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- Title
Construction of risk prediction model of sarcopenia in senile patients with stroke based on Logistic regression and decision tree.
- Authors
KONG Linghui; YU Jie; ZHANG Huijun; CHEN Ping
- Abstract
Objective: To explore the factors affecting sarcopenia in senile patients with stroke, construct risk prediction models, and evaluate their accuracy of prediction. Methods: A total of 489 senile patients with stroke from neurology department of a tertiary grade A hospital in Liaoning province were selected as the research subjects from September 2022 to April 2023. The risk prediction models of sarcopenia were constructed according to the results of Logistic regression analysis. The Nomogram and decision tree were painted, and the prediction efficiency of models were evaluated according to area under the curve (AUC) of receiver operator characteristic and confusion matrix. Results: The incidence of sarcopenia in senile patients with stroke was 37. 6%. The results of logistic regression analysis show that smoking, age, activity of daily living (ADL), fall risk, nutrition and exercise habits were effect factors for sarcopenia in senile patients with stroke (P<0.05). The results of decision tree model showed that smoking, age, ADL, nutrition and exercise habits were decision-making factors for the sarcopenia in senile patients with stroke. The AUC of Logistic regression model was 0.959, and that of decision tree model training set and test set were 0.892 and 0.826, respectively. Conclusions: The Logistic regression model and decision tree model construct in this study have good predictive performance, which is helpful for clinical medical staff to screen the high-risk group of sarcopenia.
- Subjects
CHINA; PREDICTION models; EXERCISE; RECEIVER operating characteristic curves; SMOKING; LOGISTIC regression analysis; AGE distribution; DESCRIPTIVE statistics; STROKE rehabilitation; THEORY; SENILE dementia; STROKE patients; DECISION trees; SARCOPENIA; ACTIVITIES of daily living; ACCIDENTAL falls; NUTRITION
- Publication
Chinese Nursing Research, 2024, Vol 38, Issue 10, p1703
- ISSN
1009-6493
- Publication type
Article
- DOI
10.12102/j.issn.1009-6493.2024.10.002