Background: Digitalization and artificial intelligence-supported clinical decision support systems (AI-DSS), defined here as tools that generate patient-specific alerts, risk estimates, prioritization prompts, documentation suggestions, or related recommendation outputs intended to support rather than replace professional nursing judgment, can improve clinical decision-making, yet they may also amplify technostress and burnout, with downstream effects on missed nursing care and implementation readiness. Methods: We surveyed 239 registered nurses from a tertiary-care hospital in Timișoara, Romania (January–March 2025), including critical care (n = 60) and general wards (n = 179). Measures included a 15-item technostress scale, eHEALS, Maslach Burnout Inventory–Human Services Survey (MBI-HSS), Safety Attitudes Questionnaire (SAQ) teamwork and safety climate subscales, a 10-item missed nursing care inventory, and a six-item AI-DSS acceptance scale reflecting perceived usefulness, trust, and stated willingness to use such tools if available as an attitudinal readiness outcome rather than as routine observed use. Multivariable regression, exploratory mediation models, cluster analysis, and exploratory ROC analysis were performed. Results: Higher technostress was associated with higher emotional exhaustion (r = 0.52) and more missed care (r = 0.41), whereas eHealth literacy correlated with higher AI-DSS acceptance (r = 0.35) and lower technostress (r = −0.34). In adjusted models, technostress (per 10 points) was associated with higher missed care (β = 0.28, p < 0.001) (equivalent to 0.14 points per 5-point increase) and higher odds of low AI-DSS acceptance (OR = 1.38, p = 0.001), while eHealth literacy was associated with lower odds of low acceptance (OR = 0.71 per 5 points, p < 0.001). Burnout and the safety climate statistically accounted for approximately 35% of the technostress–missed care association. Three workflow phenotypes were identified, with the high-strain/low-literacy cluster showing the most missed care (3.5 ± 1.8) and the lowest AI acceptance (19.7 ± 5.2). An exploratory in-sample ROC model for intention to leave achieved an AUC of 0.82. Conclusions: Higher technostress clustered with worse nurse well-being, more care omissions, and lower AI-DSS acceptance, whereas eHealth literacy appeared protective. Interventions combining digital skills support, usability-focused redesign, and a stronger safety climate may reduce missed care and support safer AI implementation.

Background and Objectives: Hospitals increasingly use AI tools that give nurses on-screen alerts and recommendations (AI-supported clinical decision support, AI-DSS). When nurses override these alerts too often, useful guidance can be lost; when they trust them blindly, errors can slip through. We examined which work and wellbeing factors are associated with nurses’ self-reported intention to override AI alerts, rather than observed override behavior. Methods: We surveyed 239 registered nurses (76.6% female; mean age 33.7 years) at a large hospital in Timișoara, Romania, from January to March 2025. Questionnaires measured alert fatigue, moral distress, mental workload, sleep problems, resilience, team psychological safety, and how strongly nurses intended to override AI alerts. Results: Nurses fell into three groups: those who tended to over-trust AI (26.8%), those with balanced trust (41.0%), and those who resisted it (32.2%). The resistant group had the strongest intention to override alerts and the weakest sense of psychological safety. Alert fatigue was the factor most strongly associated with override intention, and this association was partly accounted for by moral distress. The indirect association was weaker among nurses reporting higher team psychological safety. An exploratory model using these factors distinguished nurses with high self-reported override intention with acceptable accuracy. Because all variables were measured at a single time point, findings are associative and hypothesis-generating rather than causal. Conclusions: How nurses respond to AI alerts depends less on the technology than on their workload, ethical strain, and team climate. Cutting unnecessary alerts, easing moral distress, and building psychological safety may help nurses use AI more safely.

Background/Objectives: Atrial fibrillation (AF) and heart failure (HF) frequently coexist, but later-pandemic data from Eastern Europe are limited. We evaluated their documented coexistence in patients hospitalized with COVID-19; the study was not designed to determine whether SARS-CoV-2 modifies the established AF–HF relationship. Methods: We retrospectively analysed 395 adults admitted with RT-PCR-confirmed SARS-CoV-2 infection between 1 September 2022 and 31 December 2024. AF was identified from the admission record, while HF was determined by an audited, rule-based review of cardiovascular free-text entries. A modified Poisson model with robust variance was the primary analysis and estimated adjusted prevalence ratios (aPRs) after adjustment for age, sex, body mass index, hypertension, ischaemic heart disease, pre-existing type 2 diabetes, chronic kidney disease, chronic obstructive pulmonary disease, smoking, and prior ischaemic stroke. Logistic regression and a restrictive NYHA-coded HF definition were sensitivity analyses. Results: AF was documented in 68 patients (17.2%), HF in 106 (26.8%), and both conditions in 26 (6.6%). HF prevalence was 38.2% among patients with AF and 24.5% among those without AF. AF was associated with documented HF in the primary model (aPR 1.51, 95% confidence interval 1.06–2.16; p = 0.022); age was also associated with HF (aPR 1.02 per year, 95% confidence interval 1.01–1.04; p = 0.010). Results were similar with the restrictive HF definition (aPR 1.54, 95% confidence interval 1.07–2.23) and logistic regression (adjusted odds ratio 1.89, 95% confidence interval 1.06–3.37). Exploratory mortality estimates were imprecise and were not used for prognostic inference. Conclusions: AF identified a subgroup with a higher prevalence of documented HF within this hospitalized COVID-19 cohort. The findings are best interpreted as evidence of cardiovascular and multimorbidity complexity, not as proof of a COVID-specific, temporal, or causal AF–HF effect.

Background and Objectives: Anthropometric measures incompletely capture the inflammatory and adipokine heterogeneity underlying insulin resistance. We examined associations of tumor necrosis factor-alpha (TNF-α), serum uric acid, and the leptin/adiponectin ratio with HOMA-IR after adjustment. Materials and Methods: This single-center cross-sectional secondary analysis included 147 of 239 hospitalized adults with HOMA-IR and predefined core data; fasting glucose, insulin, and biomarker testing was elective and patient-funded. Continuous ln(HOMA-IR) was the primary outcome. A secondary upper-quartile phenotype (HOMA-IR ≥ 4.78; n = 37) was cohort-specific and non-diagnostic. Robust linear regression used HC3 standard errors. Sensitivity analyses excluded T2DM and replaced BMI with waist circumference or visceral fat area. Binary and AUC analyses were exploratory (expanded complete-case model, n = 112). Results: Each doubling of (TNF-α + 1) was associated with a 10.2% higher HOMA-IR in the expanded model (p = 0.021). The association persisted after excluding T2DM (12.1% higher HOMA-IR per doubling; n = 81; p = 0.012) and when BMI was replaced by waist circumference (p = 0.022) or visceral fat area (p = 0.018). Uric acid and the leptin/adiponectin ratio did not reach statistical significance in the adjusted continuous-outcome model. The 112 complete cases and 35 incomplete cases were broadly similar in measured baseline characteristics, although uric acid was higher among complete cases (p = 0.020). Conclusions: In this selected hospitalized cohort, TNF-α showed the most consistent adjusted association with the observed fasting glucose-insulin phenotype. The binary and discrimination analyses remain preliminary and do not establish diagnostic or clinical prediction utility. Prospective external validation is required.

Background/Objectives: General cardio-oncology guidance describes cardiovascular toxicity from BRAF/MEK inhibitors and immune checkpoint inhibitors (ICIs), but it does not resolve how treatment decisions should change when heart failure (HF) is already present. This critical narrative review separates direct HF-specific evidence from indirect evidence and proposes an HF-centered framework for systemic melanoma. Methods: MEDLINE/PubMed, Embase, and the Cochrane Library were searched for English-language sources published from January 2015 to April 2026, with targeted guideline, prescribing-information, interaction, and geriatric-oncology updates through July 2026. Sources were classified as direct, indirect, or extrapolated. This review follows SANRA principles but does not claim systematic-review conduct, pooled estimates, formal risk-of-bias assessment, or GRADE certainty. Results: Only case-level evidence directly describes systemic melanoma treatment in established HF; most cardiotoxicity rates are derived from selected longitudinal and real-world cohorts. We therefore organize decisions across four domains: HF phenotype and current stability; oncological urgency and alternatives; treatment-specific toxicity phenotype; and detectability, reversibility, and patient priorities. Each domain is graded ordinally and generates its own decision output, and explicit precedence rules resolve situations in which several domains are simultaneously abnormal; the pathway from assessment to documented output is presented as a decision flowchart and applied to illustrative clinical cases. This approach modifies interpretation of symptoms, biomarkers, ventricular function, surveillance, drug interactions, treatment interruption, and rechallenge. Every actionable statement is labeled as guideline-supported or author-proposed, therapy-specific baseline and follow-up monitoring are tabulated separately for each treatment class, and heart failure with preserved ejection fraction is addressed as a phenotype in which clinical deterioration may occur without any change in ejection fraction. Patient-level priorities in older adults—survival, quality of life, avoidance of hospitalization, tolerance of frequent monitoring, oral versus infusion treatment, and functional expectation—are specified as items to be recorded rather than inferred. Conclusions: Stable HF is not an automatic contraindication to effective melanoma therapy, whereas recent or active decompensation requires stabilization or monitored treatment when oncological delay is unsafe. The proposed framework operationalizes guideline principles for an understudied population while making the limits of the evidence explicit. It makes a procedural rather than an empirical claim: it is a transparent structure for documenting a decision that must be made anyway, not an algorithm derived from or validated against outcome data.

Background and Objectives: Type 2 diabetes mellitus (T2DM), visceral adiposity, and hypertension frequently coexist, but their independent associations with carotid intima–media thickness (cIMT) remain uncertain. We compared cardiometabolic profiles by diabetes status and evaluated adjusted associations with cIMT. Materials and Methods: This retrospective cross-sectional study included 147 adults admitted to Clinical CF Hospital Timișoara between 1 July 2025 and 30 June 2026. Bilateral cIMT was available in 141 patients. Group comparisons, Spearman correlation, and heteroscedasticity-robust multivariable linear regression were performed. Results: T2DM was present in 39 patients (26.5%), hypertension in 125 (85.0%), and obesity in 120 (81.6%). Compared with participants without diabetes, those with T2DM had higher homeostasis model assessment of insulin resistance (HOMA-IR; 4.11 vs. 3.08; p = 0.005) and fasting glucose (6.60 vs. 5.06 mmol/L; p < 0.001), but lower total cholesterol (205.2 vs. 225.9 mg/dL; p = 0.009) and non-high-density lipoprotein cholesterol (156.5 vs. 176.6 mg/dL; p = 0.006). These lower lipid values may reflect treatment or other residual confounding and should not be interpreted as an intrinsic T2DM phenotype. Mean cIMT was numerically higher in T2DM (0.619 vs. 0.592 mm; p = 0.284). Visceral fat area correlated with cIMT before adjustment (ρ = 0.237; p = 0.005), but not after adjustment. Age (β = 0.057 mm/decade; p < 0.001) and hypertension (β = 0.046 mm; p = 0.042) retained adjusted associations with cIMT. Conclusions: T2DM identified metabolic dysregulation, whereas age and hypertension showed the strongest adjusted associations with cIMT. Visceral adiposity showed an unadjusted but not independent association, supporting comprehensive cardiovascular risk assessment and rigorous blood pressure management.