2564. Clinical Validation of a Commercial LAMP Test for Ruling Out Malaria in Returning Travelers: A Prospective Diagnostic Trial
Bibliographic record
Abstract
Abstract Background The mainstay of malaria diagnosis relies on rapid diagnostic tests (RDT) and Giemsa-stained microscopy both of which lack analytical sensitivity. This leads to repeat testing to rule out malaria. Nucleic acid amplification (NAT) methods are more sensitive, but testing requires technical proficiency beyond the average clinical laboratory. Methods We conducted a prospective diagnostic trial of the Meridian illumigene Malaria assay (LAMP) compared with reference microscopy and RDT (BinaxNOW Malaria) in returning travelers in Western Canada between June 2017 and January 2018. Returning travelers with signs and symptoms of fever were enrolled into the study. RDT, microscopy, and LAMP assays were performed simultaneously. To increase the yield of positive specimens for all species of malaria, retrospective specimens of Plasmodium vivax, P. ovale, and P. malariae species were supplemented. Real-time (RT)–PCR testing was performed on all specimens to resolve discrepancies. A cost–benefit analysis was performed. Results A total of 296 consecutive patients (50.7% male, mean age 32.5) were enrolled, most visiting friends and relatives (43.2%), traveling to Asia (48.4%), presenting with fever (88.9%), not taking prophylaxis (82.8%), and treated as outpatients (84.3%). In the prospective arm, LAMP had a sensitivity of 98.1% (95% CI 89.9–99.9) and a specificity of 97.6% (95% CI 95.2–99.0) versus microscopy. After discrepant resolution with RTPCR, LAMP had a sensitivity of 100% (95% CI 93.9–100) and a specificity of 100% (95% CI 98.7–100) versus microscopy. When including retrospective specimens, LAMP had a sensitivity of 98.7% (95% CI 92.7–99.9) and a specificity of 97.6% (95% CI 95.2–99.1) versus microscopy, and after discrepant resolution of this set, LAMP had a sensitivity of 100% (95% CI 95.5–100) and a specificity of 100% (95% CI 98.7–100). The rate of invalid tests with LAMP was 3.05%. After discrepant resolution, RDT had a sensitivity of 83.3% (95% CI 58.6–96.4) and a specificity of 96.2% (95% CI 93.2–98.1) versus microscopy. A cost–benefit analysis of reagents and labor suggests up to USD 13 savings per specimen using a revised algorithm with LAMP screening. Conclusion A novel, highly sensitive testing algorithm for malaria screening with associated cost savings in the nonendemic setting is proposed. Disclosures D. Pillai, Meridian Biosciences: None, Diagnostic testing material for study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".