Search Results (1,998)

Background and Objective: Accurate medication allergy documentation is essential for patient safety. This study evaluated discrepancies and documentation quality between medication allergy records in the primary care information system (AP-Madrid) and the Hospital Clinical Information System (HCIS) at a tertiary care hospital. Methods: A retrospective cohort study included 223 patients with medication allergy records during 2022. Agreement between systems was assessed using Cohen’s kappa, and documentation quality was classified as adequate, moderate, or poor based on four predefined parameters. Results: Agreement between AP-Madrid and HCIS was poor (Cohen’s κ = −0.25). Of the 223 patients, medication allergy records were documented exclusively in HCIS for 94 patients, exclusively in AP-Madrid for 36 patients, and in both systems for 88 patients, while 5 patients had medication allergy information identified only in free-text clinical documentation. Documentation quality was predominantly poor in both HCIS (60.4%, n = 134) and AP-Madrid (61.9%, n = 138). Beta-lactam antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs) were the most frequently implicated therapeutic groups. Two positive medication re-exposure events were identified. Conclusions: Substantial discrepancies and poor documentation quality were identified between primary care and hospital medication allergy records, highlighting limited interoperability between the two systems. Improving the completeness, consistency, and standardized exchange of medication allergy information across healthcare settings may enhance patient safety and continuity of care. Full article

Background/Objectives: Multiple Sclerosis (MS) is a complex, chronic autoimmune neuroinflammatory disorder that significantly impacts patients’ health-related quality of life (HRQoL) and increases the burden of healthcare costs. However, evidence that quantifies the covariate-adjusted differences between the MS and non-MS populations is limited due to the presence of outliers, which frequentist approaches may not adequately address. Therefore, this study aims to examine healthcare expenditure and HRQoL in patients with MS compared to the non-MS population using a robust Bayesian approach. Methods: This retrospective cross-sectional study includes adults ($\ge$18 years) with MS and those without MS using the 2017–2022 Medical Expenditure Panel Survey (MEPS) data. The Bayesian quantile LASSO (BQL) is applied to examine the association between MS and different response variables under three quantile levels. Markov Chain Monte Carlo (MCMC) with Gibbs sampling was used to estimate the coefficients from the posterior distribution of model parameters. The convergence of the MCMC chain has also been assessed to ensure the reliability and stability of the posterior estimates. Alternative methods, including Bayesian LASSO and the multivariate Generalized Linear Models (GLMs), are also applied for comparison in both prediction and estimation. Results: The results of the BQL show that for the healthcare expenditures, the estimated total healthcare expenditure in patients diagnosed with MS is $29,860.11 (95% credible interval $27,826.96 to $31,825.63) more compared to those without MS under the quantile level 0.5. With the coefficient −3.16 (95% credible interval −4.31 to −2.07), MS is negatively related to the mental component of VR-12 under the median quantile. Compared with individuals without MS, patients with MS have a 13.40-point lower score on the physical component of the VR-12 (95% credible interval −14.56 to −12.19) at the 0.5 quantile. At the median quantile, BQL achieves prediction errors of 6723.86 for healthcare expenditure, 6.57 and 6.41 for the mental and physical components of the VR-12, respectively. Conclusions: BQL with a quantile level of 0.5 shows the lowest in-sample prediction error when examining the healthcare costs and HRQoL in MS. Full article

Background/Objectives: Romania faces a shortage of family physicians, particularly in rural areas. Undergraduate Family Medicine rotations may influence students’ perceptions before residency selection, but the contribution of rural versus urban training settings remains insufficiently documented. This study examined differences between post-rotation perceptions and students’ recalled pre-rotation perceptions, and explored whether these differences varied according to placement setting. Methods: This single-centre study used a single-administration retrospective pretest–posttest observational design. After completing the mandatory one-month Family Medicine rotation, 141 sixth-year medical students completed a questionnaire containing recalled pre-rotation and post-rotation ratings on 12 five-point Likert items. Paired comparisons used Wilcoxon signed-rank tests; multiple-testing corrections were applied within the defined item families. A baseline-adjusted model with robust standard errors examined rural placement. Results: The composite score increased from 3.62 ± 0.84 to 4.10 ± 0.74 (mean change 0.483; W = 602.5; Z = −7.990; p < 0.001; r = 0.673). All 12 paired item comparisons remained significant after Bonferroni correction. No rural–urban item comparison remained significant after correction. In the adjusted model, rural placement was not associated with a statistically significant difference in the post-rotation score (β = 0.197, 95% CI −0.002 to 0.397; p = 0.053). Among the 86 rurally exposed students, the mean willingness to practice rurally was 2.87/5. The apparent association with career intention was not retained when excluding the overlapping career-likelihood item (11-item composite: H = 6.695, p = 0.153). Conclusions: Students reported more favourable perceptions after the rotation than they recalled having before it, but their willingness to practice rurally remained limited. The uncontrolled, retrospective design does not permit causal attribution, and the data do not establish a rural-placement advantage or subsequent specialty choice. Full article

Background: Burnout, defined by emotional exhaustion, depersonalization, and reduced personal accomplishment, is a major occupational health concern among healthcare professionals. Burnout has been linked to depression, anxiety, and medical errors, but its association with suicidality in healthcare settings remains insufficiently explored. Objective: To investigate the association between burnout dimensions and suicidality among healthcare professionals, and to identify occupational and demographic factors influencing this relationship. Methods: A cross-sectional study was conducted among 195 healthcare professionals in Romania, recruited through voluntary participation in an anonymized online survey. Burnout was assessed using an adapted tool inspired by the Maslach Burnout Inventory, while suicidality outcomes included suicidal ideation, self-harm, and suicide attempts. Descriptive statistics were calculated for demographic and professional variables. Pearson correlations and multivariable linear regression analyses, adjusted for age, professional experience, and shift work, were performed to evaluate associations between burnout dimensions and suicidality. Results: Emotional exhaustion (r = 0.42, p < 0.001) and depersonalization (r = 0.37, p < 0.001) were positively correlated with suicidality, while reduced personal accomplishment was negatively correlated (r = −0.29, p = 0.002). In multivariable regression, emotional exhaustion remained the only statistically significant independent predictor of suicidality after adjustment for demographic and occupational characteristics. Younger age and rotating/night-shift work were associated with higher suicidality in unadjusted analyses but were not independent predictors after multivariable adjustment. Strong correlations between burnout dimensions and physical symptoms highlighted the interplay between psychosocial strain and somatic health. Conclusions: Burnout, particularly emotional exhaustion, was independently associated with suicidality among healthcare professionals, while depersonalization demonstrated a significant unadjusted association that did not remain statistically significant after multivariable adjustment. Younger clinicians, shift workers, and those with less experience appear especially vulnerable. These findings emphasize that burnout is a multifactorial construct with both psychological and somatic dimensions, shaped by systemic workplace stressors. These findings support further evaluation of systematic screening, organizational reforms, mentorship programs, and confidential mental health support as potential components of suicide-prevention strategies for healthcare professionals. Full article

Demand forecasting for emergency supplies is critical for public health emergency response, but demand sequences are strongly affected by external factors such as epidemic progression, traffic control, medical resource adjustments, and weather conditions, leading to stage-wise drift, short-term surges, and recovery-stage declines. Existing methods often rely on historical demand sequences or use external texts as sample-level auxiliary prompts, making it difficult to distinguish the semantic attributes, temporal granularity, and forecasting roles of different information sources. To address these limitations, we propose multimodal semantic alignment and trend–fluctuation forecasting (MATF). Following the information availability constraint in real forecasting processes, MATF organizes external information into four structured text fields, namely static context, observation-window events, historical constraints, and forecasting horizon prompts, and combines them with historical demand sequences to construct rolling forecasting samples. Methodologically, MATF uses Multimodal Input Encoding and Adaptation (MIEA) to preserve field boundaries and granularity differences, Context-Aware Event-to-Time Alignment (CETA) to align event semantics with historical time steps, and a Trend–Fluctuation Forecaster (TFF) to jointly model low-frequency trend evolution and semantic residual corrections. Experiments on real-world waybill-derived emergency logistics demand data and external text data from Wuhan show that MATF achieves lower forecasting errors than numerical-only baselines, text-enhanced baselines using the same information inputs, and ablation variants. Compared with AutoTimes, the best-performing baseline under identical information inputs, MATF reduces mean absolute error (MAE), root mean square error (RMSE), and symmetric mean absolute percentage error (sMAPE) by 9.9%, 9.2%, and 5.4%, respectively. Further ablation and sensitivity analyses support the effectiveness of structured text organization, event-to-time alignment, and joint trend–fluctuation modeling. Full article

Junctional hemorrhage has a high fatality rate due to how difficult it is to control rapid bleeding from major vessels. The available methods to stop junctional blood loss are prone to placement errors as well as failure during transport and during prolonged field care. On the battlefield, medical imaging with a portable ultrasound can be leveraged for visualization of the underlying tissue and application of compression at the anatomical junction to effectively stop blood flow. In this work, we developed AI models for anatomical landmark tracking using a perfused human cadaver model. These AI models were paired with an end-user clinical application to guide proper placement and compression, improving junctional hemorrhage control on the future battlefield. The trained U-Net semantic segmentation model demonstrated strong performance across predictions for both validation and hold-out, blind subjects. Overall pixel accuracy across the dataset was 98.9% for training and 98.6% for blind subjects. The artery and vein predictions achieved the highest class-specific training intersection-over-union scores, both at 0.73. This segmentation model trained to interpret human tissue provides evidence that ultrasound visualization can help guide compression at anatomical junctions. Future work will focus on improving blind performance for implementation of this AI model into closed-loop control of hardware prototypes, delivering real-time predictions and control. Full article

(1) Background: Ambulatory services are the main interface between populations and health systems, yet their benefits are unevenly distributed due to structural inequalities—systemic differences rooted in the social, economic, and political organisation of society. Inequities in access and quality are well documented, but patient safety remains the least examined. (2) Methods: Following Joanna Briggs Institute methodology and PRISMA-ScR guidelines, PubMed/MEDLINE and Scopus were searched for studies published from 2010 to 2026. In total, 44 studies were included, predominantly from the USA (n = 31), and synthesised narratively. (3) Results: Inequalities operated through geographic maldistribution, financial barriers, exclusionary institutional cultures, and market-driven practices. Access and quality were each addressed by 17 studies, whereas only four examined patient safety directly; the limited safety evidence indicated disproportionate diagnostic errors, medication-safety failures, and care-coordination breakdowns among vulnerable groups. Evidence was concentrated in the USA. (4) Conclusions: Structural inequalities are well documented for access and quality but are markedly under-studied for patient safety—the principal evidence gap identified. Patient safety warrants treatment as an equity outcome, measured with stratification by structural disadvantage and addressed through system-level interventions. Full article

The growing demand for wearable and Internet of Medical Things (IoMT) devices is driving the development of compact, low-power platforms for continuous physiological monitoring. Bioimpedance analysis represents a versatile non-invasive technique for the assessment of tissue properties, body composition, and respiratory dynamics. This work presents a comprehensive experimental validation of a compact bioimpedance measurement platform based on the SENSIPLUS chip, a CMOS sensor interface integrating a frequency-programmable lock-in amplifier for Electrochemical Impedance Spectroscopy in the 10 kHz–1 MHz range. The platform was validated at three complementary levels: (i) electrical characterization on Debye tissue-equivalent circuits using a three-point bilinear calibration, with analysis of the electrode–skin contribution and repeatability assessment; (ii) in vivo multi-frequency bioimpedance spectroscopy (BIS) with Cole–Cole model fitting and hook-effect correction; and (iii) single-frequency thoracic impedance plethysmography for respiratory monitoring. Results were compared against an Agilent E4980A precision Inductance (L), Capacitance (C), and Resistance (R) meter and a calibrated spirometer. The presented device achieved a maximum resistance error below 5.7% and reactance deviation under 6 $\mathsf{\Omega }$ across the investigated frequency range, Cole–Cole parameters consistent with reference values, and strong linear correlation ($R^2=0.97$) between thoracic impedance variations and tidal volume, with respiratory rate estimation errors below 2% across the ten sessions, specifically 1.43% during normal breathing and 1.96% during deep breathing. These results demonstrate that the SENSIPLUS-based platform achieves metrological performance compatible with the requirements of wearable IoMT applications, here demonstrated in a single-subject proof-of-concept study, while relying for all critical analog functions on a compact ($1.5\times 1.5$) mm² system-on-chip with low power consumption (1.5 mW). Full article

Background: Advancing age is incorporated as a strong risk variable in EuroSCORE II and is consistently associated with adverse outcomes after conventional coronary artery bypass grafting (CABG). Whether minimally invasive direct coronary artery bypass (MIDCAB)—which avoids both sternotomy and cardiopulmonary bypass—modifies this age-related risk in patients with single-vessel or LAD-predominant coronary artery disease remains insufficiently characterised. Methods: We retrospectively analysed 350 consecutive patients who underwent MIDCAB at Hannover Medical School between July 1999 and April 2025 (follow-up to April 2025). Elderly patients (age ≥70 years; n = 117) were compared with younger patients (age <70 years; n = 233) before and after 1:1 propensity score matching using greedy nearest-neighbour matching with a caliper of 0.2 × SD of the logit propensity score; age was excluded from the propensity model as it represented the exposure variable. A pre-specified sensitivity propensity model that additionally excluded EuroSCORE II (because EuroSCORE II contains an age component) was also evaluated. The primary endpoint was all-cause long-term mortality; secondary endpoints included perioperative complications and in-hospital outcomes. Long-term survival was assessed by Kaplan–Meier analysis and multivariable Cox proportional hazards regression, with a pre-specified parsimonious Cox model (age, LVEF, renal impairment) and cluster-robust standard errors on matched-pair identifiers. Results: Matching produced 109 pairs with excellent covariate balance (all standardized mean differences < 0.20). MIDCAB was completed without intraoperative conversion in all patients. No 30-day mortality, perioperative stroke, or new postoperative dialysis was observed in either age stratum (Clopper–Pearson 95% CI 0.00–3.33% for each zero-event outcome). Perioperative complications—including new-onset atrial fibrillation, re-exploration for bleeding, and intensive care unit and hospital length of stay—did not differ significantly between elderly and younger patients in the matched cohort (Newcombe 95% CI for risk differences all crossing zero; McNemar’s exact tests non-significant for all matched-pair binary endpoints; Hodges–Lehmann median difference for hospital length of stay +1.0 day, bootstrap 95% CI 0.0–1.0 days). At a median follow-up of 19.0 years (IQR 11.5–24.3; reverse Kaplan–Meier potential median 19.7 years), all-cause mortality was higher in the elderly (20.2% vs. 5.5%, p = 0.002; log-rank p = 0.001; exact McNemar’s p = 0.0025 for the matched-pair mortality endpoint). After multivariable adjustment, elderly age (≥70 years) was independently associated with long-term mortality (adjusted hazard ratio 4.48, 95% CI 1.79–11.20, p = 0.001), as was EuroSCORE II (HR 2.40 per unit, p = 0.034); a pre-specified parsimonious model (age, LVEF, renal impairment) with pair-cluster robust standard errors yielded an essentially identical adjusted HR for elderly age of 4.08 (95% CI 1.65–10.04, p = 0.002), and a sensitivity propensity model without EuroSCORE II yielded HR 3.95 (95% CI 1.68–9.29, p = 0.002). Conclusions: In this propensity-matched analysis with a median follow-up of ~19 years, MIDCAB was associated with excellent observed perioperative outcomes in appropriately selected elderly patients (no 30-day mortality, stroke, or new dialysis observed; upper 95% CI 3.3%) and no evidence of an excess of major in-hospital complications compared with younger patients within the statistical resolution of the cohort. The long-term mortality excess in the elderly is consistent with age-related life-expectancy curves in the source population; cause-specific mortality was not available in this cohort. External benchmarks from large MIDCAB cohorts in which long-term survival approximates that of the age-matched general population support this interpretation indirectly. These findings support MIDCAB as a feasible revascularization strategy associated with favourable observed early outcomes and long-term survival consistent with published MIDCAB literature, in appropriately selected elderly patients with single-vessel or LAD-predominant coronary artery disease treated at experienced centres. Full article

Edge detection is an essential problem in computer vision and is used in applications such as object recognition, scene analysis, and medical imaging. Conventional edge detectors based on integer-order derivatives are computationally efficient but sensitive to noise, whereas modern deep learning approaches generally achieve higher accuracy at the cost of increased model complexity. This paper presents GLF-ResFormer a lightweight hybrid CNN–Transformer architecture incorporating Grünwald–Letnikov (GL) fractional preprocessing. The discrete GL operator is approximated using a finite-difference convolution with a truncation level of $N=15$, where the fractional order $\alpha \in (0,1]$ controls the spatial memory of the operator. We establish an upper bound for the truncation error of the discrete GL approximation, $\mathcal{O}\left(h^{N-\alpha }\right)$ (Theorem 1), and present a gradient-sensitivity analysis (Lemma 1) that provides theoretical support for the proposed preprocessing strategy. Extensive experiments using 10 independent random seeds on the MNIST dataset show that, at the optimal fractional order of $\alpha =0.01$, GLF-ResFormer achieves a pixel-wise F1 score of $0.9967\pm 0.0002$ compared with $0.9891\pm 0.0033$ for a CNN baseline, while reducing the validation loss to $0.0043\pm 0.0003$. Additional experiments on the CIFAR-10 dataset and comparisons with the BSDS500 benchmark further demonstrate the effectiveness of the proposed framework across multiple edge detection evaluation settings while maintaining a lightweight architecture. Full article

Background/Objectives: Obsessive–compulsive disorder (OCD) is associated not only with obsessions and compulsions, but also with cognitive dysfunction involving inhibitory control, cognitive flexibility, working memory, attention, decision-making, feedback learning, and performance monitoring. Electroencephalography (EEG), event-related potentials (ERPs), quantitative EEG, and time–frequency analyses provide temporally precise methods for examining how these cognitive abnormalities unfold during information processing. This review aimed to synthesise electrophysiological evidence on the neural correlates of cognitive dysfunction in OCD. Methods: A systematic literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science Core Collection, Embase, PsycINFO, and Google Scholar from database inception to 30 May 2026. Eligible studies included patients with OCD and used EEG, qEEG, spectral EEG, EEG connectivity, or ERP methods in relation to cognitive performance, cognitive task demands, or cognitive dysfunction. Studies were grouped according to electrophysiological modality and ERP component. Because of methodological heterogeneity, findings were synthesised narratively and mechanistically rather than by meta-analysis. Methodological quality was assessed using ROBINS-I. Results: The search identified 1321 records, of which 42 studies met the inclusion criteria. Most studies used task-based ERP paradigms, while fewer examined resting-state EEG, qEEG, or oscillatory activity. The most consistent findings concerned altered performance monitoring and cognitive control, especially ERN, N2/N200, Pe, Pc, and P3/P300 abnormalities. ERN findings suggested excessive early error monitoring, whereas N2/N200 and P3/P300 findings indicated task-dependent abnormalities in inhibition, conflict processing, attentional allocation, stimulus evaluation, and context updating. Earlier components, including P50, N1/N100, P2/P200, and N450, suggested abnormalities in sensory gating, early attentional selection, stimulus evaluation, and interference control, particularly under emotionally salient or OCD-relevant conditions. FRN findings indicated altered feedback processing and reinforcement learning, while limited P600 evidence suggested inefficient working-memory preparation. Plain EEG and time–frequency studies further implicated abnormal theta, alpha, beta, and delta activity in monitoring, inhibition, arousal, and network efficiency. Conclusions: EEG-based evidence suggests that cognitive dysfunction in OCD reflects a dysregulated control system rather than a general reduction in cognitive ability. The disorder appears to involve excessive performance monitoring, abnormal sensory and attentional gating, inefficient inhibition, altered feedback evaluation, and reduced cognitive flexibility. However, the current evidence is limited by heterogeneity in samples, paradigms, EEG methodology, medication status, and statistical approaches. Future studies should use larger, well-characterised samples, standardised EEG/ERP paradigms, direct brain–behaviour analyses, and longitudinal designs to clarify whether electrophysiological abnormalities represent trait markers, state-dependent effects, compensatory mechanisms, or clinically useful predictors of cognitive dysfunction in OCD. Full article

Background/Objectives: Metabolic indices such as HOMA-IR, METS-IR and TyG play a critical role in determining cardiometabolic risk and insulin resistance in clinical practice. This study aims to simultaneously predict these three indices using ensemble learning models, based on demographic, clinical, and laboratory data from patients with gout. Methods: Demographic, retrospective clinical, and laboratory data from 411 patients diagnosed with gout were analyzed, and a logarithmic transformation was applied to address skewness in the target variable. A genetic algorithm was used to identify relevant features for predicting metabolic indices, and four scenarios were designed. Five ensemble learning models were optimized using fine-tuning with Optuna. The models’ decisions were clinically validated using SHAP analysis. Results: Across all scenarios, the CatBoost model combined with the GA + Log-Transformed framework achieved the highest prediction accuracy and the lowest error rates. Notably, when predicting the TyG index, it simulated the underlying formula with near-perfect accuracy. SHAP analysis confirmed that CatBoost successfully mapped clinical pathophysiology by prioritizing insulin levels for HOMA-IR, BMI for METS-IR, and triglyceride variance for the TyG index. Conclusions: The proposed GA + Log-Transformed CatBoost workflow provides a highly accurate, non-invasive and interpretable clinical decision support system. This study demonstrates that the ensemble architecture effectively captures complex pathophysiological patterns aligned with medical knowledge rather than merely memorizing statistical noise. Full article

Background and Objective: Reliable medical research depends on data integrity, yet clinical datasets often contain real and systematically missing values. This study aimed to develop a robust, clinically realistic imputation framework for a raw Multiple Sclerosis (MS) dataset affected by real-world missingness. Methods: We propose an innovative data-driven approach called the Sequential Multiple Imputation Bootstrapping (SMIB) model, which orders imputation based on feature correlation and incorporates bootstrapping to enhance stability and generalizability. Relevant features were identified using RF importance and Mutual Information scores and imputed using a hybrid machine learning framework that combines Random Forest (RF), Multilayer Perceptron (MLP), k-Nearest Neighbors (kNN), and a multiple imputation (MI) algorithm. The proposed method was evaluated using 15-fold cross-validation and a masking-based evaluation strategy. Model performance was assessed using accuracy, precision, recall, specificity, F1 score, Mean Squared Error (MSE), Mean Absolute Error (MAE), and R². Results: RF-based SMIB achieved superior performance, with final imputation accuracy reaching up to 97% for categorical outcomes and strong numerical performance (R² up to 0.999; MSE as low as 2.48 × 10⁻⁵). Sequential ordering and weighted bootstrapping improved stability in imbalanced clinical data under real-world missingness. Compared with the widely adopted Multiple Imputation by Chained Equations (MICE) approach, the proposed SMIB framework consistently demonstrated superior predictive performance across all evaluated categorical and numerical outcomes. Conclusions: The SMIB framework provides a robust and clinically aligned strategy for handling real-world missing values in MS datasets, improving imputation accuracy while preserving feature dependencies and feature relationships. The method supports reliable predictive analytics in healthcare contexts. Full article

Background/Objectives: Children with medical complexity (CMC) often suffer from chronic or evolving conditions that require increasingly complex treatment regimens over time, resulting in increased therapeutic and care burdens, costs, and the risk of adverse effects and medication errors. The study aimed to describe the variation in healthcare needs, medication regimens and medication burden over time and to describe the relationship between the number of drugs and healthcare needs. Methods: In this cohort study, conducted at the Pediatric Palliative Care Center of Padua, Italy, two repeated assessments were performed, respectively, in October 2021 (t₀) and in February 2023 (t₁). A total of 169 patients aged ≤23 years receiving at least one prescribed medication were enrolled. Longitudinal analyses were performed in the 147 patients with complete assessments at both time points. Data were collected from medical records and caregiver interviews. Drug costs were collected from the Italian Medicines Agency. Changes between baseline and follow-up were assessed using paired statistical tests (Wilcoxon signed-rank test for continuous variables and McNemar test for categorical variables). The associations between the number of drugs and the ACCAPED scale (Accertamento dei Bisogni Clinico Assistenziali Complessi in Cure Palliative Pediatriche) were assessed using linear regression models. Results: The study analyzed treatment regimens of 147 patients with a median age of 12.1 years at t₀ and 14.1 at t₁. The prevalence of patients with medication burden (42% vs. 50%, p = 0.022), do-not-resuscitate order (20% vs. 27%, p = 0.027) and an ACCAPED score > 50 (57% vs. 66%, p = 0.012) significantly increased on the second assessment. Also, the number of total daily administrations (5 [3–12] vs. 6 [3–14], p = 0.019) and the number of medications (4 [2–7] vs. 5 [3–8], p = 0.013) significantly increased. However, polypharmacy and the median daily cost per patient did not significantly increase between t₀ and t₁. The association between the number of drugs and the ACCAPED score showed that each additional prescribed drug is associated with an average increase of 3.2 points in the ACCAPED score (95% CI: 2.6, 3.9; p < 0.001). Conclusions: Over time, the degree of complexity in our population increased significantly, as evidenced by the increased healthcare needs, treatment regimens and burden on families. The number of prescribed drugs showed a strong and statistically significant relationship with patient care complexity. Treatment regimen characteristics were strongly associated with patient care complexity. Future studies should evaluate whether these pharmacological variables may have a role in supporting clinical assessment. Full article

Background and Objectives: Chronotype and sleep quality are related but non-identical dimensions of sleep health. We examined whether later chronotype is associated with subclinical visceral adiposity and atherogenic dyslipidaemia independently of perceived sleep quality, and whether this association differs by sex, in adults without previously diagnosed chronic disease. Materials and Methods: This single-centre cross-sectional study included 282 adults without previously diagnosed chronic disease or regular medication use. Chronotype was assessed with the Morningness–Eveningness Questionnaire (MEQ) and sleep quality with the Pittsburgh Sleep Quality Index (PSQI). Composite cardiometabolic indices were calculated from routine anthropometric and biochemical data. Primary dependent variables were log-transformed Visceral Adiposity Index (log[VAI]) and Atherogenic Index of Plasma (AIP). Multivariable linear models used heteroscedasticity-consistent type 3 (HC3) robust standard errors and adjusted for age, sex, education, physical activity, and PSQI; body mass index (BMI) was additionally included in the AIP model. Sex × MEQ interactions, false-discovery-rate (FDR) correction, and glycaemic sensitivity analyses were performed. Results: Median age was 28.0 years; 50.4% were women, 25.9% were evening type, and 51.8% had poor sleep quality. Higher MEQ score (greater morningness) was independently associated with lower log(VAI) (B = −0.0096; 95% confidence interval (CI), −0.0159 to −0.0033; β = −0.190; p = 0.003) and AIP (B = −0.0035; 95% CI, −0.0063 to −0.0008; β = −0.160; p = 0.011), whereas PSQI was not independently associated with either outcome. Sex × MEQ interactions were nominally significant and estimates were stronger in women, but interaction terms did not survive FDR correction. Associations persisted after excluding diabetes-range glycaemia but attenuated in strictly normoglycaemic participants. Conclusions: Later chronotype was associated with a less favourable subclinical adiposity–atherogenic profile independently of perceived sleep quality. Findings are cross-sectional, and the sex-specific pattern is hypothesis-generating. Prospective studies with objective circadian, sleep, dietary-timing, behavioural, and hormonal measures are required. Full article