Validation of predictive models identifying patients at risk for massive transfusion during liver transplantation and their potential impact on blood bank resource utilization.

BACKGROUND: Intraoperative massive transfusion (MT) is common during liver transplantation (LT). A predictive model of MT has the potential to improve use of blood bank resources. STUDY DESIGN AND METHODS: Development and validation cohorts were identified among deceased-donor LT recipients from 2010 to 2016. A multivariable model of MT generated from the development cohort was validated with the validation cohort and refined using both cohorts. The combined cohort also validated the previously reported McCluskey risk index (McRI). A simple modified risk index (ModRI) was then created from the combined cohort. Finally, a method to translate model predictions to a population-specific blood allocation strategy was described and demonstrated for the study population. RESULTS: Of the 403 patients, 60 (29.6%) in the development and 51 (25.5%) in the validation cohort met the definition for MT. The ModRI, derived from variables incorporated into multivariable model, ranged from 0 to 5, where 1 point each was assigned for hemoglobin level of less than 10 g/dL, platelet count of less than 100 × 109 /dL, thromboelastography R interval of more than 6 minutes, simultaneous liver and kidney transplant and retransplantation, and a ModRI of more than 2 defined recipients at risk for MT. The multivariable model, McRI, and ModRI demonstrated good discrimination (c statistic [95% CI], 0.77 [0.70-0.84]; 0.69 [0.62-0.76]; and 0.72 [0.65-0.79], respectively, after correction for optimism). For blood allocation of 6 or 15 units of red blood cells (RBCs) based on risk of MT, the ModRI would prevent unnecessary crossmatching of 300 units of RBCs/100 transplants. CONCLUSIONS: Risk indices of MT in LT can be effective for risk stratification and reducing unnecessary blood bank resource utilization.

Predictive Modeling of Massive Transfusion Requirements During Liver Transplantation and Its Potential to Reduce Utilization of Blood Bank Resources.

BACKGROUND: Patients undergoing liver transplantation frequently but inconsistently require massive blood transfusion. The ability to predict massive transfusion (MT) could reduce the impact on blood bank resources through customization of the blood order schedule. Current predictive models of MT for blood product utilization during liver transplantation are not generally applicable to individual institutions owing to variability in patient population, intraoperative management, and definitions of MT. Moreover, existing models may be limited by not incorporating cirrhosis stage or thromboelastography (TEG) parameters. METHODS: This retrospective cohort study included all patients who underwent deceased-donor liver transplantation at the Johns Hopkins Hospital between 2010 and 2014. We defined MT as intraoperative transfusion of > 10 units of packed red blood cells (pRBCs) and developed a multivariable predictive model of MT that incorporated cirrhosis stage and TEG parameters. The accuracy of the model was assessed with the goodness-of-fit test, receiver operating characteristic analysis, and bootstrap resampling. The distribution of correct patient classification was then determined as we varied the model threshold for classifying MT. Finally, the potential impact of these predictions on blood bank resources was examined. RESULTS: Two hundred three patients were included in the study. Sixty (29.6%) patients met the definition for MT and received a median (interquartile range) of 19.0 (14.0-27.0) pRBC units intraoperatively compared with 4.0 units (1.0-6.0) for those who did not satisfy the criterion for MT. The multivariable model for predicting MT included Model for End-stage Liver Disease score, whether simultaneous liver and kidney transplant was performed, cirrhosis stage, hemoglobin concentration, platelet concentration, and TEG R interval and angle. This model demonstrated good calibration (Hosmer-Lemeshow goodness-of-fit test P = .45) and good discrimination (c statistic: 0.835; 95% confidence interval, 0.781-0.888). A probability cutoff threshold of 0.25 was found to misclassify only 4 of 100 patients as unlikely to experience MT, with the majority such misclassifications within 4 units of the working definition for MT. For this threshold, a preoperative blood ordering schedule that allocated 6 units of pRBCs for those unlikely to experience MT and 15 for those who were likely to experience MT would prevent unnecessary crossmatching of 338 units/100 transplants. CONCLUSIONS: When clinical and laboratory parameters are included, a model predicting intraoperative MT in patients undergoing liver transplantation is sufficiently accurate that its predictions could guide the blood order schedule for individual patients based on institutional data, thereby reducing the impact on blood bank resources. Ongoing evaluation of model accuracy and transfusion practices is required to ensure continuing performance of the predictive model.

Comprehensive quality initiative leads to immediate postoperative extubation following liver transplant.

BACKGROUND: Immediate postoperative extubation (IPE) can reduce perioperative complications and length of stay (LOS), however it is performed variably after liver transplant across institutions and has historically excluded high-risk recipients from consideration. In late 2012, we planned and implemented a single academic institution structured quality improvement (QI) initiative to standardize perioperative care of liver transplant recipients without exceptions. We hypothesized that such an approach would lead to a sustained increase in IPE after primary (PAC) and delayed abdominal closure (DAC). METHODS: We retrospectively studied 591 patients from 2013 to 2018 who underwent liver transplant after initiative implementation. We evaluated trends in incidence of IPE versus delayed extubation (DE), and reintubation, LOS, and mortality. RESULTS: Overall, 476/591 (80.5%) recipients underwent PAC (278 IPE, 198 DE) and 115/591 (19.5%) experienced DAC (39 IPE, 76 DE). When comparing data from 2013 to data from 2018, the incidence of IPE increased from 9/67 (13.4%) to 78/90 (86.7%) after PAC and from 1/12 (8.3%) to 16/23 (69.6%) after DAC. For the same years, the incidence of IPE after PAC for recipients with MELD scores ≥30 increased from 0/19 (0%) to 12/17 (70.6%), for recipients who underwent simultaneous liver-kidney transplant increased from 1/8 (12.5%) to 4/5 (80.0%), and for recipients who received massive transfusion (>10 units of packed red blood cells) increased from 0/17 (0%) to 10/13 (76.9%). Reintubation for respiratory considerations <48 h after IPE occurred in 3/278 (1.1%) after PAC and 1/39 (2.6%) after DAC. IPE was associated with decreased intensive care unit (HR of discharge: 1.92; 95% CI: 1.58, 2.33; P < 0.001) and hospital LOS (HR of discharge: 1.45; 95% CI: 1.20, 1.76; P < 0.001) but demonstrated no association with mortality. CONCLUSION: A structured QI initiative led to sustained high rates of IPE and reduced LOS in all liver transplant recipients, including those classified as high risk.

Continuous cerebral blood flow autoregulation monitoring in patients undergoing liver transplantation.

BACKGROUND: Clinical monitoring of cerebral blood flow (CBF) autoregulation in patients undergoing liver transplantation may provide a means for optimizing blood pressure to reduce the risk of brain injury. The purpose of this pilot project is to test the feasibility of autoregulation monitoring with transcranial Doppler (TCD) and near-infrared spectroscopy (NIRS) in patients undergoing liver transplantation and to assess changes that may occur perioperatively. METHODS: We performed a prospective observational study in 9 consecutive patients undergoing orthotopic liver transplantation. Patients were monitored with TCD and NIRS. A continuous Pearson's correlation coefficient was calculated between mean arterial pressure (MAP) and CBF velocity and between MAP and NIRS data, rendering the variables mean velocity index (Mx) and cerebral oximetry index (COx), respectively. Both Mx and COx were averaged and compared during the dissection phase, anhepatic phase, first 30 min of reperfusion, and remaining reperfusion phase. Impaired autoregulation was defined as Mx ≥ 0.4. RESULTS: Autoregulation was impaired in one patient during all phases of surgery, in two patients during the anhepatic phase, and in one patient during reperfusion. Impaired autoregulation was associated with a MELD score >15 (p = 0.015) and postoperative seizures or stroke (p < 0.0001). Analysis of Mx categorized in 5 mmHg bins revealed that MAP at the lower limit of autoregulation (MAP when Mx increased to ≥ 0.4) ranged between 40 and 85 mmHg. Average Mx and average COx were significantly correlated (p = 0.0029). The relationship between COx and Mx remained when only patients with bilirubin >1.2 mg/dL were evaluated (p = 0.0419). There was no correlation between COx and baseline bilirubin (p = 0.2562) but MELD score and COx were correlated (p = 0.0458). Average COx was higher for patients with a MELD score >15 (p = 0.073) and for patients with a neurologic complication than for patients without neurologic complications (p = 0.0245). CONCLUSIONS: These results suggest that autoregulation is impaired in patients undergoing liver transplantation, even in the absence of acute, fulminant liver failure. Identification of patients at risk for neurologic complications after surgery may allow for prompt neuroprotective interventions, including directed pressure management.