open access publication

Article, 2024

Effects of IV fluid restriction according to site‐specific intensity of standard fluid treatment—protocol

Acta Anaesthesiologica Scandinavica, ISSN 1399-6576, 0001-5172, 10.1111/aas.14423

Contributors

Sivapalan, Praleene 0000-0002-0442-0032 (Corresponding author) [1] [2] Kaas-Hansen, Benjamin Skov 0000-0003-1023-0371 [1] [2] [3] Meyhoff, Tine Sylvest [1] [2] [4] Hjortrup, Peter Buhl 0000-0002-3847-3247 [1] [2] Kjær, Maj-Brit Nørregaard 0000-0002-6536-0504 [1] [2] Laake, Jon Henrik 0000-0001-6157-5359 [2] [5] Cronhjort, Maria B 0000-0002-0444-8553 [2] [6] [7] Jakob, Stephan M. [2] [8] Cecconi, Maurizio 0000-0002-4376-6538 [9] [10] Nalos, Marek 0000-0002-7977-9410 [2] [11] Ostermann, Marlies E 0000-0001-9500-9080 [2] [12] Malbrain, Manu L N G 0000-0002-1816-5255 [2] [13] Møller, Morten Hylander [1] [2] [3] Perner, Anders 0000-0002-4668-0123 [1] [2] [3] Granholm, Anders 0000-0001-5799-7655 [1] [2]

Affiliations

  1. [1] Rigshospitalet
  2. [NORA names: Capital Region of Denmark; Hospital; Denmark; Europe, EU; Nordic; OECD];
  3. [2] Collaboration for Research in Intensive Care
  4. [NORA names: Other Hospitals; Hospital; Denmark; Europe, EU; Nordic; OECD];
  5. [3] University of Copenhagen
  6. [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD];
  7. [4] Lillebaelt Hospital
  8. [NORA names: Region of Southern Denmark; Hospital; Denmark; Europe, EU; Nordic; OECD];
  9. [5] Oslo University Hospital
  10. [NORA names: Norway; Europe, Non-EU; Nordic; OECD];

Abstract

BACKGROUND: Variation in usual practice in fluid trials assessing lower versus higher volumes may affect overall comparisons. To address this, we will evaluate the effects of heterogeneity in treatment intensity in the Conservative versus Liberal Approach to Fluid Therapy of Septic Shock in Intensive Care trial. This will reflect the effects of differences in site-specific intensities of standard fluid treatment due to local practice preferences while considering participant characteristics. METHODS: We will assess the effects of heterogeneity in treatment intensity across one primary (all-cause mortality) and three secondary outcomes (serious adverse events or reactions, days alive without life support and days alive out of hospital) after 90 days. We will classify sites based on the site-specific intensity of standard fluid treatment, defined as the mean differences in observed versus predicted intravenous fluid volumes in the first 24 h in the standard-fluid group while accounting for differences in participant characteristics. Predictions will be made using a machine learning model including 22 baseline predictors using the extreme gradient boosting algorithm. Subsequently, sites will be grouped into fluid treatment intensity subgroups containing at least 100 participants each. Subgroups differences will be assessed using hierarchical Bayesian regression models with weakly informative priors. We will present the full posterior distributions of relative (risk ratios and ratios of means) and absolute differences (risk differences and mean differences) in each subgroup. DISCUSSION: This study will provide data on the effects of heterogeneity in treatment intensity while accounting for patient characteristics in critically ill adult patients with septic shock. REGISTRATIONS: The European Clinical Trials Database (EudraCT): 2018-000404-42, ClinicalTrials. gov: NCT03668236.

Keywords

Bayesian regression models, Secondary outcomes, absolute difference, adult patients, algorithm, approach, baseline, baseline predictors, boosting algorithm, care trials, characteristics, comparison, conservation, critically ill adult patients, criticism, data, days, differences, distribution, effect, effect of heterogeneity, effects of differences, fluid, fluid restriction, fluid therapy, fluid treatment, fluid trial, fluid volume, gradient, gradient boosting algorithm, group, heterogeneity, hierarchical Bayesian regression model, high volume, ill adult patients, intensity, intensive care trials, intravenous fluid volume, learning models, liberal approach, liberation, machine, machine learning models, model, mortality, outcomes, participant characteristics, participants, patient characteristics, patients, posterior distribution, practice, practice preferences, prediction, predictors, preferences, regression models, restriction, septic shock, shock, sites, study, subgroup differences, subgroups, therapy of septic shock, treatment, treatment intensity, treatment protocols, trials, variation, volume

Funders

  • Novo Nordisk Foundation

Data Provider: Digital Science