R (Version 4.0.4) was used for statistical analysis. The Shapiro–Wilk test was used to check the normal distribution of continuous data. Normally distributed continuous data were expressed as means ± standard deviation (SD) and tested using the Student’s t-test. Non-normally distributed continuous data were expressed as medians (first quartile, third quartile) and tested using the Mann–Whitney U-test. Categorical data were expressed as number (n) and percentage (%) and tested using the Chi-square test or Fisher’s exact test.
Candidate variables, selected based on statistical significance in univariate logistic analysis (P<0.05), clinical experience and published data were included in the multivariate logistic analysis using stepwise methods. The final variables in the prediction model were selected by the clinical significance, principle of statistics19 (link) and the results of the multivariate logistic analysis (P<0.05). The final regression model was visualized by a nomogram to predict the CRE BSI. Furthermore, the receiver-operating characteristic (ROC) curve (area under the curve [AUC]) and the C-statistic were used to assess the discrimination ability of this nomogram. The calibration curves and Brier score were used to assess the calibration ability of this nomogram. In addition, enhanced bootstrap internal validation was performed to verify the diagnostic efficiency of the model. Furthermore, decision curve analysis (DCA) was performed to determine the model’s clinical usefulness. P<0.05 was considered statistically significant. An online prediction tool (Shiny App) was prepared using the DynNom package in R. The construction process was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines.20 (link)
Candidate variables, selected based on statistical significance in univariate logistic analysis (P<0.05), clinical experience and published data were included in the multivariate logistic analysis using stepwise methods. The final variables in the prediction model were selected by the clinical significance, principle of statistics19 (link) and the results of the multivariate logistic analysis (P<0.05). The final regression model was visualized by a nomogram to predict the CRE BSI. Furthermore, the receiver-operating characteristic (ROC) curve (area under the curve [AUC]) and the C-statistic were used to assess the discrimination ability of this nomogram. The calibration curves and Brier score were used to assess the calibration ability of this nomogram. In addition, enhanced bootstrap internal validation was performed to verify the diagnostic efficiency of the model. Furthermore, decision curve analysis (DCA) was performed to determine the model’s clinical usefulness. P<0.05 was considered statistically significant. An online prediction tool (Shiny App) was prepared using the DynNom package in R. The construction process was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines.20 (link)
