Predicting medical emergency team calls, cardiac arrest calls and re-admission after intensive care discharge

creation of a tool to identify at-risk patients

Research output: Contribution to journalArticleResearchpeer-review

Abstract

We aimed to develop a predictive model for intensive care unit (ICU)-discharged patients at risk of post-ICU deterioration. We performed a retrospective, single-centre cohort observational study by linking the hospital admission, patient pathology, ICU, and medical emergency team (MET) databases. All patients discharged from the Alfred Hospital ICU to wards between July 2012 and June 2014 were included. The primary outcome was a composite endpoint of any MET call, cardiac arrest call or ICU re-admission. Multivariable logistic regression analysis was used to identify predictors of outcome and develop a risk-stratification model. Four thousand, six hundred and thirty-two patients were included in the study. Of these, 878 (19%) patients had a MET call, 51 (1.1%) patients had cardiac arrest calls, 304 (6.5%) were re-admitted to ICU during the same hospital stay, and 964 (21%) had MET calls, cardiac arrest calls or ICU re-admission. A discriminatory predictive model was developed (area under the receiver operating characteristic curve 0.72 [95% confidence intervals {CI} 0.70 to 0.73]) which identified the following factors: increasing age (odds ratio [OR] 1.012 [95% CI 1.007 to 1.017] P <0.001), ICU admission with subarachnoid haemorrhage (OR 2.26 [95% CI 1.22 to 4.16] P=0.009), admission to ICU from a ward (OR 1.67 [95% CI 1.31 to 2.13] P <0.001), Acute Physiology and Chronic Health Evaluation (APACHE) III score without the age component (OR 1.005 [95% CI 1.001 to 1.010] P=0.025), tracheostomy on ICU discharge (OR 4.32 [95% CI 2.9 to 6.42] P <0.001) and discharge to cardiothoracic (OR 2.43 [95%CI 1.49 to 3.96] P <0.001) or oncology wards (OR 2.27 [95% CI 1.05 to 4.89] P=0.036). Over the two-year period, 361 patients were identified as having a greater than 50% chance of having post-ICU deterioration. Factors are identifiable to predict patients at risk of post-ICU deterioration. This knowledge could be used to guide patient follow-up after ICU discharge, optimise healthcare resources, and improve patient outcomes and service delivery.

Original languageEnglish
Pages (from-to)88-96
Number of pages9
JournalAnaesthesia and intensive care
Volume46
Issue number1
DOIs
Publication statusPublished - Jan 2018

Keywords

  • clinical decision-making
  • medical emergency team
  • clinical deterioration
  • prediction
  • adverse event
  • ICU re-admissions

Cite this

@article{3a90d4192e224a0db67fe529abfdd680,
title = "Predicting medical emergency team calls, cardiac arrest calls and re-admission after intensive care discharge: creation of a tool to identify at-risk patients",
abstract = "We aimed to develop a predictive model for intensive care unit (ICU)-discharged patients at risk of post-ICU deterioration. We performed a retrospective, single-centre cohort observational study by linking the hospital admission, patient pathology, ICU, and medical emergency team (MET) databases. All patients discharged from the Alfred Hospital ICU to wards between July 2012 and June 2014 were included. The primary outcome was a composite endpoint of any MET call, cardiac arrest call or ICU re-admission. Multivariable logistic regression analysis was used to identify predictors of outcome and develop a risk-stratification model. Four thousand, six hundred and thirty-two patients were included in the study. Of these, 878 (19{\%}) patients had a MET call, 51 (1.1{\%}) patients had cardiac arrest calls, 304 (6.5{\%}) were re-admitted to ICU during the same hospital stay, and 964 (21{\%}) had MET calls, cardiac arrest calls or ICU re-admission. A discriminatory predictive model was developed (area under the receiver operating characteristic curve 0.72 [95{\%} confidence intervals {CI} 0.70 to 0.73]) which identified the following factors: increasing age (odds ratio [OR] 1.012 [95{\%} CI 1.007 to 1.017] P <0.001), ICU admission with subarachnoid haemorrhage (OR 2.26 [95{\%} CI 1.22 to 4.16] P=0.009), admission to ICU from a ward (OR 1.67 [95{\%} CI 1.31 to 2.13] P <0.001), Acute Physiology and Chronic Health Evaluation (APACHE) III score without the age component (OR 1.005 [95{\%} CI 1.001 to 1.010] P=0.025), tracheostomy on ICU discharge (OR 4.32 [95{\%} CI 2.9 to 6.42] P <0.001) and discharge to cardiothoracic (OR 2.43 [95{\%}CI 1.49 to 3.96] P <0.001) or oncology wards (OR 2.27 [95{\%} CI 1.05 to 4.89] P=0.036). Over the two-year period, 361 patients were identified as having a greater than 50{\%} chance of having post-ICU deterioration. Factors are identifiable to predict patients at risk of post-ICU deterioration. This knowledge could be used to guide patient follow-up after ICU discharge, optimise healthcare resources, and improve patient outcomes and service delivery.",
keywords = "clinical decision-making, medical emergency team, clinical deterioration, prediction, adverse event, ICU re-admissions",
author = "Ng, {Y. H.} and Pilcher, {D. V.} and M. Bailey and Bain, {C. A.} and C. MacManus and Bucknall, {T. K.}",
year = "2018",
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doi = "10.1177/0310057X1804600113",
language = "English",
volume = "46",
pages = "88--96",
journal = "Anaesthesia and intensive care",
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publisher = "Australian Society of Anaesthetists",
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TY - JOUR

T1 - Predicting medical emergency team calls, cardiac arrest calls and re-admission after intensive care discharge

T2 - creation of a tool to identify at-risk patients

AU - Ng, Y. H.

AU - Pilcher, D. V.

AU - Bailey, M.

AU - Bain, C. A.

AU - MacManus, C.

AU - Bucknall, T. K.

PY - 2018/1

Y1 - 2018/1

N2 - We aimed to develop a predictive model for intensive care unit (ICU)-discharged patients at risk of post-ICU deterioration. We performed a retrospective, single-centre cohort observational study by linking the hospital admission, patient pathology, ICU, and medical emergency team (MET) databases. All patients discharged from the Alfred Hospital ICU to wards between July 2012 and June 2014 were included. The primary outcome was a composite endpoint of any MET call, cardiac arrest call or ICU re-admission. Multivariable logistic regression analysis was used to identify predictors of outcome and develop a risk-stratification model. Four thousand, six hundred and thirty-two patients were included in the study. Of these, 878 (19%) patients had a MET call, 51 (1.1%) patients had cardiac arrest calls, 304 (6.5%) were re-admitted to ICU during the same hospital stay, and 964 (21%) had MET calls, cardiac arrest calls or ICU re-admission. A discriminatory predictive model was developed (area under the receiver operating characteristic curve 0.72 [95% confidence intervals {CI} 0.70 to 0.73]) which identified the following factors: increasing age (odds ratio [OR] 1.012 [95% CI 1.007 to 1.017] P <0.001), ICU admission with subarachnoid haemorrhage (OR 2.26 [95% CI 1.22 to 4.16] P=0.009), admission to ICU from a ward (OR 1.67 [95% CI 1.31 to 2.13] P <0.001), Acute Physiology and Chronic Health Evaluation (APACHE) III score without the age component (OR 1.005 [95% CI 1.001 to 1.010] P=0.025), tracheostomy on ICU discharge (OR 4.32 [95% CI 2.9 to 6.42] P <0.001) and discharge to cardiothoracic (OR 2.43 [95%CI 1.49 to 3.96] P <0.001) or oncology wards (OR 2.27 [95% CI 1.05 to 4.89] P=0.036). Over the two-year period, 361 patients were identified as having a greater than 50% chance of having post-ICU deterioration. Factors are identifiable to predict patients at risk of post-ICU deterioration. This knowledge could be used to guide patient follow-up after ICU discharge, optimise healthcare resources, and improve patient outcomes and service delivery.

AB - We aimed to develop a predictive model for intensive care unit (ICU)-discharged patients at risk of post-ICU deterioration. We performed a retrospective, single-centre cohort observational study by linking the hospital admission, patient pathology, ICU, and medical emergency team (MET) databases. All patients discharged from the Alfred Hospital ICU to wards between July 2012 and June 2014 were included. The primary outcome was a composite endpoint of any MET call, cardiac arrest call or ICU re-admission. Multivariable logistic regression analysis was used to identify predictors of outcome and develop a risk-stratification model. Four thousand, six hundred and thirty-two patients were included in the study. Of these, 878 (19%) patients had a MET call, 51 (1.1%) patients had cardiac arrest calls, 304 (6.5%) were re-admitted to ICU during the same hospital stay, and 964 (21%) had MET calls, cardiac arrest calls or ICU re-admission. A discriminatory predictive model was developed (area under the receiver operating characteristic curve 0.72 [95% confidence intervals {CI} 0.70 to 0.73]) which identified the following factors: increasing age (odds ratio [OR] 1.012 [95% CI 1.007 to 1.017] P <0.001), ICU admission with subarachnoid haemorrhage (OR 2.26 [95% CI 1.22 to 4.16] P=0.009), admission to ICU from a ward (OR 1.67 [95% CI 1.31 to 2.13] P <0.001), Acute Physiology and Chronic Health Evaluation (APACHE) III score without the age component (OR 1.005 [95% CI 1.001 to 1.010] P=0.025), tracheostomy on ICU discharge (OR 4.32 [95% CI 2.9 to 6.42] P <0.001) and discharge to cardiothoracic (OR 2.43 [95%CI 1.49 to 3.96] P <0.001) or oncology wards (OR 2.27 [95% CI 1.05 to 4.89] P=0.036). Over the two-year period, 361 patients were identified as having a greater than 50% chance of having post-ICU deterioration. Factors are identifiable to predict patients at risk of post-ICU deterioration. This knowledge could be used to guide patient follow-up after ICU discharge, optimise healthcare resources, and improve patient outcomes and service delivery.

KW - clinical decision-making

KW - medical emergency team

KW - clinical deterioration

KW - prediction

KW - adverse event

KW - ICU re-admissions

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U2 - 10.1177/0310057X1804600113

DO - 10.1177/0310057X1804600113

M3 - Article

VL - 46

SP - 88

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JO - Anaesthesia and intensive care

JF - Anaesthesia and intensive care

SN - 0310-057X

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