Construction and Validation of a Nomogram for Predicting Acute Kidney Injury After Pancreatoduodenectomy

Construction and Validation of a Nomogram for Predicting Acute Kidney Injury After Pancreatoduodenectomy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Construction and Validation of a Nomogram for Predicting Acute Kidney Injury After Pancreatoduodenectomy Wenwen Zhang, Zengyuan Qin, JunTao Wang, Chunling Huang, Xiaoru Zhao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7141144/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Acute kidney injury (AKI) after pancreatoduodenectomy is common and early identification of such patients is critical. The nomogram, a visual predictive model, is commonly used to predict AKI after various types of surgery. We aimed to construct and evaluate a predictive nomogram for postoperative AKI in patients undergoing pancreaticoduodenectomy. Methods In a retrospective cohort study, we examined 844 adult patients who underwent pancreaticoduodenectomy from December 2016 to June 2020. All enrolled patients were randomly assigned to the training and validation cohorts in a 7:3 ratio. We utilized LASSO regression for feature selection. A nomogram was constructed using multivariate logistic regression. The nomogram's performance was assessed using various metrics such as the receiver operating characteristic curve, calibration curves, Hosmer-Lemeshow goodness of fit, and decision curve analysis. Results In this cohort, AKI was observed in 98 out of 844 patients, representing an incidence rate of 11.6%. Multivariate logistic analysis showed that direct bilirubin (DBIL), blood loss, urine output, intensive care unit (ICU) transfer were independent influencing factors of postoperative AKI. The nomogram, incorporating the four identified factors, demonstrated moderate discrimination in both the training and validation cohorts, achieving AUC values of 0.720 and 0.772, respectively. The Hosmer-Lemeshow goodness of fit test and the calibration curve demonstrate good agreement between predicted and observed values. The decision curve analysis (DCA) indicated a positive net clinical benefit. Conclusions We developed and validated a nomogram model that could help identify individuals at risk of AKI following pancreaticoduodenectomy. This model may help clinicians optimize perioperative management for these patients. acute kidney injury pancreatoduodenectomy nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Pancreatoduodenectomy is currently the primary treatment for pancreatic and ampulla lesions, both benign and malignant. Despite significant improvements in surgical techniques and perioperative care reducing mortality rates for this procedure from over 20% to less than 2% [ 1 ], complication rates remain high, occurring in 29–50% of cases [ 2 – 4 ]. Notably, acute kidney injury (AKI) constitutes a significant proportion of these complications, occurring in 5.9%-20.4% of cases [ 5 – 10 ]. A study with over 800,000 participants identified a heightened risk of AKI necessitating kidney replacement therapy within 14 days post-pancreatic surgery [ 11 ]. In addition, AKI after pancreaticoduodenectomy is associated with adverse outcomes. Swartling et al. discovered that post-pancreaticoduodenectomy AKI is linked to a higher Clavien-Dindo classification, as well as an elevated risk of ICU transfer and 30- and 90-day mortality [ 9 ]. According to a Chinese cohort study [ 10 ], the development of AKI following pancreaticoduodenectomy in pancreatic ductal adenocarcinoma patients independently predicted both major complications and mortality within 30 days. Thus, early detection of AKI after pancreaticoduodenectomy, as well as active intervention, are critical for lowering the incidence of postoperative AKI. Existing literature has identified multiple perioperative factors associated with AKI development following pancreaticoduodenectomy [ 9 , 10 ]. Efforts have been made to create strategies to prevent and treat AKI after pancreaticoduodenectomy, based on identified risk factors. However, clinical prediction models for this surgical complication are still lacking. To our knowledge, only one study has created a predictive model for AKI after pancreatic surgery [ 12 ]. However, pancreaticoduodenectomy is a vital and complex abdominal surgery with a lengthy operation time, severe trauma, a high complication rate, and we believe it should be studied independently. Consequently, the present study was designed to develop a predictive nomogram for postoperative AKI in pancreaticoduodenectomy patients. This tool could help surgeons stratify patients based on the risk of AKI, allowing them to implement appropriate prevention and treatment methods. 2. Materials and Methods 2.1 Study population This retrospective single-center study was conducted at Henan Provincial People's Hospital with approval from the ethics committee (Approval No. 2021-Lunshen-77). Due to the study's retrospective design, informed consent was not required. The study analyzed 844 adult patients who had pancreaticoduodenectomy from December 2016 to June 2020. All participants had documented preoperative serum creatinine levels and at least one postoperative measurement within the first 7 days after surgery. Exclusion criteria included: (1) a history of urologic procedures such as nephrectomy, renal transplantation, or urinary obstruction relief, due to potential confounding effects on postoperative creatinine levels; (2) preoperative AKI; (3) chronic kidney disease, indicated by an estimated glomerular filtration rate (eGFR) below 60 mL/min/1.73 m²; and (4) a need for dialysis. 2.2. Data collection Data extracted from the electronic medical record database encompassed demographic details, comorbidities, medication history prior to surgery, preoperative lab results, intraoperative information, and postoperative factors. Demographic characteristics encompassed both age and sex. Comorbidities included hypertension, coronary heart disease, diabetes mellitus. The preoperative medication history comprised contrast agents, diuretics, non-steroidal anti-inflammatory drugs(NSAIDs), angiotensin-converting enzyme inhibitors(ACEI), and angiotensin receptor blockers (ARB). Preoperative laboratory data encompassed measurements of white blood cells (WBC), neutrophils (NEUT), lymphocytes (LYMPH), red blood cells (RBC), hemoglobin (HGB), platelets (PLT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin (ALB), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), blood urea nitrogen (BUN), serum creatinine (SCr), estimated glomerular filtration rate (eGFR), uric acid (UA), retinol-binding proteins (RBP), cystatin C (CysC), prothrombin time (PT), prothrombin time activity (PTA), international normalized ratio (INR), activated partial thromboplastin time (APTT), fibrinogen (FBG), and thrombin time (TT). Intraoperative variables encompassed vasopressor use, blood transfusion, blood loss, urine output, operative duration, and hypotension. Postoperative factors include immediate ICU transfer following surgery, termed as ICU transfer. Intraoperative hypotension was characterized by a mean arterial pressure below 65 mmHg for over 10 cumulative minutes during surgery [ 13 ]. The eGFR was determined using the Chronic Kidney Disease Epidemiology Collaboration Group formula [ 14 ]. 2.3. Outcome The main outcome was postoperative AKI, defined by KDIGO criteria as either a rise in serum creatinine (SCr) of ≥ 0.3 mg/dL (≥ 26.5 µmol/L) within 48 hours post-surgery or an increase in SCr to ≥ 1.5 times the baseline within 7 days post-surgery. Since the urine volume of most postoperative patients was not available, the KDIGO urine output standard was not used in this study. 2.4. Statistical analysis Patients were randomly divided into two groups: 70% for training and 30% for validation. Create the nomogram using the training cohort, then utilize the validation cohort for internal validation. Visualize missing data through the plot_missing function in the DataExplorer package. Then use multiple imputation to fill in missing data. Continuous variables with a normal distribution are presented as mean ± standard deviation, non-normally distributed variables as median (interquartile range), and categorical variables as percentages of the total. An independent sample t-test was used to compare the two groups for measurements with a normal distribution and equal variance. Data with non-normal distribution or non-equal variance will be compared between groups using the Mann-Whitney U test. The counting data for the two groups were analyzed using Pearson's chi-square test or Fisher's exact test. Comparison between two groups using CBCgrps package [ 16 ]. We employed LASSO analysis using the glmnet package to filter the variables. A multivariate logistic regression analysis was conducted using the stats package to identify independent influencing factors. The rms package's nomogram function is used to built a nomogram model from the selected independent influencing factors. The nomogram's discrimination ability was evaluated by calculating the area under the curve (AUC). The pROC function from the rms package was utilized to create a receiver operating characteristic (ROC) curve. The Hosmer-Lemeshow goodness of fit test was conducted using the ResourceSelection package, while the rms package facilitated the calibration curve to assess the nomogram model's fit. To assess clinical practicability, the rmda package was used to generate a decision curve analysis (DCA) curve. Statistical analyses were performed using R software (version 4.2.1), with significance defined as p 0.05 was considered significant. 3. Results 3.1. Baseline Characteristics of the Study Participants The study included 844 patients (Fig. 1 ), and 11.6% of them experienced postoperative AKI. All patients' clinical data were collected, and it was found that the missing ratio of CysC and RBP was 15.28% and 15.05%, respectively, and the missing ratio of other clinical data was less than 0.36%, or even not ( Supplementary Fig. 1 ). In a 7:3 ratio, we assigned 590 patients to the training cohort and 254 patients to the validation cohort. Table 1 presents the baseline data for both the training and validation cohorts. The two groups showed no significant differences in demographic characteristics, laboratory data, or surgery-related factors. In the training cohort, 11.7% (69/590) of patients developed AKI, and AKI group exhibited elevated TBIL, DBIL, IBIL, and CysC levels, reduced eGFR, and a greater incidence of ICU transfers (all p < 0.05) ( Supplementary Table 1 ). In the validation group, 11.4% (29 out of 254) of patients experienced AKI, and those in the AKI group exhibited elevated BUN and CysC levels, longer surgery times, and a greater likelihood of ICU transfer (all p <0.05) ( Supplementary Table 2 ). Table 1 Demographic, clinical characteristics and surgery-related factors of the training and validation cohorts Variables All (n = 844) Validation cohorts (n = 254) Training cohorts (n = 590) P value Baseline variables Age (years) 60.00 (51.00, 67.00) 59.50 (51.00, 66.00) 61.00 (51.00, 67.00) 0.403 Sex, (%) 0.931 Man 492 (58.29%) 147 (57.87%) 345 (58.47%) Women 352 (41.71%) 107 (42.13%) 245 (41.53%) Coexisting conditions Hypertension, (%) 223 (26.42%) 62 (24.41%) 161 (27.29%) 0.433 Coronary heart disease, (%) 58 (6.87%) 17 (6.69%) 41 (6.95%) 1.000 Diabetes mellitus, (%) 141 (16.71%) 38 (14.96%) 103 (17.46%) 0.429 Medication history Contrast 690 (81.75%) 208 (81.89%) 482 (81.69%) 1.000 Diuretics 77 (9.12%) 28 (11.02%) 49 (8.31%) 0.259 NSAIDs 144 (17.06%) 41 (16.14%) 103 (17.46%) 0.714 ACEI/ARB 206 (24.41%) 57 (22.44%) 149 (25.25%) 0.432 Laboratory parameters WBC (10 9 /L) 6.14 (4.93, 7.40) 6.08 (4.89, 7.47) 6.16 (4.97, 7.34) 0.656 NEUT (10 9 /L) 3.86 (2.94, 5.13) 3.82 (2.88, 5.03) 3.88 (2.98, 5.14) 0.458 LYMPH (10 9 /L) 1.43 (1.10, 1.83) 1.46 (1.11, 1.90) 1.42(1.10, 1.81) 0.508 RBC (10 12/ L) 4.03 ± 0.59 4.05 ± 0.57 4.01 ± 0.60 0.389 HGB (g/L) 124.00 (111.00, 135.00) 125.50 (113.00, 135.00) 124.00 (111.00, 135.00) 0.653 PLT (10 9 /L) 234.00 (187.00, 293.00) 235.00 (189.25, 289.00) 233.50 (186.25, 293.00) 0.590 ALT (U/L) 110.10 (32.15, 226.57) 109.60 (34.42, 238.98) 110.10 (31.55, 217.30) 0.542 AST (U/L) 76.35 (29.00, 157.00) 72.50 (27.02, 142.02) 78.95 (29.70, 160.88) 0.497 ALB (g/L) 37.60 (34.10, 40.82) 37.65 (34.80, 40.98) 37.60 (33.90, 40.80) 0.301 TBIL (µmol/L) 60.60 (12.45, 172.12) 46.55 (12.03, 159.73) 67.40 (13.12, 177.90) 0.235 DBIL (µmol/L) 41.75 (4.47, 127.15) 32.40 (3.90, 120.67) 46.20 (4.82, 130.62) 0.144 IBIL (µmol/L) 16.90 (7.30, 44.25) 15.70 (7.20, 41.10) 18.05 (7.32, 45.77) 0.266 BUN (mmol/L) 4.60 (3.67, 5.58) 4.72 (3.76, 5.74) 4.55 (3.65, 5.50) 0.134 SCr (µmol/L) 54.00 (45.00, 62.00) 54.00 (46.00, 62.00) 53.00 (45.00, 62.00) 0.462 eGFR [ml●min − 1 ●(1.73m 2 ) −1 ] 104.90 (96.82, 114.02) 105.12 (96.97, 113.83) 104.88 (96.80, 114.15) 0.973 UA (µmol/L) 215.50 (164.00, 271.25) 221.50 (167.00, 281.75) 213.00 (162.00, 269.75) 0.211 RBP (mg/L) 29.00 (23.00, 35.50) 30.00 (24.00 36.00) 28.65 (23.00, 35.20) 0.193 CysC (mg/L) 0.89 (0.79, 1.00) 0.87 (0.79, 0.99) 0.90 (0.79, 1.01) 0.143 PT (s) 11.90 (11.20, 12.70) 11.90 (11.10, 12.60) 11.90 (11.20, 12.80) 0.148 PTA(%) 117.00 (99.00, 140.00) 119.00 (103.25, 144.00) 115.50 (97.00, 139.00) 0.064 INR(INR) 0.92 (0.84, 1.00) 0.91 (0.84, 0.99) 0.93 (0.85, 1.02) 0.059 APTT (s) 33.10 (30.20, 36.30) 33.00 (29.80, 36.08) 33.15 (30.40, 36.38) 0.184 FBG(g/L) 3.95 (3.18, 4.80) 3.94 (3.06, 4.79) 3.96 (3.28, 4.81) 0.326 TT(s) 17.10 (16.00, 18.12) 16.95 (16.00, 17.80) 17.10 (16.10, 18.20) 0.226 Intraoperative variables Vasopressor use, (%) 67 (7.94%) 21 (8.27%) 46 (7.8%) 0.926 Blood transfusion, (%) 393 (46.56%) 115 (45.28%) 278 (47.12%) 0.677 Blood loss (ml) 300 (200, 500) 300 (200, 500) 300 (200, 500) 0.405 Urine output (ml) 775 (500, 1000) 700 (500, 1000) 800 (500, 1000) 0.820 Operative duration (min) 370.00 (315.00, 455.00) 362.50 (320.00, 459.25) 372.50 (315.00, 451.50) 0.949 Intraoperative hypotension, (%) 102 (12.09%) 28 (11.02%) 74 (12.54%) 0.613 Postoperative variables ICU transfer, (%) 157 (18.60%) 43 (16.93%) 114 (19.32%) 0.470 NSAIDs non-steroidal anti-inflammatory drugs; ACEI/ARB, angiotensin-converting enzyme inhibitors and angiotensin receptor blockers; WBC, white blood cells; NEUT, neutrophil; LYMPH, lymphocytes; RBC, red blood cells; HGB, hemoglobin; PLT, platelets; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALB, albumin; TBIL, total bilirubin; DBIL, direct bilirubin; IBIL, indirect bilirubin; BUN, blood urea nitrogen; SCr, serum creatinine; eGFR, estimated glomerular filtration rate; UA, uric acid; RBP, retinol-binding proteins; CysC, cystatin C; PT, prothrombin time; PTA, prothrombin time activity; INR, international normalized ratio; APTT, partial thromboplastin time; FBG, fibrinogen; TT, thrombin time; ICU, intensive care unit. 3.2 Construction of Nomogram To identify potential influencing factors, all of the variables in Table 1 were included in the LASSO regression model (Fig. 2 A and Fig. 2 B). The results showed that in the training group, the coefficients of DBIL, eGFR, CysC, blood loss, urine output, and ICU transfer are non-zero. These variables were further rescreened using multivariate logistic regression. Finally, DBIL (OR = 1.005, 95%CI: 1.001–1.008, p = 0.005), blood loss (OR = 1.001, 95%CI: 1.000-1.002, p = 0.020), urine output (OR = 0.999, 95%CI: 0.999–0.9998, p = 0.009), and ICU transfer (OR = 3.865, 95%CI: 2.155–6.904, p < 0.001) were identified as independent influencing factors (Table 2 ). A nomogram was developed using these four characteristics to evaluate the risk of postoperative AKI in patients undergoing pancreaticoduodenectomy(Fig. 3 ). Table 2 Multivariable analysis in the training cohort Variable OR 95%CI P value DBIL (µmol/L) 1.005 1.001–1.008 0.005 eGFR [ml●min − 1 ●(1.73m 2 ) −1 ] 0.987 0.966–1.009 0.265 CysC (mg/L) 1.342 0.367–4.068 0.623 Blood loss (ml) 1.001 1.000-1.002 0.020 Urine output (ml) 0.999 0.999–0.9998 0.009 ICU transfer, (%) 3.865 2.155–6.904 < 0.001 DBIL, direct bilirubin; eGFR, estimated glomerular filtration rate; CysC, cystatin C; ICU, intensive care unit. 3.3 Evaluation of the nomogram model To determine the discrimination of the nomogram model, we plot the ROC curve in the training sample and calculate the AUC. The training group's AUC was 0.720 with a 95% CI of 0.655 to 0.785 (Fig. 4 A). The calibration curve indicates strong alignment between predicted and actual probabilities (Fig. 5 A). The Hosmer-Lemeshow goodness of fit test for the training set yields a P value of 0.273, exceeding the 0.05 threshold, thus demonstrating the model's strong fitting capability (Fig. 5 A). Decision curve analysis demonstrated that the nomogram model provides clinical utility, as indicated by a net benefit ratio greater than zero for threshold probabilities between 0.05 and 0.83 (Fig. 6 A). 3.4 Confirming the Nomogram's Accuracy in the Validation Group External validation involved 254 patients, revealing that the nomogram model's ROC curve AUC was 0.772 (95% CI, 0.673–0.872), demonstrating moderate differentiation in the validation group. (Fig. 4 B). The calibration curve demonstrates consistency between actual and predicted AKI probabilities in the validation group (Fig. 5 B). The Hosmer-Lemeshow test yielded a P value of 0.319 (Fig. 5 B). Decision curve analysis demonstrated that the nomogram model offers clinical utility, as indicated by a net benefit ratio greater than zero for threshold probabilities between 0.05 and 0.42. (Fig. 6 B). 4. Discussion This study developed a nomogram model incorporating DBIL, blood loss, urine output, and ICU transfer to predict early-stage AKI risk following pancreaticoduodenectomy. The nomogram model demonstrates good differentiation, as evidenced by AUC values exceeding 0.7 in both the training and validation groups. The calibration curve shows that the nomogram model's predicted and actual probabilities are consistent, indicating high calibration ability. The decision curve showed a good net benefit rate for diagnosing AKI after pancreaticoduodenectomy using this model. There are some differences in the incidence of pancreaticoduodenectomy-related AKI among the available reports. In a Japanese nationwide cohort (n = 84,036), the AKI rate after pancreaticoduodenectomy was similar for robotic-assisted (7.0%) and open procedures (6.5%) [ 5 ]. In their retrospective analysis of 395 pancreaticoduodenectomy cases, Mahmooth et al.found a substantial 19.7% incidence of postoperative AKI. It should be noted that their study population was limited to patients who had received intraoperative intravenous fluids and had documented preoperative weights [ 6 ]. Park et al.conducted a study involving 809 pancreaticoduodenectomy patients, in which the AKI rate was documented at 5.3% [ 8 ]. Ji et al. analyzed 1312 patients having pancreaticoduodenectomy in a large hospital in China between 2013 and 2020 and discovered that the incidence of AKI was 10.7% [ 10 ]. Our study identified an 11.6% pooled incidence of postoperative AKI following pancreaticoduodenectomy, showing a slight variation from earlier research findings. First, our study included individuals undergoing pancreaticoduodenectomy for various reasons, such as malignant tumors, benign tumors, and inflammation, unlike the study by Ji et al. exclusively included patients with pancreatic ductal adenocarcinoma [ 10 ]. Secondly, the modes of operation are different. Aguayo et al. studied robotic-assisted vs open pancreaticoduodenectomy [ 5 ], while Park et al. discussed pylorus-preserving pancreaticoduodenectomy [ 8 ]. The low frequency of these two procedures in our study might have influenced the variation in incidence rates. Finally, variations in patient race across studies may affect AKI onset and progression. Bilirubin, a key indicator of liver function, results from the breakdown of aging red blood cells in the liver, spleen, and bone marrow mononuclear phagocyte system. The effects of bilirubin on the kidneys have been the subject of much debate in the medical field, and there are two conflicting views: whether bilirubin is nephrotoxic or nephroprotective. A case-control study of 36 jaundiced patients (serum total bilirubin > 3mg/dL) and 38 non-jaundiced patients found that urinary biomarkers indicating renal tubular injury were elevated in the jaundiced group. Logistic regression analysis revealed that jaundice heightened the risk of increased u-NGAL and u-B2M levels [ 17 ]. One major cause of AKI is ischemia-reperfusion damage. Hyperbilirubinemia, in cell and animal models, can aggravate renal ischemia reperfusion injury by enhancing mitophagy [ 18 ]. Elevated bilirubin concentrations have been consistently associated with heightened AKI risk across various clinical contexts, as demonstrated in recent investigations [ 10 , 19 – 22 ]. Similarly, in our investigation, DBIL was the important predictor, with a 1.005-fold increase in AKI risk for every unit (1µmol/L) increase in DBIL. In addition, bilirubin is also related to the poor prognosis of AKI. A study of 182,683 veterans identified elevated bilirubin levels as a risk factor for mortality in AKI patients within one year post-discharge [ 23 ]. Wang et al. discovered that total bilirubin level has been identified as a risk factor for mortality in patients with hemophagocytic lymphohistiocytosis complicated by AKI [ 22 ]. On the other side, bilirubin has been shown to lower oxidative stress, which may provide kidney protection. Chinese researchers developed multifunctional liposomes with bilirubin that alleviated acute kidney injury by reducing apoptosis, promoting mitochondrial autophagy, and lowering inflammation [ 24 ]. Huang et al. reported a kind of ε-polylysine-bilirubin conjugate nanoparticles coated with hyaluronic acid, which can be targeted to accumulate in the site of renal injury, protect mitochondrial structure and function, inhibit renal tubular cell apoptosis, and promote renal recovery [ 25 ]. The authors administered bilirubin to AKI rats post-ischemia and reperfusion, discovering its independent anti-oxidative and anti-apoptotic properties [ 25 ]. A South Korean study showed that higher bilirubin has a protective effect on the kidneys, although the overall bilirubin concentration in the study population was low, which cannot explain the effect of excessive bilirubin on the kidneys [ 26 ]. The association between bilirubin and the kidney needs to be further investigated. In addition, several clinical studies only include total bilirubin, which limits our understanding of the effects of direct and indirect bilirubin on the kidney. Future research should clarify the interaction between the three forms of bilirubin and the kidney. Our findings corroborate previous studies identifying intraoperative blood loss as an independent risk factor for postoperative AKI [ 27 ]. Several other studies have identified a link between intraoperative blood loss and postoperative AKI, although with different definitions for blood loss. A study indicated that intraoperative blood loss exceeding 1000 ml heightened the risk of postoperative AKI [ 10 ]. Li et al. identified intraoperative blood loss exceeding 400mL as a risk factor for AKI following pancreatic surgery [ 12 ]. An international prospective, observational, multicenter study found intraoperative bleeding to be a risk factor for AKI within 72 hours after major surgery, without specifying the bleeding volume [ 28 ]. Mechanically, intraoperative blood loss may produce hypotension, and the kidneys are vulnerable to ischemia and hypoxia, which can lead to AKI [ 29 ]. As a controllable factor, steps to limit intraoperative bleeding may aid in the reduction of postoperative AKI. Reduced urine volume during surgery is frequently a sign of renal hypoperfusion. Perspectives differ regarding the link between intraoperative urine output and postoperative acute kidney injury (AKI). A meta-analysis of nine noncardiac surgery-related studies revealed that intraoperative oliguria significantly elevates the risk of postoperative AKI and is linked to higher hospitalization mortality and increased postoperative renal replacement therapy demand [ 30 ]. A study of 2444 patients undergoing major abdominal surgery revealed that intraoperative oliguria is linked to a heightened risk of AKI [ 31 ]. A study examining the relationship between intraoperative urine output and AKI following laparoscopic pancreatic surgery found that low urine output was associated with AKI in cases without vascular reconstruction, but not in those with reconstruction [ 32 ]. Additionally, Goren et al. found that intraoperative oliguria was not a predictor of acute kidney injury (AKI) following open pancreatic surgery [ 33 ]. In our study, intraoperative urine output predicted postoperative AKI, but we did not define oliguria due to missing weight data in our cohort. Postoperative ICU transfers are frequent, with approximately 8-9.6% of major surgery patients requiring ICU care [ 34 , 35 ]. Pancreatoduodenectomy is a time-consuming and traumatic operation, which is a great challenge for both patients and doctors. In our analysis, 17.08% of the overall cohort was sent to the ICU following surgery, which is a considerable proportion. Previous studies have focused on the impact of AKI on ICU transfer. Bhasin et al. investigated patients receiving hematopoietic cell transplantation and found that individuals with AKI had a higher number of ICU transfers [ 36 ]. A Chinese study identified AKI linked to rhabdomyolysis as an independent risk factor for ICU transfer [ 37 ]. A study with the same cohort as ours also discovered that postoperative AKI increases the chance of ICU admission [ 9 ]. Our study identified immediate postoperative ICU admission as an independent risk factor for postoperative AKI. Patients transferred to the ICU are typically more gravely ill; their state of bodies is more complex, and their kidneys are more likely to be damaged as a result of many circumstances. We should be concerned about the renal function of such patients rather than focusing solely on the impact of AKI on negative outcomes such as death, duration of stay, hospitalization expenditures, ICU transfer, and so on. The study's limitations include the lack of routine postoperative urine output measurement, restricting AKI diagnosis to serum creatinine levels and potentially underestimating its incidence due to the inability to identify cases based on reduced urine volume. Additionally, the case source of this study is single, and while internal validation was performed, larger and multi-center sample sizes are still required to assess the clinical findings. 5. Conclusion In conclusion, the study identified four key predictors: preoperative DBIL, intraoperative blood loss, intraoperative urine output, and immediate postoperative ICU transfer to develop nomograms with good discriminative power in both training and validation cohorts, confirming the model's validity and applicability. The incidence of AKI after pancreaticoduodenectomy could be predicted clinically based on the sum of scores for each risk factor. Declarations Ethics approval and consent to participate Approval for the study was obtained from the Ethics Committees of Henan Provincial People's Hospital (Ethics approval number: 2021-Lunshen-77), and informed consent was secured from all participants. Consent for publication Not applicable. Availability of data and materials The data supporting this study's findings can be obtained from the corresponding author upon request. Competing interests The authors state they have no conflicts of interest. Funding This study was conducted at the Henan Provincial Clinical Research Center for Kidney Disease and the Henan Provincial Key Laboratory of Nephrology and Immunology, with support from the Funding of Zhongyuan Scholars of Henan Provincial Health Commission (No. 224000510005), Zhongyuan Scholar Workstation (No. 234400510024), Technology Attack Plan Project of Henan Province (No. 242102311062), and Medical Science and Technology Attack Plan Project of Henan Province (No. SBGJ202302002), the National Natural Science Foundation of China (No. 82100731), the Joint Fund Project of Henan Provincial Science and Technology Research and Development Plan (No. 225200810101). Authors' contributions WZ, ZQ and JW performed the data analyses and wrote the manuscript; CH, XZ and ZL arranged and collected data; LW and LY helped perform the analysis with profound discussions; YG and FS contributed to the conception and revision of the study. All authors reviewed and endorsed the final manuscript. Acknowledgments We express our gratitude to the patients who participated in this study. References Winter JM, Cameron JL, Campbell KA, Arnold MA, Chang DC, Coleman J, Hodgin MB, Sauter PK, Hruban RH, Riall TS, Schulick RD, Choti MA, Lillemoe KD, Yeo CJ . 1423 pancreaticoduodenectomies for pancreatic cancer: A single-institution experience. Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract. 2006;10(9):1199-210; discussion 210-1. Warrillow SJ, Weinberg L, Parker F, Calzavacca P, Licari E, Aly A, Bagshaw S, Christophi C, Bellomo R . Perioperative fluid prescription, complications and outcomes in major elective open gastrointestinal surgery. Anaesthesia and intensive care. 2010;38(2):259-65. van Rijssen LB, Zwart MJ, van Dieren S, de Rooij T, Bonsing BA, Bosscha K, van Dam RM, van Eijck CH, Gerhards MF, Gerritsen JJ, van der Harst E, de Hingh IH, de Jong KP, Kazemier G, Klaase J, van der Kolk BM, van Laarhoven CJ, Luyer MD, Molenaar IQ, Patijn GA, Rupert CG, Scheepers JJ, van der Schelling GP, Vahrmeijer AL, Busch ORC, van Santvoort HC, Groot Koerkamp B, Besselink MG . Variation in hospital mortality after pancreatoduodenectomy is related to failure to rescue rather than major complications: a nationwide audit. HPB : the official journal of the International Hepato Pancreato Biliary Association. 2018;20(8):759-67. Aoki S, Miyata H, Konno H, Gotoh M, Motoi F, Kumamaru H, Wakabayashi G, Kakeji Y, Mori M, Seto Y, Unno M . Risk factors of serious postoperative complications after pancreaticoduodenectomy and risk calculators for predicting postoperative complications: a nationwide study of 17,564 patients in Japan. Journal of hepato-biliary-pancreatic sciences. 2017;24(5):243-51. Aguayo E, Antonios J, Sanaiha Y, Dobaria V, Kwon OJ, Sareh S, Benharash P, King JC . Readmission and Resource Use After Robotic-Assisted versus Open Pancreaticoduodenectomy: 2010-2017. J SURG RES. 2020;255:517-24. Mahmooth Z, Jajja MR, Maxwell D, Ferez-Pinzon A, Sarmiento JM . Ultrarestrictive intraoperative intravenous fluids during pancreatoduodenectomy is not associated with an increase in post-operative acute kidney injury. AM J SURG. 2020;220(2):264-69. Mizota T, Dong L, Takeda C, Shiraki A, Matsukawa S, Shimizu S, Kai S . Transient acute kidney injury after major abdominal surgery increases chronic kidney disease risk and 1-year mortality. Journal of critical care. 2019;50:17-22. Park YS, Jun IG, Go Y, Song JG, Hwang GS . Comparison of acute kidney injury between open and laparoscopic pylorus-preserving pancreaticoduodenectomy: Propensity score analysis. PLoS One. 2018;13(8):e0202980. Swartling O, Evans M, Larsson P, Gilg S, Holmberg M, Klevebro F, Löhr M, Sparrelid E, Ghorbani P . Risk factors for acute kidney injury after pancreatoduodenectomy, and association with postoperative complications and death. PANCREATOLOGY. 2023;23(2):227-33. Ji Y, Zhou Y, Shen Z, Chen H, Zhao S, Deng X, Shen B . Risk factors for and prognostic values of postoperative acute kidney injury after pancreaticoduodenectomy for pancreatic ductal adenocarcinoma: A retrospective, propensity score-matched cohort study of 1312 patients. Cancer Med. 2023;12(7):7823-34. Wilson TA, de Koning L, Quinn RR, Zarnke KB, McArthur E, Iskander C, Roshanov PS, Garg AX, Hemmelgarn BR, Pannu N, James MT . Derivation and External Validation of a Risk Index for Predicting Acute Kidney Injury Requiring Kidney Replacement Therapy After Noncardiac Surgery. JAMA Netw Open. 2021;4(8):e2121901. Li S, Ren W, Ye X, Zhang L, Song B, Guo Z, Bian Q . An online-predictive model of acute kidney injury after pancreatic surgery. AM J SURG. 2024;228:151-58. Salmasi V, Maheshwari K, Yang D, Mascha EJ, Singh A, Sessler DI, Kurz A . Relationship between Intraoperative Hypotension, Defined by Either Reduction from Baseline or Absolute Thresholds, and Acute Kidney and Myocardial Injury after Noncardiac Surgery: A Retrospective Cohort Analysis. ANESTHESIOLOGY. 2017;126(1):47-65. 2017(0003-3022):47-65. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J . A new equation to estimate glomerular filtration rate. ANN INTERN MED. 2009;150(9):604-12. Kellum JA, Lameire N . Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). CRIT CARE. 2013;17(1):204. Zhang Z, Gayle AA, Wang J, Zhang H, Cardinal-Fernández P . Comparing baseline characteristics between groups: an introduction to the CBCgrps package. ANN TRANSL MED. 2017;5(24):484. 2017(2305-5839): 484. Scilletta S, Leggio S, Di Marco M, Miano N, Musmeci M, Marrano N, Natalicchio A, Giorgino F, Bosco G, Di Giacomo Barbagallo F, Scamporrino A, Di Mauro S, Filippello A, Scicali R, Russello M, Spadaro L, Purrello F, Piro S, Di Pino A . Acute hyperbilirubinemia determines an early subclinical renal damage: Evaluation of tubular biomarkers in cholemic nephropathy. LIVER INT. 2024;44(9):2341-50. Liao P, Wang X, Dong H, Chai D, Yue Z, Lyu L . HYPERBILIRUBINEMIA AGGRAVATES RENAL ISCHEMIA REPERFUSION INJURY BY EXACERBATING PINK1-PARKIN-MEDIATED MITOPHAGY. SHOCK. 2023;60(2):262-71. Kim H, Ali R, Short S, Kaunfer S, Krishnamurthy S, Durai L, Yilmam O, Shenoy T, Monson AE, Thomas C, Park I, Martini D, Newcomb R, Shapiro RM, Soiffer RJ, DeFilipp Z, Baron RM, Gupta S, Sise ME, Leaf DE . AKI treated with kidney replacement therapy in critically Ill allogeneic hematopoietic stem cell transplant recipients. BONE MARROW TRANSPL. 2024;59(2):178-88. Tyson LD, Atkinson S, Hunter RW, Allison M, Austin A, Dear JW, Forrest E, Liu T, Lord E, Masson S, Nunes J, Richardson P, Ryder SD, Wright M, Thursz M, Vergis N . In severe alcohol-related hepatitis, acute kidney injury is prevalent, associated with mortality independent of liver disease severity, and can be predicted using IL-8 and micro-RNAs. ALIMENT PHARM THERAP. 2023;58(11-12):1217-29. Zheng L, Lin Y, Fang K, Wu J, Zheng M . Derivation and validation of a risk score to predict acute kidney injury in critically ill cirrhotic patients. HEPATOL RES. 2023;53(8):701-712. Wang S, Zhou J, Yang J, Wang X, Chen X, Ji L, Yang L . Clinical features and prognostic factors of acute kidney injury caused by adult secondary hemophagocytic lymphohistiocytosis. J NEPHROL. 2022;35(4):1223-33. Griffin BR, Vaughan-Sarrazin M, Perencevich E, Yamada M, Swee M, Sambharia M, Girotra S, Reisinger HS, Jalal D . Risk Factors for Death Among Veterans Following Acute Kidney Injury. AM J MED. 2023;136(5):449-57. Shen Z, Wang X, Lu L, Wang R, Hu D, Fan Z, Zhu L, Zhong R, Wu M, Zhou X, Cao X . Bilirubin-Modified Chondroitin Sulfate-Mediated Multifunctional Liposomes Ameliorate Acute Kidney Injury by Inducing Mitophagy and Regulating Macrophage Polarization. ACS APPL MATER INTER. 2024;16(45):62693-709. Huang ZW, Shi Y, Zhai YY, Du CC, Zhai J, Yu RJ, Kou L, Xiao J, Zhao YZ, Yao Q . Hyaluronic acid coated bilirubin nanoparticles attenuate ischemia reperfusion-induced acute kidney injury. J CONTROL RELEASE. 2021;334:275-89. Park S, Kim DH, Hwang JH, Kim YC, Kim JH, Lim CS, Kim YS, Yang SH, Lee JP . Elevated bilirubin levels are associated with a better renal prognosis and ameliorate kidney fibrosis. PLoS One. 2017;12(2):e0172434. Yu Y, Zhang C, Zhang F, Liu C, Li H, Lou J, Xu Z, Liu Y, Cao J, Mi W . Development and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study. BMC Anesthesiol. 2022;22(1):22. Zarbock A, Weiss R, Albert F, Rutledge K, Kellum JA, Bellomo R, Grigoryev E, Candela-Toha AM, Demir ZA, Legros V, Rosenberger P, Galán Menéndez P, Garcia Alvarez M, Peng K, Léger M, Khalel W, Orhan-Sungur M, Meersch M . Epidemiology of surgery associated acute kidney injury (EPIS-AKI): a prospective international observational multi-center clinical study. INTENS CARE MED. 2023;49(12):1441-55. Lankadeva YR, May CN, Bellomo R, Evans RG . Role of perioperative hypotension in postoperative acute kidney injury: a narrative review. BRIT J ANAESTH. 2022;128(6):931-48. Pang Z, Liang S, Xing M, Zhou N, Guo Q, Zou W . The correlation of intraoperative oliguria with acute kidney injury after noncardiac surgery: a systematic review and meta-analysis. INT J SURG. 2023;109(3):449-57. Myles PS, McIlroy DR, Bellomo R, Wallace S . Importance of intraoperative oliguria during major abdominal surgery: findings of the Restrictive versus Liberal Fluid Therapy in Major Abdominal Surgery trial. BRIT J ANAESTH. 2019;122(6):726-33. Valencia Morales DJ, Plack DL, Kendrick ML, Schroeder DR, Sprung J, Weingarten TN . Urine output and acute kidney injury following laparoscopic pancreas operations. HPB. 2022;24(11):1967-74. Goren O, Levy A, Cattan A, Lahat G, Matot I . Acute kidney injury in pancreatic surgery; association with urine output and intraoperative fluid administration. AM J SURG. 2017;214(2):246-50. Pearse RM, Moreno RP, Bauer P, Pelosi P, Metnitz P, Spies C, Vallet B, Vincent JL, Hoeft A, Rhodes A . Mortality after surgery in Europe: a 7 day cohort study. LANCET. 2012;380(9847):1059-65. Jerath A, Laupacis A, Austin PC, Wunsch H, Wijeysundera DN . Intensive care utilization following major noncardiac surgical procedures in Ontario, Canada: a population-based study. INTENS CARE MED. 2018;44(9):1427-35. Bhasin B, Ber Ce P, Szabo A, Chhabra S, D'Souza A . Correlates and Outcomes of Early Acute Kidney Injury after Hematopoietic Cell Transplantation. AM J MED SCI. 2021;362(1):72-77. Zhu DC, Li WY, Zhang JW, Tong JS, Xie WY, Qin XL, Zhang XC . Rhabdomyolysis-associated acute kidney injury: clinical characteristics and intensive care unit transfer analysis. INTERN MED J. 2022;52(7):1251-57. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7141144","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499116462,"identity":"039506f7-4a73-4be3-885c-51d2b5f49310","order_by":0,"name":"Wenwen Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie3PsWoCQRCA4Q3C2Sy5dsTkQhrrkYNUQl5lh4WrDFpeYbFycleovY9hmXJlYatN0lpeEFKbJlhYxD7BPTuL/er5mRnGguAKRW1F9R4hieNiU4t84k9uud71V+NB2llZibWz/iQB+uryfUZrPex1PmetBodxI5GjIaVdlJOKWFzNheeXkmpAk06Lpd3S6x0D97b2bHESEc19wd6zLbmIIbx4Ehj9gEBzU7Lh05jKVpNEZKAxe1ycEtYs4Vr2FQ5SACtBOMu9vzxUinbHIyTPH8Xm+5BPkrhank/+4JeNB0EQBP/6BbaAT8LLPO2hAAAAAElFTkSuQmCC","orcid":"","institution":"Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Wenwen","middleName":"","lastName":"Zhang","suffix":""},{"id":499116463,"identity":"9cacf02a-f29a-4fa1-98dc-b4eb62e3cfd2","order_by":1,"name":"Zengyuan Qin","email":"","orcid":"","institution":"Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zengyuan","middleName":"","lastName":"Qin","suffix":""},{"id":499116464,"identity":"7ccb170a-b8b1-41a7-9a73-bffa836ff612","order_by":2,"name":"JunTao Wang","email":"","orcid":"","institution":"The First People's Hospital of Shangqiu","correspondingAuthor":false,"prefix":"","firstName":"JunTao","middleName":"","lastName":"Wang","suffix":""},{"id":499116465,"identity":"af23d34a-0a83-45a6-ba16-bd8e46178c5b","order_by":3,"name":"Chunling Huang","email":"","orcid":"","institution":"Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunling","middleName":"","lastName":"Huang","suffix":""},{"id":499116466,"identity":"706f15f1-ac82-4099-ba34-9a05411544f1","order_by":4,"name":"Xiaoru Zhao","email":"","orcid":"","institution":"Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoru","middleName":"","lastName":"Zhao","suffix":""},{"id":499116467,"identity":"3dbe4ac8-4168-4985-81d6-41649c75f318","order_by":5,"name":"Ziyang Liu","email":"","orcid":"","institution":"Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ziyang","middleName":"","lastName":"Liu","suffix":""},{"id":499116468,"identity":"57334f03-bf97-4831-b943-501c3b41d132","order_by":6,"name":"Limeng Wang","email":"","orcid":"","institution":"Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Limeng","middleName":"","lastName":"Wang","suffix":""},{"id":499116469,"identity":"d448dc33-d7d9-4e61-b60e-6da6671d9c43","order_by":7,"name":"Lei Yan","email":"","orcid":"","institution":"Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Yan","suffix":""},{"id":499116470,"identity":"353538be-90fd-42f5-b2f9-daa20565dae4","order_by":8,"name":"Yue Gu","email":"","orcid":"","institution":"Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Gu","suffix":""},{"id":499116471,"identity":"c24df260-b4bf-4fd2-8131-e55863bf5ff7","order_by":9,"name":"Fengmin Shao","email":"","orcid":"","institution":"Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fengmin","middleName":"","lastName":"Shao","suffix":""}],"badges":[],"createdAt":"2025-07-16 14:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7141144/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7141144/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89231409,"identity":"f42351dc-9362-4f88-a51d-c34070b41058","added_by":"auto","created_at":"2025-08-17 14:19:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":375404,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of patients’ exclusion process.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7141144/v1/38ad5bddf15c864cb4b1fe7f.png"},{"id":89232593,"identity":"e6f7c939-09d8-4aae-9aa1-7cc21b66fa36","added_by":"auto","created_at":"2025-08-17 14:27:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108366,"visible":true,"origin":"","legend":"\u003cp\u003ePredictors were chosen using a LASSO logistic regression model. (A) LASSO coefficient of variables with the change of log lambda. (B) Optimal variable selection in the LASSO model used a tenfold cross-validation. LASSO: least absolute shrinkage and selection operator.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7141144/v1/15663c426ebdc04ed0b7c879.png"},{"id":89232595,"identity":"b90716ec-0435-47aa-9bdc-b268a7904278","added_by":"auto","created_at":"2025-08-17 14:27:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50205,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram for predicting postoperative AKI in patients undergoing pancreaticoduodenectomy. DBIL, direct bilirubin; ICU, intensive care unit; AKI: acute kidney injury.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7141144/v1/0b914d42fb91f676863eb29e.png"},{"id":89231420,"identity":"082b7f29-353f-462a-92ff-09dfe908c272","added_by":"auto","created_at":"2025-08-17 14:19:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":48231,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves of the predictive model in the training group (A) and validation group (B). AUC, area under the curve.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7141144/v1/dcd4a3bb33806b7cdab716f0.png"},{"id":89233332,"identity":"d2353615-cabc-470c-ba84-dc81f2dc0332","added_by":"auto","created_at":"2025-08-17 14:35:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84131,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves of the predictive model in the training group (A) and validation group (B).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7141144/v1/edff85d32d595a3555427f50.png"},{"id":89231424,"identity":"a2247445-938c-44f6-939c-cd47e27e49c8","added_by":"auto","created_at":"2025-08-17 14:19:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":75077,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the prediction model in the training group (A) and validation group (B).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7141144/v1/6b1bf581dc84b07d94998d14.png"},{"id":92360886,"identity":"a138382a-3cee-436b-af41-b223852efa56","added_by":"auto","created_at":"2025-09-28 16:46:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1700264,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7141144/v1/2b9c5004-e681-4a89-ae46-81caa9e90da5.pdf"},{"id":89232596,"identity":"9c9e0bf1-c444-4fdf-aaf2-081e0fe350cf","added_by":"auto","created_at":"2025-08-17 14:27:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":239221,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7141144/v1/7419ccdf524a83193e907c99.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction and Validation of a Nomogram for Predicting Acute Kidney Injury After Pancreatoduodenectomy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePancreatoduodenectomy is currently the primary treatment for pancreatic and ampulla lesions, both benign and malignant. Despite significant improvements in surgical techniques and perioperative care reducing mortality rates for this procedure from over 20% to less than 2% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], complication rates remain high, occurring in 29\u0026ndash;50% of cases [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Notably, acute kidney injury (AKI) constitutes a significant proportion of these complications, occurring in 5.9%-20.4% of cases [\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA study with over 800,000 participants identified a heightened risk of AKI necessitating kidney replacement therapy within 14 days post-pancreatic surgery [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, AKI after pancreaticoduodenectomy is associated with adverse outcomes. Swartling et al. discovered that post-pancreaticoduodenectomy AKI is linked to a higher Clavien-Dindo classification, as well as an elevated risk of ICU transfer and 30- and 90-day mortality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. According to a Chinese cohort study [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], the development of AKI following pancreaticoduodenectomy in pancreatic ductal adenocarcinoma patients independently predicted both major complications and mortality within 30 days. Thus, early detection of AKI after pancreaticoduodenectomy, as well as active intervention, are critical for lowering the incidence of postoperative AKI.\u003c/p\u003e\u003cp\u003eExisting literature has identified multiple perioperative factors associated with AKI development following pancreaticoduodenectomy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Efforts have been made to create strategies to prevent and treat AKI after pancreaticoduodenectomy, based on identified risk factors. However, clinical prediction models for this surgical complication are still lacking. To our knowledge, only one study has created a predictive model for AKI after pancreatic surgery [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, pancreaticoduodenectomy is a vital and complex abdominal surgery with a lengthy operation time, severe trauma, a high complication rate, and we believe it should be studied independently.\u003c/p\u003e\u003cp\u003eConsequently, the present study was designed to develop a predictive nomogram for postoperative AKI in pancreaticoduodenectomy patients. This tool could help surgeons stratify patients based on the risk of AKI, allowing them to implement appropriate prevention and treatment methods.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study population\u003c/h2\u003e\u003cp\u003e This retrospective single-center study was conducted at Henan Provincial People's Hospital with approval from the ethics committee (Approval No. 2021-Lunshen-77). Due to the study's retrospective design, informed consent was not required.\u003c/p\u003e\u003cp\u003eThe study analyzed 844 adult patients who had pancreaticoduodenectomy from December 2016 to June 2020. All participants had documented preoperative serum creatinine levels and at least one postoperative measurement within the first 7 days after surgery. Exclusion criteria included: (1) a history of urologic procedures such as nephrectomy, renal transplantation, or urinary obstruction relief, due to potential confounding effects on postoperative creatinine levels; (2) preoperative AKI; (3) chronic kidney disease, indicated by an estimated glomerular filtration rate (eGFR) below 60 mL/min/1.73 m\u0026sup2;; and (4) a need for dialysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Data collection\u003c/h2\u003e\u003cp\u003eData extracted from the electronic medical record database encompassed demographic details, comorbidities, medication history prior to surgery, preoperative lab results, intraoperative information, and postoperative factors. Demographic characteristics encompassed both age and sex. Comorbidities included hypertension, coronary heart disease, diabetes mellitus. The preoperative medication history comprised contrast agents, diuretics, non-steroidal anti-inflammatory drugs(NSAIDs), angiotensin-converting enzyme inhibitors(ACEI), and angiotensin receptor blockers (ARB). Preoperative laboratory data encompassed measurements of white blood cells (WBC), neutrophils (NEUT), lymphocytes (LYMPH), red blood cells (RBC), hemoglobin (HGB), platelets (PLT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin (ALB), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), blood urea nitrogen (BUN), serum creatinine (SCr), estimated glomerular filtration rate (eGFR), uric acid (UA), retinol-binding proteins (RBP), cystatin C (CysC), prothrombin time (PT), prothrombin time activity (PTA), international normalized ratio (INR), activated partial thromboplastin time (APTT), fibrinogen (FBG), and thrombin time (TT). Intraoperative variables encompassed vasopressor use, blood transfusion, blood loss, urine output, operative duration, and hypotension. Postoperative factors include immediate ICU transfer following surgery, termed as ICU transfer. Intraoperative hypotension was characterized by a mean arterial pressure below 65 mmHg for over 10 cumulative minutes during surgery [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The eGFR was determined using the Chronic Kidney Disease Epidemiology Collaboration Group formula [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Outcome\u003c/h2\u003e\u003cp\u003eThe main outcome was postoperative AKI, defined by KDIGO criteria as either a rise in serum creatinine (SCr) of \u0026ge;\u0026thinsp;0.3 mg/dL (\u0026ge;\u0026thinsp;26.5 \u0026micro;mol/L) within 48 hours post-surgery or an increase in SCr to \u0026ge;\u0026thinsp;1.5 times the baseline within 7 days post-surgery. Since the urine volume of most postoperative patients was not available, the KDIGO urine output standard was not used in this study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.4. Statistical analysis\u003c/b\u003e\u003c/h2\u003e\u003cp\u003ePatients were randomly divided into two groups: 70% for training and 30% for validation. Create the nomogram using the training cohort, then utilize the validation cohort for internal validation. Visualize missing data through the plot_missing function in the DataExplorer package. Then use multiple imputation to fill in missing data. Continuous variables with a normal distribution are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, non-normally distributed variables as median (interquartile range), and categorical variables as percentages of the total. An independent sample t-test was used to compare the two groups for measurements with a normal distribution and equal variance. Data with non-normal distribution or non-equal variance will be compared between groups using the Mann-Whitney U test. The counting data for the two groups were analyzed using Pearson's chi-square test or Fisher's exact test. Comparison between two groups using CBCgrps package [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe employed LASSO analysis using the glmnet package to filter the variables. A multivariate logistic regression analysis was conducted using the stats package to identify independent influencing factors. The rms package's nomogram function is used to built a nomogram model from the selected independent influencing factors. The nomogram's discrimination ability was evaluated by calculating the area under the curve (AUC). The pROC function from the rms package was utilized to create a receiver operating characteristic (ROC) curve. The Hosmer-Lemeshow goodness of fit test was conducted using the ResourceSelection package, while the rms package facilitated the calibration curve to assess the nomogram model's fit. To assess clinical practicability, the rmda package was used to generate a decision curve analysis (DCA) curve. Statistical analyses were performed using R software (version 4.2.1), with significance defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, except for the Hosmer-Lemeshow goodness of fit test, where \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Baseline Characteristics of the Study Participants\u003c/h2\u003e\u003cp\u003eThe study included 844 patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and 11.6% of them experienced postoperative AKI. All patients' clinical data were collected, and it was found that the missing ratio of CysC and RBP was 15.28% and 15.05%, respectively, and the missing ratio of other clinical data was less than 0.36%, or even not (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). In a 7:3 ratio, we assigned 590 patients to the training cohort and 254 patients to the validation cohort. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline data for both the training and validation cohorts. The two groups showed no significant differences in demographic characteristics, laboratory data, or surgery-related factors. In the training cohort, 11.7% (69/590) of patients developed AKI, and AKI group exhibited elevated TBIL, DBIL, IBIL, and CysC levels, reduced eGFR, and a greater incidence of ICU transfers (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). In the validation group, 11.4% (29 out of 254) of patients experienced AKI, and those in the AKI group exhibited elevated BUN and CysC levels, longer surgery times, and a greater likelihood of ICU transfer (all \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic, clinical characteristics and surgery-related factors of the training and validation cohorts\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;844)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eValidation cohorts\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;254)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTraining cohorts\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;590)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBaseline variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.00 (51.00, 67.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.50 (51.00, 66.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61.00 (51.00, 67.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e492 (58.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147 (57.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e345 (58.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e352 (41.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107 (42.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e245 (41.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCoexisting conditions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e223 (26.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (24.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e161 (27.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.433\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary heart disease, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (6.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (6.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (6.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141 (16.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (14.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 (17.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.429\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedication history\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContrast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e690 (81.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e208 (81.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e482 (81.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiuretics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77 (9.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (11.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (8.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNSAIDs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 (17.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (16.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 (17.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.714\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACEI/ARB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e206 (24.41%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (22.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e149 (25.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory parameters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.14 (4.93, 7.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.08 (4.89, 7.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.16 (4.97, 7.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEUT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.86 (2.94, 5.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.82 (2.88, 5.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.88 (2.98, 5.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLYMPH (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.43 (1.10, 1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.46 (1.11, 1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.42(1.10, 1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC (10\u003csup\u003e12/\u003c/sup\u003eL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHGB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124.00 (111.00, 135.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125.50 (113.00, 135.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124.00 (111.00, 135.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e234.00 (187.00, 293.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e235.00 (189.25, 289.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e233.50 (186.25, 293.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.590\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110.10 (32.15, 226.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109.60 (34.42, 238.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110.10 (31.55, 217.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76.35 (29.00, 157.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.50 (27.02, 142.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78.95 (29.70, 160.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.497\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALB (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.60 (34.10, 40.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.65 (34.80, 40.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.60 (33.90, 40.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBIL (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.60 (12.45, 172.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.55 (12.03, 159.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.40 (13.12, 177.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBIL (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.75 (4.47, 127.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.40 (3.90, 120.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.20 (4.82, 130.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBIL (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.90 (7.30, 44.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.70 (7.20, 41.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.05 (7.32, 45.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.60 (3.67, 5.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.72 (3.76, 5.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.55 (3.65, 5.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCr (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.00 (45.00, 62.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.00 (46.00, 62.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.00 (45.00, 62.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.462\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR [ml●min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e●(1.73m\u003csup\u003e2\u003c/sup\u003e)\u003csup\u003e\u0026minus;1\u003c/sup\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104.90 (96.82, 114.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105.12 (96.97, 113.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e104.88 (96.80, 114.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e215.50 (164.00, 271.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e221.50 (167.00, 281.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e213.00 (162.00, 269.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.00 (23.00, 35.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.00 (24.00 36.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.65 (23.00, 35.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCysC (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.89 (0.79, 1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87 (0.79, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.79, 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT (s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.90 (11.20, 12.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.90 (11.10, 12.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.90 (11.20, 12.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTA(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117.00 (99.00, 140.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119.00 (103.25, 144.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115.50 (97.00, 139.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR(INR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.92 (0.84, 1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91 (0.84, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93 (0.85, 1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT (s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.10 (30.20, 36.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.00 (29.80, 36.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.15 (30.40, 36.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.95 (3.18, 4.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.94 (3.06, 4.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.96 (3.28, 4.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT(s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.10 (16.00, 18.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.95 (16.00, 17.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.10 (16.10, 18.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntraoperative variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVasopressor use, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (7.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (8.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood transfusion, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e393 (46.56%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115 (45.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e278 (47.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood loss (ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300 (200, 500)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300 (200, 500)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e300 (200, 500)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine output (ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e775 (500, 1000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e700 (500, 1000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e800 (500, 1000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.820\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOperative duration (min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e370.00 (315.00, 455.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362.50 (320.00, 459.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e372.50 (315.00, 451.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.949\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative hypotension, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102 (12.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (11.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74 (12.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePostoperative variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU transfer, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157 (18.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (16.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e114 (19.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.470\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNSAIDs non-steroidal anti-inflammatory drugs; ACEI/ARB, angiotensin-converting enzyme inhibitors and angiotensin receptor blockers; WBC, white blood cells; NEUT, neutrophil; LYMPH, lymphocytes; RBC, red blood cells; HGB, hemoglobin; PLT, platelets; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALB, albumin; TBIL, total bilirubin; DBIL, direct bilirubin; IBIL, indirect bilirubin; BUN, blood urea nitrogen; SCr, serum creatinine; eGFR, estimated glomerular filtration rate; UA, uric acid; RBP, retinol-binding proteins; CysC, cystatin C; PT, prothrombin time; PTA, prothrombin time activity; INR, international normalized ratio; APTT, partial thromboplastin time; FBG, fibrinogen; TT, thrombin time; ICU, intensive care unit.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Construction of Nomogram\u003c/h2\u003e\u003cp\u003eTo identify potential influencing factors, all of the variables in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were included in the LASSO regression model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The results showed that in the training group, the coefficients of DBIL, eGFR, CysC, blood loss, urine output, and ICU transfer are non-zero. These variables were further rescreened using multivariate logistic regression. Finally, DBIL (OR\u0026thinsp;=\u0026thinsp;1.005, 95%CI: 1.001\u0026ndash;1.008, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), blood loss (OR\u0026thinsp;=\u0026thinsp;1.001, 95%CI: 1.000-1.002, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), urine output (OR\u0026thinsp;=\u0026thinsp;0.999, 95%CI: 0.999\u0026ndash;0.9998, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), and ICU transfer (OR\u0026thinsp;=\u0026thinsp;3.865, 95%CI: 2.155\u0026ndash;6.904, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were identified as independent influencing factors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A nomogram was developed using these four characteristics to evaluate the risk of postoperative AKI in patients undergoing pancreaticoduodenectomy(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable analysis in the training cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBIL (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.001\u0026ndash;1.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR [ml●min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e●(1.73m\u003csup\u003e2\u003c/sup\u003e)\u003csup\u003e\u0026minus;1\u003c/sup\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.966\u0026ndash;1.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCysC (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.367\u0026ndash;4.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood loss (ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000-1.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrine output (ml)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.999\u0026ndash;0.9998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU transfer, (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.155\u0026ndash;6.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eDBIL, direct bilirubin; eGFR, estimated glomerular filtration rate; CysC, cystatin C; ICU, intensive care unit.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Evaluation of the nomogram model\u003c/h2\u003e\u003cp\u003eTo determine the discrimination of the nomogram model, we plot the ROC curve in the training sample and calculate the AUC. The training group's AUC was 0.720 with a 95% CI of 0.655 to 0.785 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The calibration curve indicates strong alignment between predicted and actual probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The Hosmer-Lemeshow goodness of fit test for the training set yields a \u003cem\u003eP\u003c/em\u003e value of 0.273, exceeding the 0.05 threshold, thus demonstrating the model's strong fitting capability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Decision curve analysis demonstrated that the nomogram model provides clinical utility, as indicated by a net benefit ratio greater than zero for threshold probabilities between 0.05 and 0.83 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Confirming the Nomogram's Accuracy in the Validation Group\u003c/h2\u003e\u003cp\u003eExternal validation involved 254 patients, revealing that the nomogram model's ROC curve AUC was 0.772 (95% CI, 0.673\u0026ndash;0.872), demonstrating moderate differentiation in the validation group. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The calibration curve demonstrates consistency between actual and predicted AKI probabilities in the validation group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The Hosmer-Lemeshow test yielded a P value of 0.319 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Decision curve analysis demonstrated that the nomogram model offers clinical utility, as indicated by a net benefit ratio greater than zero for threshold probabilities between 0.05 and 0.42. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study developed a nomogram model incorporating DBIL, blood loss, urine output, and ICU transfer to predict early-stage AKI risk following pancreaticoduodenectomy. The nomogram model demonstrates good differentiation, as evidenced by AUC values exceeding 0.7 in both the training and validation groups. The calibration curve shows that the nomogram model's predicted and actual probabilities are consistent, indicating high calibration ability. The decision curve showed a good net benefit rate for diagnosing AKI after pancreaticoduodenectomy using this model.\u003c/p\u003e\u003cp\u003eThere are some differences in the incidence of pancreaticoduodenectomy-related AKI among the available reports. In a Japanese nationwide cohort (n\u0026thinsp;=\u0026thinsp;84,036), the AKI rate after pancreaticoduodenectomy was similar for robotic-assisted (7.0%) and open procedures (6.5%) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In their retrospective analysis of 395 pancreaticoduodenectomy cases, Mahmooth et al.found a substantial 19.7% incidence of postoperative AKI. It should be noted that their study population was limited to patients who had received intraoperative intravenous fluids and had documented preoperative weights [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Park et al.conducted a study involving 809 pancreaticoduodenectomy patients, in which the AKI rate was documented at 5.3% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ji et al. analyzed 1312 patients having pancreaticoduodenectomy in a large hospital in China between 2013 and 2020 and discovered that the incidence of AKI was 10.7% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Our study identified an 11.6% pooled incidence of postoperative AKI following pancreaticoduodenectomy, showing a slight variation from earlier research findings. First, our study included individuals undergoing pancreaticoduodenectomy for various reasons, such as malignant tumors, benign tumors, and inflammation, unlike the study by Ji et al. exclusively included patients with pancreatic ductal adenocarcinoma [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Secondly, the modes of operation are different. Aguayo et al. studied robotic-assisted vs open pancreaticoduodenectomy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], while Park et al. discussed pylorus-preserving pancreaticoduodenectomy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The low frequency of these two procedures in our study might have influenced the variation in incidence rates. Finally, variations in patient race across studies may affect AKI onset and progression.\u003c/p\u003e\u003cp\u003eBilirubin, a key indicator of liver function, results from the breakdown of aging red blood cells in the liver, spleen, and bone marrow mononuclear phagocyte system. The effects of bilirubin on the kidneys have been the subject of much debate in the medical field, and there are two conflicting views: whether bilirubin is nephrotoxic or nephroprotective. A case-control study of 36 jaundiced patients (serum total bilirubin\u0026thinsp;\u0026gt;\u0026thinsp;3mg/dL) and 38 non-jaundiced patients found that urinary biomarkers indicating renal tubular injury were elevated in the jaundiced group. Logistic regression analysis revealed that jaundice heightened the risk of increased u-NGAL and u-B2M levels [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. One major cause of AKI is ischemia-reperfusion damage. Hyperbilirubinemia, in cell and animal models, can aggravate renal ischemia reperfusion injury by enhancing mitophagy [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Elevated bilirubin concentrations have been consistently associated with heightened AKI risk across various clinical contexts, as demonstrated in recent investigations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, in our investigation, DBIL was the important predictor, with a 1.005-fold increase in AKI risk for every unit (1\u0026micro;mol/L) increase in DBIL. In addition, bilirubin is also related to the poor prognosis of AKI. A study of 182,683 veterans identified elevated bilirubin levels as a risk factor for mortality in AKI patients within one year post-discharge [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Wang et al. discovered that total bilirubin level has been identified as a risk factor for mortality in patients with hemophagocytic lymphohistiocytosis complicated by AKI [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. On the other side, bilirubin has been shown to lower oxidative stress, which may provide kidney protection. Chinese researchers developed multifunctional liposomes with bilirubin that alleviated acute kidney injury by reducing apoptosis, promoting mitochondrial autophagy, and lowering inflammation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Huang et al. reported a kind of ε-polylysine-bilirubin conjugate nanoparticles coated with hyaluronic acid, which can be targeted to accumulate in the site of renal injury, protect mitochondrial structure and function, inhibit renal tubular cell apoptosis, and promote renal recovery [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The authors administered bilirubin to AKI rats post-ischemia and reperfusion, discovering its independent anti-oxidative and anti-apoptotic properties [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A South Korean study showed that higher bilirubin has a protective effect on the kidneys, although the overall bilirubin concentration in the study population was low, which cannot explain the effect of excessive bilirubin on the kidneys [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The association between bilirubin and the kidney needs to be further investigated. In addition, several clinical studies only include total bilirubin, which limits our understanding of the effects of direct and indirect bilirubin on the kidney. Future research should clarify the interaction between the three forms of bilirubin and the kidney.\u003c/p\u003e\u003cp\u003eOur findings corroborate previous studies identifying intraoperative blood loss as an independent risk factor for postoperative AKI [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Several other studies have identified a link between intraoperative blood loss and postoperative AKI, although with different definitions for blood loss. A study indicated that intraoperative blood loss exceeding 1000 ml heightened the risk of postoperative AKI [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Li et al. identified intraoperative blood loss exceeding 400mL as a risk factor for AKI following pancreatic surgery [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. An international prospective, observational, multicenter study found intraoperative bleeding to be a risk factor for AKI within 72 hours after major surgery, without specifying the bleeding volume [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Mechanically, intraoperative blood loss may produce hypotension, and the kidneys are vulnerable to ischemia and hypoxia, which can lead to AKI [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. As a controllable factor, steps to limit intraoperative bleeding may aid in the reduction of postoperative AKI.\u003c/p\u003e\u003cp\u003eReduced urine volume during surgery is frequently a sign of renal hypoperfusion. Perspectives differ regarding the link between intraoperative urine output and postoperative acute kidney injury (AKI). A meta-analysis of nine noncardiac surgery-related studies revealed that intraoperative oliguria significantly elevates the risk of postoperative AKI and is linked to higher hospitalization mortality and increased postoperative renal replacement therapy demand [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A study of 2444 patients undergoing major abdominal surgery revealed that intraoperative oliguria is linked to a heightened risk of AKI [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A study examining the relationship between intraoperative urine output and AKI following laparoscopic pancreatic surgery found that low urine output was associated with AKI in cases without vascular reconstruction, but not in those with reconstruction [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, Goren et al. found that intraoperative oliguria was not a predictor of acute kidney injury (AKI) following open pancreatic surgery [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In our study, intraoperative urine output predicted postoperative AKI, but we did not define oliguria due to missing weight data in our cohort.\u003c/p\u003e\u003cp\u003ePostoperative ICU transfers are frequent, with approximately 8-9.6% of major surgery patients requiring ICU care [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Pancreatoduodenectomy is a time-consuming and traumatic operation, which is a great challenge for both patients and doctors. In our analysis, 17.08% of the overall cohort was sent to the ICU following surgery, which is a considerable proportion. Previous studies have focused on the impact of AKI on ICU transfer. Bhasin et al. investigated patients receiving hematopoietic cell transplantation and found that individuals with AKI had a higher number of ICU transfers [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. A Chinese study identified AKI linked to rhabdomyolysis as an independent risk factor for ICU transfer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A study with the same cohort as ours also discovered that postoperative AKI increases the chance of ICU admission [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Our study identified immediate postoperative ICU admission as an independent risk factor for postoperative AKI. Patients transferred to the ICU are typically more gravely ill; their state of bodies is more complex, and their kidneys are more likely to be damaged as a result of many circumstances. We should be concerned about the renal function of such patients rather than focusing solely on the impact of AKI on negative outcomes such as death, duration of stay, hospitalization expenditures, ICU transfer, and so on.\u003c/p\u003e\u003cp\u003eThe study's limitations include the lack of routine postoperative urine output measurement, restricting AKI diagnosis to serum creatinine levels and potentially underestimating its incidence due to the inability to identify cases based on reduced urine volume. Additionally, the case source of this study is single, and while internal validation was performed, larger and multi-center sample sizes are still required to assess the clinical findings.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the study identified four key predictors: preoperative DBIL, intraoperative blood loss, intraoperative urine output, and immediate postoperative ICU transfer to develop nomograms with good discriminative power in both training and validation cohorts, confirming the model's validity and applicability. The incidence of AKI after pancreaticoduodenectomy could be predicted clinically based on the sum of scores for each risk factor.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval for the study was obtained from the Ethics Committees of Henan Provincial People\u0026apos;s Hospital (Ethics approval number: 2021-Lunshen-77), and informed consent was secured from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study\u0026apos;s findings can be obtained from the corresponding author upon request.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state they have no conflicts of interest.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted at the Henan Provincial Clinical Research Center for Kidney Disease and the Henan Provincial Key Laboratory of Nephrology and Immunology, with support from the Funding of Zhongyuan Scholars of Henan Provincial Health Commission (No. 224000510005), Zhongyuan Scholar Workstation (No. 234400510024), Technology Attack Plan Project of Henan Province (No. 242102311062), and Medical Science and Technology Attack Plan Project of Henan Province (No. SBGJ202302002), the National Natural Science Foundation of China (No. 82100731), the Joint Fund Project of Henan Provincial Science and Technology Research and Development Plan (No. 225200810101).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWZ, ZQ and JW performed the data analyses and wrote the manuscript; CH, XZ and ZL arranged and collected data; LW and LY helped perform the analysis with profound discussions; YG and FS contributed to the conception and revision of the study. All authors reviewed and endorsed the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to the patients who participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWinter JM, Cameron JL, Campbell KA, Arnold MA, Chang DC, Coleman J, Hodgin MB, Sauter PK, Hruban RH, Riall TS, Schulick RD, Choti MA, Lillemoe KD, Yeo CJ\u003cstrong\u003e. \u003c/strong\u003e1423 pancreaticoduodenectomies for pancreatic cancer: A single-institution experience. Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract. 2006;10(9):1199-210; discussion 210-1.\u003c/li\u003e\n\u003cli\u003eWarrillow SJ, Weinberg L, Parker F, Calzavacca P, Licari E, Aly A, Bagshaw S, Christophi C, Bellomo R\u003cstrong\u003e. \u003c/strong\u003ePerioperative fluid prescription, complications and outcomes in major elective open gastrointestinal surgery. Anaesthesia and intensive care. 2010;38(2):259-65.\u003c/li\u003e\n\u003cli\u003evan Rijssen LB, Zwart MJ, van Dieren S, de Rooij T, Bonsing BA, Bosscha K, van Dam RM, van Eijck CH, Gerhards MF, Gerritsen JJ, van der Harst E, de Hingh IH, de Jong KP, Kazemier G, Klaase J, van der Kolk BM, van Laarhoven CJ, Luyer MD, Molenaar IQ, Patijn GA, Rupert CG, Scheepers JJ, van der Schelling GP, Vahrmeijer AL, Busch ORC, van Santvoort HC, Groot Koerkamp B, Besselink MG\u003cstrong\u003e. \u003c/strong\u003eVariation in hospital mortality after pancreatoduodenectomy is related to failure to rescue rather than major complications: a nationwide audit. HPB : the official journal of the International Hepato Pancreato Biliary Association. 2018;20(8):759-67.\u003c/li\u003e\n\u003cli\u003eAoki S, Miyata H, Konno H, Gotoh M, Motoi F, Kumamaru H, Wakabayashi G, Kakeji Y, Mori M, Seto Y, Unno M\u003cstrong\u003e. \u003c/strong\u003eRisk factors of serious postoperative complications after pancreaticoduodenectomy and risk calculators for predicting postoperative complications: a nationwide study of 17,564 patients in Japan. Journal of hepato-biliary-pancreatic sciences. 2017;24(5):243-51.\u003c/li\u003e\n\u003cli\u003eAguayo E, Antonios J, Sanaiha Y, Dobaria V, Kwon OJ, Sareh S, Benharash P, King JC\u003cstrong\u003e. \u003c/strong\u003eReadmission and Resource Use After Robotic-Assisted versus Open Pancreaticoduodenectomy: 2010-2017. J SURG RES. 2020;255:517-24.\u003c/li\u003e\n\u003cli\u003eMahmooth Z, Jajja MR, Maxwell D, Ferez-Pinzon A, Sarmiento JM\u003cstrong\u003e. \u003c/strong\u003eUltrarestrictive intraoperative intravenous fluids during pancreatoduodenectomy is not associated with an increase in post-operative acute kidney injury. AM J SURG. 2020;220(2):264-69.\u003c/li\u003e\n\u003cli\u003eMizota T, Dong L, Takeda C, Shiraki A, Matsukawa S, Shimizu S, Kai S\u003cstrong\u003e. \u003c/strong\u003eTransient acute kidney injury after major abdominal surgery increases chronic kidney disease risk and 1-year mortality. Journal of critical care. 2019;50:17-22.\u003c/li\u003e\n\u003cli\u003ePark YS, Jun IG, Go Y, Song JG, Hwang GS\u003cstrong\u003e. \u003c/strong\u003eComparison of acute kidney injury between open and laparoscopic pylorus-preserving pancreaticoduodenectomy: Propensity score analysis. PLoS One. 2018;13(8):e0202980.\u003c/li\u003e\n\u003cli\u003eSwartling O, Evans M, Larsson P, Gilg S, Holmberg M, Klevebro F, L\u0026ouml;hr M, Sparrelid E, Ghorbani P\u003cstrong\u003e. \u003c/strong\u003eRisk factors for acute kidney injury after pancreatoduodenectomy, and association with postoperative complications and death. PANCREATOLOGY. 2023;23(2):227-33.\u003c/li\u003e\n\u003cli\u003eJi Y, Zhou Y, Shen Z, Chen H, Zhao S, Deng X, Shen B\u003cstrong\u003e. \u003c/strong\u003eRisk factors for and prognostic values of postoperative acute kidney injury after pancreaticoduodenectomy for pancreatic ductal adenocarcinoma: A retrospective, propensity score-matched cohort study of 1312 patients. Cancer Med. 2023;12(7):7823-34.\u003c/li\u003e\n\u003cli\u003eWilson TA, de Koning L, Quinn RR, Zarnke KB, McArthur E, Iskander C, Roshanov PS, Garg AX, Hemmelgarn BR, Pannu N, James MT\u003cstrong\u003e. \u003c/strong\u003eDerivation and External Validation of a Risk Index for Predicting Acute Kidney Injury Requiring Kidney Replacement Therapy After Noncardiac Surgery. JAMA Netw Open. 2021;4(8):e2121901.\u003c/li\u003e\n\u003cli\u003eLi S, Ren W, Ye X, Zhang L, Song B, Guo Z, Bian Q\u003cstrong\u003e. \u003c/strong\u003eAn online-predictive model of acute kidney injury after pancreatic surgery. AM J SURG. 2024;228:151-58.\u003c/li\u003e\n\u003cli\u003eSalmasi V, Maheshwari K, Yang D, Mascha EJ, Singh A, Sessler DI, Kurz A\u003cstrong\u003e. \u003c/strong\u003eRelationship between Intraoperative Hypotension, Defined by Either Reduction from Baseline or Absolute Thresholds, and Acute Kidney and Myocardial Injury after Noncardiac Surgery: A Retrospective Cohort Analysis. ANESTHESIOLOGY. 2017;126(1):47-65. 2017(0003-3022):47-65.\u003c/li\u003e\n\u003cli\u003eLevey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J\u003cstrong\u003e. \u003c/strong\u003eA new equation to estimate glomerular filtration rate. ANN INTERN MED. 2009;150(9):604-12.\u003c/li\u003e\n\u003cli\u003eKellum JA, Lameire N\u003cstrong\u003e. \u003c/strong\u003eDiagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). CRIT CARE. 2013;17(1):204.\u003c/li\u003e\n\u003cli\u003eZhang Z, Gayle AA, Wang J, Zhang H, Cardinal-Fern\u0026aacute;ndez P\u003cstrong\u003e. \u003c/strong\u003eComparing baseline characteristics between groups: an introduction to the CBCgrps package. ANN TRANSL MED. 2017;5(24):484. 2017(2305-5839): 484.\u003c/li\u003e\n\u003cli\u003eScilletta S, Leggio S, Di Marco M, Miano N, Musmeci M, Marrano N, Natalicchio A, Giorgino F, Bosco G, Di Giacomo Barbagallo F, Scamporrino A, Di Mauro S, Filippello A, Scicali R, Russello M, Spadaro L, Purrello F, Piro S, Di Pino A\u003cstrong\u003e. \u003c/strong\u003eAcute hyperbilirubinemia determines an early subclinical renal damage: Evaluation of tubular biomarkers in cholemic nephropathy. LIVER INT. 2024;44(9):2341-50.\u003c/li\u003e\n\u003cli\u003eLiao P, Wang X, Dong H, Chai D, Yue Z, Lyu L\u003cstrong\u003e. \u003c/strong\u003eHYPERBILIRUBINEMIA AGGRAVATES RENAL ISCHEMIA REPERFUSION INJURY BY EXACERBATING PINK1-PARKIN-MEDIATED MITOPHAGY. SHOCK. 2023;60(2):262-71.\u003c/li\u003e\n\u003cli\u003eKim H, Ali R, Short S, Kaunfer S, Krishnamurthy S, Durai L, Yilmam O, Shenoy T, Monson AE, Thomas C, Park I, Martini D, Newcomb R, Shapiro RM, Soiffer RJ, DeFilipp Z, Baron RM, Gupta S, Sise ME, Leaf DE\u003cstrong\u003e. \u003c/strong\u003eAKI treated with kidney replacement therapy in critically Ill allogeneic hematopoietic stem cell transplant recipients. BONE MARROW TRANSPL. 2024;59(2):178-88.\u003c/li\u003e\n\u003cli\u003eTyson LD, Atkinson S, Hunter RW, Allison M, Austin A, Dear JW, Forrest E, Liu T, Lord E, Masson S, Nunes J, Richardson P, Ryder SD, Wright M, Thursz M, Vergis N\u003cstrong\u003e. \u003c/strong\u003eIn severe alcohol-related hepatitis, acute kidney injury is prevalent, associated with mortality independent of liver disease severity, and can be predicted using IL-8 and micro-RNAs. ALIMENT PHARM THERAP. 2023;58(11-12):1217-29.\u003c/li\u003e\n\u003cli\u003eZheng L, Lin Y, Fang K, Wu J, Zheng M\u003cstrong\u003e. \u003c/strong\u003eDerivation and validation of a risk score to predict acute kidney injury in critically ill cirrhotic patients. HEPATOL RES. 2023;53(8):701-712.\u003c/li\u003e\n\u003cli\u003eWang S, Zhou J, Yang J, Wang X, Chen X, Ji L, Yang L\u003cstrong\u003e. \u003c/strong\u003eClinical features and prognostic factors of acute kidney injury caused by adult secondary hemophagocytic lymphohistiocytosis. J NEPHROL. 2022;35(4):1223-33.\u003c/li\u003e\n\u003cli\u003eGriffin BR, Vaughan-Sarrazin M, Perencevich E, Yamada M, Swee M, Sambharia M, Girotra S, Reisinger HS, Jalal D\u003cstrong\u003e. \u003c/strong\u003eRisk Factors for Death Among Veterans Following Acute Kidney Injury. AM J MED. 2023;136(5):449-57.\u003c/li\u003e\n\u003cli\u003eShen Z, Wang X, Lu L, Wang R, Hu D, Fan Z, Zhu L, Zhong R, Wu M, Zhou X, Cao X\u003cstrong\u003e. \u003c/strong\u003eBilirubin-Modified Chondroitin Sulfate-Mediated Multifunctional Liposomes Ameliorate Acute Kidney Injury by Inducing Mitophagy and Regulating Macrophage Polarization. ACS APPL MATER INTER. 2024;16(45):62693-709.\u003c/li\u003e\n\u003cli\u003eHuang ZW, Shi Y, Zhai YY, Du CC, Zhai J, Yu RJ, Kou L, Xiao J, Zhao YZ, Yao Q\u003cstrong\u003e. \u003c/strong\u003eHyaluronic acid coated bilirubin nanoparticles attenuate ischemia reperfusion-induced acute kidney injury. J CONTROL RELEASE. 2021;334:275-89.\u003c/li\u003e\n\u003cli\u003ePark S, Kim DH, Hwang JH, Kim YC, Kim JH, Lim CS, Kim YS, Yang SH, Lee JP\u003cstrong\u003e. \u003c/strong\u003eElevated bilirubin levels are associated with a better renal prognosis and ameliorate kidney fibrosis. PLoS One. 2017;12(2):e0172434.\u003c/li\u003e\n\u003cli\u003eYu Y, Zhang C, Zhang F, Liu C, Li H, Lou J, Xu Z, Liu Y, Cao J, Mi W\u003cstrong\u003e. \u003c/strong\u003eDevelopment and validation of a risk nomogram for postoperative acute kidney injury in older patients undergoing liver resection: a pilot study. BMC Anesthesiol. 2022;22(1):22.\u003c/li\u003e\n\u003cli\u003eZarbock A, Weiss R, Albert F, Rutledge K, Kellum JA, Bellomo R, Grigoryev E, Candela-Toha AM, Demir ZA, Legros V, Rosenberger P, Gal\u0026aacute;n Men\u0026eacute;ndez P, Garcia Alvarez M, Peng K, L\u0026eacute;ger M, Khalel W, Orhan-Sungur M, Meersch M\u003cstrong\u003e. \u003c/strong\u003eEpidemiology of surgery associated acute kidney injury (EPIS-AKI): a prospective international observational multi-center clinical study. INTENS CARE MED. 2023;49(12):1441-55.\u003c/li\u003e\n\u003cli\u003eLankadeva YR, May CN, Bellomo R, Evans RG\u003cstrong\u003e. \u003c/strong\u003eRole of perioperative hypotension in postoperative acute kidney injury: a narrative review. BRIT J ANAESTH. 2022;128(6):931-48.\u003c/li\u003e\n\u003cli\u003ePang Z, Liang S, Xing M, Zhou N, Guo Q, Zou W\u003cstrong\u003e. \u003c/strong\u003eThe correlation of intraoperative oliguria with acute kidney injury after noncardiac surgery: a systematic review and meta-analysis. INT J SURG. 2023;109(3):449-57.\u003c/li\u003e\n\u003cli\u003eMyles PS, McIlroy DR, Bellomo R, Wallace S\u003cstrong\u003e. \u003c/strong\u003eImportance of intraoperative oliguria during major abdominal surgery: findings of the Restrictive versus Liberal Fluid Therapy in Major Abdominal Surgery trial. BRIT J ANAESTH. 2019;122(6):726-33.\u003c/li\u003e\n\u003cli\u003eValencia Morales DJ, Plack DL, Kendrick ML, Schroeder DR, Sprung J, Weingarten TN\u003cstrong\u003e. \u003c/strong\u003eUrine output and acute kidney injury following laparoscopic pancreas operations. HPB. 2022;24(11):1967-74.\u003c/li\u003e\n\u003cli\u003eGoren O, Levy A, Cattan A, Lahat G, Matot I\u003cstrong\u003e. \u003c/strong\u003eAcute kidney injury in pancreatic surgery; association with urine output and intraoperative fluid administration. AM J SURG. 2017;214(2):246-50.\u003c/li\u003e\n\u003cli\u003ePearse RM, Moreno RP, Bauer P, Pelosi P, Metnitz P, Spies C, Vallet B, Vincent JL, Hoeft A, Rhodes A\u003cstrong\u003e. \u003c/strong\u003eMortality after surgery in Europe: a 7 day cohort study. LANCET. 2012;380(9847):1059-65.\u003c/li\u003e\n\u003cli\u003eJerath A, Laupacis A, Austin PC, Wunsch H, Wijeysundera DN\u003cstrong\u003e. \u003c/strong\u003eIntensive care utilization following major noncardiac surgical procedures in Ontario, Canada: a population-based study. INTENS CARE MED. 2018;44(9):1427-35.\u003c/li\u003e\n\u003cli\u003eBhasin B, Ber Ce P, Szabo A, Chhabra S, D\u0026apos;Souza A\u003cstrong\u003e. \u003c/strong\u003eCorrelates and Outcomes of Early Acute Kidney Injury after Hematopoietic Cell Transplantation. AM J MED SCI. 2021;362(1):72-77.\u003c/li\u003e\n\u003cli\u003eZhu DC, Li WY, Zhang JW, Tong JS, Xie WY, Qin XL, Zhang XC\u003cstrong\u003e. \u003c/strong\u003eRhabdomyolysis-associated acute kidney injury: clinical characteristics and intensive care unit transfer analysis. INTERN MED J. 2022;52(7):1251-57.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"acute kidney injury, pancreatoduodenectomy, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7141144/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7141144/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAcute kidney injury (AKI) after pancreatoduodenectomy is common and early identification of such patients is critical. The nomogram, a visual predictive model, is commonly used to predict AKI after various types of surgery. We aimed to construct and evaluate a predictive nomogram for postoperative AKI in patients undergoing pancreaticoduodenectomy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn a retrospective cohort study, we examined 844 adult patients who underwent pancreaticoduodenectomy from December 2016 to June 2020. All enrolled patients were randomly assigned to the training and validation cohorts in a 7:3 ratio. We utilized LASSO regression for feature selection. A nomogram was constructed using multivariate logistic regression. The nomogram's performance was assessed using various metrics such as the receiver operating characteristic curve, calibration curves, Hosmer-Lemeshow goodness of fit, and decision curve analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn this cohort, AKI was observed in 98 out of 844 patients, representing an incidence rate of 11.6%. Multivariate logistic analysis showed that direct bilirubin (DBIL), blood loss, urine output, intensive care unit (ICU) transfer were independent influencing factors of postoperative AKI. The nomogram, incorporating the four identified factors, demonstrated moderate discrimination in both the training and validation cohorts, achieving AUC values of 0.720 and 0.772, respectively. The Hosmer-Lemeshow goodness of fit test and the calibration curve demonstrate good agreement between predicted and observed values. The decision curve analysis (DCA) indicated a positive net clinical benefit.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eWe developed and validated a nomogram model that could help identify individuals at risk of AKI following pancreaticoduodenectomy. This model may help clinicians optimize perioperative management for these patients.\u003c/p\u003e","manuscriptTitle":"Construction and Validation of a Nomogram for Predicting Acute Kidney Injury After Pancreatoduodenectomy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-17 14:19:16","doi":"10.21203/rs.3.rs-7141144/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5b11e69-0654-452a-9e8d-68acd71c8bd7","owner":[],"postedDate":"August 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-28T16:38:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-17 14:19:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7141144","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7141144","identity":"rs-7141144","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}