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Aim: This study sought to evaluate the effectiveness of lactate/albumin ratio for ICU mortality prediction in a large cohort of patients with severe Coronavirus Disease-2019 (COVID-19) admitted to an intensive care unit (ICU). Methods: This is a single-center retrospective cohort study of prospectively collected data derived from the COVID-19 dataset for all critically ill patients admitted to an academic ICU. Data were used to determine the relation between lactate/albumin ratio and other laboratory parameters measured on the first day of the ICU stay and to evaluate the prognostic performance for ICU mortality prediction. Results: A total of 805 ICU patients were included, and the median age (IQR) was 67 (57-76) years, with 68% being male. ICU mortality was 48%, and the median lactate/albumin ratio was 0.53 (0.39-0.59). A survival analysis showed that patients with higher lactate/albumin ratio values had significantly lower survival rates (Log Rank p < 0.001). A multivariable analysis revealed that the lactate/albumin ratio was an independent risk factor for ICU mortality with a hazard ratio of 1.39 (CI: 1.27-1.52). The lactate/albumin ratio showed a receiver operating characteristics area under the curve (ROC-AUC) value to predict ICU mortality significantly higher than that of lactate alone (0.71 vs. 0.68, DeLong test p < 0.001). The optimal lactate/albumin ratio cut-off for predicting ICU mortality was 0.57, with 63% sensitivity and 73% specificity. A subgroup analysis revealed that the lactate/albumin ratio was significantly associated with mortality across different patient groups, including age and sex categories, and those with or without hypertension and coronary heart disease. Conclusions : Lactate/albumin ratio is a reliable prognostic marker in critically ill COVID-19 patients and could predict ICU mortality more accurately than lactate alone.

Purpose: The ETOP 10-16 BOOSTER study was a randomized phase II trial of osimertinib and bevacizumab therapy versus osimertinib therapy in patients with an acquired EGFR T790M mutation. The mechanisms of acquired resistance to osimertinib and bevacizumab have not been described previously., Experimental Design: Next-generation sequencing (Guardant360) was conducted in serial plasma samples. The association between ctDNA and efficacy outcomes was explored, and molecular alterations at progression were described., Results: A total of 136 patients (88% of 155 randomized) had plasma samples at baseline (68 per arm), 110 (71%) at week 9, and 65 (42%) at progression. In a multivariable model for progression-free survival (PFS), the treatment effect was found to differ by smoking status (interaction P = 0.046), with the effect of smoking also differing by baseline EGFR T790M (interaction P = 0.033), whereas both TP53 at baseline and the tissue EGFR exon 21 L858R mutation were significantly associated with worse PFS outcome. Smokers (current/former) without baseline EGFR T790M showed a significant improvement in PFS under combination treatment, albeit with small numbers (P = 0.015). Week-9 EGFR T790M clearance was associated with improved PFS in the osimertinib arm (P = 0.0097). Acquired EGFR C797S mutations were detected in 22% and 13% of patients in the combination and osimertinib arms, respectively., Conclusions: The differential effect of treatment by smoking was not explained by TP53 mutations or other molecular alterations examined. Molecular mechanisms of acquired resistance were detected, but no novel molecular alterations were identified in the combination arm., (©2024 American Association for Cancer Research.)

Background: The aim of this study was to investigate the usefulness of serum procalcitonin (PCT), C-reactive protein (CRP), neutrophil to lymphocyte count ratio (NLR), and their combination, in distinguishing candidemia from bacteremia in intensive care unit (ICU) patients. Methods: This is a retrospective study in ICU patients with documented bloodstream infections (BSIs) and with both serum PCT and CRP measurements on the day of the positive blood sample. Illness severity was assessed by sequential organ failure assessment (SOFA) score on both admission and BSI day. Demographic, clinical, and laboratory data, including PCT and CRP levels and NLR on the day of the BSI, were recorded. Results: A total of 63 patients were included in the analysis, of whom 32 had bacteremia and 31 had candidemia. PCT, CRP, and NLR values were all significantly lower in candidemia compared with bacteremia (0.29 (0.14-0.69) vs. 1.73 (0.5-6.9) ng/mL, p < 0.001, 6.3 (2.4-11.8) vs. 19 (10.7-24.8) mg/dl, p < 0.001 and 6 (3.7-8.6) vs. 9.8 (5.3-16.3), p = 0.001, respectively). PCT was an independent risk factor for candidemia diagnosis (OR 0.153, 95%CI: 0.04-0.58, p = 0.006). A multivariable model consisting of the above three variables had better predictive ability (AUC-ROC = 0.88, p < 0.001), for candidemia diagnosis, as compared to that of PCT, CRP, and NLR, whose AUC-ROCs were all lower (0.81, p < 0.001, 0.78, p < 0.001, and 0.68, p = 0.015, respectively). Conclusions: A combination of routinely available laboratory tests, such as PCT, CRP, and NLR, could prove useful for the early identification of ICU patients with candidemia.

Purpose: Emergency laparotomy (EL) is a common operation with high risk for postoperative complications, thereby requiring accurate risk stratification to manage vulnerable patients optimally. We developed and internally validated a predictive model of serious complications after EL., Methods: Data for eleven carefully selected candidate predictors of 30-day postoperative complications (Clavien-Dindo grade >  = 3) were extracted from the HELAS cohort of EL patients in 11 centres in Greece and Cyprus. Logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) was applied for model development. Discrimination and calibration measures were estimated and clinical utility was explored with decision curve analysis (DCA). Reproducibility and heterogeneity were examined with Bootstrap-based internal validation and Internal-External Cross-Validation. The American College of Surgeons National Surgical Quality Improvement Program's (ACS-NSQIP) model was applied to the same cohort to establish a benchmark for the new model., Results: From data on 633 eligible patients (175 complication events), the SErious complications After Laparotomy (SEAL) model was developed with 6 predictors (preoperative albumin, blood urea nitrogen, American Society of Anaesthesiology score, sepsis or septic shock, dependent functional status, and ascites). SEAL had good discriminative ability (optimism-corrected c-statistic: 0.80, 95% confidence interval [CI] 0.79-0.81), calibration (optimism-corrected calibration slope: 1.01, 95% CI 0.99-1.03) and overall fit (scaled Brier score: 25.1%, 95% CI 24.1-26.1%). SEAL compared favourably with ACS-NSQIP in all metrics, including DCA across multiple risk thresholds., Conclusion: SEAL is a simple and promising model for individualized risk predictions of serious complications after EL. Future external validations should appraise SEAL's transportability across diverse settings., (© 2023. The Author(s).)