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Research on osteoporotic fragility fracture prediction model based on machine learning

Research on osteoporotic fragility fracture prediction model based on machine learning

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600126739
Enrollment
Unknown
Registered
2026-06-15
Start date
2026-06-15
Completion date
Unknown
Last updated
2026-06-22

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Osteoporotic fragility fracture

Interventions

Case series :None

Sponsors

Yuebei People’s Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Patients who have completed lumbar and hip bone density testing during hospitalization and have been diagnosed with osteoporosis; 2. Patients with a clear history of low-energy injury exposure; 3. Patients with complete clinical data (height, weight, gender, age), relevant biochemical tests (such as red blood cell distribution width (SD), monocyte percentage), and bone density data with a completeness rate of >=70% (meeting data cleaning requirements);

Exclusion criteria

Exclusion criteria: 1. Exclusion of fracture types (non-fragile fractures) High-energy trauma fractures: fractures caused by car accidents, falls from heights, heavy object injuries, etc. Pathological fractures: secondary osteoporosis caused by bone tumors, metastatic cancer of the bone, multiple myeloma, Paget's disease, bone tuberculosis, etc. Fragility Fracture Secondary to Osteoporosis: Fractures due to long-term hormone therapy (>=3 months), hyperparathyroidism, Cushing's syndrome, chronic kidney/liver disease, rheumatoid arthritis, etc. with clear secondary factors. 2. Exclusion of underlying diseases and conditions Severe heart, lung, liver, and kidney failure, advanced malignant tumors, expected survival <12 months, mental illness, cognitive impairment, long-term bedridden/paralysis. 3. Data and study feasibility Exclusion of clinical data with severe missingness (core variables: age, gender, bone density, fracture history, missing key medications), loss to follow-up, data falsification/outliers that cannot be corrected, and participation in other clinical intervention trials that interfere with this study;

Design outcomes

Primary

MeasureTime frame
Laboratory biochemical testing indicators;;Drug exposure measurement indicators;Clinical history collection indicators;Quantitative bone density measurement indicators;Anthropometric indicators;

Countries

China

Contacts

Public ContactSi Zebing

Yuebei People’s Hospital

szb520zd@163.com+86 751 6913542

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Jun 29, 2026