Search Results (117)

Background/Objectives: We investigated the association of serum ferritin and procalcitonin (PCT) with Sepsis-related Organ Failure Assessment (SOFA)-2 score-based organ dysfunction severity in intensive care patients with COVID-19-associated sepsis. Methods: Patients were stratified by day 5 ferritin (ng/mL) and PCT (μg/L) levels; associations were analysed across severity groups defined by an SOFA-2 score of <5 (mild) or ≥5 (severe). Results: Day 5 PCT did not predict the SOFA-2 score (p > 0.05). The optimal day 5 ferritin cut-off was >1191 ng/mL (35.78% sensitivity, 82.38% specificity; area under the curve (AUC) = 0.608). Day 5 ferritin was associated with SOFA-2 severity in the univariable analysis but did not remain an independent correlate after adjustment for C-reactive protein (CRP) and lactate dehydrogenase (LDH); in a mortality model, neither ferritin nor PCT independently predicted intensive care unit (ICU) death. PCT provided no predictive value beyond existing inflammatory markers, consistent with its suppression during viral infections. Conclusions: Day 5 ferritin reflects, rather than independently predicts, organ dysfunction severity and may complement, rather than replace, established multi-parameter scoring. Relative to the independent determinants of severity and mortality (PaO₂/FiO₂ ratio, LDH, CRP, and age), day 5 ferritin is a specific, rule-in adjunctive marker of concurrent organ dysfunction rather than a standalone prognostic tool. Whether these associations extend to non-COVID sepsis populations requires prospective study. Full article

Background and Objectives: The COVID-19 pandemic disrupted cancer diagnosis and treatment pathways worldwide. Glioblastoma is an aggressive primary brain malignancy requiring timely multimodal care. This study evaluated survival outcomes among glioblastoma patients diagnosed before and during the COVID-19 pandemic and prepared a dataset for machine learning-based prediction of 12-month mortality. Materials and Methods: Patients aged ≥20 years diagnosed with glioblastoma between 2018 and 2021 were identified from the SEER database using ICD-O-3 histology codes 9440/3, 9441/3, and 9442/3. Patients were categorized as pre-COVID period (2018–2019) or COVID period (2020–2021). OS and CSS were evaluated using Kaplan–Meier curves, log-rank tests, and Cox regression models. Machine learning models predicted 12-month all-cause mortality using registry variables. Results: The final cohort included 9914 patients; 4819 were diagnosed pre-COVID and 5095 during COVID. Median OS was 11 months pre-COVID and 10 months during COVID; 12-month OS was 44.3% and 41.2%, respectively. Median CSS was 11 months in both periods; 12-month CSS was 46.9% and 44.1%, respectively. COVID-period diagnosis was modestly associated with poorer OS (adjusted HR 1.050, 95% CI 1.006–1.095, p = 0.025) and CSS (adjusted HR 1.048, 95% CI 1.003–1.095, p = 0.035). Machine learning models showed moderate discrimination for 12-month mortality prediction. Conclusions: Glioblastoma patients diagnosed during the COVID period had modestly poorer OS and CSS in conventional survival analyses; however, competing-risk analysis did not show a significant association with cancer-specific death. Registry-based machine learning models provided moderate 12-month mortality prediction, supporting their potential utility for population-level prognostic assessment. Full article

Background/Objectives: Although COVID-19 is generally more severe in males, data on sex-specific differences in the predictive value of commonly used inflammatory biomarkers remain limited. The study aimed to evaluate the sex-specific prognostic performance of selected biomarkers during the Alpha variant wave. Methods: In single-center study, univariate and multivariable regressions analyses, along with receiver operating characteristic curve (ROC) analyses, were performed to assess the association of acute-phase proteins, cytokines, and white blood cell indices (at admission and 7 days later) and disease severity and mortality in patients with severe-to-critical COVID-19. Results: At admission, the combined assessment of ferritin and D-dimer predicted disease severity in both sexes; however, optimal cut-off values and diagnostic performance (specificity and sensitivity) differed between males and females. In males, neutrophil and lymphocyte counts provided additional clinically relevant predictive value. Seven days after admission, the combination of ferritin, D-dimer, and fibrinogen in males, and ferritin, as an independent predictor within a model including lactate dehydrogenase, in females demonstrated strong predictive performance for severe-to-critical COVID-19. At this time-point, lymphocyte count in males was also identified as an independent predictor of disease severity. Notably, C-reactive protein and neutrophil count correlated with mortality in males with severe-to-critical disease. Conclusions: Severe COVID-19 is predicted by distinct acute-phase proteins and shared, sex-specific biomarkers, but with distinct cut-offs and predictive accuracy. In males, white blood cell indices also serve as independent predictors. Furthermore, prognostic utility changes of these biomarkers over the course of the disease, suggesting sex-specific and time-dependent role in COVID-19 pathogenesis. Full article

Coronavirus disease 2019 (COVID-19) has been associated with an increased long-term cardiovascular risk, potentially mediated by magnitude of the acute inflammatory response inflammation. Interleukin-6 (IL-6) and serum amyloid A (SAA) are key components of the inflammatory cascade and may serve as biomarkers of post-COVID cardiovascular vulnerability. This longitudinal observational study investigated the association between post- COVID-19 infection IL-6 and SAA levels and major cardiovascular events over a six-year follow-up period. A total of 97 individuals with documented prior SARS-CoV-2 infection were included. Circulating IL-6 and SAA concentrations were measured in the acute phase. The composite endpoint included incident arrhythmia, myocardial infarction, and all-cause mortality. Biomarker distributions were right-skewed and were therefore analyzed using non-parametric methods and penalized logistic regression models. During follow-up, 14.4% of participants experienced the composite endpoint. Individuals with adverse outcomes had significantly higher IL-6 and SAA levels compared with event-free participants. IL-6 demonstrated the strongest association with mortality, whereas SAA showed particularly robust associations with the composite endpoint, and with myocardial infarction. Both biomarkers independently predicted long-term adverse events. Circulating IL-6 and SAA concentrations measured during the acute phase of SARS-CoV-2 infection were analyzed in relation to long-term cardiovascular outcomes. These findings support the hypothesis that the magnitude of the acute inflammatory response during SARS-CoV-2 infection may be associated with long-term cardiovascular outcomes and suggest that combined assessment of IL-6 and SAA may have potential utility for hypothesis-generating prognostic signal requiring validation, pending validation in larger studies. Full article

Background/Objectives: While acute cardiac injury during COVID-19 is well-documented, the long-term risk of new-onset heart failure (HF) in survivors remains a critical clinical concern. This study aims to quantify the risk of new-onset heart failure during a 25 months prognostic follow-up period following recovery from SARS-CoV-2. Methods: We conducted a systematic review and meta-analysis of nine high-quality studies (n > 400,000 survivors) in accordance with PRISMA 2020 guidelines. Databases including PubMed/MEDLINE and Scopus were searched through January 2026. A quantitative meta-analysis was performed on six studies using a random-effects model to pool adjusted hazard ratios (aHR). Results: The pooled analysis revealed a significant 35% increased risk of new-onset heart failure following COVID-19 recovery (aHR 1.35; 95% CI: 1.14–1.60; p = 0.001). Significant heterogeneity was observed (I² = 92.62%), reflecting diverse risk profiles among survivors. The risk was most pronounced in immunocompromised kidney transplant recipients (aHR 2.32) and younger adults under the age of 65 (aHR 1.53). Subclinical myocardial damage, characterized by reduced left ventricular longitudinal strain, was identified even in survivors who experienced mild initial infections. Conclusions: COVID-19 recovery serves as a significant independent risk factor for chronic heart failure, emphasizing that cardiovascular impact extends far beyond the acute phase. These findings necessitate the implementation of structured cardiovascular monitoring and biomarker screening for at least one year post-infection to address this emerging chronic disease burden. Full article

Background/Objectives: Patients with pre-existing heart failure (HF) represent a clinically vulnerable population with increased susceptibility to adverse outcomes during acute systemic illnesses, including coronavirus disease 2019 (COVID-19). Systemic inflammation is increasingly recognized as a central pathophysiological mechanism linking cardiovascular vulnerability with infection-related organ dysfunction. However, the prognostic role of inflammatory biomarkers in hospitalized COVID-19 patients with pre-existing HF remains incompletely defined. This study aimed to evaluate the association between inflammatory biomarkers and clinical outcomes in this high-risk population. Methods: This retrospective single-center cohort study included 395 consecutive adult patients hospitalized with confirmed COVID-19 between March 2020 and December 2024 at a tertiary referral center. Pre-existing HF was documented in 143 patients (36.2%). Inflammatory biomarkers, including C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin, and D-dimer, were measured at admission. The primary outcomes were development of sepsis and in-hospital mortality. Multivariable logistic regression models were constructed to identify independent predictors of adverse outcomes after adjustment for demographic characteristics, comorbidities, disease severity, and cardiac biomarkers. Results: Patients with pre-existing HF had significantly higher in-hospital mortality compared with those without HF (11.9% vs. 4.8%, p = 0.016) and showed a trend toward increased intensive care unit admission. HF patients exhibited higher admission IL-6 levels, indicating enhanced inflammatory activation. In univariable analysis, HF was associated with mortality (OR 2.67, 95% CI 1.22–5.83, p = 0.014). After multivariable adjustment, the association between HF and mortality was attenuated, whereas IL-6 remained an independent predictor of mortality (adjusted OR 1.38, 95% CI 1.04–1.82, p = 0.021). Elevated IL-6 and procalcitonin levels were also independently associated with sepsis development. Conclusions: Pre-existing heart failure identifies a population at increased risk of adverse outcomes in hospitalized COVID-19 patients, and this excess risk appears to be partly mediated by systemic inflammatory activation. Interleukin-6 emerged as a key biomarker linking cardiovascular vulnerability, immune dysregulation, and clinical deterioration. These findings support the potential role of inflammation-based risk stratification to improve prognostic assessment and guide personalized management in high-risk patients with underlying cardiovascular disease. Full article

Background and Objectives: Pulmonary embolism (PE) is a frequent thrombotic complication associated with SARS-CoV-2 infection and is linked to significant early mortality. Accurate early risk stratification in the emergency department (ED) remains challenging, and it is unclear how well commonly used PE prognostic tools perform in patients with concomitant COVID-19. Materials and Methods: We conducted a retrospective, single-centre study including 538 consecutive patients with acute PE and with or without confirmed SARS-CoV-2 infection admitted through the ED. Univariate analysis and machine learning models were employed to assess mortality risk. Results: In univariate analysis, mortality was strongly associated with sepsis (OR 11.68) and PESI class V (OR 5.56) and was also linked to higher neutrophil count (OR 1.19), platelet count (OR 1.12), and NT-proBNP (OR 1.20). In the non-COVID cohort, XGBoost and RF showed better discrimination than PESI class (AUC 0.864 and 0.834 vs. 0.725), while Support Vector Machines (SVM) was lower (AUC 0.740). On COVID-19 external validation, discrimination decreased: XGBoost AUC was 0.635, RF 0.614, PESI 0.584, and SVM showed no discrimination. Conclusions: ML models using routinely available ED variables improved in-hospital mortality prediction compared with PESI in non-COVID PE, but performance declined in COVID-19 patients, suggesting limited generalizability and the need for COVID-specific refinement and prospective multicenter validation. Full article

Background and Objectives: The COVID-19 pandemic profoundly disrupted global healthcare systems, limiting access to diagnostic and therapeutic services for chronic diseases. Patients with decompensated liver cirrhosis were particularly vulnerable due to their fragile clinical status and dependence on continuous medical care. This study aimed to evaluate the temporal evolution of clinical, biological, and prognostic parameters in patients admitted emergently with decompensated liver cirrhosis across three distinct phases: pre-pandemic, pandemic, and post-pandemic. Materials and Methods: A retrospective, single-center study was conducted at the Department of Gastroenterology, Municipal Clinical Emergency Hospital, Timișoara, Romania, including 355 patients hospitalized between February 2018 and February 2024. Clinical, biochemical, and outcome data were collected and analyzed using univariate and multivariate logistic regression models to identify independent predictors of in-hospital mortality for each study period. Results: Significant temporal variations were observed in disease severity, management, and outcomes. The mean MELD score increased from 18.7 to 21.0 during the pandemic (p = 0.043), while endoscopic evaluations declined markedly (59.4% pre-pandemic vs. 42.7% pandemic, p = 0.037). Mortality rose from 21.7% to 30.2% during the pandemic (p = 0.044) and remained elevated post-pandemic (26.4%). Multivariate regression identified Child–Pugh, MELD, and Baveno scores as consistent mortality predictors, though their relative weight varied by period. During the pandemic, acute complications—particularly jaundice (OR = 294) and upper gastrointestinal bleeding (OR = 355)—became dominant determinants of death. Conclusions: The pandemic transformed cirrhosis from a chronic, manageable disease into an acutely unstable condition, primarily due to delayed presentation and restricted procedural access. Although post-pandemic recovery was evident, residual increases in mortality and severity indicate lasting effects of healthcare disruption, underscoring the need to strengthen system resilience and continuity of care for patients with chronic liver disease. Full article

Background and Objectives: The use of a standard chest X-ray has become a routine diagnostic method in daily clinical practice for the evaluation of a wide range of lung diseases. During the COVID-19 pandemic, significant challenges occurred in achieving accurate diagnostics and selecting appropriate therapies for patients with different symptoms of diseases. The aim was to cross-correlate radiological findings and clinical data and to develop models to predict hospitalization status, while evaluating the prognostic importance of the different variables. Materials and Methods: A set of variables including Brixia score, and clinical data: gender, age, hypertension, and diabetes was used to explore their association with patient hospitalization. Four different machine learning (ML) methods (Decision Tree—DT, Logistic Regression—LR, Random Forest—RF and Support Vector Machine—SVM) were used for hospitalization outcome prediction. Results: SVM appeared to be with the highest AUC (0.851), with low sensitivity, while DT was the most balanced in the context of AUC, accuracy, sensitivity, and specificity. Brixia score appeared to be the most important predictor for hospitalization within the group of predictors (gender, age, hypertension and diabetes). Conclusions: All four ML models that used in this study provided “good” prediction capabilities (AUC > 0.8), with the exception of SVM that had low sensitivity, emphasizing Brixia score as the strongest predictor of hospitalization. Application of ML methods have considerable potential in various aspects of medical clinical practice and future studies could potentially indicate the importance of applying the ML model in more precise diagnosis, therapy and prognosis of the patient’s clinical condition. Full article

Vitamin D deficiency is common in hospitalized COVID-19 patients and is associated with increased severity. However, single-biomarker approaches provide insufficient prognostic precision. We developed an integrative inflammatory-metabolic risk index combining vitamin D status, systemic inflammation, and coagulation activation. This is a prospective cohort study of 512 hospitalized COVID-19 patients (September 2022–December 2023) with serum 25(OH)D3 measurement at admission. The primary analysis (N = 301) included patients with complete data for VDIBS-Core components (CRP, ferritin, D-dimer, LDH). The Vitamin D Inflammatory Burden Score-Core (VDIBS-Core; range 0–7) integrated the following: (1) vitamin D tier (deficient < 30 nmol/L: 3 points; insufficient 30–50: 2; non-optimal 50–75: 1; sufficient > 75: 0), (2) inflammation score (CRP ≥ 100, ferritin ≥ 1000 each +1 point; 0–2 total), and (3) coagulation score (D-dimer ≥ 1000, LDH ≥ 3–6 or ≥ 6 each +0–2 points; 0–2 total). The IL-6 measurement (N = 48, 9.4%) was explored separately as VDIBS-Plus in the secondary analysis. The outcomes were severe COVID-19 (defined as the worst severity classification during hospitalization per WHO criteria), ICU admission, and mortality. The mean vitamin D was 63.4 ± 33.2 nmol/L (68.1% deficient). Among N = 301 with complete VDIBS-Core data, severe disease occurred in 221 (73.4%), ICU admission in 15 (5.0%), and mortality in 8 (2.7%). VDIBS-Core risk stratification showed the following: low-risk (VDIBS 0–2, n = 178) 8.4% severe; moderate-risk (VDIBS 3–5, n = 245) 45.7% severe; and high-risk (VDIBS 6–7, n = 89) 78.6% severe; χ² = 142.3, p < 0.001. VDIBS-Core predicted severe disease with AUC 0.78 (95% CI 0.74–0.82), with excellent calibration (Hosmer–Lemeshow p = 0.40). When compared to complex multivariate models incorporating all seven individual biomarkers, VDIBS-Core demonstrated equivalent discrimination (AUC 0.82, Δ = 0.04, p = 0.08, not statistically significant) with superior clinical simplicity. Bootstrap internal validation confirmed modest optimism (optimism-corrected AUC 0.76). An incremental value analysis demonstrated that the vitamin D component contributes a significant additional predictive value compared to inflammation/coagulation biomarkers alone (LR test p = 0.004). VDIBS-Core provides bedside-implementable risk stratification using three simple components measurable in <5 min, integrating vitamin D-dependent immune regulation with systemic inflammation and coagulation activation. This composite approach offers a practical tool for treatment intensity escalation and monitoring frequency assignment in hospitalized COVID-19 patients. External validation in geographically diverse cohorts is required before widespread clinical implementation. Full article

Invasive meningococcal disease (IMD) is classically associated with meningitis and septic shock, but an increasing proportion of cases present with atypical, extra-meningeal manifestations. Following the COVID-19 pandemic, major epidemiological shifts have occurred in France, including a rebound in IMD incidence and changes in circulating serogroups and clonal complexes. We conducted a nationwide retrospective study including all laboratory-confirmed IMD cases analysed by the French National Reference Centre between July 2014 and June 2025. Clinical presentations were coded as non-exclusive entities. Associations with age, serogroup, clonal complex, antimicrobial susceptibility and early mortality (≤72 h) were assessed using descriptive analyses and multivariable logistic regression models. Among 4328 IMD cases, sepsis/shock (61.1%) and meningeal involvement (54.9%) predominated, while atypical forms were frequent, including bacteraemic pneumonia (7.7%), abdominal presentations (8.0%) and arthritis (6.0%). Bacteraemic pneumonia was strongly associated with older age and serogroups W and Y, whereas abdominal forms predominated in adolescents and young adults and were independently associated with serogroups W and Y and clonal complex (cc) cc11. Abdominal presentations were independently associated with early mortality (adjusted odds ratio [aOR] 2.40) but not meningococcal pneumonia. Abdominal presentations were associated with serogroup W (aOR 2.27; 95% CI 1.35–3.83) and serogroup Y (aOR 2.92; 95% CI 1.79–4.75) and with cc11 (aOR 1.77; 95% CI 1.07–2.94). In contrast, cc23 was associated with lower odds of abdominal involvement (aOR 0.42; 95% CI 0.25–0.70). Overall, atypical presentations now represent a substantial proportion of IMD in France and are strongly shaped by age and meningococcal lineage. These findings highlight diagnostic challenges, prognostic heterogeneity and the need for continued integrated clinical, microbiological and genomic surveillance in the context of evolving vaccination strategies. Full article

Background and Objectives: Dysglycemia is a major determinant of adverse outcomes in COVID-19, yet the separate contributions of poor glycemic control and glycemic variability (GV) remain incompletely defined. We conducted a systematic review and meta-analysis of observational cohort studies (both prospective and retrospective) to quantify the impact of chronic hyperglycemia and glucose instability on disease severity, intensive care requirements, and mortality in patients with COVID-19. Materials and Methods: We searched PubMed, Scopus, and Web of Science from January 2020 to October 2024 for observational cohort studies reporting clinically relevant COVID-19 outcomes stratified by glycemic control or GV. Dysglycemia definitions varied across studies (HbA1c-based chronic hyperglycemia, fasting glucose, or admission/in-hospital hyperglycemia). GV was assessed using metrics including mean amplitude of glycemic excursions (MAGE), standard deviation (SD), coefficient of variation (CV), or maximum daily glucose difference. Twelve studies met inclusion criteria and were included in qualitative synthesis; five studies were eligible for quantitative synthesis of clinical outcomes. Random-effects DerSimonian–Laird models were applied due to anticipated clinical heterogeneity. Heterogeneity was evaluated using Cochran’s Q, τ², and I² statistics. Results: Overall, 12 observational studies (9 prospective and 3 retrospective cohorts; n = 1,008,310 patients) were included. In quantitative analyses of five eligible cohorts, poor glycemic control was associated with a significantly increased risk of severe or critical COVID-19 (pooled RR = 1.75, 95% CI: 1.45–2.11; I² = 29%), ICU admission (RR = 1.54, 95% CI: 1.18–2.01), and mechanical ventilation (RR = 1.72, 95% CI: 1.31–2.26). Three studies evaluating GV demonstrated a strong association with adverse outcomes (pooled RR = 2.07, 95% CI: 1.71–2.50; I² = 0%); this low heterogeneity should be interpreted cautiously given the limited number of studies. GV remained associated with mortality in multivariable models, indicating that glycemic variability is separately associated with mortality as a clinically relevant prognostic risk marker in hospitalized COVID-19 patients. Conclusions: Both chronic hyperglycemia and elevated glycemic variability are each associated with increased risk of severe COVID-19 outcomes. Glycemic variability appeared to be a consistent, low-heterogeneity prognostic marker of mortality, being separately associated with higher death risk in hospitalized COVID-19 patients, highlighting its potential utility as a dynamic metabolic biomarker. Early identification and targeted management of dysglycemia—especially glucose instability—may improve prognosis in hospitalized COVID-19 patients. PROSPERO: CRD420251250718. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection results in dysregulated inflammatory and coagulation pathways that drive immunothrombosis and contribute to adverse clinical outcomes. While individual cytokines and endothelial biomarkers have been associated with disease severity and mortality, the prognostic relevance of combined inflammatory and endothelial signatures remains incompletely characterised. To identify inflammatory cytokines and markers of endothelial activation associated with mortality in patients with severe COVID-19 requiring supplemental oxygen. This retrospective observational study included 73 consecutive adults admitted to a dedicated supplemental oxygen unit with severe COVID-19. Plasma concentrations of IL-1α, IL-1β, IL-6, IL-8, IL-10, TNF-α, von Willebrand factor (VWF) antigen and propeptide, ADAMTS13 antigen and activity, and ADAMTS13 autoantibodies were measured on admission using ELISA-based assays. Associations with mortality were assessed using non-parametric analyses, age-adjusted logistic regression, multivariable logistic regression, and receiver operating characteristic (ROC) curve analysis. Increasing age was independently associated with mortality. After adjustment for age, higher IL-1α concentrations were associated with increased odds of death, whereas a higher IL-6/IL-10 ratio was independently protective. In multivariable models, including non-ratio variables, ADAMTS13 autoantibody levels remained independently associated with mortality. In ratio-based multivariable analysis, both the ADAMTS13 activity/autoantibody ratio and the IL-6/IL-10 ratio were independently protective, while age was no longer significant. IL-10 and ADAMTS13 autoantibodies demonstrated moderate discriminative performance for mortality prediction (AUC ~0.70). A combined biomarker model incorporating IL-1α, IL-8, IL-10, and ADAMTS13 autoantibodies yielded very high predicted mortality probabilities. Our findings highlight IL-1α and ADAMTS13 autoantibodies as independent predictors of mortality in severe COVID-19, reflecting the interplay between inflammatory and endothelial pathways. Biomarker ratios capturing immune and endothelial balance—particularly the ADAMTS13 activity/autoantibody ratio—may enhance risk stratification and support integrated prognostic models. Full article

Although the impact of COVID-19, caused by SARS-CoV-2, may appear to have diminished in recent years, the emergence of new variants still continues to cause significant global health and economic challenges. While numerous metabolomic studies have explored serum-based alterations linked to the infection, investigations utilizing urine as a biological matrix remain notably limited. This gap is especially significant given the potential advantages of urine, a non-invasive and easily obtainable biofluid, in clinical settings. In the context of patients in intensive care units (ICUs), temporal monitoring through such non-invasive samples may offer a practical and effective approach for tracking disease progression and tailoring therapeutic interventions. This study retrospectively explored the longitudinal metabolomic alterations in COVID-19 patients admitted to the ICU, stratified into three prognostic outcome groups: healthy discharged (HD), polyneuropathic syndrome (PS), and Exitus. A total of 32 urine samples, collected at four distinct time points per patient during April 2020 and preserved at −80 °C, were analyzed by proton nuclear magnetic resonance (¹H-NMR) spectroscopy for comprehensive metabolic profiling. Statistical evaluation using two-way ANOVA and ANOVA–Simultaneous Component Analysis (ASCA) identified significant prognostic variations (p < 0.05) in the levels of taurine, 3-hydroxyvaleric acid and formic acid. Complementary supervised classification via random forest modeling yielded moderate predictive performance with out-of-bag error rate of 40.6% based on prognostic categories. Particularly, taurine, 3-hydroxyvaleric acid and formic acid levels were highest in the PS group. However, no significant temporal changes were observed for any metabolite in analyses. Additionally, metabolic pathway analysis conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database highlighted the “taurine and hypotaurine metabolism” pathway as the most significantly affected (p < 0.05) across prognostic classifications. Harnessing urinary metabolomics, as indicated in our preliminary study, could offer valuable insights into the dynamic metabolic responses of ICU patients, thereby facilitating more personalized and responsive critical care strategies in COVID-19 patients. Full article

Background/Objectives: Teledermatology has transformed access to dermatologic care, yet its association with melanoma prognostic parameters and diagnostic pathways in tertiary settings remains incompletely characterized. To compare the clinicopathologic profile of melanomas diagnosed under face-to-face consultation (2019) versus teledermatology-based referral (teleconsultation) (2022). Methods: A retrospective observational study comparing two patient cohorts: those diagnosed with melanoma via in-person consultation in 2019, and those diagnosed through teleconsultation in 2022. These years were selected to reflect the structural shift in care delivery models before and after the COVID-19 pandemic, during which teledermatology was formally implemented. Sociodemographic, clinical, and histopathological variables were collected. A multivariable logistic regression model assessed variables associated with being diagnosed in the 2022 teledermatology cohort versus the 2019 face-to-face cohort. Statistical analyses were performed using R (v. 4.4.3). Results: A total of 151 patients were included (89 in-person in 2019, 62 via teleconsultation in 2022). Multivariable analysis identified three variables independently associated with being diagnosed via teleconsultation. Increasing Breslow thickness was inversely associated with teleconsultation diagnosis (OR 0.60 per 1 mm increase; 95% CI 0.40–0.91; p= 0.017). Similarly, the presence of histologic regression (OR 0.28; 95% CI 0.09–0.90; p = 0.032) and immunosuppression (OR 0.08; 95% CI 0.008–0.86; p = 0.037) were inversely associated with teleconsultation diagnosis. No significant associations were found for sex, age, tumor location, ulceration, mitosis, or clinical stage. Conclusions: In this retrospective single-center comparison of two care models, melanomas diagnosed through teleconsultation in 2022 were associated with a more favorable clinicopathologic profile at diagnosis than those diagnosed via face-to-face consultation in 2019. These findings support the role of teledermatology-based referral pathways in facilitating timely melanoma assessment, although causal inference is limited by the observational design. Full article