Implementation of a pediatric early warning score tool in a pediatric oncology Ward in Palestine

Abstract Background Pediatric Early Warning Scores (PEWS) are nurse-administered clinical assessment tools utilizing vital signs and patient signs and symptoms to screen for patients at risk for clinical deterioration.1–3 When utilizing a PEWS system, which consists of an escalation algorithm to alert physicians of high risk patients requiring a bedside evaluation and assessment, studies have demonstrated that PEWS systems can decrease pediatric intensive care (PICU) utilization, in-hospital cardiac arrests, and overall decreased mortality in high income settings. Yet, many hospital based settings in low and lower middle income countries (LMIC) lack systems in place for early identification of patients at risk for clinical deterioration. Methods A contextually adapted 16-h pediatric resuscitation program included training of a PEWS tool followed by implementation and integration of a PEWS system in a pediatric hematology/oncology ward in Beit Jala, Palestine. Four PDSA cycles were implemented post-implementation to improve uptake and scoring of PEWS which included PEWS tool integration into an existing electronic medical record (EMR), escalation algorithm and job aid implementation, data audits and ward feedback. Results Frequency of complete PEWS vital sign documentation reached a mean of 89.9%. The frequency and accuracy of PEWS scores steadily increased during the post-implementation period, consistently above 89% in both categories starting from data audit four and continuing thereafter. Accuracy of PEWS scoring was unable to be assessed during week 1 and 2 of data audits due to challenges with PEWS integration into the existing EMR (PDSA cycle 1) which were resolved by the 3rd week of data auditing (PDSA cycle 2). Conclusions Implementation of a PEWS scoring tool in an LMIC pediatric oncology inpatient unit is feasible and can improve frequency of vital sign collection and generate accurate PEWS scores. Contribution to the literature This study demonstrates how to effectively implement a PEWS scoring tool into an LMIC clinical setting. This study demonstrates how to utilize a robust feedback mechanism to ensure a quality program uptake. This study demonstrates an effective international partnership model that other institutions may utilize for implementation science.

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Glucose as an additional parameter to National Early Warning Score (NEWS) in prehospital setting enhances identification of patients at risk of death: an observational cohort study

Emergency Medicine Journal ◽

10.1136/emermed-2018-208309 ◽

2020 ◽

pp. emermed-2018-208309

Author(s):

Hanna Vihonen ◽

Mitja Lääperi ◽

Markku Kuisma ◽

Jussi Pirneskoski ◽

Jouni Nurmi

Keyword(s):

At Risk ◽

Blood Glucose ◽

Regression Model ◽

Early Warning ◽

Prehospital Setting ◽

Early Warning Score ◽

Calibration Model ◽

Risk Of Death ◽

Patients At Risk ◽

National Early Warning Score

BackgroundTo determine if prehospital blood glucose could be added to National Early Warning Score (NEWS) for improved identification of risk of short-term mortality.MethodsRetrospective observational study (2008–2015) of adult patients seen by emergency medical services in Helsinki metropolitan area for whom all variables for calculation of NEWS and a blood glucose value were available. Survival of 24 hours and 30 days were determined. The NEWS parameters and glucose were tested by multivariate logistic regression model. Based on ORs we formed NEWSgluc model with hypoglycaemia (≤3.0 mmol/L) 3, normoglycaemia 0 and hyperglycaemia (≥11.1 mmol/L) 1 points. The scores from NEWS and NEWSgluc were compared using discrimination (area under the curve), calibration (Hosmer-Lemeshow test), likelihood ratio tests and reclassification (continuous net reclassification index (cNRI)).ResultsData of 27 141 patients were included in the study. Multivariable regression model for NEWSgluc parameters revealed a strong association with glucose disturbances and 24-hour and 30-day mortality. Likelihood ratios (LRs) for mortality at 24 hours using a cut-off point of 15 were for NEWSgluc: LR+ 17.78 and LR− 0.96 and for NEWS: LR+ 13.50 and LR− 0.92. Results were similar at 30 days. Risks per score point estimation and calibration model showed glucose added benefit to NEWS at 24 hours and at 30 days. Although areas under the curve were similar, reclassification test (cNRI) showed overall improvement of classification of survivors and non-survivors at 24 days and 30 days with NEWSgluc.ConclusionsIncluding glucose in NEWS in the prehospital setting seems to improve identification of patients at risk of death.

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MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model

Journal of Clinical Medicine ◽

10.3390/jcm9020343 ◽

2020 ◽

Vol 9 (2) ◽

pp. 343 ◽

Cited By ~ 4

Author(s):

Arash Kia ◽

Prem Timsina ◽

Himanshu N. Joshi ◽

Eyal Klang ◽

Rohit R. Gupta ◽

...

Keyword(s):

Machine Learning ◽

At Risk ◽

Area Under The Curve ◽

Learning Model ◽

Clinical Deterioration ◽

Early Warning Score ◽

Support Vector ◽

Adult Age ◽

Machine Learning Model ◽

Patients At Risk

Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.

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Accuracy and Monitoring of Pediatric Early Warning Score (PEWS) Scores Prior to Emergent Pediatric Intensive Care Unit (ICU) Transfer: Retrospective Analysis (Preprint)

10.2196/preprints.25991 ◽

2020 ◽

Author(s):

Rebecca L Kowalski ◽

Laura Lee ◽

Michael C Spaeder ◽

J Randall Moorman ◽

Jessica Keim-Malpass

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Pediatric Intensive Care Unit ◽

Early Warning ◽

Severity Of Illness ◽

Pediatric Intensive Care ◽

Clinical Deterioration ◽

Early Warning Score ◽

Transfer Event ◽

Patients At Risk

BACKGROUND Current approaches to early detection of clinical deterioration in children have relied on intermittent track-and-trigger warning scores such as the Pediatric Early Warning Score (PEWS) that rely on periodic assessment and vital sign entry. There are limited data on the utility of these scores prior to events of decompensation leading to pediatric intensive care unit (PICU) transfer. OBJECTIVE The purpose of our study was to determine the accuracy of recorded PEWS scores, assess clinical reasons for transfer, and describe the monitoring practices prior to PICU transfer involving acute decompensation. METHODS We conducted a retrospective cohort study of patients ≤21 years of age transferred emergently from the acute care pediatric floor to the PICU due to clinical deterioration over an 8-year period. Clinical charts were abstracted to (1) determine the clinical reason for transfer, (2) quantify the frequency of physiological monitoring prior to transfer, and (3) assess the timing and accuracy of the PEWS scores 24 hours prior to transfer. RESULTS During the 8-year period, 72 children and adolescents had an emergent PICU transfer due to clinical deterioration, most often due to acute respiratory distress. Only 35% (25/72) of the sample was on continuous telemetry or pulse oximetry monitoring prior to the transfer event, and 47% (34/72) had at least one incorrectly documented PEWS score in the 24 hours prior to the event, with a score underreporting the actual severity of illness. CONCLUSIONS This analysis provides support for the routine assessment of clinical deterioration and advocates for more research focused on the use and utility of continuous cardiorespiratory monitoring for patients at risk for emergent transfer.

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Examining the utility of the Hamilton early warning scores (HEWS) at triage: Retrospective pilot study in a Canadian emergency department

CJEM ◽

10.1017/cem.2017.21 ◽

2017 ◽

Vol 20 (2) ◽

pp. 266-274 ◽

Cited By ~ 6

Author(s):

Steven Skitch ◽

Benjamin Tam ◽

Michael Xu ◽

Laura McInnis ◽

Anthony Vu ◽

...

Keyword(s):

At Risk ◽

Emergency Department ◽

Early Warning ◽

Vital Signs ◽

Roc Curves ◽

Early Warning Score ◽

Critical Event ◽

Discriminative Ability ◽

Early Warning Scores ◽

Patients At Risk

ABSTRACTObjectivesEarly warning scores use vital signs to identify patients at risk of critical illness. The current study examines the Hamilton Early Warning Score (HEWS) at emergency department (ED) triage among patients who experienced a critical event during their hospitalization. HEWS was also evaluated as a predictor of sepsis.MethodsThe study population included admissions to two hospitals over a 6-month period. Cases experienced a critical event defined by unplanned intensive care unit admission, cardiopulmonary resuscitation, or death. Controls were randomly selected from the database in a 2-to-1 ratio to match cases on the burden of comorbid illness. Receiver operating characteristic (ROC) curves were used to evaluate HEWS as a predictor of the likelihood of critical deterioration and sepsis.ResultsThe sample included 845 patients, of whom 270 experienced a critical event; 89 patients were excluded because of missing vitals. An ROC analysis indicated that HEWS at ED triage had poor discriminative ability for predicting the likelihood of experiencing a critical event 0.62 (95% CI 0.58-0.66). HEWS had a fair discriminative ability for meeting criteria for sepsis 0.77 (95% CI 0.72-0.82) and good discriminative ability for predicting the occurrence of a critical event among septic patients 0.82 (95% CI 0.75-0.90).ConclusionThis study indicates that HEWS at ED triage has limited utility for identifying patients at risk of experiencing a critical event. However, HEWS may allow earlier identification of septic patients. Prospective studies are needed to further delineate the utility of the HEWS to identify septic patients in the ED.

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VALIDATION OF AN EARLY WARNING SCORING TOOL FOR THE IDENTIFICATION OF PEDIATRIC CARDIAC PATIENTS AT RISK FOR CARDIOPULMONARY ARREST

Journal of the American College of Cardiology ◽

10.1016/s0735-1097(11)60481-6 ◽

2011 ◽

Vol 57 (14) ◽

pp. E481

Author(s):

Mary C. McLellan ◽

Kimberlee Gauvreau ◽

Jean Anne Connor

Keyword(s):

At Risk ◽

Early Warning ◽

Cardiopulmonary Arrest ◽

Cardiac Patients ◽

Patients At Risk ◽

Scoring Tool

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The Admission Hamilton Early Warning Score (HEWS) Predicts the Risk of Critical Event during Hospitalization

Canadian Journal of General Internal Medicine ◽

10.22374/cjgim.v11i4.190 ◽

2017 ◽

Vol 11 (4) ◽

Cited By ~ 3

Author(s):

Benjamin Tam BHSc MD ◽

Michael Xu BHSc ◽

Michelle Kwong BHSc Cand. ◽

Christine Wardell BHSc Cand. ◽

Andrew Kwong BHSc Cand. ◽

...

Keyword(s):

At Risk ◽

High Risk ◽

Early Warning ◽

Early Warning Score ◽

Surgical Patients ◽

Critical Event ◽

Critical Events ◽

Patients At Risk ◽

Icu Transfer ◽

Very High

Background: Early warning scores detect patients at risk of deterioration in hospital. Our objective was to first, demonstrate that the admission Hamilton Early Warning Score (HEWS) predicts critical events and second, estimate the workload required to identify critical events during hospitalization.Methods: We prospectively identified a consecutive cohort of medical/surgical patients for retrospective review. Critical events were defined as a composite of inpatient death, cardio-pulmonary arrest or ICU transfer. Likelihood of a critical event during hospitalization and the number needed to evaluate to detect a critical event was based on highest admission HEWS.Results: We found 506 critical events occurred in 7130 cases. HEWS identified graduated levels of risk at admission. We found 2.6 and 1.8 patients needed to be evaluated in the ‘high-risk’ and very ‘high-risk’ subgroups to detect a critical event.Conclusions: HEWS identified patients at risk for critical events during hospitalization at ward admission. Few patients with high HEWS required evaluation to detect a critical event.

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Accuracy and Monitoring of Pediatric Early Warning Score (PEWS) Scores Prior to Emergent Pediatric Intensive Care Unit (ICU) Transfer: Retrospective Analysis

JMIR Pediatrics and Parenting ◽

10.2196/25991 ◽

2021 ◽

Vol 4 (1) ◽

pp. e25991

Author(s):

Rebecca L Kowalski ◽

Laura Lee ◽

Michael C Spaeder ◽

J Randall Moorman ◽

Jessica Keim-Malpass

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Pediatric Intensive Care Unit ◽

Early Warning ◽

Severity Of Illness ◽

Pediatric Intensive Care ◽

Clinical Deterioration ◽

Early Warning Score ◽

Transfer Event ◽

Patients At Risk

Background Current approaches to early detection of clinical deterioration in children have relied on intermittent track-and-trigger warning scores such as the Pediatric Early Warning Score (PEWS) that rely on periodic assessment and vital sign entry. There are limited data on the utility of these scores prior to events of decompensation leading to pediatric intensive care unit (PICU) transfer. Objective The purpose of our study was to determine the accuracy of recorded PEWS scores, assess clinical reasons for transfer, and describe the monitoring practices prior to PICU transfer involving acute decompensation. Methods We conducted a retrospective cohort study of patients ≤21 years of age transferred emergently from the acute care pediatric floor to the PICU due to clinical deterioration over an 8-year period. Clinical charts were abstracted to (1) determine the clinical reason for transfer, (2) quantify the frequency of physiological monitoring prior to transfer, and (3) assess the timing and accuracy of the PEWS scores 24 hours prior to transfer. Results During the 8-year period, 72 children and adolescents had an emergent PICU transfer due to clinical deterioration, most often due to acute respiratory distress. Only 35% (25/72) of the sample was on continuous telemetry or pulse oximetry monitoring prior to the transfer event, and 47% (34/72) had at least one incorrectly documented PEWS score in the 24 hours prior to the event, with a score underreporting the actual severity of illness. Conclusions This analysis provides support for the routine assessment of clinical deterioration and advocates for more research focused on the use and utility of continuous cardiorespiratory monitoring for patients at risk for emergent transfer.

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