Association between Type of Fluid Received Prior to Enrollment, Type of Admission, and Effect of Balanced Crystalloid in Critically Ill Adults: A Secondary Exploratory Analysis of the BaSICS Clinical Trial

Zampieri, Fernando Godinho; Machado, Flávia Ribeiro; Biondi, Rodrigo Santos; Freitas, Flávio Geraldo Rezende; Veiga, Viviane Cordeiro; Figueiredo, Rodrigo Cruvinel; Lovato, Wilson José; Amêndola, Cristina Prata; Neto, Ary Serpa; Paranhos, Jorge L R; Lúcio, Eraldo; Oliveira-Júnior, Lúcio C; Lisboa, Thiago; Lacerda, Fábio Holanda; Maia, Israel Silva; Grion, Cintia Mc; Assunção, Murillo Santucci César de; Manoel, Airton L O; Corrêa, Thiago Domingos; Guedes, Marco Antonio Va; Azevedo, Luciano César Pontes; Miranda, Tamiris Abait; Damiani, Lucas Petri; Silva, Nilton Brandão da; Cavalcanti, Alexandre Biasi

doi:10.1164/rccm.202111-2484oc

Cited by 33 publications

(19 citation statements)

References 13 publications

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“…Third, we did not impute for missing data. Fourth, we did not adjust for multiplicity of testing, though the concern for type I error may be reduced with Bayesian analysis compared with a frequentist analysis, and our findings were coherent with the main STARRT-AKI trial [3,11]. Fifth, we used margins for equivalence and for defining large effect sizes that may be questionable; however, we also present results based on consensus definition of MCID, which corroborates with consistent interpretation.…”

Section: Discussionsupporting

confidence: 71%

“…The STARRT-AKI trial was designed as a frequentist trial and was interpreted using a traditional framework of null hypothesis testing with a dichotomous interpretation of p values under a Neyman-Pearson concept [9]. The reinterpretation of the STARRT-AKI trial through a Bayesian framework may align more naturally with clinician decision-making and provide a more straightforward context, including the provision of direct probabilities of benefit or harm, probabilities of the effect size being within a range of relevant effect sizes, and estimates of equivalence [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian reanalysis of the Standard versus Accelerated Initiation of Renal-Replacement Therapy in Acute Kidney Injury (STARRT-AKI) trial

Zampieri

Costa²,

McAuley³

et al. 2022

Crit Care

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Background Timing of initiation of kidney-replacement therapy (KRT) in critically ill patients remains controversial. The Standard versus Accelerated Initiation of Renal-Replacement Therapy in Acute Kidney Injury (STARRT-AKI) trial compared two strategies of KRT initiation (accelerated versus standard) in critically ill patients with acute kidney injury and found neutral results for 90-day all-cause mortality. Probabilistic exploration of the trial endpoints may enable greater understanding of the trial findings. We aimed to perform a reanalysis using a Bayesian framework. Methods We performed a secondary analysis of all 2927 patients randomized in multi-national STARRT-AKI trial, performed at 168 centers in 15 countries. The primary endpoint, 90-day all-cause mortality, was evaluated using hierarchical Bayesian logistic regression. A spectrum of priors includes optimistic, neutral, and pessimistic priors, along with priors informed from earlier clinical trials. Secondary endpoints (KRT-free days and hospital-free days) were assessed using zero–one inflated beta regression. Results The posterior probability of benefit comparing an accelerated versus a standard KRT initiation strategy for the primary endpoint suggested no important difference, regardless of the prior used (absolute difference of 0.13% [95% credible interval [CrI] − 3.30%; 3.40%], − 0.39% [95% CrI − 3.46%; 3.00%], and 0.64% [95% CrI − 2.53%; 3.88%] for neutral, optimistic, and pessimistic priors, respectively). There was a very low probability that the effect size was equal or larger than a consensus-defined minimal clinically important difference. Patients allocated to the accelerated strategy had a lower number of KRT-free days (median absolute difference of − 3.55 days [95% CrI − 6.38; − 0.48]), with a probability that the accelerated strategy was associated with more KRT-free days of 0.008. Hospital-free days were similar between strategies, with the accelerated strategy having a median absolute difference of 0.48 more hospital-free days (95% CrI − 1.87; 2.72) compared with the standard strategy and the probability that the accelerated strategy had more hospital-free days was 0.66. Conclusions In a Bayesian reanalysis of the STARRT-AKI trial, we found very low probability that an accelerated strategy has clinically important benefits compared with the standard strategy. Patients receiving the accelerated strategy probably have fewer days alive and KRT-free. These findings do not support the adoption of an accelerated strategy of KRT initiation.

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Section: Discussionsupporting

confidence: 71%

Section: Introductionmentioning

confidence: 99%

A Bayesian reanalysis of the Standard versus Accelerated Initiation of Renal-Replacement Therapy in Acute Kidney Injury (STARRT-AKI) trial

Zampieri

Costa²,

McAuley³

et al. 2022

Crit Care

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“…probability of harm, indeterminate result in same clincal setting RCT, 32 found that the probability of harm of the intervention was > 90%. Bayesian analysis has also been used to elucidate the effect of interventions in specific subgroups, 10 to evaluate the effect of selection bias 33 and in metaanalysis. 34 This current analysis has several limitations which need to be addressed.…”

Section: Discussionmentioning

confidence: 99%

Effect of intraoperative PEEP with recruitment maneuvers on the occurrence of postoperative pulmonary complications during general anesthesia––protocol for Bayesian analysis of three randomized clinical trials of intraoperative ventilation

Mazzinari

Zampieri²,

Ball³

et al. 2022

F1000Res

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Background: Using the frequentist approach, a recent meta–analysis of three randomized clinical trials in patients undergoing intraoperative ventilation during general anesthesia for major surgery failed to show the benefit of ventilation that uses high positive end–expiratory pressure with recruitment maneuvers when compared to ventilation that uses low positive end–expiratory pressure without recruitment maneuvers. Methods: We designed a protocol for a Bayesian analysis using the pooled dataset. The multilevel Bayesian logistic model will use the individual patient data. Prior distributions will be prespecified to represent a varying level of skepticism for the effect estimate. The primary endpoint will be a composite of postoperative pulmonary complications (PPC) within the first seven postoperative days, which reflects the primary endpoint of the original studies. We preset a range of practical equivalence to assess the futility of the intervention with an interval of odds ratio (OR) between 0.9 and 1.1 and assess how much of the 95% of highest density interval (HDI) falls between the region of practical equivalence. Ethics and dissemination: The used data derive from approved studies that were published in recent years. The findings of this current analysis will be reported in a new manuscript, drafted by the writing committee on behalf of the three research groups. All investigators listed in the original trials will serve as collaborative authors.

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“…A possible cofounding factor of trials investigating the effect of different types of crystalloids on the final outcome could be related to the volume and type of fluid administration prior to enrollment. In fact, a secondary post hoc analysis of the BaSICS trial categorized the enrolled patients according to fluid use in the 24 h before enrollment and according to admission type, showing a high probability that 90-day mortality was reduced in patients who exclusively received balanced fluids [81].…”

Section: Fluids and Icu Outcomes: Does The Type Of Fluid Matter?mentioning

confidence: 99%

Pathophysiology of fluid administration in critically ill patients

Messina

Bakker

Chew

et al. 2022

ICMx

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Fluid administration is a cornerstone of treatment of critically ill patients. The aim of this review is to reappraise the pathophysiology of fluid therapy, considering the mechanisms related to the interplay of flow and pressure variables, the systemic response to the shock syndrome, the effects of different types of fluids administered and the concept of preload dependency responsiveness. In this context, the relationship between preload, stroke volume (SV) and fluid administration is that the volume infused has to be large enough to increase the driving pressure for venous return, and that the resulting increase in end-diastolic volume produces an increase in SV only if both ventricles are operating on the steep part of the curve. As a consequence, fluids should be given as drugs and, accordingly, the dose and the rate of administration impact on the final outcome. Titrating fluid therapy in terms of overall volume infused but also considering the type of fluid used is a key component of fluid resuscitation. A single, reliable, and feasible physiological or biochemical parameter to define the balance between the changes in SV and oxygen delivery (i.e., coupling “macro” and “micro” circulation) is still not available, making the diagnosis of acute circulatory dysfunction primarily clinical.

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Association between Type of Fluid Received Prior to Enrollment, Type of Admission, and Effect of Balanced Crystalloid in Critically Ill Adults: A Secondary Exploratory Analysis of the BaSICS Clinical Trial

Cited by 33 publications

References 13 publications

A Bayesian reanalysis of the Standard versus Accelerated Initiation of Renal-Replacement Therapy in Acute Kidney Injury (STARRT-AKI) trial

A Bayesian reanalysis of the Standard versus Accelerated Initiation of Renal-Replacement Therapy in Acute Kidney Injury (STARRT-AKI) trial

Effect of intraoperative PEEP with recruitment maneuvers on the occurrence of postoperative pulmonary complications during general anesthesia––protocol for Bayesian analysis of three randomized clinical trials of intraoperative ventilation

Pathophysiology of fluid administration in critically ill patients

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