scholarly journals The positive predictive value of genetic screening tests

2019 ◽  
Author(s):  
Jill Hagenkord ◽  
Birgit Funke ◽  
Emily Qian ◽  
Madhuri Hegde ◽  
Kevin B Jacobs ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Disease Prevalence ◽  
Screening Tests ◽  
Clinical Indication ◽  
Confirmatory Test ◽  
Pathogenic Variants ◽  
Test Population ◽  
Health Communities ◽  
Asymptomatic Individuals

As the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities. However, the application of screening principles to inherited diseases with unknown prevalence in an unselected (low-risk) test population raises unique challenges. This paper examines the effect of disease prevalence on the positive predictive value of a test result. Many inherited conditions have very low prevalence in an unselected population, which increases the probability that some likely pathogenic variants may be false positives. In situations where the adverse impact of a false positive result is significant, laboratories should address this issue by either increasing the interpretive specificity of the test, performing a clinical confirmatory test to establish the presence of disease, or restricting the test to a population with increased disease prevalence. Here, we review the statistical concepts relevant to screening tests, apply these concepts to genetic disease screening, create a model to estimate prevalence and positive predictive value, and provide a framework for further discussion.

scholarly journals Bayesian updating and sequential testing: overcoming inferential limitations of screening tests

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Jacques Balayla
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Bayesian Updating ◽  
Bayes Theorem ◽  
Disease Prevalence ◽  
Positive Test ◽  
Screening Tests ◽  
Sequential Testing ◽  
Widespread Application ◽  
Test Probability

Abstract Background Bayes’ theorem confers inherent limitations on the accuracy of screening tests as a function of disease prevalence. Herein, we establish a mathematical model to determine whether sequential testing with a single test overcomes the aforementioned Bayesian limitations and thus improves the reliability of screening tests. Methods We use Bayes’ theorem to derive the positive predictive value equation, and apply the Bayesian updating method to obtain the equation for the positive predictive value (PPV) following repeated testing. We likewise derive the equation which determines the number of iterations of a positive test needed to obtain a desired positive predictive value, represented graphically by the tablecloth function. Results For a given PPV ($$\rho$$ ρ ) approaching k, the number of positive test iterations needed given a prevalence of disease ($$\phi$$ ϕ ) is: $$n_i =\lim _{\rho \rightarrow k}\left\lceil \frac{ln\left[ \frac{\rho (\phi -1)}{\phi (\rho -1)}\right] }{ln\left[ \frac{a}{1-b}\right] }\right\rceil \qquad \qquad (1)$$ n i = lim ρ → k l n ρ ( ϕ - 1 ) ϕ ( ρ - 1 ) l n a 1 - b ( 1 ) where $$n_i$$ n i = number of testing iterations necessary to achieve $$\rho$$ ρ , the desired positive predictive value, ln = the natural logarithm, a = sensitivity, b = specificity, $$\phi$$ ϕ = disease prevalence/pre-test probability and k = constant. Conclusions Based on the aforementioned derivation, we provide reference tables for the number of test iterations needed to obtain a $$\rho (\phi )$$ ρ ( ϕ ) of 50, 75, 95 and 99% as a function of various levels of sensitivity, specificity and disease prevalence/pre-test probability. Clinical validation of these concepts needs to be obtained prior to its widespread application.

scholarly journals Bayesian Updating and Sequential Testing: Overcoming Inferential Limitations of Screening Tests

2021 ◽  
Author(s):  
Jacques Balayla
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Bayesian Updating ◽  
Bayes Theorem ◽  
Disease Prevalence ◽  
Positive Test ◽  
Screening Tests ◽  
Sequential Testing ◽  
Widespread Application ◽  
Test Probability

Abstract Background: Bayes’ Theorem confers inherent limitations on the accuracy of screening tests as a function of disease prevalence. Herein, we establish a mathematical model to determine whether sequential testing with a single test overcomes the aforementioned Bayesian lim- itations and thus improves the reliability of screening tests. Methods: We use Bayes’ Theorem to derive the positive predictive value equation, and apply the Bayesian updating method to obtain the equation for the positive predictive value (PPV) following repeated testing. We likewise derive the equation which determines the number of iterations of a positive test needed to obtain a desired positive pre- dictive value, represented graphically by the tablecloth function. Results: For a given PPV ρ approaching k, the number of positive test iterations given a prevalence φ needed is: [see equation], where ni = number of testing iterations necessary to achieve ρ, the desired positive predictive value, ln = the natural logarithm, a = sensitivity, b = specificity, φ = disease prevalence/pre-test probability and k = constant. Conclusions: Based on the aforementioned derivation, we provide reference tables for the number of test iterations needed to obtain a ρ(φ) of 50, 75, 95 and 99% as a function of various levels of sensitivity, specificity and disease prevalence/pre-test probability. Clinical vali- dation of these concepts needs to be obtained prior to its widespread application.

scholarly journals Use of SNP chips to detect rare pathogenic variants: retrospective, population based diagnostic evaluation

BMJ ◽  
2021 ◽  
pp. n214
Author(s):  
Weedon MN ◽  
Jackson L ◽  
Harrison JW ◽  
Ruth KS ◽  
Tyrrell J ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Population Based ◽  
Genome Project ◽  
Personal Genome ◽  
Uk Biobank ◽  
Sequencing Data ◽  
Snp Chip ◽  
Pathogenic Variants ◽  
The Uk

Abstract Objective To determine whether the sensitivity and specificity of SNP chips are adequate for detecting rare pathogenic variants in a clinically unselected population. Design Retrospective, population based diagnostic evaluation. Participants 49 908 people recruited to the UK Biobank with SNP chip and next generation sequencing data, and an additional 21 people who purchased consumer genetic tests and shared their data online via the Personal Genome Project. Main outcome measures Genotyping (that is, identification of the correct DNA base at a specific genomic location) using SNP chips versus sequencing, with results split by frequency of that genotype in the population. Rare pathogenic variants in the BRCA1 and BRCA2 genes were selected as an exemplar for detailed analysis of clinically actionable variants in the UK Biobank, and BRCA related cancers (breast, ovarian, prostate, and pancreatic) were assessed in participants through use of cancer registry data. Results Overall, genotyping using SNP chips performed well compared with sequencing; sensitivity, specificity, positive predictive value, and negative predictive value were all above 99% for 108 574 common variants directly genotyped on the SNP chips and sequenced in the UK Biobank. However, the likelihood of a true positive result decreased dramatically with decreasing variant frequency; for variants that are very rare in the population, with a frequency below 0.001% in UK Biobank, the positive predictive value was very low and only 16% of 4757 heterozygous genotypes from the SNP chips were confirmed with sequencing data. Results were similar for SNP chip data from the Personal Genome Project, and 20/21 individuals analysed had at least one false positive rare pathogenic variant that had been incorrectly genotyped. For pathogenic variants in the BRCA1 and BRCA2 genes, which are individually very rare, the overall performance metrics for the SNP chips versus sequencing in the UK Biobank were: sensitivity 34.6%, specificity 98.3%, positive predictive value 4.2%, and negative predictive value 99.9%. Rates of BRCA related cancers in UK Biobank participants with a positive SNP chip result were similar to those for age matched controls (odds ratio 1.31, 95% confidence interval 0.99 to 1.71) because the vast majority of variants were false positives, whereas sequence positive participants had a significantly increased risk (odds ratio 4.05, 2.72 to 6.03). Conclusions SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation.

scholarly journals Foundational Statistical Principles in Medical Research: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value

Medicina ◽  
2021 ◽  
Vol 57 (5) ◽  
pp. 503
Author(s):  
Thomas F. Monaghan ◽  
Syed N. Rahman ◽  
Christina W. Agudelo ◽  
Alan J. Wein ◽  
Jason M. Lazar ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Medical Literature ◽  
Disease Prevalence ◽  
Positive Test Result ◽  
Predictive Values ◽  
Sensitivity Specificity ◽  
Test Result ◽  
Test Probability

Sensitivity, which denotes the proportion of subjects correctly given a positive assignment out of all subjects who are actually positive for the outcome, indicates how well a test can classify subjects who truly have the outcome of interest. Specificity, which denotes the proportion of subjects correctly given a negative assignment out of all subjects who are actually negative for the outcome, indicates how well a test can classify subjects who truly do not have the outcome of interest. Positive predictive value reflects the proportion of subjects with a positive test result who truly have the outcome of interest. Negative predictive value reflects the proportion of subjects with a negative test result who truly do not have the outcome of interest. Sensitivity and specificity are inversely related, wherein one increases as the other decreases, but are generally considered stable for a given test, whereas positive and negative predictive values do inherently vary with pre-test probability (e.g., changes in population disease prevalence). This article will further detail the concepts of sensitivity, specificity, and predictive values using a recent real-world example from the medical literature.

scholarly journals Design and reporting considerations for genetic screening tests

2019 ◽  
Author(s):  
Jill Hagenkord ◽  
Birgit Funke ◽  
Emily Qian ◽  
Madhuri Hegde ◽  
Kevin B Jacobs ◽  
...  
Keyword(s):  
Genetic Screening ◽  
World Health ◽  
Screening Tests ◽  
Clinical Indication ◽  
Test Design ◽  
Design Strategies ◽  
Predictive Values ◽  
Inherited Diseases ◽  
Health Communities ◽  
Health Organization

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

scholarly journals Design and reporting considerations for genetic screening tests

2019 ◽  
Author(s):  
Jill Hagenkord ◽  
Birgit Funke ◽  
Emily Qian ◽  
Madhuri Hegde ◽  
Kevin B Jacobs ◽  
...  
Keyword(s):  
Genetic Screening ◽  
World Health ◽  
Screening Tests ◽  
Clinical Indication ◽  
Test Design ◽  
Design Strategies ◽  
Predictive Values ◽  
Inherited Diseases ◽  
Health Communities ◽  
Health Organization

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

scholarly journals Quantifying the bias associated with use of discrepant analysis

1998 ◽  
Vol 44 (1) ◽  
pp. 108-115 ◽  
Author(s):  
Harvey B Lipman ◽  
J Rex Astles
Keyword(s):  
Positive Predictive Value ◽  
Laboratory Test ◽  
Sensitivity And Specificity ◽  
Predictive Value ◽  
Comparison Method ◽  
Confirmatory Test ◽  
Analytical Sensitivity ◽  
Performance Parameters ◽  
Alternative Approaches ◽  
Biased Estimates

Abstract Discrepant analysis is a widely used technique for estimating the performance parameters of a laboratory test. In discrepant analysis, each specimen is initially tested with the candidate test and a comparison method, and when the results of the two tests disagree, a confirmatory test is used to resolve the discrepancy. Discrepant analysis usually produces biased estimates. This report quantifies this bias and shows that it is usually positive, leading to overestimation of the performance parameters of a laboratory test. The direction and magnitude of the bias are predictably influenced by the analytical sensitivity and specificity of the candidate test, comparison method, and confirmatory test. The proportion of abnormal specimens tested also affects the magnitude of the bias, particularly the estimates of analytical sensitivity and positive predictive value when this proportion is low. Alternative approaches are suggested.

scholarly journals Evaluation of the risk of malignancy index in preoperative diagnosis of ovarian masses

2018 ◽  
Vol 7 (10) ◽  
pp. 4202
Author(s):  
Royyuru Suchitra ◽  
Kaustubh Burde ◽  
Nilima G. ◽  
P. L. S. Sahithi
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Low Cost ◽  
Age Groups ◽  
Screening Tests ◽  
Anatomical Location ◽  
Risk Of Malignancy ◽  
Risk Of Malignancy Index ◽  
Grass Root Level

Background: Ovarian cancer possesses a challenge to screening tests due to its anatomical location, poor natural history, lack of specific lesion, symptoms and signs and low prevalence. Authors shall be considering RMI 2 and RMI 4 (forms of RMI) and comparing them with histopathology report to derive the sensitivity, specificity and other parameters of these tests.Methods: A prospective   study was conducted from September 2016- September 2017 at Mazumdar Shaw Hospital, Narayana Hrudayalaya, Bangalore.73 patients met the inclusion criteria. RMI 2   and RMI4 were calculated for all the patients and these scores were compared to the final histopathology reports.Results: In present study of 73 patients RMI2 showed a sensitivity of 86.6%, specificity of 86.5 %, Positive predictive value of 81.25% and negative predictive value of 90.24 %. Whereas RMI4 showed a sensitivity of 86.6%, specificity of 86.5 %, Positive predictive value of 83.87 and negative predictive value of 90.48 %. These results are comparable to other studies conducted.  The risk of malignancy index 2 and 4 are also almost comparable with each other and so either can be used for determining the risk of malignancy in patients with adnexal masses. These results were derived in an Indian population across all age groups showing that authors can apply this low-cost method even in resource limited settings.Conclusions: Authors found that Risk of malignancy index is a simple and affordable method to determine the likelihood of a patient having adnexal mass to be malignant. This can thus help save the resources and make the services available at grass root level.

scholarly journals Design and reporting considerations for genetic screening tests

2019 ◽  
Author(s):  
Jill Hagenkord ◽  
Birgit Funke ◽  
Emily Qian ◽  
Madhuri Hegde ◽  
Kevin B Jacobs ◽  
...  
Keyword(s):  
Genetic Screening ◽  
World Health ◽  
Screening Tests ◽  
Clinical Indication ◽  
Test Design ◽  
Design Strategies ◽  
Predictive Values ◽  
Inherited Diseases ◽  
Health Communities ◽  
Health Organization

Testing asymptomatic individuals for unsuspected conditions is not new to the medical and public health communities and protocols to develop screening tests are well-established. However, the application of screening principles to inherited diseases presents unique challenges. Unlike most screening tests, the natural history and disease prevalence of most rare inherited diseases in an unselected population are unknown. It is difficult or impossible to obtain a “truth set” cohort for clinical validation studies. As a result, it is not possible to accurately calculate clinical positive and negative predictive values for “likely pathogenic” genetic variants, which are commonly returned in genetic screening assays. In addition, many of the genetic conditions included in screening panels do not have clinical confirmatory tests. All of these elements are typically required to justify the development of a screening test, according to the World Health Organization screening principles. Nevertheless, as the cost of DNA sequencing continues to fall, more individuals are opting to undergo genomic testing in the absence of a clinical indication. Despite the challenges, reasonable estimates can be deduced and used to inform test design strategies. Here, we review test design principles and apply them to genetic screening.

Sign in / Sign up

Export Citation Format

Share Document