Abstract Introduction Burn patients require significant blood products during their hospitalization. Blood is a critical and limited resource. Up to one-third of blood products are transfused intraoperatively. Prior research identified amount of tissue excised, hemoglobin, INR, and platelets as predictive of transfusions of fresh frozen plasma (FFP) and red blood cells (RBCs). However, development of a model based on both transfused and non-transfused patients intraoperatively has not been performed. Predicting a patient’s likelihood of requiring a transfusion, as well as describing how much blood product to type-and-cross preoperatively, will result in improved utilization of a limited resource. Methods This retrospective analysis examined 254 burn operations representing 104 unique patients between October 2019-August 2020 at a single burn unit. Initial model features included age, TBSA, 2nd/3rd degree burn, ICU/hospital length of stay, preoperative hemoglobin, amount of tissue excised/grafted, case length, if case was a re-excision, and total blood transfused. Few missing variables were imputed using Multi-imputation by Chained Equation. We first built a multiclass logistic regression model for units of RBCs and FFP. Variables with the strongest coefficients were preoperative hemoglobin, case length, amount second-degree burn, and amount excised. Four models were assessed for performance metrics: index cases (the first operation for each patient) and FFP, index cases and RBCs, full cohort (including re-operations) and FFP, full cohort and RBCs. Given poor discrimination at higher levels of transfusion, we then applied linear regression on a subset of patients who received transfusions. Results Models had accuracy ranges from 0.56 (full cohort and FFP) to 0.76 (index cohort and RBC). Models had excellent precision, recall, and f1 scores in predicting patients not any requiring transfusions, with declining performance at higher transfusion levels. Interestingly, machine learning models (Random forest, XGBoost, SVM, Decision Tree) had similar accuracy ranges compared to logistic regression. With respect to linear regression performed on the transfused patient cohort, R^2 values were 0.43 and 0.31 for RBCs and FFP, respectively. RBC and FFP transfusion requirements (mL) can be broadly estimated using the following three variables: RBC_ml = [Anticipated Area Excised (cm^2 ) × 0.1] + [Anticipated Case Length (min) × 6.2] - [Hgb_Preop × 70] + 350 FFP_mL = [Anticipated Area Excised (cm^2 ) × 0.1] + [Anticipated Case Length (min) × 3] - [Hgb_Preop × 35] + 275 Conclusions This modeling allows for moderate accuracy in type-and-cross predictions preoperatively, with excellent accuracy in predicting the need for a transfusion overall. Increased sample size may permit greater model predictive ability using machine learning techniques. Applicability of Research to Practice Increased accuracy in preoperative type-and-cross improves blood resource utilization.

This review outlines the current clinical research investigating how the haptoglobin (Hp) genetic polymorphism and stroke occurrence are implicated in sickle cell disease (SCD) pathophysiology. Hp is a blood serum glycoprotein responsible for binding and removing toxic free hemoglobin from the vasculature. The role of Hp in patients with SCD is critical in combating blood toxicity, inflammation, oxidative stress, and even stroke. Ischemic stroke occurs when a blocked vessel decreases oxygen delivery in the blood to cerebral tissue and is commonly associated with SCD. Due to the malformed red blood cells of sickle hemoglobin S, blockage of blood flow is much more prevalent in patients with SCD. This review is the first to evaluate the role of the Hp polymorphism in the incidence of stroke in patients with SCD. Overall, the data compiled in this review suggest that further studies should be conducted to reveal and evaluate potential clinical advancements for gene therapy and Hp infusions.

Circulating hemopexin is the primary protein responsible for the clearance of heme; therefore, it is a systemic combatant against deleterious inflammation and oxidative stress induced by the presence of free heme. This role of hemopexin is critical in hemolytic pathophysiology. In this review, we outline the current research regarding how the dynamic activity of hemopexin is implicated in sickle cell disease, which is characterized by a pathological aggregation of red blood cells and excessive hemolysis. This pathophysiology leads to symptoms such as acute kidney injury, vaso-occlusion, ischemic stroke, pain crises, and pulmonary hypertension exacerbated by the presence of free heme and hemoglobin. This review includes in vivo studies in mouse, rat, and guinea pig models of sickle cell disease, as well as studies in human samples. In summary, the current research indicates that hemopexin is likely protective against these symptoms and that rectifying depleted hemopexin in patients with sickle cell disease could improve or prevent the symptoms. The data compiled in this review suggest that further preclinical and clinical research should be conducted to uncover pathways of hemopexin in pathological states to evaluate its potential clinical function as both a biomarker and therapy for sickle cell disease and related hemoglobinopathies.