Nomogram for Predicting Biliary Complication (NPBC)

Nomogram for Predicting Postoperative Early Biliary Complication in Adult Patients Following Liver Transplantation: a Retrospective Study

The goal of this observational study is to develop a nomogram model to predict biliary complications within 90 days in adult patients after liver transplantation. The main questions it aims to answer are:

Can the nomogram predict biliary complications within 90 days in adult patients after liver transplantation? What about the performance of the nomogram? Researchers will compare patients' preoperative variables, intraoperative factors, and postoperative outcomes to see what factors are associated with biliary complications.Subsequently, multivariable logistic regression analysis was conducted to evaluate the factors associated with biliary complications occurring within 90 days post-liver transplantation. Finally, a nomogram was developed.

Study Overview

• Status: Not yet recruiting
• Conditions: Liver Transplant Disorder

Detailed Description

Background： Liver transplantation (LT) is a critical therapeutic intervention for patients with end-stage liver disease or acute liver failure, offering favorable outcomes with survival rates of 76-90% at 1 year and approximately 70% at 5 years . Despite significant advancements in surgical techniques, immunosuppressive protocols, and organ preservation, biliary complications (BCs) remain a substantial cause of morbidity and mortality, frequently necessitating long-term interventions such as endoscopic, percutaneous, or surgical procedures. The incidence of BCs post-LT is estimated to range from 5% to 20%, with most occurring within the first three months postoperatively, although some may present years after LT . Among BCs, the most common are anastomotic strictures (AS), non-anastomotic strictures (NAS), and bile leaks. Treatment approaches include endoscopic techniques, percutaneous transhepatic biliary drainage, and surgical interventions. NAS and bile leaks typically manifest within the first year post-transplant but may arise later, with bile leaks occurring immediately or within weeks following surgery . Early postoperative monitoring and timely management are essential to preventing and addressing these complications.

In adult liver transplantation, several factors contribute to bile duct complications, including biliary ischemia, surgical techniques, infections, and donor characteristics . Among these, insufficient arterial blood supply to the bile ducts is recognized as a key factor likely contributing to the development of biliary complications. Unlike the liver parenchyma, which receives blood from both the portal vein and hepatic artery, the bile ducts depend solely on arterial blood flow. About 60% of the common bile duct's blood comes from the gastroduodenal artery, with the remaining 30-40% from hepatic artery branches. After liver transplantation, blood flow from the gastroduodenal artery is often disrupted, making the hepatic artery's role in supplying the distal bile duct even more critical. This reduced arterial support highlights the significance of maintaining adequate hepatic artery perfusion to prevent bile duct complications.

Ultrasound is essential for monitoring post-liver transplantation biliary complications, providing detailed imaging for early detection of issues like strictures and leaks. Parameters such as arterial resistive index (ARI) and flow velocity are critical for assessing hepatic artery condition and blood supply post-transplantation, offering insights into graft perfusion and viability. These non-invasive metrics are crucial for evaluating hepatic arterial circulation, may provide effective postoperative care and improving patient outcomes.

After liver transplantation, biliary complications most commonly occur during the early postoperative period, particularly within the first three months. More than half of all biliary complications are early complications occurring at the anastomotic site. Hence, early monitoring and intervention are crucial to promptly detect and manage these potential complications, thereby improving patient prognosis and survival rates in clinical settings. However, there is limited research on the risk factors and predict model for 90 days biliary complications after adult liver transplantation. Nomogram is a simple visual graph of statistical prediction model with an easy-to-use graphical interface. Importantly, it could generate personalized predictions, thereby, widely used in risk stratification and personally providing approach to disease management. This study aimed to establish a nomogram model to evaluate patients who underwent LT to help clinicians predict the risk of 90 days biliary complications and provide a more personalized treatment strategy.

Method Patients Data from patients who underwent OLT between 1 January 2016 to 31 December 2021 at the First Affiliated Hospital of Sun Yat-sen University were retrospectively extracted from the electronic medical record system. Exclusion criteria included age <18 years old, multiple organ transplantation, retransplantation, died intraoperatively or within 3 months after LT, and cases with missing important data or over 20% of data missing. Finally, a total of 757 patients were enrolled. This research study was reviewed and approved by the Ethics Committee at Hospital, waiving the requirement for informed consent in alignment with the Declaration of Helsinki. All operations were performed in accordance with the standard procedures by experienced surgical groups in our department.

Study protocol The work was approved by the Ethics Committee and registered on Clinicaltrial.gov. It was conducted and reported in line with the strengthening the reporting of cohort, cross-sectional and case-control studies in surgery (STROCSS) criteria.

Difinition of biliary complications Biliary complications were suspected when patients exhibited fever, jaundice, or pale stools, or when lab results showed elevated conjugated bilirubin, gamma-glutamyltransferase, or alanine aminotransferase, with or without intrahepatic duct dilation on ultrasound. The diagnosis was primarily based on ultrasound findings and confirmed through Magnetic Resonance Cholangiopancreatography (MRCP) or endoscopic retrograde cholangiopancreatography (ERCP). Anastomotic strictures were defined as those within 1 cm of the surgical anastomosis, while non-anastomotic strictures were located at least 1 cm beyond it. Bile leakage was defined as the unintended escape of bile, confirmed by contrast extravasation during MRCP or ERCP.

Data collection and potential predictors Preoperative recipient variables included sex, age, height, weight, BMI, comorbidities (hypertension or diabetes mellitus), etiology of liver disease (viral hepatitis, malignancy, hepatic failure, cirrhosis, alcohol-related liver disease, and others), MELD score, Child-Pugh grade, and preoperative lab values (routine blood tests, coagulation markers, electrolytes, and biochemistry). Donor variables included sex, age, height, weight, BMI, type of donation, HBV infection status, and preoperative lab results. Intraoperative factors involved surgical technique , and specific biliary events (choledochojejunostomy or biliary stenting). Postoperative outcomes included arterial resistive index within 24 hours, transaminase levels, International Normalized Ratio, and ICU stay duration.

Logistic regression analysis and nomogram model development A total of 757 patients were randomly allocated into training and validation sets in a 7:3 ratio. To identify potential predictive factors, LASSO regression analysis was applied to the training set. This method effectively removed several irrelevant and multicollinear independent variables, thereby simplifying the high-dimensional data[. Subsequently, multivariable logistic regression analysis was conducted to evaluate the factors associated with biliary complications occurring within 90 days post-liver transplantation. Finally, a nomogram was developed from the training set and validated using the validation set.

Apparent performance of the nomogram Discrimination and calibration were utilized to evaluate the predictive accuracy of the developed model. Harrell's concordance index (C-index) and the receiver operating characteristic (ROC) curve (AUC) were calculated to assess the nomogram's discrimination capability. A C-index approaching 1 suggests a strong predictive ability of the model, while an AUC exceeding 0.80 indicates good discrimination. Furthermore, decision curve analysis (DCA) was conducted to examine the clinical applicability of the nomograms [Reporting and interpreting decision curve analysis: a guide for investigators.]. Both discrimination and calibration were evaluated using bootstrapping with 1000 resamples.

Statistical analysis： Continuous variables were not normally distributed according to the Kolmogorov-Smirnov test and were therefore analyzed using the Mann-Whitney U-test and presented as median (interquartile range). Categorical variables were compared using the χ2-test or Fisher's exact test and presented as absolute numbers (percentage). Statistical analysis was performed using R Software v.4.0.2 (The R Project for Statistical Computing, www.r-project. org) with the 'rms' package utilized for logistic regression analysis and nomogram construction. A two-sided P-value less than 0.05 was considered statistically significant.

• Study Type: Observational
• Enrollment (Estimated): 900

Contacts and Locations

• Study Contact: Name: Zepeng Lin; Phone Number: +86 13421560359; Email: 13421560359@163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Retrospective collection of cases of patients who underwent liver transplantation in the Organ Transplantation Department at Sun Yat-sen University First Affiliated Hospital between January 1, 2016, and December 31, 2021.

Description

Inclusion Criteria:

①Patients aged ≥ 18 years.

②Patients who underwent their first liver transplantation at Sun Yat-sen University First Affiliated Hospital between January 1, 2016, and December 31, 2021.

Exclusion Criteria:

• Living donor liver transplantation. ② Split liver transplantation.

  • Multi-organ transplantation. ④ Missing preoperative baseline indicators and postoperative ultrasound examination results.

    • Patients undergoing re-transplantation.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
No biliary complication group; The liver transplant patients who did not experience biliary complications within 90 days post-operation.
biliary complication group; The liver transplant patients who experience biliary complications within 90 days post-operation.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Body Mass Index (BMI):	Calculated as weight (kg) / height (m²).	1-2 days before surgery
Surgical Technique	Type of technique used (orthotopic, piggyback, or ischemia-free liver transplantation).	Intraoperative time
Weight	Measured in kilograms.	1-2 days before surgery
Height	Measured in meters.	1-2 days before surgery
Blood Loss	Measured in milliliters (mL).	1-2 days before surgery
Cold Ischemic Time	Measured in minutes.	intraoperative time
Warm Ischemic Time	Measured in minutes.	intraoperative time

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Transaminase Levels	Measured in units per liter (U/L)	24 hours post-surgery.
ICU Stay Duration	Measured in hours.	24 hours post-surgery

Collaborators and Investigators

• Sponsor: Sun Yat-sen University

Publications and helpful links

General Publications

• Tingle SJ, Thompson ER, Ali SS, Figueiredo R, Hudson M, Sen G, White SA, Manas DM, Wilson CH. Risk factors and impact of early anastomotic biliary complications after liver transplantation: UK registry analysis. BJS Open. 2021 Mar 5;5(2):zrab019. doi: 10.1093/bjsopen/zrab019.
• Feng LH, Sun HC, Zhu XD, Liu XF, Zhang SZ, Li XL, Li Y, Tang ZY. Prognostic nomograms and risk classifications of outcomes in very early-stage hepatocellular carcinoma patients after hepatectomy. Eur J Surg Oncol. 2021 Mar;47(3 Pt B):681-689. doi: 10.1016/j.ejso.2020.10.039. Epub 2020 Oct 31.
• Ahmad T, Chavhan GB, Avitzur Y, Moineddin R, Oudjhane K. Doppler Parameters of the Hepatic Artery as Predictors of Graft Status in Pediatric Liver Transplantation. AJR Am J Roentgenol. 2017 Sep;209(3):671-675. doi: 10.2214/AJR.17.17902. Epub 2017 Jun 28.
• Asthana S, McClean P, Stringer MD. Does the pediatric end-stage liver disease score or hepatic artery resistance index predict outcome after liver transplantation for biliary atresia? Pediatr Surg Int. 2006 Sep;22(9):697-700. doi: 10.1007/s00383-006-1737-1. Epub 2006 Aug 1.
• Perrakis A, Fortsch T, Niebling N, Croner RS, Nissler V, Yedibela S, Lohmuller C, Zopf S, Kammerer F, Hohenberger W, Muller V. The diagnostic value of systolic acceleration time and resistive index as noninvasive modality for detection of graft rejection after orthotopic liver transplantation. Transplant Proc. 2013 Jun;45(5):1961-5. doi: 10.1016/j.transproceed.2013.01.058.
• Magro B, Tacelli M, Mazzola A, Conti F, Celsa C. Biliary complications after liver transplantation: current perspectives and future strategies. Hepatobiliary Surg Nutr. 2021 Jan;10(1):76-92. doi: 10.21037/hbsn.2019.09.01.

Study record dates

Study Major Dates

• Study Start (Estimated): November 1, 2024
• Primary Completion (Estimated): November 15, 2024
• Study Completion (Estimated): December 30, 2024

Study Registration Dates

• First Submitted: October 23, 2024
• First Submitted That Met QC Criteria: October 24, 2024
• First Posted (Actual): October 26, 2024

Study Record Updates

• Last Update Posted (Actual): October 26, 2024
• Last Update Submitted That Met QC Criteria: October 24, 2024
• Last Verified: October 1, 2024

More Information

Terms related to this study

• Keywords: liver transplantation; prediction model; nomogram; biliary complication
• Other Study ID Numbers: FAH-SYSU

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: The data sets used and/or analyzed for the present study are available from the corresponding author on reasonable request.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No