Lower versus higher oxygenation targets in critically ill patients with severe hypoxaemia: secondary Bayesian analysis to explore heterogeneous treatment effects in the Handling Oxygenation Targets in the Intensive Care Unit (HOT-ICU) trial

Thomas L Klitgaard, Olav L Schjørring, Theis Lange, Morten H Møller, Anders Perner, Bodil S Rasmussen, Anders Granholm, Thomas L Klitgaard, Olav L Schjørring, Theis Lange, Morten H Møller, Anders Perner, Bodil S Rasmussen, Anders Granholm

Abstract

Background: In the Handling Oxygenation Targets in the Intensive Care Unit (HOT-ICU) trial, a lower (8 kPa) vs a higher (12 kPa) PaO2 target did not affect mortality amongst critically ill adult patients. We used Bayesian statistics to evaluate any heterogeneity in the effect of oxygenation targets on mortality between different patient groups within the HOT-ICU trial.

Methods: We analysed 90-day all-cause mortality using adjusted Bayesian logistic regression models, and assessed heterogeneous treatment effects according to four selected baseline variables using both hierarchical models of subgroups and models with interactions on the continuous scales. Results are presented as mortality probability (%) and relative risk (RR) with 95% credibility intervals (CrI).

Results: All 2888 patients in the intention-to-treat cohort of the HOT-ICU trial were included. The adjusted 90-day mortality rates were 43.0% (CrI: 38.3-47.8%) and 42.3% (CrI: 37.7-47.1%) in the lower and higher oxygenation groups, respectively (RR 1.02 [CrI: 0.93-1.11]), with 36.5% probability of an RR <1.00. Analyses of heterogeneous treatment effects suggested a dose-response relationship between baseline norepinephrine dose and increased mortality with the lower oxygenation target, with 95% probability of increased mortality associated with the lower oxygenation target as norepinephrine doses increased.

Conclusions: A lower oxygenation target was unlikely to affect overall mortality amongst critically ill adult patients with acute hypoxaemic respiratory failure. However, our results suggest an increasing mortality risk for patients with a lower oxygen target as the baseline norepinephrine dose increases. These findings warrant additional investigation.

Clinical trial registration: NCT03174002.

Keywords: Bayesian analysis; heterogeneity of treatment effects; intensive care unit; oxygen therapy; respiratory insufficiency.

Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Figures

Fig 1
Fig 1
Posterior probability distribution for the adjusted relative risk (RR) for 90-day all-cause mortality in the primary analysis using weakly informative priors. Upper part: cumulative posterior probability distribution for the adjusted RR. P(RR ≤ X) is the probability that the RR is smaller or equal to any given value specified on the X-axis, being ‘X’; P(RR > X) is the probability that the RR is larger than any given value specified on the X-axis, being ‘X’. An RR <1 indicates benefit from the lower oxygenation target; an RR >1 indicates benefit of the higher oxygenation target. Lower part: full posterior probability distribution; full vertical line=median value; coloured area=95% credibility interval.
Fig 2
Fig 2
Posterior probability distributions of the adjusted relative risks (RRs) of the treatment effect on 90-day all-cause mortality according to the four pre-specified baseline variables in the primary analysis using weakly informative priors. The posterior probability distributions of RRs in each subgroup from the subgroup-based models are displayed together with the posterior distribution from the corresponding analysis of all patients not considering subgroups. An RR 1 indicates benefit of the higher oxygenation target. PaO2:FiO2FiO2, ratio of partial pressure of arterial oxygen to fraction of inspired oxygen; SOFA, Sequential Organ Failure Assessment.
Fig 3
Fig 3
Conditional effects plots for 90-day all-cause mortality, using weakly informative priors. These plots illustrate the estimated interactions between treatment allocation and 90-day all-cause mortality on the continuous scale. The levels of the individual variables of interest are plotted on the X-axes; the probabilities of mortality are plotted on the Y-axes. Within each subplot, the odds ratio (OR) with 95% credibility interval for the interaction effect between the lower oxygenation target and the baseline variable assessed is presented. The posterior probabilities that the interaction OR is 1.00 (positive interaction) are also presented. PaO2:Fio2, ratio of partial pressure of arterial oxygen to fraction of inspired oxygen; SOFA, Sequential Organ Failure Assessment. In total, 95% of patients had a PaO2:FiO2 ratio <35.5 kPa.

References

    1. Barbateskovic M., Schjørring O.L., Krauss S.R., et al. Higher vs lower oxygenation strategies in acutely ill adults. Chest. 2020;159:154–173.
    1. Girardis M., Busani S., Damiani E., et al. Effect of conservative vs conventional oxygen therapy on mortality among patients in an intensive care unit the Oxygen-ICU randomized clinical trial. JAMA. 2016;316:1583–1589.
    1. Barrot L., Asfar P., Mauny F., et al. Liberal or conservative oxygen therapy for acute respiratory distress syndrome. N Engl J Med. 2020;382:999.
    1. The ICU-ROX investigators and the Australian and New Zealand Intensive Care Society Clinical Trials Group Conservative oxygen therapy during mechanical ventilation in the ICU. N Engl J Med. 2020;382:989–998.
    1. Schjørring O.L., Klitgaard T.L., Perner A., et al. Lower or higher oxygenation targets for acute hypoxemic respiratory failure. N Engl J Med. 2021;384:1301–1311.
    1. Iwashyna T.J., Burke J.F., Sussman J.B., Prescott H.C., Hayward R.A., Angus D.C. Implications of heterogeneity of treatment effect for reporting and analysis of randomized trials in critical care. Am J Respir Crit Care Med. 2015;192:1045–1051.
    1. Young P.J. Effect of oxygen therapy on mortality in the ICU. N Engl J Med. 2021;384:1361–1363.
    1. Kent D.M., Steyerberg E., Van Klaveren D. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ. 2018;363:k4245.
    1. Goligher E.C., Tomlinson G., Hajage D., et al. Extracorporeal membrane oxygenation for severe acute respiratory distress syndrome and posterior probability of mortality benefit in a post hoc Bayesian analysis of a randomized clinical trial. JAMA. 2018;320:2251–2259.
    1. Zampieri F.G., Costa E.L., Iwashyna T.J., et al. Heterogeneous effects of alveolar recruitment in acute respiratory distress syndrome: a machine learning reanalysis of the Alveolar Recruitment for Acute Respiratory Distress Syndrome trial. Br J Anaesth. 2019;123:88–95.
    1. Zampieri F.G., Damiani L.P., Bakker J., et al. Effects of a resuscitation strategy targeting peripheral perfusion status versus serum lactate levels among patients with septic shock. A Bayesian reanalysis of the ANDROMEDA-SHOCK trial. Am J Respir Crit Care Med. 2020;201:423–429.
    1. Granholm A., Marker S., Krag M., et al. Heterogeneity of treatment effect of prophylactic pantoprazole in adult ICU patients: a post hoc analysis of the SUP-ICU trial. Intensive Care Med. 2020;46:717–726.
    1. The REMAP-CAP Investigators Interleukin-6 receptor antagonists in critically ill patients with Covid-19. N Engl J Med. 2021;384:1491–1502.
    1. The Writing Committee for the REMAP-CAP Investigators Effect of hydrocortisone on mortality and organ support in patients with severe COVID-19: the REMAP-CAP COVID-19 corticosteroid domain randomized clinical trial. JAMA. 2020;324:1317–1329.
    1. Angus D.C., Berry S., Lewis R.J., et al. The REMAP-CAP (randomized embedded multifactorial adaptive platform for community-acquired pneumonia) study rationale and design. Ann Am Thorac Soc. 2020;17:879–891.
    1. Klitgaard T.L., Schjørring O.L., Lange T., et al. Bayesian and heterogeneity of treatment effect analyses of the HOT-ICU trial—a secondary analysis protocol. Acta Anaesthesiol Scand. 2020;9:1376–1381.
    1. Schjørring O.L., Perner A., Wetterslev J., et al. Handling Oxygenation Targets in the Intensive Care Unit (HOT-ICU)—protocol for a randomised clinical trial comparing a lower vs a higher oxygenation target in adults with acute hypoxaemic respiratory failure. Acta Anaesthesiol Scand. 2019;63:956–965.
    1. Schjørring O.L., Klitgaard T.L., Perner A., et al. The handling oxygenation targets in the intensive care unit (HOT-ICU) trial: detailed statistical analysis plan. Acta Anaesthesiol Scand. 2020;64:847–856.
    1. Ferreira D., Barthoulot M., Pottecher J., Torp K.D., Diemunsch P., Meyer N. A consensus checklist to help clinicians interpret clinical trial results analysed by Bayesian methods. Br J Anaesth. 2020;125:208–215.
    1. Ferreira D., Barthoulot M., Pottecher J., Torp K.D., Diemunsch P., Meyer N. Theory and practical use of Bayesian methods in interpreting clinical trial data: a narrative review. Br J Anaesth. 2020;125:1–7.
    1. Granholm A., Marker S., Krag M., et al. Heterogeneity of treatment effect of stress ulcer prophylaxis in ICU patients: a secondary analysis protocol. Acta Anaesthesiol Scand. 2019;63:1251–1256.
    1. Sung L., Hayden J., Greenberg M.L., Koren G., Feldman B.M., Tomlinson G.A. Seven items were identified for inclusion when reporting a Bayesian analysis of a clinical study. J Clin Epidemiol. 2005;58:261–268.
    1. von Elm E., Altman D.G., Egger M., Pocock S.J., Gøtzsche P.C., Vandenbroucke J.P. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344–349.
    1. Carpenter B., Gelman A., Hoffman M.D., et al. Stan: a probabilistic programming language. J Stat Softw. 2017;76:1–31.
    1. Bürkner P.C. brms: an R package for Bayesian multilevel models using Stan. J Stat Softw. 2017;80:1–28.
    1. Bürkner P.C. Advanced Bayesian multilevel modeling with the R package brms. R J. 2018;10:395–411.
    1. Kruschke J. 2nd Edn. Academic Press; Cambridge, MA: 2014. Doing bayesian data analysis—a tutorial with R, JAGS, and stan.
    1. Ryan E.G., Harrison E.M., Pearse R.M., Gates S. Perioperative haemodynamic therapy for major gastrointestinal surgery: the effect of a Bayesian approach to interpreting the findings of a randomised controlled trial. BMJ Open. 2019;9
    1. Vincent J.-L., Moreno R., Takala J., et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. Intensive Care Med. 1996;22:707–710.
    1. McGlothlin A.E., Viele K. Bayesian hierarchical models. JAMA. 2018;320:2365–2366.
    1. ANZICS. MEGA-ROX trial (ANZICS . CTG1920-01) 2021. Available from (accessed April 14 2021).
    1. Intensive Care National Audit & Research Centre. UK-ROX (ICNARC project number: NIHR130508) 2021. Available from (accessed April 14 2021).
    1. Young P., Mackle D., Bellomo R., et al. Conservative oxygen therapy for mechanically ventilated adults with sepsis: a post hoc analysis of data from the intensive care unit randomized trial comparing two approaches to oxygen therapy (ICU-ROX) Intensive Care Med. 2020;46:17–26.
    1. Granholm A., Møller M.H., Krag M., Perner A., Hjortrup P.B. Predictive performance of the Simplified Acute Physiology Score (SAPS) II and the initial Sequential Organ Failure Assessment (SOFA) score in acutely ill intensive care patients: post-hoc analyses of the SUP-ICU inception cohort study. PLoS One. 2016;11
    1. Singer M., Deutschman C.S., Seymour C., et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3) JAMA. 2016;315:801–810.

Source: PubMed

3
구독하다