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Model derivation

YL Yuanxing Li
HL Haixia Luo
XZ Xiu Zhang
JC Jingjing Chang
YZ Yueyang Zhao
JL Jing Li
DL Dongyan Li
WW Wei Wang
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Baseline characteristics were summarized as frequency tables to study the distributions of categorical variables and as medians and interquartile ranges for continuous variables. Restricted cubic splines were used to explore nonlinearity in the effect of continuous variables. We performed univariable analysis using the chi-square test for each potential predictor to detect any important differences in proportions, with χ2 values and P values for categorical variables. Logistic regression was used to estimate multivariable regression coefficients, and the prediction strength was quantified as odds ratios (ORs) with 95% confidence intervals (CIs).

Given the large number of predictors, we used least absolute shrinkage and selection operator (LASSO) regression [25] to reduce the number of candidate predictors and to select the most significant ECC-positive predictors to build the final prediction model. To control the shrinkage procedure, 10-fold cross-validation was used to determine the penalty parameter λ and to develop the optimal model.

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