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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/31839
Title: Optimal sampling designs for estimation of Plasmodium falciparum clearance rates in patients treated with artemisinin derivatives
Authors: Jennifer A. Flegg
Philippe J. Guérin
Francois Nosten
Elizabeth A. Ashley
Aung Pyae Phyo
Arjen M. Dondorp
Rick M. Fairhurst
Duong Socheat
Steffen Borrmann
Anders Björkman
Andreas Mårtensson
Mayfong Mayxay
Paul N. Newton
Delia Bethell
Youry Se
Harald Noedl
Mahamadou Diakite
Abdoulaye A. Djimde
Tran T. Hien
Nicholas J. White
Kasia Stepniewska
University of Oxford
Nuffield Department of Clinical Medicine
Mahidol University
National Institute of Allergy and Infectious Diseases
National Center for Parasitology, Entomology and Malaria Control
Wellcome Trust Research Laboratories Nairobi
Karolinska Institutet
Mahosot Hospital
Armed Forces Research Institute of Medical Sciences, Thailand
Armed Forces Research Institute of Medical Sciences
Medizinische Universitat Wien
University of Bamako
Oxford University Clinical Research Unit (OUCRU)
Keywords: Immunology and Microbiology;Medicine
Issue Date: 13-Nov-2013
Citation: Malaria Journal. Vol.12, No.1 (2013)
Abstract: Abstract. Background: The emergence of Plasmodium falciparum resistance to artemisinins in Southeast Asia threatens the control of malaria worldwide. The pharmacodynamic hallmark of artemisinin derivatives is rapid parasite clearance (a short parasite half-life), therefore, the in vivo phenotype of slow clearance defines the reduced susceptibility to the drug. Measurement of parasite counts every six hours during the first three days after treatment have been recommended to measure the parasite clearance half-life, but it remains unclear whether simpler sampling intervals and frequencies might also be sufficient to reliably estimate this parameter. Methods. A total of 2,746 parasite density-time profiles were selected from 13 clinical trials in Thailand, Cambodia, Mali, Vietnam, and Kenya. In these studies, parasite densities were measured every six hours until negative after treatment with an artemisinin derivative (alone or in combination with a partner drug). The WWARN Parasite Clearance Estimator (PCE) tool was used to estimate "reference" half-lives from these six-hourly measurements. The effect of four alternative sampling schedules on half-life estimation was investigated, and compared to the reference half-life (time zero, 6, 12, 24 (A1); zero, 6, 18, 24 (A2); zero, 12, 18, 24 (A3) or zero, 12, 24 (A4) hours and then every 12 hours). Statistical bootstrap methods were used to estimate the sampling distribution of half-lives for parasite populations with different geometric mean half-lives. A simulation study was performed to investigate a suite of 16 potential alternative schedules and half-life estimates generated by each of the schedules were compared to the "true" half-life. The candidate schedules in the simulation study included (among others) six-hourly sampling, schedule A1, schedule A4, and a convenience sampling schedule at six, seven, 24, 25, 48 and 49 hours. Results: The median (range) parasite half-life for all clinical studies combined was 3.1 (0.7-12.9) hours. Schedule A1 consistently performed the best, and schedule A4 the worst, both for the individual patient estimates and for the populations generated with the bootstrapping algorithm. In both cases, the differences between the reference and alternative schedules decreased as half-life increased. In the simulation study, 24-hourly sampling performed the worst, and six-hourly sampling the best. The simulation study confirmed that more dense parasite sampling schedules are required to accurately estimate half-life for profiles with short half-life (≤three hours) and/or low initial parasite density (≤10,000 per μL). Among schedules in the simulation study with six or fewer measurements in the first 48 hours, a schedule with measurements at times (time windows) of 0 (0-2), 6 (4-8), 12 (10-14), 24 (22-26), 36 (34-36) and 48 (46-50) hours, or at times 6, 7 (two samples in time window 5-8), 24, 25 (two samples during time 23-26), and 48, 49 (two samples during time 47-50) hours, until negative most accurately estimated the "true" half-life. For a given schedule, continuing sampling after two days had little effect on the estimation of half-life, provided that adequate sampling was performed in the first two days and the half-life was less than three hours. If the measured parasitaemia at two days exceeded 1,000 per μL, continued sampling for at least once a day was needed for accurate half-life estimates. Conclusions: This study has revealed important insights on sampling schedules for accurate and reliable estimation of Plasmodium falciparum half-life following treatment with an artemisinin derivative (alone or in combination with a partner drug). Accurate measurement of short half-lives (rapid clearance) requires more dense sampling schedules (with more than twice daily sampling). A more intensive sampling schedule is, therefore, recommended in locations where P. falciparum susceptibility to artemisinins is not known and the necessary resources are available. Counting parasite density at six hours is important, and less frequent sampling is satisfactory for estimating long parasite half-lives in areas where artemisinin resistance is present. © 2013 Flegg et al.; licensee BioMed Central Ltd.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84887365151&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/31839
ISSN: 14752875
Appears in Collections:Scopus 2011-2015

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