Development and validation of sex-specific hip fracture prediction models using electronic health records: a retrospective, population-based cohort study

Li, Gloria Hoi-Yee; Cheung, Ching‐Lung; Tan, Kathryn Choon Beng; Kung, Annie W. C.; Lau, Wallis Cheuk-Yin; Wong, Janus Siu-Him; Hsu, Warrington W.Q.; Fang, Christian; Wong, Ian C. K.

doi:10.1016/j.eclinm.2023.101876

Cited by 13 publications

(3 citation statements)

References 36 publications

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“…Following the design of conventional machine learning studies 44 , the CDARS cohort was randomly split into the training (70%) and internal test (30%) sets, and the entire THIN dataset served as an independent external validation cohort (Supplementary Fig. 1 ).…”

Section: Methodsmentioning

confidence: 99%

Unveiling unique clinical phenotypes of hip fracture patients and the temporal association with cardiovascular events

Hsu,

Zhang,

Sing

et al. 2024

Nat Commun

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Cardiovascular events are the leading cause of death among hip fracture patients. This study aims to identify subphenotypes of hip fracture patients and investigate their association with incident cardiovascular events, all-cause mortality, and health service utilisation in Hong Kong and the United Kingdom populations. By the latent class analysis, we show three distinct clusters in the Hong Kong cohort (n = 78,417): Cluster 1 has cerebrovascular and hypertensive diseases, hyperlipidemia, and diabetes; Cluster 2 has congestive heart failure; Cluster 3 consists of relatively healthy patients. Compared to Cluster 3, higher risks of major adverse cardiovascular events are observed in Cluster 1 (hazard ratio 1.97, 95% CI 1.83 to 2.12) and Cluster 2 (hazard ratio 4.06, 95% CI 3.78 to 4.35). Clusters 1 and 2 are also associated with a higher risk of mortality, more unplanned accident and emergency visits and longer hospital stays. Self-controlled case series analysis shows a significantly elevated risk of major adverse cardiovascular events within 60 days post-hip fracture. Similar associations are observed in the United Kingdom cohort (n = 27,948). Pre-existing heart failure is identified as a unique subphenotype associated with poor prognosis after hip fractures.

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Section: Methodsmentioning

confidence: 99%

Unveiling unique clinical phenotypes of hip fracture patients and the temporal association with cardiovascular events

Hsu,

Zhang,

Sing

et al. 2024

Nat Commun

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“…Currently, the gold standard for the diagnosis of OP or osteopenia is BMD determined by dual-energy X-ray absorptiometry (DXA). 3 Unfortunately, since DXA scanners are a limited resource and because of radiation, especially in developing countries, 7 it may not be suitable as a large-scale screening tool. Therefore, developing a convenient, accurate, and noninvasive prediction model that does not rely on BMD data and which contributes to prioritization of DXA scans is of great significance in public health management, assisting in the early detection and treatment of BMD decreases.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Risk of Bone Mineral Density Decrease in Type 2 Diabetes Mellitus Patients Based on Traditional Multivariate Logistic Regression and Machine Learning: A Preliminary Study

Zhang,

Xu,

et al. 2023

DMSO

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There remains a lack of a machine learning (ML) model incorporating body composition to assess the risk of bone mineral density (BMD) decreases in type 2 diabetes mellitus (T2DM) patients. We aimed to use ML algorithms and the traditional multivariate logistic regression to establish prediction models for BMD decreases in T2DM patients over 50 years of age, and compare the performance of the two methods. Patients and Methods: This cross-sectional study was conducted among 450 patients with T2DM from 1 August 2016 to 31 December 2022. The participants were divided into a normal BMD group and a decreased BMD group. Traditional multivariate logistic regression and six ML algorithms were selected to construct male and female models. Two nomograms were constructed to evaluate the risk of BMD decreases in the male and female T2DM patients, respectively. The ML models with the highest area under the curve (AUC) were compared with the traditional multivariate logistic regression models in terms of discriminant ability and clinical applicability. Results: The optimal ML model was the extreme gradient boost (XGBoost) model. The AUCs of the traditional multivariate logistic regression and the XGBoost models were 0.722 and 0.800 in the male testing dataset, respectively, and 0.876 and 0.880 in the female testing dataset, respectively. The decision curve analysis results suggested that using the XGBoost models to predict the risk of BMD decreases obtained more net benefits compared with the traditional models in both sexes. Conclusion:We preliminarily proved that the XGBoost models outperformed most other ML models in both sexes and achieved higher accuracy than traditional analyses. Due to the limited sample size in the study, it is necessary to validate our findings in larger prospective cohort studies.

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“…The Scottish Intercollegiate Guidelines Network guideline prefers QFracture over FRAX in predicting fractures [ 8 ]. Thus, our team has developed multiple osteoporosis prediction tools [ [9] , [10] , [11] ] based on the data from the Hong Kong population, either from the Hong Kong Osteoporosis Study [ 7 ], which is a cohort study established since 1995, or the big data from the population-based electronic health records [ 9 ], and these tools are expected to be highly generalizable to the local population as well as to other Chinese population potentially.…”

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confidence: 99%

Reply on “Identification of people with high risk of osteoporosis in Asia”

Cheung

2023

Osteoporosis and Sarcopenia

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Development and validation of sex-specific hip fracture prediction models using electronic health records: a retrospective, population-based cohort study

Cited by 13 publications

References 36 publications

Unveiling unique clinical phenotypes of hip fracture patients and the temporal association with cardiovascular events

Unveiling unique clinical phenotypes of hip fracture patients and the temporal association with cardiovascular events

Prediction of the Risk of Bone Mineral Density Decrease in Type 2 Diabetes Mellitus Patients Based on Traditional Multivariate Logistic Regression and Machine Learning: A Preliminary Study

Reply on “Identification of people with high risk of osteoporosis in Asia”

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