Study Design
We conducted a retrospective cohort study based on a large US-based critical database named Medical Information Mart for Intensive Care III (MIMIC-III) [18]. The MIMIC-III (v1.4) contains comprehensive and high-quality data of well-defined and characterized ICU patients admitted to ICUs at the Beth Israel Deaconess Medical Center between 2001 and 2012. One author (HC) obtained the access of database and was responsible for the data extraction (certification number 27252652). Our study was complied with the Reporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement [19].
Selection of participants
Patients in the MIMIC-III fulfilled the definition of sepsis were eligible for inclusion. The diagnoses of sepsis were consistent with the third sepsis definition [20], briefly, patients with documented or suspected infection and an acute change in total SOFA score ≥ 2 points. Infection was identified from the ICD-9 code in the MIMIC-III. We excluded patients who were younger than 18 years or stayed less than 24 h in the ICU. Additionally, we only analyzed the first ICU stay for patients who were admitted to the ICU more than once. Included patients in whom initial CVP measurements were completed within 24 h after ICU admission were divided into CVP group, and the rest of patients making up the no CVP group.
Variable extraction
The primary exposure was whether the patients had measurements of CVP, time to initial CVP measurement and the initial level of CVP were collected. Baseline characteristics within the first 24 h after ICU admission were collected using structured query language (SQL): age, gender, weight, ICU type, severity at admission measured by Sequential Organ Failure Assessment (SOFA) score, Simplified Acute Physiology Score II (SAPS II), and Elixhauser comorbidity score. Use of mechanical ventilation, use of renal replacement therapy (RRT), and administration of vasopressors. Vital signs including mean arterial pressure (MAP), heart rate, temperature (°C) and respiratory rate. Laboratory variables of white blood cell (WBC) count, hemoglobin, platelet count, lactate, pH, partial pressure of oxygen (PO2) and partial pressure of carbon dioxide (PCO2) were measured during the first 24 h of ICU stay. If a variable was recorded more than once in the first 24 h, we used the value related to the greatest severity of illness. The incidence of acute kidney injury (AKI) was also extracted, the definition of AKI was according to the Kidney Disease Improving Global Outcomes (KDGIO) criteria.
Comorbidities including congestive heart failure (CHF), atrial fibrillation (AFIB), chronic renal disease, liver disease, chronic obstructive pulmonary disease (COPD), stroke, and malignant tumor were also collected for analysis based on the recorded ICD-9 codes in MIMIC-III.
Outcomes
The primary outcome in present study was 28-day mortality. Secondary outcomes included in-hospital and 1-year morality; the incidence of AKI within 7 days after ICU admission; volume (L) of intravenous fluid (IVF) during the first, second and third day in the ICU; the number of ventilator-free and vasopressor-free days within 28 days after ICU admission; and reduction in serum lactate (calculated as the difference between the maximum lactate level on day 1 and day 3).
Statistical analysis
Values were presented as mean (standard deviation) or median [interquartile range (IQR)] for continuous variables as appropriate, and categorical variables as total number and percentage. Comparisons between groups were made using X2 test or Fisher’s exact test for categorical variables and student’s t test, or Mann-Whitney U test for continuous variables as appropriate.
Multivariate regression was selected to characterize the relationship between CVP measurement and the primary outcome. Baseline variables that were considered clinically relevant or that showed a univariate relationship with the outcome (p < 0.10) were entered into a multivariate logistic regression model as covariates, including age, gender, weight, admission period, severity scores, use of mechanical ventilation, use of RRT, use of vasopressors, comorbidities, AKI, vital signs and initial lactate level. To avoid bias induced by missing data, the analysis of the primary outcome was duplicated after multiple imputation.
Propensity score matching (PSM) and propensity score based inverse probability of treatment weighing (IPTW) were also used for adjusting the covariates to ensure the robustness of our findings [21, 22]. A multivariate logistic regression model was used to estimate the patient’s propensity scores for CVP measurement. A 1:1 nearest neighbor matching was applied with a caliper width of 0.05 in present study. An IPTW model was created using the estimated propensity scores as weights. The standardized mean differences (SMDs) were calculated to evaluate the effectiveness of the PSM and IPTW. A logistic regression was then performed on the matched cohort and weighted cohort, separately.
CMA is a method for separating the total effect of a treatment into direct and indirect effects. The indirect effect on the outcome is mediated via a mediator. The analysis reports consist of the average causal mediation effect (ACME), average direct effect (ADE), and total effect. To explore whether the effect of CVP measurement on the primary outcome is proportionally mediated by the reduction of serum lactate, we used CMA to characterize the causality relationship in our retrospective study.
All statistical analyses were performed using the RStudio (version 1.2.5019), and p < 0.05 was considered statistically significant.