Relationship between Bowel/Bladder Function and Discharge in Older Stroke Patients in Convalescent Rehabilitation Wards: A Retrospective Cohort Study

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.

Association between switching prescribed drugs for lower urinary tract symptoms and independence of urination in post-stroke patients: A retrospective cohort study

OBJECTIVES

Stroke patients frequently exhibit loss of independence of urination, and their lower urinary tract symptoms change with the phase of stroke. However, it is unclear whether switching prescribed drugs for lower urinary tract symptoms during hospitalization from acute care wards to convalescence rehabilitation wards affects patients' independence of urination at discharge. It is also unclear whether the impact of switching varies by stroke type. This retrospective cohort study aimed to examine these issues.

MATERIALS AND METHODS

We analyzed 990 patients registered in the Kaga Regional Cooperation Clinical Pathway for Stroke database during 2015-2019. Prescriptions for lower urinary tract symptoms from pre-onset to convalescence rehabilitation were surveyed. Logistic regression analysis was performed to examine the association between switching drugs and independence of urination based on bladder management and voiding location at discharge. Stroke types were also examined in subgroup analyses.

RESULTS

About 21 % of patients had their lower urinary tract symptoms prescriptions switched during hospitalization. Switching was positively associated with independence of bladder management (odds ratio 1.65, 95 % confidence interval 1.07 to 2.49) and voiding location (odds ratio 2.72, 95 % confidence interval 1.72 to 4.37). Similar associations were observed in different stroke types.

CONCLUSIONS

Approximately 20 % of patients had their lower urinary tract symptoms medications switched upon transfer from acute to convalescence rehabilitation wards. Switching was significantly associated with improved urinary independence at discharge. Consistent results were observed across different stroke types, suggesting that switching medications contributes to urinary independence after stroke, regardless of the etiology or severity of stroke.