Association between prehospital time and injury severity in traffic crash patients.

Although intensive Care Unit (ICU) personnel share a moral imperative to provide palliative care, their compassionate intentions are often undermined by communication constraints, structurally inadequate systems and care environments, for example electronic health record demands superseding time spent with a dying patient, or family separation at the time of death. Moral distress and burnout among ICU healthcare professionals are a growing global concern, exacerbated by the complex demands of critical care settings. High patient acuity, emotional strain, ethical dilemmas, and exposure to death and suffering may present extraordinary pressures on ICU professionals. Effective interprofessional teamwork practices can be hindered by challenges in communication, unclear roles and responsibilities, and the constant reconfiguration of new teams in the ICU. Teamwork affects not only patient outcomes but also staff wellbeing and burnout rates. Understanding modifiable factors on moral distress and burnout and developing strategies to improve staff wellbeing and patient outcomes is essential. While several factors have been studied, the relationship between palliative care integration, moral distress, and burnout has not yet been adequately explored. This synthesis explores recent research on the interplay between stressors or interventions and clinician burnout in the ICU.

Preparing Army pharmacists for the future fight: An Army pharmacy critical care short course for large-scale combat operations.

Abstract Rationale Central Venous Catheters (CVCs) are increasingly used in Intensive Care Units (ICUs) for the management of critically ill patients. They are associated with complications which increase morbidity and mortality. Central Line Associated Blood Stream Infections (CLABSIs) are a preventable complication. Bundled interventions for CLABSI reduction include huddles. In these huddles the CLABSI reduction team meet with the bedside team to advocate for the removal of CVCs which qualify for de-escalation. This is a resource intensive intervention and is usually difficult to implement consistently. We implemented a daily E-huddle to overcome these resource limitations. Methods This is a single center, quality improvement project, implemented in the Medical ICU of a large academic medical center. Implementation occurred in January 2023. Data up to September 2025 are shown. The intervention included a clinical nurse specialist compiling a daily list of CVCs which met prespecified criteria for de-escalation. The chat function of the electronic health record was used to communicate in the morning with the first-on-call provider and nurse to flag these CVCs for de-escalation. The list was also e-mailed to the attending, fellow and medical director. If a patient remained on the list the next day the medical director communicated with the attending or fellow to encourage removal. Results Data from 2022 was used as a baseline. There was a reduction in average central line days from 656/month in 2022 to 560/month in 2023. Total line days were reduced by 1,152 days. CLABSIs were reduced from 11 to 7. The CLABSI rate decreased from 1.4 to 0.96. Data in 2024 and 2025 showed an increase back to the baseline but were still less than data prior to 2022. The increase was in the setting of increased number of ICU beds and patient acuity. Conclusions In our study there was a significant reduction in central line days, CLABSIs and CLABSI rate in the intervention year compared to baseline and they have remained low compared to historic data. The rise seen in 2024 and 2025 is in part explained by an increase in total ICU beds to 48 from 44. There was also a systematic reduction in medicine boarders in the ICU which increased patient acuity. Daily E-huddles can be implemented where in-person huddles may be difficult as they are easier and less resource intensive. This abstract is funded by: None

To examine 11-year trends in adverse events (AEs) in a pediatric critical care unit (PCCU), assess the impact of the COVID-19 pandemic on patient safety, and evaluate associations between patient acuity and AE severity. Retrospective cohort study using interrupted time series analyses and mixed-effects multinomial regression. A single-center PCCU monitored via the Adverse Event Management System from January 2013 to December 2023. A total of 7290 critically ill and injured pediatric patients admitted to the PCCU over the study period. None (observational study). Exposure variables included the COVID-19 pandemic period, invasive mechanical ventilation and noninvasive ventilation. Demographics, length of stay (LoS), disposition, and AE severity were assessed. The baseline AE rate was 11.94 events per 100 cases. At pandemic onset, AE rates rose by 5.20 events per 100 cases (p = 0.004), then declined 0.81 events per 100 cases quarterly (p = 0.010). Post-pandemic, rates increased 1.94 events per 100 cases quarterly (p = 0.009). LoS decreased 0.01 days quarterly pre-pandemic (p = 0.009), was stable during the pandemic, then increased 0.25 days quarterly post-pandemic (p = 0.033). Higher Pediatric Index of Mortality 2 scores were associated with fewer "near miss" events and more "MinModSev" (minimal, moderate, or severe) AEs. Both mechanical ventilation (p = 0.039) and noninvasive ventilation (p = 0.015) increased the odds of "MinModSev" AEs. This PCCU experienced a transient increase in AEs during COVID-19, followed by recovery and a post-pandemic rise in both AE rates and LoS. Higher illness severity and respiratory support were associated with more severe AEs. These findings underscore the importance of data-driven monitoring systems to sustain patient safety during and after healthcare crises.

Algorithm to reduce inter-rater variability in assessment of intermacs patient profiles.

Escalating emergency department overcrowding poses a serious threat to healthcare delivery, leading to reduced quality of care, increased risk of infection transmission, and heightened tension between medical staff and patients. This study aimed to investigate the status of emergency department visits by young and middle-aged patients triaged as non-urgent and to explore the influencing factors. This study retrospectively analyzed clinical data from 55,578 young and middle-aged patients who visited the emergency department of a tertiary class A hospital in Guangdong Province between January 1 and December 31, 2024. Patients were grouped into urgent and non-urgent based on triage classification. The differences in the clinical indicators between the two groups were compared. Logistic regression analysis was conducted to identify the factors associated with non-urgent triage classification. Among the included young and middle-aged patients, 35,314 (63.5%) were classified as non-urgent cases, while 20,264 (36.5%) were classified as urgent. Binary logistic regression analysis revealed that non-urgent triage classification was significantly associated with the following factors (p < 0.05): visiting Emergency Fever (OR = 1.513) and Emergency Gastroenterology (OR = 4.278) compared to Emergency Internal Medicine and Surgery; arriving on foot or by other modes (OR = 6.073) compared to ambulance transport; visiting during the afternoon shift (OR = 1.110) compared to morning shift, on weekends (OR = 1.111), and during the autumn (OR = 1.140) and winter seasons (OR = 1.083) compared to spring. The rate of non-urgent triage among young and middle-aged patients is high. Triage nurses should be particularly vigilant in assessing patients presenting to departments with a higher likelihood of non-urgent classification, such as the Emergency Fever and Emergency Gastroenterology, as well as those arriving without ambulance transport, during non-working hours and peak flu seasons. These findings can inform nurse-led strategies, such as dynamic staffing and guiding appropriate patients to alternative services, such as internet hospitals, thereby mitigating emergency department overcrowding. It should be noted that in this study, non-urgent status was defined solely by the triage system and was not validated against clinical outcomes; therefore, the findings describe patterns of triage classification rather than objective patient acuity.

Abstract Introduction Inappropriate oral antibacterial prescribing contributes to antimicrobial resistance. Antibacterial prescribing decisions are complex and may vary across care settings.[1] Out-of-hours (OOH) services (weekdays 18:30–08:00, weekends, and holidays) add further complexity, with perceived urgency and service pressures.[2] In Wales, OOH services are accessed through National Health Service (NHS) 111 telephone triage. Clinical support hubs (CSHs), an integral part of NHS 111, provide remote care delivered exclusively by general practitioners (GPs) and pharmacist independent prescribers (PIPs). Although OOH prescribing has been studied elsewhere, little is known about antibacterial prescribing decision-making in NHS 111 CSHs. Aim To explore GPs’ and PIPs’ experiences and decision-making processes when prescribing oral antibacterials in NHS 111 CSHs during OOH periods. Methods A qualitative methodology was employed using online semi-structured interviews and convenience sampling. The interview schedule was informed by literature, input from OOH experts, and a prior OOH prescribing study. It covered decision-making experiences, including factors affecting prescribing, strategies used, available resources, and perceived needs. GPs and PIPs who had completed their NHS 111 Wales training portfolios or at least 30 hours of training in CSHs were eligible. Gatekeepers shared study documentation with potential participants. Snowballing and social media advertisements were used to extend recruitment. Consenting participants were interviewed by one researcher. Interviews were audio- and video-recorded, transcribed verbatim, and thematically analysed independently by two researchers, with themes compared to ensure reliability and depth. Results Interviews were conducted with three PIPs (two females, one male), one of whom had completed the training portfolio. Four main themes were constructed: cognitive approach in practice, determinants of decision-making prescribing, strategies to improve prescribing, and strengthening antimicrobial stewardship (AMS) and professional development. Participants described adopting a patient-centred approach to decision-making that involved identifying patient concerns, gathering information, conducting examinations when possible, evaluating findings, making clinical judgments, and communicating decisions with appropriate follow-up guidance. They highlighted decision-making considerations, including accessibility and availability of other services, diagnostics, and recent laboratory results. The limitations posed by working remotely, such as the lack of physical examination and non-verbal cues were also noted. Prescribing decisions were made on a case-by-case basis, shaped by patient acuity and individual circumstances. Participants elaborated on strategies to support prescribing decisions, including peer consultations, referrals, evidence-based tools and resources, education and safety-netting, and engaging patients through communication and shared decision-making. They valued available training, ongoing education, and shared learning opportunities to strengthen AMS and professional growth, while noting insufficient audit and feedback on their prescribing. Conclusion This study provides the first exploration of PIPs’ experiences and oral antibacterial prescribing decision-making in NHS 111 CSHs. It highlights the complexity of prescribing, shaped by access constraints, patient acuity, and the challenges of remote consultation, alongside the critical role of effective communication. Context-specific understanding is essential to evaluate prescribing and design feasible and effective AMS interventions. Despite offering valuable insights to guide future research, recruitment challenges resulted in only a few PIPs and no GPs being interviewed, likely due to their workload, locum status, or lack of incentives, which limits transferability.

Sleep is a fundamental biophysiological requirement, essential for both physiological and psychological health. Patients admitted to intensive care are vulnerable to sleep disturbances due to continuous monitoring and the intensity of care provided. Despite this, the relationship between clinical care interactions and sleep quality remains underexplored. To examine the association between clinical interactions, patient acuity and inflammatory biomarkers on sleep, and to characterise ICU patient sleep using objective and subjective measures. A secondary data analysis of a clinical trial conducted within a 36-bed tertiary intensive care unit. The primary study employed a within-subjects design, integrating patient self-reported sleep experiences (Richard-Campbell's Sleep Questionnaire) and Actigraphy (biophysiological sleep monitoring) to characterise the sleep quality of ICU patients. Data on clinical interactions were extracted from electronic patient records (Metavision: iMDsoft). Patients (n = 37) reported poor sleep quality, with mean RCSQ scores below 50 mm across all subscales. Actigraphy reported a median nocturnal sleep duration of 5.6 h (IQR 303.3-422 min), with a mean of 33.4 awakenings per hour (SD ± 16.3). Patients experienced a median of nine interactions per hour, with clinical assessments (32.8%) being the most frequent. Higher SOFA scores correlated with increased clinical interaction (rs(35) = 0.33, p = 0.05), whilst lower APACHEII scores were associated with greater sleep disturbance (rs(33) = -0.39, p = 0.02). Intensive care patients experience impaired sleep characterised by fragmentation and poor perceived quality. Whilst frequent nocturnal interactions were documented, these did not appear to be the primary contributor to patients' perceived poor sleep quality. Patients with reduced acuity were highly susceptible to impaired quality of sleep, indicating the need to implement tailored strategies to support sleep in the ICU setting. Routine clinical care practices need to be re-evaluated to better align with the physiological requirements for sleep. Lower acuity patients cared for in ICU are disproportionately experiencing sleep disturbance and poor-quality sleep. Therefore, future ICU models of care must integrate sleep-supportive strategies as a component of standard care. The primary study was formally registered as a clinical trial with the Australian New Zealand Clinical Trial Registry (ACTRN12615000945527).

This study integrates a machine learning (ML) based Score for Emergency Risk Prediction (SERP), developed using objective mortality endpoints with the Patient Acuity Category Scale (PACS) and evaluated its effectiveness in clinical use. This single-centre, retrospective cohort study included all ED patients from a large tertiary hospital between 1 January 2018 and 31 December 2019. Using a reclassification framework, SERP was incorporated into PACS to derive two enhanced triage models. PACS+ model 1 downtriaged patients with low predicted 30-day mortality risk and up-triaged those with high risk. PACS+ model 2 up-triaged only high-risk patients, while low-risk patients retained their original category. Predictive performance in the test cohort was assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The derivation cohort included 97,188 ED visits, and test cohort included 97,212 ED visits. In the derivation set, the mean (SD) age of patients was 58.97 (18.41) years old and 47,993 (49.4%) were females. Of all patients, 19.9%, 57.5%, 22.5%, and 0.2% were triaged to PACS categories 1-4 respectively. The 30-day mortality rate in the derivation set was 2.8% and 2.7% in the validation cohort. For 30-day mortality prediction, PACS+ model 1 (AUC 0.828 [95% CI 0.820-0.836]) and PACS+ model 2 (AUC 0.812 [95% CI 0.805-0.818]) outperformed PACS (AUC 0.722 [95% CI 0.714-0.729]). PACS+ model 1 consistently achieved greater net benefit across the range of clinical thresholds. Integrating ML-based SERP with PACS improved 30-day mortality prediction in ED triage.

Association of objective and subjective nurse staffing metrics with patient fall rate by unit type.

To examine whether income- and geography-related disparities in in-hospital mortality after major cardiovascular procedures arise from differences in patient acuity, hospital characteristics, or inequities within hospitals. This observational study analyzed national data on eight major cardiovascular procedures performed between 2016 and 2022. We used multivariable logistic regression with progressive adjustment for demographics, clinical severity (All Patient Refined Diagnosis Related Groups [APR-DRG] risk and severity scores), and hospital characteristics. We analyzed secondary data from the National Inpatient Sample including 1,120,235 discharges (weighted N = 5,906,795) representing adults undergoing percutaneous coronary intervention, coronary artery bypass grafting, carotid endarterectomy/stenting, surgical valve replacement, transcatheter valve procedures, non-carotid endarterectomy, aneurysm repair, or peripheral bypass. Patient income was proxied using ZIP code-level median household income quartiles. Geographic location was classified as large metropolitan (≥ 1 million population), smaller metropolitan (50,000-999,999), or non-metropolitan. Lowest-income patients presented with mean APR-DRG risk scores 0.15-0.25 points higher than highest-income patients. After full adjustment with hospital fixed effects, in-hospital mortality was 0.67% points higher (95% CI: 0.08-1.26) among lowest-income patients. Geographic patterns were complex: after adjusting for hospital characteristics, non-metropolitan location was associated with 0.48% points higher mortality, though this was not statistically significant (95% CI: -0.01 to 0.97), and smaller metropolitan areas with 1.03% points higher mortality (95% CI: 0.30-1.76). Between-hospital differences explained 11.6% of mortality variance. Socioeconomic and geographic disparities in mortality following major cardiovascular procedures persist after adjustment for clinical and hospital factors. These disparities remain, with slightly larger point estimates, in within-hospital analyses, suggesting that hospital-level differences alone do not account for observed inequities. Interventions should address both social determinants and intra-hospital inequities. Multilevel interventions targeting both social determinants and within-hospital processes may be needed.

Accurately forecasting nursing demand is essential for effective workforce planning in the context of increasing patient volumes and the growing complexity of healthcare systems. Reliable forecasting supports optimal staffing, enhances patient care quality, and reduces operational risks. However, traditional forecasting approaches are increasingly challenged by evolving clinical demands, rising chronic disease burden, and rapid technological advancements. This narrative review aimed to (1) map existing models used for predicting nursing demand, (2) identify key predictive methodologies, data inputs, and evaluation metrics, and (3) highlight existing gaps and implications for healthcare policy and workforce management. A comprehensive narrative literature synthesis was conducted, examining a range of forecasting approaches used in healthcare settings. The review included time-series models (e.g., ARIMA), machine learning techniques (e.g., Random Forest and Long Short-Term Memory [LSTM]), and hybrid modeling frameworks. Relevant studies were analyzed to compare methodologies, performance, and applicability in nursing workforce prediction. The findings indicate that advanced forecasting models, particularly machine learning and hybrid approaches, improve prediction accuracy and enable proactive workforce planning. However, significant limitations were identified, including inconsistencies in data quality, lack of standardized validation methods, and limited contextual adaptation. Additionally, many models fail to incorporate dynamic factors such as patient acuity, seasonal variations, and unit-specific characteristics, which restrict their real-world applicability. Forecasting models hold substantial potential to enhance nursing workforce planning, but their effectiveness depends on methodological rigor, high-quality data infrastructure, and organizational readiness. Future efforts should focus on integrating dynamic healthcare variables and improving model validation to ensure reliable and context-sensitive predictions. This review provides guidance for researchers, healthcare administrators, and policymakers in developing evidence-informed and sustainable nursing workforce strategies.

Substance use can elevate the risk and severity of domestic assault. Using US emergency medical services (EMS) data, this study examined domestic assault severity and associated substance use across the life course. This study used 2019 National Emergency Medical Services Information System (NEMSIS) data to describe patient substance use and injury severity among domestic assault cases (n=176 931). Substance use was compared across demographic groups and severity of injury (patient acuity, neurological impairment and transport to trauma centre). Multinomial regression analysed the association between substance use and injury severity indicators. While alcohol was the most frequent substance used, drug use and combined use were more common with severe injuries. Male patients had higher odds of all severe injury indicators with drug use or combined use. Female patients had increased odds of severe injury with any substance use. When stratified by sex, alcohol use significantly contributed to injury differences between male and female patients. Specifically, alcohol use increased the risk of severe injuries for female patients and decreased or had no impact on the risk of severe injuries for male patients. NEMSIS data showed that substance use increased the risk of severe injury from domestic assault across every stage of life, furthering the call for gender-specific interventions in emergency medical care that address these co-occurring issues to prevent severe violence.

Mechanical Thrombectomy and Catheter-Directed Thrombolysis in Acute Pulmonary Embolism: Trends and Practice Patterns in the PERT Consortium Registry (2016-2024).

A mixed-methods systematic review of factors affecting missed nursing care in intensive care.

Introduction: Sepsis is a leading cause of morbidity, mortality and hospitalisation. Severity score is therefore vital to improve the outcome of patients with sepsis and septic shock. VISmax score (Maximum Vasoactive-Inotropic Score) is simpler and easier to use compared to other scales such as Paediatric Risk of Mortality Score (PRISM) Paediatric Logistic Organ Dysfunction score (PELOD) and Paediatric Sequential Organ Failure Assessment score (pSOFA) score. Aim: To determine the association of VISmax score with outcome of critically-ill children in Paediatric Intensive Care Unit (PICU) and also to evaluate the association between VISmax score and PRISM III score to predict the outcome in PICU. Materials and Methods: This prospective cohort study was done in PICU of a Gandhi Medical College and associated Hamidiya hospital, a tertiary care hospital of central India during the period of September 2022- October 2023. The study population included 330 critically-ill children between 1-13 years of age with the requirement of Vasoactive medications since admission. Maximal VIS score (VISmax) in the initial six hours after admission was calculated using the highest doses of vasoactive and inotropic medications administered. Five categories of VISmax were established: 0-5, &gt;5-15, &gt;15-30, &gt;30-45, and &gt;45 points. The association of different categories of VISmax with outcome of children in term of mortality was evaluated using Student’s t-test. Results: A total of 330 patients satisfying the inclusion criteria were included in study with a mean age of five years (IQR 1-13 years) and male (n=188) outnumbered female. The median VISmax was 10.0 (IQR: 0.0-37.0). The (median (IQR) VISmax of non survivors was significantly higher than that of survivors (37.0 (10-54.0) vs. 5.0 (5.0-18.0); p-value &lt;0.001). Significant association was found between VISmax in the first six hours of admission and outcome. A positive correlation between PRISM III and VISmax scores (r-value=0.362, p-value &lt;0.001), indicating that these two severity measures align in assessing patient acuity. Mortality was 10.9% overall and 46.34% in the highest VISmax group (&gt;45 points). Conclusion: VISmax in the first six hours of admission in PICU was significantly associated with outcome and mortality, hence can be used to guide intensive therapy accordingly

Singapore will become a 'super-aged' society by 2026, with an expected rise in geriatric emergency department (ED) attendances. Conventional triage tools such as the Patient Acuity Category Scale (PACS) and Emergency Severity Index (ESI) may not fully capture the complex presentations of older adults. This study compared the triage outcomes between geriatric and non-geriatric patients. A retrospective observational study was conducted in adult patients presenting to a tertiary ED between 1 February 2021 and May 2021. Patients were stratified into geriatric (age ≥65 years) and non-geriatric groups. Primary outcomes were ED length of stay (EDLOS) and hospital admission rates. Multivariable analyses were performed to adjust for confounders. Overall EDLOS was longer in geriatric than non-geriatric patients in both unadjusted (317.4 vs. 217.2 min, P < 0.05) and adjusted (300.2 vs. 224.1 min, P < 0.05) analyses. Admission rates were also higher in geriatric patients (75.1% vs. 33.2%, P < 0.001). Stratified analyses showed significantly longer EDLOS in older patients in PACS 2 (346.3 vs. 286.7 min, P < 0.001) and ESI 3 (353.4 vs. 281.2 min, P < 0.001). Admission rates were higher in PACS 1-3 and ESI 1-4, with the highest odds in ESI 1 (odds ratio [OR] 65.9) and PACS 1 (OR 28.3). Despite being assigned to higher acuity, older adults had longer EDLOS and higher admission rates than younger adults within the same triage category. This highlights a mismatch between triage allocation and care needs, requiring enhanced geriatric-specific risk-stratification approaches.

Teaching hospitals are vital for training future physicians, yet there is concern that the presence of learners may increase unscheduled emergency department (ED) return visits. Previous studies of this phenomenon are limited to a sample of EDs in a region. Our study aimed to address this gap using a comprehensive regional dataset and multiple academic years of routinely collected electronic medical record data. Our objective was to determine whether ED patients seen by supervised learners had higher rates of unscheduled return visits within 72hours compared to those seen by attending physicians alone. Secondary outcomes were return visits resulting in hospital admission and the impact of learner training level. We conducted a cohort study of over 1 million ED discharges across nine linked hospitals in a large Canadian health region between July 1, 2015, and June 30, 2018. The primary outcome was the rate of unscheduled return visits within 72hours. Secondary outcomes included return visits with hospital admission and critical care admission. Logistic regression models were adjusted for patient demographics, acuity, and site characteristics. Among 1,033,026 patient visits, 7.4% returned within 72hours. There was no statistically significant difference in unscheduled return visits between supervised learners and attending physicians alone (adjusted RR 0.98, 95% CI 0.96-1.00, p = 0.054). Return visit admissions were slightly higher among patients seen by learners (adjusted RR 1.06, 95% CI 1.01-1.13), primarily those seen by medical students and off-service residents. Emergency medicine residents in mid-training had lower return visit rates than attendings. Patients seen by supervised learners in a regional ED network did not have increased rates of unscheduled return visits. Further research is warranted to explore factors underlying return visit admissions in learner-involved care.

Nursing is fundamentally a profession of compassion, yet continuous self-giving risks compassion fatigue (CF), defined as the "loss of the ability to nurture". This study investigated CF among 198 registered nurses in Level 2 private hospitals in Puerto Princesa, Palawan, focusing on how work, client, and personal environments influence professional quality of life. Employing a descriptive-correlational design, the research utilized a multi-part questionnaire including the ProQOL v.5 a modified Work Environment Questionnaire, a Client Environment Survey based on patient acuity, and the Highly Sensitive Person Scale. Results indicated moderate levels of both CF and compassion satisfaction (CS) among respondents. Statistical analysis showed that a supportive work environment significantly decreased CF (r = -0.149) and increased CS (rho = 0.438). A demanding client environment significantly increased CF (r = 0.174) but did not affect CS. Notably, a highly sensitive personality was significantly associated with both higher CF (r = 0.320) and higher CS (rho = 0.266). Among demographics, only monthly income showed a significant difference in CF (p = 0.042), with the lowest earners experiencing the highest fatigue. A significant negative correlation (r = -0.251) confirmed that CS buffers CF. The study concluded that environmental conditions and personal traits concurrently shape the nursing experience. While challenging patient demands cause emotional exhaustion, they do not necessarily diminish the fulfillment gained from care. To address these findings, the CARE (Compassion Advancement &amp; Resilience Enhancement) Program was proposed to strengthen leadership, workload management, emotional resilience and economic well-being.