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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.

SUBMITTER: Wollborn J

PROVIDER: S-EPMC8674404 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesAmylase concentration in respiratory secretions was reported to be a potentially useful marker for aspiration and pneumonia. The aim of this study was to determine accuracy of α-amylase in diagnosing microaspiration in critically ill patients.MethodsRetrospective analysis of prospectively collected data collected in a medical ICU. All patients requiring mechanical ventilation for at least 48 h, and included in a previous randomized controlled trial were eligible for this study, provided that at least one tracheal aspirate was available for α-amylase measurement. As part of the initial trial, pepsin was quantitatively measured in all tracheal aspirates during a 48-h period. All tracheal aspirates were frozen, allowing subsequent measurement of α-amylase for the purpose of the current study. Microaspiration was defined as the presence of at least one positive tracheal aspirate for pepsin (>200 ng.mL-1). Abundant microaspiration was defined as the presence of pepsin at significant level in >74% of tracheal aspirates.ResultsAmylase was measured in 1055 tracheal aspirates, collected from 109 patients. Using mean α-amylase level per patient, accuracy of α-amylase in diagnosing microaspiration was moderate (area under the receiver operator curve 0.72±0.05 [95%CI 0.61-0.83], for an α-amylase value of 1685 UI.L-1). However, when α-amylase levels, coming from all samples, were taken into account, area under the receiver operator curve was 0.56±0.05 [0.53-0.60]. Mean α-amylase level, and percentage of tracheal aspirates positive for α-amylase were significantly higher in patients with microaspiration, and in patients with abundant microaspiration compared with those with no microaspiration; and similar in patients with microaspiration compared with those with abundant microaspiration. α-amylase and pepsin were significantly correlated (r2 = 0.305, p = 0.001).ConclusionAccuracy of mean α-amylase in diagnosing microaspiration is moderate. Further, when all α-amylase levels were taken into account, α-amylase was inaccurate in diagnosing microaspiration, compared with pepsin.

Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyteÂ® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyteÂ® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyteÂ® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyteÂ® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyteÂ® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyteÂ® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples Retrospective, mutli-site sutdy using retrospective physician adjudication as a comparator

Project description:Background and aimsRecently, glucose variability (GV) has been reported as an independent risk factor for mortality in non-diabetic critically ill patients. However, GV is not incorporated in any severity scoring system for critically ill patients currently. The aim of this study was to establish and validate a modified Simplified Acute Physiology Score II scoring system (SAPS II), integrated with GV parameters and named GV-SAPS II, specifically for non-diabetic critically ill patients to predict short-term and long-term mortality.MethodsTraining and validation cohorts were exacted from the Multiparameter Intelligent Monitoring in Intensive Care database III version 1.3 (MIMIC-III v1.3). The GV-SAPS II score was constructed by Cox proportional hazard regression analysis and compared with the original SAPS II, Sepsis-related Organ Failure Assessment Score (SOFA) and Elixhauser scoring systems using area under the curve of the receiver operator characteristic (auROC) curve.Results4,895 and 5,048 eligible individuals were included in the training and validation cohorts, respectively. The GV-SAPS II score was established with four independent risk factors, including hyperglycemia, hypoglycemia, standard deviation of blood glucose levels (GluSD), and SAPS II score. In the validation cohort, the auROC values of the new scoring system were 0.824 (95% CI: 0.813-0.834, P< 0.001) and 0.738 (95% CI: 0.725-0.750, P< 0.001), respectively for 30 days and 9 months, which were significantly higher than other models used in our study (all P < 0.001). Moreover, Kaplan-Meier plots demonstrated significantly worse outcomes in higher GV-SAPS II score groups both for 30-day and 9-month mortality endpoints (all P< 0.001).ConclusionsWe established and validated a modified prognostic scoring system that integrated glucose variability for non-diabetic critically ill patients, named GV-SAPS II. It demonstrated a superior prognostic capability and may be an optimal scoring system for prognostic evaluation in this patient group.

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

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