Background/Objectives: Critically ill adults in intensive care units (ICUs) remain at high risk of death, while commonly used severity scores are complex and not always available at admission. We evaluated peri-admission lactate-to-albumin (LAR), C-reactive protein-to-albumin (CAR) and procalcitonin-to-albumin (PAR) ratios at ICU entry to predict ICU mortality in a cardiovascularly burdened cohort. Methods: We performed a single-centre retrospective observational cohort study in a tertiary cardiac ICU including adult admissions in 2024 with complete peri-admission lactate, C-reactive protein, procalcitonin and albumin. Results: Of 212 ICU admissions, 137 met the inclusion criteria. ICU mortality was 48.9%. Non-survivors had higher composite ratios and lower albumin than survivors. In multivariable models, LAR and CAR, but not PAR, remained independently associated with ICU mortality after adjustment for age, sex, and admission category. Receiver operating characteristic areas under the curve (AUC) were 0.692 for LAR, 0.677 for CAR and 0.625 for PAR. Cut-offs of LAR ≥ 0.106, CAR ≥ 3.18 and PAR ≥ 0.143 identified high-risk subgroups, with odds ratios for death of 6.18, 4.20 and 2.70, respectively, compared with lower-ratio patients, and LAR provided the best overall discrimination. Conclusions: Peri-admission LAR, CAR and PAR derived from routine tests in the ICU are associated with ICU mortality in critically ill adults, with LAR and CAR providing independent prognostic information and LAR showing the best discrimination. These simple composite ratios may complement severity scores for early risk stratification and warrant external validation.

Background/Objectives: Drug repositioning has emerged as a promising strategy to address the innovation crisis in pharmaceutical development. While artificial intelligence enables efficient in silico hypothesis generation, clinical translation remains challenging. This study aims to evaluate the role of Real-World Evidence (RWE) in validating AI-generated drug-repositioning candidates. Methods: A comprehensive literature review was conducted in PubMed using a predefined search strategy integrating drug repositioning, artificial intelligence, and real-world data. After multi-stage screening, 22 original research articles were included for analysis. Results: Network-based algorithms and natural language processing dominated AI-driven hypothesis generation. Validation using Electronic Health Records and insurance databases enabled retrospective assessment of drug efficacy across large populations. Successful applications were identified in neurodegenerative, metabolic, infectious, autoimmune, and psychiatric diseases. Conclusions: The integration of AI-based analytics with RWE provides a promising framework for the preliminary verification of computational predictions, potentially informing the translational pathway toward clinical practice. However, the effectiveness of this approach remains dependent on data quality and the specific therapeutic context, requiring further standardization of clinical data.

Background/Objectives: The objective of this study was to map and synthesize the current evidence on hemostasis in chronic and acute liver disease within the framework of Patient Blood Management (PBM). Methods: Because research in this field is heterogeneous—spanning mechanistic studies, observational data, randomized controlled trials, guidelines, and expert reviews—a scoping review was selected to comprehensively map concepts. Findings were synthesized narratively to reflect the breadth and heterogeneity of available research. Results: Hemostasis in liver disease is characterized by a fragile state of rebalanced coagulation, where parallel reductions in pro- and anticoagulant factors coexist with variable fibrinolytic disturbances and thrombocytopenia. Conventional coagulation tests (CCTs) do not accurately reflect bleeding risk, whereas viscoelastic assays and thrombomodulin-modified thrombin generation testing provide a more physiologic assessment, though with limitations. Most bleeding events arise from portal hypertension rather than coagulopathy, and the routine prophylactic correction of abnormal results of CCTs is not supported by evidence. PBM-aligned strategies—such as restrictive transfusion, targeted fibrinogen replacement, and use of thrombopoietin receptor agonists (TPO-RAs)—reduce unnecessary blood product use. Thrombosis burden is increasingly recognized in this patient population. Anticoagulation is generally safe when individualized to liver function and clinical context, however significant variability persists in clinical practice, and high-quality data remain limited for advanced disease. Conclusions: Hemostasis in liver disease reflects a dynamic and unstable equilibrium rather than a simple bleeding tendency. Diagnostic and therapeutic strategies grounded in PBM principles improve safety by avoiding unnecessary transfusion and emphasize individualized care. Despite advances in understanding rebalanced hemostasis, major gaps remain in predicting thrombotic risk, standardizing advanced coagulation testing, and defining optimal management across disease stages.

Background: This study evaluates whether maximal and mean lactate-to-albumin (LAR), C-reactive protein-to-albumin (CAR), and procalcitonin-to-albumin (PAR) ratios during ICU stay improve mortality prediction beyond admission values in critically ill, cardiovascularly burdened patients, and compares their performance against individual biomarkers. Methods: We conducted a retrospective single-center cohort study of consecutive adult ICU admissions (2024) in a tertiary cardiac center, using temporally aligned (±12 h) albumin-based ratios derived from serial laboratory measurements. Maximal and mean in-ICU values were analysed per admission. The primary endpoint was ICU mortality. Associations and discriminative performance were evaluated using ROC analysis and multivariable logistic regression. Results: Of 212 screened ICU admissions, 137 patients were included. The ICU mortality was 48.9%. Both maximum and mean values of biomarkers and composite ratios were more abnormal in non-survivors, with the strongest independent associations observed for maximum LAR and CAR. In adjusted analyses, maximum LAR and CAR remained independently associated with ICU mortality and showed moderate discrimination (AUC 0.704 and 0.712). Mean CAR and LAR performed numerically slightly better in ROC analysis, whereas PAR showed weaker and less consistent prognostic utility. Conclusions: Maximal and mean LAR and CAR were independently associated with ICU mortality, whereas PAR showed weaker performance. These ratios may serve as simple tools for dynamic risk stratification but require prospective multicenter validation.

Background: Severity-of-illness scores and registry-derived risk models assume that the association between a baseline characteristic and death is stable over time. Methods: A retrospective analysis of 25,344 admissions (2010–2020) to 34 intensive care units in the Silesian Registry of Anaesthesiology and Intensive Care, with unchanged variable definitions and intra-ICU death as the outcome, was conducted. For each of 48 characteristics, a characteristic-by-time interaction was tested by likelihood ratio under four implementations of time-chronological quintiles, fixed calendar periods, linear and spline continuous time, each independently Benjamini–Hochberg corrected. Results: Twenty-two of 48 characteristics showed significant heterogeneity in the primary quintile analysis (q < 0.05), with 11 under all four. Adjustment for centre left 12 significant, 6 of which also survived adjustment for case mix. An adjusted model retained discrimination out of period but over-predicted risk in the final quintile. Conclusions: For nearly half of the characteristics, the crude association with intra-ICU death is not temporally stable, although most of the movement tracks a changing population. Out of period, a fixed model loses the level of predicted risk, not the weighting of its predictors-level rather than structural drift. Multi-year registry analyses should test temporal homogeneity rather than assume it.

Background: The neutrophil-to-lymphocyte ratio (NLR) and monocyte-to-lymphocyte ratio (MLR) reflect the balance between innate and adaptive immunity and may indicate systemic inflammation. Objectives: To evaluate longitudinal changes in NLR and MLR in MS patients during relapse compared with relapse-free controls. Methods: In this retrospective single-center observational study, patients with MS were followed for 2 years. Individuals with at least one relapse and complete blood count data at predefined time points were included. Controls without relapse were selected in a 2:1 ratio and matched for age, sex, disease duration, EDSS score, and disease-modifying therapy category. NLR and MLR were assessed 3 months before relapse (or index date), during relapse, and 3 months after. Results: The study included 34 patients with relapse and 68 controls. During relapse, patients showed higher neutrophil and monocyte counts, lower lymphocyte counts, and elevated NLR and MLR compared with controls (all p < 0.001). NLR increased from 2.75 pre-relapse to 3.62 during relapse, then decreased to 2.67 (p = 0.0002). MLR rose from 0.45 to 0.54 and declined to 0.40 (p = 0.002). No significant changes were observed in controls. Conclusions: NLR and MLR demonstrated dynamic changes during MS relapse. These non-specific indices may serve as exploratory, complementary markers of relapse-associated inflammation, but require validation in larger prospective studies including MRI correlates.