Multidiscipline Stroke Post-Acute Care Transfer System: Propensity-Score-Based Comparison of Functional Status

Predicting 30-Day Readmission for Stroke Using Machine Learning Algorithms: A Prospective Cohort Study

Background

Machine learning algorithms for predicting 30-day stroke readmission are rarely discussed. The aims of this study were to identify significant predictors of 30-day readmission after stroke and to compare prediction accuracy and area under the receiver operating characteristic (AUROC) curve in five models: artificial neural network (ANN), K nearest neighbor (KNN), random forest (RF), support vector machine (SVM), naive Bayes classifier (NBC), and Cox regression (COX) models.

Methods

The subjects of this prospective cohort study were 1,476 patients with a history of admission for stroke to one of six hospitals between March, 2014, and September, 2019. A training dataset (n = 1,033) was used for model development, and a testing dataset (n = 443) was used for internal validation. Another 167 patients with stroke recruited from October, to December, 2019, were enrolled in the dataset for external validation. A feature importance analysis was also performed to identify the significance of the selected input variables.

Results

For predicting 30-day readmission after stroke, the ANN model had significantly (P < 0.001) higher performance indices compared to the other models. According to the ANN model results, the best predictor of 30-day readmission was PAC followed by nasogastric tube insertion and stroke type (P < 0.05). Using a machine learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients.

Conclusion

Using a machine-learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients. For stroke patients who are candidates for PAC rehabilitation, these predictors have practical applications in educating patients in the expected course of recovery and health outcomes.

Increased Anxiety and Depression Symptoms in Post-Acute Care Patients with Stroke during the COVID-19 Pandemic

This study aimed to explore the quality and stability of post-acute care for patients with stroke, including their functional outcomes, mental health and medical care in Taiwan during the COVID-19 pandemic. In this retrospective case-control study-based on propensity score matching-we assessed 11 patients admitted during the pandemic period (in 2021) and 11 patients admitted during the non-pandemic period (in 2020). Functional outcomes, including the scores of the modified Rankin Scale, Barthel Index, EuroQoL-5 Dimension, Lawton-Brody instrumental activities of daily living, Berg Balance Scale, 5-metre walking speed and 6-min walking distance, were determined. Data on the length of acute care, length of post-acute care, destination after discharge and 14-days readmission were used to evaluate the quality of medical care. The Wilcoxon signed-rank test was used to compare functional performance before and after rehabilitation. The pandemic group showed no significant improvement in the scores of EuroQoL-5 Dimension, a self-reported health status assessment (p = 0.13), with the anxiety or depression dimension showing a negative effect (r = 0.21). Post-acute care programmes can efficiently improve the functional performance of patients with stroke during the COVID-19 pandemic in Taiwan. Mental health should therefore be simultaneously maintained while rehabilitating physical function.

Multidisciplinary Care after Acute Care for Stroke: A Prospective Comparison between a Multidisciplinary Post-Acute Care Group and a Standard Group Matched by Propensity Score

In this large-scale prospective cohort study, a propensity score matching method was applied in a natural experimental design to investigate how post-acute care (PAC) after stroke affects functional status and to identify predictors of functional status. The main objective of this study was to examine longitudinal changes in various measures of functional status in stroke patients and predictors of scores for these measures before and after PAC. A group of patients who had received PAC for stroke at one of two medical centers (PAC group, n = 273) was compared with a group who had received standard care for stroke at one of four hospitals (three regional hospital and one district hospital; non-PAC group, n = 273) in Taiwan from March, 2014, to October, 2018. The patients completed the functional status measures before rehabilitation, the 12th week and the 1st year after rehabilitation. Generalized estimating equations were used to estimate differences-in-differences models for examining the effects of PAC. The average age was 68.0 (SD = 8.1) years, and males accounted for 57.9%. During the follow-up period, significant risk factors for poor functional outcomes were advanced age, hemorrhagic stroke, and poor function scores before rehabilitation (p < 0.05). Between-group comparisons at subsequent time points revealed significantly higher functional status scores in the PAC group versus the non-PAC group (p < 0.001). Notably, for all functional status measures, between-group differences in total scores significantly increased over time from baseline to 1 year post-rehabilitation (p < 0.001). The contribution of this study is its further elucidation of the clinical implications and health policy implications of rehabilitative care after stroke. Specifically, it improves understanding of the effects of PAC in stroke patients at different follow-up times. Therefore, a policy implication of this study is that standard care for stroke should include intensive rehabilitative PAC to maximize recovery of overall function.

Impact of Multidisciplinary Stroke Post-Acute Care on Cost and Functional Status: A Prospective Study Based on Propensity Score Matching

Few papers discuss how the economic burden of patients with stroke receiving rehabilitation courses is related to post-acute care (PAC) programs. This is the first study to explore the economic burden of stroke patients receiving PAC rehabilitation and to evaluate the impact of multidisciplinary PAC programs on cost and functional status simultaneously. A total of 910 patients with stroke between March 2014 and October 2018 were separated into a PAC group (at two medical centers) and a non-PAC group (at three regional hospitals and one district hospital) by using propensity score matching (1:1). A cost-illness approach was employed to identify the cost categories for analysis in this study according to various perspectives. Total direct medical cost in the per-diem-based PAC cohort was statistically lower than that in the fee-for-service-based non-PAC cohort (p < 0.001) and annual per-patient economic burden of stroke patients receiving PAC rehabilitation is approximately US $354.3 million (in 2019, NT $30.5 = US $1). Additionally, the PAC cohort had statistical improvement in functional status vis-à-vis the non-PAC cohort and total score of each functional status before rehabilitation and was also statistically significant with its total score after one-year rehabilitation training (p < 0.001). Early stroke rehabilitation is important for restoring health, confidence, and safe-care abilities in these patients. Compared to the current stroke rehabilitation system, PAC rehabilitation shortened the waiting time for transfer to the rehabilitation ward and it was indicated as an efficient policy for treatment of stroke in saving medical cost and improving functional status.

Effect of referral systems on costs and outcomes after hip fracture surgery in Taiwan

OBJECTIVE

To explore the economic burdens of hip fracture surgery in patients referred to lower-level medical institutions and to evaluate how referral systems affect costs and outcomes of hip fracture surgery.

DESIGN

A nationwide population-based retrospective cohort study.

SETTING

All hospitals in Taiwan.

PARTICIPANTS

A total of 7500 patients who had received hip fracture surgery (International Classification of Disease, 9th Revision, Clinical Modification (ICD-9-CM) diagnostic codes 820.0 ∼ 820.9 and procedure codes 79.15, 79.35, 81.52, 81.53) performed in 1997 to 2013.

MAIN OUTCOME MEASURES

Total costs including outpatient costs, inpatient costs and total medical costs and medical outcomes including 30-day readmission, 90-day readmission, infection, dislocation, revision and mortality.

RESULTS

The patients were referred to a lower medical institution after hip fracture surgery (downward referral group) and 3034 patients continued treatment at the same medical institution (non-referral group). Demographic characteristics, clinical characteristics and institutional characteristics were significantly associated with postoperative costs and outcomes (P < 0.05). On average, the annual healthcare cost was New Taiwan Dollars (NT$)2262 per patient lower in the downward referral group compared with the non-referral group. The annual economic burdens of the downward referral group approximated NT$241 million (2019 exchange rate, NT$30.5 = US$1).

CONCLUSIONS

Postoperative costs and outcomes of hip fracture surgery are related not only to demographic and clinical characteristics, but also to institutional characteristics. The advantages of downward referral after hip fracture surgery can save huge medical costs and provide a useful reference for healthcare authorities when drafting policies for the referral system.

Special Issue on Clinical Medicine for Healthcare and Sustainability

Recently, due to the advancement of network technology, big data and artificial intelligence, the healthcare industry has undergone many sector-wide changes. Medical care has not only changed from passive and hospital-centric to preventative and personalized, but also from disease-centric to health-centric. Healthcare systems and basic medical research are becoming more intelligent and being implemented in biomedical engineering. This Special Issue on "Clinical Medicine for Healthcare and Sustainability" selected 30 excellent papers from 160 papers presented in IEEE ECBIOS 2019 on the topic of clinical medicine for healthcare and sustainability. Our purpose is to encourage scientists to propose their experiments and theoretical researches to facilitate the scientific prediction and influential assessment of global change and development.

Walking ability and functional status after post-acute care for stroke rehabilitation in different age groups: a prospective study based on propensity score matching

Few studies have compared how rehabilitative post-acute care affects recovery of walking ability and other functions after stroke in different age groups. After propensity score matching (1:1), 316 stroke patients were separated into an aged group (age ≥65 years, n=158) and a non-aged group (age <65 years, n=158). Both groups significantly improved in Barthel index, EuroQol-5 dimension, Berg balance scale, 6-minute walking distance and 5-meter walking speed (P<0.001). The non-aged group had significantly larger improvements in Berg balance scale, instrumental activities of daily living, EuroQol-5 dimension and 6-minute walking distance (P<0.001) compared to the aged group. The two groups did not significantly differ in Barthel index, 5-meter walking speed, length of stay, and cost. The aged group had poorer walking ability and poorer instrumental activities of daily living compared to the non-aged group. After intensive rehabilitative post-acute care, however, the aged group improved in walking ability, functional performance and mental health. Intensive strength training for unaffected lower limbs in the stroke patients achieved good recovery of walking ability and other functions. Overall, intensive rehabilitative post-acute care improved self-care ability and decreased informal care costs. Rehabilitative PAC under per-diem reimbursement is efficient and economical for stroke patients in an aging society.

Artificial Neural Network and Cox Regression Models for Predicting Mortality after Hip Fracture Surgery: A Population-Based Comparison

This study purposed to validate the accuracy of an artificial neural network (ANN) model for predicting the mortality after hip fracture surgery during the study period, and to compare performance indices between the ANN model and a Cox regression model. A total of 10,534 hip fracture surgery patients during 1996–2010 were recruited in the study. Three datasets were used: a training dataset (n = 7374) was used for model development, a testing dataset (n = 1580) was used for internal validation, and a validation dataset (1580) was used for external validation. Global sensitivity analysis also was performed to evaluate the relative importances of input predictors in the ANN model. Mortality after hip fracture surgery was significantly associated with referral system, age, gender, urbanization of residence area, socioeconomic status, Charlson comorbidity index (CCI) score, intracapsular fracture, hospital volume, and surgeon volume (p < 0.05). For predicting mortality after hip fracture surgery, the ANN model had higher prediction accuracy and overall performance indices compared to the Cox model. Global sensitivity analysis of the ANN model showed that the referral to lower-level medical institutions was the most important variable affecting mortality, followed by surgeon volume, hospital volume, and CCI score. Compared with the Cox regression model, the ANN model was more accurate in predicting postoperative mortality after a hip fracture. The forecasting predictors associated with postoperative mortality identified in this study can also bae used to educate candidates for hip fracture surgery with respect to the course of recovery and health outcomes.