Multivariable Fractional Polynomial Interaction to Investigate Continuous Effect Modifiers in a Meta-analysis on Higher Versus Lower PEEP for Patients with ARDS

Overview

Journal BMJ Open

Specialty General Medicine

Date 2016 Sep 10

PMID 27609843

Citations 9

Authors

Benjamin Kasenda

Willi Sauerbrei

Patrick Royston

Alain Mercat

Arthur S Slutsky

Deborah Cook

Gordon H Guyatt

Laurent Brochard

Jean-Christophe M Richard

Thomas E Stewart

Maureen Meade

Matthias Briel

Affiliations

Soon will be listed here.

Abstract

Objectives: A recent individual patient data (IPD) meta-analysis suggested that patients with moderate or severe acute respiratory distress syndrome (ARDS) benefit from higher positive end-expiratory pressure (PEEP) ventilation strategies. However, thresholds for continuous variables (eg, hypoxaemia) are often arbitrary and linearity assumptions in regression approaches may not hold; the multivariable fractional polynomial interaction (MFPI) approach can address both problems. The objective of this study was to apply the MFPI approach to investigate interactions between four continuous patient baseline variables and higher versus lower PEEP on clinical outcomes.

Setting: Pooled data from three randomised trials in intensive care identified by a systematic review.

Participants: 2299 patients with acute lung injury requiring mechanical ventilation.

Interventions: Higher (N=1136) versus lower PEEP (N=1163) ventilation strategy.

Outcome Measures: Prespecified outcomes included mortality, time to death and time-to-unassisted breathing. We examined the following continuous baseline characteristics as potential effect modifiers using MFPI: PaO2/FiO2 (arterial partial oxygen pressure/ fraction of inspired oxygen), oxygenation index, respiratory system compliance (tidal volume/(inspiratory plateau pressure-PEEP)) and body mass index (BMI).

Results: We found that for patients with PaO2/FiO2 below 150 mm Hg, but above 100 mm Hg or an oxygenation index above 12 (moderate ARDS), higher PEEP reduces hospital mortality, but the beneficial effect appears to level off for patients with very severe ARDS. Patients with mild ARDS (PaO2/FiO2 above 200 mm Hg or an oxygenation index below 10) do not seem to benefit from higher PEEP and might even be harmed. For patients with a respiratory system compliance above 40 mL/cm H2O or patients with a BMI above 35 kg/m(2), we found a trend towards reduced mortality with higher PEEP, but there is very weak statistical confidence in these findings.

Conclusions: MFPI analyses suggest a nonlinear effect modification of higher PEEP ventilation by PaO2/FiO2 and oxygenation index with reduced mortality for some patients suffering from moderate ARDS.

Study Registration Number: CRD42012003129.

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References

Villar J, Blanco J, Anon J, Santos-Bouza A, Blanch L, Ambros A . The ALIEN study: incidence and outcome of acute respiratory distress syndrome in the era of lung protective ventilation. Intensive Care Med. 2011; 37(12):1932-41. DOI: 10.1007/s00134-011-2380-4. View

Bernard G, Artigas A, Brigham K, Carlet J, Falke K, Hudson L . The American-European Consensus Conference on ARDS. Definitions, mechanisms, relevant outcomes, and clinical trial coordination. Am J Respir Crit Care Med. 1994; 149(3 Pt 1):818-24. DOI: 10.1164/ajrccm.149.3.7509706. View

Villar J, Sulemanji D, Kacmarek R . The acute respiratory distress syndrome: incidence and mortality, has it changed?. Curr Opin Crit Care. 2013; 20(1):3-9. DOI: 10.1097/MCC.0000000000000057. View

Weinberg C . How bad is categorization?. Epidemiology. 1995; 6(4):345-7. View

Royston P, Sauerbrei W, Ritchie A . Is treatment with interferon-alpha effective in all patients with metastatic renal carcinoma? A new approach to the investigation of interactions. Br J Cancer. 2004; 90(4):794-9. PMC: 2410187. DOI: 10.1038/sj.bjc.6601622. View

Hingorani A, van der Windt D, Riley R, Abrams K, Moons K, Steyerberg E . Prognosis research strategy (PROGRESS) 4: stratified medicine research. BMJ. 2013; 346:e5793. PMC: 3565686. DOI: 10.1136/bmj.e5793. View

Sun X, Briel M, Walter S, Guyatt G . Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses. BMJ. 2010; 340:c117. DOI: 10.1136/bmj.c117. View

Ranieri V, Rubenfeld G, Thompson B, Ferguson N, Caldwell E, Fan E . Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012; 307(23):2526-33. DOI: 10.1001/jama.2012.5669. View

Altman D, Lausen B, Sauerbrei W, Schumacher M . Dangers of using "optimal" cutpoints in the evaluation of prognostic factors. J Natl Cancer Inst. 1994; 86(11):829-35. DOI: 10.1093/jnci/86.11.829. View

10.

Amato M, Meade M, Slutsky A, Brochard L, Costa E, Schoenfeld D . Driving pressure and survival in the acute respiratory distress syndrome. N Engl J Med. 2015; 372(8):747-55. DOI: 10.1056/NEJMsa1410639. View

11.

Lau B, Cole S, Gange S . Competing risk regression models for epidemiologic data. Am J Epidemiol. 2009; 170(2):244-56. PMC: 2732996. DOI: 10.1093/aje/kwp107. View

12.

Mercat A, Richard J, Vielle B, Jaber S, Osman D, Diehl J . Positive end-expiratory pressure setting in adults with acute lung injury and acute respiratory distress syndrome: a randomized controlled trial. JAMA. 2008; 299(6):646-55. DOI: 10.1001/jama.299.6.646. View

13.

Briel M, Meade M, Mercat A, Brower R, Talmor D, Walter S . Higher vs lower positive end-expiratory pressure in patients with acute lung injury and acute respiratory distress syndrome: systematic review and meta-analysis. JAMA. 2010; 303(9):865-73. DOI: 10.1001/jama.2010.218. View

14.

Dellinger R, Levy M, Rhodes A, Annane D, Gerlach H, Opal S . Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Med. 2013; 39(2):165-228. PMC: 7095153. DOI: 10.1007/s00134-012-2769-8. View

15.

Assmann S, Pocock S, Enos L, Kasten L . Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet. 2000; 355(9209):1064-9. DOI: 10.1016/S0140-6736(00)02039-0. View

16.

Brower R, Lanken P, MacIntyre N, Matthay M, Morris A, Ancukiewicz M . Higher versus lower positive end-expiratory pressures in patients with the acute respiratory distress syndrome. N Engl J Med. 2004; 351(4):327-36. DOI: 10.1056/NEJMoa032193. View

17.

Kasenda B, Sauerbrei W, Royston P, Briel M . Investigation of continuous effect modifiers in a meta-analysis on higher versus lower PEEP in patients requiring mechanical ventilation--protocol of the ICEM study. Syst Rev. 2014; 3:46. PMC: 4035853. DOI: 10.1186/2046-4053-3-46. View

18.

Koller M, Raatz H, Steyerberg E, Wolbers M . Competing risks and the clinical community: irrelevance or ignorance?. Stat Med. 2011; 31(11-12):1089-97. PMC: 3575691. DOI: 10.1002/sim.4384. View

19.

Beck D, Smith G, Pappachan J, Millar B . External validation of the SAPS II, APACHE II and APACHE III prognostic models in South England: a multicentre study. Intensive Care Med. 2003; 29(2):249-56. DOI: 10.1007/s00134-002-1607-9. View

20.

Suistomaa M, Niskanen M, Kari A, Hynynen M, Takala J . Customized prediction models based on APACHE II and SAPS II scores in patients with prolonged length of stay in the ICU. Intensive Care Med. 2002; 28(4):479-85. DOI: 10.1007/s00134-002-1214-9. View