Additional file 1 of Plasma lipid profiling for the prognosis of 90-day mortality, in-hospital mortality, ICU admission, and severity in bacterial community-acquired pneumonia (CAP)
Creators: name: Banoei, Mohammad M.
name: Vogel, Hans J.
name: Aalim M. Weljie
name: Yende, Sachin
name: Angus, Derek C.
name: Winston, Brent W.
Publisher: name: figshare
Year: 2020
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Genetics
subject: Biotechnology
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: FOS: Chemical sciences
subject: Immunology
subject: FOS: Clinical medicine
subject: FOS: Clinical medicine
subject: 69999 Biological Sciences not elsewhere classified
subject: Developmental Biology
subject: Cancer
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: FOS: Health sciences
subject: Computational Biology

description: Additional file 1: Figure S1. Coefficient plot shows a relative concentration of 20 metabolites involve in the discrimination of non-survivors from survivors by DI-MS/MS. Table S1. Unpaired t-tests show 32 metabolites with significant changes [(FDR 90 days) based on the DI-MS/MS data. Table S2. Unpaired t-test shows 65 metabolites significantly changed [(FDR 90 days) detected by DI-MS/MS of plasma. Figure S6, PLS-regression analysis shows a very strong correlation between most differentiating metabolites separating survivors from in-hospital deaths (using the most differentiating metabolites, n = 22) detected by DI-MS/MS of plasma. Figure S7. Permutation test (200 times) to validate the predictability of the model to separate in hospital deaths (n = 26) from survivors (n = 75). The test shows the OPLS-DA prediction is valid. Figure S8. Partial least square regression (PLSR) analysis shows that the most differentiating metabolites obtained by DI-MS/MS are in strong relationship with APACHE III and PSI scores, showing metabolites are highly correlated with paraclinical features. A: APACHE III, B: PSI at day 0, C: PSI at day 1, and: PSI no Age. Table S3. Logistic regression of APACHE III and PSI to predict mortality. This table shows that severity scoring systems (APACHE III and PSI) are not as good as metabolites biomarkers for the prediction of mortality in these cohorts because they have lower sensitivity, specificity, AUROC, p values and regression compared to metabolomics lipid profiling. Table S4. Characteristics of bacterial CAP ICU patients vs. ICU ventilated controls. ŧ APACHE III, ŧ ŧ APACHE II. Figure S9. OPLS-DA of NMR metabolites show a very predictable model to separate the bacterial CAP ICU patients (n = 41) from ICU ventilated control (n = 31) using 1H-NMR, R2Y = 0.871, Q2Y = 0.777, and p = 1.97× 10− 21. Figure S10. OPLS-DA of GC-MS features show an excellent prediction model to separate the bacterial CAP ICU patients (n = 41) from ICU ventilated control (n = 31) using GC-MS, R2Y = 0.915, Q2Y = 0.852, and p = 1.3× 10− 16. Table S5. Summarized OPLS-DA models of two platform to separate ICU-admitted CAP pneumonia (n = 41) from ICU ventilated controls (n = 31), Both NMR and GC-MS show an excellent prediction models as verified by Q2Y, p value, sensitivity, specificity and AUROC parameters. (AUROC = area under receiver operating curve). Table S6. Unpaired t-test shows 22 known metabolites obtained by DI-MS/MS significantly changed (p