Factors associated with fracture after stroke and TIA: a long-term follow-up

Fracture Risk Among Stroke Survivors According to Poststroke Disability Status and Stroke Type

BACKGROUND

Stroke survivors face physical and cognitive challenges, leading to an increased dependency and a higher fall risk. We aimed to investigate the impact of poststroke disability and stroke type on fracture risk at various sites compared with matched controls.

METHODS

This retrospective cohort study used data from the Korean National Health Insurance System database (2010-2018), including patients with stroke and 1:1 matched controls. Stroke survivors were grouped based on the presence and severity of their poststroke disability and stroke type. The primary outcome was a newly diagnosed fracture, analyzed by Cox proportional hazard regression analyses adjusting for potential confounders.

RESULTS

Among 223 358 stroke survivors (mean age, 64.8±10.9 years; 61.2% men), 16 344 fractures occurred during a mean follow-up of 3.7±2.5 years. In matched controls (n=322 161; mean age, 65.4±11.2 years; 61.3% men), 20 398 fractures were identified. Stroke survivors had increased overall fracture risk compared with matched controls (adjusted hazard ratio [aHR], 1.40 [95% CI, 1.37-1.43]). Specifically, hip fracture risk was even greater in stroke survivors (incidence rate per 1000 person-years, 4.7 [95% CI, 4.5-4.8]; aHR, 2.42 [95% CI, 2.30-2.55]) than controls (incidence rate, 2.2 [95% CI, 2.1-2.3]). The risk of vertebral fractures (aHR, 1.29 [95% CI, 1.25-1.34]) and other fractures (aHR, 1.19 [95% CI, 1.15-1.23]) was also higher than that of the control group. Hip fracture risk was the highest among stroke survivors with severe poststroke disability (aHR, 4.82 [95% CI, 4.28-5.42]), although vertebral or other fracture risk was the highest among those with mild poststroke disability. No significant difference in fracture risk was found between hemorrhagic and ischemic stroke survivors when stratified by disability status.

CONCLUSIONS

Our findings showed increased subsequent fracture risk among stroke survivors, particularly those with poststroke disability and for hip fracture. Bone health assessment and treatment should be emphasized as an essential part of stroke management.

Fracture Risk Increases After Stroke or Transient Ischemic Attack and Is Associated With Reduced Quality of Life

BACKGROUND

Fractures are a serious consequence following stroke, but it is unclear how these events influence health-related quality of life (HRQoL). We aimed to compare annualized rates of fractures before and after stroke or transient ischemic attack (TIA), identify associated factors, and examine the relationship with HRQoL after stroke/TIA.

METHODS

Retrospective cohort study using data from the Australian Stroke Clinical Registry (2009-2013) linked with hospital administrative and mortality data. Rates of fractures were assessed in the 1-year period before and after stroke/TIA. Negative binomial regression, with censoring at death, was used to identify factors associated with fractures after stroke/TIA. Respondents provided HRQoL data once between 90 and 180 days after stroke/TIA using the EuroQoL 5-dimensional 3-level instrument. Adjusted logistic regression was used to assess differences in HRQoL at 90 to 180 days by previous fracture.

RESULTS

Among 13 594 adult survivors of stroke/TIA (49.7% aged ≥75 years, 45.5% female, 47.9% unable to walk on admission), 618 fractures occurred in the year before stroke/TIA (45 fractures per 1000 person-years) compared with 888 fractures in the year after stroke/TIA (74 fractures per 1000 person-years). This represented a relative increase of 63% (95% CI, 47%-80%). Factors associated with poststroke fractures included being female (incidence rate ratio [IRR], 1.34 [95% CI, 1.05-1.72]), increased age (per 10-year increase, IRR, 1.35 [95% CI, 1.21-1.50]), history of prior fracture(s; IRR, 2.56 [95% CI, 1.77-3.70]), and higher Charlson Comorbidity Scores (per 1-point increase, IRR, 1.18 [95% CI, 1.10-1.27]). Receipt of stroke unit care was associated with fewer poststroke fractures (IRR, 0.67 [95% CI, 0.49-0.93]). HRQoL at 90 to 180 days was worse among patients with prior fracture across the domains of mobility, self-care, usual activities, and pain/discomfort.

CONCLUSIONS

Fracture risk increases substantially after stroke/TIA, and a history of these events is associated with poorer HRQoL at 90 to 180 days after stroke/TIA.

Establishment and validation of a nomogram for predicting new fractures after PKP treatment of for osteoporotic vertebral compression fractures in the elderly individuals

BACKGROUND

To investigate the risk factors for new vertebral compression fractures (NVCFs) after percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fractures (OVCFs) and to create a nomogram to predict the occurrence of new postoperative fractures.

METHODS

This was a retrospective analysis of the clinical data of 529 OVCF patients who received PKP treatment in our hospital from June 2017 to June 2020. Based on whether there were new fractures within 2 years after surgery, the patients were divided into a new fracture group and a nonnew fracture group. Univariate and multivariate analyses were used to determine the risk factors for the occurrence of NVCFs after surgery. The data were randomly divided into a training set (75%) and a testing set (25%). Nomograms predicting the risk of NVCF occurrence were created based on the results of the multivariate analysis, and performance was evaluated using receiver operating characteristic curves (ROCs), calibration curves, and decision curve analyses (DCAs). A web calculator was created to give clinicians a more convenient interactive experience.

RESULTS

A total of 56 patients (10.6%) had NVCFs after surgery. The univariate analysis showed significant differences in sex and the incidences of cerebrovascular disease, a positive fracture history, and bone cement intervertebral leakage between the two groups (P < 0.05). The multivariate analysis showed that sex [OR = 2.621, 95% CI (1.030-6.673), P = 0.043], cerebrovascular disease [OR = 28.522, 95% CI (8.749-92.989), P = 0.000], fracture history [OR = 12.298, 95% CI (6.250-24.199), P = 0.000], and bone cement intervertebral leakage [OR = 2.501, 95% CI (1.029-6.082), P = 0.043] were independent risk factors that were positively associated with the occurrence of NVCFs. The AUCs of the model were 0.795 (95% CI: 0.716-0.874) and 0.861 (95% CI: 0.749-0.974) in the training and testing sets, respectively, and the calibration curves showed high agreement between the predicted and actual states. The areas under the decision curve were 0.021 and 0.036, respectively.

CONCLUSION

Female sex, cerebrovascular disease, fracture history and bone cement intervertebral leakage are risk factors for NVCF after PKP. Based on this, a highly accurate nomogram was developed, and a webpage calculator ( https://new-fracture.shinyapps.io/DynNomapp/ ) was created.

Hypnotics and injuries among older adults with Parkinson's disease: a nested case-control design

BACKGROUND

Patients with Parkinson's disease often experience sleep disorders. Hypnotics increase the risk of adverse events, such as injuries due to falls. In this study, we evaluated the association between hypnotics and injuries among older adults with Parkinson's disease.

METHODS

The study used a nested case-control design. The participants were 5009 patients with Parkinson's disease aged ≥ 75 years based on claims data between April 2016 and March 2019 without prescription hypnotics 1 year before the study started. Hypnotics prescribed as oral medications included benzodiazepines, non-benzodiazepines, orexin receptor antagonists, and melatonin receptor agonists. The incidences of outcomes, including injuries, fractures, and femoral fractures, were determined. Each case had four matched controls. Conditional logistic regression analyses were performed to calculate the odds ratios and 95% confidence intervals for the number of hypnotics taken per day for each type of hypnotic.

RESULTS

The proportion of participants taking at least one type of hypnotic was 18.6%, with benzodiazepines being the most common. The incidence of injuries, fractures, and femoral fractures was 66.7%, 37.8%, and 10.2%, respectively. Benzodiazepines significantly increased the risk of injuries (odds ratio: 1.12; 95% confidence interval: 1.03-1.22), and melatonin receptor agonists significantly increased the risk of femoral fractures (odds ratio: 2.84; 95% confidence interval: 1.19-6.77).

CONCLUSIONS

Benzodiazepines and non-benzodiazepines, which are not recommended according to current guidelines, were the most prevalent among older adults with Parkinson's disease. Benzodiazepines significantly increased the risk of injuries, and melatonin receptor agonists significantly increased the risk of femoral fractures.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Comparison of the effectiveness of different machine learning algorithms in predicting new fractures after PKP for osteoporotic vertebral compression fractures

BACKGROUND

The use of machine learning has the potential to estimate the probability of a second classification event more accurately than traditional statistical methods, and few previous studies on predicting new fractures after osteoporotic vertebral compression fractures (OVCFs) have focussed on this point. The aim of this study was to explore whether several different machine learning models could produce better predictions than logistic regression models and to select an optimal model.

METHODS

A retrospective analysis of 529 patients who underwent percutaneous kyphoplasty (PKP) for OVCFs at our institution between June 2017 and June 2020 was performed. The patient data were used to create machine learning (including decision trees (DT), random forests (RF), support vector machines (SVM), gradient boosting machines (GBM), neural networks (NNET), and regularized discriminant analysis (RDA)) and logistic regression models (LR) to estimate the probability of new fractures occurring after surgery. The dataset was divided into a training set (75%) and a test set (25%), and machine learning models were built in the training set after ten cross-validations, after which each model was evaluated in the test set, and model performance was assessed by comparing the area under the curve (AUC) of each model.

RESULTS

Among the six machine learning algorithms, except that the AUC of DT [0.775 (95% CI 0.728-0.822)] was lower than that of LR [0.831 (95% CI 0.783-0.878)], RA [0.953 (95% CI 0.927-0.980)], GBM [0.941 (95% CI 0.911-0.971)], SVM [0.869 (95% CI 0.827-0.910), NNET [0.869 (95% CI 0.826-0.912)], and RDA [0.890 (95% CI 0.851-0.929)] were all better than LR.

CONCLUSIONS

For prediction of the probability of new fracture after PKP, machine learning algorithms outperformed logistic regression, with random forest having the strongest predictive power.

Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Utilization of screening and treatment for osteoporosis among stroke survivors

BACKGROUND

Stroke survivors are prone to osteoporosis and fractures. However, bone mineral density (BMD) testing and osteoporosis treatment were underutilized in patients with recent stroke. We aimed to examine whether stroke has an impact on the utilization of BMD testing and osteoporosis treatment as well as the determinants of their utilization in stroke patients using nationwide population-based data in Taiwan.

METHODS

We identified patients aged 55 years and older who were hospitalized for hemorrhagic or ischemic stroke as the stroke cohort, and age- and sex-matched patients hospitalized for reasons other than stroke, fracture, or fall as the non-stroke cohort. We used the Fine-Gray sub-distribution hazard competing risk regression model to determine the predictors for BMD testing and osteoporosis treatment.

RESULTS

A total of 32997 stroke patients and 32997 age- and sex-matched controls comprised the stroke and non-stroke cohorts, respectively. BMD testing and osteoporosis treatment were performed in 1.0% and 5.2% of the stroke patients, respectively, within one year after hospitalization while these measures were performed in 0.8% and 4.7% of the controls. Stroke patients were more likely to receive BMD testing (adjusted hazard ratio [HR] 1.33; 95% confidence interval [CI] 1.11-1.58) and osteoporosis treatment (adjusted HR 1.19; 95% CI 1.11-1.29). Female sex, osteoporosis, prior BMD testing, and low-trauma fractures after stroke increased the likelihood of using BMD testing and osteoporosis treatment whereas greater stroke severity reduced the likelihood of receiving both measures.

CONCLUSIONS

Both BMD testing and osteoporosis treatment were underutilized among stroke survivors even though they had a higher chance of receiving both measures than non-stroke patients.

[Individual aspects in eldery patients with fragility fractures]

BACKGROUND

Due to our ageing population, the number of elderly patients who are treated in the emergency department due to low-energy trauma (e.g., tripping) continues to rise. These minor accidents often result in fragility fractures classically located in the proximal humerus, distal radius, spine, pelvis, and near the hip joint. Pre-existing conditions, polypharmacy, and general frailty increase the risk of fragility fractures in this patient population.

METHODS

Geriatric trauma fractures and especially insufficiency fractures of the posterior pelvic ring are often difficult to diagnose by plain X‑ray. Therefore, in geriatric trauma patients, cross-sectional imaging, e.g., computed tomography (CT), dual-energy CT (DECT), or magnetic resonance imaging (MRI), should be considered early for reliable evaluation of a suspected fracture. This also allows for the identification of older fractures. Particularly in cognitively impaired elderly patients, difficult examination conditions or an unclear fall event, cross-sectional imaging is often indicated. However, this may also involve risks, e.g., use of contrast medium in patients with impaired renal function, so that each case must be considered individually. Furthermore, the diagnosis and treatment of osteoporosis, which is an underlying disease that leads to fragility fractures, is of particular importance. In the diagnostic workup, measurement of bone density using dual energy X‑ray absorptiometry (DXA) is the standard method according to guidelines. In specific situations, high-resolution peripheral quantitative CT (HR-pQCT) may also be used.

CONCLUSION

Due to the special challenges of correctly detecting fragility fractures and being able to quickly initiate adequate therapy, good cooperation between radiologists and trauma surgeons is necessary.

Predicting the Risk of Ischemic Stroke in Patients Treated with Novel Oral Anticoagulants: A Machine Learning Approach

INTRODUCTION

The aim of this cohort study was to estimate the predictors of ischemic stroke in patients treated with non-vitamin K antagonist oral anticoagulants (NOACs) in a large database containing data from general practitioners in Germany using machine learning methods.

METHODS

This retrospective cohort study included 39,652 patients with a diagnosis of atrial fibrillation (AF) and an initial prescription of NOAC in 1,278 general practices in Germany between January 2011 and December 2018. Of 39,652 patients, 2,310 (5.8%) receive the first stroke or TIA diagnosis during the follow-up time (average follow-up time 2.5 [SD: 1.8] years). Sub-Population Optimization and Modeling Solutions (SOMS) tool was used to identify subgroups at a higher risk of stroke compared to the overall population receiving NOAC based on 37 different variables.

RESULTS

Using SOMS, a total of 9 variables were considered important for the stroke prediction. Age had 59.1% of prediction importance, following by ischemic heart diseases (10.6%), urinary tract infections (4.6%), dementia (3.5%), and male sex (3.5%). Further variables with less importance were dizziness (2.2%), dorsalgia (1.5%), shoulder lesions (1.1%), and diabetes mellitus (1.1%).

DISCUSSION/CONCLUSIONS

The stroke risk in AF patients treated with NOAC could be predicted based on comorbidities like ischemic heart diseases, urinary tract infections, and dementia additionally to age and male sex. Knowing and addressing these factors may help reduce the risk of stroke in this patient population.

Risk of Fractures in Stroke Patients Treated With a Selective Serotonin Reuptake Inhibitor: A Systematic Review and Meta-Analysis

[Figure: see text].

Associations between stroke type, stroke severity, and pre-stroke osteoporosis with the risk of post-stroke fracture: A nationwide population-based study

Background Recognizing the post-stroke fracture risk factors is crucial for targeted intervention and primary fracture prevention. We aimed to investigate whether stroke types, stroke severity, and pre-stroke osteoporosis are associated with post-stroke fracture. Methods In a nationwide cohort, we identified previously fracture-free patients who suffered from first-ever stroke, either acute ischemic stroke (AIS) or intracerebral hemorrhage (ICH), between 2003 and 2015. Information regarding stroke severity, osteoporosis, comorbidity, and medication information was collected. The outcomes analyzed included hip fracture, spine fracture, and other fractures. Cumulative incidence functions (CIFs) were used to estimate the cumulative incidence of fractures over time after accounting for competing risk of death. Multivariable Fine and Gray models were used to determine the adjusted hazard ratio (HR) and 95% confidence interval (CI). Results Of the 41,895 patients with stroke, the 5-year CIFs of any incident fracture, hip fracture, spine fracture, and other fractures were 8.03%, 3.42%, 1.87%, and 3.05%, respectively. The fracture risk did not differ between patients with AIS and ICH. While osteoporosis increased the risk of post-stroke fracture (adjusted HR [95% CI],1.42 [1.22-1.66]), stroke severity was inversely associated with post-stroke fracture (moderate, 0.88 [0.81-0.96] and severe, 0.39 [0.34-0.44], compared with mild stroke severity). Conclusions Stroke survivors had an over 8% fracture risk at 5 years after stroke. Mild stroke severity and osteoporosis were significantly associated with post-stroke fracture risk, whereas stroke type was not. Our results call for effective measures for bone health screening and fracture prevention in patients with stroke.