title: The need for ICU admission in intoxicated patients: a prediction model.
abstract: BACKGROUND
  Intoxicated patients are frequently admitted from the emergency room to the ICU for observational reasons. The question is whether these admissions are indeed necessary.
  
  
  OBJECTIVE
  The aim of this study was to develop a model that predicts the need of ICU treatment (receiving mechanical ventilation and/or vasopressors <24 h of the ICU admission and/or in-hospital mortality).
  
  
  METHODS
  We performed a retrospective cohort study from a national ICU-registry, including 86 Dutch ICUs. We aimed to include only observational admissions and therefore excluded admissions with treatment, at the start of the admission that can only be applied on the ICU (mechanical ventilation or CPR before admission). First, a generalized linear mixed-effects model with binominal link function and a random intercept per hospital was developed, based on covariates available in the first hour of ICU admission. Second, the selected covariates were used to develop a prediction model based on a practical point system. To determine the performance of the prediction model, the sensitivity, specificity, positive, and negative predictive value of several cut-off points based on the assigned number of points were assessed.
  
  
  RESULTS
  9679 admissions between January 2010 until January 2015 were included for analysis. In total, 632 (6.5%) of the patients admitted to the ICU eventually turned out to actually need ICU treatment. The strongest predictors for ICU treatment were respiratory insufficiency, age >55 and a GCS <6. Alcohol and "other poisonings" (e.g., carbonmonoxide, arsenic, cyanide) as intoxication type and a systolic blood pressure ≥130 mmHg were indicators that ICU treatment was likely unnecessary. The prediction model had high sensitivity (93.4%) and a high negative predictive value (98.7%).
  
  
  CONCLUSIONS
  Clinical use of the prediction model, with a high negative predictive value (98.7%), would result in 34.3% less observational admissions.
authors.affiliation: a Department of Intensive Care Medicine , University Medical Center, University of Utrecht , Utrecht , The Netherlands.|b Dutch National Poisons Information Centre (NPIC) , University Medical Center, University of Utrecht , Utrecht , The Netherlands.
authors.forename: Raya
authors.initials: R
authors.lastname: Brandenburg
authors.affiliation: c Department of Medical Informatics , Academic Medical Center, University of Amsterdam , Amsterdam , The Netherlands.
authors.forename: Sylvia
authors.initials: S
authors.lastname: Brinkman
authors.affiliation: c Department of Medical Informatics , Academic Medical Center, University of Amsterdam , Amsterdam , The Netherlands.
authors.forename: Nicolette F
authors.initials: NF
authors.lastname: de Keizer
authors.affiliation: a Department of Intensive Care Medicine , University Medical Center, University of Utrecht , Utrecht , The Netherlands.
authors.forename: Jozef
authors.initials: J
authors.lastname: Kesecioglu
authors.affiliation: a Department of Intensive Care Medicine , University Medical Center, University of Utrecht , Utrecht , The Netherlands.|b Dutch National Poisons Information Centre (NPIC) , University Medical Center, University of Utrecht , Utrecht , The Netherlands.|d Institute for Risk Assessment Sciences (IRAS), University of Utrecht , Utrecht , The Netherlands.
authors.forename: Jan
authors.initials: J
authors.lastname: Meulenbelt
authors.affiliation: a Department of Intensive Care Medicine , University Medical Center, University of Utrecht , Utrecht , The Netherlands.|b Dutch National Poisons Information Centre (NPIC) , University Medical Center, University of Utrecht , Utrecht , The Netherlands.
authors.forename: Dylan W
authors.initials: DW
authors.lastname: de Lange
mesh_terms: D000328:Adult; D000367:Age Factors; D000435:Alcoholic Intoxication / Q000628:therapy; D015331:Cohort Studies; D062787:Drug Overdose / Q000628:therapy*; D004636:Emergency Service, Hospital; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010343:Patient Admission / Q000706:statistics & numerical data*; D011041:Poisoning / Q000628:therapy*; D011237:Predictive Value of Tests; D012121:Respiration, Artificial / Q000379:methods; D012131:Respiratory Insufficiency / Q000628:therapy; D012189:Retrospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity
keywords: Critical care; costs; outcome; overdose; poisoning