Development and validation of a prediction model for home discharge in patients with moderate stroke: The Korean stroke cohort for functioning and rehabilitation study

Inpatient Rehabilitation After Acute Severe Stroke: Predictive Value of the National Institutes of Health Stroke Scale Among Other Potential Predictors for Discharge Destination

Background

Research focusing on predictors for discharge destination after rehabilitation of inpatients recovering from severe stroke is scarce. The predictive value of rehabilitation admission NIHSS score among other potential predictors available on admission to rehabilitation has not been studied.

Aim

The aim of this retrospective interventional study was to determine the predictive accuracy of 24 hours and rehabilitation admission NIHSS scores among other potential socio-demographic, clinical and functional predictors for discharge destination routinely collected on admission to rehabilitation.

Material and Methods

On a university hospital specialized inpatient rehabilitation ward 156 consecutive rehabilitants with 24 hours NIHSS score ⩾15 were recruited. On admission to rehabilitation, routinely collected variables potentially associated with discharge destination (community vs institution) were analyzed using logistic regression.

Results

70 (44.9%) of rehabilitants were discharged to community, and 86 (55.1%) were discharged to institutional care. Those discharged home were younger and more often still working, had less often dysphagia/tube feeding or DNR decision in the acute phase, shorter time from stroke onset to rehabilitation admission, less severe impairment (NIHSS score, paresis, neglect) and disability (FIM score, ambulatory ability) on admission, and faster and more significant functional improvement during the in-stay than those institutionalized.

Conclusion

The most influential independent predictors for community discharge on admission to rehabilitation were lower admission NIHSS score, ambulatory ability and younger age, NIHSS being the most powerful. The odds of being discharged to community decreased with 16.1% for every 1 point increase in NIHSS. The 3-factor model explained 65.7% of community discharge and 81.9% of institutional discharge, the overall predictive accuracy being 74.7%. The corresponding figures for admission NIHSS alone were 58.6%, 70.9% and 65.4%.

Cognitive function is associated with home discharge in subacute stroke patients: a retrospective cohort study

Aim

To investigate the cognitive function and its relation to the home discharge of patients following subacute stroke.

Methods

This retrospective cohort study included 1,229 convalescent patients experiencing their first subacute stroke. We determined discharge destination and demographic and clinical information. We recorded the following measurement scores: Mini-Mental State Examination (MMSE) score, Stroke Impairment Assessment Set score, grip strength, and Functional Independence Measure (FIM). We performed a multivariable logistic regression analysis with the forced-entry method to identify factors related to home discharge.

Results

Of the 1,229 participants (mean age: 68.7 ± 13.5 years), 501 (40.8%), 735 (59.8%), and 1,011 (82.3%) were female, had cerebral infarction, and were home discharged, respectively. Multivariable logistic regression analysis revealed that age (odds ratio [OR], 0.93; 95% confidence interval [CI], 0.91 – 0.96; P < 0.001), duration from stroke onset to admission (OR, 0.98; 95% CI, 0.96 – 0.99; P = 0.003), living situation (OR, 4.40; 95% CI, 2.69 – 7.20; P < 0.001), MMSE score at admission (OR, 1.05; 95% CI, 1.00 – 1.09; P = 0.035), FIM motor score at admission (OR, 1.04; 95% CI, 1.01 – 1.06; P = 0.001), and FIM cognitive score at admission (OR, 1.08; 95% CI, 1.04 – 1.13; P < 0.001) were significantly associated with home discharge.

Conclusions

MMSE at admission is significantly associated with home discharge in patients with subacute stroke.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02745-8.

Cognitive Status Predicts Return to Functional Independence After Minor Stroke: A Decision Tree Analysis

About two-thirds of patients with minor strokes are discharged home. However, these patients may have difficulties returning to their usual living activities. To investigate the factors associated with successful home discharge, our aim was to provide a decision tree (based on clinical data) that could identify if a patient discharged home could return to pre-stroke activities and to perform an external validation of this decision tree on an independent cohort. Two cohorts of patients with minor strokes gathered from stroke registries at the Hôpital Pitié-Salpêtrière and University Hospital Bern were included in this study (n = 105 for the construction cohort coming from France; n = 100 for the second cohort coming from Switzerland). The decision tree was built using the classification and regression tree (CART) analysis on the construction cohort. It was then applied to the validation cohort. Accuracy, sensitivity, specificity, false positive, and false-negative rates were reported for both cohorts. In the construction cohort, 60 patients (57%) returned to their usual, pre-stroke level of independence. The CART analysis produced a decision tree with the Montreal Cognitive Assessment (MoCA) as the first decision point, followed by discharge NIHSS score or age, and then by the occupational status. The overall prediction accuracy to the favorable outcome was 80% in the construction cohort and reached 72% accuracy in the validation cohort. This decision tree highlighted the role of cognitive function as a crucial factor for patients to return to their usual activities after a minor stroke. The algorithm may help clinicians to tailor planning of patients' discharge.

Temporal Validation of an Assessment Tool that Predicts a Possibility of Home Discharge for Patients with Acute Stroke

OBJECTIVES

Several prediction models have been developed to assess discharge destinations for patients with acute stroke; however, few studies have performed external validation. We aimed to perform a temporal external validation of a prediction tool to identify stroke patients with a high possibility of discharge to home.

MATERIALS AND METHODS

From December 2017 to July 2019, consecutive patients with acute stroke were included. Clinical nurses and physical therapists applied the prediction model to assess the patients' possibility of home discharge. Whether or not the patient was discharged their own home was the outcome measured. We calculated the sensitivity and specificity of the model and evaluated the discrimination and calibration based on the area under the curve (AUC) and the calibration plot.

RESULTS

Of the 1214 patients assessed, 618 (51%) were discharged home. Using the same cutoff values recommended in the study that first described the tool, we determined the sensitivity and specificity of 91% and 59%, respectively. The AUC to assess the model discrimination was 0.80 (95% confidence interval, 0.77-0.82) and the calibration plot showed acceptable agreement between the predicted and observed outcomes.

CONCLUSIONS

The tool showed a high sensitivity, as expected, in the present study, which examined external validity during the different study periods.

A Data-Driven Investigation on Surface Electromyography Based Clinical Assessment in Chronic Stroke

Background: Surface electromyography (sEMG) based robot-assisted rehabilitation systems have been adopted for chronic stroke survivors to regain upper limb motor function. However, the evaluation of rehabilitation effects during robot-assisted intervention relies on traditional manual assessments. This study aimed to develop a novel sEMG data-driven model for automated assessment. Method: A data-driven model based on a three-layer backpropagation neural network (BPNN) was constructed to map sEMG data to two widely used clinical scales, i.e., the Fugl-Meyer Assessment (FMA) and the Modified Ashworth Scale (MAS). Twenty-nine stroke participants were recruited in a 20-session sEMG-driven robot-assisted upper limb rehabilitation, which consisted of hand reaching and withdrawing tasks. The sEMG signals from four muscles in the paretic upper limbs, i.e., biceps brachii (BIC), triceps brachii (TRI), flexor digitorum (FD), and extensor digitorum (ED), were recorded before and after the intervention. Meanwhile, the corresponding clinical scales of FMA and MAS were measured manually by a blinded assessor. The sEMG features including Mean Absolute Value (MAV), Zero Crossing (ZC), Slope Sign Change (SSC), Root Mean Square (RMS), and Wavelength (WL) were adopted as the inputs to the data-driven model. The mapped clinical scores from the data-driven model were compared with the manual scores by Pearson correlation. Results: The BPNN, with 15 nodes in the hidden layer and sEMG features, i.e., MAV, ZC, SSC, and RMS, as the inputs to the model, was established to achieve the best mapping performance with significant correlations (r > 0.9, P < 0.001), according to the FMA. Significant correlations were also obtained between the mapped and manual FMA subscores, i.e., FMA-wrist/hand and FMA-shoulder/elbow, before and after the intervention (r > 0.9, P < 0.001). Significant correlations (P < 0.001) between the mapped and manual scores of MASs were achieved, with the correlation coefficients r = 0.91 at the fingers, 0.88 at the wrist, and 0.91 at the elbow after the intervention. Conclusion: An sEMG data-driven BPNN model was successfully developed. It could evaluate upper limb motor functions in chronic stroke and have potential application in automated assessment in post-stroke rehabilitation, once validated with large sample sizes. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT02117089.

Comparison of Psychological Health Problems between Families Living with Stroke Survivors and the General Population in the Community

This study aimed to identify and assess the differences in psychological health problems between families living with stroke survivors (FwSS) and the general population without stroke families (GwoSF). A total of 4,514 cases of FwSS were selected for analysis from the 2013 Korea Community Health Survey. In order to determine control groups in GwoSF, propensity scores were generated based on the sociodemographic characteristics of age, gender, residential region, marital status, educational level, monthly household income, and employment status. Each FwSS was matched to 3 controls of GwoSF (13,542 controls) using a greedy matching algorithm with 8 to 1 digit matching. After propensity score-matching, the proportion of usual stress (30.2% vs 24.6%), depressive mood (7.1% vs 6.1%), and suicidal ideation (13.0% vs 11.1%) in FwSS were all significantly higher than those in GwoSF (Ps<0.05). Compared to GwoSF, the adjusted odds ratios (aORs) with 95% confidence intervals (CIs) for psychological health problems in FwSS were calculated using multiple logistic regression analysis. The aORs for usual stress (aOR 1.32, 95% CI 1.21–1.42), depressive mood (aOR 1.14, 95% CI 0.99–1.31; borderline significance), and suicidal ideation (aOR 1.17, 95% CI 1.05–1.30) were significantly higher among FwSS than GwoSF. Moreover, the psychological health problems of FwSS were more evident in females than in males. This study shows that FwSS have poorer psychological health outcomes than GwoSF with similar sociodemographic characteristics. Community-based strategies and family support programs, especially for female family members of stroke survivors, are essential to improve the psychological health of stroke families.