ArticleFrontiers in public health2024
Construction and verification of risk prediction model of osteoporotic fractures in patients with osteoporosis in China.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 9 citations in OpenAlex.
- Construction and validation of a risk prediction model for hyponatremia caused by cervical spinal cord injury.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- A longitudinal dataset of hypertensive osteoporotic fracture patients: treatments and long-term outcomes.Scientific data · 2026Article
- 20° Anchor Insertion Technique Toward the Subchondral Bone in Rotator Cuff Repairs for Patients With Osteoporosis Compared With the Traditional 45° Deadman Angle.Orthopaedic journal of sports medicine · 2026Article
- Clinical Manifestations and Risk Factors of Osteoporosis in Patients with Type 2 Diabetes Mellitus.Journal of inflammation research · 2026Article
- A Simple Osteoporosis Clinical Risk Model Based on the National Health and Nutrition Examination Survey.Indian journal of orthopaedics · 2025Article
- The association between ten anthropometric measures and osteoporosis and osteopenia among postmenopausal women.Scientific reports · 2025Article
- Development and validation of a nomogram for all-cause mortality in osteoporosis patients over five years.PloS one · 2025Article
- Establishment and Validation of a Predictive Nomogram Model for Osteoporosis in Postmenopausal Women with Type 2 Diabetes Mellitus: A Retrospective Study.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To explore the influencing factors of osteoporotic fractures (OPF) in patients with osteoporosis, construct a prediction model, and verify the model internally and externally, so as to provide reference for early screening and intervention of OPF in patients with osteoporosis. Methods: Osteoporosis patients in the First Affiliated Hospital of Soochow University were selected, and the medical records of patients were consulted through the Hospital Information System (HIS) and the data management platform of osteoporosis patients, so as to screen patients who met the criteria for admission and discharge and collect data. SPSS 26.0 software was used for single factor analysis to screen statistically significant variables ( Results: In this study, the data of modeling set and verification set were 1,435 and 580, respectively. There were 493 (34.4%) cases with OPF and 942 (65.6%) cases without OPF in the modeling set. There were 204 (35.2%) cases with OPF and 376 (64.8%) cases without OPF. The variables with statistically significant differences in univariate analysis are Age, BMI, History of falls, Usage of glucocorticoid, ALP, Serum Calcium, BMD of lumbar, BMD of feminist neck, Conclusion: In this study, a OPF risk prediction model is established and its prediction efficiency is verified, which can help identify the high fracture risk subgroup of osteoporosis patients in order to choose stronger intervention measures and management.
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