the FAA and other regulatory organizations have not, or have rarely, required tritan color vision screening in their occupational screening because of the following three factors: the rarity of the congenital defect, the unknown number of individuals affected by acquired deficiencies, and the lack of effective, reliable, valid, and affordable equipment with which to diagnose the deficiency.

To identify the psychological impact of receipt of an abnormal yet benign screening test result, the authors examined the response to a transvaginal ultrasound screening (TVS) test for ovarian cancer (OC) in asymptomatic women (N=540) undergoing an initial TVS screening test. Interviews were conducted prior to undergoing TVS screening and at 2 weeks and 4 months following this baseline. Women receiving an abnormal yet benign TVS test result (n=33) reported elevated OC-specific, but not general, distress at 2-week follow-up. Distress returned to baseline levels at 4-month follow-up. Consistent with the monitoring process and cognitive-social health information processing models, response to an abnormal TVS result was moderated by a monitoring coping style, low optimism, and a family history of OC.

P127 Detection of MICA antibodies: Comparison between MICA Screening and Single Antigen tests

Testing for the presence of a condition may give an incorrect result Sensitivity and specificity of a test depend upon the chosen cut-off value ‘No difference’ usually means ‘these data do not suggest a difference’ The power of a test needs to be known to conclude that there is no difference Power calculations should be considered if the null hypothesis is not rejected Calvin Coolidge was a taciturn president of the United States, nicknamed ‘Silent Cal’. When told that he had died, the acerbic writer Dorothy Parker remarked, ‘How could they tell?’ Telling if something has not happened is a perennial problem in science, particularly so in laboratory experiments. Why should this be? In the case of Cal Coolidge, they probably could tell. But in science, random events make it hard to tell, with certainty. There are also different ways of ‘telling’. Mortality is a good categorical measure, an unequivocal endpoint. When diagnostic tests are used to define categories, we meet the concepts of ‘false positive’ and ‘false negative’. Here, we use the expression ‘false’ to indicate that the conclusion we drew from the test would be incorrect. In the example we use below, we have a test for ‘alive’. If we know the true condition, we can classify test results as true or false. Classifying a dead animal as alive would be a false positive. True and false are words usually applied to a diagnosis or similar categorical events but also crop up in other aspects of statistical inference. Categorical analysis and categorical logic (it's either there or it isn’t) are not the same as the frequentist logic (the likelihood is small) that is used in common statistical analysis, and this leads to a great deal of argument and misunderstanding. Our previous comment that statistics has an impoverished vocabulary rings true: in this instance, the concepts of false positive and false negative conclusions are applied in different ways to different procedures, causing confusion. Considering categories, imagine that a pool that has become overheated in the sun: some of the frogs in it have died. Our observations suggest that the frogs on the surface of the pool are still alive, and the ones at the bottom of the pool are dead. Is this correct? A random sample produces the data in Fig. 1. Not all the frogs on the surface are alive. If we used ‘on the surface’ to indicate being alive, we would falsely attribute life to 10 frogs. Since 107 frogs are dead, this is a false positive rate of 10/107 (which we only know if we have an unequivocal means of determining death). The positive predictive power of the test for life being present, if we were to look at the next frog on the surface, would be 80/90. Unfortunately, expressions such as sensitivity, false positive, and so on can be used in two different senses. Here we assume we already have an exact means to determine death, much in the way that some statistics theory will start by defining a population. Thus we consider that the false positive rate of our test for ‘alive’ is the proportion of dead frogs on the surface, expressed as a proportion of the frogs that really ARE dead. An alternative view would be if we were to consider the proportion of frogs on the surface that are later found to be dead, we would then be considering the outcome of the test. The diagnostic false positive rate would be 10/90. This is a diagnostic probability, and this form is less commonly used. Many diagnostic tests don't rely on categories like this: a continuous variable is often used to distinguish categories. Suppose we measure the blood lactate concentration in frogs that are still alive, and relate these concentrations to subsequent survival, we might obtain the results shown in Fig. 2. Plasma lactate values in frogs after overheating, related to survival The groups are different (P < 0.001, Mann–Whitney U test). The median value for survivors is 1.29, and for non-survivors, 1.86 (mmol l−1). Although the median concentrations are different, some frogs with high lactate concentrations can survive. Is there a particular lactate concentration that will distinguish survival from non-survival? The capacity to predict outcome (sensitivity and specificity) alters as we choose different cut-off values. Using a series of cut-off values we can plot sensitivity and (1 – specificity) for each chosen value (Fig. 3). This is known as a Receiver-Operator Characteristic Curve, or ROC curve, because one of its first applications was to find how operators of radar receivers distinguish ‘real’ echoes from background noise. In this case, we are looking for survivors using a lactate concentration that is less than the cut-off value we have chosen. A Receiver Operator Characteristic curve for the capacity of a lactate concentration below a chosen value to predict survival An effective diagnostic test should have a large area below the curve, which would show a combination of good sensitivity and specificity. Since tests vary, so does opinion about the area below the curve that indicates a good test. It should be substantially greater that 50%, which would be no better than chance alone, and the closer to 100% the better. The large overlap of individual values between the two groups in Fig. 2 suggests that lactate concentration is a poor indicator of survival, as there is no value that combines useful specificity and sensitivity. Any cut-off values we could choose would have too many false positive or false negative conclusions. Despite this, there is a statistically significant difference according to the Mann–Whitney test, showing that a significant difference doesn't mean the measure is the basis of a good diagnostic test. In other forms of statistical inference, an expression such as ‘false positive’ is less appropriate and is often not well defined, hidden in the logic of these tests. For example, the t test does not provide black or white conclusions: statistical inference is restricted to judging how large a part chance could play in what we observe. Frequentist statistical tests such as the t tests are measures of uncertainty. The usual laboratory experiment question ‘do these data show an effect?’ has to be worded ‘are these samples statistically different?’ As we know, the question then is re-worded ‘How likely are these results if the samples came from the same population?’ Let's say our test result is that P= 0.01, which would usually be taken to indicate there is an ‘effect’. This is not so: this P value is the probability that we conclude there is an effect, and we still could be wrong. Although small, a chance still exists that we would be wrong, about 1 in 100 times. This is known as the alpha error, the possibility that we might classify our result as showing an effect, when in fact there is none. In the long run, if we were to repeatedly sample two populations that actually were the same, then results like the ones we found would be found 1 in 100 times. This is sometimes called the ‘false positive’ rate. However, it's based on the premise that there is NO difference and is perhaps better called the ‘false conclusion’ rate. Reasoning for ‘no effect’ is even less certain, and also often misconstrued. If the P value is 0.12, then we judge that the results are quite possible, assuming the data are from the same population. If we did the same experiment again and again, then we might obtain data like this in almost 1 out of 8 experiments. Is this unlikely? We usually choose to decide this is quite likely, and thus do not reject the null hypothesis: if we rejected it, the chance of a false conclusion is too great. This ‘chance of a false conclusion’ is often misleadingly called a ‘false negative rate’. The hypothesis the test uses, which is the NULL hypothesis, is resoundingly negative; there are no positive results lurking here. Positive results come if we are persuaded, by an estimate of probability (the P value), that the null hypothesis is not tenable. The context of the question ‘is there an effect?’ is relevant. The consequence of the answer ‘no effect’ may be unimportant. However, in some cases the consequence of concluding that there is no difference, because a difference is not apparent, could be devastating. How often do we hear the claim ‘there is no evidence that X is toxic?’ to assert that X is not toxic? Immediately, the sceptic will ask how toxic, and how much evidence? The statistician may add another qualification: how much variation is present? A reasonable example (not a perfect analogy) is the radar operator: there is a greater certainty about a blip on the screen if the signal is large (accept the answer only if it's very likely), if you can keep looking at the screen (improve the sample size), and if there is not a lot of background noise (Fig. 4). In each of these examples of signal averaging, there is an identical signal with added random noise. The signal is more easily seen if the noise is reduced (vertical arrow). Signal detection is improved by taking more samples to allow more averaging (horizontal arrow). The final feature of signal detection is how certain we wish to be (from ‘just discernible’ to ‘barn door’) that the event is not random (a concept analogous to the P value). In this example, a signal is always present, but we just can't be sure in all the cases. This is equally so when statistics are applied to the null hypothesis. We have to consider the beta error: the possibility that we might classify our result as showing no effect where in fact there could be a difference. To demonstrate, consider two populations that are known to be different, as shown in Fig. 5. A, random samples from two different populations, low and high, where the population means differ by 0.1 mmol l−1. A t test finds the P values shown. B, a valuable further analysis that should be applied before erroneously assuming ‘no difference’ is to inspect the 90% confidence limits of the difference between the means. This indicates the size of the possible differences. Here, a small sample of 20 per group, and a small true difference between the groups, does not reject the null hypothesis, with a P value of 0.05. In this particular case, there is indeed a true difference in the populations from which these samples have been taken, but the power of the test applied has been inadequate. The power of a test is the chance of finding a defined difference: a power of 90% would mean that there was only a 10% chance of failing to find a difference of the defined magnitude. Thus power is (1 −β). The 10% false negative conclusion rate or beta error, is also known as a type II error. Some call this a ‘false negative’ but this could be misleading since there is never a ‘true’ negative here. The frequentist definition is that if the experiment were repeated over and over, the conclusion would be incorrect occasionally, and the rate is the beta error. For the example we have chosen, the relationships between power, sample size and the difference we seek to detect are shown in Fig. 6. Using the samples in Fig. 4, the power of the test (the chance of finding a specified difference between samples, given that it exists) is shown, where n= 20 and n= 40, for two different P values. The populations that provided the example data in Fig. 4 were different, by 0.1 mmol l−1, indicated by the vertical dotted line. If P is accepted at 0.05 and n= 40, the power of the test is only about 40%. The retrospective use of power calculations has been often criticized, but this is usually because the authors were seeking to reject a null hypothesis (they would have liked to find a difference). If a null hypothesis cannot be rejected, this is not sufficient evidence that there is NO difference. Claiming ‘no difference’ can be a serious error. If we wish to convince ourselves, and others, that there is no difference, we must be sure that the test was capable of rejecting a specifically defined difference, if it were present. If a small difference is considered important, and variation in the population is substantial, then a small sample will be inadequate. Put simply, the study is underpowered. Although there are software packages to calculate power, a simple approach for a simple two group comparison is to plot the 90% confidence limits of the difference between the means (Fig. 5B). This will show the likely range of differences that could be detected. The logical error (concluding that no significant difference is equivalent to the same) is frequently made worse when test results are compared, for example: the effect of substance A causes a significant difference, but substance B does not; thus A has more effect than B. As we explain above, several factors, not explicitly stated, could affect this result. It could well be that B has a greater, but more varied, effect. Because most tests are looking for a positive, many scientists fail to consider negatives adequately. In the old song ‘Accentuate the positive’ the accomplished song writer Johnny Mercer also cautioned to ‘eliminate the negative, don't mess with Mr In-Between’.

Screening for prostate cancer using prostate-specific antigen (PSA) testing can lead to problems of underdiagnosis and overdiagnosis. Short, noncontrast magnetic resonance imaging (MRI) or transrectal ultrasonography might overcome these limitations. To compare the performance of PSA testing, MRI, and ultrasonography as screening tests for prostate cancer. This prospective, population-based, blinded cohort study was conducted at 7 primary care practices and 2 imaging centers in the United Kingdom. Men 50 to 69 years of age were invited for prostate cancer screening from October 10, 2018, to May 15, 2019. All participants underwent screening with a PSA test, MRI (T2 weighted and diffusion), and ultrasonography (B-mode and shear wave elastography). The tests were independently interpreted without knowledge of other results. Both imaging tests were reported on a validated 5-point scale of suspicion. If any test result was positive, a systematic 12-core biopsy was performed. Additional image fusion-targeted biopsies were performed if the MRI or ultrasonography results were positive. The main outcome was the proportion of men with positive MRI or ultrasonography (defined as a score of 3-5 or 4-5) or PSA test (defined as PSA ≥3 μg/L) results. Key secondary outcomes were the number of clinically significant and clinically insignificant cancers detected if each test was used exclusively. Clinically significant cancer was defined as any Gleason score of 3+4 or higher. A total of 2034 men were invited to participate; of 411 who attended screening, 408 consented to receive all screening tests. The proportion with positive MRI results (score, 3-5) was higher than the proportion with positive PSA test results (72 [17.7%; 95% CI, 14.3%-21.8%] vs 40 [9.9%; 95% CI, 7.3%-13.2%]; P < .001). The proportion with positive ultrasonography results (score, 3-5) was also higher than the proportion of those with positive PSA test results (96 [23.7%; 95% CI, 19.8%-28.1%]; P < .001). For an imaging threshold of score 4 to 5, the proportion with positive MRI results was similar to the proportion with positive PSA test results (43 [10.6%; 95% CI, 7.9%-14.0%]; P = .71), as was the proportion with positive ultrasonography results (52 [12.8%; 95% CI, 9.9%-16.5%]; P = .15). The PSA test (≥3 ng/mL) detected 7 clinically significant cancers, an MRI score of 3 to 5 detected 14 cancers, an MRI score of 4 to 5 detected 11 cancers, an ultrasonography score of 3 to 5 detected 9 cancer, and an ultrasonography score of 4 to 5 detected 4 cancers. Clinically insignificant cancers were diagnosed by PSA testing in 6 cases, by an MRI score of 3 to 5 in 7 cases, an MRI score of 4 to 5 in 5 cases, an ultrasonography score of 3 to 5 in 13 cases, and an ultrasonography score of 4 to 5 in 7 cases. In this cohort study, when screening the general population for prostate cancer, MRI using a score of 4 or 5 to define a positive test result compared with PSA alone at 3 ng/mL or higher was associated with more men diagnosed with clinically significant cancer, without an increase in the number of men advised to undergo biopsy or overdiagnosed with clinically insignificant cancer. There was no evidence that ultrasonography would have better performance compared with PSA testing alone.

The current cognitive tests have been developed based on and standardized against Western constructs and normative data. With older people of minority ethnic background increasing across Western countries, there is a need for cognitive screening tests to address factors which influence performance bias and timely diagnostic dementia accuracy. The diagnostic accuracy in translated and culturally adapted cognitive screening tests and their impact on test performance in diverse populations have not been well addressed to date. This review aims to highlight considerations relating to the adaptation processes, language, cultural influences, impact of immigration, and level of education to assess for dementia in non-Western and/or non-English speaking populations. We conducted a systematic search for studies addressing the effects of translation and cultural adaptations of cognitive screening tests (developed in a Western context) upon their diagnostic accuracy and test performance across diverse populations. Four electronic databases and manual searches were conducted, using a predefined search strategy. A narrative synthesis of findings was conducted. Search strategy yielded 2,890 articles, and seventeen studies (4,463 participants) met the inclusion criteria. There was variability in the sensitivity and specificity of cognitive tests, irrespective of whether they were translated only, culturally adapted only, or both. Cognitive test performance was affected by education, linguistic ability, and aspects of acculturation. We highlight the importance of translating and culturally adapting tests that have been developed in the Western context. However, these findings should be interpreted with caution as results varied due to the broad selection of included cognitive tests.

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