James Neuberger and Damian Dowling (liver unit, Queen
Elizabeth Hospital, Birmingham); Mervyn Davies and Helen
Aldersley (liver unit, St James’s Hospital, Leeds); Oliver James,
Martin Prince, and Mark Hudson (liver unit, Freeman Hospital,
Contributors: KH initiated the study and contributed to the
design, interpretation, and reporting. ET coordinated the collec-
tion of the data and contributed to the study design,
interpretation, and reporting. JD conducted the statistical analy-
ses and contributed to the interpretation and reporting. LA and
DG contributed to the design of the study, data collection,
interpretation, and reporting. JC and OB contributed to
database design, data collection, and reporting. KH is guarantor
for the study.
Funding: South East Region NHSE Research and Develop-
ment. KH is also supported by Oxfordshire Mental Healthcare
Competing interests: None declared.
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(Accepted 8 February 2001)
Antipsychotic drugs and heart muscle disorder in
international pharmacovigilance: data mining study
David M Coulter, Andrew Bate, Ronald H B Meyboom, Marie Lindquist, I Ralph Edwards
Objectives To examine the relation between
antipsychotic drugs and myocarditis and
Design Data mining using bayesian statistics
implemented in a neural network architecture.
Setting International database on adverse drug
reactions run by the World Health Organization
programme for international drug monitoring.
Main outcome measures Reports mentioning
antipsychotic drugs, cardiomyopathy, or myocarditis.
Results A strong signal existed for an association
between clozapine and cardiomyopathy and
myocarditis. An association was also seen with other
antipsychotics as a group. The association was based
on sufficient cases with adequate documentation and
apparent lack of confounding to constitute a signal.
Associations between myocarditis or cardiomyopathy
and lithium, chlorpromazine, fluphenazine,
haloperidol, and risperidone need further
Conclusions Some antipsychotic drugs seem to be
linked to cardiomyopathy and myocarditis. The study
shows the potential of bayesian neural networks in
analysing data on drug safety.
The antipsychotic drug clozapine has been reported to
cause myocarditis or cardiomyopathy.
other drugs in
the same therapeutic class may share similar toxicity.
Data mining of a large database of suspected adverse
reactions can find such new signals. As part of the
World Health Organization’s programme for inter-
national drug monitoring, national pharmacovigilance
centres in 60 countries report adverse reactions to a
central database maintained by the Uppsala Monitor-
ing Centre in Sweden.
To analyse this large database an approach using
bayesian statistics implemented in a neural network
architecture has been developed. The approach is able
to look for new adverse reactions from combinations
of drugs and also to identify previously unknown
patterns, such as risk factors for adverse events with
for example, patient age, underlying
diseases, and drug interactions. We used the bayesian
approach to look for cardiac effects related to antipsy-
chotic drugs in the WHO database of adverse
We used the bayesian confidence propagation network,
which implements bayesian statistics in a neural network
architecture, in the WHO database. The network was
used to test reports of clozapine and all other
antipsychotic drugs suspected of causing myocarditis or
cardiomyopathy against a background of all reports in
the database. We calculated the strength of dependency
between a drug (or drug group) and adverse reaction
using a logarithmic measure of disproportionality called
the information component.
An association between
the drug and the reaction was considered significant if
the information component minus 2 standard devia-
tions was positive. The value of the information compo-
Details of the
available on the
Centre for Adverse
University of Otago,
David M Coulter
1207BMJ VOLUME 322 19 MAY 2001 bmj.com