Daily Ards Research Analysis

BACKGROUND: Respiratory Distress Syndrome (RDS) remains a leading cause of mortality among preterm infants weighing < 2000 g, particularly in resource-limited settings. While Kangaroo Mother Care (KMC) has shown promise in stable preterm infants, its effectiveness for those requiring respiratory support remains unclear. This study evaluated nurse-led implementation of KMC for preterm infants with RDS. METHODS: A prospective, multicenter, randomized controlled trial was conducted across four neonatal intensive care units in Tanta, Egypt (January 2023-June 2024). Two hundred forty preterm infants (<2000 g) with RDS were randomly assigned to either nurse-implemented KMC (n = 120) or standard care (n = 120). The KMC protocol, implemented for a minimum of 6 h per day until hospital discharge, integrated continuous skin-to-skin contact, exclusive breastfeeding promotion, and structured parental education. Outcomes included 28-day survival, respiratory status (Silverman-Anderson Scores), nosocomial infections, maternal-infant bonding, growth trajectories, and clinical course metrics. RESULTS: The KMC intervention significantly improved 28-day survival (adjusted HR = 0.42, 95% CI 0.28-0.63, p < 0.001) and reduced nosocomial infections by 55% (RR = 0.45, 95% CI 0.27-0.75, p < 0.001). KMC recipients demonstrated faster respiratory improvement, shorter CPAP duration (-2.2 days, p < 0.001), and higher rates of exclusive breastfeeding at discharge (74.2% vs. 48.3%, p < 0.001). Maternal competency scores showed progressive improvement with enhanced bonding and responsiveness. CONCLUSION: Nurse-implemented KMC is a safe, effective intervention for preterm infants with RDS, yielding significant improvements in survival, clinical outcomes, and family-centered care metrics. IMPLICATIONS FOR PRACTICE: These findings support the expansion of nursing roles in implementing KMC for high-risk infants in resource-limited settings. TRIAL REGISTRATION: ClinicalTrials.gov (NCT06707376).

BACKGROUND: Manual data abstraction from electronic health records (EHRs) for research on intensive care patients is time-intensive and challenging, especially during high-pressure periods such as pandemics. Automated data extraction is a potential alternative but may raise quality concerns. This study assessed the feasibility and credibility of automated data extraction during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: We retrieved routinely collected data from the COVID-Predict Dutch Data Warehouse, a multicenter database containing the following data on intensive care patients with COVID-19: demographic, medication, laboratory results, and data from monitoring and life support devices. These data were sourced from EHRs using automated data extraction. We used these data to determine indices of wasted ventilation and their prognostic value and compared our findings to a previously published original study that relied on manual data abstraction largely from the same hospitals. RESULTS: Using automatically extracted data, we replicated the original study. Among 1515 patients intubated for over 2 days, Harris-Benedict (HB) estimates of dead space fraction increased over time and were higher in non-survivors at each time point: at the start of ventilation (0.70±0.13 CONCLUSION: Manual data abstraction from EHRs may be unnecessary for reliable research on intensive care patients, highlighting the feasibility and credibility of automated data extraction as a trustworthy and scalable solution to accelerate clinical insights, especially during future pandemics.

BACKGROUND: Sepsis-associated ARDS results in high morbidity and mortality in children. However, heterogeneity among patients makes identifying those at risk of persistent acute respiratory dysfunction challenging. Endothelial dysfunction is a key feature of ARDS pathophysiologic characteristics, contributing to lung injury in sepsis. Incorporating endothelial biomarkers into risk models may enhance prediction of those with persistent acute respiratory dysfunction. RESEARCH QUESTION: Can clinical variables and endothelial biomarkers measured early in the course of sepsis predict risk of persistent acute respiratory dysfunction among critically ill children? STUDY DESIGN AND METHODS: This was a multicenter derivation and single center test cohort study of prospectively enrolled children with sepsis. The derivation cohort was split into training and holdout validation sets. We trained TreeNet (Minitab, LLC) and classification and regression tree (CART) models using clinical and endothelial biomarkers measured on day 1 of septic shock to predict risk of sepsis-associated acute respiratory dysfunction (SA ARD) on day 3. The performance of the CART model was tested in the holdout validation data set and in the independent test cohort. RESULTS: In the derivation (n = 625) and test (n = 162) cohorts, children with day 3 SA ARD showed increased mortality, length of mechanical ventilation, and PICU length of stay compared with those without. The TreeNet and CART models yielded comparable results. The variables included in the final CART model were presence of SA ARD on day 1, Pao INTERPRETATION: We derived and validated predictive models incorporating clinical and endothelial biomarkers to identify pediatric patients with septic shock at high risk of persistent acute respiratory dysfunction. Pending prospective validation, such models may facilitate enrichment and targeted intervention in future clinical trials.