Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection

Abstract

Background: The concomitant occurrence of the symptoms intravascular hypovolemia, peripheral edema and hemodynamic instability is typically named Capillary Leak Syndrome (CLS) and often occurs in surgical critical ill patients. However, neither a unitary definition nor standardized diagnostic criteria exist so far. We aimed to investigate common characteristics of this phenomenon with a subsequent scoring system, determining whether CLS contributes to mortality.

Methods: We conducted this single-center, observational, multidisciplinary, prospective trial in two separately run surgical ICUs of a tertiary academic medical center. 200 surgical patients admitted to the ICU and 30 healthy volunteers were included. Patients were clinically diagnosed as CLS or No-CLS group (each N = 100) according to the grade of edema, intravascular hypovolemia, hemodynamic instability, and positive fluid balance by two independent attending physicians with > 10 years of experience in ICU. We performed daily measurements with non-invasive body impedance electrical analysis, ultrasound and analysis of serum biomarkers to generate objective diagnostic criteria. Receiver operating characteristics were used, while we developed machine learning models to increase diagnostic specifications for our scoring model.

Results: The 30-day mortility was increased among CLS patients (12 vs. 1%, P = 0.002), while showing higher SOFA-scores. Extracellular water was increased in patients with CLS with higher echogenicity of subcutaneous tissue [29(24-31) vs. 19(16-21), P < 0.001]. Biomarkers showed characteristic alterations, especially with an increased angiopoietin-2 concentration in CLS [9.9(6.2-17.3) vs. 3.7(2.6-5.6)ng/mL, P < 0.001]. We developed a score using seven parameters (echogenicity, SOFA-score, angiopoietin-2, syndecan-1, ICAM-1, lactate and interleukin-6). A Random Forest prediction model boosted its diagnostic characteristics (AUC 0.963, P < 0.001), while a two-parameter decision tree model showed good specifications (AUC 0.865).

Conclusions: Diagnosis of CLS in critically ill patients is feasible by objective, non-invasive parameters using the CLS-Score. A simplified two-parameter diagnostic approach can enhance clinical utility. CLS contributes to mortality and should, therefore, classified as an independent entity.

Trial registration: German Clinical Trials Registry (DRKS No. 00012713), Date of registration 10/05/2017, www.drks.de.

Keywords: Capillary leak syndrome; Critical care; Endothelial permeability; Fluid balance; Sepsis.

Fig. 1

Study flow chart

Fig. 2

Study parameters in the No-CLS and CLS groups on ICU days 1 and 2 (Box plots with 5–95% whiskers)

Fig. 3

A Multivariate binary logistic regression for independent risk factor analysis. B, C. Evaluation of parameters from the multivariate analysis using Receiver Operating Characteristics (patient data from ICU day 1). D, E. Development of a 7-item scoring system (values > 75th percentile of cohort led to one point in score; UE  upper extremity)

Fig. 4

Machine learning based prediction model based on ICU data from day 1. A, B show a decision tree model which was computed from the 7-parameter input variables showing a reliable differentiation using only the two parameters of echogenicity and angiopoietin-2 level. C shows the comparison of diagnostic specifications of different machine-learning models computing the 7-parameter approach (echogenicity, SOFA-Score, angiopoietin-2, syndecan-1, ICAM-1, lactate, IL-6)