BACKGROUND

Pediatric Early Warning Scores (PEWS) help identify children at risk of clinical deterioration, but their accuracy across diverse settings, populations, interventions, and outcomes remains unexplored.

To determine the accuracy of PEWS in detecting clinical deterioration events (CDE) among pediatric patients seen at the emergency department (ED).

Retrospective cohort study.

Pediatric patients aged 1 month to 18 years seen at the ED.

Southern Philippines Medical Center Emergency Department, Davao City, Philippines from January 2021 to December 2022.

Area under the curve (AUC) of PEWS in detecting CDE; Brighton PEWS optimal cut-off and its sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (+LR), and negative likelihood ratio (-LR).

Among the 345 patients, 56 experienced CDE and 289 did not. Patients with CDE had significantly lower median age (1.00 year vs 5.00 years; p < 0.001), oxygen saturation (93.00% vs 98.00%; p < 0.001), and pediatric Glasgow Coma Scale scores (8.00 vs 15.00; p < 0.0001) compared to those without CDE. Heart rate (135.00 vs 111.00 beats per minute; p < 0.001), and respiratory rate (32.50 vs 24.00 breaths per minute; p < 0.001) were significantly higher in patients with CDE. The two groups also differed significantly in terms of comorbidity distribution (p < 0.001) and diagnosis (p < 0.001). The AUC of Brighton PEWS was 0.9064 (95% CI 0.8716 to 0.9357), with an optimal cut-off score of ≥4.00. This threshold yielded 76.79% sensitivity, 88.58% specificity, 56.60% PPV, 95.20% NPV, 6.72 LR+, and 0.26 LR-.

The Brighton PEWS demonstrates strong diagnostic accuracy in predicting CDE among pediatric patients. A cut-off score of ≥4.00 offers a balanced combination of sensitivity, specificity, and likelihood ratios for ED application.

Background: Burnout is a pressing concern among Emergency Ward (EW) nurses, stemming from the intense demands of their profession, including long hours, exposure to traumatic events, and the need for quick decision-making. This issue not only affects nurses' well-being but also has repercussions for patient care and the healthcare system. Objective: This study aimed to investigate the association between effort-reward and work-life imbalance on burnout among EW nurses in a public hospital. Methods: The study was a cross-sectional analytical study conducted from February to May 2022 involving 32 EW nurses employed at Public Hospital I.A. Moeis Hospital in Samarinda City, Indonesia. The research employed several instruments for data collection through stratified random sampling. Chi square and logistic regression analysis were performed to assess the factors contributing to burnout among EW nurses, including effort-reward imbalance, worklife balance, namely monotonous work, self-efficacy, communication among healthcare professionals, and workload. Result: There was positive correlation between effort-reward and work-life imbalance on burnout among EW nurses. The multivariate test results showed that workload, self-efficacy, communication, and monotonous work had higher risk of developing burnout in EW nurses. Conclusion In this study, we found that effort-reward and work-life imbalance were significantly associated with burnout in EW nurses in a public hospital. The development of comprehensive assessment of burnout among EW nurses and its early intervention should be warranted.
Burnout, Psychological ; Emergency Ward ; Emergency Service, Hospital ; Nurses ; Hospitals, Public

BACKGROUND

Palliative Care focuses on the needs of both the patient and their families, aiming to enhance their overall quality of life. It achieves this by anticipating, preventing, reducing, and treating suffering through comprehensive support across various aspects of life. This approach promotes patient autonomy, provides access to information, and encourages the freedom to make choices. Referring patients to Supportive and Palliative Care in a timely manner enhances their quality of life, improving symptom control, mood, and understanding of their illness. Additionally, it reduces distress for both the patient and their caregiver during the end-of-life period.

This study aimed to determine the proportion of late referrals among patients diagnosed with debilitating illnesses consulting at the Emergency Department of Batangas Medical Center from July to December 2022 and who needed Supportive and Palliative Care using the Palliative Care and Rapid Emergency Screening Tool, considering age, sex, and the specific diagnosis of the debilitating illness. Additionally, the timing of referrals for both discharged and admitted patients requiring Supportive and Palliative Care was described, with categories including those referred within one week, beyond one week, and those not referred at all.

This retrospective study was conducted via chart review of all patients with debilitating illnesses who visited the Emergency Department of Batangas Medical Center, admitted or discharged, from July 2022 to December 2022. Timing of referral to Supportive and Palliative Care was obtained from the admission charts and/or the SPC referral logbook. Frequency analysis specifically frequency tabulation was used to summarize data.

Between July and December 2022, 2,097 patients diagnosed with debilitating illnesses at the Emergency Department were identified. Only 2.52% of them received referrals to Supportive and Palliative Care, and among this group, more than half (38) were referred later than one week after diagnosis.

This study identified the need for comprehensive improvements in the referral process, emphasizing timely access to Supportive and Palliative Care for patients of all demographic facing debilitating illnesses. The call for systemic changes advocates for clear protocols and guidelines, reducing oversight and delays. The Palliative Care and Rapid Emergency Screening Tool can streamline referrals, while collaboration between healthcare providers and the palliative care team ensures a more efficient system. Strategies advocating for healthcare infrastructure improvement and awareness campaigns may be developed to facilitate timely referrals for patients across age and gender spectrums.

Humans ; Pilot Projects ; Singapore ; Hospitals ; Primary Health Care ; Accidents ; Emergency Service, Hospital

INTRODUCTION: Creatinine has limitations in identifying and predicting acute kidney injury (AKI). Our study examined the utility of neutrophil gelatinase-associated lipocalin (NGAL) in predicting AKI in patients presenting to the emergency department (ED), and in predicting the need for renal replacement therapy (RRT), occurrence of major adverse cardiac events (MACE) and all-cause mortality at three months post visit. METHODS: This is a single-centre prospective cohort study conducted at Singapore General Hospital (SGH). Patients presenting to SGH ED from July 2011 to August 2012 were recruited. They were aged ≥21 years, with an estimated glomerular filtration rate <60 mL/min/1.73 m², and had congestive cardiac failure, systemic inflammatory response syndrome or required hospital admission. AKI was diagnosed by researchers blinded to experimental measurements. Serum NGAL was measured as a point-of-care test. RESULTS: A total of 784 patients were enrolled, of whom 107 (13.6%) had AKI. Mean serum NGAL levels were raised (P < 0.001) in patients with AKI (670.0 ± 431.9 ng/dL) compared with patients without AKI (490.3 ± 391.6 ng/dL). The sensitivity and specificity of NGAL levels >490 ng/dL for AKI were 59% (95% confidence interval [CI] 49%-68%) and 65% (95% CI 61%-68%), respectively. Need for RRT increased 21% per 100 ng/dL increase in NGAL (P < 0.001), whereas odds of death in three months increased 10% per 100 ng/dL increase in NGAL (P = 0.028). No clear relationship was observed between NGAL levels and MACE. CONCLUSION Serum NGAL identifies AKI and predicts three-month mortality.
Humans ; Lipocalin-2 ; Prospective Studies ; Lipocalins ; Proto-Oncogene Proteins ; Acute-Phase Proteins ; Biomarkers ; Acute Kidney Injury/diagnosis* ; Emergency Service, Hospital ; Predictive Value of Tests

OBJECTIVE: To explore clinical rules based on the big data of the emergency department of the Second Affiliated Hospital of Guangzhou Medical University, and to establish an integrated platform for clinical research in emergency, which was finally applied to clinical practice. METHODS: Based on the hospital information system (HIS), laboratory information system (LIS), emergency specialty system, picture archiving and communication systems (PACS) and electronic medical record system of the Second Affiliated Hospital of Guangzhou Medical University, the structural and unstructured information of patients in the emergency department from March 2019 to April 2022 was extracted. By means of extraction and fusion, normalization and desensitization quality control, the database was established. In addition, data were extracted from the database for adult patients with pre screening triage level III and below who underwent emergency visits from March 2019 to April 2022, such as demographic characteristics, vital signs during pre screening triage, diagnosis and treatment characteristics, diagnosis and grading, time indicators, and outcome indicators, independent risk factors for poor prognosis in patients were analyzed. RESULTS: (1) The data of 338 681 patients in the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to April 2022 were extracted, including 15 modules, such as demographic information, triage information, visit information, green pass and rescue information, diagnosis information, medical record information, laboratory examination overview, laboratory information, examination information, microbiological information, medication information, treatment information, hospitalization information, chest pain management and stroke management. The database ensured data visualization and operability. (2) Total 140 868 patients with pre-examination and triage level III and below were recruited from the emergency department database. The gender, age, type of admission to the hospital, pulse, blood pressure, Glasgow coma scale (GCS) and other indicators of the patients were included. Taking emergency admission to operating room, emergency admission to intervention room, emergency admission to intensive care unit (ICU) or emergency death as poor prognosis, the poor prognosis prediction model for patients with pre-examination and triage level III and below was constructed. The receiver operator characteristic curve and forest map results showed that the model had good predictive efficiency and could be used in clinical practice to reduce the risk of insufficient emergency pre-examination and triage. CONCLUSIONS The establishment of high-quality clinical database based on big data in emergency department is conducive to mining the clinical value of big data, assisting clinical decision-making, and improving the quality of clinical diagnosis and treatment.
Adult ; Humans ; Big Data ; Emergency Service, Hospital ; Triage/methods* ; Intensive Care Units ; Hospitalization ; Retrospective Studies

Humans ; COVID-19/epidemiology* ; Contact Tracing ; Pandemics ; SARS-CoV-2 ; Emergency Service, Hospital

Child ; Humans ; Nitrous Oxide ; Emergency Service, Hospital

Adult ; Humans ; Heart Arrest/therapy* ; Cardiopulmonary Resuscitation ; Emergency Service, Hospital ; Out-of-Hospital Cardiac Arrest/therapy* ; Treatment Outcome

Humans ; Hospital Mortality ; ROC Curve ; Emergency Service, Hospital ; Heart Failure ; Renal Insufficiency, Chronic ; Retrospective Studies