B Morton1,2, K Nweze3, J O'Connor3, P Turton3, E Joekes4, J D Blakey1,2, I D Welters3,5. 1. From the Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK. 2. Critical Care Department, Aintree University Hospital NHS Foundation Trust, Aintree, UK. 3. Department of Critical Care Medicine, Royal Liverpool University Hospital, Liverpool, UK. 4. Department of Radiology, Royal Liverpool University Hospital, Liverpool, UK. 5. Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
Abstract
BACKGROUND: : Pandemic influenza has potential to overwhelm healthcare resources. There is uncertainty over performance of existing triage tools for hospital admission and discharge decisions. AIM: : Our aim was to identify clinical criteria that predict safe discharge from hospital and develop a pragmatic triage tool to guide physician decision-making. DESIGN: : We retrospectively examined an existing database of patients who presented to the Royal Liverpool University Hospital during the 2010-11 influenza pandemic. METHODS: Inclusion criteria: patients ≥18 years, with PCR confirmed H1N1 influenza. Exclusion criteria: died in the emergency department or case notes unavailable. Successful discharge was defined as discharge within 24 h of presentation and no readmission within 7 days. RESULTS: Eighty-six patients were included and 16 were successfully discharged. Estimated P/F ratio and C-reactive protein predicted safe discharge in a multivariable logistic regression model (AUC 0.883). A composite univariate predictor (estimated P/F minus C-reactive protein, AUC 0.877) was created to calculate specific cut off points for sensitivity and specificity. A pragmatic decision tool was created to incorporate these thresholds and relevant guidelines. Discharge: SpO 2 (in air) ≥ 94% and CRP <50. Observe: SpO 2 ≥ 94% and CRP >50 or SpO 2 ≤ 93% and CRP <50. Admit: SpO 2 ≤ 93% and CRP >50. CONCLUSIONS: We identified that oxygen exchange and CRP, a marker of acute inflammation, were the most important predictors of safe discharge. Our proposed simple triage model requires validation but has the potential to aid clinical decisions in the event of a future pandemic, and potentially for seasonal influenza.
BACKGROUND: : Pandemic influenza has potential to overwhelm healthcare resources. There is uncertainty over performance of existing triage tools for hospital admission and discharge decisions. AIM: : Our aim was to identify clinical criteria that predict safe discharge from hospital and develop a pragmatic triage tool to guide physician decision-making. DESIGN: : We retrospectively examined an existing database of patients who presented to the Royal Liverpool University Hospital during the 2010-11 influenza pandemic. METHODS: Inclusion criteria: patients ≥18 years, with PCR confirmed H1N1 influenza. Exclusion criteria: died in the emergency department or case notes unavailable. Successful discharge was defined as discharge within 24 h of presentation and no readmission within 7 days. RESULTS: Eighty-six patients were included and 16 were successfully discharged. Estimated P/F ratio and C-reactive protein predicted safe discharge in a multivariable logistic regression model (AUC 0.883). A composite univariate predictor (estimated P/F minus C-reactive protein, AUC 0.877) was created to calculate specific cut off points for sensitivity and specificity. A pragmatic decision tool was created to incorporate these thresholds and relevant guidelines. Discharge: SpO 2 (in air) ≥ 94% and CRP <50. Observe: SpO 2 ≥ 94% and CRP >50 or SpO 2 ≤ 93% and CRP <50. Admit: SpO 2 ≤ 93% and CRP >50. CONCLUSIONS: We identified that oxygen exchange and CRP, a marker of acute inflammation, were the most important predictors of safe discharge. Our proposed simple triage model requires validation but has the potential to aid clinical decisions in the event of a future pandemic, and potentially for seasonal influenza.
Authors: Christian Kraef; Marc van der Meirschen; Dominic Wichmann; Meike Kutza; Cordula Restemeyer; Marylyn M Addo; Ansgar W Lohse; Stefan Schmiedel; Stefan Kluge; Julian Schulze Zur Wiesch Journal: Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz Date: 2019-07 Impact factor: 1.513