Background Emerging evidence showed that bone metabolism and cardiovascular diseases (CVD) are closely related. We previously observed a potential immediate risk of cardiovascular mortality after hip fracture. However, whether there is an immediate risk of cardiovascular events after hip fracture is unclear. The aim of this study was to evaluate the risk for major adverse cardiovascular events (MACEs) between patients having experienced falls with and without hip fracture. Methods This retrospective population-based cohort study used data from a centralized electronic health record database managed by Hong Kong Hospital Authority. Patients having experienced falls with and without hip fracture were matched by propensity score (PS) at a 1:1 ratio. Adjusted associations between hip fracture and risk of MACEs were evaluated using competing risk regression after accounting for competing risk of death. Results Competing risk regression showed that hip fracture was associated with increased one-year risk of MACEs (hazard ratio [HR], 1.27; 95% CI, 1.21 to 1.33; p<0.001), with a 1-year cumulative incidence difference of 2.40% (1.94% to 2.87%). The HR was the highest in the first 90-day after hip fracture (HR of 1.32), and such an estimate was continuously reduced in 180-day, 270-day, and 1-year after hip fracture. Conclusions Hip fracture was associated with increased immediate risk of MACEs. This study suggested that a prompt evaluation of MACE among older adults aged 65 years and older who are diagnosed with hip fracture irrespectively of cardiovascular risk factors may be important, as early management may reduce subsequent risk of MACE.

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Background Hip fracture is associated with immobility, morbidity, mortality, and high medical cost. Due to limited availability of dual-energy X-ray absorptiometry (DXA), hip fracture prediction models without using bone mineral density (BMD) data are essential. We aimed to develop and validate 10-year sex-specific hip fracture prediction models using electronic health records (EHR) without BMD. Methods In this population-based study, the derivation cohort comprised 161,051 public healthcare service users (91,926 female; 69,125 male) in Hong Kong aged≥60. Sex-stratified derivation cohort was randomly split to 80% training and 20% internal testing datasets. An external validation cohort comprised 3,046 community-dwelling participants. With 395 potential predictors (age, diagnosis and drug prescription records from EHR), 10-year sex-specific hip fracture prediction models were developed using stepwise selection by logistic regression (LR) and four machine learning (ML) algorithms (gradient boosting machine, random forest, eXtreme gradient boosting, and single-layer neural networks) in the training cohort. Model performance was evaluated in both internal and external validation cohorts. Findings In female, the LR model had the highest AUC (0.815) and adequate calibration in internal validation. Reclassification metrics showed ML algorithms could not further improve the performance of the LR model. Similar performance was attained by the LR model in external validation, with high AUC (0.841) comparable to other ML algorithms. In internal validation for male, LR model had high AUC (0.818) and it outperformed all ML models as indicated by reclassification metrics, with adequate calibration. In external validation, the LR model had high AUC (0.898) comparable to ML algorithms. Reclassification metrics demonstrated that LR model had the best discrimination performance. Interpretation Even without using BMD data, the 10-year hip fracture prediction models developed by conventional LR had better discrimination performance than the models developed by ML algorithms. Upon further validation in independent cohorts, the LR models could be integrated into the routine clinical workflow, aiding the identification of people at high risk for DXA scan. Funding This study was funded by the Health and Medical Research Fund, Food and Health Bureau, Hong Kong SAR Government (reference: 17181381).