Evidence map›Paper›PMID 36782162›Full record

ArticleMalaria journal2023

Quantification of parasite clearance in Plasmodium knowlesi infections.

Jeyamalar T Thurai Rathnam, Matthew J Grigg, Saber Dini, Timothy William, Sitti Saimah Sakam, Daniel J Cooper, Giri S Rajahram, Bridget E Barber, Nicholas M Anstey, Ali Haghiri and 2 more

Open access · goldAbstract read
In one paragraph

Article in Malaria journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 33% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Tropical medicine and infectious disease · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 5 institutions in 3 countries.

Jeyamalar T Thurai RathnamCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
Matthew J GriggMenzies School of Health Research and Charles Darwin University, Darwin, NT, Australia.
Saber DiniCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
Timothy WilliamInfectious Diseases Society Sabah-Menzies School of Health Research Clinical Research Unit, Kota Kinabalu, Sabah, Malaysia.
Sitti Saimah SakamInfectious Diseases Society Sabah-Menzies School of Health Research Clinical Research Unit, Kota Kinabalu, Sabah, Malaysia.
Daniel J CooperMenzies School of Health Research and Charles Darwin University, Darwin, NT, Australia.
Giri S RajahramInfectious Diseases Society Sabah-Menzies School of Health Research Clinical Research Unit, Kota Kinabalu, Sabah, Malaysia.
Bridget E BarberMenzies School of Health Research and Charles Darwin University, Darwin, NT, Australia.
Nicholas M AnsteyMenzies School of Health Research and Charles Darwin University, Darwin, NT, Australia.
Ali HaghiriCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
Megha RajasekharCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
Julie A SimpsonCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia. julieas@unimelb.edu.au.
University of Melbourne · AUCharles Darwin University · AUMinistry of Health · MYQIMR Berghofer Medical Research Institute · AUUniversity of Cambridge · GB

Funding

Emerging zoonotic malaria in Malaysia: strengthening surveillance and evaluating population genetic structure to improve regional risk prediction toolsR01AI160457 · NIAID · INFECTIOUS DISEASE SOCIETY KOTA KINABALU · PI RAJAHRAM, GIRI · 2021 to 2025
$674k
NIAID NIH HHS R01 AI160457
6 · The paper itself

Abstract

backgroundThe incidence of zoonotic Plasmodium knowlesi infections in humans is rising in Southeast Asia, leading to clinical studies to monitor the efficacy of anti-malarial treatments for knowlesi malaria. One of the key outcomes of anti-malarial drug efficacy is parasite clearance. For Plasmodium falciparum, parasite clearance is typically estimated using a two-stage method, that involves estimating parasite clearance for individual patients followed by pooling of individual estimates to derive population estimates. An alternative approach is Bayesian hierarchical modelling which simultaneously analyses all parasite-time patient profiles to determine parasite clearance. This study compared these methods for estimating parasite clearance in P. knowlesi treatment efficacy studies, with typically fewer parasite measurements per patient due to high susceptibility to anti-malarials.

methodsUsing parasite clearance data from 714 patients with knowlesi malaria and enrolled in three trials, the Worldwide Antimalarial Resistance Network (WWARN) Parasite Clearance Estimator (PCE) standard two-stage approach and Bayesian hierarchical modelling were compared. Both methods estimate the parasite clearance rate from a model that incorporates a lag phase, slope, and tail phase for the parasitaemia profiles.

resultsThe standard two-stage approach successfully estimated the parasite clearance rate for 678 patients, with 36 (5%) patients excluded due to an insufficient number of available parasitaemia measurements. The Bayesian hierarchical estimation method was applied to the parasitaemia data of all 714 patients. Overall, the Bayesian method estimated a faster population mean parasite clearance (0.36/h, 95% credible interval [0.18, 0.65]) compared to the standard two-stage method (0.26/h, 95% confidence interval [0.11, 0.46]), with better model fits (compared visually). Artemisinin-based combination therapy (ACT) is more effective in treating P. knowlesi than chloroquine, as confirmed by both methods, with a mean estimated parasite clearance half-life of 2.5 and 3.6 h, respectively using the standard two-stage method, and 1.8 and 2.9 h using the Bayesian method.

conclusionFor clinical studies of P. knowlesi with frequent parasite measurements, the standard two-stage approach (WWARN's PCE) is recommended as this method is straightforward to implement. For studies with fewer parasite measurements per patient, the Bayesian approach should be considered. Regardless of method used, ACT is more efficacious than chloroquine, confirming the findings of the original trials.

Indexed as

AntimalarialsArtemisininsMalariaParasitesPlasmodium knowlesiAnimalsBayes TheoremChloroquineHumansParasitemiaPlasmodium falciparumZoonosesAntimalarialsArtemisininsChloroquineBayesian hierarchical modellingMalariaParasite clearance ratePlasmodium knowlesi

Identifiers

PMID36782162
PMCPMC9926767
OpenAlexW4320480610

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.