Links To And Excerpts From “Accuracy of Diagnostic Tests”

What follows is from Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s Association Workgroup [PubMed Abstract] [Full-Text HTML] [Full-Text PDF]. Alzheimers Dement. 2024 Jun 27. doi: 10.1002/alz.13859. Online ahead of print:

We recommend, as a minimum requirement, an accuracy of 90% for the identification of moderate/frequent neuritic plaques at autopsy (or an approved surrogate, which, at this point, would be amyloid PET or CSF) in the intended-use population. For blood-based biomarker assays, this translates to an accuracy equivalent to that of approved CSF assays. We focus on accuracy (true positive + true negative)/(true positive + true negative + false positive + false negative) as a concise metric because it is equally important that a test used clinically is correct when the test result is positive and is correct when it is negative. The specification of accurate “in the intended-use population” addresses positive and negative predictive values, which depend on the prior probability of AD in the population of interest.

Today, I review, link to, and excerpt from Accuracy of Diagnostic Tests [PubMed Abstract] [Full-Text HTML] [Full-Text PDF]. Ario Santini,* 1 , 2 Adrian Man, 1 and Septimiu Voidăzan 1. J Crit Care Med (Targu Mures). 2021 Aug 5;7(3):241-248. doi: 10.2478/jccm-2021-0022. eCollection 2021 Jul.

All that follows is from the above resource.


Following the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, design, development, validation, verification and implementation of diagnostic tests were actively addressed by a large number of diagnostic test manufacturers. This paper deals with the biases and sources of variation which influence the accuracy of diagnostic tests, including calculating and interpreting test characteristics, defining what is meant by test accuracy, understanding the basic study design for evaluating test accuracy, understanding the meaning of Sensitivity, Specificity, Positive Predictive Value and Negative Predictive Value, and evaluating them numerically, and the ROC curve (or Receiver Operating Characteristic ) and the Area under the Curve (AUC).

Keywords: 2019 (COVID-19) pandemic; ROC curve; Receiver Operating Characteristic; diagnostic tests.

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