Value of Central Venous Pressure Monitoring in the Patients with Sepsis-Associated Acute Kidney Injury

Abstract

Background. Although the measurement of central venous pressure (CVP) is a common clinical tool, the role of CVP monitoring in the outcome of sepsis is controversial because threshold values of CVP are uncertain, and there are only limited data on short-term survival of patients with septic acute kidney injury (AKI). Methods. This retrospective cohort study was based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (source of the training dataset). Multivariate regression analysis was performed to clarify the relation between CVP measurement and clinical outcomes, and a univariate regression model after propensity score matching was utilized to validate our findings. A mortality prediction model for septic AKI and a risk stratification scoring approach were developed, and the emergency intensive care unit (eICU) database was used for external validation. Results. Of the 9170 patients in the training set, 2446 (26.7%) underwent CVP measurement. No significant association was found between CVP monitoring and 28-day mortality among patients with septic AKI (odds ratio=0.479; 95% confidence interval 0.213-1.076, P=0.075), even after adjustments (propensity score matching; P=0.178). Length of ICU stay and hospital stay was markedly reduced in patients undergoing CVP measurement within 3 hours (median 6.2 and 10.9 days, respectively, P<0.001). The addition of the mean perfusion pressure initial, CVP, and the magnitude of the CVP change within 48 hours to the model significantly increased model discrimination (area under the receiver operating characteristic curve: 0.867 and 0.780, respectively, P<0.001). Conclusions. These findings suggest that CVP measurement alone has little effect on the outcome of septic AKI. Nonetheless, initial CVP levels and the dynamic changes in CVP within the first 48 hours after ICU admission and the mean perfusion pressure initial can improve the accuracy of outcome prediction models.