Validation of non-western blot based algorithms for HIV testing Susan Best NRL, Melbourne, Australia Outline Reasons for validating non-WB algorithms Brief how-to What we can do to minimise false positive classifications Types of Tests - Serology Confirmation using immunoblot Disadvantages Advantages Reactivity to each Subjective reading protein can be visualised Indeterminate reactions Auxiliary reactions Labour intensive IgG control Expertise Well established $$$ interpretation criteria Increased cost with Stage in disease small numbers Seroconversion CDC algorithm for HIV diagnosis Figure 1. Laboratory Testing Algorithm for the Diagnosis of HIV Infection[CDC HIV Testing] WHO HIV testing strategies Prevalence > 5% Prevalence < 5% http://www.who.int/diagnostics_laboratory/evaluations/hiv/131107_hiv_assays17_final.pdf Positive predictive value The likelihood of a sample identified as reactive by a test being truly POSITIVE for the analyte in question Prevalence and predictive value Example: sensitivity 100%; specificity 99.0% Population: 100,000 Prevalence (%) TRUE Positives FALSE Positives Ratio of True:False* PPV (%) 20 20,000 800 25:1 96.1 5 5,000 950 5:1 84.0 0.5 500 995 1:2 33.4 0.1 100 999 1:10 5.0 0.01 10 1000 1:100 1.0 0.002 2 1000 1:500 0.2 * Rounded PPV of sequential testing Assay 1 Assay 2 Assays 1 & 2 (in sequence) Sensitivity 100% 100% Specificity 99% 99% Prevalence PPV PPV PPV 0.2% 16.6% 16.6% 95.2% 2.0% 67.1% 67.1% 99.5% 20.0% 96.1% 96.1% 99.9% Example: common false reactivity Test 1: 100% sensitive; 99% specific Test 2: 100% sensitive: 99% specific Prevalence: 0.5% Test 1 and 2 share 1% common false reactivity # tested # reactive # true reactive #false reactive Test 1 10,000 150 50 100 Test 2 150 51 50 1 • Using a combination of 2 tests with these characteristics means that, in 10,000 tests, 1 result will be reported incorrectly as positive • PPV of algorithm: 98% Validating non-WB algorithms PRINCIPLES Conduct centrally Choose from well pedigreed tests Independently assessed WHO pre-qualified; USAID list; widely used and cited; Screening assay: choose high sensitivity test Supplemental assays: different antigen sources; good specificity Validating non-WB algorithms (cont’d) More assays than required Test negative and false positive samples in each For significance, numbers could be large Compare the results for common false reactivity Validating non-WB algorithms (cont’d) True negative sample Test 1 Falsely reactive Test 2 R Incorrect result Test 3 N Test 4 Test 5 N R Incorrect result False Positive Reactivity Sample ID Screening test z369911 A/1058/08 False Postiive z370966 False Positive z370967 False Positive z372341 False Positive z371067 False Positive V512 Negative V583 Test A Test B Test C Test D Test E Negative Positive Negative Negative Negative Positive Negative Negative Positive Negative Negative Negative Negative Negative Positive Positive Positive Negative Negative Negative Negative Negative Positive Positive Negative Negative Negative Negative Negative Negative Negative Negative Positive Negative V860 Negative Negative Negative Negative Positive Negative z371780 Negative Negative Negative Negative Negative Positive Validating non-WB algorithms True negative sample CONSIDERED: Not practical Test 1 Falsely reactive Too expensive 3 Test 4 Test 2 Too time Test consuming Test 5 R R Incorrect result Incorrect result Unnecessary Risk of false positive misclassification MINIMISED RISK Increasing risk Stringent IVD regulatory environment Independent assessment of assays Validated algorithms Functioning QMS in testing sites Trained operators HIGH RISK Weak / no IVD regulation Weak / no criteria for rejection of assays Many assays available Weak / no understanding of quality at test sites Insufficient operator training Algorithms for under-regulated environments Choose widely used, published algorithms Similar prevalence; similar region Use tests in same sequence National / regional false positive sample banks Monitor E.g. Pacific algorithm: Determine / Unigold / Insti All reactives, ~ 250 negatives confirmed at NRL Results Obtained on Ongoing Referred Samples Reactive Samples HIV Status Determine (+) UniGold (+) Insti (+) NRL (+) HIV - 54 0 6 0 HIV + 8 8 8 8 Negative Samples 267 negative in Determine negative in gold standard algorithm at NRL Conclusion - No false results reported from the algorithm. Algorithm delivered correct results when used in the field - Three assay algorithm avoided false positive results - Two assay algorithm would produce significant false positive reports Summary Risk of false positive reports from unvalidated algorithms Not restricted to HIV Degree of risk is multi-factorial Experimental validation, although best practice, is resource intensive Regulate; choose high quality tests; train; monitor
© Copyright 2024 ExpyDoc
