The dose - response association between fluid overload and hospital mortality in critically ill patients: a multicenter, prospective, observational cohort study

DOI: https://doi.org/10.21203/rs.2.20792/v1

Abstract

Background

Fluid management is important for ensuring hemodynamic stability in critically ill patients but easily leads to fluid overload. However, the optimal fluid balance plot or range for critically ill patients is unknown. This study aimed to explore the dose-response relationship between fluid overload (FO) and hospital mortality in critically ill patients.

Methods

Data were derived from the China Critical Care Sepsis Trial (CCCST). Patients with sequential fluid data for the first 3 days of admission to the ICU were included. FO was expressed as the ratio of the cumulative fluid balance (L) and initial body weight (kg) at ICU admission as a percentage. Maximum fluid overload (MFO) was defined as the peak FO value during the first 3 days of ICU admission. We used logistic regression models with restricted cubic splines to assess the relationship between MFO and the risk of hospital mortality.

Results

In total, 3850 patients were included, 929 (24.1%) of whom died in hospital. For each 1% L/kg increase in the FO, the risk of hospital mortality increased by 4% (HR 1.04, 95% CI 1.03 - 1.05, P < 0.001). FO greater than 10% was associated with a 44% increased HR of hospital mortality compared with FO less than 5% (HR 1.44, 95% CI 1.27 - 1.67). Notably, we also found a non-linear dose-response association between MFO and hospital mortality.

Conclusions

Both higher and lower fluid balance were associated with an increased risk of hospital mortality. Further studies should explore this relationship and seek for the optimal fluid management strategies for critically ill patients.

Background

Similar to mechanical ventilation (MV), renal replacement therapy (RRT) and the use of vasopressors, fluid management is also an indispensable component of life support for critically ill patients. Optimal fluid resuscitation can ensure hemodynamic stability and improve tissue and organ perfusion[1], but ordinary fluid administration may frequently lead to fluid overload (FO)[2, 3]. FO is associated with a poor prognosis, including a prolonged length of hospital stay and duration of MV[4], and FO is also related to a higher mortality rate in critically ill patients[510].

A majority of studies have defined FO as a cumulative fluid excess of 10% of body weight and found that it is an independent risk factor for mortality that has been used to predict increased mortality or other adverse events[5, 1113]. A study on critically ill children showed that an FO greater than 5% was associated with a significantly increased risk of mortality and acute kidney injury (AKI)[2]. According to a study performed by the “Dose-Response Multicenter Investigation on Fluid Assessment (DoReMIFA)” group, any positive fluid balance is associated with an increased probability of death[14]. However, all of those studies proposed a linear association between FO and outcomes, as a higher percentage of FO was related to an increased risk of adverse outcomes. This raised the question of whether there is an optimal volume plot or range of fluid balance for ICU patients and the dose-response association between FO and outcomes in critically ill patients is still limited[15].

Using advantage of the very large, multicenter, prospective CCCST study, we mainly assessed the dose-response relationship of MFO and hospital mortality in the ICU patients, and try to examine the hypothesis of there might be an optimal range for fluid balance.

Methods

Study protocol and participants

This study used data from the CCCST, a prospective, multicenter, observational cohort study that investigated the epidemiology of sepsis in critically ill patients at 18 intensive care units (ICUs) of 16 tertiary hospitals covering seven geographical regions across China from 1 January 2014 to 31 August 2015. All eligible patients were 18 years or older, stayed in the ICU for more than 24 hours and were consecutively admitted to the ICUs. For the patients who were admitted to the ICU more than once, only the first ICU admission was considered. Patients who had missing related to fluid balance were excluded.

Data collection

Baseline data on demographic characteristics, the diagnosis at admission, comorbidities, clinical and laboratory values that were used to calculate the illness severity scores, such as the Acute Physiology and Chronic Health Evaluation II (APACHE II)[16] and the Sequential Organ Failure Assessment (SOFA) scores[17] were collected. Data on RRT, MV, the use of vasopressors, were continuously recorded for 7 days or until discharge from the ICU, whichever occurred earlier. Fluid balance was calculated daily. Dates of discharge from the ICU and the hospital were also documented.

The primary outcome was hospital mortality. The secondary outcomes included in ICU mortality and length of stay in the ICU and hospital.

Definition and calculation

Daily fluid balance (DFB) was calculated daily as the ratio of total fluid intake (liter, L) minus total fluid output (L) and body weight (kilograms, kg) at the initial ICU admission as a percentage during the first 3 days of ICU admission. Fluid intake included oral intake and intravenous fluid, and total fluid output included urine output, drain fluid, ultrafiltration fluid and estimated gastrointestinal losses. Insensible loss was not included in our study because it is difficult to assess. FO was reported as the ratio of accumulated fluid balance (liter, L) and body weight (kg) at initial ICU admission as a percentage. Maximum fluid overload (MFO) was defined as the peak value of FO during the first 5 days of ICU admission, and the time of maximum fluid overload (TMFO) was the interval between ICU admission and MFO. Shock was defined as a systolic blood pressure (SBP) < 90 mmHg or mean arterial pressure (MAP) < 70 mmHg or a SBP decrease ≥ 40 mmHg or less than two standard deviations below normal for age or uses any kind of vasopressors to maintain tissue perfusion. Sepsis was defined as life-threatening organ failure caused by infection within 48 hours upon admission to the ICU according to the “Surviving Sepsis Campaign: 2016”[18].

Management and data missing

Less than 10% of clinical and laboratory data used to calculate the illness severity scores were missing in the whole cohort and were assumed to be normal. Weight data were missing for 1.3% of patients, and a mean weight of 65 kg was inputted; fluid data were missing in less than 7.8% of patients, and were censored during the statistical analysis. No values for the outcomes were missing.

Ethics

The study protocol was approved by the ethics committees of Fuxing Hospital, Capital Medical University (approval notice number 2013FXHEC-KY018) and all other centers (Additional file 2). The Institutional Review Board specifically approved the informed consent waiver because of the anonymous and purely observational nature of this study.

Statistical analysis

The categorical variables are presented as numbers with percentages and the continuous variables as means ± standard deviations (SD) or medians with interquartile ranges (IQRs). We examined the distribution of baseline variables between the surviving patients and deceased patients using the χ2 test for categorical variables and the t-test or Wilcoxon sum of rank test for continuous variables. The difference in CFB between the surviving and deceased groups during the first 3 days of ICU admission is presented in a box plot. The patients were divided into 3 groups according to the MFO value: MFO < 5%, 5% ≤ MFO < 10% and FO ≥ 10%. A Kaplan-Meier analysis was used to separately predict the time to death in the hospital for the three MFO groups. Differences between the three groups were assessed using a log-rank test.

The association of MFO with hospital mortality was assessed using multivariate Cox proportional hazard regression models. MFO was entered as a continuous and categorical variable, respectively. Analyses were first performed using a crude model (model 1), followed by three multivariate regression models. Model 2 was adjusted for age, gender, and APACHE II and SOFA scores on admission. In model 3, we additionally adjusted for the main diagnosis upon admission to the ICU and comorbidities (respiratory, cardiovascular, hypertension, chronic renal dysfunction, tumor and no comorbidity). In model 4, we additionally adjusted for MV, RRT and the use of vasopressors. In models 2, 3 and 4, the center was included as a random effect. This multivariable Cox model were also used in the subgroups analysis, the interaction effect of the MFO by predefined subgroups was also tested in the model. The possible confounders considered were the same as those included in model 4. The results are presented in forest plots, and adjusted hazard ratios (HRs) with their 95% confidence intervals (CIs) were estimated.

Additionally, we further evaluated the dose-response associations between MFO and hospital mortality using logistic regression models with restricted cubic splines (RCS). An MFO of 0 was treated as a reference, and 4 knots for the spline were placed at the 5th, 35th, 65th and 95th percentiles of the MFO.

In sensitivity analysis, we explore the potential dose-response association of fluid overload in patients with or without shock on the risk of hospital mortality.

We also used a propensity score matching (PSM) method to control for potential confounders. The propensity score was assigned based on the presence or absence of an MFO ≥ 5% L/kg and estimated using a multivariate logistic regression model. A 1:1 nearest neighbor matching algorithm was applied using a caliper width of 0.01. The following variables were selected to generate the propensity score: age, gender, APACHE II score, SOFA score at admission, main diagnosis upon admission to the ICU, comorbidities (respiratory, cardiovascular, hypertension, chronic renal dysfunction, tumor and no comorbidity), MV, RRT and use of the vasopressors.

A two-sided p value < 0.05 was considered statistically significant. All analyses were conducted with IBM SPSS Statistics software, version 25.0 for Windows (IBM, Armonk, NY, USA) and Stata statistical software (version 15, Stata Corp LP, College Station, TX, USA).

Results

Clinical characteristics of the subjects

Among the 4910 participants, 3850 for whom the first 3 days of sequential fluid data upon admission to the ICU were available were included in this study (Fig. 1). The mean age of the study subjects was 61.8 (18.1) years with 2501 (65.0%) were male, and 929 (24.1%) died in hospital. Among the 3850 patients, 1882 (48.9%) presented with an MFO less than 5% L/kg, 1030 (26.8%) with an MFO less than 10% L/kg and 938 (24.3%) with an MFO equal to or greater than 10% L/kg. Compared with the patients with a MFO less than 5% (L/kg), the other two groups of patients were more often males with higher illness severity scores and were likely to be diagnosed with sepsis, trauma, and gastrointestinal conditions upon admission to the ICU. They also needed more MV (64.3% vs 74.6% vs 84.0%, p < 0.001), RRT (15.7% vs 18.1% vs 25.1%, p < 0.001), and vasopressors during the first 7 days of the ICU stay (42.0% vs 50.2 vs 54.6%, p < 0.001). These patients also had longer ICU and hospital stays (Table 1 and Additional file 1: Table S1).

Table 1
Characteristics and outcomes of the patients stratified according to the percentage of maximum fluid overload
Characteristics
 
Percentage of MFO, L/kg
P value
All patients (n = 3850)
MFO < 5%
(n = 1882)
5% ≤ MFO < 10%
(n = 1030)
MFO ≥ 10%
(n = 938)
Age in years, mean ± SD
61.8 ± 18.1
61.0 ± 18.2
62.3 ± 18.2
62.8 ± 17.9
0.023
Sex, male, n (%)
2501 (65.0)
1201 (63.8)
693 (67.3)
607 (64.7)
0.170
Illness severity scores, median (IQR)
         
APACHE II
17.0 (12.0–23.0)
16.0 (11.0–22.0)
18.0 (12.0–23.0)
20.0 (14.0–26.0)
< 0.001
SOFA
8.5 (5.5–11.3)
7.0 (4.0–10.0)
8.5 (5.0–11.3)
10.0 (7.0–13.0)
< 0.001
Main diagnosis, n (%)
       
< 0.001
Sepsis
1258 (32.7)
510 (27.1)
359 (34.9)
389 (41.5)
 
Respiratory disease
707 (18.4)
376 (20.0)
195 (18.9)
136 (14.5)
 
Cardiovascular disease
299 (7.8)
227 (12.1)
48 (4.7)
24 (2.6)
 
Neurological disease
239 (6.2)
136 (7.2)
72 (7.0)
31 (3.3)
 
Trauma
240 (6.2)
98 (5.2)
66 (6.4)
76 (8.1)
 
Postoperative care
558 (14.5)
269 (14.3)
150 (14.6)
139 (14.8)
 
Gastrointestinal disease
300 (7.8)
120 (6.4)
81 (7.9)
99 (10.6)
 
Others
249 (6.6)
146 (7.8)
59 (5.7)
44 (4.6)
 
Comorbidities, n (%)
         
Respiratory disease
290 (7.5)
126 (6.7)
88 (8.5)
76 (8.1)
0.146
Cardiovascular disease
663 (17.2)
334 (17.7)
171 (16.6)
158 (16.8)
0.692
Hypertension
1321 (34.3)
680 (36.1)
333 (32.3)
308 (32.8)
0.065
Diabetes mellitus
716 (18.6)
345 (18.3)
204 (19.8)
167 (17.8)
0.479
Chronic renal dysfunction
245 (6.4)
129 (6.9)
59 (5.7)
57 (6.1)
0.452
Malignant tumor
373 (9.7)
174 (9.2)
105 (10.2)
94 (10.0)
0.656
Cirrhosis
106 (2.8)
31 (1.6)
35 (3.4)
40 (4.3)
< 0.001
None
1145 (29.7)
577 (30.7)
305 (29.6)
263 (28.0)
0.356
Mechanical ventilation, n (%)
2766 (71.8)
1210 (64.3)
768 (74.6)
788 (84.0)
< 0.001
Renal replacement therapy, n (%)
716 (18.6)
295 (15.7)
186 (18.1)
235 (25.1)
< 0.001
Vasopressors, n (%)
1820 (47.3)
791 (42.0)
517 (50.2)
512 (54.6)
< 0.001
Mortality, n (%)
         
In hospital
929 (24.1)
330 (17.5)
266 (25.8)
333 (35.5)
< 0.001
In ICU
812 (21.1)
293 (15.6)
223 (21.7)
296 (31.6)
< 0.001
Length of stay, days (IQR)
         
ICU
8.0 (4.0–15.0)
6.5 (4.0–12.5)
8.0 (4.5–15.0)
10.0 (67.0–17.0)
< 0.001
Hospital
19.0 (11.0–28.0)
18.0 (11.0–27.0)
19.0 (12.0–29.0)
19.0 (13.0–30.0)
< 0.001
SD, standard deviation; IQR, interquartile range; APACHE II, Acute Physiology and Chronic Health Evaluation II; SOFA, Sequential Organ Failure Assessment; ICU, intensive care unit; MFO, maximum fluid overload.

Association between FO and hospital mortality

A progressive CFB was observed in both surviving and deceased patients since their ICU admission, and the two groups showed significantly different degrees of CFB at all time points (Additional file 1: Fig S1). The MFO values were 6.5% L/kg and 3.4% L/kg in deceased and surviving patients, respectively (Additional file 1: Fig S1). With the increase in the MFO, in hospital mortality increased from 17.5–35.5%, and in ICU mortality increased from 15.6–31.6% (Table 1). The Kaplan-Meier analysis including the first 28 days of hospital stays revealed a significant survival benefit for patients with an MFO less than 5% L/kg (p < 0.001); the patients with the highest percentage of MFO had the lowest survival rate (Additional file 1: Fig S2).

In the multivariate Cox regression analysis of the MFO, a 1.4-fold increase in the risk of in hospital mortality was observed in the group with an MFO > 10% L/kg compared to the group with an MFO < 5% L/kg (HR 1.44, 95% CI: 1.25–1.67, p < 0.001, Table 2, model 4), after adjusting for potential confounders. When the MFO was included as a continuous variable, the MFO was also significantly associated with hospital mortality (HR 1.04, 95% CI 1.03–1.05, p < 0.001), regardless of potential confounders (Table 2, model 4), indicating that for each 1% L/kg increase in the MFO, the risk of in hospital mortality increased by 4%.

Table 2
Results of the Cox proportional hazard regression analysis of the risk of in hospital mortality
 
MFO*
 
MFO stratified by percentage
HR (95% CI)
P value
MFO < 5% (n = 1475)
Reference
5% ≤ MFO < 10% (n = 773)
HR (95% CI)
MFO ≥ 10% (n = 596)
HR (95% CI)
Model 1
1.06 (1.04–1.08)
< 0.001
 
1.000
1.38 (1.14–1.62)
2.11 (1.86–2.42)
Model 2
1.05 (1.03–1.07)
< 0.001
 
1.000
1.36 (1.15–1.61)
1.81 (1.56–2.11)
Model 3
1.04 (1.01–1.06)
< 0.001
 
1.000
1.30 (1.07–1.48)
1.63 (1.37–1.93)
Model 4
1.04 (1.03–1.05)
< 0.001
 
1.000
1.27 (1.10–1.52)
1.44 (1.25–1.67)
*MFO was entered as a continuous variable per 1% (L/kg) increase. Model 1, crude HR. Model 2, adjusted for age, sex, and APACHE II score. Model 3, additionally adjusted for the main diagnosis and comorbidities. Model 4, additionally adjusted for time of maximum fluid overload, mechanical ventilation, renal replacement therapy, and use of vasopressors. MFO, maximum fluid overload.

Dose-response association between the MFO and hospital mortality

The MFO during the first 3 days of ICU admission exhibited a skewed distribution with a median of 3.9% (IQR 1.3–8.2%) L/kg (Fig. 2). Using a multivariate logistic regression model with a RCS, We observed non-linear association between MFO and hospital mortality, and patients with an MFO of 2.4% L/kg had the lowest risk of hospital mortality. Higher MFO values were significantly associated with an increased risk of in hospital mortality, and patients with an MFO less than 0% exhibited a slightly increased risk of hospital mortality (Fig. 3A). A similar curve was observed when we explore the potential effects of MFO on the risk of hospital mortality in patients with or without shock in sensitivity analysis (Fig. 4), and found the MFO greater than 9.6% L/kg was an independent risk in patients with shock (Fig. 4A), but in patients without shock this value was 6.3% L/kg (Fig. 4B).

Propensity score matching analyses

Used PSM method to match the patients who with or without an MFO ≥ 5% L/kg, there were 1078 (2156 patients) matched pairs produced (Additional file 1: Table S2). There was no significant difference in baseline characteristics, diagnosis on admission, comorbidities and uses of RRT and MV in matched study subjects. For every 1% increase in MFO was significantly associated with a 3% increase in risk of hospital mortality, and when MFO was entered as a categorical variable, MFO greater than 10% L/kg had 41% increased risk of hospital mortality compared with those MFO lower than 5% (Additional file 1: Table S3).

In dose-response analysis, we also found similar associations between the MFO and risk of hospital mortality with a nadir of 4.0% L/kg. Higher and lower MFO values were significantly associated with an increased risk of in hospital mortality (Fig. 3B).

Discussion

In this multicenter prospective cohort study, we founded that a higher level of MFO during the first 3 days of ICU admission was associated with an increased risk of hospital mortality in critically ill patients. Notably, we further demonstrated that this association was non-linear with a nadir of 2.4% L/kg. Which indicated that MFO values greater than 5% or 10% and less than 0% were associated with an increased risk of hospital mortality. This study address that there might be an optimal “plot” or “range” of fluid balance for the critical ill patients, and which might be different for the critically ill patients with different characteristics.

Fluid administration is an integral component of the management of critically ill patients to maintain hemodynamic stability, organ function and tissue perfusion. Early fluid resuscitation has been shown to reverse tissue hypoperfusion and improve patient outcomes in several studies[1921]. However, when fluid resuscitation is excessive, it may be harmful. Several studies have supported this hypothesis[4, 8, 13, 22]. According to Alobaidi et al, a positive fluid balance in patients with severe bronchiolitis during the first 24 hours after admission to the pediatric intensive care unit (PICU) resulted in longer durations of PICU and hospital stays and a longer duration of MV[4]. However, fluid balance recorded during the second 24 hours[8] or within 72 hours[13] after ICU admission, but not during the first 24 hours, is strongly associated with an increased risk of mortality. However, the studies described above did not explicitly define FO but rather presented the association between positive fluid balance and outcomes.

Several clinical studies have reported that FO increases mortality in patients with sepsis[13, 23], acute respiratory distress syndrome[9] and AKI[10, 11, 22]. The Beijing Acute Kidney Injury Trial (BAKIT) group[11] and other researchers[12, 24] have considered FO as an accumulation of a fluid balance greater than 10% of the body weight in kilograms and showed that FO was a risk factor for the incidence of AKI. In our study, critically ill patients tended to accumulate fluid beginning on the day of ICU admission, and the deceased patients accumulated much more fluid. In addition, as the volume of FO increased, the in hospital mortality rate increased.

Diminishing the effect of a negative fluid balance, the MFO might better explain the status of fluid accumulation in critically ill patients in the period of study[14]. The FO aggregate of the underlying status of patients with AKI and MFO is an independent risk factor for the incidence of AKI and increases the severity of AKI[11, 14]. In our study, the MFO was an independent risk factor for in hospital mortality (HR 1.04, 95% CI 1.03–1.05) and for every 10 ml/kg increase in the MFO, the risk of mortality increased by 4%. A higher MFO resulted in a lower survival probability. Compared with the patients presenting the lowest percentage of MFO (MFO < 5%), the risk of mortality increased 1.3 times in patients with a middle percentage of MFO (5% ≤ MFO < 10%) (HR 1.27, 95% CI 1.10–1.52) and 1.4 times in patients with the highest percentage of MFO (HR 1.44, 95% CI 1.25–1.67). Therefore, an MFO greater than 10% and between 5 and 10% both increased the risk of hospital mortality. This result was similar to a study on AKI in critically ill children that defined FO as CFB greater than five percent of the body weight at admission and found that FO was associated with an increased risk of AKI and PICU mortality[2].

However, a study in the United States identified different FO cut-off values associated with hospital mortality[22]. Garzotto F[14] and colleagues postulated that FO should not only be considered as a level greater than a fixed value but also any levels of positive fluid balance. Furthermore, there also studied showed both positive and negative fluid balance might be related with adverse outcomes[25, 26]. We used logistic regression models with RCSs to better illustrate this dose-response relationship between FO and outcomes and observed a non-linear association between the MFO and hospital mortality, with a nadir of 2.4% L/kg. These associations suggest that a higher MFO is significantly associated with an increased risk of hospital mortality. Although the sample size of patients with an MFO less than 0% was small, we still observed a slightly increased risk of hospital mortality. In the analysis of predefined subgroups, the MFO was also associated with an increased risk of hospital mortality and similar associations in those patients with or without shock, but in shock patients, we seemed to have observed a larger optimal range of fluid balance. When we used PSM to control for some potential confounders, the similar association between the MFO and hospital mortality still persisted. In other words, a “spot” or “range” for an optimal fluid balance may exist. This non-linear association has been reported in several studies[15, 25, 26]. This finding appears to be logical because a volume depletion causes hypovolemia and tissue or organ hypoperfusion, among other conditions. Further studies are needed to confirm the precise margins of this “spot” or “range” of fluid balance in patients with acute critical illnesses.

There are several limitations in this study. First, the observational study design is not warranted for inferring causal relationships between FO and outcomes. Second, we may have failed to adjust for other potential confounders in our study, although we used PSM to balance some important confounders. Third, we did not consider the fluid input and output before ICU admission or in the operating theatre, which cannot be ignored. Forth, diuretic use and the type of intravenous fluids were failed to collected, which may influence the fluid output and outcomes[15, 2729]. Fifth, most of the body weights were reported by patients or their family members, which may influence the accurate estimation of FO. Finally, we used the MFO to assess the degree of FO, but these values may not represent trends in the fluid balance. We need to perform a latent growth model analysis to further explore the relationship between fluid balance and outcomes.

Conclusions

Our multicenter study revealed an association between FO and an increased risk of in hospital mortality, and a non-linear association suggested that higher and lower fluid balance levels were associated with an increased risk of in hospital mortality. However, fluid balance is accompanied by diverse risks of mortality in critically ill patients with or without shock. Further studies should be performed to explore the relationship between FO and mortality in patients with various diseases and determine which fluid management strategies are optimal.

Key message

Fluid overload is an independent predictor of hospital mortality for critically ill patients.

A non-linear association was observed between FO and hospital mortality, which suggested that higher and lower fluid balance levels are associated with an increased risk of hospital mortality.

Patients with different characteristics may have variety optimal plot or range of fluid balance.

Additional information

Additional file 1: Table S1 Characteristics of the patients according to the outcome. SD, standard deviation; APACHE II, Acute Physiology and Chronic Health Evaluation II; SOFA, Sequential Organ Failure Assessment; IQR, interquartile range; ICU, intensive care unit; MV, mechanical ventilation; RRT, renal replacement therapy. Table S2 Characteristics, therapies and outcomes of the patients after propensity score matching. SD, standard deviation; APACHE II, Acute Physiology and Chronic Health Evaluation II; SOFA, Sequential Organ Failure Assessment; IQR, interquartile range; ICU, intensive care unit; MV, mechanical ventilation; RRT, renal replacement therapy. Table S3 Results of the Cox proportional hazard regression analysis of the risk of hospital mortality after propensity score matching. *MFO was entered as a continuous variable per 1% (L/kg) increase. Model 1, crude HR. Model 2, adjusted for age, sex, APACHE II score and center. Model 3, additionally adjusted for the main diagnosis and comorbidities. Model 4, additionally adjusted for mechanical ventilation, renal replacement therapy, and use of vasopressors. MFO, maximum fluid overload. HR, hazard ratio. Fig. S1 Cumulative fluid balance and maximum fluid overload (&) in the first 3 days on admission ICU according to the survival status of patients. &, maximum fluid overload. * p < 0.001. Fig. S2 Kaplan-Meier survival curves estimating the cumulative probabilities of 28-day survival for all patients stratified by the categories of MFO values. The patients with an MFO ≥ 10% L/kg had the lowest 28-day survival (log-rank test, p < 0.001). MFO, maximum fluid overload.

Additional file 2: All other ethical bodies that approved our study in the various centres involved.

Abbreviations

CCCST:China Critical Care Sepsis Trial; ICU:intensive care unit; MV:mechanical ventilation; RRT:renal replace therapy; AKI:acute kidney injury; APACHE II:Acute Physiology and Chronic Health Evaluation II; SOFA:Sequential Organ Failure Assessment; DFB:Daily fluid balance; FO:fluid overload; L:liter; kg:kilogram; MFO:maximum fluid overload; TMFO:time of maximum fluid overload; SBP:systolic blood pressure; MAP:mean arterial pressure; SD:standard deviations; IQR:interquartile ranges; HR:hazard ratio; CI:confidence interval; RCS:restricted cubic splines; PSM:propensity score matching.

Declarations

Ethical approval and consent to participate

The study protocol was approved by the ethics committees of Fuxing Hospital, Capital Medical University (approval notice number 2013FXHEC-KY018) and all other centers. The Institutional Review Board specifically approved the informed consent waiver because of the anonymous and purely observational nature of this study.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated during the current study are available in the China Critical Care Sepsis Trial (CCCST) workgroup repository.

Competing interests

The authors have no competing interests to declare.

Funding

This study was supported by the National Science and Technology Supporting Plan of the Ministry of Science and Technology of the People’s Republic of China (2012BAI11B05). The funding source had no role in writing the manuscript or the decision to submit it for publication.

Authors’ contributions

MW and BZ designed and carried out the study, performed the statistical analysis, and drafted the manuscript. LJ, YW and BD was involved in design and in acquisition of data and helped to revise the manuscript critically for important content. WL was involved in the design and cleaning data. GL, WL, JW helped statistical analysis. The China Critical Care Sepsis Trial (CCCST) workgroup participated in acquisition and interpretation of data. YH was involved in the design, statistical analysis and revised the paper. XMX was the chief investigator, designed the CCCST, secured funding, contributed to writing the manuscript, and critically revised the paper. All authors read and critically revised the protocol and this article prior to submission.

Acknowledgments

We gratefully acknowledge the National Science and Technology Supporting Plan of the Ministry of Science and Technology of the People’s Republic of China, a government fund used to improve health-care quality and data collection, for providing financial support. We also acknowledge all the following members of the CCCST workgroup who contributed data and samples and have enabled this study to be conducted: Bin Du, Medical Intensive Care Unit, Peking Union Medical College Hospital, Beijing, China; Li Weng, Medical Intensive Care Unit, Peking Union Medical College Hospital, Beijing, China; Tong Li, Department of Critical Care Medicine, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Meili Duan, Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing China; Wenxiong Li, Surgical Intensive Care Unit, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China; Bing Sun, Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China; Jianxin Zhou, Department of Critical Care Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Jianguo Jia, Surgical Intensive Care Unit, Xuanwu Hospital, Capital Medical University, Beijing, China; Xi Zhu, Department of Critical Care Medicine, Peking University Third Hospital, Beijing, China; Qingyuan Zhan, Department of Critical Care Medicine, China-Japan Friendship Hospital, Beijing, China; Xiaochun Ma, Department of Critical Care Medicine, The First Affiliated Hospital of China Medical University, Shenyang, China; Tiehe Qin and Shouhong Wang, Department of Critical Care Medicine, Guangdong Geriatric Institute, Guangdong General Hospital, Guangdong, China; Yuhang Ai, Department of Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, China; Yan Kang and Xuelian Liao, Department of Critical Care Medicine, West China Hospital, Sichuan University, Sichuan, China; Xiangyuan Cao, Department of Critical Care Medicine, General Hospital of Ningxia Medical University, Ningxia, China; Yushan Wang, Intensive Care Unit, The First Hospital of Jilin University, Changchun, China; and Duming Zhu, Surgical Intensive Care Unit, Department of Anaesthesiology, ZhongShan Hospital, FuDan University, Shanghai, China.

Authors' information

1Department of Epidemiology and Health Statistics,School of Public Health,Capital Medical University, NO.10 Xitoutiao, Youanmen, Fengtai District, Beijing 100069, China; 2Department of Critical Care Medicine, Fuxing Hospital, Capital Medical University, NO.20 Fuxingmenwai Street, Xicheng District, Beijing 100038, China; 3Department of Critical Care Medicine, Xuanwu Hospital, Capital Medical University, NO.45 Changchun Street, Xicheng District, Beijing, 100053, China; 4Beitaipingzhuang Community Health Service Centre, Haidian District, Beijing 100038, China. 5Medical Intensive Care Unit, Peking Union Medical College Hospital, NO.1 Shuaifuyuan,  Wangfujing, Dongcheng District, Beijing 100730, China.

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