Association of Pre-stroke Frailty With Prognosis of Elderly Patients With Acute Cerebral Infarction: A Cohort Study

Propensity Score Matching Analysis on Risk Factors and Their Diagnostic Value of Frailty in Elderly Patients With Cerebral Infarction

OBJECTIVES

Cerebral infarction is an acute ischemic cerebrovascular disease; elderly patients with cerebral infarction often have frailty syndrome. Therefore, the aim of this study was to analyze the risk factors and their diagnostic value of frailty in elderly patients with cerebral infarction.

METHODS

In this retrospective cohort study, the medical records (from October 2017 to September 2020) of patients with cerebral infarction (n=395) were analyzed. According to different frailty statuses, patients were divided into the health group (n=166) and the prefrailty group (n=229). The diagnostic value of various parameters for the occurrence of pre-frailty was assessed by the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). The risk factors were analyzed by single-factor and multi-factor analyses.

RESULTS

Interleukin-6 (IL-6, P<0.001), C-reactive protein (CRP), prealbumin (PA, P<0.001), albumin (ALB, P<0.001), and brain natriuretic peptide (BNP, P<0.001) had higher diagnostic value for the occurrence of prefrailty in elderly patients with cerebral infarction. Single-factor and multi-factor analysis results revealed that the length of stay (LOS), comorbidity with nervous system diseases, high Charlson comorbidity index (CCI), high National Institute of Health stroke scale score, IL-6, BNP, ALB, PA, and creatine kinase might risk the factors for prefrailty occurrence (P<0.05). After the elimination of interference factors, LOS, high CCI, IL-6, and ALB were confirmed to be key risk factors for the occurrence of prefrailty (P<0.05).

CONCLUSION

LOS, high CCI, IL-6, and ALB are the underlying risk factors for the occurrence of prefrailty in elderly patients with cerebral infarction, which provides a theoretical basis for clinical disease diagnosis.

Precision in Stroke Care: Novel Model for Predicting Functional Independence in Urgent Carotid Intervention Patients

BACKGROUND

Stroke requires timely intervention, with carotid endarterectomy (CEA) and carotid artery stenting (CAS) increasingly used in select acute carotid-related stroke patients. We aimed to build a model to predict neurologic functional independence (modified Rankin scale [mRS] ≤ 2) in this high-risk group.

STUDY DESIGN

We analyzed data from 302 stroke patients undergoing urgent CEA or CAS between 2015 and 2023 at a tertiary comprehensive stroke center. Predictors included (1) stroke severity (NIH Stroke Scale), (2) time to intervention (≤48 hours), (3) thrombolysis use, and (4) frailty risk score. Two-way interactions were included to enhance generalizability without overfitting. Multiple models were constructed and selected based on the area under the receiver operating characteristic curve. The primary endpoint was discharge neurological functional independence (mRS ≤ 2).

RESULTS

Presenting clinical factors and neurological outcomes data from 302 patients undergoing urgent CEA and CAS during the index hospitalization from 2015 to 2023 at a tertiary comprehensive stroke center formed the model's foundation. Most patients (72.8%, 220 of 302) were discharged functionally independent (mRS ≤ 2). The combined 30-day rate of stroke, death, and MI was 8.3% (25 of 302), 6.5% (14 of 214) for CEA alone, and 12.5% (11 of 88) for CAS. The model, incorporating thrombolysis, time to intervention, stroke severity (NIH Stroke Scale), and frailty risk, correctly predicted 93% of functional independence outcomes (area under the receiver operating characteristic curve 0.808).

CONCLUSIONS

We present a novel model using 4 clinical factors-stroke severity, time to intervention, thrombolysis use, and frailty risk-to predict functional neurologic independence with 93% accuracy in patients undergoing urgent carotid interventions for acute stroke. This high predictive capability can enhance clinical decision-making and improve patient outcomes by identifying those most likely to benefit from timely carotid revascularization.

Development and Validation of a Nomogram for Predicting Frailty Risk Among Older Patients With Ischaemic Stroke

AIM

To investigate the risk factors associated with frailty in older patients with ischaemic stroke, develop a nomogram and apply it clinically.

DESIGN

A cross-sectional study.

METHODS

Altogether, 567 patients who experienced ischaemic strokes between March and December 2023 were temporally divided into training (n = 452) and validation (n = 115) sets and dichotomised into frail and non-frail groups using the Tilburg Frailty Indicator scale. In the training set, feature selection was performed using least absolute shrinkage and selection operator regression and random forest recursive feature elimination, followed by nomogram construction using binary logistic regression. Internal validation was performed through bootstrap re-sampling and the validation set was used to assess model generalisability. The receiver operating characteristic curve, Hosmer-Lemeshow test, Brier score, calibration curve, decision curve analysis and clinical impact curve were used to evaluate nomogram performance.

RESULTS

The prevalence of frailty was 58.6%. Marital status, smoking, history of falls (in the preceding year), physical exercise, polypharmacy, albumin levels, activities of daily living, dysphagia and cognitive impairment were predictors in the nomogram. Receiver operating characteristic curve analysis indicated outstanding discrimination of the nomogram. The Hosmer-Lemeshow test, calibration curve and Brier score results confirmed good model consistency and predictive accuracy. The clinical decision and impact curve demonstrated notable clinical utility. This free, dynamic nomogram, created for interactive use and promotion, is available at: https://dongdongshen.shinyapps.io/DynNomapp/.

CONCLUSION

This nomogram may serve as an effective tool for assessing frailty risk in older patients with ischaemic stroke.

RELEVANCE TO CLINICAL PRACTICE

The nomogram in this study may assist healthcare professionals in identifying high-risk patients with frailty and understanding related factors, thereby providing more personalised risk management.

REPORTING METHOD

TRIPOD checklist.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Association between patient-reported frailty and nonhome discharge among older patients with acute stroke: A prospective study

OBJECTIVE

To investigate the association between prestroke frailty and nonhome discharge, prolonged length of stay as well as functional outcomes.

DESIGN

Prospective observational study.

SETTING

Single urban teaching hospital in Guangzhou, China.

PARTICIPANTS

Consecutive sample of 271 older patients admitted with acute stroke.

INTERVENTION

N/A.

MAIN MEASURES

A five-item FRAIL scale (0∼5 points) and the stroke severity at onset were measured. The primary outcome of interest was nonhome discharge, with secondary outcomes including prolonged length of stay and worse short-term prognosis. Multivariable logistic regression adjusting for confounding factors was used to determine the association between patient-reported frailty and nonhome discharge, prolonged length of stay, worse short-term prognosis.

RESULTS

The population had a median age of 68 [interquartile range (IQR), 64∼74)]years, with 50 individuals (18.5%) identified as frail. After adjusting for age, sex, Barthel index, National Institutes of Health Stroke Scale, and Mini-Mental Status Exam score at admission, patients with self-reported frailty were significantly likely to experience nonhome discharge (Odds Ratio [OR] = 4.788; 95% confidence interval [CI] = 1.272∼18.017; p = .021), prolonged length of stay (OR = 4.76; 95% CI = 1.80∼12.56; p = .002), mRS scores at 30 days (OR = 6.72;95% CI = 1.79∼25.20; p = .005) and three months postdischarge and three-month (OR = 8.94; 95% CI = 2.10∼38.08; p = .003).

CONCLUSIONS

In older adults with stroke, frailty is associated with nonhome discharge, prolonged length of stay, and worse short-term prognosis, regardless of the stroke severity, cognition, and Barthel index score at admission. FRAIL scale can be used as a practical screening tool in acute care setting by multidisciplinary team in supporting discharge process.

Development and validation of a dynamic nomogram for high care dependency during the hospital-family transition periods in older stroke patients

BACKGROUND

This research aimed to develop and validate a dynamic nomogram for predicting the risk of high care dependency during the hospital-family transition periods in older stroke patients.

METHODS

309 older stroke patients in the hospital-family transition periods who were treated in the Department of Neurology outpatient clinics of three general hospitals in Jinzhou, Liaoning Province from June to December 2023 were selected as the training set. The patients were investigated with the General Patient Information Questionnaire, the Care Dependency Scale (CDS), the Tilburg Frailty Inventory (TFI), the Hamilton Anxiety Rating Scale (HAMA), the Hamilton Depression Rating Scale-17 (HAMD-17), and the Mini Nutrition Assessment Short Form (MNA-SF). Lasso-logistic regression analysis was used to screen the risk factors for high care dependency in older stroke patients during the hospital-family transition period, and a dynamic nomogram model was constructed. The model was uploaded in the form of a web page based on Shiny apps. The Bootstrap method was employed to repeat the process 1000 times for internal validation. The model's predictive efficacy was assessed using the calibration plot, decision curve analysis curve (DCA), and area under the curve (AUC) of the receiver operator characteristic (ROC) curve. A total of 133 older stroke patients during the hospital-family transition periods who visited the outpatient department of Neurology of three general hospitals in Jinzhou from January to March 2024 were selected as the validation set for external validation of the model.

RESULTS

Based on the history of stroke, chronic disease, falls in the past 6 months, depression, malnutrition, and frailty, build a dynamic nomogram. The AUC under the ROC curves of the training set was 0.830 (95% CI: 0.784-0.875), and that of the validation set was 0.833 (95% CI: 0.766-0.900). The calibration curve was close to the ideal curve, and DCA results confirmed that the nomogram performed well in terms of clinical applicability.

CONCLUSION

The online dynamic nomogram constructed in this study has good specificity, sensitivity, and clinical practicability, which can be applied to senior stroke patients as a prediction and assessment tool for high care dependency. It is of great significance to guide the development of early intervention strategies, optimize resource allocation, and reduce the care burden on families and society.

Association between pre-stroke frailty status and stroke risk and impact on outcomes: a systematic review and meta-analysis of 1,660,328 participants

The prevalence of frailty is increasing, and it is associated with increased risk of diseases and adverse outcomes. Although substantial research has focused on post-stroke frailty, understanding of pre-stroke frailty remains limited. Our aim was to synthesize literature on pre-stroke frailty and stroke risk to explore their relationship and impact on prognosis. A systematic search of multiple databases was conducted to identify cohort studies published until October 28, 2023. Meta-analysis was conducted using a random effects model. Heterogeneity was assessed with the I² statistic, and publication bias was evaluated using Begg's test. Finally, we included 11 studies (n = 1,660,328 participants). The pooled hazard ratios (HRs) for stroke risk associated with pre-stroke frailty compared to non-frail individuals was 1.72 (95% confidence interval, CI: 1.46-2.02, p = 0.002, I2 = 69.2%, Begg's test: p = 0.536). The pooled HRs for mortality and the pooled relative risk (RRs) modified Rankin Scale (mRs) associated with pre-stroke frailty were 1.68 (95% CI: 1.10-2.56, p = 0.136, I2 = 49.9%, Begg's test: p = 0.296) and 3.11 (95% CI: 1.77-5.46, p = 0.192, I2 = 39.4%, Begg's test: p = 1.000), respectively. In conclusion, pre-stroke frailty is strongly associated with stroke risk and impacts its prognosis, irrespective of the measurement method. Future research should focus on prospective studies to assess the effects of early intervention for frailty. This has significant implications for primary healthcare services and frailty management.

The impact of palliative care on the frailty-stroke continuum: from theoretical concepts to practical aspects

With a constant increase in prevalence and incidence worldwide, stroke remains a public health issue in the 21st century. Additionally, population aging inevitably leads to increased vulnerability in the general population, a clinical state known as frailty. While there are adequate guidelines on the treatment of stroke in the acute setting, there are a lot of gaps regarding the chronic management of stroke patients, particularly the frail ones. From the therapeutic point of view, palliative care could be the key to offering complex and individualized treatment to these frail chronic stroke patients. In the context of the heterogeneous data and incomplete therapeutic guidelines, this article provides a new and original perspective on the topic, aiming to increase awareness and understanding and improve palliative care management in stroke patients. Based on current knowledge, the authors describe a new concept called the frailty-stroke continuum and offer a detailed explanation of the intricate stroke-frailty connection in the first part. After understanding the role of palliative care in managing this kind of patients, the authors discuss the most relevant practical aspects aiming to offer an individualized framework for daily clinical practice. The novel approach consists of developing a four-step scale for characterizing frail stroke patients, with the final aim of providing personalized treatment and correctly evaluating prognosis. By pointing out the limitations of current guidelines and the challenges of new research directions, this article opens the pathway for the better evaluation of frail stroke patients, offering a better perception of patients' prognosis.

Machine learning decision support model for discharge planning in stroke patients

BACKGROUND/AIM

Efficient discharge for stroke patients is crucial but challenging. The study aimed to develop early predictive models to explore which patient characteristics and variables significantly influence the discharge planning of patients, based on the data available within 24 h of admission.

DESIGN

Prospective observational study.

METHODS

A prospective cohort was conducted at a university hospital with 523 patients hospitalised for stroke. We built and trained six different machine learning (ML) models, followed by testing and tuning those models to find the best-suited predictor for discharge disposition, dichotomized into home and non-home. To evaluate the accuracy, reliability and interpretability of the best-performing models, we identified and analysed the features that had the greatest impact on the predictions.

RESULTS

In total, 523 patients met the inclusion criteria, with a mean age of 61 years. Of the patients with stroke, 30.01% had non-home discharge. Our model predicting non-home discharge achieved an area under the receiver operating characteristic curve of 0.95 and a precision of 0.776. After threshold was moved, the model had a recall of 0.809. Top 10 variables by importance were National Institutes of Health Stroke Scale (NIHSS) score, family income, Barthel index (BI) score, FRAIL score, fall risk, pressure injury risk, feeding method, depression, age and dysphagia.

CONCLUSION

The ML model identified higher NIHSS, BI, and FRAIL, family income, higher fall risk, pressure injury risk, older age, tube feeding, depression and dysphagia as the top 10 strongest risk predictors in identifying patients who required non-home discharge to higher levels of care. Modern ML techniques can support timely and appropriate clinical decision-making.

RELEVANCE TO CLINICAL PRACTICE

This study illustrates the characteristics and risk factors of non-home discharge in patients with stroke, potentially contributing to the improvement of the discharge process.

REPORTING METHOD

STROBE guidelines.

Role of frailty in predicting outcomes after stroke: a systematic review and meta-analysis

Background

Stroke is considered the second most common cause of death and the third leading cause of disability worldwide. Frailty, characterized by increased vulnerability to stressors, is emerging as a key factor affecting outcomes in older adults and stroke patients. This study aimed to estimate the prevalence of frailty in acute stroke patients and assess its association with mortality and poor functional outcome.

Methods

Medline, Google Scholar, and Science Direct databases were systematically searched for English-language studies that included adult stroke patients (>16 years), have defined frailty, and reported mortality and functional outcomes. Meta-analysis was done using STATA 14.2, and the results were expressed as pooled odds ratios (OR) with 95% confidence intervals (CI). Heterogeneity was assessed using the I2 statistic and the Chi-square test. Study quality was evaluated using the Newcastle Ottawa Scale (NOS).

Results

Twenty-five studies were included in the analysis. Frailty prevalence in stroke patients was 23% (95% CI 22% - 23%). Unadjusted analysis showed an OR of 2.66 (95% CI: 1.93 - 3.67) for mortality and 2.04 (95% CI: 1.49 - 2.80) for poor functional outcome. Adjusted estimates indicated an OR of 1.22 (95% CI: 1.1 - 1.35) for mortality and 1.21 (95% CI: 1.04 - 1.41) for poor functional outcome, with substantial heterogeneity for both adjusted and unadjusted analyses. No publication bias was detected for the prevalence of frailty. However, there was a publication bias for the association between frailty and mortality.

Conclusions

Frailty was significantly associated with increased mortality and poorer functional outcomes in stroke patients. Our study highlights the need to focus on frailty in stroke patients to improve outcomes and quality of life. Further research should aim to standardize assessment of frailty and reduce heterogeneity in study outcomes.

Systematic review registration

https://www.crd.york.ac.uk/prospero/#searchadvanced, CRD42023470325.

Genetic liability to frailty in relation to functional outcome after ischemic stroke

BACKGROUND

Frailty appears to be associated with unfavorable prognosis after stroke in observational studies, but the causality remains largely unknown.

AIMS

The aim of this study is to investigate the potential causal effect of frailty on functional outcome at 3 months after ischemic stroke using the Mendelian randomization (MR) framework.

METHODS

Genetic instruments for frailty index were identified in a genome-wide association study meta-analysis including 175,226 individuals of European descent. Corresponding genetic association estimates for functional outcome after ischemic stroke at 90 days were taken from the Genetic of Ischemic Stroke Functional Outcome (GISCOME) network of 6021 patients. We performed inverse-variance weighted MR as the main analyses, followed by several alternate methods and sensitivity analyses.

RESULTS

In univariable MR, we found evidence that genetically predicted higher frailty index (odds ratio (OR) = 5.12; 95% confidence interval (CI) = 1.31-20.09; p = 0.019) was associated with worse functional outcome (modified Rankin Scale score ⩾3) after ischemic stroke. In further multivariable MR adjusting for potential confounding traits including body mass index, C-reactive protein, inflammatory bowel disease, and smoking initiation, the overall patterns between genetic liability to frailty and poor functional outcome status remained. Sensitivity analyses with complementary methods and with model unadjusted for baseline stroke severity (OR = 4.19; 95% CI = 1.26-13.90; p = 0.019) yielded broadly concordant results.

CONCLUSIONS

The present MR study suggested a possible causal effect of frailty on poor functional outcome after ischemic stroke. Frailty might represent a potential target for intervention to improve recovery after ischemic stroke.

Functioning and recovery during stroke rehabilitation: a comparison between pre-stroke frail and non-frail patients

PURPOSE

Pre-stroke frailty in older adults is associated with adverse outcomes after stroke in community-based and hospital-based populations. The aim of our study was to investigate the prevalence of pre-stroke frailty among older stroke survivors receiving medical specialistic rehabilitation and its association with outcomes and recovery.

METHODS

Pre-stroke frailty was measured by the Groningen Frailty Indicator (GFI, score ≥ 4 indicates frailty) in patients ≥ 65 years receiving stroke medical specialistic rehabilitation. Baseline, follow-up and change (i.e. recovery) scores of the Barthel index (BI), Stroke Impact Scale (SIS) 'mobility', 'communication', and 'memory and thinking', Hospital Anxiety and Depression Scale (HADS) and the EuroQoL-5 dimensions (EQ-5D) were compared between frail and non-frail patients with a multivariable regression model adjusting for confounders.

RESULTS

Of 322 included patients (34.2% females, median age 70 years), 43 (13.4%) patients reported pre-stroke frailty. There were no differences in BI or in destination of discharge between pre-stroke frail and non-frail stroke survivors receiving inpatient rehabilitation. However, pre-stroke frailty was associated with worse follow-up scores for all other measures. Recovery in pre-stroke frail patients was less favorable compared to non-frail patients for SIS mobility, HADS subscales and EQ-5D index and visual analogue scale.

CONCLUSION

Pre-stroke frailty was present in a minority of older stroke survivors receiving medical specialistic rehabilitation. BI and destination of discharge did not differ. Nevertheless, pre-stroke frailty was associated with worse functioning at follow-up for most measures of health status and with smaller improvements in mobility, mood and quality of life.

Effect of admission in the stroke care unit versus intensive care unit on in-hospital mortality in patients with acute ischemic stroke

BACKGROUND/OBJECTIVE

Few reports have directly compared the outcomes of patients with acute ischemic stroke (AIS) who are managed in a stroke care unit (SCU) with those who are managed in an intensive care units (ICU). This large database study in Japan aimed to compare in-hospital mortality between patients with AIS admitted into SCU and those admitted into ICU.

METHODS

Patients with AIS who were admitted between April 1, 2014, and March 31, 2019, were selected from the administrative database and divided into the SCU and ICU groups. We calculated the propensity score to match groups for which the admission unit assignment was independent of confounding factors, including the modified Rankin scale (mRS) score. The primary outcome was in-hospital mortality, and secondary outcomes were the mRS score at discharge, length of stay (LOS), and total hospitalization cost.

RESULTS

Overall, 8,683 patients were included, and 960 pairs were matched. After matching, the in-hospital mortality rates of the SCU and ICU groups were not significantly different (5.9% vs. 7.9%, P = 0.106). LOS was significantly shorter (SCU = 20.9 vs. ICU = 26.2 days, P < 0.001) and expenses were significantly lower in the SCU group than in the ICU group (SCU = 1,686,588 vs. ICU = 1,998,260 yen, P < 0.001). mRS scores (score of 1-3 or 4-6) at discharge were not significantly different after matching. Stratified analysis showed that the in-hospital mortality rate was lower in the ICU group than in the SCU group among patients who underwent thrombectomy.

CONCLUSIONS

In-hospital mortality was not significantly different between the ICU and SCU groups, with significantly lower costs and shorter LOS in the SCU group than in the ICU group.

Evaluating the FRAIL Questionnaire as a Trigger for Palliative Care Consultation After Acute Stroke

The American Heart Association and the American Stroke Association jointly released guidelines stating that all patients with a new diagnosis of stroke should receive palliative care consultation starting in the acute phase of care. The purpose of this project was to increase palliative care consultation rates for patients after an acute stroke by using a frailty score to trigger a palliative care consult. Provider education on palliative care and a 5-question fatigue, resistance, ambulation, illnesses, and loss of weight (FRAIL) questionnaire was delivered by a presentation, handouts, and a follow-up email using previously developed content. Patients included adults admitted to the neuroscience critical care unit of a Midwestern comprehensive stroke center with an admission diagnosis of acute stroke (n = 120). The charge nurse completed the FRAIL questionnaire as a screening tool to trigger a palliative care consult. A survey was also distributed to providers (n = 54) to understand their knowledge, thoughts, and feelings toward palliative care. There was an increase in patients who received palliative care consultation from 14.9% to 21.7% after implementation of the FRAIL questionnaire. Also, providers felt better able to provide symptom management to patients after acute stroke. Further research is necessary to determine if the FRAIL survey is an adequate trigger for palliative care consultation.

Neutrophil-to-leukocyte ratio and admission glycemia as predictors of short-term death in very old elderlies with lobar intracerebral hemorrhage

BACKGROUND

The incidence of spontaneous intracerebral hemorrhage (SICH) is highest in very old elderlies (≥75 years). The increasing use of antithrombotic drugs is shifting the epidemiology of SICH towards predominance of lobar subtype, suggesting an incremented propensity of bleeding associated with underlying cerebral amyloid angiopathy. With population aging and antithrombotic use, a parallel raise of proportion of lobar SICH is occurring. Improvement of prognostication in this specific age group and SICH type is needed. Routine blood biomarkers can contribute to prediction of short-term mortality after SICH.

OBJECTIVE

Our aim was to investigate the contribution of routine blood biomarkers for short-term mortality (30-days) in elderly patients with lobar SICH.

METHODS

Retrospective analysis of consecutive 130 patients with ≥ 75 years and lobar SICH. The outcome was 30-day mortality. Logistic regression analysis was used to investigate whether admission routine biomarkers can be used as predictors.

RESULTS

The case fatality was 40.8%. Admission glycaemia level, neutrophil to lymphocyte ratio and mean platelet volume were significantly different between groups (p = 0.001, p = 0.024, p = 0.038, respectively). There was no significant difference in all other routine biomarkers. On multivariate analysis, admission higher mean BG level (odds ratio [OR]: 1.010, 95% confidence interval [CI]: 1.001-1.019, p = 0.026) and neutrophil to lymphocyte ratio (OR: 1.070, 95% CI: 1.008-1.136, p = 0.027) emerged as predictors.

CONCLUSION

In very old patients with lobar SICH, higher BG level and neutrophil to lymphocyte ratio are associated with increased risk of short-term death.

Morphology of paraspinal muscles in frail and non-frail older adults evaluated through FRAIL scale

BACKGROUND

Frailty is a condition characterized by the progressive deterioration of physiological functioning, which is closely related to adverse events. Multiple previous investigations applied frailty scales for spine research, and the purpose of this study is to investigate the differences in the morphology of the paraspinal muscles in frail and non-frail older adults evaluated through FRAIL scale.

METHODS

The sample of this retrospectively cross-sectional study consisted of individuals who were ≥ 60 years of age and with lumbar degenerative disease. We divided patients into two groups (0-2 = non-Frail, 3-5 = Frail) according to the FRAIL scale. The cross-sectional area (CSA) and percentage of the fatty infiltration (FI%) of the paraspinal muscles were compared between the two groups.

RESULTS

The fCSA (functional cross-sectional area) of the non-Frail group (32.78 [28.52, 38.28]) (cm²) was significantly greater than that of the Frail group (28.50 [24.11, 34.77]) (p < 0.001). The ES FI% (erector spinae fatty infiltration rate) (24.83 ± 6.61 vs. 29.60 ± 7.92, p < 0.001) and MF FI% (multifidus fatty infiltration rate) (31.68 ± 5.63 vs. 41.12 ± 7.04, p < 0.001) of the non-Frail group were significantly lower than that of Frail group.

CONCLUSIONS

The paraspinal muscles of elderly Frail patients screened by the FRAIL scale are worse than those of the non-Frail patients, and the ability of the FRAIL scale to distinguish paraspinal muscle morphology has important clinical significance.

Association between pre-stroke sarcopenia risk and stroke-associated infection in older people with acute ischemic stroke

Background

Stroke-associated infection (SAI) is a common complication after a stroke. The incidence of infection was higher in people with sarcopenia than in the general population. However, the relationship between pre-stroke sarcopenia risk and SAI in older patients has not been confirmed. This study aimed to investigate the association between pre-stroke sarcopenia risk and SAI in older patients with acute ischemic stroke (AIS).

Methods

This retrospective study was conducted by the Peking University People's Hospital. We evaluated the pre-stroke sarcopenia risk by applying the SARC-F questionnaire. Multivariate logistic regression was applied to explore the association between pre-stroke sarcopenia risk and SAI.

Results

A total of 1,002 elder patients with AIS (592 men; 72.9 ± 8.6 years) were enrolled in our study. Pre-stroke sarcopenia risk was found in 29.1% of the cohort. The proportion of patients with pre-stroke sarcopenia risk was larger in the SAI group than in the non-SAI group (43.2 vs. 25.3%, p   < 0.001). In multivariate logistic analysis, pre-stroke sarcopenia risk was shown to be independently associated with SAI (OR = 1.454, 95% CI: 1.008-2.097, p = 0.045) after adjusting for potential factors. This association remained consistent across the subgroups based on age, sex, body mass index, smoking status, drinking status, diabetes, hypertension, and dyslipidemia.

Conclusion

Pre-stroke sarcopenia risk was independently associated with SAI in older patients with AIS. Our findings highlight the significance of pre-stroke sarcopenia identification in the prevention and management of SAI in this population.