PloS one
2026-01-01
Opracowanie i walidacja modelu predykcyjnego ryzyka długoterminowej kruchości poznawczej u pacjentów po udarze na podstawie danych CHARLS
Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based on CHARLS data.
Recenzja AI
Cel badania
Celem badania było opracowanie i walidacja modeli uczenia maszynowego do przewidywania ryzyka kruchości poznawczej u starszych osób po udarze mieszkających w społeczności.
Metoda
Badanie obejmowało 2 325 pacjentów po udarze z badania CHARLS, analizując 22 zmienne behawioralne przy użyciu regresji LASSO i ośmiu modeli uczenia maszynowego, w tym XGBoost i Random Forest, w celu identyfikacji optymalnego modelu predykcyjnego.
Wyniki
Modele XGBoost (AUC=0,810) i Random Forest (AUC=0,795) wykazały najwyższą skuteczność predykcyjną, a kluczowe czynniki predykcyjne to wiek, poziom wykształcenia, status żywieniowy, aktywność fizyczna i IADL.
Znaczenie dla praktyki
Model XGBoost może być użytecznym narzędziem do wczesnego wykrywania ryzyka kruchości poznawczej u starszych pacjentów po udarze w podstawowej opiece zdrowotnej, co pozwala na opracowanie ukierunkowanych strategii interwencyjnych w celu spowolnienia progresji choroby.
Abstrakt oryginalny
BACKGROUND: This study aimed to develop and validate machine learning (ML) models for predicting the risk of cognitive frailty in community-dwelling elderly adults with stroke. METHODS: This study involved 2,325 stroke survivors from the China Health and Retirement Longitudinal Study (CHARLS), conducted between 2018 and 2020. We examined 22 behavioral variables, encompassing indicators from the sociodemographic, physical, psychological, cognitive, and social domains. LASSO regression was employed to identify predictive factors, and eight machine learning models-Logistic Regression, Decision Tree, XGBoost, Support Vector Machine, k-Nearest Neighbors, Naïve Bayes, Random Forest, and LightGBM-were utilized to ascertain the optimal model for predicting cognitive frailty among stroke survivors. SHapley Additive exPlanations (SHAP) values were applied to interpret the contributions of the variables. RESULTS: A total of 2,325 stroke patients were included in the study, among whom 688 (29.59%) exhibited symptoms of cognitive frailty. Of the eight models evaluated, XGBoost (AUC = 0.810) and Random Forest (AUC = 0.795) demonstrated the highest predictive performance for stroke-related cognitive frailty. Key predictors identified were education, nutritional status, physical exercise, Instrumental Activities of Daily Living (IADL), and age, with corresponding SHAP values of 0.28, 0.18, 0.16, 0.21, and 0.32, respectively. The SHAP values indicated that age and education level are the most significant factors in predicting the risk of cognitive frailty in this population. CONCLUSION: This study developed eight risk prediction models for post-stroke cognitive frailty utilizing machine learning, with the XGBoost algorithm demonstrating superior performance. Leveraging readily available clinical and demographic indicators, the optimized XGBoost model serves as a practical tool for the early screening of cognitive frailty risk among community-dwelling elderly stroke survivors, particularly within primary care settings. This model can aid clinicians in devising targeted intervention strategies to mitigate disease progression and establish a foundation for future prospective studies examining the mechanisms underlying cognitive frailty in stroke populations. Further external validation is necessary to confirm its generalizability across various clinical contexts.