5 research outputs found
MOESM1 of Does metformin exposure before ICU stay have any impact on patients’ outcome? A retrospective cohort study of diabetic patients
Additional file 1: Figure S1. Study flowchart. Figure S2. Linear regression between blood creatinine and lactate levels in metformin users patients. Figure S3. Linear regression between blood creatinine and lactate levels in non-metformin users patients. Figure S4. Lactate levels. Table S1. Main admission pattern of ICU-admitted diabetics. Table S2. ICU-admitted diabetics with preadmission metformin treatment with or without usual metformin contraindication. Table S3. Aetiologies and germs responsible for septic shocks in ICU-diabetics. Table S4. Septic shocks without aetiology at the end of hospital stay. Table S5. Hospital death among ICU-admitted diabetic patients: univariate analysis and conditional forward stepwise multivariate analysis with metformin as analysis factor. Table S6. Hospital death among metformin patients: univariate analysis and conditional forward stepwise multivariate analysis with usual contraindication as analysis factor
Representative pairs of baseline and follow-up HRCT images.
<p>(A) Better case: pulmonary fibrosis (PF) improved and ground glass opacity (GGO) worsened by the visual assessment in whole lung; (B) annotated HRCT of (A) with a CAD system: QLF and QILD in whole lung decreased, by 2.9% (3.9% to 1.0%) and 0.2% (19.9% to 19.7%), respectively. (C) Stable case; (D) annotated CAD of (C): QLF in the worst lobe (lower left) increased by 2% (64% to 66%). In whole lung, QLF and QILD increased, by 2.3% (39.5% to 41.8%) and 1.8% (72.4% to 74.2%), respectively. (E) Worse case: PF worsened and GGO improved; (F) annotated CAD of (E): QLF increased by 6.0% (28% to 34%) in the most severe lobe (lower right) and 1.8% (9.8% to 11.6%) in whole lung. QILD in whole lung was stable (41.3% to 40.5%).</p
Baseline demographics and disease characteristics of SSc patients (n = 31).
<p>Baseline demographics and disease characteristics of SSc patients (n = 31).</p
HRCT trends across improvers and non-improvers.
<p>(A) QGG trends. (B) QLF trends. (C) Baseline [QGG-QLF] marker vs. intrinsic subset. (D) Baseline [QGG-QLF] marker vs. response status.</p