Abstract
Objective Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing what we know from Randomised control trials (RCTs). We Propose a text mining approach to detect adverse events and medication episodes from the clinical text to enhance our understanding of adverse effects related to Clozapine, the most effective antipsychotic drug for the management of treatment-resistant schizophrenia, but underutilised due to concerns over its side effects. Material and methods We used data from de-identified EHRs of three mental health trusts in the UK (>50 million documents, over 500,000 patients, 2835 of which were prescribed Clozapine). We explored the prevalence of 33 adverse effects by age, gender, ethnicity, smoking status and admission type three months before and after the patients started Clozapine treatment. Where possible, we compared the prevalence of adverse effects with those reported in the Side Effects Resource (SIDER). Results Sedation, fatigue, agitation, dizziness, hypersalivation, weight gain, tachycardia, headache, constipation and confusion were amongst the highest recorded Clozapine adverse effect in the three months following the start of treatment. Higher percentages of all adverse effects were found in the first month of Clozapine therapy. Using a significance level of (p< 0.05) our chi-square tests show a significant association between most of the ADRs and smoking status and hospital admission, and some in gender, ethnicity and age groups in all trusts hospitals. Later we combined the data from the three trusts hospitals to estimate the average effect of ADRs in each monthly interval. In gender and ethnicity, the results show significant association in 7 out of 33 ADRs, smoking status shows significant association in 21 out of 33 ADRs and hospital admission shows the significant association in 30 out of 33 ADRs. Conclusion A better understanding of how drugs work in the real world can complement clinical trials.
Original language | English |
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Article number | e0243437 |
Journal | PLoS ONE |
Volume | 15 |
Issue number | 12 December |
DOIs | |
State | Published - Dec 2020 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2020 Iqbal et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding
This paper represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, and by the NIHR Oxford Health Biomedical Research Centre at Oxford University and Oxford Health NHS Foundation Trust (grant BRC-1215-20005). This research was supported by researchers at the National Institute for Health Research University College London Hospitals Biomedical Research Centre, and by awards establishing the Farr Institute of Health Informatics Research at UCL Partners, from the Medical Research Council, Arthritis Research UK, British Heart Foundation, Cancer Research UK, Chief Scientist Office, Economic and Social Research Council, Engineering and Physical Sciences Research Council, National Institute for Health Research, National Institute for Social Care and Health Research, and Wellcome Trust (grant MR/ K006584/1). This study was supported by the Case Record Interactive Search (CRIS) system funded and developed by the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London using the system within the NIHR Oxford Health Biomedical Research Centre (Oxford University and Oxford Health NHS Foundation Trust). Disclaimer: The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
Funders | Funder number |
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Case Record Interactive Search | |
Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust | |
National Institute for Social Care and Health Research | |
King’s College London | BRC-1215-20005 |
South London and Maudsley NHS Foundation Trust | |
Wellcome Trust | MR/ K006584/1 |
Medical Research Council | |
Engineering and Physical Sciences Research Council | |
National Institute for Health and Care Research | |
British Heart Foundation | |
Cancer Research UK | |
Arthritis Research UK | |
Chief Scientist Office | |
UCLH Biomedical Research Centre | |
NIHR Oxford Biomedical Research Centre |