METHOD: This was a descriptive cross-sectional design study. Data were collected from 25 to 31 December 2022, at six hospitals in Sichuan province. Using convenience sampling, a total of 717 participants were recruited, using the revised version of the Maslach Burnout InventoryGeneral Survey, and the Resilience Assessment Scale for healthcare workers.

RESULTS: More than half of nurses reported a moderate level of emotional exhaustion (66.50%, n = 484), cynicism (68.20%, n = 489), and personal accomplishment (68.76%, n = 493); nearly one-third and one-fourth of nurses experienced a high level of emotional exhaustion (27.48%, n = 197) and cynicism (20.78%, n = 149), respectively. In resilience, the highest scoring dimension was interpersonal connectedness, followed by decisional coping, flexible self-adaptation, and rational thinking. Satisfaction with work income, patient-nurse conflict, frequency of overtime work, age, and marital status were significant factors influencing burnout among nurses (p < .05).

DESIGN: Descriptive, cross-sectional survey.

METHODS: The research method employed in this study is characterized as a methodological study. Self-reported survey data were collected among community-dwelling older adults with chronic diseases residing in suburban counties in China. Including the following psychometric characteristics, item analysis was performed using the decision value method and Pearson's correlation analysis. Content validity was assessed through expert panel evaluation. The internal consistency of the questionnaire was determined by calculating Cronbach's alpha coefficient and corrected item-total correlation. Additionally, confirmatory factor analysis (CFA) was utilized to assess the construct validity of the ACPRS-C.

RESULTS: A total of 228 older adults participated in this psychometric study from August to October 2023. The item content validity index ranged from 0.80 to 1.00, while the scale content validity index was 0.945. The scale demonstrated excellent internal consistency (Cronbach's alpha = 0.931), and the correlation between items and total score was satisfactory. The structural validity was deemed robust (CFA model fit: chi-square/df = 1.121, comparative fit index = 0.992).

METHODS: This study included 965 Chinese community-dwelling older adults. Pearson correlation coefficient was conducted to assess the relationship between readiness toward ACP, death anxiety, and family cohesion. Structural equation model was used to examine the study hypothetical model.

RESULTS: 965 valid questionnaires were collected. Death anxiety is significantly related to the readiness toward ACP (r = -0.437, P < 0.01) and family cohesion (r = -0.444, P < 0.01), and family cohesion exhibited a positive correlation with readiness toward ACP (r = 0.499, P < 0.01). Family cohesion partially mediated the effect of death anxiety on readiness toward ACP, accounting for 35.94 % of the total effect.

AIMS: To validate the performance of the dual-cutoffs (8/12 kPa) and the proposed algorithm to identify patients with cACLD in three well-characterised Asian nonalcoholic fatty liver disease (NAFLD) cohorts.

METHODS: We included 830 patients with biopsy-proven NAFLD. Liver stiffness was measured using transient elastography (FibroScan).

RESULTS: cACLD was found in 21.8% of patients. Compared with the original Baveno VI elastography criteria (10/15 kPa), the new cutoffs showed a comparable specificity and a higher sensitivity for identifying cACLD. We developed a simplified risk model incorporating age, liver stiffness value, and platelet count, which outperformed liver stiffness measurement alone in two Chinese cohorts (P = 0.001), and was further validated in a Malaysian cohort (P = 0.04). Overall, the "two-step" screening of cACLD improved classification rates from 73.5% by the original dual-cutoffs to 86.7%. Notably, usage of our simplified risk model resulted in significantly lower false-negative rate than the refined screening approach by Papatheodoridi et al (27.1% vs 41.4%; P = 0.01).

METHODS: This is a post hoc analysis of a cluster-randomized clinical trial that assesses the effect of implementing a feeding protocol on mortality in critically ill patients. Patients who stayed in the ICUs for at least 7 days and received exclusive EN were included in this analysis. Multivariable Cox hazard regression models and restricted cubic spline models were used to assess the relationship between the different doses of EN delivery and 28-day mortality. Subgroups with varying lactate levels at enrollment were additionally analyzed to address the potential confounding effect brought in by the presence of shock-related hypoperfusion.

RESULTS: Overall, 1322 patients were included in the analysis. The median (interquartile range) daily energy and protein delivery during the first week of enrollment were 14.6 (10.3-19.6) kcal/kg and 0.6 (0.4-0.8) g/kg, respectively. An increase of 5 kcal/kg energy delivery was associated with a significant reduction (approximately 14%) in 28-day mortality (adjusted hazard ratio [HR] = 0.865, 95% confidence interval [CI]: 0.768-0.974, P = 0.016). For protein intake, a 0.2 g/kg increase was associated with a similar mortality reduction with an adjusted HR of 0.868 (95% CI 0.770-0.979). However, the benefits associated with enhanced nutrition delivery could be observed in patients with lactate concentration ≤ 2 mmol/L (adjusted HR = 0.804 (95% CI 0.674-0.960) for energy delivery and adjusted HR = 0.804 (95% CI 0.672-0.962) for protein delivery, respectively), but not in those > 2 mmol/L.

CONCLUSIONS: During the first week of critical illness, enhanced nutrition delivery is associated with reduced mortality in critically ill patients receiving exclusive EN, only for those with lactate concentration ≤ 2 mmol/L.

METHODS AND RESULTS: For 12 years, we followed a prospective nationwide cohort of 15 151 patients (aged 22-101 years, median age 63 years; 72.3% male; 66.7% Chinese, 19.8% Malay, 13.5% Indian) who were hospitalized for acute myocardial infarction between 2000 and 2005. There were 6463 deaths (4534 cardiovascular, 1929 noncardiovascular). Compared with men, women had a higher risk of cardiovascular death (age-adjusted hazard ratio [HR] 1.3, 95% CI 1.2-1.4) but a similar risk of noncardiovascular death (HR 0.9, 95% CI 0.8-1.0). Sex differences in cardiovascular death varied by ethnicity, age, and time. Compared with Chinese women, Malay women had the greatest increased hazard of cardiovascular death (HR 1.4, 95% CI 1.2-1.6) and a marked imbalance in death due to heart failure or cardiomyopathy (HR 3.4 [95% CI 1.9-6.0] versus HR 1.5 [95% CI 0.6-3.6] for Indian women). Compared with same-age Malay men, Malay women aged 22 to 49 years had a 2.5-fold (95% CI 1.6-3.8) increased hazard of cardiovascular death. Sex disparities in cardiovascular death tapered over time, least among Chinese patients and most among Indian patients; the HR comparing cardiovascular death of Indian women and men decreased from 1.9 (95% CI 1.5-2.4) at 30 days to 0.9 (95% CI 0.5-1.6) at 10 years.

METHODS: C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation.

RESULTS: Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%.