Background:
Major depressive disorder (MDD) is common and often has sub-optimal response to treatment. Difficult-to-treat depression (DTD) is a new concept that describes ‘depression that continues to cause significant burden despite usual treatment efforts’.
Major depressive disorder (MDD) is one of the biggest causes of disability worldwide, affecting 300 million people, the equivalent to 4.4% of the world’s population (World Health Organization (WHO), 2017). In 2015, depressive disorders led to a global total of over 50 million years lived with disability (WHO, 2017). In England, the total cost of services for depression (health and social care, criminal justice services and informal care from family members) was estimated to be in the region of £1.7 billion; adding lost employment increased this by a further £5.8 billion – 2007 data from McCrone et al. (2008).
MDD is highly comorbid with many other mental health conditions including anxiety, post-traumatic stress disorder (PTSD), eating disorders and substance misuse (Rush et al., 2005). It is also frequently comorbid with a range of physical disorders such as type 2 diabetes, asthma, gastrointestinal and musculoskeletal conditions (Gagnon and Patten, 2002; Nouwen et al., 2010). There are strong suggestions of a particular association with cardiovascular dysfunction (Cai et al., 2019; Feng et al., 2019). Conversely, depression is twice as likely in those individuals with multimorbidity than those with only one comorbidity, and three times as likely than those with no comorbidity (Read et al., 2017). The presence of comorbidity is a major driver of the economic burden of MDD, with 62% of total health care costs being due to comorbid conditions rather than MDD itself (Greenberg et al., 2015).
Aims:
To identify patients with likely DTD in UK secondary care and examine demographic, disease and treatment data as compared with ‘non-DTD’ MDD patients.
Methods:
Anonymised electronic health records (EHRs) of five specialist mental health National Health Service (NHS) Trusts in the United Kingdom were analysed using a natural language processing model. Data on disease characteristics, comorbidities and treatment histories were extracted from structured fields and using natural language algorithms from unstructured fields. Patients with MDD aged ⩾18 years were included in the analysis; those with presumed DTD were identified on the basis of MDD history (duration and recurrence) and number of treatments prescribed.
Results:
In a sample of 28,184 patients with MDD, 19% met criteria for DTD. Compared to the non-DTD group, patients with DTD were more likely to have severe depression, suicidal ideation, and comorbid psychiatric and/or physical illness, as well as higher rates of hospitalisation. They were also more likely to be in receipt of unemployment and sickness/disability benefits. More intensive treatment strategies were used in the DTD group, including higher rates of combination therapy, augmentation, psychotherapy and electroconvulsive therapy.
Conclusion:
This study demonstrates the feasibility of identifying patients with probable DTD from EHRs and highlights the increased burden associated with MDD in these patients.