Search Results (4,577)

Background: Accurate perioperative cardiovascular risk stratification remains challenging in patients undergoing noncardiac surgery. Although treadmill testing (TMT) is widely used for functional assessment, its ability to identify truly high-risk patients is limited. Natriuretic peptides reflect integrated myocardial stress and may provide complementary prognostic information, particularly in patients with abnormal functional test results. Methods: In this prospective multicenter observational study, 178 patients with at least one Revised Cardiac Risk Index risk factor undergoing noncardiac surgery were included. All patients underwent preoperative TMT and had available N-terminal pro–B-type natriuretic peptide (NT-proBNP) measurements. The primary endpoint was 30-day major adverse cardiac events (MACE), defined as a composite of cardiac death, nonfatal myocardial infarction, myocardial injury after noncardiac surgery, pulmonary edema with heart failure, and clinically significant arrhythmias. Incremental prognostic value was assessed using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI), with internal validation using bootstrap resampling. Results: At 30 days, 26 patients (14.6%) experienced MACE, of whom seven experienced more than one event. Log-transformed NT-proBNP was independently associated with perioperative events in parsimonious multivariable models. Elevated NT-proBNP, particularly NT-proBNP ≥ 1000 pg/mL, was independently associated with perioperative events after multivariable adjustment. Importantly, the incremental prognostic value of NT-proBNP was most pronounced in patients with a positive TMT, in whom NT-proBNP improved risk discrimination (ΔAUC = +0.09) and reclassification (NRI = 1.00). In contrast, among patients with a negative TMT, the additional prognostic contribution of NT-proBNP was modest and not statistically significant. Subgroup findings should be interpreted cautiously, given the limited number of events. Conclusions: Preoperative NT-proBNP provides modest but independent incremental prognostic value beyond treadmill testing, with the greatest impact observed in patients with positive TMT results. Although improvements in discrimination were moderate, NT-proBNP may help refine perioperative risk assessment in selected intermediate- to high-risk patients. These findings support a complementary biomarker-based approach to MACE. Full article

Background: Artificial intelligence (AI) shows promising results in lymphoma detection, prediction, and classification. However, translating these findings into practice requires a rigorous assessment of potential biases, clinical utility, and further validation of research models. Objective: The goal of this study was to summarize existing studies on artificial intelligence models for the histopathological detection of lymphoma. Design: This study adhered to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted across three major databases (Scopus, PubMed, Web of Science) for English-language articles and reviews published between 2016 and 2025. Seven precise search queries were applied to identify relevant publications, accounting for variations in study modality, algorithmic architectures, and disease-specific terminology. Results: The search identified 612 records, of which 36 articles met the inclusion criteria. These studies presented 36 AI models, comprising 30 diagnostic and six prognostic applications, with Convolutional Neural Networks (CNNs) being the predominant architecture. Regarding data sources, 83% (30/36) of datasets utilized Hematoxylin and Eosin (H&E)-stained images, while the remainder relied on diverse modalities, including IHC-stained slides, bone marrow smears, and other tissue preparations. Studies predominantly utilized retrospective, private cohorts with sample sizes typically ranging from 50 to 400 patients; only a minority leveraged open-access repositories (e.g., Kaggle, TCGA). The primary application was slide-level multi-class classification, distinguishing between specific lymphoma subtypes and non-neoplastic controls. Beyond diagnosis, a subset of studies explored advanced prognostic tasks, such as predicting chemotherapy response and disease progression (e.g., in CLL), as well as automated biomarker quantification (c-MYC, BCL2, PD-L1). Reported diagnostic performance was generally high, with accuracy ranging from 60% to 100% (clustering around 90%) and AUC values spanning 0.70 to 0.99 (predominantly > 0.90). Conclusions: While AI models demonstrate high diagnostic accuracy, their translation into practice is limited by unstandardized protocols, morphological complexity, and the “black box” nature of algorithms. Critical issues regarding data provenance, image noise, and lack of representativeness raise risks of systematic bias, hence the need for rigorous validation in diverse clinical environments. Full article

Background/Objectives: Penile squamous cell carcinoma (PSCC) is a rare malignancy with a potential major impact on survival. Prognostic assessment remains challenging, particularly in underrepresented eastern European populations, where region-specific evidence is lacking. This paper aimed to identify independent predictors of overall survival in surgically treated patients with PSCC from a Romanian high-volume tertiary center. Methods: This retrospective cohort study analyzed 60 patients who were surgically treated for PSCC between October 2020 and December 2024. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify independent prognostic factors. Results: The mean patient age was 62 ± 12 years. T-stage distribution showed 30% pT1, 35% pT2, 31.67% pT3, and 3.33% pT4, with 55% of patients presenting with nodal metastases. Univariate analyses demonstrated significant associations between lymphovascular invasion (p < 0.001), perineural invasion (p = 0.022), and positive surgical margins (p = 0.030) and risk of death. Multivariate analysis identified three independent prognostic factors: absence of histologically documented urethral invasion (HR 0.32; p = 0.027), T3–T4 disease (HR 8.26; p = 0.005 vs. T1), and N3 stage (HR 3.53; p = 0.030 vs. N0–N1). Patients without urethral invasion demonstrated significantly longer median overall survival (63 months vs. 11 months). The final three-variable prognostic model demonstrated good discrimination (C-index 0.78), providing a potential practical risk stratification tool. Conclusions: Urethral invasion, advanced T-stage, and N3 disease independently predict poor survival in surgically treated PSCC. The identification of urethral invasion as an independent prognostic factor warrants consideration in clinical practice. This is the first study of a Romanian cohort to provide critical data for risk-adapted treatment strategies in underrepresented eastern European populations. 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

Sepsis-induced liver injury (SILI) stands as an independent prognostic factor for mortality among patients diagnosed with sepsis. Maresin-1 (MaR1) is a proresolving lipid mediator. However, its significance in SILI is uncertain. The current study sought to investigate MaR1’s effectiveness in treating SILI, as well as its molecular mechanism. In male C57BL/6J mice, we generated a SILI model by using cecal ligation and puncture (CLP). We further investigated how MaR1 influences inflammation, hepatic autophagy and apoptosis. We showed that treatment with MaR1 ameliorates SILI-induced hepatic injury, as reflected in decreased blood level of the alanine aminotransferase (ALT) and aspartate aminotransferase (AST) enzymes, as well as better appearance of liver tissues. Furthermore, this medication markedly reduced the expression of inflammatory mediators. Importantly, MaR1 inhibited hepatocyte apoptosis by regulating the Bax/Bcl-2 ratio, decreasing cleaved caspase-3 expression, lowering apoptotic cell count, and increasing autophagy. The findings demonstrated that MaR1 treatment reduced p62 protein expression while raising Beclin1 levels and the LC3-II/LC3-I ratio, and facilitated autophagosome formation. The observed effects were most likely due to the stimulation of PI3K/Akt signaling, which was completely prevented by LY294002 (LY), a specific PI3K inhibitor. MaR1’s protective effect in SILI may be mediated via stimulation of the PI3K/Akt pathway, which reduces inflammation and regulates apoptosis and autophagy. Our results give additional experimental evidence of the potential therapeutic uses of MaR1 in the treatment of SILI. Full article

Acute gastrointestinal bleeding (GIB) is one of the most common and dangerous condition in patients admitted in Emergency Departments. The incidence and the mortality of acute GIB remain significant, although some positive trends were observed in recent years. Initial evaluation of GIB needs an accurate assessment of the medical history and the clinical presentation. Physicians should pay attention about the presence of hemorrhagic shock that usually requires urgent diagnosis and treatment. Only a prompt diagnostic approach can identify the source of bleeding and improve the outcomes in acute GIB patients. Risk stratification and time of endoscopy are fundamental issues in the management of upper and lower GIB. Small bowel capsule enteroscopy (SBCE) and device-assisted enteroscopy (DAE) are the basic approaches to suspected small bowel bleeding. Machine Learning Prognostic Models have been proposed, such as alternative prognostic tools in GIB, but they are currently recommended only to identify low-risk outpatients. Full article

Colorectal cancer (CRC) remains a leading cause of cancer mortality globally, yet current histopathological diagnostics capture only limited features. This study aimed to discover subtle, prognostically significant histomorphological patterns in CRC tissues using unsupervised deep learning. We developed a framework integrating convolutional neural networks with deep clustering, trained on 23,341 image patches from 493 patients. We identified 30 distinct histomorphological clusters from CRC tissue images. Through univariate and multivariate survival analyses, three clusters (Cluster13, Cluster19, and Cluster24) were consistently associated with patient prognosis. These clusters were integrated with clinical factors (T stage, N stage, and differentiation degree) to construct a prognostic risk model. Patients stratified into high-risk and low-risk groups based on model predictions showed significant survival differences in both the training set (N = 493) and an independent validation set (N = 2590). Furthermore, logistic regression and multivariate Cox analyses demonstrated that incorporating the three histomorphological clusters alongside clinical factors yielded a modest but statistically significant improvement in predictive performance compared to clinical factors alone, indicating their complementary value for prognosis. This work demonstrates that computational pathology can uncover novel, visually elusive morphological features with independent prognostic value, offering potential to refine CRC patient stratification and inform clinical decision-making. Full article

Background: Frailty strongly influences outcomes after transcatheter aortic valve implantation (TAVI), but conventional risk models insufficiently capture functional and cognitive vulnerability. We compared conventional surgical risk scores with multidimensional frailty assessment and a biological score. Methods: This observational registry included 528 consecutive patients with severe symptomatic aortic stenosis undergoing TAVI between January 2019 and November 2024. Frailty was assessed using the Essential Frailty Toolset (EFT), Katz Index, and cognitive screening, alongside French Aortic National CoreValve and Edwards 2 (FRANCE-2) and Age, Creatinine, and Ejection Fraction (ACEF) scores. HALP was calculated as (haemoglobin × albumin × lymphocytes) ÷ platelets. Primary endpoints were 30-day, 6-month, and 1-year all-cause mortality. Secondary outcomes included non-fatal major adverse cardiovascular events (MACE), complications, and quality-of-life improvement. Results: One-year mortality was 12.7%. EFT and Katz Index showed the strongest discrimination for 1-year mortality (AUC 0.72 and 0.75), outperforming EuroSCORE II and STS-PROM (AUC 0.66 and 0.68). After adjustment, EFT (HR 1.91, 95% CI 1.47–2.48), Katz Index (HR 0.57, 95% CI 0.47–0.70, and cognitive impairment (HR 2.24, 95% CI 1.34–3.75) independently predicted 1-year mortality. HALP was not associated with outcomes. FRANCE-2 independently predicted 1-year MACE (HR 1.24, p = 0.019). Conclusions: Functional frailty and cognitive impairment add prognostic value beyond conventional comparator models, whereas HALP does not. Brief functional and cognitive screening may help Heart Teams identify patients who need closer peri-procedural optimisation, rehabilitation planning, and discharge support rather than relying on surgical risk scores alone. Full article

Background: Hepatocellular carcinoma (HCC) stands as a prevalent global health issue with increasing incidence and mortality rates. Hepatocellular carcinoma (HCC) exhibits profound molecular and clinical heterogeneity, which limits the effectiveness of current therapeutic strategies. Circadian rhythm disruption has been implicated in metabolic reprogramming, proliferation, and immune modulation in cancer, but its role in shaping HCC heterogeneity remains poorly defined. Methods: Four public HCC transcriptomic cohorts (TCGA-LIHC, CHCC, LIRI, LICA) were integrated using RMA normalization and ComBat for batch correction. Consensus clustering based on 31 core circadian clock genes (CCGs) identified robust molecular subtypes. Multi-omics characterization—including genomic alterations, pathway activity (GSEA/GSVA), immune microenvironment profiling (CIBERSORT, EPIC, MCP-counter, xCell), and drug-sensitivity prediction (pRRophetic/oncoPredict)—was performed to delineate subtype-specific biological properties. A nine-gene CCG-based RiskScore model was constructed using LASSO Cox regression to internally validate subtype robustness and intra-subtype risk stratification. Results: Using consensus clustering of 31 core CCGs in TCGA-LIHC and three independent validation cohorts (CHCC, LIRI, LICA), we identified three reproducible subtypes—Cluster-1 (metabolic–quiescent), Cluster-2 (transition–intermediate), and Cluster-3 (proliferation–inflammatory)—which were recapitulated across cohorts and showed distinct overall survival (Cluster-3 worst; log-rank p values significant across datasets). Multi-omic characterization revealed that Cluster-3 exhibits the highest tumor mutational burden and CNV burden with enrichment of TP53/AXIN1/TERT alterations, strong activation of cell-cycle, E2F, and G2M programs, and an immune-hot yet immunosuppressed microenvironment enriched for TAMs, Tregs and MDSCs. By contrast, Cluster-1 shows relative genomic stability, dominant hepatic metabolic signatures (fatty-acid oxidation, bile-acid and xenobiotic metabolism) and an immune-cold phenotype. Single-cell mapping linked ALAS1 expression to malignant hepatocytes predominating in Cluster-1, whereas NONO and CSNK1D localized to stromal (CAFs/TECs) and both malignant/immune compartments respectively in Cluster-3, providing a cellular mechanism for subtype-specific metabolism, angiogenesis and immune modulation. Finally, a nine-gene CCG-based RiskScore validated prognostic stratification and drug-sensitivity predictions indicated subtype-specific therapeutic vulnerabilities (notably increased predicted TKI sensitivity in Cluster-3). Conclusion: In conclusion, this study proposes a robust circadian rhythm-based molecular classification of hepatocellular carcinoma, revealing three biologically and clinically distinct subtypes characterized by divergent genomic alterations, metabolic programs, immune microenvironment states, and prognostic patterns. By integrating bulk and single-cell transcriptomic data, we identify subtype-specific roles of key circadian regulators—including ALAS1, NONO, and CSNK1D—in shaping tumor metabolism, proliferation, stromal remodeling, and immune suppression. These findings highlight circadian dysregulation as a potential upstream factor associated with HCC heterogeneity and provide a conceptual framework for developing subtype-tailored mechanistic studies and circadian-informed therapeutic strategies. Full article

Background: Up to one-third of brucellosis patients develop focal organ involvement, contributing to increased morbidity and therapeutic failure, yet no clinically validated instrument exists to stratify risk at presentation. Methods: In this three-center retrospective cohort from Türkiye (2015–2025), 355 adults with confirmed brucellosis were enrolled. Thirty-two candidate variables spanning demographics, comorbidities, symptoms, routine laboratory values, and composite inflammation indices underwent LASSO-penalized regression with 10-fold cross-validation for predictor selection, after which a nomogram was constructed and internally validated via 1000-iteration bootstrap resampling. Results: Ninety-two patients (25.9%) developed focal complications. Five predictors were retained by LASSO—prognostic nutritional index (PNI), erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), chronic disease stage, and hypertension—and combined with age and sex (retained a priori) into a seven-predictor nomogram. PNI was the strongest contributor (OR = 0.901, 95% CI: 0.857–0.948). Apparent C-statistic reached 0.782 (optimism-corrected 0.762), with a calibration slope of 0.894 and Brier score of 0.154. Decision curve analysis indicated net clinical benefit over the 5–55% threshold probability range. Conclusions: This PNI-anchored LASSO nomogram offers a practical bedside risk stratification instrument for brucellosis-related focal involvement. Prospective external validation across geographically diverse endemic regions is warranted before clinical adoption. Full article

Background/Objectives: This study aimed to investigate the prognostic significance of the triglyceride–glucose index (TGI), triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio, and inflammatory biomarkers in predicting short-term mortality among intensive care unit (ICU) patients with sepsis. Additionally, this study evaluated whether combining these indices with conventional clinical scores improves prognostic accuracy. Methods: This retrospective cohort study included 600 adult ICU patients diagnosed with sepsis according to Sepsis-3 criteria between January 2020 and April 2025. Clinical, biochemical, and hematological data were collected within the first 24 h of ICU admission. Metabolic indices (TGI, TG/HDL-C) and inflammatory markers (neutrophil-to-lymphocyte ratio [NLR], systemic immune-inflammation index [SII], and pan-immune-inflammation value [PIV]) were analyzed. The primary outcome was 28-day mortality. Receiver operating characteristic (ROC) analyses, Kaplan–Meier survival curves, and a multivariable logistic regression model were applied to determine prognostic performance. Results: Non-survivors exhibited significantly higher levels of TGI, TG/HDL-C, NLR, SII, and PIV compared to survivors (all p < 0.001). In ROC analysis, TGI (AUC = 0.75, 95% CI: 0.71–0.79), TG/HDL-C (AUC = 0.72, 95% CI: 0.68–0.76), and PIV (AUC = 0.78, 95% CI: 0.74–0.82) demonstrated good discriminative power for predicting 28-day mortality. Multivariate logistic regression identified TGI > 8.95 (OR = 1.44, 95% CI: 1.19–1.74, p < 0.001), TG/HDL-C > 3.95 (OR = 1.31, 95% CI: 1.08–1.59, p = 0.005), and PIV > 260 (OR = 1.49, 95% CI: 1.22–1.82, p < 0.001) as independent predictors of mortality. Integrating TGI and PIV with the SOFA score improved prognostic performance (ΔAUC = +0.04). Conclusions: Both TGI and TG/HDL-C are independent predictors of short-term mortality in septic ICU patients, reflecting the contribution of metabolic dysregulation to disease severity. The PIV demonstrated comparable predictive ability to conventional severity scores. Combining metabolic and inflammatory biomarkers with established clinical indices may enhance early risk stratification and guide personalized management strategies in sepsis. Full article

Background/Objectives: Acute heart failure (AHF) is a leading cause of hospitalization and mortality among very old patients, yet this group is underrepresented in prognostic studies. Carbohydrate antigen 125 (CA125) has emerged as a potential biomarker of congestion and inflammation, but its value in patients aged 80 years and over remains unclear. We aimed to evaluate the prognostic value of plasma CA125 measured at admission for 12-month all-cause mortality and the composite outcome of mortality or heart failure (HF) readmission in very elderly patients hospitalized for AHF. Methods: We conducted a prospective observational study of patients aged ≥80 years admitted to an acute geriatric unit for AHF. CA125 and NT-proBNP were measured within 24 h of admission. Outcomes were assessed at 12 months. Survival analyses were performed using Kaplan–Meier curves, Cox regression models, and restricted cubic splines. Results: A total of 210 patients (mean age 89.8 ± 5.3 years; 75.3% females; 88.1% frail) were recruited. During the one-year follow-up, 70 deaths (37.2%) and 68 HF hospital readmissions (36.1%) were recorded. Patients in the highest CA125 tertile had an increased cumulative mortality risk (log-rank p = 0.061). A CA125 value ≥ 100 U/mL independently predicted both mortality (HR 1.88, 95% CI 1.15–3.09; p = 0.012) and the composite endpoint (HR 1.54, 95% CI 1.04–2.29; p = 0.031). Measures of functional dependence and frailty demonstrated greater discriminative ability than biomarkers. Conclusions: In very elderly patients hospitalized for AHF, elevated CA125 at admission independently predicted 12-month mortality and HF readmission. CA125 provides complementary prognostic information to geriatric assessment and may support risk stratification in this vulnerable population. Full article

Peritoneal dialysis (PD)-related infections caused by rare pathogens are heterogeneous and clinically challenging, and available evidence is largely limited to isolated reports that hinder comparative interpretation. We conducted a controlled pooled narrative synthesis with exploratory comparative analyses of previously published, author-derived literature, designed to compare clinical outcomes across rare pathogen groups using a consistent analytical framework rather than a systematic review or meta-analysis. Infectious episodes were categorized into four groups: Gram-positive bacteria, Gram-negative bacteria, nontuberculous Mycobacteria (NTM) and fungal pathogens (Aspergillus spp.). Primary outcomes were catheter removal and infection-related mortality, while secondary outcomes were analyzed descriptively. In total, 135 infectious episodes were included (17 Gram-positive, 39 Gram-negative, 25 NTM and 55 Aspergillus). Catheter removal occurred in 11.8% of Gram-positive, 12.8% of Gram-negative, 95.8% of NTM and 85.5% of Aspergillus infections, while infection-related mortality was observed only in NTM (4.0%) and Aspergillus infections (38.2%). Exploratory comparisons suggested a gradient of severity across pathogen categories. In conclusion, rare PD pathogens show distinct, pathogen-specific outcome patterns. Rather than a validated prognostic model, we propose descriptive, hypothesis-generating pathogen-based severity tiers that may support early risk appraisal, guide timely decisions regarding catheter salvage versus early removal, and facilitate more tailored, pathogen-informed management in high-risk infections. Full article

Background: Gastric cancer (GC) remains a global health challenge, with high mortality rates often linked to late-stage diagnosis. Novel, non-invasive biomarkers are urgently needed to improve the detection and prognosis of this malignant pathology. This study aimed to evaluate the diagnostic and prognostic utility of serum Cluster of Differentiation 276 (CD276) and Dickkopf Related Protein 3 (DKK3) in patients with GC. Methods: In this case–control study, serum levels of CD276 and DKK3 were quantified in 40 GC patients and 40 age-matched healthy controls. The diagnostic performance of each marker and their combination was assessed using Receiver Operating Characteristic (ROC) curve analysis. Correlations between biomarker levels and clinicopathological features were evaluated using Spearman’s correlation. The Kaplan–Meier method and the Cox Proportional Hazards Regression Model were used to assess survival. Results: Serum CD276 levels were found to be significantly elevated in GC patients compared to healthy controls (median 60.06 vs. 18.71 units, p < 0.001). Conversely, serum DKK3 levels were significantly suppressed in the GC group (median 92.47 vs. 121.02 units, p < 0.001). In ROC analysis, CD276 demonstrated excellent diagnostic accuracy as a standalone biomarker (AUC: 0.836). DKK3 showed independent diagnostic value (AUC: 0.792), but adding DKK3 to CD276 did not provide statistically significant incremental benefit (DeLong’s p = 0.443). Survival analysis was underpowered due to limited events and short follow-up duration. Conclusions: In patients with predominantly locally advanced gastric cancer, CD276 can be a primary diagnostic marker, and the addition of DKK3 does not demonstrate a statistically significant improvement but may provide complementary information. Performance in early-stage disease requires validation in future studies. The opposing dysregulation of these markers, reflecting immune checkpoint activation (CD276) and tumor suppressor loss (DKK3), provides a robust and synergistic noninvasive signature. To assess the prognostic value of these two markers, studies involving a larger number of patients and a longer follow-up period are needed. Full article

Introduction: Immune checkpoint inhibitors (ICIs) have transformed the treatment of metastatic melanoma; however, predictive markers of therapeutic response remain poorly defined. This study systematically assesses clinical, histological, and molecular predictors associated with survival outcomes in melanoma patients treated with ICIs. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines, a systematic search was conducted in MEDLINE, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) for studies published between January 2018 and October 2025. Eligible studies reported associations between predictive factors and overall survival (OS) or progression-free survival (PFS) in adult melanoma patients receiving ICIs. Pooled hazard ratios (HRs) with corresponding 95% confidence intervals (CIs) from univariate (UVA) and multivariate analyses (MVA) were synthesized using random-effects meta-analyses. Results: Sex was not a consistent predictor (contradictory effects; PFS heterogeneity I² ≈ 90%), whereas older age predicted worse OS (MVA continuous: HR 1.05, 95% CI 1.02–1.08; UVA ≥ 65 vs. <65: HR 1.70, 95% CI 1.36–2.12). Poor performance status, assessed using the Eastern Cooperative Oncology Group (ECOG) scale, strongly predicted inferior outcomes (ECOG ≥ 1 vs. 0: MVA OS HR 2.01, 95% CI 1.61–2.51; MVA PFS HR 1.49, 95% CI 1.18–1.88; ECOG ≥ 2 vs. <2: MVA OS HR 2.24, 95% CI 1.79–2.81). Elevated lactate dehydrogenase (LDH) was consistently associated with poorer survival (MVA OS HR 1.71, 95% CI 1.53–1.91; MVA PFS HR 1.61, 95% CI 1.41–1.85), whereas body mass index (BMI) > 25 kg/m² was associated with improved OS (HR 0.82, 95% CI 0.68–0.98). Higher disease burden predicted worse prognosis (Stage IV vs. III: MVA OS HR 1.57, 95% CI 1.16–2.13; >2 metastatic sites vs. ≤2: MVA OS HR 2.38, 95% CI 1.40–4.07; brain metastases: MVA OS HR 1.69, 95% CI 1.30–2.20; MVA PFS HR 1.52, 95% CI 1.00–2.33). Histologic and molecular factors showed prognostic value: ulceration worsened OS (UVA HR 2.08, 95% CI 1.25–3.44) and PFS (UVA HR 2.97, 95% CI 1.39–6.32); acral subtype had poorer OS than cutaneous melanoma (MVA HR 2.99, 95% CI 1.63–5.48); high tumor mutational burden (TMB) improved PFS (UVA HR 0.47, 95% CI 0.33–0.70); and cutaneous immune-related adverse events (irAEs) were associated with favorable outcomes (skin disorders: UVA OS HR 0.26, 95% CI 0.14–0.47; UVA PFS HR 0.50, 95% CI 0.34–0.74). In contrast, detectable circulating tumor DNA (ctDNA) predicted markedly worse PFS (MVA HR 4.72, 95% CI 2.31–9.65) and a non-significant trend toward worse OS (MVA HR 3.34, 95% CI 0.96–11.67). Liver metastases and programmed death-ligand 1 (PD-L1) expression were not significantly associated with survival. Discussion: This meta-analysis synthesizes evidence on clinicopathologic, laboratory, and histopathologic predictors of immunotherapy outcomes in metastatic melanoma. Performance status, age, LDH, BMI, and metastatic burden consistently correlated with prognosis, while ulceration, disease stage, and TMB emerged as key histologic determinants. Conversely, PD-L1 and gender showed no consistent predictive value, whereas cutaneous immune-related adverse events and ctDNA reflected favorable and poor outcomes, respectively. These findings highlight the multifactorial nature of immunotherapy response and support the further development of integrated prognostic models to refine patient stratification and optimize treatment outcomes. Full article