scholarly journals COVID outcome prediction in the emergency department (COPE): using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e051468
Author(s):  
David van Klaveren ◽  
Alexandros Rekkas ◽  
Jelmer Alsma ◽  
Rob J C G Verdonschot ◽  
Dick T J J Koning ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Outcome Prediction ◽  
Characteristic Curve ◽  
Hospital Death ◽  
Hospital Data ◽  
Dutch Hospital ◽  
Icu Admission ◽  
Second Wave

ObjectivesDevelop simple and valid models for predicting mortality and need for intensive care unit (ICU) admission in patients who present at the emergency department (ED) with suspected COVID-19.DesignRetrospective.SettingSecondary care in four large Dutch hospitals.ParticipantsPatients who presented at the ED and were admitted to hospital with suspected COVID-19. We used 5831 first-wave patients who presented between March and August 2020 for model development and 3252 second-wave patients who presented between September and December 2020 for model validation.Outcome measuresWe developed separate logistic regression models for in-hospital death and for need for ICU admission, both within 28 days after hospital admission. Based on prior literature, we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. We assessed model performance by the area under the receiver operating characteristic curve (AUC) and by calibration plots.ResultsOf 5831 first-wave patients, 629 (10.8%) died within 28 days after admission. ICU admission was fully recorded for 2633 first-wave patients in 2 hospitals, with 214 (8.1%) ICU admissions within 28 days. A simple model—COVID outcome prediction in the emergency department (COPE)—with age, respiratory rate, C reactive protein, lactate dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well calibrated and showed good discrimination for mortality in second-wave patients (AUC in four hospitals: 0.82 (95% CI 0.78 to 0.86); 0.82 (95% CI 0.74 to 0.90); 0.79 (95% CI 0.70 to 0.88); 0.83 (95% CI 0.79 to 0.86)). COPE was also able to identify patients at high risk of needing ICU admission in second-wave patients (AUC in two hospitals: 0.84 (95% CI 0.78 to 0.90); 0.81 (95% CI 0.66 to 0.95)).ConclusionsCOPE is a simple tool that is well able to predict mortality and need for ICU admission in patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.

scholarly journals COVID Outcome Prediction in the Emergency Department (COPE)

2021 ◽  
Author(s):  
David van Klaveren ◽  
Alexandros Rekkas ◽  
Jelmer Alsma ◽  
Rob JCG Verdonschot ◽  
Dick TJJ Koning ◽  
...  
Keyword(s):  
Emergency Department ◽  
Hospital Admission ◽  
Outcome Prediction ◽  
Model Performance ◽  
Hospital Death ◽  
Icu Admission ◽  
Risk Of Death ◽  
Increased Risk ◽  
Temporal Validation ◽  
Second Wave

ABSTRACTBackground and aimThe COVID-19 pandemic is putting extraordinary pressure on emergency departments (EDs). To support decision making about hospital admission, we aimed to develop a simple and valid model for predicting mortality and need for admission to an intensive care unit (ICU) in suspected-COVID-19 patients presenting at the ED.MethodsFor model development, we included patients that presented at the ED and were admitted to 4 large Dutch hospitals with suspected COVID-19 between March and August 2020, the first wave of the pandemic in the Netherlands. Based on prior literature we included patient characteristics, vital parameters and blood test values, all measured at ED admission, as potential predictors. Logistic regression analyses with post-hoc uniform shrinkage was used to obtain predicted probabilities of in-hospital death and of being admitted to the ICU, both within 28 days after admission. Model performance (AUC; calibration plots, intercepts and slopes) was assessed with temporal validation in patients who presented between September and December 2020 (second wave). We used multiple imputation to account for missing predictor values.ResultsThe development data included 5,831 patients who presented at the ED and were hospitalized, of whom 629 (10.8%) died and 5,070 (86.9%) were discharged within 28 days after admission. A simple model – named COVID Outcome Prediction in the Emergency Department (COPE) – with linear age and logarithmic transforms of respiratory rate, CRP, LDH, albumin and urea captured most of the ability to predict death within 28 days. Patients who were admitted in the first month of the pandemic had substantially increased risk of death (odds ratio 1.99; 95% CI 1.61-2.47). COPE was well-calibrated and showed good discrimination for predicting death in 3,252 patients of the second wave (AUC in 4 hospitals: 0.82; 0.82; 0.79; 0.83). COPE was also able to identify patients at high risk of needing IC in second wave patients below the age of 70 (AUC 0.84; 0.81), but overestimated ICU admission for low-risk patients. The models are implemented as a web-based application.ConclusionCOPE is a simple tool that is well able to predict mortality and ICU admission for patients who present to the ED with suspected COVID-19 and may help to inform patients and doctors when deciding on hospital admission.

scholarly journals In-Hospital Outcomes in Patients Admitted to the Intensive Care Unit after a Return Visit to the Emergency Department

Healthcare ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 431
Author(s):  
Chun-Fu Lin ◽  
Yi-Syun Huang ◽  
Ming-Ta Tsai ◽  
Kuan-Han Wu ◽  
Chien-Fu Lin ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Regression Analysis ◽  
Intensive Care ◽  
Medical Cost ◽  
Total Medical Cost ◽  
Hospital Outcomes ◽  
Return Visit ◽  
Icu Admission ◽  
Ed Visits

Background: Intensive care unit (ICU) admission following a short-term emergency department (ED) revisit has been considered a particularly undesirable outcome among return-visit patients, although their in-hospital prognosis has not been discussed. We aimed to compare clinical outcomes between adult patients admitted to the ICU after unscheduled ED revisits and those admitted during index ED visits. Method: This retrospective study was conducted at two tertiary medical centers in Taiwan from 1 January 2016 to 31 December 2017. All adult non-trauma patients admitted to the ICU directly via the ED during the study period were included and divided into two comparison groups: patients admitted to the ICU during index ED visits and those admitted to the ICU during return ED visits. The outcomes of interest included in-hospital mortality, mechanical ventilation (MV) support, profound shock, hospital length of stay (HLOS), and total medical cost. Results: Altogether, 12,075 patients with a mean (standard deviation) age of 64.6 (15.7) years were included. Among these, 5.3% were admitted to the ICU following a return ED visit within 14 days and 3.1% were admitted following a return ED visit within 7 days. After adjusting for confounding factors for multivariate regression analysis, ICU admission following an ED revisit within 14 days was not associated with an increased mortality rate (adjusted odds ratio (aOR): 1.08, 95% confidence interval (CI): 0.89 to 1.32), MV support (aOR: 1.06, 95% CI: 0.89 to 1.26), profound shock (aOR: 0.99, 95% CI: 0.84 to 1.18), prolonged HLOS (difference: 0.04 days, 95% CI: −1.02 to 1.09), and increased total medical cost (difference: USD 361, 95% CI: −303 to 1025). Similar results were observed after the regression analysis in patients that had a 7-day return visit. Conclusion: ICU admission following a return ED visit was not associated with major in-hospital outcomes including mortality, MV support, shock, increased HLOS, or medical cost. Although ICU admissions following ED revisits are considered serious adverse events, they may not indicate poor prognosis in ED practice.

scholarly journals NT-ProBNP Predicts Total Mortality, Emergency Department Visits, Hospitalization, Intensive-Care Unit Admission, and Cardiovascular Events in Hemodialysis Patients

2019 ◽  
Vol 8 (2) ◽  
pp. 238
Author(s):  
Yi-Hsin Chen ◽  
Yun-Ching Fu ◽  
Ming-Ju Wu
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Cardiovascular Events ◽  
Predictive Ability ◽  
Hemodialysis Patients ◽  
Emergency Department Visits ◽  
Time Dependent ◽  
Icu Admission ◽  
Ed Visits

N-terminal pro b-type natriuretic peptide (NT-proBNP) was considered a prognostic factor for mortality in hemodialysis patients in previous studies. However, NT-proBNP has not been fully explored in terms of predicting other clinical outcomes in hemodialysis patients. This study aimed to investigate if NT-proBNP could predict emergency department (ED) visits, hospitalization, admission to intensive-care unit (ICU), and cardiovascular incidents in hemodialysis patients. Serum NT-proBNP and other indicators were collected in 232 hemodialysis patients. Patients were followed up for three years or until mortality. Outcomes included mortality, number of ED visits, hospitalizations, admissions to ICU, and cardiovascular events. NT-proBNP was found to predict recurrent ER visits, hospitalization, admission to ICU, cardiovascular events, and mortality, after adjusting for covariates. Time-dependent area under the curve (AUC) was used to evaluate the NT-proBNP predicting ability. Using time-dependent AUC, NT-proBNP has good predictive ability for mortality, ED visit, hospitalization, ICU admission, and cardiovascular events with the best predictive ability occurring at approximately 1 year, and 5th, 62nd, 63rd, and 63rd days respectively. AUC values for predicting mortality, hospitalization, and ICU admission decreased significantly after one year. NT-proBNP can be applied in predicting ED visits but is only suitable for the short-term. NT-proBNP may be used for predicting mortality in the long term.

scholarly journals Factors contributing to delay intensive care unit admission of critically ill patients from the adult emergency Department in Tikur Anbessa Specialized Hospital

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Helen Teklie ◽  
Hywet Engida ◽  
Birhanu Melaku ◽  
Abdata Workina
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Intensive Care Unit Admission ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Transfer Time ◽  
Multivariate Logistic Regression ◽  
Icu Admission ◽  
Specialized Hospital

Abstract Background The transfer time for critically ill patients from the emergency department (ED) to the Intensive care unit (ICU) must be minimal; however, some factors prolong the transfer time, which may delay intensive care treatment and adversely affect the patient’s outcome. Purpose To identify factors affecting intensive care unit admission of critically ill patients from the emergency department. Patients and methods A cross-sectional study design was conducted from January 13 to April 12, 2020, at the emergency department of Tikur Anbesa Specialized Hospital. All critically ill patients who need intensive care unit admission during the study period were included in the study. A pretested structured questionnaire was adapted from similar studies. The data were collected by chart review and observation. Then checked data were entered into Epi-data version 4.1 and cleaned data was exported to SPSS Version 25 for analysis. Descriptive statistics, bivariate and multivariate logistic regression were used to analyze the data. Result From the total of 102 critically ill patients who need ICU admission 84.3% of them had prolonged lengths of ED stay. The median length of ED stay was 13.5 h with an IQR of 7–25.5 h. The most common reasons for delayed ICU admission were shortage of ICU beds 56 (65.1%) and delays in radiological examination results 13(15.1%). On multivariate logistic regression p < 0.05 male gender (AOR = 0.175, 95% CI: (0.044, 0.693)) and shortage of ICU bed (AOR = 0.022, 95% CI: (0.002, 0.201)) were found to have a significant association with delayed intensive care unit admission. Conclusion there was a delay in ICU admission of critically ill patients from the ED. Shortage of ICU bed and delay in radiological investigation results were the reasons for the prolonged ED stay.

scholarly journals Validation of Modified Paediatric Triage Score in a Tertiary Care Hospital

2020 ◽  
Author(s):  
Bandya Sahoo ◽  
Reshmi Mishra ◽  
Mukesh Kumar Jain ◽  
Sibabratta Patnaik
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Scoring System ◽  
Tertiary Care ◽  
P Value ◽  
Care Hospital ◽  
Clinical Variables ◽  
Icu Admission ◽  
Physical Variables

Introduction: The global burden of paediatric mortality is high and majority of the deaths are preventable by providing timely access to specialised emergency care. An appropriate triage in a busy emergency department can identify the sickest patient for early intervention. Aim: To develop a simple score based on physical variables alone and assess its validation so as to predict Intensive Care Unit (ICU) admission. Materials and Methods: This prospective hospital based study included 936 children, aged 1 month to 18 years. Baseline demographic data along with clinical variables were noted in a pre-designed proforma at the time of admission. A scoring system was developed based on severity of various clinical variables i.e., heart rate, respiratory rate, respiratory effort, Oxygen Saturation (SpO2), Capillary Refill Time (CFT), temperature, level of consciousness and behaviour. The outcome i.e., admission to ward or Pediatric Intensive Care Unit (PICU) of the patient was correlated with the study variables and total score. An association of modified PETS with the PICU admission was done using Chi-square test. A p-value of <0.01 was considered as statistically significant. Results: The modified Paediatric Triage Score (PETS) which is developed based on eight physical variables, is reliable in discriminating the children with ward and ICU admission. A score of ≥6 leads to 14.8 times higher risk of getting admitted to ICU as compared to a child with score of <6. A cut-off of ≥6 for modified PETS score has a sensitivity of 79.6% and specificity of 79.2% in predicting ICU admission. Conclusion: This simple clinically developed scoring system based on physical variables alone with an optimal cut-off of ≥6 can predict severity of illness and need for PICU admission in Emergency Department with acceptable validity and can serve as a potentially excellent screening tool.

scholarly journals Evaluation of N/LP Ratio as a Predictor of Disease Progression and Mortality in COVID-19 Patients Admitted to the Intensive Care Unit

2021 ◽  
Author(s):  
Başak Çakır Güney ◽  
Mert Hayıroğlu ◽  
Didar Şenocak ◽  
Vedat Çiçek ◽  
Tufan Çınar ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multivariable Analysis ◽  
Hospital Death ◽  
Survival Status ◽  
Inflammatory Index ◽  
Icu Stay

Objective: This research aimed to evaluate whether the neutrophil to lymphocyte and platelet (N/LP) ratio may be used to predict the risk of admission to the intensive care unit (ICU), the need for mechanical ventilation and in-hospital mortality in Coronavirus disease 2019 (COVID-19) cases. Methods: The study was conducted retrospectively on the data of 134 COVID-19 patients who were admitted to the ICU. The N/LP ratio was calculated as follows: neutrophil count x 100 / (lymphocyte count x platelet count). Each member of the research cohort was categorised into 1 of 2 groups based on their survival status (survivor and non-survivor groups). Results: In total, 82 (61%) patients died during the ICU stay. Patients who required mechanical ventilation and died in the ICU stay had significantly higher N/LP ratio than those who did not require it and survived [10 (IQR=4.94-19.38) vs 2.51 (IQR=1.67-5.49), p<0.001] and [11.27 (IQR=4.53-30.02) vs 1.65 (IQR=1-3.24), p<0.001], respectively. The N/LP ratio was linked with the requirement of mechanical ventilation and in-hospital death according to multivariable analysis. In receiver operating characteristic curve analysis, we found that N/LP in predicting admission to the ICU was >4.18 with 61% sensitivity and 62% specificity, it was >5.07 with 74% sensitivity and 73% specificity for the need for mechanical ventilation, and >3.69 with 81% sensitivity and 81% specificity to predict in-hospital death. Conclusion: To our knowledge, this is the first study showing that the N/LP ratio, which is a novel and widely applicable inflammatory index, may be used to predict the risk of ICU admission, mechanical ventilation and in-hospital death in patients with COVID-19 disease.

scholarly journals Emergency calls are early indicators of ICU bed requirement during the COVID-19 epidemic

2020 ◽  
Author(s):  
◽  
Bruno Riou
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Icu Patients ◽  
Ambulance Dispatch ◽  
Polymerase Chain ◽  
Gp Visits ◽  
Second Wave

AbstractBackgroundAlthough the number of intensive care unit (ICU) beds is crucial during the COVID-19 epidemic caring for the most critically ill infected patients, there is no recognized early indicator to anticipate ICU bed requirements.MethodsIn the Ile-de-France region, from February 20 to May 5, 2020, emergency medical service (EMS) calls and the response provided (ambulances) together the percentage of positive reverse transcriptase polymerase chain reaction (RT-PCR) tests, general practitioner (GP) and emergency department (ED) visits, and hospital admissions of COVID-19 patients were recorded daily and compared to the number of COVID-19 ICU patients. Correlation curve analysis was performed to determine the best correlation coefficient (R), depending on the number of days the indicator has been shifted. A delay ≥7 days was considered as an early alert, and a delay ≥14 days a very early alert.FindingsEMS calls, percentage of positive RT-PCR tests, ambulances used, ED and GP visits of COVID-19 patients were strongly associated with COVID-19 ICU patients with an anticipation delay of 23, 15, 14, 13, and 12 days respectively. Hospitalization did not anticipate ICU bed requirement.InterpretationThe daily number of COVID19-related telephone calls received by the EMS and corresponding dispatch ambulances, and the proportion of positive RT-PCR tests were the earliest indicators of the number of COVID19 patients requiring ICU care during the epidemic crisis in the Ile-de-France region, rapidly followed by ED and GP visits. This information may help health authorities to anticipate a future epidemic, including a second wave of COVID19 or decide additional social measures.FundingOnly institutional funding was provided.Research in contextEvidence before the studyWe searched PubMed and preprint archives for articles published up to May 17, 2020, that contained information about the anticipation of intensive care unit (ICU) bed requirement during the COVID-19 outbreak using the terms “coronavirus”, “2009-nCOV”, “COVID-19”, SARS-CoV2”, “prediction” “resource” and “intensive care”. We also reviewed relevant references in retrieved articles and the publicly available publication list of the COVID-19 living systematic review.22 This list contains studies on covid-19 published on PubMed and Embase through Ovid, bioRxiv, and medRxiv, and is continuously updated. Although many studies estimated the number of patients who would have severe COVID-19 requiring ICU, very few contained assessment for early signals (from internet or social media), and we retrieved no study whose data came from suspected or infected patients.Added values of this studyDuring the COVID-19 epidemic, emergency medical system (EMS) calls, percentage of positive reverse transcriptase polymerase chain reaction (RT-PCR) tests, ambulance dispatch, emergency department (ED) and general practitioner (GP) visits of COVID-19 patients were strongly associated with COVID-19 ICU patients with an anticipation delay of 23, 15, 14, 13, and 12 days respectively. Hospitalization did not anticipated COVID-19 ICU bed requirement.Implication of all available evidenceEMS calls and ambulance dispatch, percent of positive RT-PCR, and ED and GP visits could be valuable tools as daily alert signals to set up plan to face the burden of ICU bed requirement during the initial wave of the COVID-19 epidemic, and may possibly also help anticipating a second wave. These results are important since mortality has been reported being correlated to health care resources.

scholarly journals A Peculiar Case of Invasive Streptococcus pneumoniae

2017 ◽  
Vol 2017 ◽  
pp. 1-3
Author(s):  
Adeel Rafi Ahmed ◽  
Liam Townsend ◽  
Helen Tuite ◽  
Catherine Fleming
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Streptococcus Pneumoniae ◽  
Respiratory Distress ◽  
Multiorgan Failure ◽  
Community Acquired Pneumonia ◽  
Icu Admission ◽  
Severe Community Acquired Pneumonia ◽  
Peculiar Case

Patients commonly present to the emergency department with acute respiratory distress; however, the differentials are broad and at times difficult to distinguish. We describe a case of severe community-acquired pneumonia (CAP) secondary to invasive Streptococcus pneumoniae. The patient was intubated within 3 h of presentation and suffered multiorgan failure within 72 h of intensive care unit (ICU) admission. This case is a stark illustration of how the most common bacteria associated with CAP can be fatal and highlights the associated markers of severity. It also outlines other potential complications including a very rare phenomenon of cardiomyopathy with myocarditis associated with S. pneumoniae bacteraemia.

scholarly journals National Early Warning Score for predicting intensive care unit admission among elderly patients with influenza infections in the emergency department: an effective disposition tool during the influenza season

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e044496
Author(s):  
Te-Hao Wang ◽  
Jing-Cheng Jheng ◽  
Yen-Ting Tseng ◽  
Li-Fu Chen ◽  
Jui-Yuan Chung
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Logistic Regression ◽  
Intensive Care ◽  
Elderly Patients ◽  
Early Warning Score ◽  
Influenza Epidemic ◽  
Epidemic Season ◽  
Icu Admission ◽  
National Early Warning Score

ObjectiveDuring the influenza epidemic season, the fragile elderlies are not only susceptible to influenza infections, but are also more likely to develop severe symptoms and syndromes. Such circumstances may pose a significant burden to the medical resources especially in the emergency department (ED). Disposition of the elderly patients with influenza infections to either the ward or intensive care unit (ICU) accurately is therefore a crucial issue.Study designRetrospective cohort study.Setting and participantsElderly patients (≥65 years) with influenza visiting the ED of a medical centre between 1 January 2010 and 31 December 2015.Primary outcome measuresDemographic data, vital signs, medical history, subtype of influenza, national early warning score (NEWS) and outcomes (mortality) were analysed. We investigated the ability of NEWS to predict ICU admission via logistic regression and the receiver operating characteristic (ROC) analysis.ResultsWe included 409 geriatric patients in the ED with a mean age of 79.5 years and approximately equal sex ratio. The mean NEWS ±SD was 3.4±2.9, and NEWS ≥8 was reported in 11.0% of the total patients. Logistic regression revealed that NEWS ≥8 predicted ICU admission with an OR of 5.37 (95% CI 2.61 to 11.04). The Hosmer-Lemeshow goodness-of-fit test was calculated as 0.95, and the adjusted area under the ROC was 0.72. An NEWS ≥8 is associated with ICU-admission and may help to triage elderly patients with influenza infections during the influenza epidemic season.ConclusionThe high specificity of NEWS ≥8 to predict ICU admission in elderly patients with influenza infection during the epidemic season may avoid unnecessary ICU admissions and ensure proper medical resource allocation.

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