scholarly journals Prospective validation of an 11-gene mRNA host response score for mortality risk stratification in the intensive care unit

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew R. Moore ◽  
Jonasel Roque ◽  
Brian T. Shaller ◽  
Tola Asuni ◽  
Melissa Remmel ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Whole Blood ◽  
Messenger Rna ◽  
Emergency Department Presentation ◽  
Gene Score ◽  
Net Reclassification Improvement ◽  
Improvement Index ◽  
Gene Mrna

AbstractSeveral clinical calculators predict intensive care unit (ICU) mortality, however these are cumbersome and often require 24 h of data to calculate. Retrospective studies have demonstrated the utility of whole blood transcriptomic analysis in predicting mortality. In this study, we tested prospective validation of an 11-gene messenger RNA (mRNA) score in an ICU population. Whole blood mRNA from 70 subjects in the Stanford ICU Biobank with samples collected within 24 h of Emergency Department presentation were used to calculate an 11-gene mRNA score. We found that the 11-gene score was highly associated with 60-day mortality, with an area under the receiver operating characteristic curve of 0.68 in all patients, 0.77 in shock patients, and 0.98 in patients whose primary determinant of prognosis was acute illness. Subjects with the highest quartile of mRNA scores were more likely to die in hospital (40% vs 7%, p < 0.01) and within 60 days (40% vs 15%, p = 0.06). The 11-gene score improved prognostication with a categorical Net Reclassification Improvement index of 0.37 (p = 0.03) and an Integrated Discrimination Improvement index of 0.07 (p = 0.02) when combined with Simplified Acute Physiology Score 3 or Acute Physiology and Chronic Health Evaluation II score. The test performed poorly in the 95 independent samples collected > 24 h after emergency department presentation. Tests will target a 30-min turnaround time, allowing for rapid results early in admission. Moving forward, this test may provide valuable real-time prognostic information to improve triage decisions and allow for enrichment of clinical trials.

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 Assessment of atrial fibrillation in emergency department

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Manzo-Silberman ◽  
T Chouihed ◽  
L Fraticelli ◽  
A Peiretti ◽  
C Claustre ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Vitamin K ◽  
De Novo ◽  
Bleeding Event ◽  
Vitamin K Antagonist ◽  
Funding Source ◽  
Private Company

Abstract Introduction Atrial Fibrillation (AF) is the most common arrythmia, especially in older adults. AF represents 1% of emergency department (ED) visits a third of which are de novo or recurrent. While the diagnosis is given quickly by reading the electrocardiogram (ECG), its management both remains complex. European guidelines have been published in 2016. Purpose Our study aimed to investigate guidelines implementation in French ED. Methods Prospective national multicenter study (clinical trials NCT 03836339) and core interpretation of ECG. Consecutive patients admitted in 32 French ED for AF confirmed by ECG were prospectively included. Clinical characteristics at admission were recorded by the physician. The 3-months telephone follow-up was ensured by one operator. Results From 1/10/2018 to 30/11/2018, 1369 patients with AF were included, of whom 295 (21.55%) had a de novo AF. Patients were 80 [65; 87] years old, 51.17% of men, 71.53% self-ruling, 91.53% living at home, 65.42% transported by firemen or by ambulances and 4,07% by a mobile intensive care unit. Twenty-six (8.84%) patients had a history of stroke or transient ischemic stroke and none of them on anticoagulants. CHA2DS2-VASC score was performed in 66.78% of patients and was 0 in 14 (7.11%) patients. HAS-BLED score = 2 [1; 3]. At admission 50.17% of patients received anticoagulants, of whom 49.32% a non-vitamin K antagonist oral anticoagulant, 0.68% Vitamin K antagonists, 50.68% UFH or LMWH. Beta-blockers were administered in 102 (24.01%) patients and amiodarone in 38 (12.89%). Cardiac echography has been performed in 20.34% of patients. Atrial fibrillation was the primary diagnosis in 42.71% of patients. It has been associated to a pneumopathy in 25.17% of patients, a pulmonary embolism in 4.76% and acute alcoholism in 1.36% of them. Precipitating factor was often undetermined. The discharge to the home concerned 18.64% of patients, 26.78% of patients were hospitalized in ED hospitalization unit, 23.05% in cardiology or intensive care unit. At 3 months, 49% of patients were on anticoagulants, of whom 90% on non-vitamin K antagonist oral anticoagulants, 95% of them didn't report any bleeding event and 41.77% of them were able to have a cardiology consultation within three months. Three-months mortality was about 22.09%, and rehospitalization rate about 22.89%. Conclusion It seems to be a reticence to initiate anticoagulation of patients admitted to ED with a de novo AF. It could be explained by both the advanced age of the patients and the lack of an organized access to a systematic cardiology consultation at discharge. Patients with chronic AF are subject to high mortality at 3 months and a significant risk of readmission. The application of the guidelines could be optimized by a better training program and the implementation of a dedicated pathway of care. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Bayer

scholarly journals Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eyal Klang ◽  
Benjamin R. Kummer ◽  
Neha S. Dangayach ◽  
Amy Zhong ◽  
M. Arash Kia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Learning Model ◽  
Free Text ◽  
Combined Model ◽  
Text Data ◽  
Machine Learning Model ◽  
Record Data

AbstractEarly admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and free text data to identify patients requiring NSICU admission shortly after arrival to the emergency department (ED). We conducted a single-center, retrospective cohort study of adult patients at the Mount Sinai Hospital, an academic medical center in New York City. All patients presenting to our institutional ED between January 2014 and December 2018 were included. Structured (tabular) demographic, clinical, bed movement record data, and free text data from triage notes were extracted from our institutional data warehouse. A machine learning model was trained to predict likelihood of NSICU admission at 30 min from arrival to the ED. We identified 412,858 patients presenting to the ED over the study period, of whom 1900 (0.5%) were admitted to the NSICU. The daily median number of ED presentations was 231 (IQR 200–256) and the median time from ED presentation to the decision for NSICU admission was 169 min (IQR 80–324). A model trained only with text data had an area under the receiver-operating curve (AUC) of 0.90 (95% confidence interval (CI) 0.87–0.91). A structured data-only model had an AUC of 0.92 (95% CI 0.91–0.94). A combined model trained on structured and text data had an AUC of 0.93 (95% CI 0.92–0.95). At a false positive rate of 1:100 (99% specificity), the combined model was 58% sensitive for identifying NSICU admission. A machine learning model using structured and free text data can predict NSICU admission soon after ED arrival. This may potentially improve ED and NSICU resource allocation. Further studies should validate our findings.

scholarly journals THE ROLE OF EMERGENCY DEPARTMENT IN THE EFFICIENCY OF INTENSIVE CARE UNIT OF THE HOSPITAL

2017 ◽  
Vol 18 (2) ◽  
pp. 64-68 ◽  
Author(s):  
V. M. Teplov ◽  
Y. S. Polushin ◽  
A. S. Povzun ◽  
A. G. Miroshnichenko ◽  
I. P. Minnullin ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care

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.

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