The Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
SLAM BioResource for Mental Health, South London and Maudsley NHS Foundation Trust and King's College London, London, United Kingdom.
PLoS One. 2020 Dec 8;15(12):e0243437. doi: 10.1371/journal.pone.0243437. eCollection 2020.
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.
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).
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.
A better understanding of how drugs work in the real world can complement clinical trials.
挖掘电子健康记录（EHR）中包含的数据，可能会增进我们对药物在真实世界中的作用的了解，补充我们从随机对照试验（RCT）中所了解到的信息。我们提出了一种文本挖掘方法，从临床文本中检测不良事件和药物事件，以增进我们对氯氮平相关不良影响的了解。氯氮平是治疗抗药性精神分裂症最有效的抗精神病药物，但由于对其副作用的担忧，使用较少。
我们使用了来自英国三家精神健康信托机构的匿名 EHR 数据（超过 5000 万份文件，超过 50 万名患者，其中 2835 名患者开了氯氮平）。我们探讨了 33 种不良影响在患者开始氯氮平治疗前三个月和后三个月的年龄、性别、种族、吸烟状况和入院类型中的患病率。在可能的情况下，我们将不良影响的患病率与 SIDER（不良反应资源）报告的进行了比较。
镇静、疲劳、激动、头晕、过度流涎、体重增加、心动过速、头痛、便秘和意识混乱是治疗开始后三个月内氯氮平记录的最高不良影响之一。在氯氮平治疗的第一个月中，所有不良影响的百分比都更高。使用（p<0.05）的显著性水平，我们的卡方检验显示，大多数不良反应与吸烟状况和住院之间存在显著关联，而在所有信托医院的性别、种族和年龄组中，一些也存在关联。后来，我们将来自三家信托医院的数据合并，以估计每个月间隔的不良反应的平均效应。在性别和种族方面，33 种不良反应中有 7 种显示出显著关联，吸烟状况显示出 21 种不良反应中的显著关联，住院状况显示出 33 种不良反应中的显著关联。
更好地了解药物在现实世界中的作用可以补充临床试验。
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