Clinicians' Probability Calculator to Convert Pre-Test to Post-Test Probability of SARS-CoV-2 Infection Based on Method Validation from Each Laboratory.

BACKGROUND: Despite best efforts, false positive and false negative test results for SARS-CoV-2 are unavoidable. Likelihood ratios convert a clinical opinion of pre-test probability to post-test probability, independently of prevalence of disease in the test population. METHODS: The authors examined results of PPA (Positive Percent Agreement, sensitivity) and NPA (Negative Percent Agreement, specificity) from 73 laboratory experiments for molecular tests for SARS-CoV-2 as reported to the FIND database, and for two manufacturers' claims in FDA EUA submissions.PPA and NPA were converted to likelihood ratios to calculate post-test probability of disease based on clinical opinion of pre-test probability. Confidence intervals were based on the number of samples tested. An online calculator was created to help clinicians identify false-positive, or false-negative SARS-CoV-2 test results for COVID-19 disease. RESULTS: Laboratory results from the same test methods did not mirror each other or the manufacturer. Laboratory studies showed PPA from 17% to 100% and NPA from 70.4% to 100%. The number of known samples varied 8 to 675 known patient samples, which greatly impacted confidence intervals. CONCLUSION: Post-test probability of the presence of disease (true-positive or false-negative tests) varies with clinical pre-test probability, likelihood ratios and confidence intervals.The Clinician's Probability Calculator creates reports to help clinicians estimate post-test probability of COVID-19 based on the testing laboratory's verified PPA and NPA.

COVID-19 Testing.

OBJECTIVES: To illustrate how patient risk and clinical costs are driven by false-positive and false-negative results. METHODS: Molecular, antigen, and antibody testing are the mainstay to identify infected patients and fight the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To evaluate the test methods, sensitivity (percent positive agreement [PPA]) and specificity (percent negative agreement [PNA]) are the most common metrics utilized, followed by the positive and negative predictive value-the probability that a positive or negative test result represents a true positive or negative patient. The number, probability, and cost of false results are driven by combinations of prevalence, PPA, and PNA of the individual test selected by the laboratory. RESULTS: Molecular and antigen tests that detect the presence of the virus are relevant in the acute phase only. Serologic assays detect antibodies to SARS-CoV-2 in the recovering and recovered phase. Each testing methodology has its advantages and disadvantages. CONCLUSIONS: We demonstrate the value of reporting probability of false-positive results, probability of false-negative results, and costs to patients and health care. These risk metrics can be calculated from the risk drivers of PPA and PNA combined with estimates of prevalence, cost, and Reff number (people infected by 1 positive SARS-CoV-2 carrier).

Use some horse sense with QC.

What do we need to do to improve Lab QC practice? Maybe it is time to take a lesson from the folks with horse sense. Recognize there is a problem. Look closely and critically at QC processes. Help each other improve by benchmarking performance against performance standards and sharing best practices. Begin to change practices and share stories to encourage others. When a mistake is made when training or riding a horse, repercussions are immediate and personal. But a mistake made in the design or application of quality control means the patient and physician suffer the pain.