Depression-Anxiety Coupling Strength as a predictor of relapse in major depressive disorder: A CAN-BIND wellness monitoring study report.

BACKGROUND: A critical challenge in the study and management of major depressive disorder (MDD) is predicting relapse. We examined the temporal correlation/coupling between depression and anxiety (called Depression-Anxiety Coupling Strength, DACS) as a predictor of relapse in patients with MDD. METHODS: We followed 97 patients with remitted MDD for an average of 394 days. Patients completed weekly self-ratings of depression and anxiety symptoms using the Quick Inventory of Depressive Symptoms (QIDS-SR) and the Generalized Anxiety Disorder 7-item scale (GAD-7). Using these longitudinal ratings we computed DACS as random slopes in a linear mixed effects model reflecting individual-specific degree of correlation between depression and anxiety across time points. We then tested DACS as an independent variable in a Cox proportional hazards model to predict relapse. RESULTS: A total of 28 patients (29 %) relapsed during the follow-up period. DACS significantly predicted confirmed relapse (hazard ratio [HR] 1.5, 95 % CI [1.01, 2.22], p = 0.043; Concordance 0.79 [SE 0.04]). This effect was independent of baseline depressive or anxiety symptoms or their average levels over the follow-up period, and was identifiable more than one month before relapse onset. LIMITATIONS: Small sample size, in a single study. Narrow phenotype and comorbidity profiles. CONCLUSIONS: DACS may offer opportunities for developing novel strategies for personalized monitoring, early detection, and intervention. Future studies should replicate our findings in larger, diverse patient populations, develop individual patient prediction models, and explore the underlying mechanisms that govern the relationship of DACS and relapse.

Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.

BACKGROUND: Patients with cancer often have unmet psychosocial needs. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This work used natural language processing to predict which patients will see a counsellor or psychiatrist from a patient's initial oncology consultation document. We believe this is the first use of artificial intelligence to predict psychiatric outcomes from non-psychiatric medical documents. METHODS: This retrospective prognostic study used data from 47,625 patients at BC Cancer. We analyzed initial oncology consultation documents using traditional and neural language models to predict whether patients would see a counsellor or psychiatrist in the 12 months following their initial oncology consultation. RESULTS: Here, we show our best models achieved a balanced accuracy (receiver-operating-characteristic area-under-curve) of 73.1% (0.824) for predicting seeing a psychiatrist, and 71.0% (0.784) for seeing a counsellor. Different words and phrases are important for predicting each outcome. CONCLUSION: These results suggest natural language processing can be used to predict psychosocial needs of patients with cancer from their initial oncology consultation document. Future research could extend this work to predict the psychosocial needs of medical patients in other settings.

Patients with cancer often need support for their mental health. Early detection of who requires referral to a counsellor or psychiatrist may improve their care. This study trained a type of artificial intelligence (AI) called natural language processing to read the consultation report an oncologist writes after they first see a patient to predict which patients will see a counsellor or psychiatrist. The AI predicted this with performance similar to other uses of AI in mental health, and used different words and phrases to predict who would see a psychiatrist compared to seeing a counsellor. We believe this is the first use of AI to predict mental health outcomes from medical documents written by clinicians outside of mental health. This study suggests this type of AI can predict the mental health needs of patients with cancer from this widely-available document.

A Randomized Evaluation of MoodFX, a Patient-Centred e-Health Tool to Support Outcome Measurement for Depression: Une évaluation randomisée de MoodFX, un outil de santé en ligne centré sur le patient pour soutenir la mesure du résultat dans la dépression.

BACKGROUND: e-Health tools using validated questionnaires to assess outcomes may facilitate measurement-based care for psychiatric disorders. MoodFX was created as a free online symptom tracker to support patients for outcome measurement in their depression treatment. We conducted a pilot randomized evaluation to examine its usability, and clinical utility. METHODS: Patients presenting with a major depressive episode (within a major depressive or bipolar disorder) were randomly assigned to receive either MoodFX or a health information website as the intervention and control condition, respectively, with follow-up assessment surveys conducted online at baseline, 8 weeks and 6 months. The primary usability outcomes included the percentage of patients with self-reported use of MoodFX 3 or more times during follow up (indicating minimally adequate usage) and usability measures based on the System Usability Scale (SUS). Secondary clinical outcomes included the Quick Inventory of Depressive Symptomatology, Self-Rated (QIDS-SR) and Patient Health Questionnaire (PHQ-9). RESULTS: Forty-nine participants were randomized (24 to MoodFX and 25 to the control condition). Of the 23 participants randomized to MoodFX who completed the user survey, 18 (78%) used MoodFX 3 or more times over the 6 months of the study. The mean SUS score of 72.7 (65th-69th percentile) represents good usability. Compared to the control group, the MoodFX group had significantly better improvement on QIDS-SR and PHQ-9 scores, with large effect sizes and higher response rates at 6 months. There were no differences between conditions on other secondary outcomes such as functioning and quality of life. CONCLUSION: MoodFX demonstrated good usability and was associated with reduction in depressive symptoms. This pilot study supports the use of digital tools in depression treatment.

E-health tools may be useful for measuring and tracking symptoms and other outcomes during treatment for depression. This study is a randomized evaluation of MoodFX, a free web-based app that helps patients track their symptoms using validated questionnaires, and also offers depression information and self-management tips. A total of 49 participants with clinical depression were randomized to using MoodFX or a health information website, for 6 months. In a survey, the participants that used MoodFX found it easy and useful to use. In addition, the participants that used MoodFX had greater improvement in depressive symptoms after 6 months, compared to those who used the health information website. These results suggest that MoodFX may be a useful tool to monitor outcomes and support depression treatment.

Supportive care and healthcare service utilisation in older adults with a new cancer diagnosis: a population-based review.

OBJECTIVES: Older adults have unique needs and may benefit from additional supportive services through their cancer journey. It can be challenging for older adults to navigate the siloed systems within cancer centres and the community. We aimed to document the use of supportive care services in older adults with a new cancer diagnosis in a public healthcare system. METHODS: We used population-based databases in British Columbia to document referrals to supportive care services. Patients aged 70 years and above with a new diagnosis of solid tumour in the year 2015 were included. Supportive care services captured were social work, psychiatry, palliative care, nutrition and home care. Chart review was used to assess visits to the emergency room and extra calls to the cancer centre help line. RESULTS: 2014 patients were included with a median age of 77, 30% had advanced cancer. 459 (22.8%) of patients accessed one or more services through the cancer centre. The most common service used was patient and family counselling (13%). 309 (15.3%) of patients used community home care services. Patients aged 80 years and above were less likely to access supportive care resources (OR 0.57) compared with those 70-79 years. Patients with advanced cancer, those treated at smaller cancer centres, and patients with colorectal, gynaecological and lung cancer were more likely to have received a supportive care referral. CONCLUSIONS: Older adults, particularly those above 80 years, have low rates of supportive care service utilisation. Barriers to access must be explored, in addition to novel ways of holistic care delivery.