Retrospective Cohort Study Open Access
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. May 28, 2025; 31(20): 105269
Published online May 28, 2025. doi: 10.3748/wjg.v31.i20.105269
Nonlinear association between estimated plasma volume status and acute kidney injury in acute pancreatitis patients
Wen Wu, Yu-Pei Zhang, Yi-Lan Zhang, Xing-Guang Qu, Zhao-Hui Zhang, Rong Zhang, Department of Emergency and Critical Care Medicine, Yichang Central People’s Hospital, Yichang 443000, Hubei Province, China
Wen Wu, Yu-Pei Zhang, Yi-Lan Zhang, Xing-Guang Qu, Zhao-Hui Zhang, Rong Zhang, The First College of Clinical Medical Science, China Three Gorges University, Yichang 443000, Hubei Province, China
Wen Wu, Zhi-Yong Peng, Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, Hubei Province, China
Wen Wu, Zhi-Yong Peng, Clinical Research Center of Hubei Critical Care Medicine, Wuhan 430071, Hubei Province, China
ORCID number: Rong Zhang (0009-0008-9686-3543); Zhi-Yong Peng (0000-0002-0849-5648).
Co-corresponding authors: Rong Zhang and Zhi-Yong Peng.
Author contributions: Wu W and Zhang YP conceived, designed the study and obtained the data, which was analyzed by Wu W; Zhang YP, Zhang YL, Qing XG and Zhang ZH interpreted the data and results and drafted the manuscript; Zhang R and Peng ZY critically revised the manuscript for intellectual content; All authors were responsible for interpretation of data and for approving the draft manuscript.
Institutional review board statement: The present retrospective study was approved by the Ethics Committee of Yichang Central People’s Hospital, (ethical approval number: 2023-130-01), and performed following Declaration of Helsinki.
Informed consent statement: Informed consent was waived because of the retrospective nature of the study and the analysis used de-identified data.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
STROBE statement: The authors have read the STROBE Statement—a checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-a checklist of items.
Data sharing statement: Data is available from the corresponding author on reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Rong Zhang, MD, Doctor, Department of Emergency and Critical Care Medicine, Yichang Central People’s Hospital, No. 183 Yiling Avenue, Wujiagang District, Yichang 443000, Hubei Province, China. zhangrong20240304@163.com
Received: January 18, 2025
Revised: April 18, 2025
Accepted: May 12, 2025
Published online: May 28, 2025
Processing time: 131 Days and 23 Hours

Abstract
BACKGROUND

Acute pancreatitis (AP), a severe pancreatic inflammatory condition, with a mortality rate reaching up to 40%. Recently, AP shows a steadily elevating prevalence, which causes the greater number of hospital admissions, imposing the substantial economic burden. Acute kidney injury (AKI) complicates take up approximately 15% of AP cases, with an associated mortality rate of 74.7%-81%.

AIM

To evaluate the efficacy of estimated plasma volume status (ePVS) in forecasting AKI in patients with AP.

METHODS

In this retrospective cohort study, AP cases were recruited from the First College of Clinical Medical Science of China Three Gorges University between January 2019 and October 2023. Electronic medical records were adopted for data extraction, including demographic data and clinical characteristics. The association between ePVS and AKI was analyzed using multivariate logistic regression models, with potential confounders being adjusted. Nonlinear relationship was examined with smooth curve fitting, and infection points were calculated. Further analyses were performed on stratified subgroups and interaction tests were conducted.

RESULTS

Among the 1508 AP patients, 251 (16.6%) developed AKI. ePVS was calculated using Duarte (D-ePVS) and Kaplan-Hakim (KH-ePVS) formulas. After adjusting for covariates, the AKI risk exhibited 46% [odds ratio (OR) = 1.46, 95% confidence interval (CI): 0.96-2.24] and 11% (OR = 1.11, 95%CI: 0.72-1.72) increases in the low tertile (T1) of D-ePVS and KH-ePVS, respectively, and 101% (OR = 2.01, 95%CI: 1.31-3.05) and 51% (OR = 1.51, 95%CI: 1.00-2.29) increases in the high tertile (T3) relative to the reference tertile (T2). Nonlinear curve fitting revealed a U-shaped association of D-ePVS with AKI and a J-shaped association for KH-ePVS, with inflection points at 4.3 dL/g and -2.8%, respectively. Significant interactions were not observed in age, gender, hypertension, diabetes mellitus, sequential organ failure assessment score, or AP severity (all P for interaction > 0.05).

CONCLUSION

Our results indicated that ePVS demonstrated the nonlinear association with AKI incidence in AP patients. A U-shaped curve was observed with an inflection point at 4.3 dL/g for the Duarte formula, and a J-shaped curve at -2.8% for the Kaplan-Hakim formula.

Key Words: Acute pancreatitis; Acute kidney injury; Estimated plasma volume status; Cohort study; Duarte formula; Kaplan-Hakim formula

Core Tip: This study is the largest to date examining acute kidney injury (AKI) in patients with acute pancreatitis (AP), and it is the first to explore the association between estimated plasma volume status (ePVS) and the incidence of AKI. The study utilized two formulas (Duarte and Kaplan-Hakim) to assess ePVS and its relationship with AKI risk. Through smooth curve fitting analysis, the results revealed a nonlinear relationship between ePVS and AKI risk, including U-shaped and J-shaped curves. These findings provide important reference for the prediction and management of AKI in patients with AP.
INTRODUCTION

Acute pancreatitis (AP), a severe pancreatic inflammatory condition, is a frequently seen gastrointestinal disease requiring hospitalization, with a mortality rate reaching up to 40%[1,2]. Recently, AP shows a steadily elevating prevalence, which causes the greater number of hospital admissions, imposing the substantial economic burden[3,4]. Acute kidney injury (AKI) complicates take up approximately 15% of AP cases, with an associated mortality rate of 74.7%-81%[5-7]. In managing AKI among hospitalized AP patients, fluid volume management is crucial, which addresses both significant intravascular fluid loss and third-space fluid leakage. This involves fluid resuscitation for depletion and de-resuscitation for overload.

Directly quantifying plasma volume (PV) is challenging, which is considered the gold standard, and requires the administration of radiolabeled tracer molecules[8]. Nevertheless, the approach can be invasive, technically demanding, and impractical in continual clinical application. To address the existing limitations, estimated PV status (ePVS) has been developed as a biomarker. This metric is cost-effective, easily performed through standard blood tests, and time-efficient, demonstrating a strong correlation with radiolabeled albumin (ALB) techniques[9,10]. As it is derived from standard tests conducted at admission, ePVS is readily available, making it highly suitable for clinical application. ePVS is demonstrated as a valuable indicator of fluid status, which correlates well with cardiac filling pressures and hemodynamics[11]. It has also exhibited prognostic value in various clinical scenarios, including heart failure[12-15], dyspnea, fever[16], cardiac surgery[9,17], as well as in the general population[18]. However, based on our knowledge, the PVS prevalence and its association with volume status-related AKI within pancreatitis remains unexplored.

This study investigates the correlation of ePVS with AKI incidence among AP cases for the first time, offering insights for early risk assessment and fluid management strategies in this population.

MATERIALS AND METHODS
Patients

This retrospective, observational cohort study involving human samples was approved by Hospital of Medicine Ethics Committee (ethical approval number: 2023-130-01). During data processing, patient information was anonymous. This study was performed following Declaration of Helsinki. Patient data were obtained through retrospectively reviewing electronic medical records of AP cases from Yichang Central People’s Hospital, the First College of Clinical Medical Science of China Three Gorges University between January 2019 and October 2023.

Eligibility criteria

Patients who satisfied the criteria below were included: Inpatients from the First College of Clinical Medical Science of China Three Gorges University; and AP cases. The following patients were excluded: Those aged < 18 or > 80 years; Women during pregnancy or breastfeeding; Those with a hospital stay of ≤ 2 days; Patients with chronic kidney disease; Cancer cases; Those developing chronic pancreatitis; Those undergoing nephrectomy or renal transplantation; Patients with insufficient medical information. Figure 1 shows the patient collection and review process.

Figure 1
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Figure 1 Flowchart of the screening and enrollment of study participants. AKI: Acute kidney injury.
Definitions and laboratory test results

The ePVS was performed based on the following two different methodologies: (1) Duarte formula-based ePVS (D-ePVS)[13]: This method employs a single equation: D-ePVS = [100 - hematocrit (%)]/hemoglobin (g/dL); and (2) Kaplan-Hakim formula-based ePVS (KH-ePVS)[11,14,15]: This approach involves the calculation of actual and PVs (aPV and iPV, respectively), and ePVS using the below equations: aPV = [1 - hematocrit (%)] × [a + b × weight (kg)], a = 1530, b = 41 for males, whereas a = 864, b = 47.9 for females; iPV = c × weight (kg), For males: c = 39, for females: c = 40; KH-ePVS = [(aPV - iPV) / iPV] × 100%.

AP diagnosis and severity classification

The diagnosis of AP was performed according to symptoms, physical examinations, laboratory results, medical history, and imaging findings (including abdominal ultrasound, contrast-enhanced computed tomography, or magnetic resonance imaging)[19,20]. Finally, exploratory laparotomy was used to confirm few AP cases.

AP severity could be classified according to the revised Atlanta classification. AP with no local/systemic complication or organ failure was deemed as mild, while that accompanied by local/systemic complications, and transient rather than persistent organ failure (which resolved in 2 days) was considered as moderate[21], and AP accompanied by persistent organ failure (single or multiple) lasting for over 48 hours was regarded as severe.

AKI diagnosis

Following the KIDGO guidelines (2012), AKI was diagnosed and classified[22], with the serum creatinine standard as the increase in serum creatinine level ≥ 0.3 mg/dL (≥ 26.4 μmol/L) or the percentage increase by ≥ 50% in 48 hours. In addition, the lowest serum creatinine concentration detected in 48 hours prior to hospital admission was considered as its baseline content. If serum creatinine was not determined, we regarded the concentration during the initial measurement in 48 hours after hospital admission as its level.

Data extraction

General data were collected, including age, gender, body mass index (BMI), history of hypertension, coronary heart disease (CHD), diabetes mellitus (DM), the length of hospital stays (LOS), respiratory rate (RR), heart rate (HR), pulse oxygen saturation (SpO2), and systolic blood pressure (SBP), necessity of blood transfusion, mechanical ventilation (MV) and continuous renal replacement treatment (CRRT), bedside index of severity in AP (BISAP) score, and sequential organ failure assessment (SOFA) score in 24 hours after admission. Blood routine factors were extracted, including hemoglobin (HGB) level, white blood cell count, and hematocrit. Biochemical factors like blood urea nitrogen, ALB, fasting plasma glucose (FPG), serum creatinine, calcium ion level (Ca2+), sodium ion level, aspartate aminotransferase (AST), alanine aminotransferase, C-reactive protein (CRP), triglyceride (TG), and procalcitonin (PCT), were collected. AP severity was assessed using BISAP score[23,24] and SOFA score[25]. Our endpoint was AP-induced AKI during hospital stay.

Statistical analysis

Normally-distributed continuous data were indicated by means ± SDs and analyzed by one-way analysis of variance, while non-normally-distributed continuous data were explored by Kruskal-Wallis H test. In addition, categorical counterparts were indicated by frequencies or percentages and analyzed by Fisher’s exact and χ2 tests.

Patients were classified according to ePVS level in tertiles (D-ePVS < 4.231, 4.231 ≤ D-ePVS < 5.358, D-ePVS ≥ 5.358; KH-ePVS < -7.425, -7.425 ≤ KH-ePVS < 2.669, KH-ePVS ≥ 2.669). Subsequently, the association of ePVS with AKI was analyzed by multivariate logistic regression and smooth curve fitting. Based on the strengthening the reporting of observational studies statement[26], models not adjusted or adjusted for multiple variables were simultaneously analyzed. Covariate adjustment was performed when variables satisfied P < 0.1 using univariate regression, or when model covariate addition or removal changed the odds ratio (OR) by > 10%. Three models were used, including model 1 (adjusted for age, sex and BMI), model 2 (adjusted for age, sex, BMI, hypertension, DM, CHD, HR, RR and SpO2), and model 3 (adjusted for age, sex, BMI, hypertension, DM, CHD, HR, RR, SpO2, BISAP score, SOFA score, TG, Ca2+, ALB, FPG, PCT, CRP, amylase and white blood cell). Baseline factors with clinical relevance or > 10% effect estimate change were selected as confounders. This study constructed the smooth curve fitting and adjusted it based on covariables used in model 3. Logistic regression suggested a non-linear relation of ePVS level with AKI. A two-piecewise binary logistic regression model was also used for further explaining nonlinearity. The covariate missing value percentage was lower than 20% in each analysis. Next, a multiple imputation approach was adopted for filling in those absent values[27]. Descriptive analysis merely reports the observed data, whereas regression models also incorporate estimated data. For further exploratory analysis, interaction testing and stratified analysis were performed to assess the potential modifications concerning the association of ePVS with AKI.

Free Statistics software versions 1.9 and statistical software packages R (http://www.R-project.org, The R Foundation) were used for analysis. P < 0.05 (two-sided) represented statistical significance.

RESULTS
Basic and clinical data of participants

Totally 1508 cases were included, including 251 (16.6%) developing AKI. We classified those included AP cases as AKI (n = 251) or non-AKI group (n = 1257) based on whether they developed AKI or not. BMI, the history of DM, chronic obstructive pulmonary disease, HGB, hematocrit, AST and D-ePVS were not of significance difference between two groups (P > 0.05). By contrast, age, gender, LOS, necessity of blood transfusion, MV and CRRT, PCT, ALB, TG, CRP, serum creatinine, and KH-ePVS were significantly different (P < 0.05). Median KH-ePVS was 0.9% in AKI group, reflecting that PV exceeded 0.9% of iPV (Table 1).

Table 1 Baseline characteristics of included patients grouped by occurrence of acute kidney injury, mean ± SD/n (%).
Variables
Non-AKI (n = 1257)
AKI (n = 251)
P value
Male642 (51.1)164 (65.3)< 0.001
Age (years), median (IQR)53.0 (41.0, 64.0)56.0 (44.0, 69.0)0.002
Hypertension304 (24.2)88 (35.1)< 0.001
DM177 (14.1)47 (18.7)0.059
CHD77 (6.1)26 (10.4)0.015
COPD48 (3.8)15 (6)0.119
Weight (kg), median (IQR)64.0 (56.0, 75.0)66.0 (55.0, 75.0)0.675
Height (cm), median (IQR)163.0 (158.0, 170.0)166.0 (158.0, 172.0)0.003
BMI (kg/m2), median (IQR)24.1 (21.8, 26.7)24.2 (20.9, 26.9)0.534
HR (bpm), median (IQR)78.0 (72.0, 90.0)90.0 (76.0, 109.5)< 0.001
SBP (mmHg), median (IQR)130.0 (119.0, 143.0)123.0 (110.0, 140.5)< 0.001
DBP (mmHg), median (IQR)80.0 (74.0, 90.0)80.0 (66.0, 89.0)0.001
RR (bpm), median (IQR)20.0 (19.0, 20.0)20.0 (19.0, 21.0)0.010
SpO2 (%), median (IQR)99.0 (98.0, 100.0)98.0 (96.0, 100.0)< 0.001
Mechanical ventilation73 (5.8)83 (33.1)< 0.001
Transfusion70 (5.6)93 (37.1)< 0.001
CRRT21 (1.7)66 (26.3)< 0.001
WBC (× 109/L), median (IQR)9.6 (6.4, 13.2)11.2 (7.8, 15.8)< 0.001
HGB (g/L), median (IQR)129.0 (116.0, 144.0)128.0 (106.0, 150.5)0.255
HCT (%), median (IQR)38.7 (34.9, 42.3)37.6 (32.2, 43.2)0.057
PLT (× 109/L), median (IQR)181.0 (139.0, 234.0)172.0 (111.0, 226.5)0.005
PCT (ng/mL), median (IQR)0.1 (0.0, 0.6)1.1 (0.2, 6.2)< 0.001
CRP (mg/L), median (IQR)31.4 (5.9, 119.0)90.8 (17.2, 198.6)< 0.001
AST (U/L), median (IQR)41.0 (23.0, 143.0)44.0 (25.0, 132.5)0.422
ALB (g/dL), median (IQR)39.1 (34.9, 42.7)34.9 (28.6, 39.7)< 0.001
FPG (mmol/L), median (IQR)6.6 (5.3, 8.9)7.5 (5.7, 11.2)< 0.001
AMY (U/L), median (IQR)163.0 (64.5, 716.0)193.0 (90.0, 580.0)0.124
TG (mmol/L), median (IQR)1.5 (1.0, 3.4)1.8 (1.2, 6.4)< 0.001
Na+ (mmol/L), median (IQR)139.7 (137.1, 141.8)137.9 (135.0, 141.1)< 0.001
Ca2+ (mmol/L), median (IQR)2.2 (2.1, 2.4)2.1 (1.9, 2.3)< 0.001
BUN (mmol/L), median (IQR)5.0 (3.8, 6.3)9.6 (5.9, 15.1)< 0.001
CREA (mmol/L), median (IQR)71.0 (59.0, 83.0)95.5 (84.0, 187.0)< 0.001
D-ePVS (dL/g), median (IQR)4.7 (4.0, 5.6)4.9 (3.8, 6.4)0.181
KH-ePVS (%), median (IQR)-2.5 (-10.0, 4.5)0.9 (-10.7, 13.1)0.002
Death5 (0.4)18 (7.2)< 0.001
LOS (day), median (IQR)13.0 (10.0, 18.0)16.0 (10.0, 26.0)< 0.001
ICU115 (9.1)127 (50.6)< 0.001
SOFA (score)2.2 ± 1.95.0 ± 3.6< 0.001
BISAP (score)1.2 ± 1.12.0 ± 1.3< 0.001
Biliary887 (70.6)130 (51.8)
Hyperlipidemic289 (23)64 (25.5)
Alcoholic42 (3.3)8 (3.2)
Others39 (3.1)49 (19.5)
Severity of AP (%)< 0.001
Mild and moderate800 (63.6)90 (35.9)
Severe457 (36.4)161 (64.1)
IQR: Interquartile range; AKI: Acute kidney injury; DM: Diabetes mellitus; CHD: Coronary heart disease; COPD: Chronic obstructive pulmonary disease; BMI: Body mass index; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; HR: Heart rate; RR: Respiratory rate; SpO2: Pulse oxygen saturation; CRRT: Continuous renal replacement treatment; WBC: White blood cell count; HGB: Hemoglobin; HCT: Hematocrit; PCT: Procalcitonin; CRP: C-reactive protein; PLT: Platelet; AST: Aspartate aminotransferase; ALB: Albumin; AMY: Amylase; FPG: Fasting plasma glucose; TG: Triglyceride; Na+: Sodium; Ca2+: Calcium; BUN: Blood urea nitrogen; CREA: Creatinine; D-ePVS: Duarte formula-based estimated plasma volume status; KH-ePVS: Kaplan-Hakim formula-based estimated plasma volume status; ICU: Intensive care unit; LOS: Length of hospital stay; SOFA: Sequential organ failure assessment score; BISAP: Bedside index of severity in acute pancreatitis score; AP: Acute pancreatitis; Mechanical ventilation: Noninvasive or invasive mechanical ventilation; Transfusion: Blood transfusion.

Table 2 presents the basic patient demographic data classified based on ePVS tertiles. All patients had a median age of 53.0 (41.0, 65.0) years, including 53.4% males and 46.6% females. Among patients in the second tertile of ePVS levels, the length of intensive care unit (ICU) stays was lower, the demand for ventilation, transfusion and CRRT was lower, the HR, PCT and BISAP score were lower and the AKI patient proportion decreased relative to those in the ePVS higher and lower tertiles. Table 2 displays the distribution of PVS.

Table 2 Baseline characteristics of included patients grouped by tertile of estimated plasma volume status using Duarte and Kaplan-Hakim formula, mean ± SD/n (%).
CharacteristicsD-ePVS formula
KH-ePVS formula
T1 (n = 505) (< 4.231 dL/g)
T2 (n = 504) (4.231-5.358 dL/g)
T3 (n = 505) (≥ 5.358 dL/g)
P value
T1 (n = 505) (< -7.425)
T2 (n = 504) (-7.425-2.669)
T3 (n = 505) (≥ 2.669)
P value
Male401 (79.7)239 (47.6)166 (33)< 0.001363 (72.2)211 (42)232 (46.1)< 0.001
Age (years), median (IQR)46.0 (36.0, 57.0)55.0 (44.0, 65.0)59.0 (48.0, 69.0)< 0.00145.0 (35.0, 56.0)54.0 (46.0, 66.0)60.0 (50.0, 70.0)< 0.001
Death9 (1.8)2 (0.4)12 (2.4)0.031493 (98)500 (99.6)492 (97.8)0.04
ICU75 (14.9)51 (10.2)116 (23.1)< 0.00175 (14.9)51 (10.2)116 (23.1)< 0.001
LOS (day), median (IQR)12.0 (10.0, 17.0)13.0 (10.0, 18.0)14.0 (10.0, 22.0)< 0.00112.0 (10.0, 17.0)12.0 (10.0, 18.0)14.0 (10.0, 23.0)< 0.001
Hypertension119 (23.7)132 (26.3)141 (28)0.281118 (23.5)149 (29.7)125 (24.9)0.062
DM85 (16.9)62 (12.4)77 (15.3)0.12195 (18.9)60 (12)69 (13.7)0.006
CHD477 (94.8)473 (94.2)455 (90.5)0.01226 (5.2)32 (6.4)45 (8.9)0.053
SBP (mmHg), median (IQR)130.0 (120.0, 144.0)130.0 (118.0, 144.0)125.0 (112.0, 140.0)< 0.001130.0 (120.0, 146.0)130.0 (118.0, 142.0)125.0 (111.0, 140.0)< 0.001
DBP (mmHg), median (IQR)85.0 (78.0, 94.0)80.0 (72.0, 90.0)78.0 (69.0, 85.5)< 0.00186.0 (78.0, 95.0)80.0 (72.0, 90.0)77.0 (69.0, 85.0)< 0.001
HR (bpm), median (IQR)82.0 (75.0, 100.0)78.0 (72.0, 90.0)80.0 (72.0, 92.0)< 0.00182.0 (75.0, 99.0)78.0 (72.0, 89.8)80.0 (72.0, 94.0)< 0.001
RR (bpm), median (IQR)20.0 (19.0, 20.0)20.0 (19.0, 20.0)20.0 (18.0, 20.0)0.00220.0 (19.0, 20.0)20.0 (19.0, 20.0)20.0 (18.0, 20.0)< 0.001
SpO2 (%), median (IQR)99.0 (97.0, 100.0)99.0 (97.0, 100.0)99.0 (98.0, 100.0)0.46499.0 (97.0, 100.0)99.0 (97.0, 100.0)99.0 (97.0, 100.0)0.318
Mechanical ventilation (%)52 (10.3)37 (7.4)67 (13.3)0.00846 (9.1)31 (6.2)79 (15.7)< 0.001
Transfusion (%)41 (8.2)30 (6)92 (18.3)< 0.00135 (7)27 (5.4)101 (20.1)< 0.001
CRRT (%)38 (7.6)12 (2.4)37 (7.4)< 0.00137 (7.4)12 (2.4)38 (7.6)< 0.001
WBC (× 109/L), median (IQR)11.9 (8.4, 15.2)9.6 (6.5, 12.8)8.3 (5.6, 11.9)< 0.00111.8 (8.5, 14.8)9.3 (6.5, 12.9)8.3 (5.6, 12.3)< 0.001
HGB (g/L), median (IQR)152.0 (144.0, 162.0)129.0 (125.0, 134.0)108.0 (98.0, 114.0)< 0.001151.0 (141.0, 161.0)128.0 (120.0, 136.0)109.0 (98.0, 119.0)< 0.001
HCT (%), median (IQR)44.5 (42.5, 47.2)38.6 (37.2, 40.0)32.8 (30.0, 34.6)< 0.00144.3 (42.0, 47.2)38.0 (36.4, 40.1)33.0 (30.0, 35.6)< 0.001
PCT (ng/mL), median (IQR)0.2 (0.1, 0.9)0.1 (0.0, 0.6)0.3 (0.1, 1.2)< 0.0010.2 (0.1, 0.7)0.1 (0.0, 0.5)0.3 (0.1, 1.4)< 0.001
CRP (mg/L), median (IQR)37.3 (7.3, 130.7)27.4 (4.1, 123.2)53.3 (7.6, 139.0)0.00734.7 (7.1, 138.1)32.8 (5.3, 134.7)45.9 (7.2, 133.4)0.194
AST (U/L), median (IQR)38.0 (23.2, 121.5)44.2 (24.0, 152.5)41.0 (22.0, 143.5)0.43538.0 (23.0, 134.0)42.0 (22.7, 143.8)42.0 (22.0, 137.4)0.919
ALB (g/dL), median (IQR)41.1 (37.4, 44.3)38.9 (35.3, 42.5)35.0 (29.6, 39.2)< 0.00141.4 (37.9, 44.4)38.4 (34.9, 41.9)35.2 (29.6, 39.4)< 0.001
FPG (mmol/L), median (IQR)7.5 (6.0, 11.5)6.5 (5.3, 8.7)6.1 (5.0, 7.8)< 0.0017.8 (6.0, 11.9)6.4 (5.2, 8.4)6.2 (5.0, 7.8)< 0.001
TG (mmol/L), median (IQR)2.4 (1.2, 7.7)1.4 (0.9, 2.7)1.3 (0.9, 2.2)< 0.0012.5 (1.2, 7.4)1.4 (1.0, 2.7)1.2 (0.9, 2.2)< 0.001
Na+ (mmol/L), median (IQR)138.8 (135.5, 141.1)139.8 (137.2, 142.2)139.9 (137.3, 141.9)< 0.001139.1 (135.8, 141.2)139.9 (137.3, 142.4)139.6 (136.9, 141.8)< 0.001
Ca2+ (mmol/L), median (IQR)2.3 (2.1, 2.4)2.2 (2.1, 2.4)2.1 (2.0, 2.3)< 0.0012.2 (2.1, 2.4)2.2 (2.1, 2.4)2.1 (2.0, 2.3)< 0.001
AMY (U/L), median (IQR)236.0 (85.0, 875.5)193.0 (68.3, 650.8)117.0 (59.0, 433.0)< 0.001251.0 (78.2, 921.0)182.7 (68.3, 574.0)128.0 (60.0, 466.0)< 0.001
CREA (μmol/L), median (IQR)80.0 (69.0, 92.0)71.1 (59.0, 84.1)71.0 (58.0, 88.0)< 0.00179.0 (67.0, 91.0)70.0 (58.7, 81.8)75.0 (60.0, 90.0)< 0.001
AKI91 (18.1)55 (11)105 (20.9)< 0.00181 (16.1)62 (12.4)108 (21.5)< 0.001
Etiology< 0.001< 0.001
Biliary276 (54.9)367 (73.1)374 (74.4)272 (54.1)372 (74.1)373 (74.2)
Hyperlipidemic186 (37)97 (19.3)70 (13.9)189 (37.6)99 (19.7)65 (12.9)
Alcoholic28 (5.6)13 (2.6)9 (1.8)29 (5.8)12 (2.4)9 (1.8)
Others13 (2.6)25 (5)50 (9.9)13 (2.6)19 (3.8)56 (11.1)
SOFA (score)2.5 ± 2.42.5 ± 2.23.1 ± 2.8< 0.0012.4 ± 2.22.5 ± 2.23.2 ± 2.9< 0.001
BISAP (score)1.3 ± 1.11.2 ± 1.21.4 ± 1.20.0041.3 ± 1.11.2 ± 1.11.5 ± 1.2< 0.001
Severity of AP0.006< 0.001
Mild and moderate434 (86.3)467 (93.0)439 (87.3)442 (87.9)472 (94.0)426 (84.7)
Severe69 (13.7)35 (7.0)64 (12.7)61 (12.1)30 (6.0)77 (15.3)
IQR: Interquartile range; D-ePVS: Duarte formula-based estimated plasma volume status; KH-ePVS: Kaplan-Hakim formula-based estimated plasma volume status; ICU: Intensive care unit; LOS: Length of hospital stay; DM: Diabetes mellitus; CHD: Coronary heart disease; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; HR: Heart rate; RR: Respiratory rate; SpO2: Pulse oxygen saturation; CRRT: Continuous renal replacement treatment; WBC: White blood cell count; HGB: Hemoglobin; HCT: Hematocrit; PCT: Procalcitonin; CRP: C-reactive protein; AST: Aspartate aminotransferase; ALB: Albumin; FPG: Fasting plasma glucose; TG: Triglyceride; Na+: Sodium; Ca2+: Calcium; AMY: Amylase; CREA: Creatinine; AKI: Acute kidney injury; SOFA: Sequential organ failure assessment score; BISAP: Bedside index of severity in acute pancreatitis score; AP: Acute pancreatitis.
Univariate/multivariate analyses on ePVS and AKI

Univariate regression identified age, gender, CHD, hypertension, SBP, HR, RR, SpO2, blood transfusion, MV, CRRT, BISAP score, SOFA score, ALB, white blood cell count, CRP, PCT, FPG, and Ca2+ as obvious confounders which affect AKI occurrence (P < 0.001) (Supplementary Table 1).

Using multivariate logistic regression (Table 3), relative to the D-ePVS reference tertile (T2: 4.231-5.358), the adjusted ORs for D-ePVS lower tertile (T1 < 4.231) and higher tertile (T3 ≥ 5.358) were determined to be 1.56 [95% confidence interval (CI): 1.01-2.41, P = 0.045] and 1.87 (95%CI: 1.20-2.91, P = 0.005), respectively, which were associated with an increased AKI risk. At the same time, relative to the KH-ePVS reference tertile (T2: -7.425 to 2.669), we determined the adjusted ORs for KH-ePVS lower tertile (T1 < -7.425) and higher tertile (T3 ≥ 2.669) to be 1.11 (95%CI: 0.72-1.72, P = 0.629) and 1.51 (95%CI: 1.00-2.29, P = 0.05), respectively, after adjusting for covariates.

Table 3 Multiple logistic regression analysis for estimated plasma volume status and acute kidney injury in acute pancreatitis population.
Non-adjusted model
Adjusted model 1
Adjusted model 2
Adjusted model 3
OR (95%CI)
P value
OR (95%CI)
P value
OR (95%CI)
P value
OR (95%CI)
P value
D-ePVS, tertiles
T11.8 (1.25-2.57)0.0011.65 (1.13-2.41)0.011.54 (1.04-2.29)0.0321.56 (1.01-2.41)0.045
T2Ref.Ref.Ref.Ref.
T32.14 (1.51-3.05)< 0.0012.32 (1.61-3.35)< 0.0012.31 (1.57-3.38)< 0.0011.87 (1.2-2.91)0.005
P for trend0.2360.0430.0280.469
KH-ePVS, tertiles
T11.36 (0.95-1.95)0.0891.28 (0.87~1.89)0.2111.18 (0.79-1.77)0.4141.11 (0.72-1.72)0.629
T2Ref.Ref.Ref.Ref.
T31.94 (1.38-2.73)< 0.0011.83 (1.28-2.62)0.0011.4 (1.19-2.54)0.0041.51 (1-2.29)0.05
P for trend0.0230.0510.050.167
Kaplan-Hakim formula-based estimated plasma volume status of tertile 1: < -7.425; tertile 2: -7.425 to 2.669; tertile 3: ≥ 2.669. Duarte formula-based estimated plasma volume status of tertile 1: < 4.231; tertile 2: 4.231-5.358; tertile 3: ≥ 5.358. Model 1: Adjusted for age, sex and body mass index. Model 2: Adjusted for age, sex, body mass index, hypertension, diabetes mellitus, coronary heart disease, heart rate, respiratory rate and pulse oxygen saturation. Model 3: Adjusted for age, sex, hypertension, diabetes mellitus, coronary heart disease, heart rate, respiratory rate, pulse oxygen saturation, sequential organ failure assessment score, bedside index of severity in acute pancreatitis score, triglyceride, calcium, albumin, fasting plasma glucose, amylase, procalcitonin, C-reactive protein and white blood cell count. D-ePVS: Duarte formula-based estimated plasma volume status; KH-ePVS: Kaplan-Hakim formula-based estimated plasma volume status; OR: Odds ratio; CI: Confidence interval.
The nonlinear relationship of ePVS with AKI

When covariates from model 3 were adjusted, the nonlinear relations of D-ePVS and KH-ePVS with AKI were detected, exhibiting the U-shaped and J-shaped curves in the restricted cubic spline model, respectively (P for non-linearity < 0.01) (Figure 2).

Figure 2
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Figure 2 The nonlinear association between estimated plasma volume status and acute kidney injury in patients with acute pancreatitis. A: Duarte formula-based estimated plasma volume status; B: Kaplan-Hakim formula-based estimated plasma volume status. Adjusted for all covariates as model 3. Solid lines indicate the odds ratio of acute kidney injury and dotted lines represent the corresponding 95% confidence interval. Odds ratio = 1 was set as the reference line (takes the upper limit of 99.9%). D-ePVS: Duarte formula-based estimated plasma volume status; KH-ePVS: Kaplan-Hakim formula-based estimated plasma volume status; AKI: Acute kidney injury.

In threshold analysis, the data were fitted into the piecewise multiple logistic regression model using 2 distinct slopes (Table 4). An inflection point at 4.3 dL/g (D-ePVS) and -2.8% (KH-ePVS) was found in the population. AKI was positively related to ePVS above the inflection point for both D-ePVS (OR = 1.235, 95%CI: 1.079-1.413, P = 0.002) and KH-ePVS (OR = 1.032, 95%CI: 1.005-1.060, P = 0.019). By contrast, below the inflection point, a negative correlation was observed between AKI and D-ePVS (OR = 0.579, 95%CI: 0.335-0.998, P = 0.049), while AKI was not significantly related to KH-ePVS (OR = 0.990, 95%CI: 0.953-1.028, P = 0.601).

Table 4 Threshold effect analysis of estimated plasma volume status and acute kidney injury using two-piecewise regression models.
Two-piecewise linear regression modelD-ePVS
KH-ePVS
OR (95%CI)
P value
OR (95%CI)
P value
Inflection point (K)4.3-2.8
ePVS < K0.579 (0.335-0.998)0.0490.990 (0.953-1.028)0.601
ePVS ≥ K1.235 (1.079-1.413)0.0021.032 (1.005-1.06)0.019
Likelihood ratio test0.0020.006
Non-linear test0.0210.025
Adjusted for all covariates as model 3. ePVS: Estimated plasma volume status; D-ePVS: Duarte formula-based estimated plasma volume status; KH-ePVS: Kaplan-Hakim formula-based estimated plasma volume status; OR: Odds ratio; CI: Confidence interval.
Subgroup analysis

Our exploratory subgroup analyses, using two different formulae, consistently revealed an association between ePVS and AKI across various stratified variables including age, sex, hypertension, DM, SOFA score, and AP severity, and significant interaction effects were not observed (P for interaction > 0.05) (Figure 3).

Figure 3
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Figure 3 The relationship between estimated plasma volume status and acute kidney injury according to basic features. Except for the stratification component itself, each stratification factor was adjusted for all covariates as model 3. D-ePVS: Duarte formula-based estimated plasma volume status; KH-ePVS: Kaplan-Hakim formula-based estimated plasma volume status; OR: Odds ratio; CI: Confidence interval; AP: Acute pancreatitis; SOFA: Sequential organ failure assessment.
DISCUSSION

Fluid management is a crucial and manageable risk factor related to AKI, especially for critical cases. Our study assessed the calculated PVS as the fluid status indicator in a large cohort with AP, showing several key results. At first, most AKI patients show the PVS on admission with KH-ePVS, suggesting excessive PV. Secondly, the U-shaped and J-shaped curves for ePVS corresponded with a graded increase in AKI risk in AP, with the optimal serum ePVS for minimal AKI risk identified at 4.3 dL/g using the Duarte formula and -2.8% with the Kaplan-Hakim formula, respectively. Finally, both formulae indicated the same nonlinear association, and subgroup analysis demonstrated the robust relationship between ePVS and AKI. Our findings indicate that ePVS is the valuable predictor for AKI risk among AP patients and warrants further investigation as a potential therapeutic target.

ePVS has been shown to associate with actual PV based on the 125Iodine-labeled human serum ALB method[28] or Technetium (99Tc)-labeled red blood cells[15] techniques, which is a key factor suggesting fluid status in chronic heart failure patients. Expansion of PV is correlated with dismal clinical results for heart failure patients[28-30]. In addition, ePVS has been used in the non-cardiovascular settings. Kurokawa et al[31] suggested the association of ePVS with the severity and unfavorable results of lower extremity artery disease. Gao et al[32] identified a J-shaped relationship between extracellular fluid volume (ePVS) and 28-day mortality in septic patients at the time of ICU admission, reporting a significantly increased mortality risk when ePVS exceeds 6.52 dL/g. Similarly, Kobayashi et al[30] demonstrated that an optimal cutoff of ePVS greater than 5.5 mL/g is associated with adverse outcomes in patients with heart failure and preserved ejection fraction, with mortality rates of 21.6% and hospitalization rates of 24.7% due to heart failure. Our study, which identified a breakpoint of 4.3 dL/g, presents comparable breakpoint values that may reflect differences in the patient populations analyzed. Based on Laou et al[33], increased perioperative KH-ePVS level was related to organ damage and complications postoperatively. Turcato et al[16] proposed the application of ePVS as a valuable additional predictive tool for assessing illness severity in fever patients when ePVS exceeded 4.52 dL/g. Based on Marawan and Qayyum[34], the elevated ePVS was related to a higher all-cause mortality risk among healthy subjects, and the hazard ratio was 1.29 (1.24-1.35, P < 0.001). The PARADISE cohort[35] demonstrated that higher ePVS values at admission in emergency department were correlated with the increased acute heart failure or in-hospital mortality risk among acute dyspnea cases. This study extended the above results by examining the role of ePVS among AP patients.

AKI and AP are both characterized by significant inflammatory responses. Elevated levels of pro-inflammatory cytokines, such as tumor necrosis factor-α and interleukin-6, can lead to complications including endothelial dysfunction and microvascular injury in both the kidneys and the pancreas[36,37]. Endothelial dysfunction may result from impaired nitric oxide production and increased vascular permeability, which can contribute to fluctuations in fluid status, potentially manifesting as U-shaped and J-shaped curves in D-ePVS and KH-ePVS, respectively. Furthermore, the release of pro-inflammatory cytokines in patients with AP may exacerbate fluid imbalances and alter vascular reactivity, thereby reinforcing the associations observed in our study.

Our study has first investigated the association of ePVS with AKI among AP, concentrating on volume status as a key factor in AKI development[1,38,39]. In early AP, capillary leak and fluid retention in the “third space” may result in hemodynamic instability, notable hypovolemia and hypotension, impairing renal perfusion and increasing the risk of pre-renal AKI, accompanied by a reduction in ePVS. Aggressive volume resuscitation is often necessary to reverse AKI. By contrast, volume overload can increase ePVS, potentially triggered by reduced urine output, inadequately fluid administered for over-resuscitation. The excess circulating volume can cause renal congestion, renal interstitial oedema, renal compartment syndrome and intra-abdominal hypertension[38], which may further impair renal function[40-42]. Therefore, both volume overload and depletion adversely impact renal function, which can highlight the complex interplay of factors affecting renal outcomes in AP.

Managing fluid balance in AP patients is challenging, and ePVS provides a valuable supplementary measure. Point-of-care ultrasound (POCUS) is the main non-invasive method for the assessment of volume status in the critical care settings[43]. However, its use in AP patients can be challenging owing to complications including acute dyspnea, abdominal distension, ascites, abdominal pain and bowel gas obstruction. In addition, POCUS is not widely used to manage AKI in most centers worldwide. Furthermore, it is of note that, as a tertiary teaching hospital, our medical center usually receives AP patients who have already undergone initial fluid resuscitation elsewhere before transfer, complicating the evaluation of fluid status. Considering these challenges, we believe ePVS can easily and quickly assess volume status upon hospital admission, especially for critical AP cases.

This study examined ePVS using two methods for the calculation of PV: D-ePVS and KH-ePVS. The D-ePVS formula, based on the Strauss formula[44], uses hematocrit and HGB for an ‘instantaneous’ PV measurement, as described by Duarte et al[45] in 2015. By contrast, the KH-ePVS formula (expressed as a percentage deviation from iPV) calculates hematocrit and patient body dry weight[28,46], while these two values can hardly be obtained in fluid-overload patients[35], and weight estimation among critical cases is usually unreliable. Severe AP patients are more vulnerable to develop AKI owing to insufficient blood volume and overload. Therefore, D-ePVS may be more dependable and practical for critically ill patients compared with KH-ePVS. Furthermore, D-ePVS follows a U-shaped curve, aligning closely with pathophysiology of AKI and clinical characteristics, which indicates that both lower and higher volumes can cause AP-AKI, whereas KH-ePVS is represented as a J-shaped curve. Our study suggests that D-ePVS of 4.3 dL/g may serve as a promising complement to other fluid status evaluation methods in AP, like physical examinations, biomarkers, ultrasound, or dynamic fluid responses to maneuvers including passive leg raise. Specifically, the inflection point of 4.3 dL/g for D-ePVS may signify a critical balance in hemodynamic status, whereby deviations from this threshold could indicate an increased risk of fluid overload or compromised perfusion, potentially resulting in adverse clinical outcomes. For the J-shaped curve of KH-ePVS, the inflection point of -2.8% highlights a notable shift in the association between fluid status and clinical outcomes. This suggests that patients with KH-ePVS values above this threshold may be at an elevated risk for fluid overload and subsequent organ dysfunction. Besides, focusing on AP cases with ePVS to achieve optimal fluid management and early de-resuscitation may be a promising approach requiring further investigation. Nevertheless, the optimal method for fluid balance assessment at the ICU is still unclear[38,39]. More studies may also examine the association of PVS with physical indicators for volume status, relevant biomarkers, as well as its correlation with imaging techniques like ultrasound.

CONCLUSION

To conclude, this study reveals the obvious nonlinear relation of serum ePVS with AKI, with the inflection points at 4.3 dL/g (Duarte formula) and -2.8% (Kaplan-Hakim formula). Given its wide availability, low cost, rapid quantification, and routine use in hemogram prescriptions, ePVS can serve as the important approach to diagnose and stratify AKI patients. Furthermore, more investigations are needed to validate our findings and explore mechanisms related to the ePVS-AKI relationship.

Footnotes

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Gastroenterology and hepatology

Country of origin: China

Peer-review report’s classification

Scientific Quality: Grade B, Grade C

Novelty: Grade A, Grade B

Creativity or Innovation: Grade B, Grade C

Scientific Significance: Grade B, Grade B

P-Reviewer: Mao EQ; Xu M S-Editor: Fan M L-Editor: A P-Editor: Wang WB

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