Physiological abnormalities in early warning scores are related to mortality in adult inpatients †

Goldhill, D. R.; McNarry, Alistair F.

doi:10.1093/bja/aeh113

Cited by 291 publications

(204 citation statements)

References 3 publications

Supporting

Mentioning

185

Contrasting

Unclassified

Order By: Relevance

“…The relationship between physiological abnormalities and hospital mortality has been well described in hospital patients. [20][21][22] In the present study, 84.9% of patients had at least one documented physiological abnormality in the 24 h preceding transfer, which is higher than the reports of abnormal observations in other studies. Studies of adult ward patients show 15%-67.9% of patients have one or more abnormal observation 20,22 and 3%-9% of ward patients fulfil rapid response system activation criteria at any point in time.…”

Section: Discussioncontrasting

confidence: 67%

“…[20][21][22] In the present study, 84.9% of patients had at least one documented physiological abnormality in the 24 h preceding transfer, which is higher than the reports of abnormal observations in other studies. Studies of adult ward patients show 15%-67.9% of patients have one or more abnormal observation 20,22 and 3%-9% of ward patients fulfil rapid response system activation criteria at any point in time. [23][24][25] Whether the high rates of physiological abnormalities in the present study cohort is a function of the older patient group, clinical deterioration or both warrants further investigation.…”

Section: Discussioncontrasting

confidence: 64%

See 1 more Smart Citation

Multisite analysis of the timing and outcomes of unplanned transfers from subacute to acute care

Considine¹,

Street²,

Botti³

et al. 2015

Aust. Health Review

View full text Add to dashboard Cite

Objective. The aim of the present study was to examine the timing and outcomes of patients requiring an unplanned transfer from subacute to acute care.Methods. Subacute care in-patients requiring unplanned transfer to an acute care facility within four Victorian health services from 1 January to 31 December 2010 were included in the study. Data were collected using retrospective audit. The primary outcome was transfer within 24 h of subacute care admission.Results. In all, 431 patients (median age 81 years) had unplanned transfers; of these, 37.8% had a limitation of medical treatment (LOMT) order. The median subacute care length of stay was 43 h: 29.0% of patients were transferred within 24 h and 83.5% were transferred within 72 h of subacute care admission. Predictors of transfer within 24 h were comorbidity weighting (odds ratio (OR) 1.1, P = 0.02) and LOMT order (OR 2.1, P < 0.01). Hospital admission occurred in 87.2% of patients and 15.4% died in hospital. Predictors of in-hospital mortality were comorbidity weighting (OR 1.2, P < 0.01) and the number of physiological abnormalities in the 24 h preceding transfer (OR 1.3, P < 0.01).Conclusions. There is a high rate of unplanned transfers to acute care within 24 h of admission to subacute care. Unplanned transfers are associated with high hospital admission and in-hospital mortality rates.What is known about the topic? Subacute care is becoming a high acuity environment where many patients are at significant risk of clinical deterioration. Systems for recognising and responding to deteriorating patients are well developed in acute care, but still developing in subacute care. What does this paper add? This is the first Australian multisite study of clinical deterioration in patients situated in subacute care facilities. One-third of unplanned transfers occur within 24 h of admission to subacute care. Patients who require unplanned transfer from subacute to acute care have unexpectedly high hospital admission rates and high in-hospital mortality rates. The frequency and completeness of physiological monitoring preceding transfer was low. What are the implications for practitioners? Patients in subacute care require regular physiological assessment and early escalation of care if there are physiological abnormalities. Risk of clinical deterioration should be a factor in the decision to admit patients to subacute care after an acute illness or injury. There is a need to improve systems for recognising and responding to deteriorating patients in subacute care settings.

show abstract

Section: Discussioncontrasting

confidence: 67%

Section: Discussioncontrasting

confidence: 64%

Multisite analysis of the timing and outcomes of unplanned transfers from subacute to acute care

Considine¹,

Street²,

Botti³

et al. 2015

Aust. Health Review

View full text Add to dashboard Cite

show abstract

“…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%

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

Kwon

Lee²,

Lee³

et al. 2018

JAHA

234

205

View full text Add to dashboard Cite

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.

show abstract

“…This is much higher than the overall PICU mortality for this centre which runs at 3.5-4%. Increased mortality of emergency admissions to PICU has also been described in adults (Goldhill and McNarry, 2004) and paediatrics (Odetola et al, 2007).…”

Section: Discussionmentioning

confidence: 96%

What impact did a Paediatric Early Warning system have on emergency admissions to the paediatric intensive care unit? An observational cohort study

Sefton

McGrath

Tume

et al. 2015

Intensive and Critical Care Nursing

View full text Add to dashboard Cite

Physiological abnormalities in early warning scores are related to mortality in adult inpatients †

Abstract: Simple physiological observations identify high-risk hospital inpatients. Those who die are often inpatients for days or weeks before death, allowing time for clinicians to intervene and potentially change outcome. Access to critical care beds could decrease mortality.

Cited by 291 publications

References 3 publications

Multisite analysis of the timing and outcomes of unplanned transfers from subacute to acute care

Multisite analysis of the timing and outcomes of unplanned transfers from subacute to acute care

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

What impact did a Paediatric Early Warning system have on emergency admissions to the paediatric intensive care unit? An observational cohort study

Contact Info

Product

Resources

About