Fractures after stroke-A Danish register-based study of 106 001 patients

Pathophysiological changes of muscle after ischemic stroke: a secondary consequence of stroke injury

Sufficient clinical evidence suggests that the damage caused by ischemic stroke to the body occurs not only in the acute phase but also during the recovery period, and that the latter has a greater impact on the long-term prognosis of the patient. However, current stroke studies have typically focused only on lesions in the central nervous system, ignoring secondary damage caused by this disease. Such a phenomenon arises from the slow progress of pathophysiological studies examining the central nervous system. Further, the appropriate therapeutic time window and benefits of thrombolytic therapy are still controversial, leading scholars to explore more pragmatic intervention strategies. As treatment measures targeting limb symptoms can greatly improve a patient's quality of life, they have become a critical intervention strategy. As the most vital component of the limbs, skeletal muscles have become potential points of concern. Despite this, to the best of our knowledge, there are no comprehensive reviews of pathophysiological changes and potential treatments for post-stroke skeletal muscle. The current review seeks to fill a gap in the current understanding of the pathological processes and mechanisms of muscle wasting atrophy, inflammation, neuroregeneration, mitochondrial changes, and nutritional dysregulation in stroke survivors. In addition, the challenges, as well as the optional solutions for individualized rehabilitation programs for stroke patients based on motor function are discussed.

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.

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.

Interleukin-13 Affects the Recovery Processes in a Mouse Model of Hemorrhagic Stroke with Bilateral Tibial Fracture

As one form of stroke, intracerebral hemorrhage (ICH) is a fatal cerebrovascular disease, which has high morbidity and mortality and lacks effective medical treatment. Increased infiltration of inflammatory cytokines coupled with pyroptotic cell death is involved in the pathophysiological process of ICH. However, little is known about whether concomitant fracture patients have the same progression of inflammation and pyroptosis. Hence, we respectively established the mouse ICH model and ICH with bilateral tibial fracture model (MI) to explore the potential cross-talk between the above two injuries. We found that MI obviously reversed the expressions of pyroptosis-associated proteins, which were remarkably up-regulated at the acute phase after ICH. Similar results were observed in neuronal expressions via double immunostaining. Furthermore, brain edema was also significantly alleviated in mice who suffered MI, when compared with ICH alone. To better clarify the potential mechanisms that mediated this cross-talk, recombinant mouse interleukin-13 (IL-13) was used to investigate its effect on pyroptosis in the mouse MI model, in which a lower level of IL-13 was observed. Remarkably, IL-13 administration re-awakened cell death, which was mirrored by the re-upregulation of pyroptosis-associated proteins and PI-positive cell counts. The results of hemorrhage volume and behavioral tests further confirmed its critical role in regulating neurological functions. Besides, the IL-13-treated MI group showed poor outcomes of fracture healing. To sum up, our research indicates that controlling the IL-13 content in the acute phase would be a promising target in influencing the outcomes of brain injury and fracture, and meanwhile, provides new evidence in repairing compound injuries in clinics.

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.

Falls After Stroke: A Follow-up after Ten Years in Lund Stroke Register

OBJECTIVES

To evaluate incidence of self-reported falls and associated factors in a ten-year perspective after stroke.

METHODS

From a population-based cohort of first-ever stroke patients (n = 416) included in the Lund Stroke Register between March 1, 2001, and February 28, 2002, we performed a follow up of all 145 survivors ten years after stroke. We collected data on age, gender, main stroke type, living and housing situation, general health status (question 1 in the Short Form Health Survey (SF-36), dizziness, physical activity, Barthel Index, mobility aids, moving ability inside/outside, and health-related quality of life as defined by the EuroQol 3 dimension scale (EQ-5D-3L). Factors that may relate to falls were compared between those who had experienced falls after stroke or not.

RESULTS

Ten years after stroke, 49 patients (34 %) reported falls and 96 patients (66 %) reported no falls. Compared to patients with no falls, those who reported falls were older (median age 83.3 years vs 75.6 years; p < 0.001), more often lived alone, were more dependent in daily living, had less physical activity, poorer general health status, more often needed mobility aids, were more often unable to move alone outside, and had poorer health-related quality of life in all items in EQ-5D-3L except pain/discomfort.

CONCLUSIONS

Falls had occurred in approximately one third of the participants ten years after the stroke, and were strongly associated with several measures of frailty. Our results indicate that fall prevention should in particular focus on those at high risk of falls.

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

We assessed the long-term incidence of fractures after stroke and TIA and analyzed associated factors. The fracture incidence increases with age and is higher in stroke than in TIA. Dementia is associated with fractures after both. Our results indicate tailored measures are necessary for preventing fractures after stroke or TIA.

INTRODUCTION

In the present study, we aimed to assess the long-term incidence of fractures and analyze associated factors after stroke or transient ischemic attack (TIA).

METHODS

The current cohort study included patients who had received an initial ischemic stroke or TIA diagnosis documented anonymously in the Disease Analyzer database (IQVIA) between 2000 and 2016 by physicians in 1262 general practices in Germany. Univariate Cox and multivariate regression models were carried out.

RESULTS

Three groups (stroke, TIA, no stroke/TIA), each with 12,265 individuals, were selected (mean age 67.3 years, 48.1% female). A fracture was diagnosed in 12.9% of stroke patients and in 11.4% of TIA patients. Among male stroke patients, 11.1% had a fracture (15.4% among female stroke patients). The hazard ratio (HR) for fractures after stroke was 1.26 (CI: 1.15-1.39) and for fractures after TIA, it was 1.14 (CI: 1.03-1.25). In female stroke patients, the HR for fractures was 1.32 (CI: 1.15-1.60), while in males, it was 1.20 (CI: 1.03-1.39). Among TIA patients, females had an elevated HR for fractures (HR: 1.21; CI: 1.06-1.37). In individuals aged ≥ 80 years, an increased risk for fractures was only detected among TIA patients (HR: 1.26; CI: 1.05-1.51). Dementia and non-opioid analgesic therapy were positively associated with fracture after both stroke and TIA.

CONCLUSION

Stroke was positively associated with fracture in patients < 80 years, while TIA was positively associated with fracture in patients ≥ 80 years and females. Dementia and analgesic therapy were also associated with fracture after either stroke or TIA.