scholarly journals A Software Tool for Calculating the Uncertainty of Diagnostic Accuracy Measures

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 406
Author(s):  
Theodora Chatzimichail ◽  
Aristides T. Hatjimihail
Keyword(s):  
Decision Making ◽  
Diagnostic Accuracy ◽  
Diagnostic Tests ◽  
Clinical Decision Making ◽  
Single Point ◽  
Medical Decision ◽  
Interactive Program ◽  
Diagnostic Uncertainty ◽  
Diagnostic Threshold ◽  
Accuracy Measures

Screening and diagnostic tests are applied for the classification of people into diseased and non-diseased populations. Although diagnostic accuracy measures are used to evaluate the correctness of classification in clinical research and practice, there has been limited research on their uncertainty. The objective for this work was to develop a tool for calculating the uncertainty of diagnostic accuracy measures, as diagnostic accuracy is fundamental to clinical decision-making. For this reason, the freely available interactive program Diagnostic Uncertainty has been developed in the Wolfram Language. The program provides six modules with nine submodules for calculating and plotting the standard combined, measurement and sampling uncertainty and the resultant confidence intervals of various diagnostic accuracy measures of screening or diagnostic tests, which measure a normally distributed measurand, applied at a single point in time to samples of non-diseased and diseased populations. This is done for differing sample sizes, mean and standard deviation of the measurand, diagnostic threshold and standard measurement uncertainty of the test. The application of the program is demonstrated with an illustrative example of glucose measurements in samples of diabetic and non-diabetic populations, that shows the calculation of the uncertainty of diagnostic accuracy measures. The presented interactive program is user-friendly and can be used as a flexible educational and research tool in medical decision-making, to calculate and explore the uncertainty of diagnostic accuracy measures.

scholarly journals A software tool for calculating the uncertainty of diagnostic accuracy measures

2020 ◽  
Author(s):  
Theodora Chatzimichail ◽  
Aristides T. Hatjimihail
Keyword(s):  
Decision Making ◽  
Diagnostic Accuracy ◽  
Diagnostic Tests ◽  
Clinical Decision Making ◽  
Single Point ◽  
Population Sample ◽  
Medical Decision ◽  
Interactive Program ◽  
Diagnostic Threshold ◽  
Accuracy Measures

Abstract Background: Screening and diagnostic tests are used to classify people with and without a disease. Although diagnostic accuracy measures are used to evaluate the correctness of a classification in clinical research and practice, there has been limited research on their uncertainty. The objective for this work is to develop a tool for calculating the uncertainty of diagnostic accuracy measures, as diagnostic accuracy is fundamental to clinical decision-making.Results: For this reason, a freely available interactive program has been developed in Wolfram Language. The program provides six modules with nine submodules, for calculating and plotting the standard and expanded uncertainty and the resultant confidence intervals of various diagnostic accuracy measures of screening or diagnostic tests, which measure a normally distributed measurand, applied at a single point in time in non-diseased and diseased populations. This is done for differing population sample sizes, mean and standard deviation of the measurand, diagnostic threshold and standard measurement uncertainty of the test.The application of the program is illustrated with a case study of glucose measurements in diabetic and non-diabetic populations, that demonstrates the calculation of the uncertainty of diagnostic accuracy measures.Conclusion: The presented interactive program is user-friendly and can be used as a flexible educational and research tool in medical decision making, to calculate and explore the uncertainty of diagnostic accuracy measures.

scholarly journals A software tool for exploring the relation between diagnostic accuracy and measurement uncertainty

2020 ◽  
Author(s):  
Theodora Chatzimichail ◽  
Aristides T. Hatjimihail
Keyword(s):  
Decision Making ◽  
Diagnostic Accuracy ◽  
Measurement Uncertainty ◽  
Clinical Decision Making ◽  
Single Point ◽  
Screening Tests ◽  
Medical Decision ◽  
Interactive Program ◽  
Diagnostic Threshold ◽  
Accuracy Measures

Abstract Background Screening and diagnostic tests are used to classify people with and without a disease. Diagnostic accuracy measures are used to evaluate the correctness of a classification in clinical research and practice. Although the correctness of a classification based on a measurand depends on the uncertainty of measurement, there has been limited research on their relation. The objective for this work is to develop an exploratory tool for the relation between diagnostic accuracy measures and measurement uncertainty, as diagnostic accuracy is fundamental to clinical decision making, while measurement uncertainty is critical to quality and risk management in laboratory medicine. Results For this reason, a freely available interactive program has been developed, written in Wolfram Language. The program provides four modules for calculating, optimizing, plotting and comparing various diagnostic accuracy measures and the corresponding risk of diagnostic or screening tests measuring a normally distributed measurand, applied at a single point in time in non-diseased and diseased populations. This is done for differing prevalence of the disease, mean and standard deviation of the measurand, diagnostic threshold, standard measurement uncertainty of the tests and expected loss. The application of the program is illustrated with a case study of glucose measurements in diabetic and non-diabetic populations, that demonstrates the relation between diagnostic accuracy measures and measurement uncertainty. Conclusion The presented interactive program is user-friendly and can be used as a flexible educational and research tool in medical decision making, to explore the relation between diagnostic accuracy measures and measurement uncertainty.

scholarly journals A Software Tool for Exploring the Relation between Diagnostic Accuracy and Measurement Uncertainty

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 610
Author(s):  
Theodora Chatzimichail ◽  
Aristides T. Hatjimihail
Keyword(s):  
Decision Making ◽  
Diagnostic Accuracy ◽  
Measurement Uncertainty ◽  
Clinical Decision Making ◽  
Single Point ◽  
Software Tool ◽  
Screening Tests ◽  
Medical Decision ◽  
Diagnostic Threshold ◽  
Accuracy Measures

Screening and diagnostic tests are used to classify people with and without a disease. Diagnostic accuracy measures are used to evaluate the correctness of a classification in clinical research and practice. Although this depends on the uncertainty of measurement, there has been limited research on their relation. The objective of this work was to develop an exploratory tool for the relation between diagnostic accuracy measures and measurement uncertainty, as diagnostic accuracy is fundamental to clinical decision-making, while measurement uncertainty is critical to quality and risk management in laboratory medicine. For this reason, a freely available interactive program was developed for calculating, optimizing, plotting and comparing various diagnostic accuracy measures and the corresponding risk of diagnostic or screening tests measuring a normally distributed measurand, applied at a single point in time in non-diseased and diseased populations. This is done for differing prevalence of the disease, mean and standard deviation of the measurand, diagnostic threshold, standard measurement uncertainty of the tests and expected loss. The application of the program is illustrated with a case study of glucose measurements in diabetic and non-diabetic populations. The program is user-friendly and can be used as an educational and research tool in medical decision-making.

scholarly journals Rational use of diagnostic tests for clinical decision making

2019 ◽  
Vol 65 (3) ◽  
pp. 452-459 ◽  
Author(s):  
Anna Maria Buehler ◽  
Bruna de Oliveira Ascef ◽  
Haliton Alves de Oliveira Júnior ◽  
Cleusa Pinheiro Ferri ◽  
Jefferson Gomes Fernandes
Keyword(s):  
Decision Making ◽  
Clinical Practice ◽  
Diagnostic Accuracy ◽  
Diagnostic Tests ◽  
Randomized Clinical Trials ◽  
Clinical Decision Making ◽  
Cross Sectional ◽  
Rational Use ◽  
Cross Sectional Design ◽  
The Impact

SUMMARY OBJECTIVE: To assist clinicians to make adequate interpretation of scientific evidence from studies that evaluate diagnostic tests in order to allow their rational use in clinical practice. METHODS: This is a narrative review focused on the main concepts, study designs, the adequate interpretation of the diagnostic accuracy data, and making inferences about the impact of diagnostic testing in clinical practice. RESULTS: Most of the literature that evaluates the performance of diagnostic tests uses cross-sectional design. Randomized clinical trials, in which diagnostic strategies are compared, are scarce. Cross-sectional studies measure diagnostic accuracy outcomes that are considered indirect and insufficient to define the real benefit for patients. Among the accuracy outcomes, the positive and negative likelihood ratios are the most useful for clinical management. Variations in the study's cross-sectional design, which may add bias to the results, as well as other domains that contribute to decreasing the reliability of the findings, are discussed, as well as how to extrapolate such accuracy findings on impact and consequences considered important for the patient. Aspects of costs, time to obtain results, patients’ preferences and values should preferably be considered in decision making. CONCLUSION: Knowing the methodology of diagnostic accuracy studies is fundamental, but not sufficient, for the rational use of diagnostic tests. There is a need to balance the desirable and undesirable consequences of tests results for the patients in order to favor a rational decision-making approach about which tests should be recommended in clinical practice.

scholarly journals Physician-Specific Maximum Acceptable Risk in Personalized Medicine: Implications for Medical Decision Making

2018 ◽  
Vol 38 (5) ◽  
pp. 593-600
Author(s):  
Marco Boeri ◽  
Alan J. McMichael ◽  
Joseph P. M. Kane ◽  
Francis A. O’Neill ◽  
Frank Kee
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Discrete Choice Experiments ◽  
Medical Decision ◽  
Random Parameters ◽  
Acceptable Risk ◽  
Syndrome Scale ◽  
Unit Increase ◽  
Negative Syndrome

Background. In discrete-choice experiments (DCEs), respondents are presented with a series of scenarios and asked to select their preferred choice. In clinical decision making, DCEs allow one to calculate the maximum acceptable risk (MAR) that a respondent is willing to accept for a one-unit increase in treatment efficacy. Most published studies report the average MAR for the whole sample, without conveying any information about heterogeneity. For a sample of psychiatrists prescribing drugs for a series of hypothetical patients with schizophrenia, this article demonstrates how heterogeneity accounted for in the DCE modeling can be incorporated in the derivation of the MAR. Methods. Psychiatrists were given information about a group of patients’ responses to treatment on the Positive and Negative Syndrome Scale (PANSS) and the weight gain associated with the treatment observed in a series of 26 vignettes. We estimated a random parameters logit (RPL) model with treatment choice as the dependent variable. Results. Results from the RPL were used to compute the MAR for the overall sample. This was found to be equal to 4%, implying that, overall, psychiatrists were willing to accept a 4% increase in the risk of an adverse event to obtain a one-unit improvement of symptoms – measured on the PANSS. Heterogeneity was then incorporated in the MAR calculation, finding that MARs ranged between 0.5 and 9.5 across the sample of psychiatrists. Limitations. We provided psychiatrists with hypothetical scenarios, and their MAR may change when making decisions for actual patients. Conclusions. This analysis aimed to show how it is possible to calculate physician-specific MARs and to discuss how MAR heterogeneity could have implications for medical practice.

Clinical Decision Making by Emergency Room Physicians and Residents

2008 ◽  
pp. 217-223
Author(s):  
Ken J. Farion ◽  
Michael J. Hine ◽  
Wojtek Michalowski ◽  
Szymon Wilk
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Clinical Decision Making ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Community ◽  
Medical Decision ◽  
Best Interest ◽  
Complex Process

Clinical decision-making is a complex process that is reliant on accurate and timely information. Clinicians are dependent (or should be dependent) on massive amounts of information and knowledge to make decisions that are in the best interest of the patient. Increasingly, information technology (IT) solutions are being used as a knowledge transfer mechanism to ensure that clinicians have access to appropriate knowledge sources to support and facilitate medical decision making. One particular class of IT that the medical community is showing increased interest in is clinical decision support systems (CDSSs).

scholarly journals Number Needed to Diagnose, Predict, or Misdiagnose: Useful Metrics for Non-Canonical Signs of Cognitive Status?

2018 ◽  
Vol 8 (3) ◽  
pp. 321-327 ◽  
Author(s):  
A.J. Larner
Keyword(s):  
Diagnostic Accuracy ◽  
Diagnostic Tests ◽  
Cognitive Status ◽  
Data Sets ◽  
Head Turning ◽  
Neurological Signs ◽  
Intuitive Appeal ◽  
Applause Sign ◽  
Single Exception ◽  
Accuracy Measures

Background/Aims: “Number needed to” metrics may hold more intuitive appeal for clinicians than standard diagnostic accuracy measures. The aim of this study was to calculate “number needed to diagnose” (NND), “number needed to predict” (NNP), and “number needed to misdiagnose” (NNM) for neurological signs of possible value in assessing cognitive status. Methods: Data sets from pragmatic diagnostic accuracy studies examining easily observed and dichotomised neurological signs (“attended alone” sign, “attended with” sign, head turning sign, applause sign, la maladie du petit papier) were analysed to calculate the NND, NNP, and NNM. Results: All measures of discrimination showed broad ranges. The range of NND and NNP suggested that these signs were, with a single exception, of value for correctly diagnosing or predicting cognitive status (presence or absence of cognitive impairment) when between 2 and 4 patients were examined. However, NNM showed similar values (range 1–5 patients) suggesting risk of misdiagnosis. Conclusion: NND, NNP, and NNM may be useful, intuitive, metrics in assessing the utility of diagnostic tests in day-to-day clinical practice. A ratio of NNM to either NND or NNP, termed the likelihood to diagnose or misdiagnose, may clarify the utility or inutility of diagnostic tests.

scholarly journals Value-impregnated factual claims may undermine medical decision-making

2018 ◽  
Vol 13 (3) ◽  
pp. 151-158 ◽  
Author(s):  
Niels Lynøe ◽  
Gert Helgesson ◽  
Niklas Juth
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Clinical Decision Making ◽  
Negative Impact ◽  
Personal Values ◽  
Healthcare Providers ◽  
Clinical Decision ◽  
Medical Decision ◽  
Clinical Decisions ◽  
Soft Laws

Clinical decisions are expected to be based on factual evidence and official values derived from healthcare law and soft laws such as regulations and guidelines. But sometimes personal values instead influence clinical decisions. One way in which personal values may influence medical decision-making is by their affecting factual claims or assumptions made by healthcare providers. Such influence, which we call ‘value-impregnation,’ may be concealed to all concerned stakeholders. We suggest as a hypothesis that healthcare providers’ decision making is sometimes affected by value-impregnated factual claims or assumptions. If such claims influence e.g. doctor–patient encounters, this will likely have a negative impact on the provision of correct information to patients and on patients’ influence on decision making regarding their own care. In this paper, we explore the idea that value-impregnated factual claims influence healthcare decisions through a series of medical examples. We suggest that more research is needed to further examine whether healthcare staff’s personal values influence clinical decision-making.

392 Role of MRI in indeterminate renal mass: Diagnostic accuracy and impact on clinical decision making

2014 ◽  
Vol 13 (1) ◽  
pp. e392
Author(s):  
T. Kwon ◽  
I.G. Jeong ◽  
D. You ◽  
B. Lim ◽  
K-S. Han ◽  
...  
Keyword(s):  
Decision Making ◽  
Diagnostic Accuracy ◽  
Renal Mass ◽  
Clinical Decision Making ◽  
Clinical Decision

scholarly journals CRT-400.10 Diagnostic Accuracy of FFR-CT: Implications for Clinical Decision Making

2017 ◽  
Vol 10 (3) ◽  
pp. S50
Author(s):  
Christopher Cook ◽  
Ricardo Petraco ◽  
Yousif Ahmad ◽  
Matthew Shun-Shin ◽  
Sukhjinder Nijjer ◽  
...  
Keyword(s):  
Decision Making ◽  
Diagnostic Accuracy ◽  
Clinical Decision Making ◽  
Clinical Decision
Sign in / Sign up

Export Citation Format

Share Document