Predicting length of stay in patients admitted to stroke rehabilitation with severe and moderate levels of functional impairments

Hyponatremia as a predictor of cognitive deterioration in hospitalized post-stroke patients

PURPOSE

Evidence is scarce regarding the association between hyponatremia and alterations in cognitive function among hospitalized older patients. We aimed to investigate the associations between hyponatremia and the baseline cognitive status, as well as the improvement in cognitive function, in hospitalized post-stroke patients.

METHODS

This retrospective cohort study included consecutive hospitalized post-stroke patients. Serum sodium concentrations were extracted from medical records based on blood tests performed within 24 h of admission, with hyponatremia defined as a serum sodium concentration < 135 mEq/L. The main outcomes included admission and discharge scores for cognitive levels, assessed through the cognitive domain of the Functional Independence Measure (FIM-cognition), as well as the score changes observed during the hospitalization period. Multivariate linear regression analyses were used to determine the association between hyponatremia and outcomes of interest, adjusted for potential confounders.

RESULTS

Data from 955 patients (mean age 73.2 years; 53.6 % men) were included in the analysis. The median baseline blood sodium level was 139 [137, 141], and 84 patients (8.8 %) exhibited hyponatremia. After full adjustment for confounders, the baseline hyponatremia was significantly and negatively associated with FIM-cognition values at admission (β = -0.009, p = 0.016), discharge (β = -0.038, p = 0.043), and the gain during hospital stay (β = -0.040, p = 0.011).

CONCLUSION

Baseline hyponatremia has demonstrated a correlation with decline in cognitive level over the course of rehabilitation in individuals after stroke. Assessing hyponatremia at the outset proves to be a pivotal prognostic indicator.

Predictors of social risk for post-ischemic stroke reintegration

After stroke rehabilitation, patients need to reintegrate back into their daily life, workplace and society. Reintegration involves complex processes depending on age, sex, stroke severity, cognitive, physical, as well as socioeconomic factors that impact long-term outcomes post-stroke. Moreover, post-stroke quality of life can be impacted by social risks of inadequate family, social, economic, housing and other supports needed by the patients. Social risks and barriers to successful reintegration are poorly understood yet critical for informing clinical or social interventions. Therefore, the aim of this work is to predict social risk at rehabilitation discharge using sociodemographic and clinical variables at rehabilitation admission and identify factors that contribute to this risk. A Gradient Boosting modelling methodology based on decision trees was applied to a Catalan 217-patient cohort of mostly young (mean age 52.7), male (66.4%), ischemic stroke survivors. The modelling task was to predict an individual's social risk upon discharge from rehabilitation based on 16 different demographic, diagnostic and social risk variables (family support, social support, economic status, cohabitation and home accessibility at admission). To correct for imbalance in patient sample numbers with high and low-risk levels (prediction target), five different datasets were prepared by varying the data subsampling methodology. For each of the five datasets a prediction model was trained and the analysis involves a comparison across these models. The training and validation results indicated that the models corrected for prediction target imbalance have similarly good performance (AUC 0.831-0.843) and validation (AUC 0.881 - 0.909). Furthermore, predictor variable importance ranked social support and economic status as the most important variables with the greatest contribution to social risk prediction, however, sex and age had a lesser, but still important, contribution. Due to the complex and multifactorial nature of social risk, factors in combination, including social support and economic status, drive social risk for individuals.

Is communication key in stroke rehabilitation and recovery? National linked stroke data study

BACKGROUND

Information on the characteristics or long-term outcomes of people with communication support needs post-stroke is limited. We investigated associations between communication gains in rehabilitation and long-term outcomes (quality-of-life [EuroQOL-ED-3 L], mortality) by post-stroke communication support need status.

METHODS

Retrospective cohort study using person-level linked data from the Australian Stroke Clinical Registry and the Australasian Rehabilitation Outcomes Centre (2014-2017). Communication support needs were assessed using the Functional Independence Measure™ comprehension and expression items recorded on admission indicated by scores one (total assistance) to five (standby prompting). Multivariable multilevel and Cox regression models were used to determine associations with long-term outcomes.

RESULTS

Of 8,394 patients who received in-patient rehabilitation after stroke (42% female, median age 75.6 years), two-thirds had post-stroke communication support needs. Having aphasia (odds ratio [OR] 4.34, 95% CI 3.67-5.14), being aged ≥65 years (OR 1.21, 95% CI 1.08-1.36), greater stroke severity (unable to walk on admission; OR 1.48, 95% CI 1.32-1.68) and previous stroke (OR 1.25, 95% CI 1.11-1.41) were associated with increased likelihoods of having communication support needs. One-point improvement in FIM™ expression was associated with reduced likelihood of self-reporting problems related to mobility (OR 0.85, 95% CI: 0.80-0.90), self-care (OR 0.79, 95% CI: 0.74-0.86) or usual activities (OR 0.84, 95% CI: 0.75-0.94) at 90-180 days. Patients with communication support needs had greater mortality rates within one-year post-stroke (adjusted hazard ratio 1.99, 95% CI: 1.65-2.39).

CONCLUSIONS

Two-thirds of patients with stroke require communication support to participate in healthcare activities. Establishing communication-accessible stroke care environments is a priority.

Prediction of hospitalization time and independence level with functional outcomes for patients with acute stroke: a retrospective study

OBJECTIVES

The objective of this study was to develop predictive models for estimating the length of stay (LOS) with standardized clinical outcome measures (Functional Independence Measure, Trunk Impairment Scale, Postural Assessment Scale for Stroke Patients, Fugl Meyer Assessment Scale, and Functional Ambulation Category) during acute care setting.

METHODS

One hundred sixty-nine patients were included in the retrospective study. Predictors chosen for the LOS included scores of functional outcome measures at admission. We used Spearman's rank correlation coefficients to calculate correlations among clinical outcome measures and LOS, stepwise multiple regression analysis to develop a predictive model, and receiver operating characteristics curve to analyze the predictive value of explanatory factors obtained from the previous model for discharge Functional Independence Measure score.

RESULTS

The predictive equation explained 81% of the variance in LOS. The most important predictors were trunk impairment, motor function of the upper extremity, walking ability, and independence level at admission. The receiver operating characteristic curve was obtained with a cut-off score of 13 points for the Trunk Impairment Scale, 47 points for Fugl Meyer Assessment-Upper Extremity, and 2 points for Functional Ambulation Category, demonstrating the highest percentage of the accurately predicted ability of independence level at discharge.

DISCUSSION

The models presented in this study could help clinicians and researchers to predict the LOS and discharge independence level of clinical outcomes for patients with acute stroke enrolled in an acute care setting.

Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks

Background

Accurate estimation of prolonged length of hospital stay after acute ischemic stroke provides crucial information on medical expenditure and subsequent disposition. This study used artificial neural networks to identify risk factors and build prediction models for a prolonged length of stay based on parameters at the time of hospitalization.

Methods

We retrieved the medical records of patients who received acute ischemic stroke diagnoses and were treated at a stroke center between January 2016 and June 2020, and a retrospective analysis of these data was performed. Prolonged length of stay was defined as a hospital stay longer than the median number of days. We applied artificial neural networks to derive prediction models using parameters associated with the length of stay that was collected at admission, and a sensitivity analysis was performed to assess the effect of each predictor. We applied 5-fold cross-validation and used the validation set to evaluate the classification performance of the artificial neural network models.

Results

Overall, 2,240 patients were enrolled in this study. The median length of hospital stay was 9 days. A total of 1,101 patients (49.2%) had a prolonged hospital stay. A prolonged length of stay is associated with worse neurological outcomes at discharge. Univariate analysis identified 14 baseline parameters associated with prolonged length of stay, and with these parameters as input, the artificial neural network model achieved training and validation areas under the curve of 0.808 and 0.788, respectively. The mean accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of prediction models were 74.5, 74.9, 74.2, 75.2, and 73.9%, respectively. The key factors associated with prolonged length of stay were National Institutes of Health Stroke Scale scores at admission, atrial fibrillation, receiving thrombolytic therapy, history of hypertension, diabetes, and previous stroke.

Conclusion

The artificial neural network model achieved adequate discriminative power for predicting prolonged length of stay after acute ischemic stroke and identified crucial factors associated with a prolonged hospital stay. The proposed model can assist in clinically assessing the risk of prolonged hospitalization, informing decision-making, and developing individualized medical care plans for patients with acute ischemic stroke.

Combined Functional Assessment for Predicting Clinical Outcomes in Stroke Patients After Post-acute Care: A Retrospective Multi-Center Cohort in Central Taiwan

Background and Objective

In 2014, Taiwan’s National Health Insurance administration launched a post-acute care (PAC) program for patients to improve their functions after acute stroke. The present study was aimed to determine PAC assessment parameters, either alone or in combination, for predicting clinical outcomes.

Methods

We retrospectively enrolled stroke adult patients through one PAC network in central Taiwan between January 2014 and December 2020. We collected data on post-stroke patients’ functional ability at baseline and after PAC stay. The comprehensive assessment included the following: Modified Rankin Scale (MRS), Functional Oral Intake Scale (FOIS), Mini-Nutritional Assessment (MNA), Berg Balance Scale (BBS), Fugl-Meyer Assessment (FMA), Mini-Mental State Examination (MMSE), aphasia test, and quality of life. The above items were assessed first at baseline and again at discharge from PAC. Logistic regression was used to determine factors that were associated with PAC length of stay (LOS), 14-day hospital readmission, and 1-year mortality.

Results

A total of 267 adults (mean age 67.2 ± 14.7 years) with completed data were analyzed. MRS, activities of daily living (ADLs), instrumental activities of daily living (IADLs), BBS, and MMSE all had improved between disease onset and PAC discharge. Higher baseline and greater improvement of physical and cognitive functions between initial and final PAC assessments were significantly associated with less readmission, and lower mortality. Furthermore, the improved ADLs, FOIS, MNA, FMA-motor, and MMSE scores were related to LOS during PAC. Using logistic regression, we found that functional improvements ≥5 items [adjusted odds ratio (aOR) = 0.16; 95% confidence interval (CI) = 0.05–0.45] and improved MMSE (aOR = 0.19; 95% CI = 0.05–0.68) were significantly associated with reduced post-PAC mortality or readmission. Whereas, functional improvements ≥7 items, improved FOIS, and MNA significantly prolonged LOS during PAC.

Conclusion

Physical performance parameters of patients with acute stroke improved after PAC. PAC assessment with multiple parameters better predicted clinical outcomes. These parameters could provide information on rehabilitation therapy for acute stroke patients receiving PAC.

Transitional and Long-Term Rehabilitation Care System After Stroke in Korea

Stroke is one of the leading causes of mortality and disability in Korea. Patients who experience stroke require adequate management throughout the acute to subacute and chronic stages. Many patients with long-term functional issues require rehabilitative management even in the chronic stage. A comprehensive rehabilitation and care model for patients who experience stroke is necessary to effectively manage their needs during rehabilitation and allocate medical resources throughout the stages, thus ensuring reduced unmet needs and improved post-stroke quality of life. In Korea, the government and medical specialists are working on re-organizing the rehabilitation care model, including standardized triage and discharge planning after acute stroke treatment, and establishing systematic transitional and long-term rehabilitation care plans. This review briefly introduces the general rehabilitation triage after acute stroke and describes the current transitional and continuous care systems available for these patients in Korea. We also present the issues faced in transitional and long-term care plans of the current system and the efforts invested in resolving them and promoting long-term care in stroke cases.

Telerehabilitation for lower extremity recovery poststroke: a systematic review and meta-analysis protocol

INTRODUCTION

Approximately 30% of individuals with stroke report unmet lower extremity recovery needs after formal hospital-based rehabilitation programmes have ended. Telerehabilitation can mitigate issues surrounding accessibility of rehabilitation services by providing ongoing support to promote recovery, however, no review exists that is specific to telerehabilitation for lower extremity recovery. This paper describes the protocol of a systematic review and meta-analysis that aims to describe and evaluate the effectiveness of lower extremity-focused telerehabilitation interventions on clinical outcomes poststroke.

METHODS AND ANALYSIS

A systematic review of relevant electronic databases (MEDLINE, Embase, CINAHL, PsycINFO, Web of Science, Google Scholar, PEDro, PubMed and Cochrane Library) between inception and February 2022 will be undertaken to identify eligible interventional studies published in English that compared telerehabilitation focusing on lower extremity recovery to another intervention or usual care for individuals living in the community with stroke. Clinical outcomes examined will include those related to physical function and impairment, activities and participation that are typically assessed in clinical practice and research. Two reviewers will independently screen results, identify studies to be included for review, extract data and assess risk of bias. Meta-analyses will be performed if sufficient data exist. Sensitivity analyses will be performed by removing studies with low methodological quality, and subgroup analyses will be performed if data allow by stratifying papers based on salient demographic or stroke factors and comparing results. The reporting of the review will follow the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The quality of evidence regarding various outcomes for telerehabilitation for lower extremity recovery poststroke will be assessed according to the Grading of Recommendation, Assessment, Development and Evaluation approach.

ETHICS AND DISSEMINATION

No ethical approval or informed consent is needed for this systematic review. The findings of this review will be disseminated via peer-reviewed publications and conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42021246886.

Anti-Epileptic Medication Exposure Influences Functional Status in New Zealand Stroke Patients: A Retrospective Population-Level Study

Background

Patients who develop seizures after stroke have disproportionately poorer outcomes and increased mortality.

Objective

Our objective was to investigate whether exposure to anti-epileptic medications influenced long-term functional status after stroke.

Methods

We used linked health administrative data from a cohort of adult stroke patients in New Zealand. Demographics and prescription information were obtained from the National Minimum Dataset and Pharmaceutical Collection, respectively. Activities of daily living (ADL) scores for the same patients were obtained using the International Resident Assessment Instrument. Beta regression was used to investigate the relationship between anti-epileptic drug (AED) exposure and functional status.

Results

The study included 3606 patients with a single ischaemic stroke between 2012 and 2017. In total, 15% were dispensed an AED in the 3 months before or after stroke. The adjusted odds ratio (OR) for AED exposure was 1.29 (95% confidence interval [CI] 1.15–1.45). Overall AED exposure, categorical body mass index (BMI), ethnicity, length of hospital stay, and exposure to paracetamol, opioids, anti-psychotics, and anti-nausea medications were significantly associated with changes in the mean ADL score percentages. Considering the exposure timeframe, the ORs for AED exposure only after stroke and for exposure both before and after stroke were 1.52 (95% CI 1.31–1.78) and 1.09 (95% CI 0.93–1.27), respectively.

Conclusion

Stroke patients with AED exposure had greater odds of a higher ADL score, indicating a poorer long-term functional status than those unexposed to AEDs. The timeframe of exposure impacted on functional status, with patients exposed only after stroke having increased odds of higher ADL scores than those exposed both before and after stroke.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40801-021-00280-5.

Challenges of Outcome Prediction for Acute Stroke Treatment Decisions

Physicians often base their decisions to offer acute stroke therapies to patients around the question of whether the patient will benefit from treatment. This has led to a plethora of attempts at accurate outcome prediction for acute ischemic stroke treatment, which have evolved in complexity over the years. In theory, physicians could eventually use such models to make a prediction about the treatment outcome for a given patient by plugging in a combination of demographic, clinical, laboratory, and imaging variables. In this article, we highlight the importance of considering the limits and nuances of outcome prediction models and their applicability in the clinical setting. From the clinical perspective of decision-making about acute treatment, we argue that it is important to consider 4 main questions about a given prediction model: (1) what outcome is being predicted, (2) what patients contributed to the model, (3) what variables are in the model (considering their quantifiability, knowability at the time of decision-making, and modifiability), and (4) what is the intended purpose of the model? We discuss relevant aspects of these questions, accompanied by clinically relevant examples. By acknowledging the limits of outcome prediction for acute stroke therapies, we can incorporate them into our decision-making more meaningfully, critically examining their contents, outcomes, and intentions before heeding their predictions. By rigorously identifying and optimizing modifiable variables in such models, we can be empowered rather than paralyzed by them.