Unrecognised bipolar disorder among UK primary care patients prescribed antidepressants: an observational study

Hughes, Tom; Cardno, Alastair G.; West, Robert; Marino‐Francis, Federica; Featherstone, Imogen; Rolling, Keeley; Locker, Alice; McLintock, Kate; House, Allan

doi:10.3399/bjgp16x683437

Cited by 26 publications

(22 citation statements)

References 30 publications

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“…This rate is higher than in previous literature from the UK. In samples of primary care patients, Hughes et al 10 found a prevalence of 7.3% whereas Smith et al found a prevalence of 9.6%. Both studies assessed patients with depressive disorder who had been prescribed antidepressant medication.…”

Section: Discussionmentioning

confidence: 97%

“…Recent screening studies of bipolar disorder in UK primary care have identified that between 7 and 10% of individuals with depression may have undiagnosed bipolar disorder, usually bipolar disorder type II. 9 , 10 It is also the case that individuals with difficult to treat depression and those with poor response to antidepressants are more likely to have unrecognised bipolar disorder. 11 …”

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confidence: 99%

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Evaluation of the 13-item Hypomania Checklist and a brief 3-item manic features questionnaire in primary care

2017

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Aims and method The mean delay for bipolar disorder diagnosis is 10 years. Identification of patients with previous hypomania is challenging, sometimes resulting in misdiagnosis. The aims of this study were: (a) to estimate the proportion of primary care patients with depression currently taking antidepressants who have undiagnosed bipolar disorder and (b) to compare a brief 3-item manic features questionnaire with the Hypomania Checklist (HCL-13). The sample comprised patients with a recorded diagnosis of depression, either on long-term antidepressant therapy or with previous multiple courses of antidepressants.Results Of 149 participants assessed, 24 (16.1%) satisfied criteria for bipolar disorder. Areas under the curve (AUC) for the 3-item questionnaire and the HCL-13 were similar (0.79 and 0.72, respectively) but positive predictive values (PPV) were low.Clinical implications Bipolar disorder may be underdiagnosed in primary care. A 3-item questionnaire could be used by general practitioners to screen for bipolar disorder in their patients with depression.

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Section: Discussionmentioning

confidence: 97%

mentioning

confidence: 99%

Evaluation of the 13-item Hypomania Checklist and a brief 3-item manic features questionnaire in primary care

2017

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“…However, this approach would require a substantially larger training set size and limit the amount of user data available for future exploratory analyses. Introducing such digital instruments into primary healthcare, where resources are scarce and where symptoms of BD often remain undiagnosed 42 , has the potential to lessen the burden experienced by both patients and medical professionals, and therefore reduce the overall load on the healthcare system. In particular, such an approach could constitute a cost- and time-effective alternative to conventional, interview-based methods, while allowing for a more comprehensive symptom assessment and identification of patients who require specialty care services early in the mental health triage process.…”

Section: Discussionmentioning

confidence: 99%

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

et al. 2021

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The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

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“…The authors recommended 'When seeing patients with depression or anxiety disorder, particularly when they are young or not doing well, clinicians should review the life history for evidence of unrecognised bipolar disorder.' 3 Therefore if the patient has had periods of inappropriately elevated mood, overactivity, or disinhibited behaviour which have lasted for 4 days or more, consider referral for a specialist mental health assessment.…”

Section: Detecting Bipolar Disordermentioning

confidence: 99%

Effective management of depression in primary care: a review of the literature

2017

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Unrecognised bipolar disorder among UK primary care patients prescribed antidepressants: an observational study

Cited by 26 publications

References 30 publications

Evaluation of the 13-item Hypomania Checklist and a brief 3-item manic features questionnaire in primary care

Evaluation of the 13-item Hypomania Checklist and a brief 3-item manic features questionnaire in primary care

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

Effective management of depression in primary care: a review of the literature

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