Frailty in older adults patients: a prospective observational cohort study on subtype identification.

BACKGROUND

While the FRAIL scale has been used in primary care, cluster analysis on frail patients in a hospital setting has not been performed.

OBJECTIVES

To identify potential subtypes of frail patients, and develop a simple, clinically applicable model for improved patient management.

METHODS

The study included 214 frail patients aged 65 and above who were hospitalized in a hospital in Beijing from September 2018 to April 2019. This study applied the K-means clustering algorithm to analyze 27 variables, determining the optimal cluster number using the Elbow method and Silhouette coefficient. Key variables for predictive modeling were identified through LASSO (least absolute shrinkage and selection operator) regression, SVM-RFE (support vector machine-recursive feature elimination), and random forest techniques. A logistic regression model was then developed to predict patient subtypes, aimed at enhancing clinical identification and management of frailty subtypes.

RESULTS

Clustering analysis distinguished two unique subgroups among the frail patients, revealing significant disparities in clinical characteristics and survival outcomes. One-year survival rates for Class 1 and Class 2 were 62.51% and 47.51%, respectively. The logistic regression model exhibited robust predictive capability, with an AUC (Area under curve) of 0.88. Validation through 1000 bootstrap resamples confirmed the model's reliability, with an average AUC of 0.8707 and a 95% CI (Confidence intervals) of 0.8572 to 0.8792.

CONCLUSIONS

This study identifies two frailty subtypes in a hospital setting using unsupervised machine learning, demonstrating significant differences in survival outcomes. Clinical Trial registration ChiCTR1800017204; date of reqistration: 07/18/2018.