• Y H Ng, D V Pilcher, M Bailey, C A Bain, C MacManus, and T K Bucknall.
• School of Nursing and Midwifery, Deakin University, Melbourne, Victoria.
• Anaesth Intensive Care. 2018 Jan 1; 46 (1): 889688-96.

AbstractWe 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.

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