Validation of a modified Early Warning Score in medical admissions

Subbe, Christian P; Kruger, Mariana; Rutherford, Peter; Gemmel, L.

doi:10.1093/qjmed/94.10.521

Cited by 1,456 publications

(1,358 citation statements)

References 19 publications

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“…16 For the other three outcomes, the AUROCs (95% CI) for the other 33 EWSs ranged from 0.570 (0.553 to 0.568) 17 to 0.827 (0.814 to 0.840) 18 (unanticipated ICU admission); 0.813 (0.802 to 0.824) 15 to 0.858 (0.849 to 0.867) 16 (death); and 0.736 (0.727 to 0.745) 17 to 0.834 (0.826 to 0.842) 19 (any outcome) ( Table 2). The comparative performance of each of the 33 EWSs and NEWS, for each of the four outcomes, is shown in Figure 2.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, this suggests that the case mix factors that determine outcome for death within 24 hours and unanticipated ICU admission are essentially the same. However, the four EWS systems that include age as a co-variate, 16,[22][23][24] and only these four, perform relatively poorly for the discrimination of unanticipated ICU admission compared to their performance for death. Age may be a consideration when patients are referred for ICU admission; however, we have no evidence that the study hospital operates an ageist policy in this respect.…”

Section: Discussionmentioning

confidence: 99%

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The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death

et al. 2013

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Introduction: Early warning scores (EWS) are recommended as part of the early recognition and response to patient deterioration. The Royal College of Physicians recommends the use of a National Early Warning Score (NEWS) for the routine clinical assessment of all adult patients. Methods:We tested the ability of NEWS to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit (ICU) admission or death within 24 hours of a NEWS value and compared its performance to that of 33 other EWSs currently in use, using the area under the receiver-operating characteristics (AUROC) curve and a large vital signs database (n=198,755 observation sets) collected from 35,585 consecutive, completed acute medical admissions.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death

et al. 2013

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“…These vital signs are associated with adverse clinical outcomes and are measured periodically and frequently 6, 16, 26. Furthermore, they are objective values that are barely affected by medical staff measuring them 27.…”

Section: Methodsmentioning

confidence: 99%

“…The aggregated weighted TTS calculates a weighted score for each vital sign and then finds patients with cardiac arrest based on the sum of these scores 15. The modified early warning score (MEWS) is one of the most widely used approaches among all aggregated weighted TTSs (Table 1)16; however, traditional TTSs including MEWS have limitations, with low sensitivity or high false‐alarm rates 14, 15, 17. Sensitivity and false‐alarm rate interact: Increased sensitivity creates higher false‐alarm rates and vice versa.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Kwon

Lee²,

Lee³

et al. 2018

JAHA

234

205

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BackgroundIn‐hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track‐and‐trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false‐alarm rates. We propose a deep learning–based early warning system that shows higher performance than the existing track‐and‐trigger systems.Methods and ResultsThis retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July 2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver operating characteristic curve (AUROC), the area under the precision–recall curve (AUPRC), and the net reclassification index. Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning–based early warning system (AUROC: 0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning–based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning system, random forest, and logistic regression, respectively, at the same sensitivity.ConclusionsAn algorithm based on deep learning had high sensitivity and a low false‐alarm rate for detection of patients with cardiac arrest in the multicenter study.

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“…Advanced age is independently associated with death and cardiac arrest, and use of age further improves predictive accuracy of many of the physiology based risk models (117)(118)(119)(120)(121)(122).…”

Section: Physiologicalmentioning

confidence: 99%

Frailty Assessment in Acute Care

2015

The Gerontologist

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IntroductionFor some people, ageing is associated with the experience of increased co-morbidity, functional impairment, poor resilience and heightened vulnerability to external stressors, resulting in reduced lifespan as well as health-span. This frailty phenomenon poses challenges to health care systems in the form of increased patient complexity and resource utilisation. The acute care setting, characterised by time-pressure and high patient turn-over, is under strain and struggles to recognise and subsequently reliably intervene, to prevent, reverse or halt the decline of this vulnerable cohort. MethodsThis mixed-methods study probes existing evidence and 'real-world' processes with a systematic review of frailty assessments developed or validated in the acute care setting and a survey of contemporaneous clinical practice in London Acute Medical Units. Content validation and understanding of contextual factors for ideal frailty assessment in acute care is explored using Delphi consensus and Focus Group methodology respectively. The resultant model is developed on existing retrospective national Hospital Episode Statistics data, and prospectively tested on observational data in a local Acute Medical Unit setting. ResultsExisting frailty scores are preponderantly biophysical in nature, and have poor predictive power for adverse outcomes in the acute care setting. In clinical practice, single-dimension assessment tools predominate. Frailty syndromes and previous high resource utilisation in the form of a simple, clinically relevant tool useful to the multidisciplinary team gain consensus as optimal assessment for the setting. Retrospective testing of the frailty model displays moderate predictive powers for adverse events (inpatient mortality, emergency readmission and institutionalisation) and prospective testing provides concurrent (Frailty Index, Age, Co-Morbidity) and comparative predictive validity (Frailty Index, Co-Morbidity, 2 admission National Early Warning Score) with existing risk stratification models in this setting. ConclusionsA risk prediction model based on frailty syndromes and previous high resource utilisation is a valid, feasible and useful for the acute care setting.

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Validation of a modified Early Warning Score in medical admissions

Cited by 1,456 publications

References 19 publications

The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death

The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Frailty Assessment in Acute Care

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