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This position paper is the second ESCMID Consensus Document on this subject and aims to provide intensivists, infectious disease specialists, and emergency physicians with a standardized approach to the management of serious travel-related infections in the intensive care unit (ICU) or the emergency department. This document is a cooperative effort between members of two European Society of Clinical Microbiology and Infectious Diseases (ESCMID) study groups and was coordinated by Hakan Leblebicioglu and Jordi Rello for ESGITM (ESCMID Study Group for Infections in Travellers and Migrants) and ESGCIP (ESCMID Study Group for Infections in Critically Ill Patients), respectively. A relevant expert on the subject of each section prepared the first draft which was then edited and approved by additional members from both ESCMID study groups. This article summarizes considerations regarding clinical syndromes requiring ICU admission in travellers, covering immunocompromised patients.
In-line filtration of intravenous infusion may reduce organ dysfunction of adult critical patients
(2019)
Background: The potential harmful effects of particle-contaminated infusions for critically ill adult patients are yet unclear. So far, only significant improved outcome in critically ill children and new-borns was demonstrated when using in-line filters, but for adult patients, evidence is still missing.
Methods: This single-centre, retrospective controlled cohort study assessed the effect of in-line filtration of intravenous fluids with finer 0.2 or 1.2 μm vs 5.0 μm filters in critically ill adult patients. From a total of n = 3215 adult patients, n = 3012 patients were selected by propensity score matching (adjusting for sex, age, and surgery group) and assigned to either a fine filter cohort (with 0.2/1.2 μm filters, n = 1506, time period from February 2013 to January 2014) or a control filter cohort (with 5.0 μm filters, n = 1506, time period from April 2014 to March 2015). The cohorts were compared regarding the occurrence of severe vasoplegia, organ dysfunctions (lung, kidney, and brain), inflammation, in-hospital complications (myocardial infarction, ischemic stroke, pneumonia, and sepsis), in-hospital mortality, and length of ICU and hospital stay.
Results: Comparing fine filter vs control filter cohort, respiratory dysfunction (Horowitz index 206 (119–290) vs 191 (104.75–280); P = 0.04), pneumonia (11.4% vs 14.4%; P = 0.02), sepsis (9.6% vs 12.2%; P = 0.03), interleukin-6 (471.5 (258.8–1062.8) ng/l vs 540.5 (284.5–1147.5) ng/l; P = 0.01), and length of ICU (1.2 (0.6–4.9) vs 1.7 (0.8–6.9) days; P < 0.01) and hospital stay (14.0 (9.2–22.2) vs 14.8 (10.0–26.8) days; P = 0.01) were reduced. Rate of severe vasoplegia (21.0% vs 19.6%; P > 0.20) and acute kidney injury (11.8% vs 13.7%; P = 0.11) was not significantly different between the cohorts.
Conclusions: In-line filtration with finer 0.2 and 1.2 μm filters may be associated with less organ dysfunction and less inflammation in critically ill adult patients.
Trial registration: The study was registered at ClinicalTrials.gov (number: NCT02281604).
Highlights
• Artificial intelligence systems for mechanically ventilated patients are increasing.
• The clinical and financial impact of these models are often unexamined.
• We developed a generic health-economic model for artificial intelligence systems.
• This model assesses the cost-effectiveness for many different scenarios.
• The developed framework is easily adjustable to other (clinical) situations.
Abstract
Purpose: The health and economic consequences of artificial intelligence (AI) systems for mechanically ventilated intensive care unit patients often remain unstudied. Early health technology assessments (HTA) can examine the potential impact of AI systems by using available data and simulations. Therefore, we developed a generic health-economic model suitable for early HTA of AI systems for mechanically ventilated patients.
Materials and methods: Our generic health-economic model simulates mechanically ventilated patients from their hospitalisation until their death. The model simulates two scenarios, care as usual and care with the AI system, and compares these scenarios to estimate their cost-effectiveness.
Results: The generic health-economic model we developed is suitable for estimating the cost-effectiveness of various AI systems. By varying input parameters and assumptions, the model can examine the cost-effectiveness of AI systems across a wide range of different clinical settings.
Conclusions: Using the proposed generic health-economic model, investors and innovators can easily assess whether implementing a certain AI system is likely to be cost-effective before an exact clinical impact is determined. The results of the early HTA can aid investors and innovators in deployment of AI systems by supporting development decisions, informing value-based pricing, clinical trial design, and selection of target patient groups.