Construction and validation of a predictive model for the risk of emergence delirium in older adult patients

Figure 2 Least absolute shrinkage and selection operator regression analysis of the emergence delirium in older adult patients. A: Least absolute shrinkage and selection operator (LASSO) regression coefficient curve for variables (a illustrates the coefficients of various variables as a function of λ. As the value of λ increases, many variable coefficients are progressively driven toward zero, highlighting the effectiveness of LASSO in variable selection); B: Results of LASSO ten-fold cross-validation (in B, the two vertical lines represent critical points in the model selection process. The left black vertical line, labeled “lambda.min”, corresponds to the value of λ that achieves the lowest mean squared error during tenfold cross-validation, indicating the model’s optimal predictive performance. The right black vertical line, labeled “lambda.1se”, represents the λ value obtained by adding one standard error to “lambda.min”. This choice aims to produce a more streamlined model that typically balances strong predictive capability with a reduced number of variables, thereby enhancing the model's practicality in clinical applications).