The need for ICU admission in intoxicated patients: a prediction model

Context: 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 <24h of the ICU admission and/or in-hospital mortality). Materials and 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%). Discussion and conclusion: Clinical use of the prediction model, with a high negative predictive value (98.7%), would result in 34.3% less observational admissions.