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Bedside prediction of intradialytic hemodynamic instability in critically ill patients: the SOCRATE study.

ABSTRACT: BACKGROUND:Despite improvements in intermittent hemodialysis management, intradialytic hemodynamic instability (IHI) remains a common issue that could account for increased mortality and delayed renal recovery. However, predictive factors of IHI remain poorly explored. The objective of this study was to evaluate the relationship between baseline macrohemodynamic, tissue hypoperfusion parameters and IHI occurrence. METHODS:Prospective observational study conducted in a 18-bed medical ICU of a tertiary teaching hospital. Cardiovascular SOFA score, index capillary refill time (CRT) and lactate level were measured just before (T0) consecutive intermittent hemodialysis sessions performed for AKI. The occurrence of IHI requiring a therapeutic intervention was recorded. RESULTS:Two hundred eleven sessions, corresponding to 72 (34%) first sessions and 139 (66%) later sessions, were included. As IHI mostly occurred during first sessions (43% vs 12%, P??2 mmol/L (68% vs 29%, P?=?0.0018). Moreover, the occurrence of IHI increased with the number of macrohemodynamic and tissue perfusion impaired parameters, named SOCRATE score (cardiovascular SOFA, index CRT and lactATE): 10% (95% CI [3%, 30%]), 33% (95% CI [15%, 58%]), 55% (95% CI [35%, 73%]) and 80% (95% CI [55%, 93%]) for 0, 1, 2 and 3 parameters, respectively (AUC?=?0.79 [0.69-0.89], P?

SUBMITTER: Bige N

PROVIDER: S-EPMC7176798 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Bedside prediction of intradialytic hemodynamic instability in critically ill patients: the SOCRATE study.

Bigé Naïke N Lavillegrand Jean-Rémi JR Dang Julien J Attias Philippe P Deryckere Stéphanie S Joffre Jérémie J Dubée Vincent V Preda Gabriel G Dumas Guillaume G Hariri Geoffroy G Pichereau Claire C Baudel Jean-Luc JL Guidet Bertrand B Maury Eric E Boelle Pierre-Yves PY Ait-Oufella Hafid H

Annals of intensive care 20200422 1

<h4>Background</h4>Despite improvements in intermittent hemodialysis management, intradialytic hemodynamic instability (IHI) remains a common issue that could account for increased mortality and delayed renal recovery. However, predictive factors of IHI remain poorly explored. The objective of this study was to evaluate the relationship between baseline macrohemodynamic, tissue hypoperfusion parameters and IHI occurrence.<h4>Methods</h4>Prospective observational study conducted in a 18-bed medic ...[more]

PMID: 32323060

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Project description:BackgroundHemodynamic instability related to renal replacement therapy (HIRRT) may increase the risk of death and limit renal recovery. Studies in end-stage renal disease populations on maintenance hemodialysis suggest that some renal replacement therapy (RRT)-related interventions (e.g., cool dialysate) may reduce the occurrence of HIRRT, but less is known about interventions to prevent HIRRT in critically ill patients receiving RRT for acute kidney injury (AKI). We sought to evaluate the effectiveness of RRT-related interventions for reducing HIRRT in such patients across RRT modalities.MethodsA systematic review of publications was undertaken using MEDLINE, MEDLINE in Process, EMBASE, and Cochrane's Central Registry for Randomized Controlled Trials (RCTs). Studies that assessed any intervention's effect on HIRRT (the primary outcome) in critically ill patients with AKI were included. HIRRT was variably defined according to each study's definition. Two reviewers independently screened abstracts, identified articles for inclusion, extracted data, and evaluated study quality using validated assessment tools.ResultsFive RCTs and four observational studies were included (n = 9; 623 patients in total). Studies were small, and the quality was mostly low. Interventions included dialysate sodium modeling (n = 3), ultrafiltration profiling (n = 2), blood volume (n = 2) and temperature control (n = 3), duration of RRT (n = 1), and slow blood flow rate at initiation (n = 1). Some studies applied more than one strategy simultaneously (n = 5). Interventions shown to reduce HIRRT from three studies (two RCTs and one observational study) included higher dialysate sodium concentration, lower dialysate temperature, variable ultrafiltration rates, or a combination of strategies. Interventions not found to have an effect included blood volume and temperature control, extended duration of intermittent RRT, and slower blood flow rates during continuous RRT initiation. How HIRRT was defined and its frequency of occurrence varied widely across studies, including those involving the same RRT modality. Pooled analysis was not possible due to study heterogeneity.ConclusionsSmall clinical studies suggest that higher dialysate sodium, lower temperature, individualized ultrafiltration rates, or a combination of these strategies may reduce the risk of HIRRT. Overall, for all RRT modalities, there is a paucity of high-quality data regarding interventions to reduce the occurrence of HIRRT in critically ill patients.

Project description:Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients' AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.

Dataset Information

Bedside prediction of intradialytic hemodynamic instability in critically ill patients: the SOCRATE study.

Publications

Bedside prediction of intradialytic hemodynamic instability in critically ill patients: the SOCRATE study.

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