PurposeWith regard to large inter-individual variability of height, body weight (BW), and age, several hemodynamic parameters are adjusted for biometric data. This also applies to extravascular lung water (EVLW), which traditionally was indexed to actual BW (BW-act) resulting in EVLW-index (EVLWI; i.e., EVLWI-act). Since indexation to BW-act might inappropriately diminish EVLWI-act in obese patients, the indexation has been changed to predicted BW (BW-pred) resulting in EVLWI-pred. BW-pred is a weight estimation formula calculated from height and gender that has not been derived from population-based data. The aim of the study was to investigate the independent association of biometric data with EVLW.MethodsWe analyzed a hemodynamic monitoring database including 3,691 transpulmonary thermodilution-derived EVLW measurements (234 consecutive patients; intensive care unit of a university hospital). We performed univariate and multivariate analyses regarding the association of biometric data with the first EVLW measurement and the mean EVLW value of each patient.ResultsIn univariate analysis, the first EVLW significantly correlated with height (r = 0.254; p < 0.001), but neither with age nor BW-act. Similar findings were made in the analysis of the patients’ EVLW means of all measurements (“one point per patient”). In multivariate analysis (primary endpoint), including BW-act, height, age, and gender, only height was independently associated with EVLW, with each centimeter of height increasing the first measurement of EVLW by 6.882 mL (p < 0.001) and mean EVLW by 6.727 mL (p < 0.001).ConclusionsHeight is the only biometric parameter independently associated with the first and mean EVLW. In adult patients, EVLW should be indexed to height.

Sinus rhythm (SR) and controlled mechanical ventilation (CV) are mandatory for the applicability of respiratory changes of the arterial curve such as stroke volume variation (SVV) to predict fluid-responsiveness. Furthermore, several secondary limitations including tidal volumes <8 mL/kg and SVV-values within the "gray zone" of 9-13% impair prediction of fluid-responsiveness by SVV. Therefore, we investigated the prevalence of these four conditions in general ICU-patients. This longitudinal observational study analyzed a prospectively maintained haemodynamic database including 4801 transpulmonary thermodilution and pulse contour analysis measurements of 278 patients (APACHE-II 21.0 ± 7.4). The main underlying diseases were cirrhosis (32%), sepsis (28%), and ARDS (17%). The prevalence of SR and CV was only 19.4% (54/278) in the first measurements (primary endpoint), 18.8% (902/4801) in all measurements and 26.5% (9/34) in measurements with MAP < 65 mmHg and CI < 2.5 L/min/m and vasopressor therapy. In 69.1% (192/278) of the first measurements and in 65.9% (3165/4801) of all measurements the patients had SR but did not have CV. In 1.8% (5/278) of the first measurements and in 2.5% (119/4801) of all measurements the patients had CV but lacked SR. In 9.7% (27/278) of the first measurements and in 12.8% (615/4801) of all measurements the patients did neither have SR nor CV. Only 20 of 278 (7.2%) of the first measurements and 8.2% of all measurements fulfilled both major criteria (CV, SR) and both minor criteria for the applicability of SVV. The applicability of SVV in ICU-patients is limited due to the absence of mandatory criteria during the majority of measurements.

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Global end-diastolic volume (GEDV) has been indexed to body surface area (BSA). However, data validating this indexation of GEDV are scarce. Furthermore, it has been suggested to index GEDV to "predicted BSA" based on predicted body weight. Therefore, we aimed to identify biometric parameters independently associated with GEDV. We analyzed a database including 3812 TPTD measurements in 234 patients treated in the ICU of a German university hospital. GEDVI indexed to actual BSA was significantly lower than GEDVI indexed to predicted BSA (748 ± 179 vs. 804 ± 190 mL/m; p < 0.001). GEDV was independently associated with older age, male sex, height, and actual body weight. In a regression model for the estimation of GEDV, age and height were the most important parameters: Each year in age and each cm in height increased GEDV by 9 and 15 mL, respectively. In addition to height and weight also age and sex should be considered for indexation of GEDV.