Diagnostic accuracy measures
Abstract
BACKGROUND: An increasing number of diagnostic tests and biomarkers have been validated during the last decades, and this will still be a prominent field of research in the future because of the need for personalized medicine. Strict evaluation is needed whenever we aim at validating any potential diagnostic tool, and the first requirement a new testing procedure must fulfill is diagnostic accuracy. SUMMARY: Diagnostic accuracy measures tell us about the ability of a test to discriminate between and/or predict disease and health. This discriminative and predictive potential can be quantified by measures of diagnostic accuracy such as sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operating characteristic curve, overall accuracy and diagnostic odds ratio. Some measures are useful for discriminative purposes, while others serve as a predictive tool. Measures of diagnostic accuracy vary in the way they depend on the prevalence, spectrum and definition of the disease. In general, measures of diagnostic accuracy are extremely sensitive to the design of the study. Studies not meeting strict methodological standards usually over- or underestimate the indicators of test performance and limit the applicability of the results of the study. KEY MESSAGES: The testing procedure should be verified on a reasonable population, including people with mild and severe disease, thus providing a comparable spectrum. Sensitivities and specificities are not predictive measures. Predictive values depend on disease prevalence, and their conclusions can be transposed to other settings only for studies which are based on a suitable population (e.g. screening studies). Likelihood ratios should be an optimal choice for reporting diagnostic accuracy. Diagnostic accuracy measures must be reported with their confidence intervals. We always have to report paired measures (sensitivity and specificity, predictive values or likelihood ratios) for clinically meaningful thresholds. How much discriminative or predictive power we need depends on the clinical diagnostic pathway and on misclassification (false positives/negatives) costs.
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These parameters are only affected by the disease spectrum, so patients with clearly manifested disease have better outcomes.
“These parameters are only affected by the disease spectrum, so patients with clearly manifested disease have better outcomes.”
From Diagnostic Accuracy of a Thick Blood Smear Compared to qPCR for Malaria Associated with Pregnancy in Colombia (2023) · cites this paper as [50]
Sensitivity and specificity demonstrate the test's ability to detect positives and negatives without being affected by disease prevalence, implying that the results could be easily applied to settings with a different prevalence.
“sensitivity and specificity demonstrate the test’s ability to detect positives and negatives without being affected by disease prevalence, implying that the results could be easily applied to settings with a different prevalence.”
From Diagnostic Accuracy of a Thick Blood Smear Compared to qPCR for Malaria Associated with Pregnancy in Colombia (2023) · cites this paper as [50]
The TBS is not useful in any case or subgroup for PM.
“this indicates that this test is not useful in any case or subgroup for PM [50]”
From Diagnostic Accuracy of a Thick Blood Smear Compared to qPCR for Malaria Associated with Pregnancy in Colombia (2023) · cites this paper as [50]
Disease prevalence can influence the overall validity index or correctly classified subjects because accuracy increases as disease prevalence decreases.
“disease prevalence can influence the overall validity index or correctly classified subjects because accuracy increases as disease prevalence decreases [50]”
From Diagnostic Accuracy of a Thick Blood Smear Compared to qPCR for Malaria Associated with Pregnancy in Colombia (2023) · cites this paper as [50]
Cites
The diagnostic odds ratio: a single indicator of test performance
Glas, A. S., Lijmer, J. G., Prins, M. H., Bonsel, G. J., & Bossuyt, P. M. M. (2003) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(03)00177-x · Cited by 2,261 · see citing works
Statistics Notes: Diagnostic tests 1: sensitivity and specificity
Altman, D. G., & Bland, J. M. (1994) · BMJ · doi:10.1136/bmj.308.6943.1552 · Cited by 2,074 · see citing works
Statistics Notes: Diagnostic tests 2: predictive values
Altman, D. G., & Bland, J. M. (1994) · BMJ · doi:10.1136/bmj.309.6947.102 · Cited by 1,711 · see citing works
Diagnostic tests 4: likelihood ratios
Deeks, J. J., & Altman, D. G. (2004) · BMJ · doi:10.1136/bmj.329.7458.168 · Cited by 1,600 · see citing works
Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models
Zou, K. H., O’Malley, A. J., & Mauri, L. (2007) · Circulation · doi:10.1161/circulationaha.105.594929 · Cited by 1,473 · see citing works
Ting, K. M. (2016) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7502-7_758-1 · Cited by 207 · see citing works
Transcranial Doppler correlation with angiography in detection of intracranial stenosis.
Rorick, M. B., Nichols, F. T., & Adams, R. J. (1994) · Stroke · doi:10.1161/01.str.25.10.1931 · Cited by 137 · see citing works
The Accuracy of Transcranial Doppler in the Diagnosis of Middle Cerebral Artery Stenosis
Navarro, J. C., Lao, A. Y., Sharma, V. K., Tsivgoulis, G. K., & Alexandrov, A. V. (2007) · Cerebrovascular Diseases · doi:10.1159/000099130 · Cited by 72 · see citing works
Montori, V. M. (2005) · Canadian Medical Association Journal · doi:10.1503/cmaj.1031666 · Cited by 53 · see citing works
Blood Biomarkers for the Diagnosis of Acute Cerebrovascular Diseases: A Prospective Cohort Study
Whiteley, W. N., Wardlaw, J. M., Dennis, M. S., Lowe, G. D. O., Rumley, A., Sattar, N. A., Welsh, P. I., Green, A., Andrews, M., Graham, C., et al. (2011) · Cerebrovascular Diseases · doi:10.1159/000328517 · Cited by 43 · see citing works
Koffijberg, H., van Zaane, B., & Moons, K. G. M. (2013) · BMC Medical Research Methodology · doi:10.1186/1471-2288-13-12 · Cited by 31 · see citing works
Tamura, A., Yamamoto, Y., Nagakane, Y., Takezawa, H., Koizumi, T., Makita, N., & Makino, M. (2013) · Cerebrovascular Diseases · doi:10.1159/000348313 · Cited by 25 · see citing works
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Majbour, N. K., Chiasserini, D., Vaikath, N. N., Eusebi, P., Tokuda, T., van de Berg, W., Parnetti, L., Calabresi, P., & El-Agnaf, O. M. A. (2017) · Scientific Reports · doi:10.1038/srep40263 · Cited by 55 · see citing works
Self-citationBailey, J., & Balls, M. (2019) · BMC Medical Ethics · doi:10.1186/s12910-019-0352-3 · Cited by 51 · see citing works
Tseng, R. M. W. W., Ong, L. C., Goh, J. H. L., Chen, Y., Chen, T., Lum, E., & Tham, Y.-C. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02358-2 · Cited by 9 · see citing works
Cardona-Arias, J. A., Higuita Gutiérrez, L. F., & Carmona-Fonseca, J. (2023) · Tropical Medicine and Infectious Disease · doi:10.3390/tropicalmed8020119 · Cited by 6 · see citing works
Hong, H., Karmarkar, A. M., Kumar, A., Keysor, J., & Gore, S. (2026) · Physical Therapy · doi:10.1093/ptj/pzag076
- Authors
- Eusebi, P.
- Affiliations
- Regione Umbria (IT)
- Corresponding author
- Eusebi, Paolo
- ORCID
- Eusebi, Paolo
- Venue
- Cerebrovascular Diseases
- Publisher
- S. Karger AG
- Published
- 2013
- Volume
- 36
- Issue
- 4
- Pages
- 267-272
- ISSN
- 1015-9770 (ISSN-L)
- 1421-9786
- OpenAlex
- W2007471231
- Cited by
- 316
- References
- 12
- Citations by year
- 2026: 14 · 2025: 26 · 2024: 36 · 2023: 34 · 2022: 40 · 2021: 45 · 2020: 27 · 2019: 23 · 2018: 22 · 2017: 13 · 2016: 18 · 2015: 11 · 2014: 6 · 2013: 1
- FWCI
- 10.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cerebrovascular Diseases (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- diagnostic accuracy, sensitivity, specificity, predictive value, likelihood ratios, area under the receiver operating characteristic, diagnostic odds ratio, disease prevalence, biomarker validation, diagnostic performance, research design, misclassification cost
- MeSH terms
- Diagnostic Tests, Routine, Humans, Predictive Value of Tests, Prognosis, Reference Standards, Reference Values, ROC Curve, Severity of Illness Index, Reproducibility of Results, Models, Statistical, Discriminant Analysis, Likelihood Functions, Odds Ratio, Area Under Curve
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