Intraoperative Hemodynamic Management and Postoperative Outcomes in Liver Transplantation

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OBJECTIVES: The main objective of this study is to describe and measure the effects of intraoperative hemodynamic management on postoperative outcomes in liver transplantation in Canada. The specific objectives are: To describe the overall incidence of postoperative complications and graft outcomes ...

OBJECTIVES: The main objective of this study is to describe and measure the effects of intraoperative hemodynamic management on postoperative outcomes in liver transplantation in Canada. The specific objectives are: To describe the overall incidence of postoperative complications and graft outcomes in adult liver transplant recipients in Canada and across different recipients' characteristics. To measure the effects of fluid balance, within an overall hemodynamic instability and management strategy, on the postoperative outcomes of liver transplant recipients in Canada. The central hypotheses of this study are that an intraoperative hemodynamic management based on a restrictive fluid administration may improve postoperative outcomes in this population. EXPOSURE VARIABLES; The exposures of interest will be the intraoperative fluid balance and the intraoperative doses and types of vasopressors used. Fluid balance will be defined as the sum of the volume of administered crystalloids, colloids and blood product transfusions minus drained ascites, intraoperative diuresis and bleeding. Doses of administered vasopressors will be converted into a time-weighted total norepinephrine equivalent dose calculated as the total equipotent units of norepinephrine administered as an infusion divided by the number of hours the infusion was given during the period between entrance in the operating room and ICU admission. Types of vasopressors used will also be collected. Since a restrictive fluid management strategy is associated with a higher use of vasopressors and vice versa, both variables may covary together. However, more difficult surgeries will be associated with higher blood loss, higher volume of administered fluid and higher doses of vasopressors. The combination of these two variables will help estimate the intraoperative hemodynamic management strategy used and delineate the effects of each component. OTHER DESCRIPTIVE VARIABLES: Other patients' characteristics and perioperative practices variables will be captured for descriptive purposes. Recipients' demographic characteristics (age, sex), anthropometric variables (body mass index (BMI)), the presence of any previous abdominal surgery, presence of hepatic encephalopathy, need for preoperative organ support or any preoperative hospital admission will be collected as well. Data on the perioperative use of invasive cardiac output monitoring (thermodilution catheter, transoesophageal echocardiography, etc.), as well as coagulation management (use of thromboelastometry, tranexamic acid, transfusion thresholds, etc.) will be collected. Other donor and graft variables, such as donor sex, donor's BMI, the type of vascular and biliary anastomoses, the use of extended-criteria donor graft, the use of ex vivo perfusion, ischemia time, the practice of donation after circulatory death (donor characteristics, heparin usage, etc.) and the perioperative use of liver biopsies will also be collected. DESCRIPTIVE AND STATISTICAL ANALYSIS: Descriptive analyses and complications incidences across centers: The cumulative incidences of different complications will be described. The main descriptive analysis will be to report these incidences overall as well as across centers (anonymized). A chi-square test for each complication will be used to test if the variation across center is statistically significant. The survival up to 6 months over and across centers and will perform a log-rank test will also be reported. The alpha level will be set at 0.05. To better define the variability of complications across centers, the determination models will be fit three determination models to estimate the center effect as an independent variable, adjusted for age, sex, disease severity and the DRI. A logistic regression for primary graft dysfunction will be fitted, a Cox regression for biliary non-anastomotic strictures and a Poisson regression for the total number of complications up to 30 days per patient (count variable) will be used. The secondary analyses will be to report the cumulative incidence of each complications according to age categories (< 50, 50-60, >60), sex, nature and severity of liver disease (chronic liver disease (CLF), acute liver failure (ALF), retransplantation and MELD categories among CLD patients (< 20, 20-30, >30). Such incidences will be reported with 95% confidence intervals. Association between hemodynamic management and postoperative complications: The primary association analysis will be the association between fluid balance and primary graft dysfunctions using a multivariable mixed-effect model using a logistic link and random intercepts to estimate inter-centre variability and intra-cluster correlation, adjusted for the time-weighted dose of vasopressors and the aforementioned confounders. The investigators will consider any patient needing a retransplantation within 7 days after the index transplantation as having a primary graft dysfunction. The secondary main analysis will be the association between fluid balance and time to biliary non-anastomotic strictures using a multivariable Fine and Gray model, adjusted for the same confounders, with retransplantation and death (not caused by the biliary non-anastomotic strictures) considered as competing risks. The intra-cluster correlation will be addressed using a frailty factor. The second transplantation of any patient performed during the observation period will be excluded to avoid intra-patient correlation and censor the first transplantation follow-up at retransplantation. The other secondary analyses will be the association between fluid balance and other outcomes using similar adjusted survival models (with frailty factors) or generalized mixed effect models (with random intercepts). Statistical interaction will be explored between fluid balance and the vasopressors dose variable. Risk proportionality over time will be explored using the Harrel and Lee test and a visual inspection of the Schoenfeld residuals. The confounding effect of hypotension will be evaluated by removing it from the models and measure the observed change in estimate as well as by a sensitivity analysis restricted in the subgroup of patients without significant residual hypotension (< 150 mmHg). Sensitivity analyzes will be conducted using a generalized propensity score for fluid balance (based on probability density) that will include all confounders and the vasopressor exposure. To explore the robustness of the findings, influential analyzes will be conducted by alternatively excluding patients transplanted for amyloidosis or acute liver failure, those who received a living donor graft or cadaveric donor graft after cardiocirculatory arrest, and those with severe chronic renal failure (glomerular filtration rate < 15 mL/hour/1.73 m2 or on dialysis). The effect of vasopressor doses by type of vasopressors (instead of an overall dose equivalent) will also be explored. Economic analysis: An economic analysis to evaluate hospital costs associated with the observed incidences of postoperative complications will be conducted. The costs associated with our fluid balance exposure effect will be analyzed. Costs will be those associated with the actual procedures, as determined by every hospital using its account systems, and include both fixed and variable components previously identified as major cost drivers (mechanical ventilation days, LOS, reoperation and ICU readmission). SAMPLE SIZE Based on clinical trials in major surgery, a relative difference of 25% for all complications was considered minimally clinically significant. Assuming a proportion of primary graft dysfunction of 25% at one year, an absolute reduction of 6.25% of complications may be converted to a HR of 1.4. To be more conservative, a HR of 1.2 is selected (HR = 1.24 per liter of fluid balance for survival in previous work), which required a sample size of 427 patients to be measurable (alpha = 0.05, power = 95%, proportion of events = 0.25, R2 = 0.1 between exposure and the adjustment variables, fluid balance standard deviation = 2 L) (44). Since random effects have a minimal impact on the sample size when the intervention is not cluster randomized, it is not taken into account. To prevent potential seasonal variability in organ procurement, have enough power in case of missing data and ensure enough degree of freedom for our multivariable analyses, all eligible patients over a 1-year period are planned to be enrolled. The principal investigator anticipates an enrollment of 470 consecutive liver transplant recipients over such period according to the annual volume in each centre (CHUM: 65, MUHC: 45, TGH: 200, LHSC: 60, UAH: 100). Sample size calculation was conducted using R software (R collaboration (version 3.6.2) and the powerSurvEpi package (version 0.1.0). Different sensitivity analyses for residual power were conducted based on different assumptions and a sample size of 470 patients.

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