Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jul 4:13:875491.
doi: 10.3389/fneur.2022.875491. eCollection 2022.

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

Yu-Ching Chen  1   2 , Jo-Hsuan Chung  1 , Yu-Jo Yeh  1 , Shi-Jer Lou  1   3 , Hsiu-Fen Lin  4   5 , Ching-Huang Lin  6 , Hong-Hsi Hsien  7 , Kuo-Wei Hung  8 , Shu-Chuan Jennifer Yeh  1   9 , Hon-Yi Shi  1   3   9   10   11
Affiliations

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

Yu-Ching Chen et al. Front Neurol. .

Abstract

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.

Keywords: 30-day readmission; artificial neural network; feature importance analysis; post-acute care; stroke.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of the study.
Figure 2
Figure 2
Conceptual framework of the proposed method for predicting readmission within 30 days after stroke.
Figure 3
Figure 3
Performance indices of forecasting models used to predict 30-day readmission in patients with stroke when using (A) training dataset, (B) testing dataset. The box plot shows the median (centers) and interquartile range (borders). In analyses of accuracy and AUROC, the ANN model had significantly higher values compared to other forecasting models (P < 0.001). AUROC, area under the receiver operating characteristics; ANN, artificial neural network.
Figure 4
Figure 4
A permutation importance analysis of artificial neural network model in predicting 30-day readmission in patients with stroke. BI, Barthel Index; IADL, Instrumental Activities of Daily Living; MMSE, Mini-Mental State Examination; BBS, Berg Balance Scale; FOIS, Functional Oral Intake Scale; EQ-5D, EuroQoL Quality of Life Scale.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Ottenbacher KJ, Karmarkar A, Graham JE, Kuo YF, Deutsch A, Reistetter TA, et al. . Thirty-day hospital readmission following discharge from postacute rehabilitation in fee-for-service medicare patients. JAMA. (2014) 311:604–14. 10.1001/jama.2014.8 - DOI - PMC - PubMed
    1. Nouh AM, McCormick L, Modak J, Fortunato G, Staff I. High mortality among 30-day readmission after stroke: predictors and etiologies of readmission. Front Neurol. (2017) 8:632. 10.3389/fneur.2017.00632 - DOI - PMC - PubMed
    1. Fehnel CR, Lee Y, Wendell LC, Thompson BB, Potter NS, Mor V. Post-acute care data for predicting readmission after ischemic stroke: a nationwide cohort analysis using the minimum data set. J Am Heart Assoc. (2015) 4:e002145. 10.1161/JAHA.115.002145 - DOI - PMC - PubMed
    1. Liotta EM, Singh M, Kosteva AR, Beaumont JL, Guth JC, Bauer RM, et al. . Predictors of 30 day readmission after intracerebral hemorrhage: a single-center approach for identifying potentially modifiable associations with readmission. Crit Care Med. (2013) 41:2762–9. 10.1097/CCM.0b013e318298a10f - DOI - PMC - PubMed
    1. Amin A, Deitelzweig S, Bucior I, Lin J, Lingohr-Smith M, Menges B, et al. . Frequency of hospital readmissions for venous thromboembolism and associated hospital costs and length of stay among acute medically ill patients in the US. J Med Econ. (2019) 22:1119–25. 10.1080/13696998.2019.1618862 - DOI - PubMed

LinkOut - more resources