scholarly journals Initial emergency department vital signs may predict PICU admission in pediatric patients presenting with asthma exacerbation

2021 ◽  
Author(s):  
Michael Freedman ◽  
Erick Forno
Keyword(s):  
Emergency Department ◽  
Predictive Factors ◽  
Characteristic Curve ◽  
Vital Signs ◽  
Pediatric Intensive Care ◽  
Asthma Severity ◽  
Receiver Operator Characteristic Curve ◽  
Predictive Index ◽  
Asthma Morbidity ◽  
Lower Spo2

Objective: Severe asthma exacerbations account for a large share of asthma morbidity, mortality, and costs. Here, we aim to identify early predictive factors for pediatric intensive care unit (PICU) admission that could help improve outcomes. Methods: We performed a retrospective observational study of 6,014 emergency department (ED) encounters at a large children's hospital, including 95 (1.6%) resulting in PICU admission between 10/1/2015 and 8/31/2017 with ICD9/ICD10 codes for 'asthma,' 'bronchospasm,' or 'wheezing.' Vital signs and demographic information were obtained from EHR data and analyzed for each encounter. Predictive factors were identified using adjusted regression models, and our primary outcome was PICU admission. Results: Higher mean heartrates (HR) and respiratory rates (RR) and lower SpO2 within the first hour of ED presentation were independently associated with PICU admission. Odds of PICU admission increased 63% for each 10-beats/minute higher HR, 97% for each 10-breaths/minute higher RR, and 34% for each 5% lower SpO2. A binary predictive index using 1-hour vitals yielded OR 11.7 (95%CI 7.4-18.3) for PICU admission, area under the receiver operator characteristic curve (AUROC) 0.82 and overall accuracy of 81.5%. Results were essentially unchanged (AUROC 0.84) after adjusting for asthma severity and initial ED management. In combination with a secondary standardized clinical asthma distress score, positive predictive value increased by seven-fold (5.9% to 41%). Conclusions: A predictive index using HR, RR and SpO2 within the first hour of ED presentation accurately predicted PICU admission in this cohort. Automated vital signs trend analysis may help identify vulnerable patients quickly upon presentation.

scholarly journals Development and validation of an early warning tool for sepsis and decompensation in children during emergency department triage

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Louis Ehwerhemuepha ◽  
Theodore Heyming ◽  
Rachel Marano ◽  
Mary Jane Piroutek ◽  
Antonio C. Arrieta ◽  
...  
Keyword(s):  
Emergency Department ◽  
Severe Sepsis ◽  
Early Warning ◽  
Characteristic Curve ◽  
Vital Signs ◽  
Warning System ◽  
Receiver Operator Characteristic Curve ◽  
Warning Signs ◽  
Gradient Boosting ◽  
Stochastic Gradient Boosting

AbstractThis study was designed to develop and validate an early warning system for sepsis based on a predictive model of critical decompensation. Data from the electronic medical records for 537,837 visits to a pediatric Emergency Department (ED) from March 2013 to December 2019 were collected. A multiclass stochastic gradient boosting model was built to identify early warning signs associated with death, severe sepsis, non-severe sepsis, and bacteremia. Model features included triage vital signs, previous diagnoses, medications, and healthcare utilizations within 6 months of the index ED visit. There were 483 patients who had severe sepsis and/or died, 1102 had non-severe sepsis, 1103 had positive bacteremia tests, and the remaining had none of the events. The most important predictors were age, heart rate, length of stay of previous hospitalizations, temperature, systolic blood pressure, and prior sepsis. The one-versus-all area under the receiver operator characteristic curve (AUROC) were 0.979 (0.967, 0.991), 0.990 (0.985, 0.995), 0.976 (0.972, 0.981), and 0.968 (0.962, 0.974) for death, severe sepsis, non-severe sepsis, and bacteremia without sepsis respectively. The multi-class macro average AUROC and area under the precision recall curve were 0.977 and 0.316 respectively. The study findings were used to develop an automated early warning decision tool for sepsis. Implementation of this model in pediatric EDs will allow sepsis-related critical decompensation to be predicted accurately after a few seconds of triage.

scholarly journals Identifying High-Risk Children in the Emergency Department

2016 ◽  
Vol 31 (10) ◽  
pp. 660-666 ◽  
Author(s):  
Katie R. Nielsen ◽  
Russ Migita ◽  
Maneesh Batra ◽  
Jane L. Di Gennaro ◽  
Joan S. Roberts ◽  
...  
Keyword(s):  
Emergency Department ◽  
High Risk ◽  
Characteristic Curve ◽  
Pediatric Intensive Care ◽  
Receiver Operator Characteristic Curve ◽  
Clinical Deterioration ◽  
Chi Square ◽  
Inpatient Ward ◽  
Increased Risk ◽  
Chi Square Analysis

Purpose: Early warning scores (EWS) identify high-risk hospitalized patients prior to clinical deterioration; however, their ability to identify high-risk pediatric patients in the emergency department (ED) has not been adequately evaluated. We sought to determine the association between modified pediatric EWS (MPEWS) in the ED and inpatient ward-to-pediatric intensive care unit (PICU) transfer within 24 hours of admission. Methods: This is a case–control study of 597 pediatric ED patients admitted to the inpatient ward at Seattle Children’s Hospital between July 1, 2010, and December 31, 2011. Cases were children subsequently transferred to the PICU within 24 hours, whereas controls remained hospitalized on the inpatient ward. The association between MPEWS in the ED and ward-to-PICU transfer was determined by chi-square analysis. Results: Fifty children experienced ward-to-PICU transfer within 24 hours of admission. The area under the receiver–operator characteristic curve was 0.691. Children with MPEWS > 7 in the ED were more likely to experience ward-to-PICU transfer (odds ratio 8.36, 95% confidence interval 2.98-22.08); however, the sensitivity was only 18.0% with a specificity of 97.4%. Using MPEWS >7 for direct PICU admission would have led to 167 unnecessary PICU admissions and identified only 9 of 50 patients who required PICU care. Conclusions: Elevated MPEWS in the ED is associated with increased risk of ward-to-PICU transfer within 24 hours of admission; however, an MPEWS threshold of 7 is not sufficient to identify more than a small proportion of ward-admitted children with subsequent clinical deterioration.

scholarly journals The clinical course of COVID-19 in the outpatient setting: a prospective cohort study

2021 ◽  
Author(s):  
Paul W Blair ◽  
Diane M Brown ◽  
Minyoung Jang ◽  
Annukka A R Antar ◽  
Jeanne C Keruly ◽  
...  
Keyword(s):  
Emergency Department ◽  
Cohort Study ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Operating Characteristic ◽  
Negative Binomial ◽  
Characteristic Curve ◽  
Vital Signs ◽  
Outpatient Setting ◽  
Progression Of Disease

Abstract Background Outpatient COVID-19 has been insufficiently characterized. To determine the progression of disease and determinants of hospitalization, we conducted a prospective cohort study. Methods Outpatient adults with positive RT-PCR results for SARS-CoV-2 were recruited by phone between April 21 to July 23, 2020 after receiving outpatient or emergency department testing within a large health network in Maryland, USA. Symptoms were collected by participants on days 0, 3, 7, 14, 21, and 28 and portable pulse oximeter oxygen saturation (SaO2), heart rate, and temperature were collected for 15 consecutive days. Baseline demographics, comorbid conditions, and vital signs were evaluated for risk of subsequent hospitalization using negative binomial, and logistic regression. Results Among 118 SARS-CoV-2 infected outpatients, the median age was 56.0 years (IQR, 50.0 to 63.0) and 50 (42.4%) were male. Among individuals in the first week of illness (N=61), the most common symptoms included weakness/fatigue (65.7%), cough (58.8%), headache (45.6%), chills (38.2%), and anosmia (27.9%). Participants returned to their usual health a median of 20 days (IQR, 13 to 38) from symptom onset, and 66.0% of respondents were at their usual health during the fourth week of illness. Over 28 days, 10.9% presented to the emergency department and 7.6% required hospitalization. The area under the receiving operating characteristic curve for the initial home SaO2 for predicting subsequent hospitalization was 0.86 (CI, 0.73 to 0.99). Conclusions Symptoms often persisted but uncommonly progressed to hospitalization among outpatients with COVID-19. Home SaO2 may be a helpful tool to stratify risk of hospitalization.

scholarly journals Comparison of different prognostic scores for risk stratification in septic patients arriving to the Emergency Department

2020 ◽  
Vol 14 (2) ◽  
pp. 79-87
Author(s):  
Valeria Caramello ◽  
Valentina Beux ◽  
Alessandro Vincenzo De Salve ◽  
Alessandra Macciotta ◽  
Fulvio Ricceri ◽  
...  
Keyword(s):  
Emergency Department ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Prognostic Scores ◽  
Early Warning Score ◽  
Prognostic Performance ◽  
Icu Admission ◽  
Risk Of Mortality ◽  
Failure Assessment ◽  
Patients At Risk

We evaluated the prognostic performance of systemic inflammatory response syndrome (SIRS), sequential organ failure assessment (SOFA), quick-SOFA (qSOFA), modified early warning score (MEWS), lactates and procalcitonin in septic patients. Prospective study on adults with sepsis in the Emergency Department (ED). Area under the Receiver operator characteristic curve (AUC) was calculated to assess how scores predict mortality at 30 and 60 days (d) and upon admission to Intensive care unit (ICU). Among 469 patients, mortality was associated with higher SOFA, qSOFA, MEWS and lactates level. ICU admission was associated with higher SOFA, procalcitonin and MEWS. Prognostic performance for mortality were: SOFA AUC 30 d 0.76 (0.69-0.81); 60 d 0.74 (0.68-0.79); qSOFA AUC 30 d 0.72 (0.66-0.79); 60 d 0.73 (0.67-0.78) and lactates AUC 30 d 0.71 (0.60-0.82); 60d 0.65 (0.54- 0.73). For the outcome ICU admission, procalcitonin had the highest AUC [0.66 (0.56-0.64], followed by SOFA [0.61 (0.54-0.69)] and MEWS [0.60 (0.53-0.67)]. SOFA, qSOFA and lactates assessment after arrival in the ED have a good performance in detecting patients at risk of mortality for sepsis. Procalcitonin is useful to select patients that will need ICU admission.

scholarly journals Comparison of point-of-care peripheral perfusion assessment using pulse oximetry sensor with manual capillary refill time: clinical pilot study in the emergency department

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Koichiro Shinozaki ◽  
Lee S. Jacobson ◽  
Kota Saeki ◽  
Hideaki Hirahara ◽  
Naoki Kobayashi ◽  
...  
Keyword(s):  
Emergency Department ◽  
Pulse Oximetry ◽  
Point Of Care ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Capillary Refill Time ◽  
Peripheral Perfusion ◽  
Altman Plot ◽  
Capillary Refill ◽  
Bland Altman Plot

Abstract Background Traditional capillary refill time (CRT) is a manual measurement that is commonly used by clinicians to identify deterioration in peripheral perfusion status. Our study compared a novel method of measuring peripheral perfusion using an investigational device with standardized visual CRT and tested the clinical usefulness of this investigational device, using an existing pulse oximetry sensor, in an emergency department (ED) setting. Material and methods An ED attending physician quantitatively measured CRT using a chronometer (standardized visual CRT). The pulse oximetry sensor was attached to the same hand. Values obtained using the device are referred to as blood refill time (BRT). These techniques were compared in its numbers with the Bland-Altman plot and the predictability of patients’ admissions. Results Thirty ED patients were recruited. Mean CRT of ED patients was 1.9 ± 0.8 s, and there was a strong correlation with BRT (r = 0.723, p < 0.001). The Bland-Altman plot showed a proportional bias pattern. The ED physician identified 3 patients with abnormal CRT (> 3 s). Area under the receiver operator characteristic curve (AUC) of BRT to predict whether or not CRT was greater than 3 s was 0.82 (95% CI, 0.58–1.00). Intra-rater reliability of BRT was 0.88 (95% CI, 0.79–0.94) and that of CRT was 0.92 (0.85–0.96). Twelve patients were admitted to the hospital. AUC to predict patients’ admissions was 0.67 (95% CI, 0.46–0.87) by BRT and 0.76 (0.58–0.94) by CRT. Conclusions BRT by a pulse oximetry sensor was an objective measurement as useful as the standardized CRT measured by the trained examiner with a chronometer at the bedside.

AVALIAÇÃO DE UM INSTRUMENTO DE MENSURAÇÃO DE MORBIDADE PSÍQUICA: ESTUDO DE VALIDAÇÃO DO SELF-REPORTING QUESTIONNAIRE (SRQ-20)

1970 ◽  
Vol 34 (3) ◽  
pp. 544 ◽  
Author(s):  
Kionna Oliveira Bernardes Santos ◽  
Tânia Maria de Araújo ◽  
Paloma de Sousa Pinho ◽  
Ana Cláudia Conceição Silva
Keyword(s):  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Clinical Interview ◽  
Receiver Operator Characteristic ◽  
Interview Schedule ◽  
Para Determinar ◽  
Self Reporting Questionnaire

O Self-Reporting Questionnaire (SRQ-20), desenvolvido pela Organização Mundial de Saúde, tem sido utilizado para mensuração de nível de suspeição de transtornos mentais em estudos brasileiros, especialmente em grupos de trabalhadores. O objetivo deste estudo foi avaliar o desempenho do SRQ-20, com base em indicadores de validade (sensibilidade, especificidade, taxa de classificação incorreta e valores preditivos), e determinar o melhor ponto de corte para classificação dos transtornos mentais comuns na população estudada. O estudo incluiu 91 indivíduos selecionados aleatoriamente de um estudo de corte transversal realizado com população residente em áreas urbanas de Feira de Santana (BA). Entrevistas clínicas, realizadas por psicólogas, utilizando o Revised Clinical Interview Schedule (CIS-R), foi adotada como padrão-ouro. Na avaliação do desempenho do SRQ-20 foram estimados indicadores de validade (sensibilidade e especificidade). A curva Receiver Operator Characteristic Curve (ROC) foi utilizada para determinar o melhor ponto de corte para classificação de suspeitos/não suspeitos. O ponto de corte de melhor desempenho foi de 6/7 para a população investigada, revelando desempenho razoável com área sob a curva de 0,789. Os resultados indicam que o SRQ-20 possui característica discriminante regular.

scholarly journals Haemoconcentration as a predictor of severity in acute pancreatitis

2016 ◽  
Vol 4 (1) ◽  
pp. 3-7
Author(s):  
Tanka Prasad Bohara ◽  
Dimindra Karki ◽  
Anuj Parajuli ◽  
Shail Rupakheti ◽  
Mukund Raj Joshi
Keyword(s):  
Acute Pancreatitis ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Scoring Systems ◽  
Cost Effective ◽  
Receiver Operator Characteristic Curve ◽  
First Episode ◽  
P Value ◽  
Receiver Operator Characteristic ◽  
Chi Square

Background: Acute pancreatitis is usually a mild and self-limiting disease. About 25 % of patients have severe episode with mortality up to 30%. Early identification of these patients has potential advantages of aggressive treatment at intensive care unit or transfer to higher centre. Several scoring systems are available to predict severity of acute pancreatitis but are cumbersome, take 24 to 48 hours and are dependent on tests that are not universally available. Haematocrit has been used as a predictor of severity of acute pancreatitis but some have doubted its role.Objectives: To study the significance of haematocrit in prediction of severity of acute pancreatitis.Methods: Patients admitted with first episode of acute pancreatitis from February 2014 to July 2014 were included. Haematocrit at admission and 24 hours of admission were compared with severity of acute pancreatitis. Mean, analysis of variance, chi square, pearson correlation and receiver operator characteristic curve were used for statistical analysis.Results: Thirty one patients were included in the study with 16 (51.61%) male and 15 (48.4%) female. Haematocrit at 24 hours of admission was higher in severe acute pancreatitis (P value 0.003). Both haematocrit at admission and at 24 hours had positive correlation with severity of acute pancreatitis (r: 0.387; P value 0.031 and r: 0.584; P value 0.001) respectively.Area under receiver operator characteristic curve for haematocrit at admission and 24 hours were 0.713 (P value 0.175, 95% CI 0.536 - 0.889) and 0.917 (P value 0.008, 95% CI 0.813 – 1.00) respectively.Conclusion: Haematocrit is a simple, cost effective and widely available test and can predict severity of acute pancreatitis.Journal of Kathmandu Medical College, Vol. 4(1) 2015, 3-7

scholarly journals Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199398
Author(s):  
Jinwu Peng ◽  
Zhili Duan ◽  
Yamin Guo ◽  
Xiaona Li ◽  
Xiaoqin Luo ◽  
...  
Keyword(s):  
Random Forest ◽  
Hydatid Disease ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Random Forest Model ◽  
Hepatic Hydatid Disease ◽  
Forest Model ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

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