Challenges to improving patient outcome following massive transfusion in severe trauma

Adverse transfusion reactions and what we can do

INTRODUCTION

Transfusions of blood and blood components have inherent risks and the ensuing adverse reactions. It is very important to understand the adverse reactions of blood transfusion comprehensively for ensuring the safety of any future transfusions.

AREAS COVERED

According to the time of onset, adverse reactions of blood transfusion are divided into immediate and delayed transfusion reactions. In acute transfusion reactions, timely identification and immediate cessation of transfusion is critical. Vigilance is required to distinguish delayed responses or reactions that present non-specific signs and symptoms. In this review, we present the progress of mechanism, clinical characteristics and management of commonly encountered transfusion reactions.

EXPERT OPINION

The incidence of many transfusion-related adverse events is decreasing, but threats to transfusion safety are always emerging. It is particularly important for clinicians and blood transfusion staff to recognize the causes, symptoms and treatment methods of adverse blood transfusion reactions to improve the safety. In the future, at-risk patients will be better identified and can benefit from more closely matched blood components.

Influencing Factors and Prevention of Sepsis or Acute Kidney Injury in 85 Patients with Severe Trauma

Severe trauma can cause systemic reactions, leading to massive bleeding, shock, asphyxia, and disturbance of consciousness. At the same time, patients with severe trauma are at high risk of sepsis and acute renal injury. The occurrence of complications will increase the difficulty of clinical treatment, improve the mortality rate, and bring heavy physical and mental burdens and economic pressure to patients and their families. It is of great clinical significance to understand the high risk factors of sepsis and AKI and actively formulate prevention and treatment measures. In this study, the clinical data of 85 patients with severe trauma were analyzed by univariate and multivariate logistic regression to identify the risk factors leading to sepsis or AKI and analyze the prevention and treatment strategies. The results showed that multiple injuries, APACHE II score on admission, SOFA score on admission, and mechanical ventilation were independent influencing factors of sepsis in patients with severe trauma, while hemorrhagic shock, APACHE II score on admission, CRRT, and sepsis were independent influencing factors of AKI in patients with severe trauma. Severe trauma patients complicated with sepsis or AKI will increase the risk of death. In the course of treatment, prevention and intervention should be given as far as possible to reduce the incidence of complications.

Intelligent prediction of RBC demand in trauma patients using decision tree methods

BACKGROUND

The vital signs of trauma patients are complex and changeable, and the prediction of blood transfusion demand mainly depends on doctors' experience and trauma scoring system; therefore, it cannot be accurately predicted. In this study, a machine learning decision tree algorithm [classification and regression tree (CRT) and eXtreme gradient boosting (XGBoost)] was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.

METHODS

A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database. The vital signs, laboratory examination parameters and blood transfusion volume were used as variables, and the non-invasive parameters and all (non-invasive + invasive) parameters were used to construct an intelligent prediction model for red blood cell (RBC) demand by logistic regression (LR), CRT and XGBoost. The prediction accuracy of the model was compared with the area under the curve (AUC).

RESULTS

For non-invasive parameters, the LR method was the best, with an AUC of 0.72 [95% confidence interval (CI) 0.657-0.775], which was higher than the CRT (AUC 0.69, 95% CI 0.633-0.751) and the XGBoost (AUC 0.71, 95% CI 0.654-0.756, P < 0.05). The trauma location and shock index are important prediction parameters. For all the prediction parameters, XGBoost was the best, with an AUC of 0.94 (95% CI 0.893-0.981), which was higher than the LR (AUC 0.80, 95% CI 0.744-0.850) and the CRT (AUC 0.82, 95% CI 0.779-0.853, P < 0.05). Haematocrit (Hct) is an important prediction parameter.

CONCLUSIONS

The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method. It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment, so as to improve the success rate of patient treatment.

Impact of Perioperative Massive Transfusion on Long Term Outcomes of Liver Transplantation: a Retrospective Cohort Study

Background: Liver transplantation (LT) is associated with a significant risk of intraoperative hemorrhage and massive blood transfusion. However, there are few relevant reports addressing the long-term impacts of massive transfusion (MT) on liver transplantation recipients. Aim: To assess the effects of MT on the short and long-term outcomes of adult liver transplantation recipients. Methods: We included adult patients who underwent liver transplantation at West China Hospital from January 2011 to February 2015. MT was defined as red blood cell (RBC) transfusion of ≥10 units within 48 hours since the application of LT. Preoperative, intraoperative and postoperative information were collected for data analyzing. We used one-to-one propensity-matching to create pairs. Kaplan-Meier survival analysis was used to compare long-term outcomes of LT recipients between the MT and non-MT groups. Univariate and multivariate logistic regression analyses were performed to evaluate the risk factors associated with MT in LT. Results: Finally, a total of 227 patients were included in our study. After propensity score matching, 59 patients were categorized into the MT and 59 patients in non-MT groups. Compared with the non-MT group, the MT group had a higher 30-day mortality (15.3% vs 0, p=0.006), and a higher incidence of postoperative complications, including postoperative pulmonary infection, abdominal hemorrhage, pleural effusion and severe acute kidney injury. Furthermore, MT group had prolonged postoperative ventilation support (42 vs 25 h, p=0.007) and prolonged durations of ICU (12.9 vs 9.5 d, p<0.001) stay. Multivariate COX regression indicated that massive transfusion (OR: 2.393, 95% CI: 1.164-4.923, p=0.018) and acute rejection (OR: 7.295, 95% CI: 2.108-25.246, p=0.02) were significant risk factors affecting long-term survivals of LT patients. The 1-year and 3-year survival rates patients in MT group were 82.5% and 67.3%, respectively, while those of non-MT group were 93.9% and 90.5%, respectively. The MT group exhibited a lower long-term survival rate than the non-MT group (HR: 2.393, 95% CI: 1.164-4.923, p<0.001). Finally, the multivariate logistic regression revealed that preoperative hemoglobin <118 g/L (OR: 5.062, 95% CI: 2.292-11.181, p<0.001) and intraoperative blood loss ≥1100 ml (OR: 3.212, 95% CI: 1.586-6.506, p = 0.001) were the independent risk factor of MT in patients undergoing LT. Conclusion: Patients receiving MT in perioperative periods of LT had worse short-term and long-term outcomes than the non-MT patients. Massive transfusion and acute rejection were significant risk factors affecting long-term survivals of LT patients, and intraoperative blood loss of over 1100 ml was the independent risk factor of MT in patients undergoing LT. The results may offer valuable information on perioperative management in LT recipients who experience high risk of MT.