Diagnostic accuracy of an automated microscope solution (miLab™) in detecting malaria parasites in symptomatic patients at point-of-care in Sudan: a case-control study.

BACKGROUND: Microscopic detection of malaria parasites is labour-intensive, time-consuming, and expertise-demanding. Moreover, the slide interpretation is highly dependent on the staining technique and the technician's expertise. Therefore, there is a growing interest in next-generation, fully- or semi-integrated microscopes that can improve slide preparation and examination. This study aimed to evaluate the clinical performance of miLab™ (Noul Inc., Republic of Korea), a fully-integrated automated microscopy device for the detection of malaria parasites in symptomatic patients at point-of-care in Sudan. METHODS: This was a prospective, case-control diagnostic accuracy study conducted in primary health care facilities in rural Khartoum, Sudan in 2020. According to the outcomes of routine on-site microscopy testing, 100 malaria-positive and 90 malaria-negative patients who presented at the health facility and were 5 years of age or older were enrolled consecutively. All consenting patients underwent miLab™ testing and received a negative or suspected result. For the primary analysis, the suspected results were regarded as positive (automated mode). For the secondary analysis, the operator reviewed the suspected results and categorized them as either negative or positive (corrected mode). Nested polymerase chain reaction (PCR) was used as the reference standard, and expert light microscopy as the comparator. RESULTS: Out of the 190 patients, malaria diagnosis was confirmed by PCR in 112 and excluded in 78. The sensitivity of miLab™ was 91.1% (95% confidence interval [CI] 84.2-95.6%) and the specificity was 66.7% (95% Cl 55.1-67.7%) in the automated mode. The specificity increased to 96.2% (95% Cl 89.6-99.2%), with operator intervention in the corrected mode. Concordance of miLab with expert microscopy was substantial (kappa 0.65 [95% CI 0.54-0.76]) in the automated mode, but almost perfect (kappa 0.97 [95% CI 0.95-0.99]) in the corrected mode. A mean difference of 0.359 was found in the Bland-Altman analysis of the agreement between expert microscopy and miLab™ for quantifying parasite counts. CONCLUSION: When used in a clinical context, miLab™ demonstrated high sensitivity but low specificity. Expert intervention was shown to be required to improve the device's specificity in its current version. miLab™ in the corrected mode performed similar to expert microscopy. Before clinical application, more refinement is needed to ensure full workflow automation and eliminate human intervention. Trial registration ClinicalTrials.gov: NCT04558515.

Lessons from the pandemic: new best practices in selecting molecular diagnostics for point-of-care testing of infectious diseases in sub-Saharan Africa.

INTRODUCTION: Point-of-care molecular diagnostics offer solutions to the limited diagnostic availability and accessibility in resource-limited settings. During the COVID-19 pandemic, molecular diagnostics became essential tools for accurate detection and monitoring of SARS-CoV-2. The unprecedented demand for molecular diagnostics presented challenges and catalyzed innovations which may provide lessons for the future selection of point-of-care molecular diagnostics. AREAS COVERED: We searched PubMed from January 2020 to August 2023 to identify lessons learned from the COVID-19 pandemic which may impact the selection of point-of-care molecular diagnostics for future use in sub-Saharan Africa. We evaluated this in the context of REASSURED criteria (Real-time connectivity; Ease of specimen collection; Affordable; Sensitive; Specific; User-friendly; Rapid and robust; Equipment free; and Deliverable to users at the point of need) for point-of-care diagnostics for resource-limited settings. EXPERT OPINION: The diagnostic challenges and successes during the COVID-19 pandemic affirmed the importance of the REASSURED criteria but demonstrated that these are not sufficient to ensure new diagnostics will be appropriate for public health emergencies. Capacity for rapid scale-up of diagnostic testing and transferability of assays, data, and technology are also important, resulting in updated REST-ASSURED criteria. Few diagnostics will meet all criteria, and trade-offs between criteria will need to be context-specific.