Conservative versus Liberal Oxygenation Targets in Intensive Care Unit Patients (ICONIC): A Randomized Clinical Trial

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Abstract

Rationale

Supplemental oxygen is widely administered to ICU patients, but appropriate oxygenation targets remain unclear.

Objectives

This study aimed to determine whether a low-oxygenation strategy would lower 28-day mortality compared with a high-oxygenation strategy.

Methods

This randomized multicenter trial included mechanically ventilated ICU patients with an expected ventilation duration of at least 24 hours. Patients were randomized 1:1 to a low-oxygenation (Pa _{O
₂}, 55–80 mm Hg; or oxygen saturation as measured by pulse oximetry, 91–94%) or high-oxygenation (Pa _{O
₂}, 110–150 mm Hg; or oxygen saturation as measured by pulse oximetry, 96–100%) target until ICU discharge or 28 days after randomization, whichever came first. The primary outcome was 28-day mortality. The study was stopped prematurely because of the COVID-19 pandemic when 664 of the planned 1,512 patients were included.

Measurements and Main Results

Between November 2018 and November 2021, a total of 664 patients were included in the trial: 335 in the low-oxygenation group and 329 in the high-oxygenation group. The median achieved Pa _{O
₂} was 75 mm Hg (interquartile range, 70–84) and 115 mm Hg (interquartile range, 100–129) in the low- and high-oxygenation groups, respectively. At Day 28, 129 (38.5%) and 114 (34.7%) patients had died in the low- and high-oxygenation groups, respectively (risk ratio, 1.11; 95% confidence interval, 0.9–1.4; P = 0.30). At least one serious adverse event was reported in 12 (3.6%) and 17 (5.2%) patients in the low- and high-oxygenation groups, respectively.

Conclusions

Among mechanically ventilated ICU patients with an expected mechanical ventilation duration of at least 24 hours, using a low-oxygenation strategy did not result in a reduction of 28-day mortality compared with a high-oxygenation strategy.

Clinical trial registered with the National Trial Register and the International Clinical Trials Registry Platform (NTR7376).

Related collections

Most cited references 29

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Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients.

Jack E Zimmerman, Andrew A Kramer, Douglas S. McNair … (2006)

To improve the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) method for predicting hospital mortality among critically ill adults and to evaluate changes in the accuracy of earlier APACHE models. : Observational cohort study. A total of 104 intensive care units (ICUs) in 45 U.S. hospitals. A total of 131,618 consecutive ICU admissions during 2002 and 2003, of which 110,558 met inclusion criteria and had complete data. None. We developed APACHE IV using ICU day 1 information and a multivariate logistic regression procedure to estimate the probability of hospital death for randomly selected patients who comprised 60% of the database. Predictor variables were similar to those in APACHE III, but new variables were added and different statistical modeling used. We assessed the accuracy of APACHE IV predictions by comparing observed and predicted hospital mortality for the excluded patients (validation set). We tested discrimination and used multiple tests of calibration in aggregate and for patient subgroups. APACHE IV had good discrimination (area under the receiver operating characteristic curve = 0.88) and calibration (Hosmer-Lemeshow C statistic = 16.9, p = .08). For 90% of 116 ICU admission diagnoses, the ratio of observed to predicted mortality was not significantly different from 1.0. We also used the validation data set to compare the accuracy of APACHE IV predictions to those using APACHE III versions developed 7 and 14 yrs previously. There was little change in discrimination, but aggregate mortality was systematically overestimated as model age increased. When examined across disease, predictive accuracy was maintained for some diagnoses but for others seemed to reflect changes in practice or therapy. APACHE IV predictions of hospital mortality have good discrimination and calibration and should be useful for benchmarking performance in U.S. ICUs. The accuracy of predictive models is dynamic and should be periodically retested. When accuracy deteriorates they should be revised and updated.

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J Hope Kilgannon, Alan Jones, Nathan I. Shapiro … (2010)

Laboratory investigations suggest that exposure to hyperoxia after resuscitation from cardiac arrest may worsen anoxic brain injury; however, clinical data are lacking. To test the hypothesis that postresuscitation hyperoxia is associated with increased mortality. Multicenter cohort study using the Project IMPACT critical care database of intensive care units (ICUs) at 120 US hospitals between 2001 and 2005. Patient inclusion criteria were age older than 17 years, nontraumatic cardiac arrest, cardiopulmonary resuscitation within 24 hours prior to ICU arrival, and arterial blood gas analysis performed within 24 hours following ICU arrival. Patients were divided into 3 groups defined a priori based on PaO(2) on the first arterial blood gas values obtained in the ICU. Hyperoxia was defined as PaO(2) of 300 mm Hg or greater; hypoxia, PaO(2) of less than 60 mm Hg (or ratio of PaO(2) to fraction of inspired oxygen <300); and normoxia, not classified as hyperoxia or hypoxia. In-hospital mortality. Of 6326 patients, 1156 had hyperoxia (18%), 3999 had hypoxia (63%), and 1171 had normoxia (19%). The hyperoxia group had significantly higher in-hospital mortality (732/1156 [63%; 95% confidence interval {CI}, 60%-66%]) compared with the normoxia group (532/1171 [45%; 95% CI, 43%-48%]; proportion difference, 18% [95% CI, 14%-22%]) and the hypoxia group (2297/3999 [57%; 95% CI, 56%-59%]; proportion difference, 6% [95% CI, 3%-9%]). In a model controlling for potential confounders (eg, age, preadmission functional status, comorbid conditions, vital signs, and other physiological indices), hyperoxia exposure had an odds ratio for death of 1.8 (95% CI, 1.5-2.2). Among patients admitted to the ICU following resuscitation from cardiac arrest, arterial hyperoxia was independently associated with increased in-hospital mortality compared with either hypoxia or normoxia.

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Liberal or Conservative Oxygen Therapy for Acute Respiratory Distress Syndrome

Loïc Barrot, Pierre Asfar, Frédéric Mauny … (2020)

In patients with acute respiratory distress syndrome (ARDS), the National Heart, Lung, and Blood Institute ARDS Clinical Trials Network recommends a target partial pressure of arterial oxygen (Pao2) between 55 and 80 mm Hg. Prospective validation of this range in patients with ARDS is lacking. We hypothesized that targeting the lower limit of this range would improve outcomes in patients with ARDS.

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Author and article information

Contributors

Evert de Jonge:

ORCID: https://orcid.org/0000-0003-1431-8039

On behalf of : for the ICONIC investigators

Journal

Journal ID (nlm-ta): Am J Respir Crit Care Med

Journal ID (iso-abbrev): Am J Respir Crit Care Med

Journal ID (publisher-id): ajrccm

Title: American Journal of Respiratory and Critical Care Medicine

Publisher: American Thoracic Society

ISSN (Print): 1073-449X

ISSN (Electronic): 1535-4970

Publication date (Electronic, pub): 8 August 2023

Publication date (Electronic, collection): 01 October 2023

Publication date PMC-release: 8 August 2023

Volume: 208

Issue: 7

Pages: 770-779

Affiliations

[ ¹ ]Department of Intensive Care,

[ ² ]Department of Anesthesiology, and

[ ³ ]Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands;

[ ⁴ ]Department of Sustainable Health, Campus Fryslân, University of Groningen, Groningen, The Netherlands;

[ ⁵ ]Department of Intensive Care, Medical Center Leeuwarden, Leeuwarden, The Netherlands;

[ ⁶ ]Department of Intensive Care, Martini Hospital, Groningen, The Netherlands;

[ ⁷ ]Department of Intensive Care, Reinier de Graaf Hospital, Delft, The Netherlands;

[ ⁸ ]Department of Intensive Care, Diakonessenhuis, Utrecht, The Netherlands;

[ ⁹ ]Department of Intensive Care and

[ ¹⁰ ]Department of Medical Informatics, Amsterdam Public Health – Digital Health, Amsterdam University Medical Center, Location AMC, Amsterdam, The Netherlands;

[ ¹¹ ]Department of Intensive Care, Medisch Spectrum Twente, Enschede, The Netherlands;

[ ¹² ]Department of Anesthesiology and Intensive Care and

[ ¹³ ]Department of Surgical Sciences and Integrated Diagnostics, San Martino Policlinico Hospital, Scientific Institute for Research, Hospitalization and Healthcare for Oncology and Neurosciences, Genoa, Italy;

[ ¹⁴ ]Department of Intensive Care, Ikazia Hospital, Rotterdam, The Netherlands;

[ ¹⁵ ]Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia;

[ ¹⁶ ]Department of Critical Care Medicine, Albert Einstein Israelite Hospital, São Paulo, Brazil;

[ ¹⁷ ]Department of Intensive Care, Austin Hospital, Melbourne, Australia;

[ ¹⁸ ]Mahidol – Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand; and

[ ¹⁹ ]Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom

Author notes

Correspondence and requests for reprints should be addressed to L. Imeen van der Wal, M.D., Department of Intensive Care, Leiden University Medical Center, Albinusdreef 2, 2333ZA Leiden, The Netherlands. E-mail: l.i.van_der_wal@ 123456lumc.nl .

[*]

These authors contributed equally to this work.

[ † ]

Deceased.