Additional file 1 of Impact of macrolide treatment on long-term mortality in patients admitted to the ICU due to CAP: a targeted maximum likelihood estimation and survival analysis
Creators: name: Reyes, Luis Felipe
name: Garcia, Esteban
name: Ibáñez-Prada, Elsa D.
name: Serrano-Mayorga, Cristian C.
name: Fuentes, Yuli V.
name: Rodríguez, Alejandro
name: Moreno, Gerard
name: Bastidas, Alirio
name: Gómez, Josep
name: Gonzalez, Angélica
name: Frei, Christopher R.
name: Celi, Leo Anthony
name: Martin-Loeches, Ignacio
name: Waterer, Grant
Publisher: name: figshare
Year: 2023
Subjects: subject: Medicine
subject: Pharmacology
subject: Biotechnology
subject: Biological Sciences not elsewhere classified
subject: Cancer
subject: Science Policy
subject: Plant Biology
subject: FOS: Biological sciences

description: Additional file 1: Table S1. ICD-9 Codes. Fig. S1. Critical care invasive treatments and pneumonia causal agents. Panel A shows a Venn diagram of the different invasive treatments received. Panel B shows the most frequent causative microorganisms of pneumonia, being "other" specified in panel C. Table S2. Pneumonia casual agents. Table S3. Used antibiotics in the whole cohort and stratified between treatments. Fig. S2. Hosmer Lemeshow Test. Goodness of fit for logistic regression model was calculated, panel A shows result for six-months mortality and panel B for twelve-months mortality. Fig. S3. Propensity Score Matching. The original cohort is shown in panel A and the matched cohort is in panel B. Fig. S4. Logistic regression model to identify factors associated with 12 m mortality in the matched cohort. Logistic regression was performed with the optimal subset of variables obtained with the random forest model. The odds ratiosare graphically represented in the Forest plot for better medical interpretability. Fig. S5. Area under de Curve in the matched cohort. Cross-validation trial's receiver operative curvefor the subset of the selected variables. The blue curve represents the average of the ROC curves of each test, and the average area under de ROC is also presented. Fig. S6. Cox Proportional Hazard Regression to identify factors associated with 6 m and 12 m mortality. A Forest plot distribution of risk and protective factors for 6 m mortality in original cohort. B Forest plot distribution of risk and protective factors for 12 m mortality in original cohort. Table S4. Six-months mortality Cox Proportional Hazard Regression. Table S5. Twelve-months mortality Cox Proportional Hazard Regression. Fig. S7. Logistic regression model to identify factors associated with 6 m and 12 m mortality without P. aeruginosa or MRSA infected patients. Logistic regression was performed with the optimal subset of variables obtained with the random forest model. The odds ratiosare graphically represented in the Forest plot for better medical interpretability. Panel A has presented the odd proportions of the risk for 6 m mortality, and panel B is shown for 12 m mortality. Fig. S8. Area under de Curve without P. aeruginosa or MRSA infected patients. Cross-validation trial's receiver operative curvefor the subset of the selected variables. The blue curve represents the average of the ROC curves of each test, and the average area under de ROC is also presented. Panel A presents the AUC-ROC for 6 m mortality and panel B for 12 m mortality.