The Impact of Fluid Management during ICU on the Prognosis of Patients with Acute Pancreatitis

doi:10.21203/rs.3.rs-4306215/v1

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The Impact of Fluid Management during ICU on the Prognosis of Patients with Acute Pancreatitis

https://doi.org/10.21203/rs.3.rs-4306215/v1

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Background: Acute pancreatitis is a severe inflammatory condition with 6.6-7.6% of patients needing ICU admission, and a mortality rate of approximately 20%. Early and aggressive fluid resuscitation is crucial, but excessive administration leads to complications. Fluid overload in the ICU correlates with adverse outcomes. Continuous fluid management during ICU stay is necessary. Therefore, fluid management post-ICU admission is significant. This study investigates the impact of ICU fluid management on the prognosis of acute pancreatitis patients.

Methods: This study utilized the MIMIC-IV database（2.2）.Data extraction used Navicat Premium (version 15). The study focuses on analyzing the relationship between fluid balance and patient outcomes, particularly mortality risk factors. SPSS (version 26.0) was used for statistical analysis. Normality tests, descriptive statistics, and comparisons were performed. Factors with p<0.1 in univariate analysis were included in multivariate analysis using logistic regression. Odds ratios and confidence intervals were reported. Model accuracy was evaluated with ROC curve analysis. A p-value <0.05 was considered significant.

Results: A total of 469 eligible acute pancreatitis were included in the data analysis. The mortality rate was 31.6%. Deceased patients were older and had higher comorbidity index, lactate levels, and daily fluid balance(P<0.01). Logistic regression analysis identified age, comorbidity index, lactate levels, and daily fluid balance as independent predictors of outcomes (AUC,0.683 (0.616-0.750), 0.691 (0.627-0.755), 0.692 (0.626-0.759), and 0.631 (0.563-0.70)). The combined model showed higher predictive accuracy (AUC,0.778 (0.718-0.839)). The optimal cutoff value for daily fluid balance was determined to be 1.162L, with a sensitivity of 66.7% and a specificity of 57.1%.

Conclusions: This study demonstrates a correlation between cumulative positive fluid balance during ICU hospitalization and adverse outcomes in acute pancreatitis. Factors such as older age, comorbidities, and high lactate levels are associated with poorer outcomes.

Acute pancreatitis

Fluid resuscitation

Prognosis

MIMIC-IV

Acute pancreatitis is a severe inflammatory condition of the pancreas, with approximately 6.6–7.6% of patients requiring intensive care unit (ICU) admission. Among them, around one-fifth experience severe symptoms, resulting in a mortality rate of approximately 20%[1–3]. This condition involves the swelling of the pancreas, fluid leakage, and systemic edema, which can lead to reduced circulatory volume and potentially cause multi-organ dysfunction[4]. Early and aggressive fluid resuscitation is crucial for improving tissue perfusion and reducing organ dysfunction[5]. However, it is important to avoid excessive fluid administration as it can lead to complications such as pulmonary edema, abdominal compartment syndrome, and an increased risk of interventions like endotracheal intubation, mechanical ventilation, and renal replacement therapy[2, 6].

While the initial 24 hours of fluid resuscitation are of utmost importance, critically ill acute pancreatitis patients in the ICU require continuous fluid management and optimization throughout their hospital stay. Despite a significant amount of research literature on early fluid resuscitation, studies focusing on fluid management after ICU admission are limited. Moreover, extensive research has demonstrated a significant correlation between fluid overload in the intensive care unit (ICU) and adverse disease outcomes[7–9].

Therefore, it is essential not to overlook the importance of fluid management in the post-ICU admission. This study aims to investigate the impact of fluid management during ICU on the prognosis of patients with acute pancreatitis.

2.1 Sources of data and research ethics

This study utilizes the Medical Information Mart for Intensive Care (MIMIC)-IV database, a publicly available and freely accessible database containing critical care data. The Institutional Review Board at Beth Israel Deaconess Medical Center, Massachusetts, and the Massachusetts Institute of Technology have granted approval for the use of this dataset in research. Following the successful completion of the web-based training course provided by the National Institutes of Health (NIH) and the Protecting Human Research Participants exam (Certificate Number: No. 56283557), we obtained permission to extract data from the MIMIC-IV database. As the data is de-identified and publicly available, individual patient informed consent is not required. Data extraction was performed using Navicat Premium (version15).

2.2 Data collection and stay design

Based on the recorded International Classification of Diseases, Ninth and Tenth Revisions (ICD-9 and ICD-10) codes in the database, we have identified patients diagnosed with acute pancreatitis. Our study primarily focuses on patients who were admitted to the ICU for more than 24 hours. In cases where patients had multiple ICU admissions, we have only considered data from their initial ICU stay. To ensure the reliability and validity of our findings, certain exclusion criteria were applied. These criteria involved excluding patients under the age of 18, individuals with ICU stays exceeding 30 days, and those with missing data from the analysis.

We collected several data points from the patients, including their age, gender, Charlson Comorbidity Index, total fluid intake and output during their ICU stay, length of ICU stay, total length of hospital stay, and certain laboratory indicators (maximum values) on the first day of ICU admission. To assess the severity of the disease, we also extracted the SOFA score and APS III score from the patients on the first day of ICU admission. The total fluid intake during the ICU stay includes crystalloid fluid infusion, blood products and albumin transfusion, enteral nutrition, parenteral nutrition, as well as supplemental nutrition. The total fluid output includes urine output, drainage fluid, vomiting, gastric decompression, and fecal output. To evaluate the fluid balance during the ICU stay, we calculated the difference between total fluid intake and total fluid output. This difference was then divided by the number of days of ICU hospitalization to determine the daily fluid balance during the ICU stay.

The main focus of this study is to examine the relationship between fluid balance during hospitalization and patient outcomes, with a specific emphasis on analyzing the risk factors that are associated with patient mortality.

The statistical analysis will be performed using the social science statistical program SPSS (version 26.0). The normality of the data will be assessed using the Shapiro-Wilk test. Categorical variables will be presented as counts and percentages, while continuous variables will be reported as means with standard deviations or medians with interquartile ranges. Differences in continuous variables will be compared using either Student’s t-test or the Mann-Whitney U test. Categorical outcomes will be compared using the chi-squared test, with Fisher’s correction applied if necessary. In the univariate analysis, factors with a p-value below 0.1, indicating statistical significance, will be selected for inclusion in the multivariate analysis. The multivariate analysis will be conducted using binary logistic regression to account for confounding variables, using an equation to adjust for their effects. Odds ratios (OR) and 95% confidence intervals (CI) will be reported for each variable. The predictive accuracy of the model will be evaluated using the area under the receiver operating characteristic (ROC) curve. The optimal cut-off value of daily fluid balance was determined by the Youden index. A two-sided p-value below 0.05 will be considered statistically significant.

4.1Baseline demographic and clinical characteristic

Based on the MIMIC-IV database, a total of 504,412 ICU admissions were identified, out of which 4,017 were diagnosed with acute pancreatitis. Following our inclusion criteria, 469 patients were included in the study, including 148 deceased patients and 321 survivors (Fig. 1), resulting in a mortality rate of 31.6%. The baseline characteristics and laboratory indicators of the patients are presented in Table 1.

Table 1

Baseline Characteristics and Comparison between Survivors and Non-Survivors
Clinical baseline	All n = 469	Survivors n = 321	Non-Survivors n = 148	P
Age (years)	59.9 ± 17.1	56.4 ± 16.3	67.4 ± 16.5	< 0.001
Male gender, n (%)	263(56)	177(55.1)	86(58.1)	0.547
CCI, median (IQR)	4(2–6)	4(2–6)	6(4–8)	< 0.001
Myocardial infarct, n (%)	38(8.1)	15(4.7)	23(15.5)	< 0.001
Congestive heart failure, n (%)	75(16)	44(13.7)	31(20.9)	< 0.05
Chronic pulmonary disease, n (%)	103(22)	62(19.3)	41(27.7)	< 0.05
Diabetes, n (%)	7(1.5)	4(1.2)	3(2.0)	0.741
Renal disease, n (%)	70(14.9)	39(12.1)	31(20.9)	< 0.05
APS III, median (IQR)	52(36–77)	48(34–70)	60(43–88)	< 0.001
Sofa, median (IQR)	5(3–9)	5(2–8)	6(4–11)	< 0.001
Laboratory findings
WBC (10⁹ /L), median (IQR)	13.9(9.8–19.7)	15.2(10.6–20.7)	13.5(9.7–21.8)	0.615
Hematocrit (%)	35.86 ± 7.01	36.02 ± 6.98	35.53 ± 7.08	0.483
Hemoglobin(g/dl)	11.94 ± 2.36	12.03 ± 2.39	11.76 ± 2.29	0.249
Platelets (10⁹ /L), median (IQR)	216(152–313)	200(146–284)	215(149–313)	0.451
Albumin (g/dl)	3.16 ± 0.67	3.21 ± 0.68	3.04 ± 0.65	< 0.05
Lactate (mmol/L), median (IQR)	2.1(1.4–3.6)	1.8(1.3–2.9)	3.2(1.7–5.4)	< 0.001
Calcium(mg/dL), median (IQR)	8.3(7.8–8.9)	8.3(7.7–8.8)	8.3(7.7–8.8)	0.773
Total input(L)	21.31 ± 23.50	20.54 ± 23.41	22.97 ± 23.70	0.299
Fluids/Intake(L)	17.59 ± 17.22	16.80 ± 17.04	19.30 ± 17.53	0.144
Nutrition enteral(L)	4.93 ± 5.90	5.65 ± 6.24	3.43 ± 4.86	< 0.05
Nutrition parenteral(L)	9.41 ± 9.08	9.31 ± 9.28	9.63 ± 8.79	0.889
Blood /colloids (L), median (IQR)	1.00(0.50–1.71)	0.95(0.50–1.42)	1.04(0.50–2.05)	0.403
Total output(L)	14.44 ± 18.99	15.67 ± 20.05	11.76 ± 16.19	< 0.05
Cumulative fluid balance(L), median (IQR)	6.28(1.42–16.24)	4.99(0.18–11.17)	10.49(4.28–23.53)	< 0.001
Daily fluid balance(L), median (IQR)	0.98(0.05–2.61)	0.734(-0.11-2.23)	1.68(0.57–2.89)	< 0.001
Daily positive Fluid Balance, n (%)	361(77)	234(64.8)	127(35.2)
Daily negative Fluid Balance, n (%)	108(23)	87(80.6)	21(19.4)	0.02*
ICU stay (days), median (IQR)	3.1(1.8–7.3)	3(1.8–6.7)	3.4(1.8–8.2)	0.511
Hospital stay(days), median (IQR)	11.1(6.6–19.9)	11.4(6.8–20.4)	10.8(5.9–18)	0.261
Abbreviations: CCI: Charlson comorbidity index; IQR denotes interquartile range; APS III: Acute Physiology score III; SOFA: sequential organ failure assessment; WBC: White Blood Cell; *:
Compare the mortality rates between Daily positive fluid balance and Daily negative fluid balance.

The patients had an average age of 59.9 ± 17.1 years, with the deceased patients being older (56.4 ± 16.3 vs 67.4 ± 16.5, P < 0.001). There were no significant differences in the distribution of gender between the two groups(P = 0.547). The deceased patients had a higher prevalence of underlying diseases and exhibited a greater severity of illness, as indicated by higher Charlson Comorbidity Index [4(2–6) vs 6(4–8), P < 0.001], SOFA scores [5(2–8) vs 6(4–11), P < 0.001], Lactate [1.8(1.3–2.9) vs 3.2(1.7–5.4), P < 0.001],and APS III scores [48(34–70) vs 60(43–88), P < 0.001]. The total input of the two patient groups did not show statistical significance(P = 0.299). However, the survival group had a higher amount of enteral nutrition (5.65 ± 6.24 vs 3.43 ± 4.86, P < 0.05). The total output in the survival group was significantly higher than that of the deceased group, indicating statistical significance (15.67 ± 20.05 vs 11.76 ± 16.19 P < 0.001). The surviving patients had a noticeably lower cumulative fluid imbalance and daily fluid balance compared to the deceased patients (4.99(0.18–11.17) vs 10.49(4.28–23.53), P < 0.001) and (0.734(-0.11-2.23) vs 1.68(0.57–2.89), P < 0.001). The average ICU stay for both patient groups was 3.1 days, with a total hospital stay of 11.1 days(P > 0.05).

4.2 Univariate and multivariate analysis of the prognosis for patients

A binary logistic regression analysis was conducted to compare the risk factors for mortality in patients with acute pancreatitis, using both univariate and multivariate approaches. The results of the univariate logistic regression analysis showed that deceased patients were associated with higher age, Charlson comorbidity index, APS III score, SOFA score, lactate levels, cumulative fluid balance, daily fluid balance, as well as lower levels of albumin, enteral nutrition, and total output. After adjusting for all other important predictors of outcomes, the results of the multivariate logistic regression analysis revealed that age[OR 1.033, 95%CI(1.014–1.052), P < 0.001], Charlson comorbidity index[OR 1.18, 95%CI(1.057–1.316), P = 0.003], lactate levels[OR 1.164, 95%CI(1.041–1.301), P = 0.008], and daily fluid balance[OR 1.281, 95%CI(1.126–1.457), P = 0.001] were still considered as independent predictors of outcomes(Table 2).

Table 2

Univariate and multivariate analysis of the prognosis for patients with acute pancreatitis.
Clinical baseline	Univariate analysis			Multivariate analysis
Clinical baseline	OR	95%CI	P	OR	95%CI	P
Age	1.041	1.028–1.055	< 0.001	1.033	1.014–1.052	< 0.001
CCI	1.242	1.115–1.335	< 0.001	1.18	1.057–1.316	0.003
Myocardial infarct	3.754	1.896–7.431	< 0.001	-	-	-
Congestive heart failure	1.668	1.004–2.772	0.048	-	-	-
Chronic pulmonary disease	1.601	1.016–2.521	0.042	-	-	-
Renal disease	1.916	1.141–3.218	0.014	-	-	-
APS III	1.012	1.006–1.019	< 0.001	-	-	-
Sofa	1.074	1.028–1.121	0.001	-	-	-
Albumin	0.694	0.491–0.981	0.039	-	-	-
Lactate	1.24	1.119–1.375	< 0.001	1.164	1.041–1.301	0.008
Nutrition enteral	0.922	0.855–0.995	0.037	-	-	-
Total output	0.988	0.976-1.00	0.041	-	-	-
Cumulative fluid balance	1.034	1.017–1.052	< 0.001	-	-	-
Daily fluid balance	1.237	1.132–1.351	< 0.001	1.281	1.126–1.457	0.001

4.3 ROC curve analysis

We conducted receiver operating characteristic (ROC) curve analysis to compare the overall accuracy of the prognosis. The AUCs for age, Charlson comorbidity index, lactate levels, and daily fluid balance were 0.683 (0.616–0.750), 0.691 (0.627–0.755), 0.692 (0.626–0.759), and 0.631 (0.563-0.70) respectively. Furthermore, we combined these four variables into a model called the “combined model” to enhance predictive accuracy. The combined model yielded an AUC of 0.778 (0.718–0.839), which consistently outperformed the AUCs of the individual variables mentioned earlier (Fig. 2). Additionally, we determined the optimal cutoff value for daily fluid balance to be 1.162L, with a sensitivity of 66.7% and a specificity of 57.1%.

Fluid management is crucial in both the early 24 hours of acute pancreatitis and during ICU hospitalization. However, we may have excessively focused on early fluid resuscitation goals while neglecting long-term fluid management. In this study, we first assessed the relationship between fluid management during ICU hospitalization and mortality in patients with acute pancreatitis. We found that patients with daily positive fluid balance had a significantly higher mortality rate compared to those with daily negative balance (19.4% vs 35.2%, P < 0.05). Additionally, the daily cumulative fluid volume was positively correlated with mortality, making it an independent risk factor in predicting patient death (P < 0.001). However, it is important to note that despite its optimal cutoff value of 1.162L, its sensitivity and specificity are not high. This suggests that a positive fluid balance during ICU hospitalization significantly increases the risk of patient death, while appropriate negative balance is beneficial. This may be due to the occurrence of abdominal compartment syndrome and respiratory failure caused by fluid overload[3, 10]. Previous studies[2, 11, 12] have also indicated that excessive fluid resuscitation may not improve patient prognosis and instead increases the risk of fluid overload. This serves as a reminder of the importance of fluid management during ICU admission.

Furthermore, a higher Charlson Comorbidity Index indicates a greater number of complications in patients and increases their risk of death(P < 0.05), particularly in patients with a history of myocardial infarction and renal dysfunction. The possible reason is that these patients cannot tolerate excessive fluid in the early stage, leading to pancreatic necrosis. Additionally, fluid overload can aggravate heart failure and worsen renal dysfunction. The Charlson Comorbidity Index can serve as a predictive factor for mortality, which is consistent with the findings of VIKESH K’s trial[13].According to studies[14–16], elevated lactate levels are often indicative of tissue hypoxia or inadequate perfusion, and they are widely used to assess the severity and prognosis of diseases. In this study, we observed a significant elevation in lactate levels among patients in the deceased group compared to the surviving group. Furthermore, the results of the multivariate analysis indicate that lactate levels can also serve as a predictive marker for mortality (P < 0.05). Similar study[17] has also indicated that lactate is a predictive factor for acute pancreatitis, with an AUC of 0.71 (95% CI, 0.64–0.78), which is essentially consistent with the AUC of 0.692 (95% CI, 0.626–0.759) observed in our measurements.

To address the limitations of low sensitivity and specificity observed in single variable analysis, it is recommended to incorporate additional variables or risk factors in the predictive model. We discovered that a combined model incorporating age, Charlson score, lactate levels, and daily fluid balance can further enhance the accuracy of predictive information (AUC 0.778, 95% CI 0.718–0.839). These variables can be readily obtained in clinical practice, assisting clinicians in conducting a comprehensive assessment of a patient’s illness severity and prognosis. By considering multiple indicators, clinicians can consider various factors that impact a patient’s prognosis, ultimately enhancing the accuracy of predictions.

Additionally, we observed a significant difference in enteral nutrition intake between ICU patients who survived and those who did not (P < 0.05). This finding further emphasizes the importance of enteral nutrition in the management of acute pancreatitis. Bakker et al[18], found that early initiation of enteral feeding is safe and feasible in patients with mild to moderate acute pancreatitis. This approach significantly reduces hospital stay and saves costs, without causing any adverse reactions or complications. Marik[19] has also emphasized that enteral nutrition should be the preferred method of nutritional support for patients with acute pancreatitis. Overall, these findings are in line with the current guidelines on enteral nutrition[20].

Certainly, it is important to acknowledge the limitations of our study. Firstly, our study design being retrospective restricts our ability to establish a causal relationship between fluid management and acute pancreatitis, unlike prospective studies which provide more compelling evidence. This lack of a cause-and-effect relationship reduces the strength of our findings. Secondly, our study revealed that deceased patients had a higher age, lactate levels, and more comorbidities upon admission, indicating that these patients had more severe conditions. However, we did not perform baseline adjustments before conducting the comparisons. Lastly, our study utilized trial data obtained from electronic medical records, covering a wide time span. As fluid management practices for acute pancreatitis may have evolved over time, our findings may not fully represent the current landscape of fluid management. Considering these limitations, further prospective studies with larger and more diverse populations, as well as improved methodology, are necessary to provide a more comprehensive understanding of the relationship between fluid management during ICU admission and outcomes in acute pancreatitis patients.

Our study demonstrates a correlation between cumulative positive fluid balance during ICU hospitalization and adverse outcomes in acute pancreatitis. It is crucial to emphasize the significance of closely monitoring and optimizing fluid intake and output to prevent fluid overload or depletion, as these can have significant implications for patient prognosis. Older age, more comorbidities, and higher lactate levels are associated with poorer prognosis. The use of a multi-parameter analysis can further enhance the reliability of prognostic assessment in acute pancreatitis.

Contributors

ZFL and JSG conceived and designed the study. ZFL and JSG did the literature search, extracted the data, and wrote the first draft of the manuscript. ZFL and APZ analyzed the data. ZFL, JSG, APZ, and HBN contributed to the final draft and revision of the manuscript.

Ethical approval:

The MIMIC-IV database used in this study has obtained approval from the Institutional Review Board (IRB) at the Massachusetts Institute of Technology. The database does not contain any protected health information.

Conflicts of interests:

The authors declare that they have no competing interests.

Contributors

All authors contributed substantially to the writing and revision of this manuscript and approved of its contents.

Funding:

None.

Author Contribution

ZFL and JSG conceived and designed the study. ZFL 、LS and JSG did the literature search, extracted the data, and wrote the first draft of the manuscript. ZFL and APZ analyzed the data. ZFL, JSG, APZ, and HBN contributed to the final draft and revision of the manuscript.

Acknowledgments:

We would like to express our sincere gratitude to Jingsheng Guo, Aiping Zhang, Haibin Ni, and Zhenfeng Lu for their valuable insights and expertise in the clinical field. Their contributions and assistance in designing the figures have greatly enhanced the quality of this work.

Availability of data and materials:

Data are available on reasonable request.

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The Impact of Fluid Management during ICU on the Prognosis of Patients with Acute Pancreatitis

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Version 1

Abstract

Figures

1. Introduction

2. Methods

2.1 Sources of data and research ethics

2.2 Data collection and stay design

3. Statistical Analysis

4. Results

4.1Baseline demographic and clinical characteristic

4.2 Univariate and multivariate analysis of the prognosis for patients

4.3 ROC curve analysis

5. Discussion

Conclusion

Declarations

Contributors

Ethical approval:

Funding:

Author Contribution

Acknowledgments:

Availability of data and materials:

References

Additional Declarations

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