WBR0038

Author	[[PageAuthor::Associate Editor-In-Chief: Gonzalo A. Romero, M.D. [1]]]
Exam Type	ExamType::USMLE Step 1
Main Category	MainCategory::Biostatistics/ Epidemiology
Sub Category	SubCategory::Infectious Disease
Prompt	[[Prompt::A group of investigators developed a new diagnostic test for the detection of the serum marker Z-AWS, a structural component of the infectious organism CG-23. A study is evaluating the performance of the new test among 500 individuals who were followed up for the occurrence of the symptoms. In addition, blood was collected for PCR analysis, the gold standard test for this infectious disease. The infectious organism is detected in 100 individuals by PCR. The pre-test probabilities of the novel test are determined as follows: the sensitivity is 90% and the specificity is 80%. What is the probability that a subject with a positive result in the new diagnostic test actually has the disease?]]
Answer A	AnswerA::90/100
Answer A Explanation	[[AnswerAExp::The sensitivity of a test is the ability of a test to be positive when the subjects actually have the disease. The sensitivity can be calculated as follows: Sensitivity= True positive/(True positive + false negative) In this case, sensitivity=90/(90+10)= 90/100]]
Answer B	AnswerB::320/ 400
Answer B Explanation	[[AnswerBExp::The specificity of a test is the ability of a test to be negative when the subjects do not have the disease. The specificity can be calculated as follows: Specificity= True negative/(True negative+ false positive) In this case, specificity=320/(320+80)= 320/400]]
Answer C	AnswerC::90/170
Answer C Explanation	[[AnswerCExp::The positive predictive value (PPV) , or precision rate, is the proportion of individuals with a positive test result who actually have preclinical disease. It is considered the physician's gold standard, as it reflects the probability that a positive test reflects the underlying condition of interest. The PPV can be calculated as follows: PPV= True positive/(True positive + false negative) In this case, PPV= 90/(90+80)= 90/170]]
Answer D	AnswerD::320 / 330
Answer D Explanation	[[AnswerDExp::The negative predictive value is the proportion of individuals without preclinical disease who test negative using the specified testing modality. The NPV can be calculated as follows: In this case, NPV= True negative / (True negative+ false negative)]]
Answer E	AnswerE::
Answer E Explanation	AnswerEExp::
Right Answer	RightAnswer::C
Explanation	[[Explanation::In order to calculate the positive predictive value, the 2x2 table containing the true positive, false positive, true negative and false negative need to be constructed. The sample size is 500 and the total number of subjects with disease is 100; therefore, the total number of subjects without the disease is 400. Since the sensitivity is 90%, 90 subjects of the 100 who have the disease are true positive while 10 are false negative. Similarly, since the specificity is 80%, 320 subjects out of the 400 are the true negative and 80 (400-320) are false negative. The positive predictive value (PPV) , or precision rate, is the proportion of individuals with a positive test result who actually have preclinical disease. It is considered the physician's gold standard, as it reflects the probability that a positive test reflects the underlying condition of interest. The PPV can be calculated as follows: PPV= True positive/(True positive + false negative) Disease No disease Positive test True positive False negative Negative test False negative true negative In this case, PPV= 90/(90+80)= 90/170 Disease No disease Total Positive test 90 80 170 Negative test 10 320 330 Total 100 400 500 Educational Objective: The positive predictive value (PPV) , or precision rate, is the proportion of individuals with a positive test result who actually have preclinical disease. It is considered the physician's gold standard, as it reflects the probability that a positive test reflects the underlying condition of interest. References: ]]
Approved	Approved::Yes
Keyword	WBRKeyword::Infectious disease, WBRKeyword::biostatistics, WBRKeyword::positive predictive value
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