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Heterogeneity of treatment effect of prophylactic pantoprazole in adult ICU patients: a post hoc analysis of the SUP-ICU trial

  • Anders GranholmEmail author
  • Søren Marker
  • Mette Krag
  • Fernando G. Zampieri
  • Hans-Christian Thorsen-Meyer
  • Benjamin Skov Kaas-Hansen
  • Iwan C. C. van der Horst
  • Theis Lange
  • Jørn Wetterslev
  • Anders Perner
  • Morten Hylander Møller
Original

Abstract

Purpose

The Stress Ulcer Prophylaxis in the Intensive Care Unit (SUP-ICU) trial compared prophylactic pantoprazole with placebo in 3291 adult ICU patients at risk of clinically important gastrointestinal bleeding (CIB). As a predefined subgroup analysis suggested increased 90-day mortality with pantoprazole in the most severely ill patients, we aimed to further explore whether heterogenous treatment effects (HTE) were present.

Methods

We assessed HTE in subgroups defined according to illness severity by SAPS II quintiles and the total number of risk factors for CIB using Bayesian hierarchical models, and on the continuous scale using Bayesian logistic regression models with interactions. Estimates were presented as posterior probability distributions of odds ratios (ORs), probabilities of different effect sizes, and marginal effects plots.

Results

We observed potential HTE for 90-day mortality according to illness severity (median subgroup OR range 0.90–1.09) with higher risk in the most severely ill, but not with different numbers of risk factors (1.00–1.02). We observed potential HTE of pantoprazole for clinically important events (0.86–1.18) and infectious adverse events (0.88–1.27) with higher risk in patients with greater illness severity and in those with more risk factors for CIB. Pantoprazole substantially and consistently reduced the risk of CIB with no indications of HTE (0.53–0.63).

Conclusions

In this post hoc analysis of the SUP-ICU trial, we found indications of HTE with increased risks of serious adverse events in patients with greater illness severity or more risk factors for CIB allocated to pantoprazole. These findings are hypothesis-generating and warrant further prospective investigation.

ClinicalTrials.gov identifier

NCT02467621

Keywords

Stress ulcer prophylaxis Gastrointestinal bleeding Pantoprazole Heterogeneity of treatment effects Bayesian analysis Subgroup analysis 

Notes

Acknowledgements

The authors thank everybody involved in the SUP-ICU trial: research staff and investigators, clinical staff, patients and their relatives.

Author contributions

The idea for this project was conceived by AG, FGZ, AP and MHM. All authors contributed to the study protocol and statistical analysis plan. AG conducted all analyses and wrote the first draft of the article, which was critically revised by all authors. SM and MK were responsible for the SUP-ICU trial database. AG, SM, MK, ICCH, TL, JW, AP and MHM were all involved in the conduct of the SUP-ICU trial. All authors approved the final version.

Funding

The SUP-ICU trial was funded by Innovation Fund Denmark (4108-00011A) and supported by Rigshospitalet, the Capital Region of Denmark, the Regions of Denmark, the Scandinavian Society of Anaesthesiology and Intensive Care Medicine, Ehrenreich’s Foundation, Aase and Ejnar Danielsens Foundation, the Danish Society of Anaesthesiology and Intensive Care Medicine, the Danish Medical Association and the European Society of Intensive Care Medicine. The present substudy received no specific funding and none of the funders of the SUP-ICU trial had any influence on any aspects of this study. HCTM and BSKH received funding from Innovation Fund Denmark (5153-00002B).

Compliance with ethical standards

Conflicts of interest

None. The Department of Intensive Care at Rigshospitalet receives support for other research projects from Ferring Pharmaceuticals, Denmark and the Novo Nordisk Foundation, Denmark. FGZ received grants for other research projects from Bactiguard, Sweden and from the Brazilian Ministry of Health, both unrelated to this work.

Supplementary material

134_2019_5903_MOESM1_ESM.pdf (1.2 mb)
Supplementary file1 (PDF 1222 kb)

References

  1. 1.
    Cook DJ, Griffith LE, Walter SD et al (2001) The attributable mortality and length of intensive care unit stay of clinically important gastrointestinal bleeding in critically ill patients. Crit Care 5:368–375CrossRefGoogle Scholar
  2. 2.
    Granholm A, Zeng L, Dionne JC et al (2019) Predictors of gastrointestinal bleeding in adult ICU patients: a systematic review and meta-analysis. Intensive Care Med 45:1347–1359CrossRefGoogle Scholar
  3. 3.
    Krag M, Perner A, Wetterslev J et al (2015) Prevalence and outcome of gastrointestinal bleeding and use of acid suppressants in acutely ill adult intensive care patients. Intensive Care Med 41:833–845CrossRefGoogle Scholar
  4. 4.
    Rhodes A, Evans LE, Alhazzani W et al (2017) Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med 43:304–377CrossRefGoogle Scholar
  5. 5.
    MacLaren R, Reynolds PM, Allen RR (2014) Histamine-2 receptor antagonists vs proton pump inhibitors on gastrointestinal tract haemorrhage and infectious complications in the intensive care unit. JAMA Intern Med 174:564–574CrossRefGoogle Scholar
  6. 6.
    Charlot M, Ahlehoff O, Norgaard ML et al (2010) Proton-pump inhibitors are associated with increased cardiovascular risk independent of clopidogrel use: a nationwide cohort study. Ann Intern Med 153:378–386CrossRefGoogle Scholar
  7. 7.
    Krag M, Marker S, Perner A et al (2018) Pantoprazole in patients at risk for gastrointestinal bleeding in the ICU. N Engl J Med 379:2199–2208CrossRefGoogle Scholar
  8. 8.
    Wetterslev J, Jakobsen JC, Gluud C (2017) Trial Sequential Analysis in systematic reviews with meta-analysis. BMC Med Res Methodol 17:39CrossRefGoogle Scholar
  9. 9.
    Barbateskovic M, Marker S, Granholm A et al (2019) Stress ulcer prophylaxis with proton pump inhibitors or histamin-2 receptor antagonists in adult intensive care patients: a systematic review with meta-analysis and trial sequential analysis. Intensive Care Med 45:143–158CrossRefGoogle Scholar
  10. 10.
    Marker S, Perner A, Wetterslev J et al (2019) Pantoprazole prophylaxis in ICU patients with high severity of disease: a post hoc analysis of the placebo-controlled SUP-ICU trial. Intensive Care Med 45:609–618CrossRefGoogle Scholar
  11. 11.
    Marker S, Krag M, Perner A et al (2019) Pantoprazole in ICU patients at risk for gastrointestinal bleeding—1-year mortality in the SUP-ICU trial. Acta Anaesthesiol Scand 63:1184–1190CrossRefGoogle Scholar
  12. 12.
    Kent DM, Steyerberg E, van Klaveren D (2018) Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ 364:k4245CrossRefGoogle Scholar
  13. 13.
    Iwashyna TJ, Burke JF, Sussman JB et al (2015) Implications of heterogeneity of treatment effect for reporting and analysis of randomized trials in critical care. Am J Respir Crit Care Med 192:1045–1051CrossRefGoogle Scholar
  14. 14.
    Granholm A, Marker S, Krag M et al (2019) Heterogeneity of treatment effect of stress ulcer prophylaxis in ICU patients: a secondary analysis protocol. Acta Anaesthesiol Scand 63:1251–1256CrossRefGoogle Scholar
  15. 15.
    Writing Group for the Alveolar Recuitment for Acute Respiratory Distress Syndrome Trial (ART) Investigators, Cavalcanti AB, Suzumura ÉA et al (2017) Effect of lung recruitment and titrated Positive End-Expiratory Pressure (PEEP) vs low PEEP on mortality in patients with acute respiratory distress syndrome - a randomized clinical trial. JAMA 318:1335–1345CrossRefGoogle Scholar
  16. 16.
    Zampieri FG, Costa EL, Iwashyna TJ et al (2019) 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 123:88–95CrossRefGoogle Scholar
  17. 17.
    von Elm E, Altman DG, Egger M et al (2008) The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol 61:344–349CrossRefGoogle Scholar
  18. 18.
    Sung L, Hayden J, Greenberg ML, Koren G, Feldman BM, Tomlinson GA (2005) Seven items were identified for inclusion when reporting a Bayesian analysis of a clinical study. J Clin Epidemiol 58:261–268CrossRefGoogle Scholar
  19. 19.
    Krag M, Perner A, Wetterslev J et al (2016) Stress ulcer prophylaxis with a proton pump inhibitor versus placebo in critically ill patients (SUP-ICU trial): study protocol for a randomised controlled trial. Trials 17:205CrossRefGoogle Scholar
  20. 20.
    Krag M, Perner A, Wetterslev J et al (2017) Stress ulcer prophylaxis in the intensive care unit trial: detailed statistical analysis plan. Acta Anaesthesiol Scand 61:859–868CrossRefGoogle Scholar
  21. 21.
    Wickham H, Averick M, Bryan J et al (2019) Welcome to the Tidyverse. J Open Source Softw 4:1686CrossRefGoogle Scholar
  22. 22.
    Carpenter B, Gelman A, Hoffman MD et al (2017) Stan: a probabilistic programming language. J Stat Softw.  https://doi.org/10.18637/jss.v076.i01 CrossRefGoogle Scholar
  23. 23.
    Bürkner P-C (2017) brms: Bayesian multilevel models using stan. J Stat Softw.  https://doi.org/10.18637/jss.v080.i01 CrossRefGoogle Scholar
  24. 24.
    Kruschke JK (2015) Doing Bayesian Data Analysis, 2nd edn. Academic Press, London, UKGoogle Scholar
  25. 25.
    Le Gall J-R, Lemeshow S, Saulnier F (1993) A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA 270:2957–2963 (Erratum, JAMA 1994;271:1321)CrossRefGoogle Scholar
  26. 26.
    Bürkner P-C (2018) Advanced bayesian multilevel modeling with the R package brms. R J 10:395–411CrossRefGoogle Scholar
  27. 27.
    Norton EC, Dowd BE, Maciejewski ML (2019) Marginal effects—quantifying the effect of changes in risk factors in logistic regression models. JAMA 27:187–209Google Scholar
  28. 28.
    Vesin A, Azoulay E, Ruckly S et al (2013) Reporting and handling missing values in clinical studies in intensive care units. Intensive Care Med 39:1396–1404CrossRefGoogle Scholar
  29. 29.
    van Buuren S, Groothuis-Oudshoorn K (2011) MICE: multivariate imputation by chained equations in R. J Stat Softw.  https://doi.org/10.18637/jss.v045.i03 CrossRefGoogle Scholar
  30. 30.
    Spiegelhalter D, Myles J, Jones D, Abrams K (2000) Bayesian methods in health technology assessment: a review. Health Technol Assess 4:1–130CrossRefGoogle Scholar
  31. 31.
    Vincent J-L, Moreno R, Takala J et al (1996) The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. Intensive Care Med 22:707–710CrossRefGoogle Scholar
  32. 32.
    Alhazzani W, Alshamsi F, Belley-Cote E et al (2018) Efficacy and safety of stress ulcer prophylaxis in critically ill patients: a network meta-analysis of randomized trials. Intensive Care Med 44:1–11CrossRefGoogle Scholar
  33. 33.
    Cook D, Guyatt G (2018) Prophylaxis against upper gastrointestinal bleeding in hospitalized patients. N Engl J Med 378:2506–2516CrossRefGoogle Scholar
  34. 34.
    Barletta JF, Buckley MS, MacLaren R (2019) The SUP-ICU trial: does it confirm or condemn the practice of stress ulcer prophylaxis? Hosp Pharm.  https://doi.org/10.1177/0018578719867687 CrossRefGoogle Scholar
  35. 35.
    Barkun A, Bardou M (2018) Proton-pump inhibitor prophylaxis in the ICU - Benefits worth the risks? N Engl J Med 379:2263–2264CrossRefGoogle Scholar
  36. 36.
    Henderson NC, Louis TA, Wang C, Varadhan R (2016) Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research. Heal Serv Outcomes Res Methodol 16:213–233CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Anders Granholm
    • 1
    Email author
  • Søren Marker
    • 1
    • 2
  • Mette Krag
    • 1
    • 2
  • Fernando G. Zampieri
    • 3
    • 4
  • Hans-Christian Thorsen-Meyer
    • 1
    • 5
  • Benjamin Skov Kaas-Hansen
    • 5
    • 6
  • Iwan C. C. van der Horst
    • 7
  • Theis Lange
    • 2
    • 8
    • 9
  • Jørn Wetterslev
    • 2
    • 10
  • Anders Perner
    • 1
    • 2
  • Morten Hylander Møller
    • 1
    • 2
  1. 1.Department of Intensive Care 4131Copenhagen University Hospital - RigshospitaletCopenhagenDenmark
  2. 2.Centre for Research in Intensive CareCopenhagenDenmark
  3. 3.Research InstituteHCor‐Hospital do CoraçãoSão PauloBrazil
  4. 4.D´Or Research and Education InstituteSão PauloBrazil
  5. 5.NNF Center for Protein ResearchUniversity of CopenhagenCopenhagenDenmark
  6. 6.Clinical Pharmacology UnitZealand University HospitalRoskildeDenmark
  7. 7.Department of Intensive Care, Maastricht University Medical Center+University MaastrichtMaastrichtThe Netherlands
  8. 8.Section of Biostatistics, Department of Public HealthUniversity of CopenhagenCopenhagenDenmark
  9. 9.Center for Statistical SciencePeking UniversityBeijingChina
  10. 10.Copenhagen Trial Unit, Centre for Clinical Intervention ResearchCopenhagen University Hospital - RigshospitaletCopenhagenDenmark

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