Sub-healthy
Conditions
Brief summary
This study aims to explore the dynamic evolution patterns of population health, sub-health, and disease states through dynamic system theory and big data mining methods, providing scientific evidence for personalized prevention and health management.
Detailed description
Specific objectives include: (1) Identifying individual health, sub-health, and disease states using unsupervised system modeling techniques, while investigating their mutual transformation pathways. (2) Identifying key indicators determining state transitions, clarifying their mechanisms and interactions. (3) Developing dynamic system models to simulate state transition trajectories under multivariate influences, predicting individual probabilities of progression from health to sub-health or disease. (4) Creating interpretable health prediction tools based on modeling results to support precision interventions. The ultimate goal is to establish a scientifically validated yet implementable health state modeling system, offering quantifiable tools for early intervention and personalized health management to reduce chronic disease incidence and healthcare burdens.
Interventions
This is an observational study.
Sponsors
Beijing Friendship Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
* Participants must have completed at least two consecutive physical examinations at the Physical Examination Center of Beijing Friendship Hospital, Capital Medical University, between June 2007 and August 2025, with a minimum interval of 6 months between adjacent records. * Data records should be relatively complete, with missing rates for key research variables (e.g., core biochemical indicators, demographic information, and essential questionnaire items) ≤30%. * Participants must have no prior history of severe organic diseases prior to their first study inclusion (as documented in medical records, primarily including: malignant tumors (non-curable/end-stage), severe cardiac insufficiency (NYHA Class III-IV), end-stage renal disease (CKD Stage 5), decompensated cirrhosis, or significant functional impairment caused by sequelae of severe cerebrovascular disease).
Exclusion criteria
\- Individuals with a severe lack of basic data (such as unique identification, key demographic information, and core indicators of detection) or who cannot be effectively anonymized.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Long-term Predictive Accuracy | Evaluate the AUC values of disease occurrence in the 1st, 2nd, 3rd, 5th, and 10th year after prediction on the internal validation dataset. | AUC stratified by age and gender, assessing the risk of disease occurrence within specific time intervals (1 year, 2 years,..., 10 years) after prediction. This indicator measures the decay of a model's predictive ability over time. |
| Discriminative Accuracy for Next Diagnosis | Evaluate on an internal validation dataset. This dataset contains individual historical data up to January 1, 2018, based on which the model predicts the next diagnostic event that will occur immediately. Calculate AUC for diseases with over 1000 ICD-10 | Age- and Sex-stratified Area Under the Receiver Operating Characteristic Curve, AUC |
| Trajectory-level Predictive Accuracy | On the validation subset, evaluate the accuracy of disease event predictions for each year from the simulation starting point (60 years old) to the following 1 to 20 years. | The proportion of correctly predicted disease events. In each simulated future year, match the generated disease events with the actual disease events that occur in individuals, and calculate the success rate (%) of the matching. |
Countries
China
Outcome results
None listed