TY  - JOUR
PY  - 2021//
TI  - A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data
JO  - Translational psychiatry
A1  - Rustogi, Nitin
A1  - Martin-Key, Nayra A.
A1  - Mirea, Dan-Mircea
A1  - Barton-Owen, Giles
A1  - Han, Sung Yeon Sarah
A1  - Tomasik, Jakub
A1  - Bahn, Sabine
A1  - Cowell, Dan
A1  - Bell, Emily
A1  - Olmert, Tony
A1  - Lago, Santiago G.
A1  - Friend, Lauren V.
A1  - Farrag, Lynn P.
A1  - Schei, Thea S.
A1  - Metcalfe, Tim
A1  - Tuytten, Robin
A1  - Thomas, Grégoire
A1  - Eljasz, Pawel
A1  - Ozcan, Sureyya
A1  - Cooper, Jason D.
SP  - e41
EP  - e41
VL  - 11
IS  - 1
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2158-3188
UR  - http://dx.doi.org/10.1038/s41398-020-01181-x
ID  - ref1
ER  -