EXPOSURE OF PEDIATRIC EMERGENCY PATIENTS TO IMAGING EXAMS, NOWADAYS AND IN TIMES OF COVID-19: AN INTEGRATIVE REVIEW

ABSTRACT Objective: To analyze literature data about unnecessary exposure of pediatric emergency patients to ionizing agents from imaging examinations, nowadays and during times of COVID-19. Data sources: Between April and July 2020, articles were selected using the databases: Virtual Health Library, PubMed and Scientific Electronic Library Online. The following descriptors were used: [(pediatrics) AND (emergencies) AND (diagnostic imaging) AND (medical overuse)] and [(Coronavirus infections) OR (COVID-19) AND (pediatrics) AND (emergencies) AND (diagnostic imaging)]. Inclusion criteria were articles available in full, in Portuguese or English, published from 2016 to 2020 or from 2019 to 2020, and articles that covered the theme. Articles without adherence to the theme and duplicate texts in the databases were excluded. Data synthesis: 61 publications were identified, of which 17 were comprised in this review. Some imaging tests used in pediatric emergency departments increase the possibility of developing future malignancies in patients, since they emit ionizing radiation. There are clinical decision instruments that allow reducing unnecessary exam requests, avoiding over-medicalization, and hospital expenses. Moreover, with the COVID-19 pandemic, there was a growing concern about the overuse of imaging exams in the pediatric population, which highlights the problems pointed out by this review. Conclusions: It is necessary to improve hospital staff training, use clinical decision instruments and develop guidelines to reduce the number of exams required, allowing hospital cost savings; and reducing children’s exposure to ionizing agents.

FACTORS RELATED TO NURSE’S RESPONSE TIME IN HANDLING OF EMERGENCY PATIENTS

Background: In emergency care, the most important thing to note is the speed of nurses in responding or acting on the first patient who enters the emergency room. This speed is often referred to as response time. Nurse response time is always a measure of the service quality of a hospital or health center. Response times depend on the speed available as well as the quality of assistance to save lives/prevent disability. Objectives: The study aimed to determine the factors related to the response time of nurses in the handling of emergency patients at Lagaligo I Hospital, East Luwu. Methods: Carrying out the research is from June to August 2020. Analytical survey design with a cross-sectional approach. The research subjects were 24 nurses. measuring tools, namely questionnaires. analysis used bivariate analysis used the Chi-Square Test. Results: This shows that there is a relationship between education and response time with a value of p-value = .013, knowledge and response time with a value of p-value = .001, the length of work and response time with a value of p-value = .000, and training and response time with p-value = .006. Conclusion: The education level of health workers is getting higher. Of course, knowledge and experience also increase, on average they are in their work longer so they are more productive and nurses have attended several pieces of training to improve their competencies.

Serum Uric Acid and Calcium Levels as Predictors of Hematoma Volume in Emergency Patients Experiencing Spontaneous Intracerebral Hemorrhage

Objective: To investigate the relationship between serum uric acid and calcium levels and hematoma volume in emergency patients who experienced spontaneous intracerebral hemorrhage (SICH).Methods: Data from 105 patients who experienced SICH and 92 with non-intracerebral hemorrhage (control group) were retrospectively analyzed. Data collected included clinical characteristics, and serum biochemical and blood coagulation indices. Hematoma volume was calculated using computed tomography (CT) imaging data.Results: Individuals who experienced SICH exhibited higher serum uric acid levels and longer activated partial thromboplastin and thrombin times compared to those with non-intracerebral hemorrhage (all P < 0.05). In contrast, serum calcium levels in patients with SICH were lower than those of the control group (P < 0.05). Hypocalcemic patients exhibited a greater median baseline hematoma volume than normocalcemic patients. Conclusion: High serum uric acid and low calcium levels may be predictors of larger hematoma volumes among individuals who experience SICH.

Declining Numbers of Neurosurgical Emergencies at a German University Medical Center during the Coronavirus Lockdown

Background The coronavirus pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is posing unprecedented challenges to health care systems around the globe. Consequently, various lockdown scenarios have been politically imposed to get control over the spread of this disease. We examined the impact of the lockdown situation on the number of neurosurgical emergency patients admitted to our tertiary care center with a catchment area of ∼2.2 million inhabitants in the south of Germany to ensure adequate neurosurgical emergency care during a pandemic lockdown. Methods All emergency admissions (with consecutive inpatient treatment) to the Department of Neurosurgery at the University Medical Center Regensburg, Germany, between March 1 and May 8 (69 days) of the years 2018, 2019, and 2020 were retrospectively identified and reviewed for this study. Demographic data, diagnoses, urgency of surgery, and duration of the journey to the emergency room were examined. Results Between March 1 and May 8, 2020, 59 emergency patients were neurosurgically treated at our department. Compared with 2018 and 2019, emergency admissions in 2020 had thus declined by 37.2 and 27.1%, respectively. Regarding the year 2020, we found a significant drop from 1.71 and 1.52 emergency patients per day in January and February 2020, respectively, to 0.86 during lockdown (p < 0.001). The decline especially concerned nontraumatic spinal cases and also patients with other neurosurgical diagnoses such as intracranial hemorrhage. Evaluation of the overall disease severity of admitted patients by means of the urgency of surgery showed no difference between the baseline years and the lockdown period. Conclusion Our findings are in line with other observational studies of neurosurgical, neurologic, and cardiologic centers in Europe that have described a drop in emergency cases. The reasons for this drop that seems to affect various medical fields and countries across Europe are still unidentified. Morbidity and mortality rates are still unknown, and efforts should be made to facilitate neurosurgical emergency care during a pandemic lockdown.

Incidence and Mortality of Emergency Patients Transported by Emergency Medical Service Personnel during the Novel Corona Virus Pandemic in Osaka Prefecture, Japan: A Population-Based Study

Although the COVID-19 pandemic affects the emergency medical service (EMS) system, little is known about the impact of the COVID-19 pandemic on the prognosis of emergency patients. This study aimed to reveal the impact of the COVID-19 pandemic on the EMS system and patient outcomes. We included patients transported by ambulance who were registered in a population-based registry of patients transported by ambulance. The endpoints of this study were the incident number of patients transported by ambulance each month and the number of deaths among these patients admitted to hospital each month. The incidence rate ratio (IRR) and 95% confidence interval (CI) using a Poisson regression model with the year 2019 as the reference were calculated. A total of 500,194 patients were transported in 2019, whereas 443,321 patients were transported in 2020, indicating a significant decrease in the number of emergency patients transported by ambulance (IRR: 0.89, 95% CI: 0.88–0.89). The number of deaths of emergency patients admitted to hospital was 11,931 in 2019 and remained unchanged at 11,963 in 2020 (IRR: 1.00, 95% CI: 0.98–1.03). The incidence of emergency patients transported by ambulance decreased during the COVID-19 pandemic in 2020, but the mortality of emergency patients admitted to hospital did not change in this study.

Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge

Background Delays in patient flow and a shortage of hospital beds are commonplace in hospitals during periods of increased infection incidence, such as seasonal influenza and the COVID-19 pandemic. The objective of this study was to develop and evaluate the efficacy of machine learning methods at identifying and ranking the real-time readiness of individual patients for discharge, with the goal of improving patient flow within hospitals during periods of crisis. Methods and performance Electronic Health Record data from Oxford University Hospitals was used to train independent models to classify and rank patients’ real-time readiness for discharge within 24 hours, for patient subsets according to the nature of their admission (planned or emergency) and the number of days elapsed since their admission. A strategy for the use of the models’ inference is proposed, by which the model makes predictions for all patients in hospital and ranks them in order of likelihood of discharge within the following 24 hours. The 20% of patients with the highest ranking are considered as candidates for discharge and would therefore expect to have a further screening by a clinician to confirm whether they are ready for discharge or not. Performance was evaluated in terms of positive predictive value (PPV), i.e., the proportion of these patients who would have been correctly deemed as ‘ready for discharge’ after having the second screening by a clinician. Performance was high for patients on their first day of admission (PPV = 0.96/0.94 for planned/emergency patients respectively) but dropped for patients further into a longer admission (PPV = 0.66/0.71 for planned/emergency patients still in hospital after 7 days). Conclusion We demonstrate the efficacy of machine learning methods at making operationally focused, next-day discharge readiness predictions for all individual patients in hospital at any given moment and propose a strategy for their use within a decision-support tool during crisis periods.