Search Results (1,813)

Liver disease represents a major global health burden, and early diagnosis is essential for reducing mortality. Machine learning (ML) approaches offer non-invasive alternatives to conventional diagnostics, yet existing studies on liver disease prediction often lack systematic feature selection, apply resampling before data splitting (introducing leakage), and report results from single train-test splits without statistical testing. This study proposes a Hybrid Feature Selection (HFS) framework combining Pearson-correlation-based redundancy elimination with a weighted Information Gain–Gain Ratio scoring function, integrated with SMOTE within a leakage-free pipeline. The framework is evaluated on two benchmarks—the Indian Liver Patient Dataset (ILPD, n = 583) and the BUPA Liver Disorders Dataset (n = 345)—across ten classifiers and ten independent train-test splits, with significance assessed via paired Wilcoxon signed-rank tests. On ILPD, the HFS + SMOTE pipeline produced statistically significant ROC-AUC improvements (p < 0.05) in five of ten classifiers and resolved majority-class collapse, raising mean Specificity from 0.00–0.33 to 0.61–0.92. A 2 × 2 ablation study confirmed that HFS and SMOTE contribute independently, with SMOTE driving the Specificity transformation and HFS reducing feature-space noise. Sensitivity analyses demonstrated robustness to the weighting parameter w and confirmed k = 6 as the optimal feature count. Replication on BUPA—which exhibits near-perfect class balance and no feature redundancy—produced a principled null result, confirming that the pipeline’s effectiveness is mechanistically linked to dataset characteristics. The HFS algorithm consistently identified four clinically meaningful core features (AST, ALT, Total Bilirubin, Age) across all runs, validated by SHAP and Permutation Importance stability analysis. Full article

Background: Early and objective identification of cognitive decline in aging populations remains a clinical challenge. Pupillary light reflex (PLR) and eye movement parameters represent non-invasive, quantitative biomarkers of autonomic and central nervous system integrity, yet their diagnostic utility for cognitive impairment in community-dwelling older adults, particularly in those with mild or borderline impairment (predominantly GDS-Stage 2), remains underexplored. Methods: This cross-sectional study analyzed 383 community-dwelling older adults (mean age 69.78 ± 6.29 years; 68.7% female). Ten PLR parameters (n = 202 with complete PLR measurements) and ten eye movement parameters were measured. Associations with cognitive decline (deterioration grade, GDS 2–4) were evaluated using Spearman correlation analysis and multivariate linear regression (adjusted for age, sex, BMI, and hypertension). Stratified analyses and ordinal logistic regression sensitivity analysis were performed to assess robustness. FDR correction (Benjamini–Hochberg) was applied for multiple comparisons. Predictive modeling was conducted using ElasticNet regression with 5-fold cross-validation. Results: After FDR correction, resting pupil diameter (ρ = −0.47, q < 0.001), constriction amplitude (ρ = −0.40, q < 0.001), mean constriction velocity (ρ = −0.36, q < 0.001), mean dilation velocity (ρ = −0.36, q < 0.001), and all eye movement velocity parameters (ρ = −0.22 to −0.41, q < 0.001) demonstrated significant negative correlations with cognitive decline. Multivariate regression confirmed resting pupil diameter (β = −0.286, q < 0.001) and constriction amplitude (β = −0.223, q < 0.001) as independent predictors. Sensitivity analysis using ordinal logistic regression yielded consistent results. Predictive modeling yielded modest performance for the primary outcome (PLR-only cross-validated R² = 0.184), whereas models using eye movement features alone or in combination with PLR features performed near chance (R² ≤ 0.04) or showed instability, indicating that these parameters are not yet suitable as standalone diagnostic tools. Exploratory analyses of depression and anxiety were limited by floor effects (≥89% zero scores). Conclusions: PLR and eye movement parameters show significant negative associations with cognitive decline in older adults, particularly in a sample skewed toward mild impairment (predominantly GDS-Stage 2). These findings provide preliminary, hypothesis-generating signals that warrant validation in clinical samples with broader cognitive impairment distributions, and these parameters should not yet be considered standalone diagnostic biomarkers. Full article

Background/Objectives: Topical imiquimod is an established non-invasive treatment for superficial basal cell carcinoma (sBCC). However, data on treatment-associated changes in tumor microvascularization remain limited. This study investigated vascular changes before and after imiquimod therapy using multimodal non-invasive imaging. Methods: In this single-center, prospective observational study, 31 basal cell carcinomas in 20 patients were examined before and 12–16 weeks after topical imiquimod therapy (5%, five times weekly for six weeks) using dermoscopy, dynamic optical coherence tomography (D-OCT), and line-field confocal optical coherence tomography (LC-OCT). Analyses were performed as paired before-and-after comparisons. While approved for sBCC, a small number of thin nodular and infiltrative BCCs were included exploratorily; subgroup analyses were not powered. Results: Dermoscopy showed a nominally significant shift toward smaller vessel diameter categories after therapy (ATS = 8.183, df = 1, p = 0.004). D-OCT-derived parameters (vessel density, vessel diameter, and depth of the vascular plexus) did not show nominally significant changes. LC-OCT showed nominally lower apparent intratumoral flow scores (ATS = 13.285, df = 1, p < 0.001), reduced occurrence of vessel-wall-associated intraluminal structures showing a rolling-like motion pattern (86.7% before treatment versus 33.3% after treatment; ATS = 13.357; df = 1, p < 0.001), and a reduction in maximum vessel diameter (ATS = 6.110, df = 1, p = 0.013). The primary LC-OCT inferential analyses were performed at the lesion level without adjustment for within-patient clustering and should therefore be interpreted as exploratory. An additional patient-cluster-adjusted paired change-score sensitivity analysis for LC-OCT maximum vessel diameter yielded a directionally consistent estimate (−17.81 µm; 95% CI: −34.40 to −1.23; p = 0.037). The primary exploratory endpoints were LC-OCT–based apparent intratumoral flow and maximum vessel diameter; secondary endpoints included dermoscopic and D-OCT–based vascular parameters. In the exploratory response-stratified analysis, the change in LC-OCT-based maximum vessel diameter did not differ significantly among the assigned response groups (Kruskal–Wallis H = 3.870, df = 2, raw p = 0.144; BH-adjusted p = 0.753). Conclusions: LC-OCT detected several exploratory vascular changes between the pre-treatment examination and follow-up and may provide complementary information for the non-invasive assessment of BCC after imiquimod therapy. Given the exploratory design, limited sample size, and lack of systematic histological confirmation, these findings are hypothesis-generating and require validation in larger prospective studies. Full article

Background: Autonomic dysfunction is increasingly recognized as a key mechanism in disorders involving the brain–gut axis and gastrointestinal symptom generation. Transcutaneous vagus nerve stimulation (tVNS) is a noninvasive neuromodulatory technique investigated for its potential effects on autonomic regulation. Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines, with protocol registration in PROSPERO. Randomized controlled trials (RCTs) investigating auricular or cervical tVNS in patients with visceral disorders were included. Continuous outcomes were pooled using mean differences (MDs) or standardized mean differences (SMDs) with 95% confidence intervals (CIs). When multiple publications originated from the same trial population, only independent datasets were considered for quantitative synthesis to avoid double-counting participants. Risk of bias was assessed using the PEDro scale and certainty of evidence with the GRADE approach. Results: Seven RCTs met the inclusion criteria. tVNS demonstrated a small but statistically significant improvement in gastrointestinal symptoms based on change-from-baseline GSRS scores (MD −0.19; 95% CI −0.29 to −0.09; p < 0.001; I² = 0%). Although statistically significant, the magnitude of this effect was modest, and its clinical relevance remains uncertain. No significant effects were observed on cardiac vagal tone (SMD 0.19; 95% CI −0.53 to 0.90; p = 0.61; I² = 73%) or systolic blood pressure (MD −1.24 mmHg; 95% CI −6.69 to 4.21; p = 0.66; I² = 0%). Evidence regarding cardiac autonomic neuropathy (CAN) was limited to a single independent randomized controlled trial, which found no significant differences between tVNS and sham stimulation. Conclusions: tVNS provides modest statistically significant improvements in gastrointestinal symptoms, supporting its role as a symptomatic neuromodulatory intervention. However, the available evidence for this outcome was based on only two studies, and the clinical relevance of the observed effect remains uncertain. No statistically significant pooled effects were observed for the cardiovascular autonomic markers assessed in this review. Evidence regarding CAN was limited to a single independent study. Therefore, the available evidence remains limited and heterogeneous, and further high-quality randomized controlled trials are warranted. Full article

Neurodevelopmental disorders (NDDs) are associated with impairments in communication, behavior, and social interaction, making accurate diagnosis clinically challenging. Autism Spectrum Disorder (ASD), a major NDD, often exhibits atypical speech patterns characterized by altered prosody and reduced emotional expressiveness. The study proposes a hybrid dual-path framework for ASD detection from emotional speech using two strategies: PCA–GMM-based acoustic modeling and a CNN–BiLSTM–Attention architecture for spectral–temporal feature learning. The proposed framework captures probabilistic, spectral, and temporal speech characteristics for robust ASD classification. Acoustic analysis demonstrated clear separability between ASD and non-ASD speech, while the deep learning framework achieved stable and reliable performance across multiple emotional conditions. Experimental evaluation achieved 98.3% accuracy, AUC values ranging from 0.9699 to 0.9864, and F1-scores up to 0.9891. The findings highlight the potential of AI-driven speech analysis as a scalable and non-invasive tool for early ASD screening and predictive healthcare applications. Full article

Background: Transient elastography (TE) is widely used for noninvasive assessment of liver fibrosis. In patients undergoing hemodialysis, however, liver stiffness measurements (LSM) may be affected by rapid intradialytic changes in volume status, venous congestion, and other non-fibrotic determinants. We prospectively evaluated peridialytic variability in liver stiffness and the concordance of serum fibrosis indices with elevated LSM in patients receiving maintenance hemodialysis. Methods: In this prospective paired pilot study, 45 adults on maintenance hemodialysis underwent LSM and controlled attenuation parameter (CAP) assessments immediately before and after a dialysis session; paired data were available for 41 patients. The Fibrosis-4 index (FIB-4) and the aspartate aminotransferase-to-platelet ratio index (APRI) were calculated from routine laboratory values. Paired comparisons, correlation analyses, and receiver operating characteristic curves were used to assess within-patient changes and the ability of serum indices to identify elevated pre-dialysis liver stiffness (LSM ≥ 8 kPa). Because no histologic or imaging reference standard for fibrosis was available, these analyses were interpreted as evidence of concordance with elevated LSM rather than as diagnostic accuracy for liver fibrosis. Results: Median LSM was 7.1 kPa (interquartile range [IQR] 5.2–12.1) pre-dialysis and 7.7 kPa (IQR 5.8–12.2) post-dialysis, with no significant paired change (median ΔLSM −0.2 kPa [IQR −1.1 to 1.2]; p = 0.898). However, the proportion with LSM ≥ 8 kPa increased from 36.6% to 46.3%, with 4 of 41 patients (9.8%) newly exceeding the threshold. CAP values showed no significant paired change (p = 0.511). Intradialytic weight loss was not associated with ΔLSM (rho = −0.13, p = 0.44). FIB-4 and APRI showed poor correlation with LSM and limited concordance with elevated LSM (area under the curve 0.553 and 0.578, respectively, with wide confidence intervals). Conclusions: In this exploratory hemodialysis cohort, cohort-level median LSM did not change significantly after dialysis, but clinically relevant individual-level reclassification occurred in approximately 10% of patients. Measurement timing may alter LSM-based classification, underscoring the need for dialysis-specific validation of LSM thresholds and noninvasive assessment strategies. Full article

(1) Background: Clinical trial data extraction from registries such as ClinicalTrials.gov remains labor-intensive and error-prone, often missing critical details hidden in unstructured protocol descriptions. Large Language Models (LLMs) offer potential to automate this process, yet systematic multi-model comparisons on real clinical trial data remain scarce. (2) Methods: Four LLMs (OpenAI o4-mini-high, Anthropic Claude-Sonnet-4, Google Gemini 2.5-Pro, and Meta Llama-4-Maverick) extracted brain stimulation parameters from 67 transcranial direct current stimulation (tDCS) trials in Parkinson’s disease via a structured JSON schema. Pairwise inter-model agreement was quantified with Cohen’s Kappa and percentage agreement across binary, categorical, and multi-component task tiers. (3) Results: Under exact-string matching, agreement was near-perfect for binary classifications (non-invasive classification: 100%; brain stimulation presence: 99.3%, $\kappa$ = 0.50) and substantial for categorical extractions (primary stimulation type: 96.4%, $\kappa$ = 0.70), but fell to 48.6% ($\kappa$ = 0.43) for complex anatomical targets. Numeric parameters revealed model-specific strengths: o4-mini-high and Claude-Sonnet-4 achieved perfect duration agreement (r = 1.000, n = 19) while Llama-4-Maverick diverged substantially (r < 0.12). Validation against an expert gold standard (100% inter-annotator agreement on a 20-trial overlap) confirmed high extraction accuracy across all features (mean 93.7–98.9%). Crucially, the low agreement on anatomical targets proved to be an artifact of exact-string scoring: under the same semantic matching used to measure accuracy, inter-model agreement rose to 97.0%, coinciding with the 95.5% expert accuracy. Inter-model agreement therefore tracks accuracy once both are measured on a common basis. (4) Conclusions: Exact-string inter-model agreement decreases with task complexity, but this decline largely reflects interchangeable free-text wording rather than reduced accuracy. Evaluated semantically, agreement and expert accuracy are both high and closely aligned. A residual risk is not low accuracy but the rare error shared across all models, which agreement cannot detect, and which overall accuracy can itself mask when one class dominates. These findings inform hybrid human–AI systematic review pipelines in which targeted expert oversight focuses on shared-error and minority-class detection. Full article

Automated assessment of coronary artery disease (CAD) severity from invasive X-ray angiography is important for diagnostic accuracy, but there are limitations due to limited label data and privacy issues in multi-institutional collaboration. This research proposes a Federated Graph-Transformer Network (FGTN) that models coronary vessel compositions as graphs and uses a transformer unit of measurement to encode global anatomic circumstances for severity scaling. The publicly available X-ray angiography images and SYNTAX-Score dataset will be used, consisting of 232 X-ray coronary angiography images with analogous clinically calculated SYNTAX tons and angiographic factors from 231 patients, manually annotated by a competent cardiologist. The vascular tree is a primary segment that transforms inside the node-edge graph representing bifurcation and vessel sections, continuing topological features, and then processes by graph convolutions integrated with transformer self-attention to capture simultaneously the local stenosis features and global vessel relationships. A Horizontal Federated Learning Strategy allowing collaborative model training on clinical sites without sharing raw data. The intended FGTN achieved overall accuracy of 99.4%, precision of 97.6%, recall of 98.8%, and F1-score of 98.2%, exceeding the usual CNNs, Attention-UNet, and Capsule Connection baselines by a margin of 4–7%. For non-obstructive, mild, moderate, and severe stenosis classes, the AUC values were 0.98, 0.97, 0.96, and 0.95, respectively. Moreover, the Federated Learning framework shows firm convergence with lower, compared to 1.8% performance degradation, when compared to centralized training, and confirms robustness via heterogeneous data distribution. These results show that the proposed solution automatically calculates the CAD severity grading from coronary angiography images. Full article

Background/Objectives: Autoimmune hepatitis (AIH) is a chronic immune-mediated liver disease that may progress to cirrhosis and liver failure if not diagnosed early. Although liver biopsy remains the reference standard for fibrosis assessment, its invasive nature limits routine use. This study aimed to compare the diagnostic performance of ultrasound-based microvascular imaging (MVI), transient elastography (TE), and serum fibrosis indices (APRI and FIB-4) in patients with biopsy-confirmed AIH. Methods: Fifty-five patients with probable or definite AIH according to IAIHG criteria were included in the study. All patients underwent liver biopsy, and fibrosis stage was assessed using the METAVIR system. TE and MVI examinations were performed, and APRI and FIB-4 scores were calculated. Diagnostic performance was evaluated using AUROC, sensitivity, and specificity. Spearman correlation and logistic regression analyses were additionally performed. Results: The mean age of the patients was 49.2 years, and most patients were women. Cirrhosis was present in 58.2% of the cohort. TE demonstrated high diagnostic accuracy, whereas FIB-4 showed moderate performance and APRI demonstrated limited utility. MVI achieved the highest diagnostic performance, with AUROC values of 0.99 for significant fibrosis and 0.97 for cirrhosis. MVI showed the strongest correlation with histological fibrosis stage (r = 0.916, p < 0.001), followed by TE (r = 0.907, p < 0.001). MVI was strongly associated with histologically confirmed cirrhosis (OR 16.7, 95% CI 2.36–118.2, p = 0.004). Conclusions: MVI demonstrates diagnostic performance comparable to TE and may represent a promising adjunctive non-invasive imaging biomarker for fibrosis assessment in AIH. Larger multicenter studies are required for external validation before routine clinical implementation. Full article

Electroencephalography (EEG) is a noninvasive tool used by healthcare professionals to measure brain electrical activity. EEG analysis can indicate various anomalies linked to different brain pathologies, including seizures. Traditionally, the analysis is confined to two-dimensional displays and relies exclusively on the visual modality, limiting a comprehensive overview. EEG analysis through visualisation is challenging and time-consuming, and artificial intelligence (AI) is increasingly used to aid the process of seizure detection. However, the educational value of AI-assisted seizure detection models depends on the explainability of the underlying models. Explainable AI can help learners understand the features and patterns associated with seizure detection and also support informed use of AI-based decision support systems. M2EEG-VR leverages the focus and immersive capabilities of virtual reality (VR) with the aim of developing a multi-modal platform for EEG seizure detection analysis with a human-in-the-loop. The ability to understand EEG and seizure patterns is key to addressing and effectively treating many neurological conditions. Neonatal seizure detection is particularly challenging where seizure patterns are subtle and context dependent. This study advances toward multi-modal analysis by encoding EEG signals into auditory representations using AI that aids in the acoustic detection of the presence of neonatal seizures in EEG. The platform also introduces a 3D brain model with a spatial mapping of seizure regions. In a user study (N = 20, 4 prior EEG experience, 16 no prior EEG experience), participants achieved higher seizure detection accuracy in the combined visual and auditory condition (mean = 7.6 ± 1.2) than in visual-only or audio-only modes. These preliminary findings suggest that a multi-modal environment may improve the accuracy of detection. However, further controlled studies are needed to ascertain the performance benefits. Usability was rated excellent (SUS = 83 ± 11), and task load remained moderate (NASA-TLX = 36.6). The findings suggest that VR multi-modal interaction can reduce cognitive load and enhance the explainability of complex EEG data in a focused virtual environment. The analysis of the diagnostic accuracy showed that participants without prior EEG knowledge performed similarly across all modalities to those with prior EEG knowledge. This implies that the accessibility barrier is reduced for novice users using the tool for the EEG review/detection task. This, together with high usability and moderate task load scores, indicates that the tool may be suitable for medical training applications. A multi-modal EEG in VR may prove useful in education and also be used as a test bench to further explore AI with human-in-the-loop paradigms for seizure detection. Full article

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality in the elderly worldwide. Over the past two decades, there has been a wealth of evidence of a close relationship between autonomic nervous system activity and cardiovascular mortality, including sudden cardiac death. Heart rate variability (HRV), derived from photoplethysmographic (PPG) signals, is increasingly recognized as a promising non-invasive digital marker for evaluating autonomic nervous system function and stratifying CVD risk. The application of machine learning algorithms to PPG-derived HRV analysis offers a promising approach for improving CVD risk stratification and facilitating the development of personalized medicine strategies. Background/Objectives: To evaluate the potential of heart rate variability indicators in predicting the risk of developing CVD in individuals aged 65 years and older. Methods: The study involved individuals aged 65 years and older, divided into two groups: those with a risk of developing CVD (n = 54) and those without risk (n = 46). The first stage included a questionnaire as well as anthropometric and hemodynamic measurements. At the second stage, a PPG was performed using the Eldar computer photoplethysmograph and Eldar-Vario software, followed by an analysis of time-domain and spectral HRV parameters. Statistical data analysis was conducted using the SPSS Statistics 22.0 software package, focusing on the evaluation of associations between HRV indicators and the presence of CVD. Interpretable machine learning models were developed using logistic regression and a random forest algorithm within a nested cross-validation framework. In addition to the discriminatory characteristics, Brier score, LogLoss, calibration analysis, error matrices, permutation importance, and SHAP interpretation were analyzed in the study. Results: In patients with cardiovascular diseases, a statistically significant decrease in heart rate variability was revealed: SDNN by 2 times (26 [Q1–Q3: 15, 35] ms), pNN50 by 3.5 times (4 [3, 5]%), TINN by 5 times (31 [20, 51] ms), and HRV by 2.5 times (6 [4, 8.7]). In addition, a decrease was seen in the spectral components of VLF by one-fold (2450 [Q1–Q3: 2450, 4500] ms²), LF by four-fold (750 [750, 1500] ms²) and HF by five-fold (450 [450, 750] ms²) (p < 0.05). At the same time, there was a significant increase in the VLF/HF and LF/HF ratios, which indicates a predominance of sympathetic activity. According to the results of the correlation analysis, statistically significant associations of HRV indicators with age, physical activity level, body mass index and systolic blood pressure were revealed. The results of machine learning also revealed the association of HRV with arterial hypertension, physical activity and BMI. The best final results were demonstrated by a random forest model with a combined set of clinical and HRV signs of HF and RMSSD (ROC-AUC was 0.9988). The signs of heart rate variability obtained by photoplethysmography demonstrated additional prognostic value in relation to clinical signs. PPG-derived HRV features demonstrated additional discriminatory value for cardiovascular risk stratification. Conclusions: The obtained data demonstrate a close association between the risk of developing cardiovascular disease and autonomic nervous system dysfunction. The decrease in heart rate variability is most pronounced in elderly individuals with existing cardiovascular disease and can be considered a potential tool for developing diagnostic, prognostic, and risk stratification strategies. The use of machine learning demonstrated that heart rate variability features obtained using photoplethysmography improve diagnostic prognostication and classification of cardiovascular diseases compared to models based solely on clinical data. Full article

Objective: This study aimed to evaluate the risk of medical device-related pressure injury (MDRPI) development among patients exposed to medical devices and to assess the clinical utility of the Medical Device-Related Pressure Injury Risk Assessment Scale (MDRPIS). Methods: This clinical assessment study included 132 patients receiving care in intensive care, palliative care, and home-care units. The MDRPIS total score ranges from 8 to 27, with scores of 8–12 indicating high risk, 13–21 indicating moderate risk, and 22–27 indicating low risk. The scale was used to assess MDRPI risk associated with life-sustaining medical devices. Its psychometric performance was evaluated through analyses of internal consistency, criterion validity against the Braden Scale, and diagnostic accuracy using receiver operating characteristic (ROC) analysis. Results: The MDRPIS demonstrated strong discriminative ability for identifying patients at risk of MDRPI, with an area under the curve (AUC) of 0.822. A cut-off score of ≤16 was identified as the optimal threshold for detecting high-risk patients. Patients with MDRPIS scores of 16 or lower had a significantly higher incidence of MDRPI than those classified as low risk (p < 0.001). Respiratory support devices, particularly non-invasive ventilation (NIV)/continuous positive airway pressure (CPAP) masks and tracheostomy flanges or securement devices, were identified as the most significant risk factors for injury development. The highest incidence of MDRPI was observed among patients in intensive care units, followed by those in palliative care and home-care settings, indicating a statistically significant concentration of device-related risk in high-acuity care environments (p < 0.05). Conclusions: Clinical settings, particularly intensive care and palliative care units, should incorporate the MDRPIS into routine risk assessment protocols to facilitate targeted preventive interventions, including prophylactic dressings and advanced fixation techniques for patients using high-risk devices such as NIV masks and tracheostomy securement systems. The systematic implementation of the MDRPIS may support more effective allocation of nursing resources and enhance patient safety by enabling the early identification and prevention of avoidable device-related pressure injuries. Furthermore, the findings indicate that an MDRPIS score of 16 or below represents a clinically meaningful threshold for initiating preventive interventions. Full article

Background/Objective: Hip osteoarthritis (HOA) is a major source of pain and disability worldwide. Although total hip arthroplasty (THA) provides substantial symptomatic improvement, a subgroup of patients remains ineligible because of severe comorbidities, frailty or elevated perioperative risk. Conservative treatments provide only temporary symptom control and transarterial periarticular embolization (TAPE) has emerged as a minimally invasive intervention targeting abnormal periarticular hypervascularity and inflammation. The aim was to compare clinical outcomes of TAPE and conservative care (CC) in patients with symptomatic HOA considered unsuitable for THA. Methods: A prospective non-randomized two-centre cohort study included consecutive adults aged ≥60 years with symptomatic HOA and baseline visual analogue scale (VAS) pain scores over 40. Patients were managed with either TAPE or structured CC. The primary endpoint was change in VAS pain score from baseline to 6 months. Secondary outcomes included Lower Extremity Functional Scale (LEFS), Timed Up-and-Go (TUG) and analgesic use. Patients were evaluated at baseline, 1, 3 and 6 months. Results: A total of 81 patients were screened, 69 were enrolled and 62 were included in the complete case longitudinal analysis. Baseline symptom severity was greater in the TAPE group, with higher VAS scores (73.6 ± 12.5 vs. 63.7 ± 14.1; p = 0.003) and lower-joint space width (1.37 ± 0.79 vs. 2.07 ± 0.89 mm; p < 0.001). The reduction in pain occurred during the first three months following embolization, after which symptom trajectories stabilized. Mean VAS pain in the TAPE group decreased from 73.6 ± 12.5 at baseline to 55.4 ± 13.0 at three months and 56.8 ± 13.6 at six months. LEFS improved in both groups throughout follow-up. Conclusions: TAPE was associated with symptom improvement and short-term safety in a small cohort of surgery-ineligible patients with HOA. The observed benefits appeared greatest within the first three months. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) has been linked to atrial fibrillation (AF); however, its relationship with specific AF subtypes remains unclear. This prospective, single-center, observational case–control study investigated whether MASLD is independently associated with AF presence and its subtypes. Materials: A total of 327 participants were analyzed, including 119 controls and 208 patients with AF. Comprehensive clinical history, anthropometric measures, laboratory testing, 24 h Holter ECG, and echocardiography were performed. Clinically inferred MASLD was defined according to the current EASL–EASD–EASO guidelines using clinical and non-invasive indices (Hepatic Steatosis Index, Fatty Liver Index, Fibrosis-4 Index). No liver biopsy or imaging confirmation of steatosis or fibrosis was performed, and therefore, the diagnosis represents a clinically inferred (“probable”) MASLD. To minimize systematic bias and improve baseline comparability between groups, propensity score matching and complementary regression analyses were applied. Results: Overall probable MASLD prevalence did not differ between AF and controls (42% vs. 44%, p = 0.742). A clear phenotypic gradient emerged across subtypes: lowest in permanent AF (PermAF, 27.1%) versus paroxysmal (47.1%) and persistent AF (51.4%) (p = 0.021). PermAF exhibited the most advanced comorbidity—highest CHF (78.6%), CKD (71.4%), HFpEF (48.6%), FIB-4 (median 2.67), the lowest TG/HDL–cholesterol ratio (1.93 vs. 3.32; p < 0.001), and progressive renal impairment. Statin therapy reached 80% in clinically inferred MASLD-positive PermAF. The elevated FIB-4 observed in PermAF must be interpreted with explicit caution: this group was substantially older (median 79.5 years) and carried the highest burden of chronic heart failure and chronic kidney disease; therefore, in this subgroup, FIB-4 most plausibly reflects age and cardio-renal comorbidity rather than histologically confirmed hepatic fibrosis. After matching, MASLD was not an independent predictor of AF presence (OR = 0.96; 95% CI: 0.59–1.46) or its clinical severity. Conclusions: Probable MASLD, defined by clinical and non-invasive indices, was not independently associated with AF in this cohort, but AF subtypes exhibited a clear phenotypic gradient—from a metabolically driven profile in early AF to a cardio-renal and fibrotic pattern in advanced, elderly AF. Elevated FIB-4 values in PermAF most plausibly reflect age and cardio-renal comorbidity rather than true histologically confirmed hepatic fibrosis. These findings support a phenotype- and population-dependent MASLD–AF relationship and underscore the need for imaging- and histology-verified longitudinal studies. Full article

Background: Pneumothorax is a clinically heterogeneous condition with a substantial nursing-care burden; yet, contemporary descriptive data that combine medical and nursing variables remain scarce in the Croatian setting. Our aim was to describe the demographic profile, treatment patterns, and nursing-care requirements of patients treated for pneumothorax at a single regional hospital. Moreover, we explored factors associated with chest drainage and with adverse nursing-sensitive outcomes. Methods: We conducted a retrospective, single-centre, cross-sectional analysis of adult and adolescent patients consecutively admitted due to pneumothorax to Varaždin General Hospital between 1 January 2019 and 31 December 2021. Sociodemographic, clinical, and nursing-care variables were extracted from the hospital information system and the electronic nursing documentation. Nursing diagnoses were classified using NANDA International (NANDA-I) terminology. Proportions are reported with 95% Wilson score confidence intervals. Bivariate associations between categorical variables were assessed using the Fisher exact test with Haldane–Anscombe-corrected odds ratios; the Kruskal–Wallis test with Bonferroni-corrected pairwise comparisons were used for continuous distributions. Independent associations with the chest drainage placement, prolonged length of stay (>14 days), and worsening of dependency category were assessed with L1-penalised logistic regression (α = 0.5), with 1000-iteration non-parametric bootstrap 95% CIs and p-values. Results: Of 60 patients included, 39 (65.0%; 95% CI 52.4–75.8) were male and 33 (55.0%; 42.5–66.9) were aged 60 years or older. Spontaneous pneumothorax accounted for 27 cases (45.0%; 33.1–57.5), traumatic for 23 (38.3%; 27.1–51.0), and iatrogenic for 10 (16.7%; 9.3–28.0). Chest drainage was used in 44 patients (73.3%; 61.0–82.9), universally in iatrogenic cases. After adjustment, age ≥ 60 years was independently associated with the receipt of chest drainage (adjusted OR 3.67; 95% CI 1.21–13.56; p = 0.026), with a prolonged length of stay (adjusted OR 3.69; 95% CI 1.02–21.00; p = 0.042) and with functional deterioration (adjusted OR 4.29; 95% CI 1.21–22.62; p = 0.028). Risk for falls (58.3%) and Bathing self-care deficit (26.7%) were the most frequent NANDA-I diagnoses; 14 patients (23.3%) deteriorated by at least one dependency category by discharge. Conclusions: Patients hospitalised with pneumothorax at our centre were predominantly older men with a substantial nursing-care workload. An older age was the most consistent independent correlate of both invasive treatment and adverse nursing-sensitive outcomes. The findings provide a descriptive baseline for the Croatian setting and should be interpreted as hypothesis-generating, given the modest sample size and the single-centre retrospective design. Full article