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. 2023 Aug 8;13(1):70.
doi: 10.1186/s13613-023-01165-2.

Angiopoietin-2 is associated with capillary leak and predicts complications after cardiac surgery

Jakob Wollborn  1   2   3 , Zilu Zhang  4 , Julie Gaa  5   6 , Moritz Gentner  5   6 , Christian Hausmann  5   6 , Felix Saenger  5   6 , Karina Weise  5   6 , Samuel Justice  7 , Jean-Luca Funk  5   6 , Hans Felix Staehle  5   6 , Marie Thomas  8   6 , Raphael R Bruno  9 , Babak Saravi  7 , Jan O Friess  10 , Markus Marx  5   6 , Hartmut Buerkle  5   6 , Georg Trummer  8   6 , Jochen D Muehlschlegel  7 , Daniel Reker  4 , Ulrich Goebel #  5   11   6 , Felix Ulbrich #  5   6
Affiliations

Angiopoietin-2 is associated with capillary leak and predicts complications after cardiac surgery

Jakob Wollborn et al. Ann Intensive Care. .

Abstract

Background: Patients undergoing cardiac surgery are prone to numerous complications. Increased vascular permeability may be associated with morbidity and mortality due to hemodynamic instability, fluid overload, and edema formation. We hypothesized that markers of endothelial injury and inflammation are associated with capillary leak, ultimately increasing the risk of postoperative complications.

Methods: In this prospective, observational, multidisciplinary cohort study at our tertiary academic medical center, we recruited 405 cardiac surgery patients. Patients were assessed daily using body impedance electrical analysis, ultrasound, sublingual intravital microscopy, and analysis of serum biomarkers. Multivariable models, as well as machine learning, were used to study the association of angiopoietin-2 with extracellular water as well as common complications after cardiac surgery.

Results: The majority of patients underwent coronary artery bypass grafting, valvular, or aortic surgeries. Across the groups, extracellular water increased postoperatively (20 ± 6 preoperatively to 29 ± 7L on postoperative day 2; P < 0.001). Concomitantly, the levels of the biomarker angiopoietin-2 rose, showing a strong correlation based on the time points of measurements (r = 0.959, P = 0.041). Inflammatory (IL-6, IL-8, CRP) and endothelial biomarkers (VE-Cadherin, syndecan-1, ICAM-1) suggestive of capillary leak were increased. After controlling for common risk factors of edema formation, we found that an increase of 1 ng/mL in angiopoietin-2 was associated with a 0.24L increase in extracellular water (P < 0.001). Angiopoietin-2 showed increased odds for the development of acute kidney injury (OR 1.095 [95% CI 1.032, 1.169]; P = 0.004) and was furthermore associated with delayed extubation, longer time in the ICU, and a higher chance of prolonged dependence on vasoactive medication. Machine learning predicted postoperative complications when capillary leak was added to standard risk factors.

Conclusions: Capillary leak and subsequent edema formation are relevant problems after cardiac surgery. Levels of angiopoietin-2 in combination with extracellular water show promising potential to predict postoperative complications after cardiac surgery.

Trial registration number: German Clinical Trials Registry (DRKS No. 00017057), Date of registration 05/04/2019, www.drks.de.

Keywords: Acute kidney injury; Angiopoietin-2; Capillary leak syndrome; Cardiac surgery; Critical care; Endothelial permeability; Fluid balance.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Fig. 1
Fig. 1
CONSORT study flow chart
Fig. 2
Fig. 2
Time course of study variables, grouped by surgical type, on edema, serum biomarkers, and sublingual microcirculation: A Extracellular water obtained from body impedance electrical analysis, B Phase angle obtained from BIA, C Echogenicity obtained from tissue ultrasound, D Angiopoietin-2 in serum, E Syndecan-1 in serum, F Interleukin-6 in serum, G Microvascular flow index obtained from sublingual microcirculation assessment, H Perfused vessel density obtained from sublingual microcirculation assessment (POD: postoperative day, FU7 = follow-up on day 7 post ICU discharge)
Fig. 3
Fig. 3
Correlation between biomarkers and extracellular water preoperatively (blue), postoperatively (orange), on POD1 (green) and POD2 (red) with the oversized diamonds showing the average for the respective time of measurement. The respective biomarker is represented on the x-axis and extracellular water is represented on the y-axis
Fig. 4
Fig. 4
Secondary analyses. A Uni- and multivariable logistic regression models for the association of Ang-2 and postoperative, acute kidney injury (AKI). In the multivariable model, common risk factors associated with AKI after cardiac surgery were controlled for (#vs. coronary artery bypass grafting). B Using Kaplan–Meier curves, time to extubation was significantly prolonged in patients showing high serum Ang-2 levels (P < 0.0001). C Discharge from the ICU occurred later in patients with high Ang-2 levels (P < 0.0001)
Fig. 5
Fig. 5
Machine learning models predicting complications based on common risk factors, and by adding a combination of Ang-2 and body impedance electrical analysis-derived measurements to the risk factors. A shows ROC-AUC of machine learning (ML) algorithms to predict acute kidney injury (AKI), low oxygenation index (P/F-ratio), dependence on vasoactive drugs, mortality, and postoperative dependence on ECMO from standard risk factors (grey curve). The red curves represent an augmented feature set, adding the phenotype of capillary leak to the standard risk factors (with Ang-2 and body impedance electrical analysis-derived measurements), thus showing a significant improvement in predicting the respective postoperative complication (blue dotted lines: random selection). B shows different cross-validation methods and their respective ROC-AUC to predict complications derived from ML. Models could maintain their robust performance in terms of ROC-AUC regardless of the splitting approaches used in various cross-validation strategies. Color from dark to light (counter-clockwise) in each segment: repeated train-test splitting validation, standard cross-validation, stratified cross-validation, leave-one-patient-out cross-validation, and leave-one-surgery-out cross-validation
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