Utility of Pediatric Early Warning Scoring System in Predicting Clinical Deterioration in Children: A Review

Background: Early Warning Scores (EWS), including the National Early Warning Score 2 (NEWS2) and Modified NEWS (NEWS-C), have been recommended for triage decision in patients with COVID-19. However, the effectiveness of these EWS in COVID-19 has not been fully validated. The study aimed to investigate the predictive value of EWS to detect clinical deterioration in patients with COVID-19.Methods: Between February 7, 2020 and February 17, 2020, patients confirmed with COVID-19 were screened for this study. The outcomes were early deterioration of respiratory function (EDRF) and need for intensive respiratory support (IRS) during the treatment process. The EDRF was defined as changes in the respiratory component of the sequential organ failure assessment (SOFA) score at day 3 (ΔSOFAresp = SOFA resp at day 3–SOFAresp on admission), in which the positive value reflects clinical deterioration. The IRS was defined as the use of high flow nasal cannula oxygen therapy, noninvasive or invasive mechanical ventilation. The performances of EWS including NEWS, NEWS 2, NEWS-C, Modified Early Warning Scores (MEWS), Hamilton Early Warning Scores (HEWS), and quick sepsis-related organ failure assessment (qSOFA) for predicting EDRF and IRS were compared using the area under the receiver operating characteristic curve (AUROC).Results: A total of 116 patients were included in this study. Of them, 27 patients (23.3%) developed EDRF and 24 patients (20.7%) required IRS. Among these EWS, NEWS-C was the most accurate scoring system for predicting EDRF [AUROC 0.79 (95% CI, 0.69–0.89)] and IRS [AUROC 0.89 (95% CI, 0.82–0.96)], while NEWS 2 had the lowest accuracy in predicting EDRF [AUROC 0.59 (95% CI, 0.46–0.720)] and IRS [AUROC 0.69 (95% CI, 0.57–0.81)]. A NEWS-C ≥ 9 had a sensitivity of 59.3% and a specificity of 85.4% for predicting EDRF. For predicting IRS, a NEWS-C ≥ 9 had a sensitivity of 75% and a specificity of 88%.Conclusions: The NEWS-C was the most accurate scoring system among common EWS to identify patients with COVID-19 at risk for EDRF and need for IRS. The NEWS-C could be recommended as an early triage tool for patients with COVID-19.

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Corrigendum to “Risk prediction using the National Early Warning Score and the Worthing Physiological Scoring System in patients who were transported to the Intensive Care Unit from the Emergency Department: A cohort study” [Intens. Crit. Care Nurs. 64 (2021) 103015]

Intensive and Critical Care Nursing ◽

10.1016/j.iccn.2021.103085 ◽

2021 ◽

pp. 103085

Author(s):

Ying An ◽

Zi-Rong Tian ◽

Fei Li ◽

Ya-Mei Guan ◽

Zi-Feng Ma ◽

...

Keyword(s):

Intensive Care Unit ◽

Emergency Department ◽

Cohort Study ◽

Intensive Care ◽

Risk Prediction ◽

Early Warning ◽

Scoring System ◽

Early Warning Score ◽

National Early Warning Score

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The Use of Prognostic Prediction Models for Mortality or Clinical Deterioration among Hospitalized and Non-hospitalized Adults with COVID-19: A Systematic Review

Acta Medica Philippina ◽

10.47895/amp.vi0.4195 ◽

2021 ◽

Author(s):

Patricia Pauline M. Remalante-Rayco ◽

Evelyn Osio-Salido

Keyword(s):

Systematic Review ◽

Emergency Department ◽

Early Warning ◽

Prediction Models ◽

External Validation ◽

Risk Of Bias ◽

Clinical Deterioration ◽

Early Warning Score ◽

Good Prediction ◽

Prediction Of Mortality

Objective. To assess the performance of prognostic models in predicting mortality or clinical deterioration among patients with COVID-19, both hospitalized and non-hospitalized Methods. We conducted a systematic review of the literature until March 8, 2021. We included models for the prediction of mortality or clinical deterioration in COVID-19 with external validation. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the GRADEpro Guideline Development Tool (GDT) to assess the evidence obtained. Results. We reviewed 33 cohort studies. Two studies had a low risk of bias, four unclear risks, and 27 with a high risk of bias due to participant selection and analysis. For the outcome of mortality, the QCOVID model had excellent prediction with high certainty of evidence but was specific for use in England. The COVID Outcome Prediction in the Emergency Department (COPE) model, the 4C Mortality Score, the Age, BUN, number of comorbidities, CRP, SpO2/FiO2 ratio, platelet count, heart rate (ABC2-SPH) risk score, the Confusion Urea Respiration Blood Pressure (CURB-65) severity score, the Rapid Emergency Medicine Score (REMS), and the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score had fair to good prediction of death among inpatients, while the quick Sepsis-related Organ Failure Assessment (qSOFA) score had poor to fair prediction. The certainty of evidence for these models was very low to low. For the outcome of clinical deterioration, the 4C Deterioration Score had fair prediction, the National Early Warning Score 2 (NEWS2) score poor to good, and the Modified Early Warning Score (MEWS) had poor prediction. The certainty of evidence for these three models was also very low to low. None of these models had been validated in the Philippine setting. Conclusion. The QCOVID, COPE, ABC2-SPH, 4C, CURB-65, REMS, RISE-UP models for prediction of mortality and the 4C Deterioration and NEWS2 models for prediction of clinical deterioration are potentially useful but need to be validated among patients with COVID-19 of varying severity in the Philippine setting.

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A wireless oxygen saturation and heart rate monitoring and alarming system based on the Qatar Early Warning Scoring system

Journal of Emergency Medicine Trauma and Acute Care ◽

10.5339/jemtac.2016.icepq.155 ◽

2016 ◽

Vol 2016 (2) ◽

pp. 155

Author(s):

Sami Saleh Alshorman ◽

Faycal Bensaali ◽

Fadi Jaber

Keyword(s):

Heart Rate ◽

Oxygen Saturation ◽

Early Warning ◽

Scoring System ◽

Heart Rate Monitoring

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IMPLEMENTATION OF EARLY WARNING SYSTEM IN NURSING WARD: A LITERATURE REVIEW

Nurse and Health: Jurnal Keperawatan ◽

10.36720/nhjk.v10i1.275 ◽

2021 ◽

Vol 10 (1) ◽

pp. 126-134

Author(s):

Meli Diana ◽

Dimas Hadi Prayoga ◽

Dini Prastyo Wijayanti

Keyword(s):

Early Detection ◽

Early Warning ◽

Scoring System ◽

Early Warning System ◽

Sudden Cardiac Arrest ◽

Warning System ◽

High Rate ◽

Early Warning Score ◽

Score System ◽

Emergency Detection

Background: Hospital service is a process that involves all elements in the hospital including nurses and inpatient rooms or nursing wards. Different inpatient conditions will be treated in separated wards, by the same token patients with unstable conditions are admitted in intensive care units, this procedure aims to reduce the mortality incidence due to sudden cardiac arrest, therefore early detection of patients’ clinical deterioration using the early warning score system performed by the nurse in the nursing wards is required. Objective: This review study is a summary of the early warning system implementation in the nursing wards. Design: The data was obtained from international journal providers Proquest and Ebsco databases. The author accessed unair.remotexs.co website. Review Methods: Narative Review. Results: Early warning score is an effective intervention for emergency detection in patients. Conclusion: Early detection clinical emergency or known as the Early Warning Score System (EWSS) is the application of a scoring system for early detection of patient's condition before a worsening situation occurs. The implementation of this scoring system is necessary due to the high rate of deterioration of patient conditions that requiring immediate management to prevent profound deterioration and its subsequent adverse effect Keywords : Early warning system;nurse care;literatur;review

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Development and validation of a prognostic model to predict clinical deterioration in hospitalized patients with COVID-19

10.21203/rs.3.rs-701172/v2 ◽

2021 ◽

Author(s):

Javid Azadbakht ◽

Sina Rashedi ◽

Soheil Kooraki ◽

Hamed Kowsari ◽

Elnaz Tabibian

Keyword(s):

Roc Curve ◽

Scoring System ◽

Prognostic Model ◽

Operating Characteristic ◽

Clinical Laboratory ◽

External Validation ◽

Clinical Deterioration ◽

Roc Curve Analysis ◽

Prospective Multicenter Study ◽

Development And Validation

Abstract Objectives We aimed to develop and validate a prognostic model to predict clinical deterioration defined as either death or intensive care unit admission of hospitalized COVID-19 patients.Methods This prospective, multicenter study investigated 172 consecutive hospitalized COVID-19 patients who underwent a chest computed tomography (CT) scan between March 20 and April 30, 2020 (development cohort), as well as an independent sample of 40 consecutive patients for external validation (validation cohort). The clinical, laboratory, and radiologic data were gathered, and logistic regression along with receiver operating characteristic (ROC) curve analysis was performed.Results The overall clinical deterioration rates of the development and validation cohorts were 28.4% (49 of 172) and 30% (12 of 40), respectively. Seven predictors were included in the scoring system with a total score of 15: CT severity score\(\ge\)15 (Odds Ratio (OR)=6.34, 4 points), pleural effusion (OR = 6.80, 2 points), symptom onset to admission ≤ 6 days (OR = 2.44, 2 points), age\(\ge\)70 years (OR = 2.44, 2 points), diabetes mellitus (OR = 2.24, 2 points), dyspnea (OR = 2.17, 1.5 points), and abnormal leukocyte count (OR = 1.89, 1.5 points). The area under the ROC curve for the scoring system in the development and validation cohorts was 0.823 (CI [0.751–0.895]) and 0.558 (CI [0.340–0.775]), respectively.Conclusion This study provided a new easy-to-calculate scoring system with external validation for hospitalized COVID-19 patients to predict clinical deterioration based on a combination of seven clinical, laboratory, and radiologic parameters.

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