Article

Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: Data Mining Study

Centre for Adverse Reactions Monitoring and Intensive Medicines Monitoring Programme, Department of Preventive and Social Medicine, University of Otago, Dunedin, New Zealand.
BMJ Clinical Research (Impact Factor: 14.09). 06/2001; 322(7296):1207-9. DOI: 10.1136/bmj.322.7296.1207
Source: PubMed
ABSTRACT
To examine the relation between antipsychotic drugs and myocarditis and cardiomyopathy.
Data mining using bayesian statistics implemented in a neural network architecture.
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.
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 investigation.
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.

Full-text

Available from: David M Coulter
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,
Newcastle).
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
Trust.
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
Abstract
Objectives To examine the relation between
antipsychotic drugs and myocarditis and
cardiomyopathy.
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
investigation.
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.
Introduction
The antipsychotic drug clozapine has been reported to
cause myocarditis or cardiomyopathy.
12
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.
3
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
specific drugs
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
reactions.
Methods
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.
4
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
methods are
available on the
BMJ’s website
Papers
Centre for Adverse
Reactions
Monitoring and
Intensive Medicines
Monitoring
Programme,
Department of
Preventive and
Social Medicine,
University of Otago,
Dunedin,
New Zealand
David M Coulter
head
continued over
BMJ 2001;322:1207–9
1207BMJ VOLUME 322 19 MAY 2001 bmj.com
Page 1
nent is based on the number of case reports for drug(s)
“x” (Cx); the number of case reports of adverse
reaction(s) “y” (Cy); the number of reports of the specific
combination (Cxy); and the total number of reports (C).
Further details of the methods are available on the
BMJ s website.
Results
Myocarditis and cardiomyopathy were reported rarely
as suspected adverse drug reactions, accounting for
less than 0.1% (2121) of almost 2.5 million reports. The
table shows the antipsychotic drugs reported to have
caused either myocarditis or cardiomyopathy on two
or more occasions. Clozapine has a much higher infor-
mation component than other antipsychotics together
and than the general background database. Most
reports predated recent publicity about clozapine. The
statistical associations of clozapine with myocarditis
and cardiomyopathy individually were also significant.
The group of other antipsychotics drugs was
significantly associated with myocarditis and cardiomy-
opathy together (table) and individually compared
with the general database, although these associations
were much weaker than for clozapine.
Chlorpromazine, lithium, and fluphenazine were
significantly associated with myocarditis and cardiomy-
opathy. The 16 cases with risperidone were not more
than expected given the high overall reporting of the
drug in the database. Chlorpromazine was also signifi-
cantly associated with myocarditis and cardiomyopathy
separately. Lithium, fluphenazine, and risperidone
were significantly associated with cardiomyopathy but
not myocarditis. In contrast, haloperidol was associated
with myocarditis but not cardiomyopathy.
Discussion
Our analysis suggests that antipsychotic drugs other
than clozapine may be associated with myocarditis and
cardiomyopathy. The findings may have three explana-
tions. The conditions for which antipsychotics are used
could be risk factors for myocarditis and cardiomyopa-
thy; the antipsychotic drug could be an innocent
bystander; or there may be a causal association. Despite
patients taking clozapine being intensively monitored
for agranulocytosis, the former two are unlikely expla-
nations for the strong relation between clozapine and
myocarditis and cardiomyopathy.
5
The association with
clozapine cannot be explained by coprescribed drugs.
In some of the cases in the other antipsychotics group
the patient was also taking clozapine or non-
antipsychotic drugs known to cause myocarditis or car-
diomyopathy. However, standardised clinical evalua-
tion
6
shows that there were sufficient cases with
adequate documentation and apparent lack of
confounding to constitute a signal for cardiomyopathy
or myocarditis in the other antipsychotics identified
above.
Choice of methods
Our results were obtained by a data mining approach.
A concern had been raised about myocarditis with
clozapine. We then examined the association between
the group of antipsychotics with myocarditis or cardio-
myopathy. Having discovered a quantitative association
between the antipsychotics group and cardiomyopathy
and myocarditis, we investigated individual antipsy-
chotic drugs and then performed a case by case analy-
sis. Our study shows that data mining can be used
successfully to detect signals of adverse reactions in the
WHO database.
Our results could have been shown using a simpler
method. However, the simpler methods rely on some-
one deciding to look for an association.
7
A data mining
approach that routinely looks for associations between
all possible combinations of drugs and adverse
reactions is computer intensive (hence the use of a
neural network). However, it increases the objectivity of
signal detection by introducing an effective quantitative
filtering step before clinical analysis.
8
We believe that
this is enormously beneficial.
Implications
The summaries of case histories in the database do not
allow us to draw definite conclusions about the
likelihood of the possible causes of the associations we
observed between antipsychotic drugs and myocarditis
and cardiomyopathy. Adverse drug reactions are
Antipsychotic drugs (anatomical, therapeutic, chemical drug classification NO5A) for
which two or more reports of cardiomyopathy or myocarditis have been registered in
WHO database
Drug
No of case
reports
Total No of
reports for drug
Information
component
Information
component 2SD
Clozapine 231 24 730 3.34 3.14
Other antipsychotics* 89 60 775 0.71 0.40
Lithium 17 6 315 1.45 0.76
Risperidone 16 10 746 0.69 0.01
Chlorpromazine 14 5 386 1.38 0.63
Haloperidol 11 8 257 0.53 0.31
Fluphenazine 8 2 242 1.59 0.62
Olanzapine 8 6 135 0.48 0.48
Thioridazine 5 3 973 0.41 0.77
Pericyazine 2 317 1.23 0.45
Pimozide 2 536 1.02 0.65
Quetiapine 2 709 0.88 0.79
Trifluoperazine 2 1 703 0.26 1.41
Zuclopenthixol 2 623 0.95 0.72
*All antipsychotic drugs other than clozapine.
In this table a single case report is counted for more than one drug adverse reaction combination if there
are two or more suspected antipsychotic drugs in that case report.
What is known on this topic
Clozapine has been reported to be associated with
myocarditis and cardiomyopathy
What this study adds
The WHO database shows that clozapine is
significantly more frequently reported in relation
to cardiomyopathy and myocarditis than other
drugs
Myocarditis and cardiomyopathy were also
particularly associated with chlorpromazine,
lithium, fluphenazine, risperidone, and
haloperidol
These associations need to be investigated further
to establish whether they are causal
Data mining is a useful tool in pharmacovigilance
Papers
Uppsala
Monitoring Centre,
WHO
Collaborating
Centre for
International Drug
Monitoring,
S-75320 Uppsala,
Sweden
Andrew Bate
programme leader,
signal research
methodology
Ronald H B
Meyboom
medical adviser
Marie Lindquist
head of research and
development
I Ralph Edwards
director
Correspondence to:
I R Edwards
ralph.edwards@
who-umc.org
1208 BMJ VOLUME 322 19 MAY 2001 bmj.com
Page 2
greatly underreported worldwide. Further study is
needed to determine if antipsychotics other than
clozapine cause myocarditis or cardiomyopathy,
particularly lithium, chlorpromazine, fluphenazine,
haloperidol, and risperidone, and to consider the com-
parative risks and effectiveness of antipsychotics. This is
especially important given the recent finding that older
and newer drugs have similar efficacy.
9
Antipsychotic
drugs should also be considered in unexplained
sudden deaths in psychotic patients.
We thank the national centres that contribute data to the WHO
international drug monitoring programme. The opinions and
conclusions, however, are not necessarily those of the various
national centres or of the WHO. Roland Orre was central in
developing the bayesian confidence propagation neural
network as a routine tool for signal detection in the WHO data-
base of drug adverse reactions.
Contributors: DMC suggested the study and made a
provisional investigation of the data, AB and IRE planned and
designed the study; AB carried out the study; and IRE, AB, and
ML evaluated the results. RHBM drafted the first report of the
study, AB and IRE wrote the paper, and all authors contributed
to modifying the manuscript and the final editing of the paper.
IRE is the guarantor.
Funding: None.
Competing interests: None declared.
1 Jensen VE, Gotzsche O. Allergic myocarditis in clozapine treatment.
Ugeskrift for Laeger 1994;156:4151-2.
2 Killian JG, Kerr K, Lawrence C, Celermajer DS. Myocarditis and cardio-
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3 Olsson S. The role of the WHO programme on international drug moni-
toring in coordinating worldwide drug safety efforts. Drug Safety
1998;19:1-10.
4 Bate A, Lindquist M, Edwards IR, Olsson S, Orre R, Lansner A, et al. A
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generation. Eur J Clin Pharmacol 1998;54:315-21.
5 Honigfeld G, Arellano F, Sethi J, Bianchini A, Schein J. Reducing
clozapine-related morbidity and mortality: 5 years of experience with the
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6 Edwards IR, Lindquist M, Wiholm B-E, Napke E. Quality criteria for early
signals of possible adverse drug reactions. Lancet 1990;336:156-8.
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ics in the treatment of schizophrenia: systematic overview and
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(Accepted 20 February 2001)
Effect of improved housing on illness in children under
5 years old in northern Malawi: cross sectional study
Christopher G Wolff, Dirk G Schroeder, Mark W Young
Abstract
Objective To evaluate the effects of a Habitat for
Humanity housing improvement programme in
northern Malawi on the prevalence of childhood
illnesses.
Design Household based cross sectional study.
Setting Rural communities centred near the small
northern Malawi town of Ekwendeni.
Subjects 318 children under 5 years old.
Main outcome measures Prevalence of respiratory,
gastrointestinal, and malarial infections according to
maternal recall, laboratory, or clinical data.
Results Children living in improved homes were less
likely to have respiratory, gastrointestinal, or malarial
illnesses (odds ratio 0.56, 95% confidence interval 0.35
to 0.91) after confounding factors were controlled for.
The reductions in individual diseases were not
significant.
Conclusion Improved housing significantly reduced
the burden of disease among children under 5 years
old.
Introduction
Poor quality housing is generally accepted to be an
important contributor to ill health.
1
Rates of disease
have been associated with the quality and specific
attributes of a house as well as the conditions that those
qualities impose.
2–11
Although the importance of housing for health is
recognised,
11213
few well designed studies have quanti-
fied this impact, especially in the developing world. The
objective of this study was to assess the impact on chil-
dren’s health of a housing improvement project in
rural Malawi. We examined the effect on illness of
living in improved housing compared with living in
traditional housing.
Participants and methods
The study was conducted in collaboration with
Ekwendeni Hospital, Homeless International UK, and
Habitat for Humanity International in the town of
Ekwendeni, Malawi. Traditional houses in the area are
constructed of mud brick walls with thatch roofing,
hard packed mud floors, and possibly a pit latrine.
Houses are usually about 25 m
2
and consist of two or
three rooms. Houses constructed under the Habitat for
Humanity programme in Ekwendeni have fired mud
bricks, tile roofing, concrete foundation, and a pit
latrine. Habitat houses have a mean size of 30 m
2
and
three rooms. The cost of a habitat house at the time of
the study was about $550 (£370), offset by a 10 year no
interest loan. Habitat houses were built next to or
replaced the traditional house of the intended owner
and were non-systematically dispersed throughout the
communities among traditional houses.
Participants in the habitat programme were
selected by a village habitat committee. Applicants had
to be unable to provide adequate housing for
themselves because of financial, social, or physical
reasons and to have shown their commitment to the
programme by spending a standardised amount of
time helping to build another applicant’s house.
Sample
We used data from two surveys conducted in March
and August 1997. Households for the first survey were
randomly selected from a list of about 300 habitat
Papers
Department of
International
Health, Rollins
School of Public
Health of Emory
University, 1518
Clifton Road,
Atlanta, GA 30322,
USA
Christopher G
Wolff
researcher
Dirk G Schroeder
associate professor
Primary Health
Care Department,
Ekwedeni Mission
Hospital,
Ekwendeni, Malawi
Mark W Young
director
Correspondence to
D G Schroeder
dschr02@sph.
emory.edu
BMJ 2001;322:1209–12
1209BMJ VOLUME 322 19 MAY 2001 bmj.com
Page 3
  • Source
    • "Despite its efficacy, the drug has been associated with serious adverse effects such as fatal agranulocytosis and toxic megacolon and cardiovascular complications including myocarditis and dilated cardiomyopathy. As a result, it is not considered a first-line treatment and is reserved for patients with treatment resistant schizophrenia/schizoaffective disorder121314 . Characteristics of patients with clozapine-induced cardiomyopathy were detailed in a systematic review from 2014, which included reviewing data from 26 cases of clozapine-induced cardiomyopathy [15]. "
    [Show abstract] [Hide abstract] ABSTRACT: A 48-year-old male with history of schizoaffective disorder on Clozapine presented with chest pain, dyspnea, and new left bundle branch block. He underwent coronary angiography, which revealed no atherosclerosis. The patient’s work up was unrevealing for a cause for the cardiomyopathy and thus it was thought that clozapine was the offending agent. The patient was taken off clozapine and started on guideline directed heart failure therapy. During the course of hospitalization, he was also discovered to have a left ventricular (LV) thrombus for which he received anticoagulation. To our knowledge, this is the first case report of clozapine-induced cardiomyopathy complicated by a LV thrombus.
    Full-text · Article · Nov 2015
  • Source
    • "They calculated the strength of dependency between a drug and adverse reaction using a logarithmic measure of disproportionality. The final analysis suggested that antipsychotic drugs other than clozapine may be associated with myocarditis and car- diomyopathy [27]. Harpaz et al. showed that a rich and diverse portfolio of data-mining approaches aligned to different strategies and objectives are now available for the analysis and detection of post approval adverse drug events. "
    [Show abstract] [Hide abstract] ABSTRACT: Data has always played an important role in assisting business decisions and overall improvement of a company’s strategies. The introduction of what has come to be named ‘BIG data’ has changed the industry paradigm altogether for a few domains like media, mobility, retail and social. Data from the real world is also considered as BIG data based on its magnitude, sources and the industry’s capacity to handle the same. Although, the healthcare industry has been using real world data for decades, digitization of health records has demonstrated its value to all the stakeholders with a reaffirmation of interest in it. Over time, companies are looking to adopt new technologies in linking these fragmented data for meaningful and actionable insights to demonstrate their value over competition. It has also been noticed that the consequences of not demonstrating the value of data are sometimes leads regulators and payers to be severe. The real challenge though is not in identifying data sets but transforming these data sets into actionable real time insights and business decisions. Evidence and value development frameworks need to work side by side, harnessing meaningful insights in parallel to product development from early phase to life-cycle management. This should in-turn create evidence and value-based insights for multiple stakeholders across the industry; ultimately supporting the patient as the end user to take informed decisions that impact access to care. This article attempts to review the current state of affairs in the area of BIG data in pharma OR BIG DIP as it is increasingly being referred to.
    Full-text · Article · Jun 2015
  • Source
    • "Reports related to drug–AR pairs picked out by the triage algorithms are sent to an expert review panel, with pattern discovery methods often useful for profiling groups of reports and suggesting alternative explanations for increased reporting. Hypotheses of suspected ARs first highlighted in automated knowledge discovery, which remain after clinical review, are then communicated to industry/regulators and published as appropriate [54, 55]. However, the risk of distortion from undiscovered data quality problems and the difficulty of obtaining complete, detailed information on reported AR incidents mean that signals of suspected ARs often remain tentative, even after clinical review [56]. "
    [Show abstract] [Hide abstract] ABSTRACT: The Patient-Reported Outcomes Safety Event Reporting (PROSPER) Consortium was convened to improve safety reporting by better incorporating the perspective of the patient. PROSPER comprises industry, regulatory authority, academic, private sector and patient representatives who are interested in the area of patient-reported outcomes of adverse events (PRO-AEs). It has developed guidance on PRO-AE data, including the benefits of wider use and approaches for data capture and analysis. Patient-reported outcomes (PROs) encompass the full range of self-reporting, rather than only patient reports collected by clinicians using validated instruments. In recent years, PROs have become increasingly important across the spectrum of healthcare and life sciences. Patient-centred models of care are integrating shared decision making and PROs at the point of care; comparative effectiveness research seeks to include patients as participatory stakeholders; and industry is expanding its involvement with patients and patient groups as part of the drug development process and safety monitoring. Additionally, recent pharmacovigilance legislation from regulatory authorities in the EU and the USA calls for the inclusion of patient-reported information in benefit–risk assessment of pharmaceutical products. For patients, technological advancements have made it easier to be an active participant in one’s healthcare. Simplified internet search capabilities, electronic and personal health records, digital mobile devices, and PRO-enabled patient online communities are just a few examples of tools that allow patients to gain increased knowledge about conditions, symptoms, treatment options and side effects. Despite these changes and increased attention on the perceived value of PROs, their full potential has yet to be realised in pharmacovigilance. Current safety reporting and risk assessment processes remain heavily dependent on healthcare professionals, though there are known limitations such as under-reporting and discordant perspectives between patient reports and clinician perceptions of adverse outcomes. PROSPER seeks to support the wider use of PRO-AEs. The scope of this guidance document, which was completed between July 2011 and March 2013, considered a host of domains related to PRO-AEs, including definitions and suitable taxonomies, the range of datasets that could be used, data collection mechanisms, and suitable analytical methodologies. PROSPER offers an innovative framework to differentiate patient populations. This framework considers populations that are prespecified (such as those in clinical trials, prospective observational studies and some registries) and non-prespecified populations (such as those in claims databases, PRO-enabled online patient networks, and social websites in general). While the main focus of this guidance is on post-approval PRO-AEs from both prespecified and non-prespecified population groups, PROSPER has also considered pre-approval, prespecified populations. The ultimate aim of this guidance is to ensure that the patient ‘voice’ and perspective feed appropriately into collection of safety data. The guidance also covers a minimum core dataset for use by industry or regulators to structure PRO-AEs (accessible in the online appendix) and how data, once collected, might be evaluated to better inform on the safe and effective use of medicinal products. Structured collection of such patient data can be considered both a means to an end (improving patient safety) as well as an end in itself (expressing the patient viewpoint). The members of the PROSPER Consortium therefore direct this PRO-AE guidance to multiple stakeholders in drug safety, including industry, regulators, prescribers and patients. The use of this document across the entirety of the drug development life cycle will help to better define the benefit–risk profile of new and existing medicines. Because of the clinical relevance of ‘real-world’ data, PROs have the potential to contribute important new knowledge about the benefits and risks of medicinal products, communicated through the voice of the patient. Electronic supplementary material The online version of this article (doi:10.1007/s40264-013-0113-z) contains supplementary material, which is available to authorized users.
    Full-text · Article · Oct 2013 · Drug Safety
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