Index for Rating Predictive Accuracy of Screening Tests

Predictive Accuracy ◽

Screening Tests ◽

Predictive Values ◽

This paper proposes a new pair of indices, the »predictive powers«, for measuring the predictive accuracy (predictivity) of screening tests.Sensitivity and specificity are indices for measuring the validity of a test. They give the probability of a certain test result given a substance of known condition (a carcinogen or a non-carcinogen). They do not describe the predictive accuracy of a test, which is the probability of a certain condition (a carcinogen or a non-carcinogen) given a known test result. Predictive values are unsuitable measures for characterizing a test since they are seriously affected by the prevalence of carcinogens. However, the predictive powers do not have this limitation and are shown to be useful indices for the purpose of rating the predictive accuracies of various screening tests.

Index for Rating Predictive Accuracy of Screening Tests

Methods of Information in Medicine ◽

10.1055/s-0038-1635401 ◽

1982 ◽

Vol 21 (03) ◽

pp. 149-153 ◽

Cited By ~ 8

Author(s):

B. C. K. Choi

Keyword(s):

Predictive Accuracy ◽

Screening Tests ◽

Predictive Values ◽

Bounding the Predictive Values of COVID-19 Antibody Tests

10.1101/2020.05.14.20102061 ◽

2020 ◽

Author(s):

Charles F. Manski

Keyword(s):

Risk Assessment ◽

Predictive Value ◽

Clinical Decision Making ◽

Clinical Decision ◽

Infected Person ◽

Predictive Values ◽

Considerable Uncertainty ◽

Antibody Tests ◽

COVID-19 antibody tests have imperfect accuracy. There has been lack of clarity on the meaning of reported rates of false positives and false negatives. For risk assessment and clinical decision making, the rates of interest are the positive and negative predictive values of a test. Positive predictive value (PPV) is the chance that a person who tests positive has been infected. Negative predictive value (NPV) is the chance that someone who tests negative has not been infected. The medical literature regularly reports different statistics, sensitivity and specificity. Sensitivity is the chance that an infected person receives a positive test result. Specificity is the chance that a non-infected person receives a negative result. Knowledge of sensitivity and specificity permits one to predict the test result given a person's true infection status. These predictions are not directly relevant to risk assessment or clinical decisions, where one knows a test result and wants to predict whether a person has been infected. Given estimates of sensitivity and specificity, PPV and NPV can be derived if one knows the prevalence of the disease, the rate of illness in the population. There is considerable uncertainty about the prevalence of COVID-19. This paper addresses the problem of inference on the PPV and NPV of COVID-19 antibody tests given estimates of sensitivity and specificity and credible bounds on prevalence. I explain the methodological problem, show how to estimate bounds on PPV and NPV, and apply the findings to some tests authorized by the FDA.

Confirmation and certainty in toxicology screening.

Clinical Chemistry ◽

10.1093/clinchem/34.8.1535 ◽

1988 ◽

Vol 34 (8) ◽

pp. 1535-1539 ◽

Cited By ~ 11

Author(s):

V R Spiehler ◽

C M O'Donnell ◽

D V Gokhale

Keyword(s):

Predictive Value ◽

Drugs Of Abuse ◽

Screening Tests ◽

True Negative ◽

Urine Drug ◽

Toxicology Screening ◽

Value Model ◽

Positive Results ◽

Abstract Confirmation of presumptive positive urine drug screens, necessary to minimize the reporting of false-positive results, can be costly and time-consuming. The predictive value model can be used to select the confirming tests and to calculate the confidence of the result. The predictive value of a test result is the probability, based on the sensitivity and specificity of the test, that the result is a true positive or a true negative. The predictive value model applied to toxicology screening tests for drugs of abuse showed that prevalence, in addition to sensitivity and specificity, was the factor controlling the confidence level of a result. For example, the predictive value of a positive result for a screening test that has a sensitivity of 99% and a specificity of 99%, applied to screening in a population with a prevalence of 1% is 0.50; for a prevalence of 10%, it is 0.92. Confirmation with a second, chemically independent, test of equal sensitivity and specificity increases the predictive value to 0.99.

Performance of diabetes screening tests: an evaluation study of Iranian diabetes screening program

10.21203/rs.2.11503/v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Fateme Kianpour ◽

Mohammad Fararouei ◽

Jafar Hassanzadeh ◽

Mohammadnabi Mohammadi

Keyword(s):

Screening Program ◽

Communicable Disease ◽

Fasting Blood Glucose ◽

Glycated Haemoglobin ◽

Screening Tests ◽

Diabetic Patients ◽

Diabetes Screening ◽

Predictive Values ◽

Cut Points

Abstract Background Diabetes is a common non-communicable disease which is responsible for about 9% of all deaths and 25% reduction in life expectancy and nearly half of the diabetic patients are not aware of their disease. In this regard, diabetes screening to identify un-known diabetic patients is of great importance.The aims of this study were first to evaluate the performance of two commonly used diabetes screening tests, which are currently used by the Iranian national screening program for diabetes (NSPD). Methods Validity of diabetes screening tests among 1057 participants older than 30 years was measured. Screening tests included Capillary fasting blood glucose (CBG) with glucometer and glycated haemoglobin (HbA1c). In addition, the validity of the tests was measured based on venous fasting plasma glucose (VPG) as golden standard test. Results According to the results, the sensitivity of CBG and HbA1c tests were 69.01% and 84.5% and the specificity of the tests were 95.7% and 79.3% respectively. Positive and negative predictive values were 53.84% and 97.72% for CBG and 22.72% and 98.61% for HbA1c respectively. New cut points for CBG (116.5 mg/dl) and HbA1c (7.15%) are obtained. Using these values as new cut points, sensitivity and specificity of CBG raised to 80.30% and decreased 89.10% respectively. Similarly, using 7.15% as cut point of HbA1c test, sensitivity and specificity changed to 77.50% and 94.20% respectively. Conclusions Compared to several other countries, the performance of NSPD is relatively higher. ROC analysis suggested new cut points for significantly better performance of NSPD.

Evaluation of binary diagnostic tests accuracy for medical researches

Turkish Journal of Biochemistry ◽

10.1515/tjb-2020-0337 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Jale Karakaya

Keyword(s):

Diagnostic Test ◽

Performance Measures ◽

Test Performance ◽

Diagnostic Tests ◽

Sample Size Calculation ◽

Rapid Test ◽

Screening Tests ◽

Youden Index ◽

Predictive Values ◽

AbstractObjectivesThe aim of this study is to introduce the features of diagnostic tests. In addition, it will be demonstrated which performance measures can be used for diagnostic tests with binary results, the properties of these measures and how to interpret them.Materials and MethodsThe evaluation of the diagnostic test performance measures may differ depending on whether the test result is numerical or binary. When the diagnostic test result is continuous numerical data, ROC analysis is often utilized. The performance of a diagnostic test with binary results are usually evaluated using the measures of sensitivity and specificity. However, there are some important measures other than these two measures for binary test results. These measures are predictive values, overall accuracy, diagnostic odds ratio, Youden index, and likelihood ratios.ResultsA hypothetical data has been produced based on the studies conducted on the performance of rapid tests (Specific IgM/IgG) according to the RT-PCR test for Covid 19 in the literature. An example of a diagnostic test (Specific IgM/IgG) with a binary result is given and all measurements and their confidence interval are obtained for this data. The performance of rapid test was examined and interpreted.ConclusionIt is important to design and evaluate the performance of diagnostic/screening tests for health care. In this review, some basic definitions, performance measures that can be used only in evaluating the diagnostic tests with binary results and their confidence intervals are mentioned. Having many different measures provides different interpretations in the evaluation of test performance. Accurately predicting the performance of a diagnostic test depends on many factors. These factors can be study design, criteria of participant selection, sample size calculation, test methods etc. There are guidelines that ensure that all information regarding the conditions under which the study was conducted is in report, in terms of such factors. Therefore, these guidelines are recommended for use of the checklist by many publishers.

How to Correct for Chance Agreement in the Estimation of Sensitivity and Specificity of Diagnostic Tests

Methods of Information in Medicine ◽

10.1055/s-0038-1635010 ◽

1994 ◽

Vol 33 (02) ◽

pp. 180-186 ◽

Cited By ~ 11

Author(s):

H. Brenner ◽

O. Gefeller

Keyword(s):

Disease Status ◽

Disease Prevalence ◽

Small Sample ◽

Small Samples ◽

Interval Estimate ◽

Traditional Concept ◽

Interval Estimates ◽

Major Disadvantage ◽

Abstract:The traditional concept of describing the validity of a diagnostic test neglects the presence of chance agreement between test result and true (disease) status. Sensitivity and specificity, as the fundamental measures of validity, can thus only be considered in conjunction with each other to provide an appropriate basis for the evaluation of the capacity of the test to discriminate truly diseased from truly undiseased subjects. In this paper, chance-corrected analogues of sensitivity and specificity are presented as supplemental measures of validity, which pay attention to the problem of chance agreement and offer the opportunity to be interpreted separately. While recent proposals of chance-correction techniques, suggested by several authors in this context, lead to measures which are dependent on disease prevalence, our method does not share this major disadvantage. We discuss the extension of the conventional ROC-curve approach to chance-corrected measures of sensitivity and specificity. Furthermore, point and asymptotic interval estimates of the parameters of interest are derived under different sampling frameworks for validation studies. The small sample behavior of the estimates is investigated in a simulation study, leading to a logarithmic modification of the interval estimate in order to hold the nominal confidence level for small samples.

Effects of Transport Medium Composition on in vitro Drug Permeation across Excised Pig Intestinal Tissue

Drug Delivery Letters ◽

10.2174/2210303110999201005214114 ◽

2020 ◽

Vol 10 ◽

Author(s):

Bianca Peterson ◽

Henrico Heystek ◽

Josias H. Hamman ◽

Johan D. Steyn

Keyword(s):

Medium Composition ◽

Screening Tests ◽

Intestinal Tissue ◽

Predictive Values ◽

Transport Medium ◽

Intestinal Fluids ◽

Lower Permeability ◽

The Impact ◽

Simulated Intestinal Fluids

Background:: Knowledge of the permeation characteristics of new chemical entities across biological membranes is essential to drug research and development. Transport medium composition may affect the absorption of compounds during in vitro drug transport testing. To preserve the predictive values of screening tests, the possible influence of transport media on the solubility of model drugs, and on the activities of tight junctions and efflux transporter proteins (e.g. P-glycoprotein) must be known. Objective:: The aim of this study was to compare the impact of different transport media on the bi-directional transport of standard compounds, selected from the four classes of the Biopharmaceutical Classification System (BCS), across excised pig intestinal tissue. Methods:: The Sweetana-Grass diffusion apparatus was used for the transport studies. Krebs-Ringer bicarbonate (KRB) buffer and simulated intestinal fluids in the fed (FeSSIF) and fasted (FaSSIF) states were used as the three transport media, while the chosen compounds were abacavir (BCS class 1), dapsone (BCS class 2), lamivudine (BCS class 3) and furosemide (BCS class 4). Results:: Abacavir exhibited lower permeability in both the simulated intestinal fluids than in the KRB buffer. Dapsone showed similar permeability in all media. Lamivudine exhibited lower permeability in FaSSIF than in the other two media. Furosemide exhibited improved transport with pronounced efflux in FaSSIF. Conclusion:: Different permeation behaviors were observed for the selected drugs in the respective media, which may have resulted from their different physico-chemical properties, as well as from the effects that dissimilar transport media components had on excised pig intestinal tissue.

The value of serum progesterone level on day of human chorionic gonadotrophin administration / metaphase II oocyte ratio in predicting IVF/ICSI outcome in patients with normal ovarian reserve

Journal of Ovarian Research ◽

10.1186/s13048-021-00800-5 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Ahmad Mahran ◽

Mohammed Khairy ◽

Reham Elkhateeb ◽

Abdel Rahman Hegazy ◽

Ayman Abdelmeged ◽

...

Keyword(s):

Ovarian Response ◽

Pregnancy Rates ◽

Clinical Pregnancy ◽

Progesterone Level ◽

Receiver Operating Curve ◽

Serum Progesterone ◽

Predictive Values ◽

Significant Difference ◽

Assisted Reproduction Treatment

Abstract Background The clinical implication of the increased serum progesterone level on the day of HCG administration in assisted reproduction treatment (ART) is still controversial. The current study aimed to compare the predictive value of serum progesterone on day of HCG administration / metaphase II oocyte (P/MII) ratio on IVF/ ICSI outcome to serum progesterone (P) level alone and the ratio of serum progesterone/estradiol level (P/E2) ratio in prediction of pregnancy rates after ART. Material & methods Two hundred patients admitted to the IVF/ICSI program at Minia IVF center in Egypt in the period from October 2016 to May 2018 were included in this study. Serum Progesterone (P) and Estradiol (E2) levels were estimated on the day of HCG administration. The ratio between serum P and the number of MII oocytes (P/MII ratio) was calculated and the predictive values of the three parameters (P, P/E2 ratio and P/MII ratio) in prediction of cycle outcomes were measured. Results P/ MII oocyte ratio was significantly lower in patients who attained clinical pregnancy (n = 97) as compared with those who couldn’t whilst there was no significant difference in P and P/E2 ratio between the two groups. Using a cut off value of 0.125, the sensitivity and specificity of progesterone/ MII ratio in prediction of no pregnancy in IVF/ICSI were 75.7 and 77.1% respectively with the area under The Receiver operating curve (ROC-AUC) = 0.808. The respective values of the ROC-AUC for the P and P/E2 ratio were 0.651 and 0.712 with sensitivity and specificity of 71.2 and 73.5%for P level and of 72.5 and 75.3% for P/E2 ratio. Implantation or clinical pregnancy rates were significantly different between patients with high and low P/MII ratio irrespective of day of embryo transfer (day 3 or 5). Conclusions In patients with normal ovarian response, serum progesterone on day of HCG / MII oocyte ratio can be a useful predictor of pregnancy outcomes and in deciding on freezing of all embryos for later transfer instead of high progesterone level alone.

Screening for aneurysms of the abdominal aorta using a simple screening device

The Ultrasound Journal ◽

10.1186/s13089-020-00192-5 ◽

2020 ◽

Vol 12 (1) ◽

Author(s):

J. W. Brakel ◽

T. A. Berendsen ◽

P. M. C. Callenbach ◽

J. van der Burgh ◽

R. J. Hissink ◽

...

Keyword(s):

Negative Predictive Value ◽

Abdominal Aorta ◽

Predictive Value ◽

Cost Effective ◽

Abdominal Ultrasound ◽

Predictive Values ◽

Bladder Scan ◽

Health Council ◽

Screening Device

Abstract Introduction Several countries advocate screening for aneurysms of the abdominal aorta (AAA) in selected patients. In the Netherlands, routine screening is currently under review by the National Health Council. In any screening programme, cost-efficiency and accuracy are key. In this study, we evaluate the Aorta Scan (Verathon, Amsterdam, Netherlands), a cost-effective and easy-to-use screening device based on bladder scan technology, which enables untrained personnel to screen for AAA. Methods We subjected 117 patients to an Aorta Scan and compared the results to the gold standard (abdominal ultrasound). We used statistical analysis to determine sensitivity and specificity of the Aorta Scan, as well as the positive and negative predictive values, accuracy, and inter-test agreement (Kappa). Results Sensitivity and specificity were 0.86 and 0.98, respectively. Positive predictive value was 0.98 and negative predictive value was 0.88. Accuracy was determined at 0.92 and the Kappa value was 0.85. When waist–hip circumferences (WHC) of > 115 cm were excluded, sensitivity raised to 0.96, specificity stayed 0.98, positive and negative predictive value were 0.98 and 0.96, respectively, accuracy to 0.97, and Kappa to 0.94. Conclusion Herein, we show that the Aorta Scan is a cost-effective and very accurate screening tool, especially in patients with WHC below 115 cm, which makes it a suitable candidate for implementation into clinical practice, specifically in the setting of screening selected populations for the presence of AAA.

A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes

Scientific Reports ◽

10.1038/s41598-021-81385-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Susanne F. Awad ◽

Soha R. Dargham ◽

Amine A. Toumi ◽

Elsy M. Dumit ◽

Katie G. El-Nahas ◽

...

Keyword(s):

High Risk ◽

Risk Score ◽

Area Under The Curve ◽

Diabetes Risk ◽

Predictive Values ◽

Predictive Variables ◽

Comparable Performance ◽

Representative Random Sample ◽

Diabetes Risk Score

AbstractWe developed a diabetes risk score using a novel analytical approach and tested its diagnostic performance to detect individuals at high risk of diabetes, by applying it to the Qatari population. A representative random sample of 5,000 Qataris selected at different time points was simulated using a diabetes mathematical model. Logistic regression was used to derive the score using age, sex, obesity, smoking, and physical inactivity as predictive variables. Performance diagnostics, validity, and potential yields of a diabetes testing program were evaluated. In 2020, the area under the curve (AUC) was 0.79 and sensitivity and specificity were 79.0% and 66.8%, respectively. Positive and negative predictive values (PPV and NPV) were 36.1% and 93.0%, with 42.0% of Qataris being at high diabetes risk. In 2030, projected AUC was 0.78 and sensitivity and specificity were 77.5% and 65.8%. PPV and NPV were 36.8% and 92.0%, with 43.0% of Qataris being at high diabetes risk. In 2050, AUC was 0.76 and sensitivity and specificity were 74.4% and 64.5%. PPV and NPV were 40.4% and 88.7%, with 45.0% of Qataris being at high diabetes risk. This model-based score demonstrated comparable performance to a data-derived score. The derived self-complete risk score provides an effective tool for initial diabetes screening, and for targeted lifestyle counselling and prevention programs.