Prognosis estimation is the basis for establishing the personal interventions in sepsis patients. Serum biomarkers are potential tools for predicting the outcomes of sepsis patients admitted to the intensive care unit (ICU). Here, we plan to perform a prospective study to investigate the prognostic value of serum biomarkers. We named this study Hospital Outcome of Host Heterogeneity, Organ dysfunction and Trajectory in sepsis (HOHHOT).

This prospective cohort study is being performed in several hospitals in the Inner Mongolia Autonomous Region between 2025 and 2027. The inclusion criteria were adult sepsis patients admitted to the ICU. A serum specimen will be collected at the onset of sepsis and stored at –80°C for future use. The endpoint of the HOHHOT study includes, but is not limited to, hospital outcome (eg, mortality, occurrence of organ dysfunction, treatment response and the progression of sepsis). We will analyse the predictive value of serum biomarkers for outcomes using a receiver operating characteristic curve and decision curve analysis. The association between baseline characteristics and serum biomarkers will be estimated using a logistic regression model, net reclassification index and integrated discriminatory index. We will also construct some predictive models using machine learning algorithms.

The HOHHOT study has been approved by the Ethics Committee of the Affiliated Hospital of Inner Mongolia Medical University (NO: KY2025005). The findings derived from the HOHHOT study will be submitted to international academic journals.

Chinese Clinical Trial Registry (ChiCTR2500100770); pre-results.

Mental health issues such as depression and anxiety are highly and disproportionally prevalent among university students. Beyond the academic rigour, stressors imposed by a new environment result in them being vulnerable to the onset and manifestation of mental health symptomatology. Leveraging smartphones and wearables for digital phenotyping capabilities is an innovative approach for monitoring and intervening in the mental health conditions of university students. This provides a unique opportunity to collect and identify digital and behavioural biomarkers, subsequently enabling the development of predictive models to identify university students at risk.

This study—Brightline—will employ an observational study design over a 6-month period, recruiting 500 students from a major public university in Singapore. Passive data collection will occur continuously throughout the monitoring period through a wearable device (Fitbit Charge 6) and smartphone sensors via the Brightline app, which uses a digital phenotyping data collection platform. Active data collection will consist of self-report questionnaires to be completed at the beginning of the study and follow-up assessments at 1, 3 and 6 months after. The passive and active data collected will be analysed to identify the digital biomarkers associated with depression, anxiety, stress, loneliness and affect among university students. Predictive models of these mental health issues will also be developed.

This study was approved by the Nanyang Technological University Institutional Review Board (IRB-2023-894). Findings from this study will be published in peer-reviewed journals and presented at academic conferences.

NCT06770075.