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

Bendayan, Rebecca; Kraljević, Željko; Shaari, Shaweena; Das‐Munshi, Jayati; Leipold, Leona; Chaturvedi, Jaya; Mirza, Luwaiza; Aldelemi, Sarah; Searle, Thomas; Chance, Natalia; Mascio, Aurelie; Skiada, Naoko; Wang, Tao; Roberts, Angus; Stewart, Robert; Bean, Daniel; Dobson, Richard

doi:10.1136/bmjopen-2021-054414

Cited by 10 publications

(6 citation statements)

References 54 publications

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“…Of the 42 full-text articles, 23 articles were excluded due to lack of relevant outcomes, not including analysis on the association between severe mental illness and physical multimorbidity, and mental health diagnoses were not obtained. This left 19 articles to be included in the systematic review 25–41…”

Section: Resultsmentioning

confidence: 99%

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Relationship between severe mental illness and physical multimorbidity: a meta-analysis and call for action

Pizzol,

Trott,

Butler

et al. 2023

BMJ Ment Health

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BackgroundPeople with severe mental illness (SMI) have a higher prevalence of several chronic physical health conditions, and the prevalence of physical multimorbidity is expected to rise. The aim of this study was to assess the strength of the association between SMI and physical multimorbidity.Study selection and analysisWe systematically searched PubMed/Medline, Scopus, Embase, Web of Science, PsycINFO and the behavioural sciences collection databases, from inception to 31 January 2023, for studies that investigated the association between SMI and physical multimorbidity. Humans of any age either clinically diagnosed and/or currently receiving treatment for SMI, specified as schizophrenia (and related psychotic disorders), bipolar disorder and psychotic depression, were eligible. Data from studies selected for inclusion were converted into ORs, with a subsequent meta-analysis conducted.FindingsWe included 19 studies with a total of 194 123 patients with SMI with different diagnoses and drawn from the general population. The pooled OR for physical multimorbidity in people with versus without SMI was 1.84 (95% CI 1.33 to 2.54), with the analysis indicating a high level of heterogeneity (98.38%). The other 15 studies included in the systematic review for which it was not possible to conduct a meta-analysis showed strong associations between SMI and physical multimorbidity.ConclusionsThe current evidence highlights the link between SMI and physical multimorbidity. A multidisciplinary approach is now urgent to develop the best models of services tailored to patients with SMI with physical multimorbidities to improve physical, mental and social outcomes.PROSPERO registration numberCRD42023395165.

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Section: Resultsmentioning

confidence: 99%

“…This left 19 articles to be included in the systematic review. [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] Descriptive data…”

Section: Literature Searchmentioning

confidence: 99%

Relationship between severe mental illness and physical multimorbidity: a meta-analysis and call for action

Pizzol,

Trott,

Butler

et al. 2023

BMJ Ment Health

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“…We used a public MedCAT model that was pre-trained based on all text data in the MIMIC-III database using a vocabulary of medical concepts defined in UMLS. The MedCAT has been validated using real-world EHR data from 3 large London hospitals (including both acute and mental health hospitals) and has shown consistently good performance across hospitals, datasets and medical concept types and it achieved precision rates (F1) above 0.90 for extracting 21 common physical comorbidities in an independent study ( 25 ). Concepts that were mentioned in a clinical notes, identified as CUIs by MedCAT, were used as features of the note.…”

Section: Methodsmentioning

confidence: 99%

Identifying encephalopathy in patients admitted to an intensive care unit: Going beyond structured information using natural language processing

Ariño

Bae

Chaturvedi

et al. 2023

Front. Digit. Health

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BackgroundEncephalopathy is a severe co-morbid condition in critically ill patients that includes different clinical constellation of neurological symptoms. However, even for the most recognised form, delirium, this medical condition is rarely recorded in structured fields of electronic health records precluding large and unbiased retrospective studies. We aimed to identify patients with encephalopathy using a machine learning-based approach over clinical notes in electronic health records.MethodsWe used a list of ICD-9 codes and clinical concepts related to encephalopathy to define a cohort of patients from the MIMIC-III dataset. Clinical notes were annotated with MedCAT and vectorized with a bag-of-word approach or word embedding using clinical concepts normalised to standard nomenclatures as features. Machine learning algorithms (support vector machines and random forest) trained with clinical notes from patients who had a diagnosis of encephalopathy (defined by ICD-9 codes) were used to classify patients with clinical concepts related to encephalopathy in their clinical notes but without any ICD-9 relevant code. A random selection of 50 patients were reviewed by a clinical expert for model validation.ResultsAmong 46,520 different patients, 7.5% had encephalopathy related ICD-9 codes in all their admissions (group 1, definite encephalopathy), 45% clinical concepts related to encephalopathy only in their clinical notes (group 2, possible encephalopathy) and 38% did not have encephalopathy related concepts neither in structured nor in clinical notes (group 3, non-encephalopathy). Length of stay, mortality rate or number of co-morbid conditions were higher in groups 1 and 2 compared to group 3. The best model to classify patients from group 2 as patients with encephalopathy (SVM using embeddings) had F1 of 85% and predicted 31% patients from group 2 as having encephalopathy with a probability >90%. Validation on new cases found a precision ranging from 92% to 98% depending on the criteria considered.ConclusionsNatural language processing techniques can leverage relevant clinical information that might help to identify patients with under-recognised clinical disorders such as encephalopathy. In the MIMIC dataset, this approach identifies with high probability thousands of patients that did not have a formal diagnosis in the structured information of the EHR.

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“…People with severe mental illness (SMI), such as schizophrenia and bipolar affective disorder, experience significantly poorer physical health than those without SMI (Firth et al., 2019), which is associated with a greater burden of disability, frailty and premature mortality (Bendayan et al., 2022; Pearson et al., 2022; Plana-Ripoll et al., 2019a). While these consumers often have both multiple psychiatric diagnoses ( psychiatric multimorbidity ) and multiple physical health conditions ( physical multimorbidity ), the healthcare they receive is notably fragmented (Emsley et al., 2008; Langan et al., 2013).…”

Section: Introductionmentioning

confidence: 99%

Making meaning of multimorbidity and severe mental illness: A viewpoint

Halstead,

Siskind,

Warren

2023

Aust N Z J Psychiatry

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People living with severe mental illness, such as schizophrenia and bipolar affective disorder, frequently experience poorer physical health compared to those without mental illness. This issue has hitherto been approached through the disease-centred construct of comorbidity, where subsequent conditions are viewed as secondary to an ‘index condition’. In contrast, this Viewpoint sets out to explain why multimorbidity, a patient-centred concept that instead refers to the coexistence of multiple chronic illnesses, is a more versatile and robust framework for tackling the issue of poor physical health in people with severe mental illness. In establishing this argument, this Viewpoint has sought to address three key areas. First, this article will discuss the epidemiology of both physical and psychiatric multimorbidity, with respect to how they manifest at greater frequency and at younger ages in people with severe mental illness. Second, the profound consequences of this multimorbidity burden will be explored, with respect to the ‘three D’ s’ of death (premature mortality), disability (functional impacts) and deficit (health-economic impacts). Finally, the utility of multimorbidity as a framework will be illustrated through a proposal for a three-dimensional multimorbidity construct composed of (1) quantity, (2) severity and (3) duration of an individual’s chronic illnesses. Consequently, this Viewpoint aims to capture why it is necessary for modern psychiatry to grasp the concept of multimorbidity to facilitate holistic healthcare for people living with severe mental illness.

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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

Cited by 10 publications

References 54 publications

Relationship between severe mental illness and physical multimorbidity: a meta-analysis and call for action

Relationship between severe mental illness and physical multimorbidity: a meta-analysis and call for action

Identifying encephalopathy in patients admitted to an intensive care unit: Going beyond structured information using natural language processing

Making meaning of multimorbidity and severe mental illness: A viewpoint

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