Cohort study to test the predictability of the NHS Institute for Innovation and Improvement Paediatric Early Warning System

ObjectiveTo test the predictability of the National Health Service Institute for Innovation and Improvement (NHSIII) Paediatric Early Warning System (PEWS) score to identify children at risk of developing critical illness.DesignCohort study.SettingAdmissions to all paediatric wards at the University Hospital of Wales between 1 December 2005 and 30 November 2006.Outcome measuresUnscheduled paediatric high dependency unit (PHDU) admission, paediatric intensive care unit (PICU) admission and death.ResultsThere were 9075 clinical observations from 1000 children. An NHSIII PEWS score of 2 or more, which triggers review, has a sensitivity of 73.2% (95% CI 62.2% to 82.4%), specificity of 75.2% (95% CI 74.3% to 76.1%), positive predictive value (PPV) of 2.6% (95% CI 2.0% to 3.4%), negative predictive value of 99.7% (95% CI 99.5% to 99.8%) and positive likelihood ratio of 3.0 (95% CI 2.6 to 3.4) for predicting PHDU admission, PICU admission or death. Six (37.5%) of the 16 children with an adverse outcome did not have an abnormal NHSIII PEWS score. The area under the receiver operating characteristic curve for the NHSIII PEWS score was 0.83 (95% CI 0.77 to 0.88).ConclusionsThe NHSIII PEWS has a low PPV and its full implementation would result in a large number of false positive triggers. The issue with PEWS scores or triggers is neither their sensitivity nor children with high scores which require clinical interventions who are not ‘false positives’; but their low specificity and low PPV arising from the large number of children with low but raised scores.

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High dependency unit admissions during the first year of a national obstetric early warning system

International Journal of Gynecology & Obstetrics ◽

10.1016/j.ijgo.2015.09.014 ◽

2015 ◽

Vol 133 (1) ◽

pp. 121-122 ◽

Cited By ~ 1

Author(s):

Patrick J. Maguire ◽

Karen A. Power ◽

Niamh Daly ◽

Maria Farren ◽

Aoife McKeating ◽

...

Keyword(s):

Early Warning ◽

Early Warning System ◽

Warning System ◽

First Year ◽

High Dependency Unit ◽

High Dependency

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Assessment of electronic disease early warning system for improved disease surveillance and outbreak response in Yemen

BMC Public Health ◽

10.1186/s12889-020-09460-4 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Fekri Dureab ◽

Kamran Ahmed ◽

Claudia Beiersmann ◽

Claire J. Standley ◽

Ali Alwaleedi ◽

...

Keyword(s):

Public Health ◽

Data Quality ◽

Early Warning ◽

Predictive Value ◽

Disease Surveillance ◽

Early Warning System ◽

Warning System ◽

Health Facilities ◽

Public Health Response ◽

Attributes Data

Abstract Background Diseases Surveillance is a continuous process of data collection, analysis interpretation and dissemination of information for swift public health action. Recent advances in health informatics have led to the implementation of electronic tools to facilitate such critical disease surveillance processes. This study aimed to assess the performance of the national electronic Disease Early Warning System in Yemen (eDEWS) using system attributes: data quality, timeliness, stability, simplicity, predictive value positive, sensitivity, acceptability, flexibility, and representativeness, based on the Centres for Disease Control & Prevention (US CDC) standard indicators. Methods We performed a mixed methods study that occurred in two stages: first, the quantitative data was collected from weekly epidemiological bulletins from 2013 to 2017, all alerts of 2016, and annual eDEWS reports, and then the qualitative method using in-depth interviews was carried out in a convergent strategy. The CDC guideline used to describe the following system attributes: data quality (reporting, and completeness), timeliness, stability, simplicity, predictive value positive, sensitivity, acceptability, flexibility and representativeness. Results The finding of this assessment showed that eDEWS is a resilient and reliable system, and despite the conflict in Yemen, the system is still functioning and expanding. The response timeliness remains a challenge, since only 21% of all eDEWS alerts were verified within the first 24 h of detection in 2016. However, identified gaps did not affect the system’s ability to identify outbreaks in the current fragile situation. Findings show that eDEWS data is representative, since it covers the entire country. Although, eDEWS covers only 37% of all health facilities, this represents 83% of all functional health facilities in all 23 governorates and all 333 districts. Conclusion The quality and timeliness of responses are major challenges to eDEWS’ functionality, the eDEWS remains the only system that provides regular data on communicable diseases in Yemen. In particular, public health response timeliness needs improvement.

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Prognostic and discriminative accuracy of the quick Sepsis-related Organ Failure Assessment compared with an early warning score: a Danish cohort study

Emergency Medicine Journal ◽

10.1136/emermed-2020-209746 ◽

2021 ◽

pp. emermed-2020-209746

Author(s):

Lise Skovgaard Svingel ◽

Merete Storgaard ◽

Buket Öztürk Esen ◽

Lotte Ebdrup ◽

Jette Ahrensberg ◽

...

Keyword(s):

Cohort Study ◽

Organ Failure ◽

Early Warning ◽

Predictive Value ◽

Primary Outcome ◽

Characteristic Curve ◽

Early Warning Score ◽

Icu Admission ◽

Hospitalised Patients ◽

Failure Assessment

BackgroundThe clinical benefit of implementing the quick Sepsis-related Organ Failure Assessment (qSOFA) instead of early warning scores (EWS) to screen all hospitalised patients for critical illness has yet to be investigated in a large, multicentre study.MethodsWe conducted a cohort study including all hospitalised patients ≥18 years with EWS recorded at hospitals in the Central Denmark Region during the year 2016. The primary outcome was intensive care unit (ICU) admission and/or death within 2 days following an initial EWS. Prognostic accuracy was examined using sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV). Discriminative accuracy was examined by the area under the receiver operating characteristic curve (AUROC).ResultsAmong 97 332 evaluated patients, 1714 (1.8%) experienced the primary outcome. The qSOFA ≥2 was less sensitive (11.7% (95% CI: 10.2% to 13.3%) vs 25.1% (95% CI: 23.1% to 27.3%)) and more specific (99.3% (95% CI: 99.2% to 99.3%) vs 97.5% (95% CI: 97.4% to 97.6%)) than EWS ≥5. The NPV was similar for the two scores (EWS ≥5, 98.6% (95% CI: 98.6% to 98.7%) and qSOFA ≥2, 98.4% (95% CI: 98.3% to 98.5%)), while the PPV was 15.1% (95% CI: 13.8% to 16.5%) for EWS ≥5 and 22.4% (95% CI: 19.7% to 25.3%) for qSOFA ≥2. The AUROC was 0.72 (95% CI: 0.70 to 0.73) for EWS and 0.66 (95% CI: 0.65 to 0.67) for qSOFA.ConclusionThe qSOFA was less sensitive (qSOFA ≥2 vs EWS ≥5) and discriminatively accurate than the EWS for predicting ICU admission and/or death within 2 days after an initial EWS. This study did not support replacing EWS with qSOFA in all hospitalised patients.

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Prospective cohort study to test the predictability of the Cardiff and Vale paediatric early warning system

Archives of Disease in Childhood ◽

10.1136/adc.2008.142026 ◽

2008 ◽

Vol 94 (8) ◽

pp. 602-606 ◽

Cited By ~ 56

Author(s):

E D Edwards ◽

C V E Powell ◽

B W Mason ◽

A Oliver

Keyword(s):

Cohort Study ◽

Prospective Cohort Study ◽

Early Warning ◽

Prospective Cohort ◽

Early Warning System ◽

Warning System

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Early warning system hypertension thresholds to predict adverse outcomes in pre-eclampsia: A prospective cohort study

Pregnancy Hypertension ◽

10.1016/j.preghy.2017.11.003 ◽

2018 ◽

Vol 12 ◽

pp. 183-188 ◽

Cited By ~ 10

Author(s):

Hannah L. Nathan ◽

Paul T. Seed ◽

Natasha L. Hezelgrave ◽

Annemarie De Greeff ◽

Elodie Lawley ◽

...

Keyword(s):

Cohort Study ◽

Prospective Cohort Study ◽

Early Warning ◽

Prospective Cohort ◽

Early Warning System ◽

Warning System ◽

Adverse Outcomes

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Prediction of epidemic cholera due toVibrio choleraeO1 in children younger than 10 years using climate data in Bangladesh

Epidemiology and Infection ◽

10.1017/s0950268807008175 ◽

2007 ◽

Vol 136 (1) ◽

pp. 73-79 ◽

Cited By ~ 17

Author(s):

F. MATSUDA ◽

S. ISHIMURA ◽

Y. WAGATSUMA ◽

T. HIGASHI ◽

T. HAYASHI ◽

...

Keyword(s):

Vibrio Cholerae ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Dhaka City ◽

Climate Variables ◽

Climate Data ◽

Number Of Children ◽

Number Of Patients ◽

Autoregression Model

SUMMARYTo determine if a prediction of epidemic cholera using climate data can be made, we performed autoregression analysis using the data recorded in Dhaka City, Bangladesh over a 20-year period (1983–2002) comparing the number of children aged <10 years who were infected withVibrio choleraeO1 to the maximum and minimum temperatures and rainfall. We formulated a simple autoregression model that predicts the monthly number of patients using earlier climate variables. The monthly number of patients predicted by this model agreed well with the actual monthly number of patients where the Pearson's correlation coefficient was 0·95. Arbitrarily defined, 39·4% of the predicted numbers during the study period were within 0·8–1·2 times the observed numbers. This prediction model uses the climate data recorded 2–4 months before. Therefore, our approach may be a good basis for establishing a practical early warning system for epidemic cholera.

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Assessing the accuracy of forecasting: Applying standard diagnostic assessment tools to a health technology early warning system

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462304001229 ◽

2004 ◽

Vol 20 (3) ◽

pp. 381-384 ◽

Cited By ~ 12

Author(s):

Sue Simpson ◽

Chris Hyde ◽

Alison Cook ◽

Claire Packer ◽

Andrew Stevens

Keyword(s):

Health Technology Assessment ◽

Technology Assessment ◽

Early Warning ◽

Predictive Value ◽

Gold Standard ◽

Early Warning System ◽

Warning System ◽

Early Warning Systems ◽

Health Technology ◽

Warning Systems

Objectives:Early warning systems are an integral part of many health technology assessment programs. Despite this finding, to date, there have been no quantitative evaluations of the accuracy of predictions made by these systems. We report a study evaluating the accuracy of predictions made by the main United Kingdom early warning system.Methods:As prediction of impact is analogous to diagnosis, a method normally applied to determine the accuracy of diagnostic tests was used. The sensitivity, specificity, and predictive values of the National Horizon Scanning Centre's prediction methods were estimated with reference to an (imperfect) gold standard, that is, expert opinion of impact 3 to 5 years after prediction.Results:The sensitivity of predictions was 71 percent (95 percent confidence interval [CI], 0.36–0.92), and the specificity was 73 percent (95 percent CI, 0.64–0.8). The negative predictive value was 98 percent (95 percent CI, 0.92–0.99), and the positive predictive value was 14 percent (95 percent CI, 0.06–0.3).Conclusions:Forecasting is difficult, but the results suggest that this early warning system's predictions have an acceptable level of accuracy. However, there are caveats. The first is that early warning systems may themselves reduce the impact of a technology, as helping to control adoption and diffusion is their main purpose. The second is that the use of an imperfect gold standard may bias the results. As early warning systems are viewed as an increasingly important component of health technology assessment and decision making, their outcomes must be evaluated. The method used here should be investigated further and the accuracy of other early warning systems explored.

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