Paediatric pharmacovigilance: use of pharmacovigilance data mining algorithms for signal detection in a safety dataset of a paediatric clinical study conducted in seven African countries.
BACKGROUND
Pharmacovigilance programmes monitor and help ensuring the safe use of medicines which is critical to the success of public health programmes. The commonest method used for discovering previously unknown safety risks is spontaneous notifications. In this study we examine the use of data mining algorithms to identify signals from adverse events reported in a phase IIIb/IV clinical trial evaluating the efficacy and safety of several Artemisinin-based combination therapies (ACTs) for treatment of uncomplicated malaria in African children.


METHODS
We used paediatric safety data from a multi-site, multi-country clinical study conducted in seven African countries (Burkina Faso, Gabon, Nigeria, Rwanda, Uganda, Zambia, and Mozambique). Each site compared three out of four ACTs, namely amodiaquine-artesunate (ASAQ), dihydroartemisinin-piperaquine (DHAPQ), artemether-lumefantrine (AL) or chlorproguanil/dapsone and artesunate (CD+A). We examine two pharmacovigilance signal detection methods, namely proportional reporting ratio and Bayesian Confidence Propagation Neural Network on the clinical safety dataset.


RESULTS
Among the 4,116 children (6-59 months old) enrolled and followed up for 28 days post treatment, a total of 6,238 adverse events were reported resulting into 346 drug-event combinations. Nine signals were generated both by proportional reporting ratio and Bayesian Confidence Propagation Neural Network. A review of the manufacturer package leaflets, an online Multi-Drug Symptom/Interaction Checker (DoubleCheckMD) and further by therapeutic area experts reduced the number of signals to five. The ranking of some drug-adverse reaction pairs on the basis of their signal index differed between the two methods.


CONCLUSIONS
Our two data mining methods were equally able to generate suspected signals using the pooled safety data from a phase IIIb/IV clinical trial. This analysis demonstrated the possibility of utilising clinical studies safety data for key pharmacovigilance activities like signal detection and evaluation. This approach can be applied to complement the spontaneous reporting systems which are limited by under reporting.
Research Institute of Health and Society (IRSS), Université catholique de Louvain, Brussels, Belgium; Uganda Malaria Surveillance project/Infectious Disease Research Collaboration, Kampala, Uganda; Santé Stat. and Analytical Research Institute (SSARI), Kampala, Uganda.
Dan K
DK
Kajungu
Institute of Tropical Medicine, Antwerp, Belgium.
Annette
A
Erhart
Malaria Public Health Department, University of Oxford-KEMRI-Wellcome Trust Programme, Nairobi, Kenya; Uganda Malaria Surveillance project/Infectious Disease Research Collaboration, Kampala, Uganda.
Ambrose Otau
AO
Talisuna
Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique/Barcelona Centre for International Health Research (CRESIB, Hospital Clínic-Universitat de Barcelona), Barcelona, Spain.
Quique
Q
Bassat
National Malaria Control Program-TRAC Plus, Ministry of Health, Kigali, Rwanda.
Corine
C
Karema
Epicentre, Paris, France/Mbarara University of Science and Technology, Faculty of Medicine, Mbarara, Uganda.
Carolyn
C
Nabasumba
Tropical Diseases Research Centre, Ndola, Zambia.
Michael
M
Nambozi
Institut de Recherches en Sciences de la Santé, Bobo Dioulasso, Burkina Faso/Centre Muraz, Bobo Dioulasso, Burkina Faso.
Halidou
H
Tinto
Institut für Tropenmedizin, Universität Tübingen, Germany and Centre de Recherches Médicales de Lambaréné, Lambaréné, Gabon.
Peter
P
Kremsner
Department of Paediatrics, University of Calabar, Calabar, Nigeria/Institute of Tropical Diseases Research & Prevention, Calabar, Nigeria.
Martin
M
Meremikwu
Institute of Tropical Medicine, Antwerp, Belgium; Medical Research Council Unit, Fajara, The Gambia.
Umberto
U
D'Alessandro
Research Institute of Health and Society (IRSS), Université catholique de Louvain, Brussels, Belgium.
Niko
N
Speybroeck
D016907:Adverse Drug Reaction Reporting Systems; D000349:Africa; D000465:Algorithms; D000962:Antimalarials / Q000009:adverse effects*; D037621:Artemisinins / Q000009:adverse effects*; D001499:Bayes Theorem; D002675:Child, Preschool; D002986:Clinical Trials as Topic; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D016903:Drug Monitoring / Q000379:methods; D006801:Humans; D007223:Infant; D008288:Malaria / Q000188:drug therapy*; D060735:Pharmacovigilance
D000962:Antimalarials; D037621:Artemisinins