Major Depressive Disorder (MDD) is a complex mental health condition characterized by a wide spectrum of symptoms. According to the Diagnostic Statistical Manual 5 (DSM-5) criteria, patients can present with up to 1,497 different symptom combinations, yet all receive the same MDD diagnosis. This diversity in symptom presentation poses a significant challenge to understanding the disorder in the wider population. Subtyping offers a way to unpick this phenotypic diversity and enable improved characterization of the disorder. According to reviews, MDD subtyping work to date has lacked consistency in results due to inadequate statistics, non-transparent reporting, or inappropriate sample choice. By addressing these limitations, the current study aims to extend past phenotypic subtyping studies in MDD.

(1) To investigate phenotypic subtypes at baseline in a sample of people with MDD;

(2) To determine if subtypes are consistent between baseline 6- and 12-month follow-ups; and

(3) To examine how participants move between subtypes over time.

This was a secondary analysis of a one-year longitudinal observational cohort study. We collected data from individuals with a history of recurrent MDD in the United Kingdom, the Netherlands and Spain (N=619). The presence or absence of symptoms was tracked at three-month intervals through the Inventory of Depressive Symptomatology: Self-Report (IDS-SR) assessment. We used latent class and three-step latent transition analysis to identify subtypes at baseline, determined their consistency at 6- and 12-month follow-ups, and examined participants’ transitions over time.

We identified a 4-class solution based on model fit and interpretability, including (Class 1) severe with appetite increase, (Class 2), severe with appetite decrease, (Class 3) moderate, and (Class 4) low severity. The classes mainly differed in terms of severity (the varying likelihood of symptom endorsement) and, for the two more severe classes, the type of neurovegetative symptoms reported (Figure 1). The four classes were stable over time (measurement invariant) and participants tended to remain in the same class over baseline and follow-up (Figure 2).

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We identified four stable subtypes of depression, with individuals most likely to remain in their same class over 1-year follow-up. This suggests a chronic nature of depression, with (for example) individuals in severe classes more likely to remain in the same class throughout follow-up. Despite the vast heterogeneous symptom combinations possible in MDD, our results emphasize differences across severity rather than symptom type. This raises questions about the meaningfulness of these subtypes beyond established measures of depression severity. Implications of these findings and recommendations for future research are made.

C. Oetzmann Grant / Research support from: C.O. is supported by the UK Medical Research Council (MR/N013700/1) and King’s College London member of the MRC Doctoral Training Partnership in Biomedical Sciences., N. Cummins: None Declared, F. Lamers: None Declared, F. Matcham: None Declared, K. White: None Declared, J. Haro: None Declared, S. Siddi: None Declared, S. Vairavan Employee of: S.V is an employee of Janssen Research & Development, LLC and hold company stocks/stock options., B. Penninx : None Declared, V. Narayan: None Declared, M. Hotopf Grant / Research support from: M.H. is the principal investigator of the RADAR-CNS programme, a precompetitive public–private partnership funded by the Innovative Medicines Initiative and the European Federation of Pharmaceutical Industries and Associations. The programme received support from Janssen, Biogen, MSD, UCB and Lundbeck., E. Carr: None Declared

Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity.

Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions.

Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms.

Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.

Cognitive symptoms are common during and following episodes of depression. Little is known about the persistence of self-reported and performance-based cognition with depression and functional outcomes.

This is a secondary analysis of a prospective naturalistic observational clinical cohort study of individuals with recurrent major depressive disorder (MDD; N = 623). Participants completed app-based self-reported and performance-based cognitive function assessments alongside validated measures of depression, functional disability, and self-esteem every 3 months. Participants were followed-up for a maximum of 2-years. Multilevel hierarchically nested modelling was employed to explore between- and within-participant variation over time to identify whether persistent cognitive difficulties are related to levels of depression and functional impairment during follow-up.

508 individuals (81.5%) provided data (mean age: 46.6, s.d.: 15.6; 76.2% female). Increasing persistence of self-reported cognitive difficulty was associated with higher levels of depression and functional impairment throughout the follow-up. In comparison to low persistence of objective cognitive difficulty (<25% of timepoints), those with high persistence (>75% of timepoints) reported significantly higher levels of depression (B = 5.17, s.e. = 2.21, p = 0.019) and functional impairment (B = 4.82, s.e. = 1.79, p = 0.002) over time. Examination of the individual cognitive modules shows that persistently impaired executive function is associated with worse functioning, and poor processing speed is particularly important for worsened depressive symptoms.

We replicated previous findings of greater persistence of cognitive difficulty with increasing severity of depression and further demonstrate that these cognitive difficulties are associated with pervasive functional disability. Difficulties with cognition may be an indicator and target for further treatment input.

Major Depressive Disorder (MDD) is prevalent, often chronic, and requires ongoing monitoring of symptoms to track response to treatment and identify early indicators of relapse. Remote Measurement Technologies (RMT) provide an exciting opportunity to transform the measurement and management of MDD, via data collected from inbuilt smartphone sensors and wearable devices alongside app-based questionnaires and tasks.

To describe the amount of data collected during a multimodal longitudinal RMT study, in an MDD population.

RADAR-MDD is a multi-centre, prospective observational cohort study. People with a history of MDD were provided with a wrist-worn wearable, and several apps designed to: a) collect data from smartphone sensors; and b) deliver questionnaires, speech tasks and cognitive assessments and followed-up for a maximum of 2 years.

A total of 623 individuals with a history of MDD were enrolled in the study with 80% completion rates for primary outcome assessments across all timepoints. 79.8% of people participated for the maximum amount of time available and 20.2% withdrew prematurely. Data availability across all RMT data types varied depending on the source of data and the participant-burden for each data type. We found no evidence of an association between the severity of depression symptoms at baseline and the availability of data. 110 participants had > 50% data available across all data types, and thus able to contribute to multiparametric analyses.

RADAR-MDD is the largest multimodal RMT study in the field of mental health. Here, we have shown that collecting RMT data from a clinical population is feasible.

No significant relationships.

People with Schizophrenia have excess mortality due to physical illness. Guidelines recommend monitoring of physical health at least once a year with both primary and secondary Care having a role to play.

The aim was to improve the monitoring of the physical health of patients with severe mental illness on antipsychotic medications.

The objectives were to look at the standards derived from NICE and Maudsley guidelines which included parameters like weight, blood pressure, ECG and blood tests and compare the current clinical practice and improve the annual monitoring of these in both primary and secondary care.

Data was collected retrospectively from case notes and pathology laboratory results from August 2009 to August 2010. Inclusion criteria were patients between 18 and 65 years in the community who were on antipsychotics. Sample size obtained was 82.

Weight, Blood Pressure and ECG were recorded in less than 10% of cases and blood parameters were recorded in less than 20% of cases in secondary care. Around 60% of patients had their blood parameters monitored in primary care. There was no communication of these results between primary and secondary care. In 60% of cases an annual physical health check was not recommended by Psychiatrists to General Practitioners.

Monitoring of Physical health needs to be more robust. We have devised a proforma to record results of the monitoring. We have developed a post for a Nurse Practitioner to facilitate liaison and more effective communication between the two services.

Mortality due to physical illness is greater in schizophrenia than in the general population. Antipsychotics have been linked with weight gain, diabetes and hyperlipidaemia. UK guidelines recommend annual health screens in primary care, and secondary care ensure patients receive this and document results in notes.

To establish whether patients taking antipsychotics received annual monitoring of U&E, FBC, Lipids, Glucose, LFT, weight and blood pressure. Standards for the audit were derived from the Maudsley Prescribing Guidelines and NICE Guidelines on Schizophrenia.

To improve physical health monitoring for our patients.

We retrospectively reviewed 82 case notes. Intervention following initial audit included education of professionals involved in monitoring and inclusion of a Prescribed Medication Monitoring Form in case notes. Re-audit occurred 12 months after initial audit. Statistical analysis used χ2-test with Yates’ Continuity Correction.

Following intervention recommendation of physical health monitoring to primary care increased from 40.2% to 76.8% (P < 0.0001), as did monitoring frequency: Weight (7.3% to 54.3%), BP (7.3% to 49.4%), U&E (64.6% to 82.5%), FBC (61.0% to 78.5%,) Lipids (47.6% to 72.2%), Glucose (59.8% to 79.7%) and LFT (61.0% to 81.3%) (P < 0.05 for all parameters). Documentation of blood results in secondary care notes was also increased (P < 0.05).

Monitoring of physical health comorbidity in patients with serious mental illness was poor in the initial audit. This can be improved in both primary and secondary care by relatively simple measures and by increasing clinicians' understanding of the importance of monitoring.

Kawasaki disease is an acute vasculitis of childhood and is the leading cause of acquired heart disease in the developed countries.

Data from hospital discharge records were obtained from the National Kids Inpatient Database for years 2009 and 2012. Hospitalisations by months, hospital regions, timing of admission, insurance types, and ethnicity were analysed. Length of stay and total charges were also analysed.

There were 10,486 cases of Kawasaki disease from 12,678,005 children hospitalisation. Kawasaki disease was more common between 0 and 5 years old, in male, and in Asian. The January–March quarter had the highest rate compared to the lowest in the July–September quarter (OR=1.62, p < 0.001). Admissions on the weekend had longer length of stay [4.1 days (95 % CI: 3.97–4.31)] as compared to admissions on a weekday [3.72 days (95 % CI: 3.64–3.80), p < 0.001]. Blacks had the longest length of stay and whites had the shortest [4.33 days (95 % CI: 4.12–4.54 days) versus 3.60 days (95 % CI: 3.48–3.72 days), p < 0.001]. Coronary artery aneurysm was identified in 2.7 % of all patients with Kawasaki disease. Children with coronary artery aneurysm were hospitalised longer and had higher hospital charge. Age, admission during weekend, and the presence of coronary artery aneurysm had significant effect on the length of stay.

This report provides the most updated epidemiological information on Kawasaki disease hospitalisation. Age, admissions during weekend, and the presence of coronary artery aneurysm are significant contributors to the length of stay.

There are few data on excess direct and indirect costs of diabetes in India and limited data on rural costs of diabetes. We aimed to further explore these aspects of diabetes burdens using a clinic-based, comparative cost-of-illness study.

Persons with diabetes (n = 606) were recruited from government, private, and rural clinics and compared to persons without diabetes matched for age, sex, and socioeconomic status (n = 356). We used interviewer-administered questionnaires to estimate direct costs (outpatient, inpatient, medication, laboratory, and procedures) and indirect costs [absence from (absenteeism) or low productivity at (presenteeism) work]. Excess costs were calculated as the difference between costs reported by persons with and without diabetes and compared across settings. Regression analyses were used to separately identify factors associated with total direct and indirect costs.

Annual excess direct costs were highest amongst private clinic attendees (INR 19 552, US$425) and lowest amongst government clinic attendees (INR 1204, US$26.17). Private clinic attendees had the lowest excess absenteeism (2.36 work days/year) and highest presenteeism (0.06 work days/year) due to diabetes. Government clinic attendees reported the highest absenteeism (7.48 work days/year) and lowest presenteeism (−0.31 work days/year). Ten additional years of diabetes duration was associated with 11% higher direct costs (p < 0.001). Older age (p = 0.02) and longer duration of diabetes (p < 0.001) were associated with higher total lost work days.

Excess health expenditures and lost productivity amongst individuals with diabetes are substantial and different across care settings. Innovative solutions are needed to cope with diabetes and its associated cost burdens in India.