Loop-mediated isothermal DNA amplification for asymptomatic malaria detection in challenging field settings

BACKGROUND: Loop-mediated isothermal DNA amplification (LAMP) methodology offers an opportunity for point-of-care (POC) molecular detection of asymptomatic malaria infections. However, there is still little evidence on the feasibility of implementing this technique for population screenings in isolated field settings.

METHODS: Overall, we recruited 1167 individuals from terrestrial ('road') and hydric ('riverine') communities of the Peruvian Amazon for a cross-sectional survey to detect asymptomatic malaria infections. The technical performance of LAMP was evaluated in a subgroup of 503 samples, using real-time Polymerase Chain Reaction (qPCR) as reference standard. The operational feasibility of introducing LAMP testing in the mobile screening campaigns was assessed based on field-suitability parameters, along with a pilot POC-LAMP assay in a riverine community without laboratory infrastructure.

RESULTS: LAMP had a sensitivity of 91.8% (87.7-94.9) and specificity of 91.9% (87.8-95.0), and the overall accuracy was significantly better among samples collected during road screenings than riverine communities (p≤0.004). LAMP-based diagnostic strategy was successfully implemented within the field-team logistics and the POC-LAMP pilot in the riverine community allowed for a reduction in the turnaround time for case management, from 12-24 hours to less than 5 hours. Specimens with haemolytic appearance were regularly observed in riverine screenings and could help explaining the hindered performance/interpretation of the LAMP reaction in these communities.

CONCLUSIONS: LAMP-based molecular malaria diagnosis can be deployed outside of reference laboratories, providing similar performance as qPCR. However, scale-up in remote field settings such as riverine communities needs to consider a number of logistical challenges (e.g. environmental conditions, labour-intensiveness in large population screenings) that can influence its optimal implementation.