Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register.
Mapping multimorbidity in individuals with schizophrenia and bipolar disorders: evidence from the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLAM BRC) case register.
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DOI：
10.1136/bmjopen-2021-054414
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发表时间：
2022-01-24
期刊：
影响因子：
2.9
通讯作者：
Dobson R
中科院分区：
文献类型：
--
作者：
Bendayan R;Kraljevic Z;Shaari S;Das-Munshi J;Leipold L;Chaturvedi J;Mirza L;Aldelemi S;Searle T;Chance N;Mascio A;Skiada N;Wang T;Roberts A;Stewart R;Bean D;Dobson R
本研究的第一个目的是设计和开发一个有效的和可复制的策略，以提取身体健康状况的临床笔记，这是常见的精神卫生服务。然后，我们研究了这些条件的患病率在严重精神疾病（SMI）的个人，并比较了他们的个人和合并患病率在双相情感障碍（BD）和精神分裂症谱系障碍（SSD）。观察性研究。来自南伦敦的二级精神卫生保健服务我们的最大样本包括17500名年龄在15岁或以上的人，他们在2007年至2018年期间接受了原发性或继发性SMI诊断（国际疾病分类，第10版，F20-31）。我们使用自然语言处理管道MedCAT设计并实施了21种常见物理合并症的数据提取策略。研究了整个队列和BD和SSD亚组的性别、SMI诊断时的年龄、种族和社会剥夺的相关性。线性回归模型被用来检查与残疾的关联，衡量国家健康结果量表。提取身体健康数据，在所有条件下均达到0.90以上的精确率（F1）。10种最常见的疾病是糖尿病、高血压、哮喘、关节炎、癫痫、脑血管意外、湿疹、偏头痛、缺血性心脏病和慢性阻塞性肺病。该人群中最常见的合并症包括糖尿病、高血压和哮喘，无论其SMI诊断如何。我们的数据提取策略被认为是足够的临床笔记，这是必不可少的未来多morphology研究使用文本记录中提取身体健康数据。我们发现，我们的队列中约有40%患有多发性硬化症，其中20%患有复杂的多发性硬化症（除了SMI之外还有两种或多种身体状况）。性别，年龄，种族和社会剥夺被认为是关键，以了解他们的异质性和他们的残疾程度在这一人群中的不同贡献。这些产出对研究人员和临床医生有直接影响。
The first aim of this study was to design and develop a valid and replicable strategy to extract physical health conditions from clinical notes which are common in mental health services. Then, we examined the prevalence of these conditions in individuals with severe mental illness (SMI) and compared their individual and combined prevalence in individuals with bipolar (BD) and schizophrenia spectrum disorders (SSD). Observational study. Secondary mental healthcare services from South London Our maximal sample comprised 17 500 individuals aged 15 years or older who had received a primary or secondary SMI diagnosis (International Classification of Diseases, 10th edition, F20-31) between 2007 and 2018. We designed and implemented a data extraction strategy for 21 common physical comorbidities using a natural language processing pipeline, MedCAT. Associations were investigated with sex, age at SMI diagnosis, ethnicity and social deprivation for the whole cohort and the BD and SSD subgroups. Linear regression models were used to examine associations with disability measured by the Health of Nations Outcome Scale. Physical health data were extracted, achieving precision rates (F1) above 0.90 for all conditions. The 10 most prevalent conditions were diabetes, hypertension, asthma, arthritis, epilepsy, cerebrovascular accident, eczema, migraine, ischaemic heart disease and chronic obstructive pulmonary disease. The most prevalent combination in this population included diabetes, hypertension and asthma, regardless of their SMI diagnoses. Our data extraction strategy was found to be adequate to extract physical health data from clinical notes, which is essential for future multimorbidity research using text records. We found that around 40% of our cohort had multimorbidity from which 20% had complex multimorbidity (two or more physical conditions besides SMI). Sex, age, ethnicity and social deprivation were found to be key to understand their heterogeneity and their differential contribution to disability levels in this population. These outputs have direct implications for researchers and clinicians.
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作者：
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DOI：
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