Precise stratification of patients into homogeneous disease subgroups could address the heterogeneity of phenotypes and enhance understanding of the pathophysiology underlying specific subtypes. Existing literature on subtyping patients with major depressive disorder (MDD) mainly utilized clinical features only. Genomic and imaging data may improve subtyping, but advanced methods are required due to the high dimensionality of features.

We propose a novel disease subtyping framework for MDD by integrating brain structural features, genotype-predicted expression levels in brain tissues, and clinical features. Using a multi-view biclustering approach, we classify patients into clinically and biologically homogeneous subgroups. Additionally, we propose approaches to identify causally relevant genes for clustering.

We verified the reliability of the subtyping model by internal and external validation. High prediction strengths (PS) (average PS: 0.896, minimum: 0.854), a measure of generalizability of the derived clusters in independent datasets, support the validity of our approach. External validation using patient outcome variables (treatment response and hospitalization risks) confirmed the clinical relevance of the identified subgroups. Furthermore, subtype-defining genes overlapped with known susceptibility genes for MDD and were involved in relevant biological pathways. In addition, drug repositioning analysis based on these genes prioritized promising candidates for subtype-specific treatments.

Our approach successfully stratified MDD patients into subgroups with distinct clinical prognoses. The identification of biologically and clinically meaningful subtypes may enable more personalized treatment strategies. This study also provides a framework for disease subtyping that can be extended to other complex disorders.

Studies examining age-stratified risk factors for suicide among individuals with bipolar disorder in different stages of life are scant, possibly because of the insufficient number of suicide cases.

This study investigated suicide mortality rates and risk profiles of suicide mortality stratified by five age groups in individuals with bipolar disorder.

This study identified patients with a diagnosis of bipolar disorder between January 1, 2000, and December 31, 2021, from Taiwan’s National Health Insurance Research Database. The study population comprised 45,211 inpatients diagnosed with bipolar disorder, with 1,370 suicide cases during the study period. We calculated the standardized mortality ratio (SMR) of the bipolar cohort relative to the general population. In the age-stratified nested case–control study, risk set sampling was performed to match 1 suicide case with 10 living controls by age, sex, and the year of first diagnosis. The age-stratified risk associated with demographic characteristics, psychiatric and physical comorbidities was estimated using multivariable conditional logistic regression.

The highest SMR (47.0) for suicide was observed in individuals with bipolar disorder aged <30 years. SMR decreased with age; patients aged >60 years had an SMR of 9.5. Among those younger than 40 years, a higher percentage of unemployment was noted among suicide cases than among controls. A significantly increased risk of the depressive phase of bipolar disorder was noted shortly before suicide mortality among patients with bipolar disorder in all age groups. Drug-induced and alcohol-induced mental disorders were associated with suicide and were highly prevalent in patients aged <30 years. Other forms of heart disease were identified in patients aged <40 years, and pneumonia was detected in the 50–59 years age group.

These findings aid the development of health-care intervention strategies for preventing suicide among patients with bipolar disorder in various stages of life.

Population-wide restrictions during the COVID-19 pandemic may create barriers to mental health diagnosis. This study aims to examine changes in the number of incident cases and the incidence rates of mental health diagnoses during the COVID-19 pandemic.

By using electronic health records from France, Germany, Italy, South Korea and the UK and claims data from the US, this study conducted interrupted time-series analyses to compare the monthly incident cases and the incidence of depressive disorders, anxiety disorders, alcohol misuse or dependence, substance misuse or dependence, bipolar disorders, personality disorders and psychoses diagnoses before (January 2017 to February 2020) and after (April 2020 to the latest available date of each database [up to November 2021]) the introduction of COVID-related restrictions.

A total of 629,712,954 individuals were enrolled across nine databases. Following the introduction of restrictions, an immediate decline was observed in the number of incident cases of all mental health diagnoses in the US (rate ratios (RRs) ranged from 0.005 to 0.677) and in the incidence of all conditions in France, Germany, Italy and the US (RRs ranged from 0.002 to 0.422). In the UK, significant reductions were only observed in common mental illnesses. The number of incident cases and the incidence began to return to or exceed pre-pandemic levels in most countries from mid-2020 through 2021.

Healthcare providers should be prepared to deliver service adaptations to mitigate burdens directly or indirectly caused by delays in the diagnosis and treatment of mental health conditions.

To identify risk factors for mortality in intensive care units (ICUs) in Asia.

Prospective cohort study.

The study included 317 ICUs of 96 hospitals in 44 cities in 9 countries of Asia: China, India, Malaysia, Mongolia, Nepal, Pakistan, Philippines, Sri Lanka, Thailand, and Vietnam.

Patients aged >18 years admitted to ICUs.

In total, 157,667 patients were followed during 957,517 patient days, and 8,157 HAIs occurred. In multiple logistic regression, the following variables were associated with an increased mortality risk: central-line–associated bloodstream infection (CLABSI; aOR, 2.36; P < .0001), ventilator-associated event (VAE; aOR, 1.51; P < .0001), catheter-associated urinary tract infection (CAUTI; aOR, 1.04; P < .0001), and female sex (aOR, 1.06; P < .0001). Older age increased mortality risk by 1% per year (aOR, 1.01; P < .0001). Length of stay (LOS) increased mortality risk by 1% per bed day (aOR, 1.01; P < .0001). Central-line days increased mortality risk by 2% per central-line day (aOR, 1.02; P < .0001). Urinary catheter days increased mortality risk by 4% per urinary catheter day (aOR, 1.04; P < .0001). The highest mortality risks were associated with mechanical ventilation utilization ratio (aOR, 12.48; P < .0001), upper middle-income country (aOR, 1.09; P = .033), surgical hospitalization (aOR, 2.17; P < .0001), pediatric oncology ICU (aOR, 9.90; P < .0001), and adult oncology ICU (aOR, 4.52; P < .0001). Patients at university hospitals had the lowest mortality risk (aOR, 0.61; P < .0001).

Some variables associated with an increased mortality risk are unlikely to change, such as age, sex, national economy, hospitalization type, and ICU type. Some other variables can be modified, such as LOS, central-line use, urinary catheter use, and mechanical ventilation as well as and acquisition of CLABSI, VAE, or CAUTI. To reduce mortality risk, we shall focus on strategies to reduce LOS; strategies to reduce central-line, urinary catheter, and mechanical ventilation use; and HAI prevention recommendations.

It is unclear whether the enhancing contact model (ECM) intervention is effective in reducing family caregiving burden and improving hope and quality of life (QOL) among family caregivers of persons with schizophrenia (FCPWS).

We conducted a cluster randomized controlled trial in FCPWS in eight rural townships in Xinjin, Chengdu, China. In total, 253 FCPWS were randomly allocated to the ECM, psychoeducational family intervention (PFI), or treatment as usual (TAU) group. FCPWS in three groups were assessed caregiving burden, QOL and state of hope at baseline (T0), post-intervention (T1), 3-month (T2), and 9-month (T3) follow-up, respectively.

Compared with participants in the TAU group, participants in the ECM group had statistically significantly lower caregiving burden scores both at T1 and T2 (p = 0.0059 and 0.0257, respectively). Compared with participants in the TAU group, participants in the PFI group had statistically significantly higher QOL scores in T1 (p = 0.0406), while participants in the ECM group had statistically significantly higher QOL scores in T3 (p = 0.0240). Participants in both ECM and PFI groups had statistically significantly higher hope scores than those in the TAU group at T1 (p = 0.0160 and 0.0486, respectively).

This is the first study to explore the effectiveness of ECM on reducing family caregiving burden and improving hope and QOL in rural China. The results indicate the ECM intervention, a comprehensive and multifaceted intervention, is more effective than the PFI in various aspects of mental wellbeing among FCPWS. Future research needs to confirm ECM's effectiveness in various population.