Search Results (347)

Background: Preoperative anemia is common among patients undergoing arthroplasty and is associated with increased transfusion requirements and worse outcomes. Current perioperative pathways rely on iron studies to guide intravenous iron supplementation, but systemic inflammation triggered by surgery profoundly alters iron markers, risking misclassification of iron deficiency. This study evaluated whether adjusting iron indices for inflammatory markers improves diagnostic accuracy after total hip arthroplasty (THA). Methods: In this prospective cohort study, 20 patients undergoing elective primary THA at a single center were enrolled. Patients with preoperative inflammation were excluded. Serum iron, transferrin, transferrin saturation (TSAT), CRP, and albumin were measured preoperatively and on postoperative days (PODs) 1, 2, 3, and 90. Serum iron was adjusted for systemic inflammation using a validated regression equation incorporating CRP and albumin, and adjusted TSAT was calculated accordingly. Absolute iron deficiency was defined as serum iron < 10 µmol/L, and functional iron deficiency was defined as TSAT < 20%. Comparisons were made using Wilcoxon’s signed-rank test and ANOVA. Results: In the 20 included patients, a pronounced systemic inflammatory response was observed, with CRP peaking on POD 2 (median, 162 mg/L) and albumin falling to 32 g/L on POD 1 (both p < 0.001). Unadjusted serum iron and TSAT fell sharply, with nearly all patients classified as iron-deficient in the first three postoperative days. Adjustment for CRP and albumin significantly attenuated these declines: on POD 2, median iron was 8.2 µmol/L adjusted versus 2.0 µmol/L unadjusted (p < 0.001), and TSAT was 19% versus 4% (p < 0.001). Misclassification of iron deficiency fell by 40–50% with adjustment, and by POD 90, adjusted indices approximated baseline values. Conclusions: Systemic inflammation after THA markedly suppresses iron indices, leading to widespread misclassification of iron deficiency. Adjustment for CRP and albumin reduces this misclassification and provides a more accurate assessment of perioperative iron status. These findings complement existing evidence supporting intravenous iron supplementation by highlighting a diagnostic refinement that could improve patient selection for therapy. Full article

As an emerging construction method, the lateral launching technique for shield tunneling can ensure launching safety while significantly reducing disturbances to urban traffic. However, the influence of its design parameters on construction stability and economic performance has not yet been systematically investigated, thereby limiting its broader application in complex urban environments. To address this gap, this study proposes a comprehensive analytical framework integrating field monitoring, numerical modeling, orthogonal experiments, and regression-based optimization. Relying on a shield lateral launching project in a central urban district of Guangzhou, a systematic investigation is conducted. Field monitoring data are used to verify the reliability of the three-dimensional finite element model, confirming that deformations of both the retaining structures and the surrounding ground remain within a stable and controllable range. On this basis, the orthogonal experimental method is, for the first time, introduced into the parameter sensitivity analysis of the shield lateral launching technique. The analysis reveals the influence ranking of support parameters on surface settlement. Key parameters are then selected for optimization design according to the sensitivity order, followed by a comprehensive evaluation of deformation control effectiveness and economic performance of the optimized scheme. The results show that the deformation of both the retaining structures and the ground during construction remains below the control limits, indicating good structural stability. Among the supporting parameters, the sensitivity coefficients from high to low are the diaphragm wall thickness H_W, the grouting reinforcement range H_G, the initial support thickness of the lateral-shifting tunnel H₁, the initial support thickness of the advance launching tunnel H₂, and the elastic modulus of the diaphragm wall E_W. Based on the sensitivity ranking, the highly sensitive parameters are selected for optimization, and the optimal parameter combination is determined to be a diaphragm wall thickness of 1000 mm, a grouting reinforcement range of 1600 mm, and an initial support thickness of 100 mm for the lateral-shifting tunnel. This combination meets the safety requirements for surface settlement while effectively reducing material consumption and improving economic performance. The study provides technical and theoretical references for shield launching under complex conditions. Full article

The increasing demand for sustainable construction materials has motivated the optimization of mortar mix designs to reduce cement consumption and its environmental impact while maintaining adequate mechanical performance. This study develops a machine learning (ML) model for optimizing mortar mixtures used in concrete roofing tiles by integrating aggregate particle packing techniques with non-linear regression algorithms, using an industry-grade dataset generated in the Central Laboratory of Wienerberger Ltd. Unlike most previous studies, which mainly focus on compressive strength, this research targets the transverse strength of industrial roof tile mortar. The proposed approach combines Tarantula Curve gradation limits, experimentally derived packing density ($\eta$), and ML regression within a unified and application-oriented workflow, representing a research direction rarely explored in the literature for optimizing concrete mix transverse strength. Fine concrete aggregates were characterized through a sand sieve analysis and subsequently adjusted according to the Tarantula Curve method to optimize packing density and minimize void content. Physical properties of cements and fine aggregates were assessed, and granulometric mixtures were evaluated using computational methods to calculate fineness modulus summation (FMS) and packing density. Mortar samples were tested for transverse strength at 1, 7, and 28 days using a three-point bending test, generating a robust dataset for modeling training. Three ML models—Random Forest Regressor (RFR), XG-Boost Regressor (XGBR), and Support Vector Regressor (SVR)—were evaluated, confirming their ability to capture nonlinear relationships between mix parameters and transverse strength. The analysis of input variables, which consistently ranked as the highest contributors according to impurity-based and permutation-based importance metrics, revealed that the duration of curing, density, and the summation of the fineness modulus significantly influenced the estimated transverse strength derived from the models. The integration of particle size distribution optimization and ML demonstrates a viable pathway for reducing cement content, lowering costs, and achieving sustainable mortar mix designs in the tile manufacturing industry. Full article

Background: Nutritional status is a key determinant of surgical outcomes, but its assessment in emergency settings remains challenging. The prognostic nutritional index (PNI), which is derived from the serum ALB concentration and lymphocyte count, is a rapid, objective measure of nutritional and immune status. This study evaluated the associations between the PNI and postoperative outcomes in patients undergoing emergency laparotomy. Methods: A retrospective observational study was conducted at a single district general hospital in England, including adult patients who underwent emergency laparotomy between January 2019 and December 2023. The PNI was calculated as PNI = serum albumin (g/L) + 0.005 × total lymphocyte count (cells/μL). Patients were classified as malnourished (PNI < 50) or not malnourished (PNI ≥ 50). The outcomes assessed included postoperative complications, length of hospital stay (LOS), 30-day readmission, and three-year all-cause mortality. Statistical analyses included chi-square, Mann–Whitney U, logistic regression, and Kaplan–Meier survival analyses. Preoperative albumin and lymphocyte counts were obtained on admission or within 24 h prior to surgery to calculate the PNI. Results: Among 482 patients (median age 68 years; 57% male), 66% were malnourished. Malnutrition was significantly associated with higher ASA grade (p < 0.001), frailty (p = 0.028), and comorbidity burden (p < 0.001). Malnourished patients had longer LOSs (≥12 days; p < 0.001) and higher 30-day readmissions (p = 0.026). After adjustment for key confounders, low PNI remained independently associated with stoma formation and prolonged length of stay. After adjustment for ASA grade, frailty, comorbidity burden, hypotension, and sepsis, low PNI remained independently associated with stoma formation and prolonged length of stay. Kaplan–Meier analysis revealed reduced three-year survival in malnourished patients (log-rank p < 0.01). Conclusions: Malnutrition, as defined by a low PNI, is highly prevalent and associated with adverse postoperative outcomes in emergency laparotomy. PNI is a simple, objective, and clinically useful tool that should be incorporated into preoperative assessments to guide early nutritional optimization. However, albumin and lymphocyte counts may be influenced by acute inflammation and resuscitation in emergency presentations, and nutritional interventions were not captured in this retrospective dataset. Full article

(1) Background: Inhalation injury significantly worsens burn outcomes but lacks a standardized definition and diagnostic consensus, complicating prognosis. Existing diagnostic tools often show limited sensitivity and specificity, reducing clinical utility. This study aimed to identify potential clinical markers, recorded at or shortly after admission, for inhalation injury prognostication. (2) Methods: A retrospective cohort study of 59 burn patients admitted to Tygerberg Hospital’s Burn Centre (South Africa) between 23 April 2016 and 15 August 2017 was conducted. Descriptive statistics were reported based on data type and distribution. Fisher’s exact test, Spearman’s rank correlation (rho), and partial least squares regression (VIP scores) assessed associations, correlations, and predictive value. p < 0.05 (two-tailed) denoted significance. (3) Results: Severe inhalation injury accounted for 61% of admissions (mean 11.2; CI = 9.5–12.9), with a 38.9% mortality rate. Significant associations (p ≤ 0.008) and positive correlations (p ≤ 0.06) were noted for total body surface area (rho = 0.357), complications (rho = 0.690), and burns intensive care unit length of stay (BICU LOS, rho = 0.908). Complications and BICU LOS showed the strongest predictive contributions (VIP = 1.229 and 1.372). Lactate (rho = 0.331, p < 0.011) and hoarseness (rho = −0.314, p < 0.015) correlated significantly but lacked association. (4) Conclusions: Findings suggest elevated lactate may serve as a prognostic marker, while BICU LOS and complications may reflect disease progression. A multi-marker approach is recommended. Full article

Background and Objectives: Polypharmacy is common among older adults and its impact on the onset of dynapenia, reduced muscle strength and function, is largely unknown. Older adults hospitalized for either post-fracture or elective orthopedic surgery (knee, femur, or hip) and undergoing rehabilitation were included to investigate the association between polypharmacy and dynapenia. A further aim is to investigate associations between polypharmacy and dynapenia with outcomes including mortality, falls, and hospitalizations. Materials and Methods: On the fifth day following surgery, medical doctors administered a structured questionnaire along with physical and instrumental assessments. Polypharmacy was defined as the concurrent and regular use of 5 or more medications, dynapenia was assessed by measuring handgrip strength. The association between dynapenia and polypharmacy was detected with logistic regression, and their impact on adverse outcomes was assessed using Cox models, Kaplan–Meier curves and log-rank tests. Results: A total of 205 older adults (mean age 77.5 years; 79.5% women) were enrolled. After adjusting for sex, age, and the presence of multidimensional frailty, dynapenia was significantly associated with increased adverse outcomes such as mortality, falls, and hospitalizations (HR 2.96, 95% CI 1.22–7.20, p = 0.016). Similarly, polypharmacy was independently linked to a higher risk of mortality, falls and hospitalizations (HR 2.23, 95% CI 1.24–4.10, p = 0.007). At 6 months follow-up, polypharmacy showed a strong and significant association with dynapenia (adjusted OR 2.63, 95% CI 1.21–4.63, p = 0.019). Conclusions: These findings suggest that polypharmacy is strongly associated with dynapenia, both conditions are associated with adverse clinical outcomes in older hospitalized patients. Close monitoring and tailored interventions are recommended to mitigate these risks and improve rehabilitation outcomes in this vulnerable population. Full article

Background: In high-dimensional, small-sample omics studies such as metabolomics, feature selection not only determines the discriminative performance of classification models but also directly affects the reproducibility and translational value of candidate biomarkers. However, most existing methods primarily optimize classification accuracy and treat stability as a post hoc diagnostic, leading to considerable fluctuations in selected feature sets under different data splits or mild perturbations. Methods: To address this issue, this study proposes FRL-TSFS, a feature selection framework synergistically driven by filter-based Robust Rank Aggregation and L1-sparse modeling. Five complementary filter methods—variance thresholding, chi-square test, mutual information, ANOVA F test, and ReliefF—are first applied in parallel to score features, and Robust Rank Aggregation (RRA) is then used to obtain a consensus feature ranking that is less sensitive to the bias of any single scoring criterion. An L1-regularized logistic regression model is subsequently constructed on the candidate feature subset defined by the RRA ranking to achieve task-coupled sparse selection, thereby linking feature selection stability, feature compression, and classification performance. Results: FRL-TSFS was evaluated on six representative metabolomics and gene expression datasets under a mildly perturbed scenario induced by 10-fold cross-validation, and its performance was compared with multiple baselines using the Extended Kuncheva Index (EKI), Accuracy, and F1-score. The results show that RRA substantially improves ranking stability compared with conventional aggregation strategies without degrading classification performance, while the full FRL-TSFS framework consistently attains higher EKI values than the other feature selection schemes, markedly reduces the number of selected features to several tens of metabolites or genes, and maintains competitive classification performance. Conclusions: These findings indicate that FRL-TSFS can generate compact, reproducible, and interpretable biomarker panels, providing a practical analysis framework for stability-oriented feature selection and biomarker discovery in untargeted metabolomics. Full article

Background/Objectives: The COVID-19 pandemic disrupted healthcare delivery worldwide, potentially delaying the diagnosis and treatment of oncologic diseases. This study aimed to evaluate the impact of the pandemic on stage at diagnosis, treatment allocation, and survival outcomes among patients with gastric cancer. Methods: We retrospectively analyzed 419 consecutive patients diagnosed with gastric cancer between January 2018 and December 2021 at a tertiary oncology–surgical center. Patients were divided into pre-pandemic (2018–2019) and pandemic (2020–2021) cohorts. Demographic, clinical, and treatment variables were compared using t-tests and χ² tests. Multivariate logistics and Cox regression models were applied to identify independent predictors of metastatic presentation and mortality. Overall survival (OS) was calculated from diagnosis to death or last contact (OS_days), with same-day events censored at time zero. Results: Baseline characteristics were comparable between cohorts (age, p = 0.098; sex, p = 0.137; residence, p = 0.345). The proportion of metastatic cases (M1) increased from 42.8% in 2018–2019 to 64.4% in 2020–2021 (χ² p < 0.001). Surgical rates remained stable (55.1% vs. 47.7%, p = 0.161). Diagnosis during the pandemic independently predicted metastatic presentation (OR = 2.63, 95% CI 1.68–4.11, p < 0.001) and higher mortality (HR = 1.72, 95% CI 1.41–2.03, p < 0.001). Kaplan–Meier analysis confirmed significantly reduced OS in the pandemic cohort (log-rank χ² = 81.29, p < 0.001). Conclusions: The pandemic was associated with delayed diagnosis, stage migration toward advanced disease, and inferior survival in gastric cancer, despite comparable demographics and treatment capacity. These findings emphasize the need to safeguard diagnostic pathways—particularly endoscopy—during healthcare crises to prevent avoidable oncologic deterioration. Full article

Accurate prediction of significant wave height (SWH) is central to coastal ocean dynamics, wave–climate assessment, and operational marine forecasting, yet many high-performing machine-learning (ML) models remain opaque and weakly connected to underlying wave physics. We propose an explainable, feature engineering-guided ML framework for coastal SWH prediction that combines extremal wave statistics, temporal descriptors, and SHAP-based interpretation. Using 30 min buoy observations from a high-energy, wave-dominated coastal site off Australia’s Gold Coast, we benchmarked seven regression models (Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression, K-Nearest Neighbors, and Neural Networks) across four feature sets: (i) Base (Hmax, Tz, Tp, SST, peak direction), (ii) Base + Temporal (lags, rolling statistics, cyclical hour/month encodings), (iii) Base + a physics-informed Wave Height Ratio, WHR = Hmax/Hs, and (iv) Full (Base + Temporal + WHR). Model skill is evaluated for full-year, 1-month, and 10-day prediction windows. Performance was assessed using R², RMSE, MAE, and bias metrics, with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) employed for multi-criteria ranking. Inclusion of WHR systematically improves performance, raising test R² from a baseline range of ~0.85–0.95 to values exceeding 0.97 and reducing RMSE by up to 86%, with a Random Forest|Base + WHR configuration achieving the top TOPSIS score (1.000). SHAP analysis identifies WHR and lagged SWH as dominant predictors, linking model behavior to extremal sea states and short-term memory in the wave field. The proposed framework demonstrates how embedding simple, physically motivated features and explainable AI tools can transform black-box coastal wave predictors into transparent models suitable for geophysical fluid dynamics, coastal hazard assessment, and wave-energy applications. Full article

Background: The lack of an accurate coronary artery spasm (CAS) risk prediction model highlights the failure to consider dynamic coronary health and reveals a gap in understanding CAS. Methods: A total of 913 Taiwanese patients (460 women and 453 men) with suspected ischemic heart disease but without angiographic obstructive coronary artery disease were subjected to intracoronary methylergonovine testing during the period 2008–2025. Results: The study included 645 CAS cases (70.6%) and 268 non-CAS controls (29.4%). The multivariable logistic regression model identified 10 variables significantly associated with CAS (p < 0.05): male sex, smoking, low systolic and diastolic blood pressure, reduced B-type natriuretic peptide levels, elevated low-density lipoprotein levels, increased relative wall thickness at end-systole, high left ventricular mass index, low e’(l) values, and high Tei index. Discrimination performance was moderate, with an AUC value of 73.8% that dropped to 72.4% after bootstrapped internal validation, suggesting the potential generalizability of the derived model. The total score ranged from 36 to 98, representing a predicted probability between 12% and 98%, respectively. Conclusions: While a total score of ≥58 with the probability of CAS exceeding 50% indicates a significant chance of undiagnosed CAS, for patients with a total score ≥ 69 and a high probability of CAS ≥ 75%, coronary catheterization with CAS provocation testing is strongly recommended for a definite diagnosis. The simple 10-variable scoring model allows ranking of at-risk populations and is designed to be used as a screening tool rather than a diagnostic adjunct, enabling more efficient diagnostic resource allocation. Full article

Construction and demolition (C&D) waste remains a critical challenge in India due to accelerated urbanisation and material-intensive construction practices. This study integrates survey-based assessment with machine learning to identify key causes of C&D waste and recommend targeted minimization strategies. Data were collected from 116 professionals representing junior, middle, and senior management, spanning age groups from 20 to 60+ years, and working across building construction, consultancy, project management, roadworks, bridges, and industrial structures. The majority of respondents (57%) had 6–20 years of experience, ensuring representation from both operational and decision-making roles. The Relative Importance Index (RII) method was applied to rank waste causes and minimization techniques based on industry perceptions. To enhance robustness, Random Forest, Gradient Boosting, and Linear Regression models were tested, with Random Forest performing best (R² = 0.62), providing insights into the relative importance of different strategies. Findings show that human skill and quality control are most critical in reducing waste across concrete, mortar, bricks, steel, and tiles, while proper planning is key for excavated soil and quality sourcing for wood. Recommended strategies include workforce training, strict quality checks, improved planning, and prefabrication. The integration of perception-based analysis with machine learning offers a comprehensive framework for minimising C&D waste, supporting cost reduction and sustainability in construction projects. The major limitation of this study is its reliance on self-reported survey data, which may be influenced by subjectivity and regional bias. Additionally, results may not fully generalize beyond the Indian construction context due to the sample size and sectoral skew. The absence of real-time site data and limited access to integrated waste management systems also restrict predictive accuracy of the machine learning models. Nevertheless, combining industry perception with robust data-driven techniques provides a valuable framework for supporting sustainable construction management. Full article

The integration of Artificial Intelligence (AI) into higher education offers new opportunities for inclusive and sustainable learning. This study investigates the impact of an AI-enabled microlearning cycle—comprising short instructional videos, formative quizzes, and structured discussions—on student engagement, inclusivity, and academic performance in postgraduate management education. A mixed-methods design was applied across two cohorts (2023, n = 138; 2024, n = 140). Data included: (1) survey responses on engagement, accessibility, and confidence (5-point Likert scale); (2) learning analytics (video views, quiz completion, forum activity); (3) academic results; and (4) qualitative feedback from open-ended questions. Quantitative analyses used Wilcoxon signed-rank tests, regressions, and subgroup comparisons; qualitative data underwent thematic analysis. Findings revealed significant improvements across all dimensions (p < 0.001), with large effect sizes (r = 0.35–0.48). Engagement, accessibility, and confidence increased most, supported by behavioural data showing higher video viewing (+19%), quiz completion (+21%), and forum participation (+65%). Regression analysis indicated that forum contributions (β = 0.39) and video engagement (β = 0.31) were the strongest predictors of grades. Subgroup analysis confirmed equitable outcomes, with non-native English speakers reporting slightly higher accessibility gains. Qualitative themes highlighted interactivity, real-world application, and inclusivity, but also noted quiz-related anxiety and a need for industry tools. The AI-enabled microlearning model enhanced engagement, equity, and academic success, aligning with SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities). By combining Cognitive Load Theory, Kolb’s experiential learning, and Universal Design for Learning, it offers a scalable, pedagogically sustainable framework. Future research should explore emotional impacts, AI co-teaching models, and cross-disciplinary applications. By integrating Kolb’s experiential learning, Universal Design for Learning, and Cognitive Load Theory, this model advances both pedagogical and ecological sustainability. Full article

Background: Triple-negative breast cancer (TNBC) is an aggressive subtype with limited targeted treatment options. Immunotherapy has recently emerged as a potential strategy. The addition of pembrolizumab to neoadjuvant chemotherapy, as established in the KEYNOTE-522 trial, represents a major advancement in targeted immunotherapy for TNBC. However, real-world data validating its feasibility and outcomes remain limited. This study aims to evaluate, in real-life settings, the impact of adding pembrolizumab to neoadjuvant chemotherapy on complete pathological response (pCR), recurrence-free survival (RFS), and overall survival (OS) in patients with non-metastatic TNBC. Methods: This retrospective cohort study included patients treated at King Hussein Cancer Center (KHCC) between 2015 and 2022. Among 8523 breast cancer cases, 761 were TNBC. Eligible patients had non-metastatic TNBC, received neoadjuvant therapy, and underwent surgery. The immunotherapy group included patients treated with neoadjuvant pembrolizumab (2019–2022); the no-immunotherapy group received standard neoadjuvant chemotherapy (2015–2022). Propensity score matching (1:1, nearest neighbor) was performed based on pre-treatment covariates including age, BMI, clinical stage, comorbidities, smoking, and histopathology. Pathological response, complication rates, RFS, and OS were analyzed using logistic regression and Kaplan–Meier curves with log-rank testing. Results: The matched cohort included 130 patients (65 per group). The study groups’ baseline characteristics were well-balanced between the two groups. Postoperative complication rates were similar across groups, with no significant increase in adverse events observed in the immunotherapy group. The mean lymph node positivity ratio was significantly lower in the immunotherapy group (2.2 ± 7.7 vs. 24.3 ± 33.1, p < 0.001), indicating reduced nodal burden. Pathologic complete response (pCR) was markedly higher with immunotherapy (66.2% vs. 9.2%, p < 0.001). However, survival outcomes were significantly improved with immunotherapy. At three years, RFS was markedly higher in the immunotherapy group (91.8%; 95% CI: 85.0–99.0%) compared to the no-immunotherapy group (53.8%; 95% CI: 42.8–67.8%), with a log-rank p < 0.001. Overall survival also significantly favored the immunotherapy group, with three-year OS of 87.2% versus 67.8% in no-immunotherapy group (p = 0.0015). Conclusions: Neoadjuvant pembrolizumab significantly enhances pathological response, reduces nodal involvement, and provides durable RFS and OS benefits in non-metastatic TNBC without increasing perioperative complications. This study supports incorporating immunotherapy into standard neoadjuvant regimens for TNBC patients and provides real-world evidence from a Middle Eastern tertiary cancer center. Full article

Objectives: Metabolic dysfunction-associated steatohepatitis (MASH) is a leading cause of cirrhosis within the spectrum of metabolic dysfunction-associated steatotic liver disease (MASLD). However, the prognostic impact of diabetes mellitus (DM) in MASH-associated cirrhosis remains unclear. This study aimed to compare clinical outcomes between cirrhotic patients with and without DM. Methods: Patients with MASH-related cirrhosis were stratified into DM (DM-MASH) and non-DM (non-DM MASH) groups. The diagnosis of MASH was based on histological evidence of steatohepatitis with underlying metabolic dysfunction. The non-DM group included both obese individuals and lean/normal-weight individuals with ≥1 metabolic risk factors. Mortality, liver-related events (LREs; ascites, variceal bleeding, encephalopathy, and hepatocellular carcinoma), and sarcopenia were compared using Kaplan–Meier analysis, log-rank tests, and Fisher’s exact test. Risk factors for sarcopenia were assessed using logistic regression. Results: Median survival was significantly shorter in DM-MASH patients compared to non-DM MASH patients (1523 vs. 2618 days; p < 0.05). The incidence of LREs during follow-up was also higher in the DM-MASH group. The prevalence of sarcopenia was significantly greater among DM-MASH patients (36.1% vs. 19.7%; p < 0.05). In multivariate analysis, DM emerged as an independent predictor of sarcopenia in patients with MASH-related cirrhosis. Conclusions: DM is associated with worse outcomes in MASH-driven cirrhosis, including increased sarcopenia and reduced survival. DM may serve as a prognostic marker for identifying high-risk patients with MASH-associated cirrhosis. Full article

Introduction: Congestive heart failure (CHF) is a major global health challenge and the leading cause of hospitalization in the U.S., with disproportionately high 30-day readmission rates among low-income and minority communities. Social drivers of health and substance use both influence CHF outcomes, yet the effect of substance use on short-term readmissions remains understudied. This study evaluated the association between substance use and all-cause 30-day readmissions at a Safety-Net Community Hospital. Methods: A retrospective chart review was conducted among 500 adults admitted with CHF between 2019 and 2021. Substance use was defined as any documented use, identified through a positive urine toxicology or patient-reported social history of cocaine, tetrahydrocannabinol (THC), opioids, amphetamines, benzodiazepines, barbiturates, methadone, or phencyclidine (PCP). Alcohol and tobacco were assessed separately. Group differences were assessed using Chi-square and Wilcoxon rank-sum tests. Multivariable logistic regression was used for 30-day readmissions, and multivariable Poisson regression was used for total hospitalizations and length of stay (LOS). Results: Evidence of substance use was present in 38% of patients, with cocaine and THC most common. Patients with a history of substance use were younger, more often male, and experienced greater socioeconomic disadvantage. They had higher all-cause 30-day readmissions (21% vs. 14%; p = 0.048), more total hospitalizations (median 2 vs. 1 stay; p < 0.0001), and shorter LOS (median 4 vs. 5 days; p = 0.04). No differences were observed in 7-day post-hospitalization. Conclusions: Substance use is common among CHF patients at a safety-net hospital and was associated with higher 30-day readmissions as well as shorter hospital stays, which may reflect premature discharge rather than improved recovery. Future studies should assess whether addressing substance use alongside socioeconomic disparities can reduce readmissions in this population. Full article