scholarly journals Procalcitonin Predicts Intensive Care Unit Admission and Mortality in Patients With A COVID-19 Infection in The Emergency Department

2021 ◽  
Author(s):  
Kirby Tong-Minh ◽  
Yuri van der Does ◽  
Susanna Engelen ◽  
Evelien de Jong ◽  
Christian Ramakers ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Clinical Decision Making ◽  
Medical Center ◽  
Composite Endpoint ◽  
Final Analysis ◽  
Clinical Decision ◽  
Erasmus Medical ◽  
Bacterial Coinfection

Abstract IntroductionPatients with a severe COVID-19 infection often require admission at an intensive care unit (ICU) when they develop acute respiratory distress syndrome (ARDS). Hyperinflammation plays an important role in the development of ARDS in COVID-19. Procalcitonin (PCT) is a biomarker which may be a predictor of hyperinflammation. When patients with COVID-19 are in the emergency department (ED), PCT could be a predictor of severe COVID-19 infection. The goal of this study is to investigate the predictive value of PCT on severe COVID-19 infections in the ED. MethodsThis was a retrospective cohort study including patients with confirmed COVID-19 infection who visited the ED of Erasmus Medical Center in Rotterdam, the Netherlands, between March and December 2020. The primary endpoint was a severe COVID-19 infection, which was defined as patients who required ICU admission, in-hospital mortality and 30-day mortality after hospital discharge. PCT levels were measured during the ED visit. We used logistic regression to calculate the odds ratio (OR) of PCT on a severe COVID-19 infection, adjusting for bacterial coinfections, age, gender and comorbidities. ResultsA total of 332 patients were included in the final analysis of this study, of which 105 patients reached the composite endpoint of a severe COVID-19 infection. PCT showed an unadjusted OR of 4.19 (CI: 2.52-7.69) on a severe COVID-19 infection. Corrected for bacterial coinfection, the OR of PCT was 4.05 (2.45 – 7.41). Adjusted for gender, bacterial coinfection, age and comorbidities, PCT was still an independent predictor of severe COVID-19 infection with an adjusted OR of 3.82 (CI: 2.26-7.48).ConclusionPCT is a predictor of severe COVID-19 infections in patients with a COVID-19 infection in the ED. The routine measurement of PCT in patients with a COVID-19 infection in the ED may assist physicians in the clinical decision making process regarding ICU disposition when PCT levels are elevated.

scholarly journals Procalcitonin Predicts Intensive Care Unit Admission and Mortality in Patients With a COVID-19 Infection in the Emergency Department

2021 ◽  
Author(s):  
Kirby Tong-Minh ◽  
Yuri van der Does ◽  
Susanna Engelen ◽  
Evelien de Jong ◽  
Christian Ramakers ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Clinical Decision Making ◽  
Medical Center ◽  
Composite Endpoint ◽  
Final Analysis ◽  
Clinical Decision ◽  
Erasmus Medical ◽  
Bacterial Coinfection

Abstract IntroductionPatients with a severe COVID-19 infection often require admission at an intensive care unit (ICU) when they develop acute respiratory distress syndrome (ARDS). Hyperinflammation plays an important role in the development of ARDS in COVID-19. Procalcitonin (PCT) is a biomarker which may be a predictor of hyperinflammation. When patients with COVID-19 are in the emergency department (ED), PCT could be a predictor of severe COVID-19 infection. The goal of this study is to investigate the predictive value of PCT on severe COVID-19 infections in the ED. MethodsThis was a retrospective cohort study including patients with confirmed COVID-19 infection who visited the ED of Erasmus Medical Center in Rotterdam, the Netherlands, between March and December 2020. The primary endpoint was a severe COVID-19 infection, which was defined as patients who required ICU admission, in-hospital mortality and 30-day mortality after hospital discharge. PCT levels were measured during the ED visit. We used logistic regression to calculate the odds ratio (OR) of PCT on a severe COVID-19 infection, adjusting for bacterial coinfections, age, gender and comorbidities. ResultsA total of 332 patients were included in the final analysis of this study, of which 105 patients reached the composite endpoint of a severe COVID-19 infection. PCT showed an unadjusted OR of 4.19 (CI: 2.52-7.69) on a severe COVID-19 infection. Corrected for bacterial coinfection, the OR of PCT was 4.05 (2.45 – 7.41). Adjusted for gender, bacterial coinfection, age and comorbidities, PCT was still an independent predictor of severe COVID-19 infection with an adjusted OR of 3.82 (CI: 2.26-7.48).ConclusionPCT is a predictor of severe COVID-19 infections in patients with a COVID-19 infection in the ED. The routine measurement of PCT in patients with a COVID-19 infection in the ED may assist physicians in the clinical decision making process regarding ICU disposition when PCT levels are elevated.

scholarly journals Using machine learning tools to predict outcomes for emergency department intensive care unit patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qiangrong Zhai ◽  
Zi Lin ◽  
Hongxia Ge ◽  
Yang Liang ◽  
Nan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Predictive Accuracy ◽  
Medical Center ◽  
Learning Tools ◽  
Critical Patients

AbstractThe number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring systems are designed for all emergency patients using a classic mathematical method, but risk factors in critically ill patients have complex interactions, so traditional scoring cannot as readily apply to them. As an accurate model for predicting the mortality of emergency department critically ill patients is lacking, this study’s objective was to develop a scoring system using machine learning optimized for the unique case of critical patients in emergency departments. We conducted a retrospective cohort study in a tertiary medical center in Beijing, China. Patients over 16 years old were included if they were alive when they entered the emergency department intensive care unit system from February 2015 and December 2015. Mortality up to 7 days after admission into the emergency department was considered as the primary outcome, and 1624 cases were included to derive the models. Prospective factors included previous diseases, physiologic parameters, and laboratory results. Several machine learning tools were built for 7-day mortality using these factors, for which their predictive accuracy (sensitivity and specificity) was evaluated by area under the curve (AUC). The AUCs were 0.794, 0.840, 0.849 and 0.822 respectively, for the SVM, GBDT, XGBoost and logistic regression model. In comparison with the SAPS 3 model (AUC = 0.826), the discriminatory capability of the newer machine learning methods, XGBoost in particular, is demonstrated to be more reliable for predicting outcomes for emergency department intensive care unit patients.

Clinical Decision Making in the ICU: Dysphagia Screening, Assessment, and Treatment

2019 ◽  
Vol 40 (03) ◽  
pp. 170-187 ◽  
Author(s):  
Martin B. Brodsky ◽  
Emily B. Mayfield ◽  
Roxann Diez Gross
Keyword(s):  
Intensive Care Unit ◽  
Decision Making ◽  
Intensive Care ◽  
Clinical Decision Making ◽  
Clinical Care ◽  
Clinical Decision ◽  
Level Of Care ◽  
Assessment And Treatment ◽  
Proper Training ◽  
Screening Assessment

AbstractClinicians often perceive the intensive care unit as among the most intimidating environments in patient care. With the proper training, acquisition of skill, and approach to clinical care, feelings of intimidation may be overcome with the great rewards this level of care has to offer. This review—spanning the ages of birth to senescence and covering oral/nasal endotracheal intubation and tracheostomy—presents a clinically relevant, directly applicable review of screening, assessment, and treatment of dysphagia in the patients who are critically ill for clinical speech–language pathologists and identifies gaps in the clinical peer-reviewed literature for researchers.

scholarly journals The Role of C-Reactive Protein and the SOFA Score as Parameter for Clinical Decision Making in Surgical Patients during the Intensive Care Unit Course

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e55964 ◽  
Author(s):  
Zainna C. Meyer ◽  
Jennifer M. J. Schreinemakers ◽  
Paul G. H. Mulder ◽  
Ruud A. L. de Waal ◽  
Antonius A. M. Ermens ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Decision Making ◽  
Intensive Care ◽  
Sofa Score ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Surgical Patients ◽  
C Reactive Protein ◽  
Reactive Protein

scholarly journals (P2-94) How One Group of Physicians Helped a Busy Emergency Department

2011 ◽  
Vol 26 (S1) ◽  
pp. s167-s167
Author(s):  
J. Hu ◽  
J. Xu ◽  
J. Botler ◽  
S. Haydar
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Length Of Stay ◽  
Medical Center ◽  
Hospital Medicine ◽  
Intermediate Care ◽  
Physician Time ◽  
The Mean ◽  
Medical Telemetry

A pilot admission leadership physician (ALP) program was experimented within a 693-bed, tertiary medical center with a 60-bed emergency department. This trial was intended to investigate whether having a physician triage potential patients would shorten patients' length-of-stay in the emergency department. After a emergency physician evaluated patients, ALP triaged them. The ALP ordered the appropriate bed for the patients if they qualified for the inpatient criteria, choosing among medical, medical telemetry, cardiac telemetry, intermediate care, or intensive care bed. The mean patient door-to-bed order time (time between patients reaching the emergency department to time to bed ordered by ALP) is 330.7 minutes (n = 234, SD = 151.68, 95% CI = 310.21–351.28) with ALP involvement. Compared with the mean door-to-bed order time of 337.8 minutes (n = 827, SD = 149.71, 95%CI = 326.98–348.57) without ALP, ALP shortened the waiting time by 7.09 minutes. During the same period, the door-to-physician time was 41.38 minutes (SD = 38.87 95%CI = 36.38–46.39), compared with 39.52 minutes (SD = 40.32, 95%CI = 36.77–42.27) before ALP. The time for patients waiting in the emergency department for other services such as surgery, psychiatry, and pediatrics also have decreased accordingly. Incorrect medical admissions such as scrambling to get the patient to the intensive care unit right after seeing patients has decreased (data not provided). Identifying physicians as physicians in the emergency department who triage potential admissions also has improved efficiencies within the hospital medicine group and bonding with ER physicians.

scholarly journals Computerized nursing process in the Intensive Care Unit: ergonomics and usability

2016 ◽  
Vol 50 (6) ◽  
pp. 998-1004 ◽  
Author(s):  
Sônia Regina Wagner de Almeida ◽  
◽  
Grace Teresinha Marcon Dal Sasso ◽  
Daniela Couto Carvalho Barra ◽  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Nursing Practice ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Nursing Process ◽  
Evaluation Instrument ◽  
Before And After ◽  
Quasi Experimental ◽  
Usability Criteria

Abstract OBJECTIVE Analyzing the ergonomics and usability criteria of the Computerized Nursing Process based on the International Classification for Nursing Practice in the Intensive Care Unit according to International Organization for Standardization(ISO). METHOD A quantitative, quasi-experimental, before-and-after study with a sample of 16 participants performed in an Intensive Care Unit. Data collection was performed through the application of five simulated clinical cases and an evaluation instrument. Data analysis was performed by descriptive and inferential statistics. RESULTS The organization, content and technical criteria were considered "excellent", and the interface criteria were considered "very good", obtaining means of 4.54, 4.60, 4.64 and 4.39, respectively. The analyzed standards obtained means above 4.0, being considered "very good" by the participants. CONCLUSION The Computerized Nursing Processmet ergonomic and usability standards according to the standards set by ISO. This technology supports nurses' clinical decision-making by providing complete and up-to-date content for Nursing practice in the Intensive Care Unit.

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