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Extrasystoles for fluid responsiveness prediction in critically ill patients.

ABSTRACT: Background:Fluid responsiveness prediction with continuously available monitoring is an unsettled matter for the vast majority of critically ill patients, and development of new and reliable methods is desired. We hypothesized that the post-ectopic beat, which is associated with increased preload, could be analyzed in relation to preceding sinus beats and that the change in cardiac performance (e.g., systolic blood pressure) at the post-ectopic beat could predict fluid responsiveness. Methods:Critically ill patients were observed when scheduled for a 500-ml volume expansion. The 30-min ECG prior to volume expansion was analyzed for the occurrence of extrasystoles. Classification variables were defined as the change in a variable (e.g., systolic blood pressure or pre-ejection period) from the median of ten preceding sinus beats to extrasystolic post-ectopic beat. A stroke volume increase >?10% following volume expansion defined fluid responsiveness. Results:Twenty-six patients were included. The change in systolic blood pressure predicted fluid responsiveness with receiver operating characteristic (ROC) area 0.79 (CI [0.52:1.00]), specificity 100%, sensitivity 67%, positive predictive value 100%, and negative predictive value 91% (threshold: 5%). The change in pre-ejection period predicted fluid responsiveness with ROC area 0.74 (CI [0.53:0.94]), specificity 78%, sensitivity 67%, positive predictive value 50%, and negative predictive value 88% (threshold 7.5 ms). Conclusions:Based on standard critical care monitoring, analysis of the extrasystolic post-ectopic beat predicts fluid responsiveness in critical care patients with good accuracy. The presented results are considered preliminary proof-of-concept results, and further validation is needed to confirm these preliminary findings.

SUBMITTER: Vistisen ST

PROVIDER: S-EPMC6103985 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Extrasystoles for fluid responsiveness prediction in critically ill patients.

Vistisen Simon Tilma ST Krog Martin Buhl MB Elkmann Thomas T Vallentin Mikael Fink MF Scheeren Thomas W L TWL Sølling Christoffer C

Journal of intensive care 20180822

<h4>Background</h4>Fluid responsiveness prediction with continuously available monitoring is an unsettled matter for the vast majority of critically ill patients, and development of new and reliable methods is desired. We hypothesized that the post-ectopic beat, which is associated with increased preload, could be analyzed in relation to preceding sinus beats and that the change in cardiac performance (e.g., systolic blood pressure) at the post-ectopic beat could predict fluid responsiveness.<h4 ...[more]

PMID: 30151211

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Project description:IntroductionDynamic predictors of fluid responsiveness have shown good performance in mechanically ventilated patients at tidal volumes (Vt) > 8 mL kg-1. Nevertheless, most critically ill conditions demand lower Vt. We sought to evaluate the operative performance of several predictors of fluid responsiveness at Vt ≤ 8 mL kg-1 by using meta-regression and subgroup analyses.MethodsA sensitive search was conducted in the Embase and MEDLINE databases. We searched for studies prospectively assessing the operative performance of pulse pressure variation (PPV), stroke volume variation (SVV), end-expiratory occlusion test (EEOT), passive leg raising (PLR), inferior vena cava respiratory variability (Δ-IVC), mini-fluid challenge (m-FC), and tidal volume challenge (VtC), to predict fluid responsiveness in adult patients mechanically ventilated at Vt ≤ 8 ml kg-1, without respiratory effort and arrhythmias, published between 1999 and 2020. Operative performance was assessed using hierarchical and bivariate analyses, while subgroup analysis was used to evaluate variations in their operative performance and sources of heterogeneity. A sensitivity analysis based on the methodological quality of the studies included (QUADAS-2) was also performed.ResultsA total of 33 studies involving 1,352 patients were included for analysis. Areas under the curve (AUC) values for predictors of fluid responsiveness were: for PPV = 0.82, Δ-IVC = 0.86, SVV = 0.90, m-FC = 0.84, PLR = 0.84, EEOT = 0.92, and VtC = 0.92. According to subgroup analyses, variations in methods to measure cardiac output and in turn, to classify patients as responders or non-responders significantly influence the performance of PPV and SVV (p < 0.05). Operative performance of PPV was also significantly affected by the compliance of the respiratory system (p = 0.05), while type of patient (p < 0.01) and thresholds used to determine responsiveness significantly affected the predictability of SVV (p = 0.05). Similarly, volume of fluids infused to determine variation in cardiac output, significantly affected the performance of SVV (p = 0.01) and PLR (p < 0.01). Sensitivity analysis showed no variations in operative performance of PPV (p = 0.39), SVV (p = 0.23) and EEOT (p = 0.15).ConclusionMost predictors of fluid responsiveness reliably predict the response of cardiac output to volume expansion in adult patients mechanically ventilated at tidal volumes ≤ 8 ml kg-1. Nevertheless, technical and clinical variables might clearly influence on their operative performance.

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.

Project description:BackgroundAlthough net ultrafiltration (UFNET) is frequently used for treatment of fluid overload in critically ill patients with acute kidney injury, the optimal intensity of UFNET is unclear. Among critically ill patients with fluid overload receiving renal replacement therapy (RRT), we examined the association between UFNET intensity and risk-adjusted 1-year mortality.MethodsWe selected patients with fluid overload ≥ 5% of body weight prior to initiation of RRT from a large academic medical center ICU dataset. UFNET intensity was calculated as the net volume of fluid ultrafiltered per day from initiation of either continuous or intermittent RRT until the end of ICU stay adjusted for patient hospital admission body weight. We stratified UFNET as low (≤ 20 ml/kg/day), moderate (> 20 to ≤ 25 ml/kg/day) or high (> 25 ml/kg/day) intensity. We adjusted for age, sex, body mass index, race, surgery, baseline estimated glomerular filtration rate, oliguria, first RRT modality, pre-RRT fluid balance, duration of RRT, time to RRT initiation from ICU admission, APACHE III score, mechanical ventilation use, suspected sepsis, mean arterial pressure on day 1 of RRT, cumulative fluid balance during RRT and cumulative vasopressor dose during RRT. We fitted logistic regression for 1-year mortality, Gray's survival model and propensity matching to account for indication bias.ResultsOf 1075 patients, the distribution of high, moderate and low-intensity UFNET groups was 40.4%, 15.2% and 44.2% and 1-year mortality was 59.4% vs 60.2% vs 69.7%, respectively (p = 0.003). Using logistic regression, high-intensity compared with low-intensity UFNET was associated with lower mortality (adjusted odds ratio 0.61, 95% CI 0.41-0.93, p = 0.02). Using Gray's model, high UFNET was associated with decreased mortality up to 39 days after ICU admission (adjusted hazard ratio range 0.50-0.73). After combining low and moderate-intensity UFNET groups (n = 258) and propensity matching with the high-intensity group (n = 258), UFNET intensity > 25 ml/kg/day compared with ≤ 25 ml/kg/day was associated with lower mortality (57% vs 67.8%, p = 0.01). Findings were robust to several sensitivity analyses.ConclusionsAmong critically ill patients with ≥ 5% fluid overload and receiving RRT, UFNET intensity > 25 ml/kg/day compared with ≤ 20 ml/kg/day was associated with lower 1-year risk-adjusted mortality. Whether tolerating intensive UFNET is just a marker for recovery or a mediator requires further research.

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Extrasystoles for fluid responsiveness prediction in critically ill patients.

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Extrasystoles for fluid responsiveness prediction in critically ill patients.

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