The usefulness of a classification and regression tree algorithm for detecting perioperative transfusion-related pulmonary complications

Estimating the incidence of transfusion-associated circulatory overload using active surveillance: A systematic review and meta-analysis

BACKGROUND

Transfusion-associated circulatory overload (TACO) is an adverse event that is the leading cause of transfusion-related death. It is underrecognized, and the aim of this study was to synthesize the available evidence from active surveillance studies to estimate its incidence.

STUDY DESIGN AND METHODS

This study is a systematic review and meta-analysis of publications reporting TACO incidence using active surveillance. A research librarian searched Medline and Embase, identifying publications between January 1991 and June 2024. Studies reporting TACO either by patient, blood component (red blood cells [RBCs], platelets, or plasma) or transfusion episode were identified, and all patient settings were eligible. A random effects model estimated TACO incidence, and potential sources of heterogeneity were evaluated using meta-regression.

RESULTS

Twenty-two studies met eligibility criteria and were included for analysis. The rate per patient was 22.2/1000 (95% CI: 16.2-29.2) based on 21 studies. The rate estimate of TACO among total blood components (RBCs, plasma, and platelets combined) reported in 10 studies was 2.2/1000 units transfused (95% CI: 1.2-3.5/1000). There was substantial between-study variation in rates and more recent studies tended to report higher rates. Although the platelet point estimate was higher than the point estimates for RBCs and plasma, the confidence intervals overlapped. Only two studies reported TACO rates per transfusion episode and the pooled estimate was 6.3/1000 (95% CI: 1-16.3/1000), about three times greater than the overall per unit estimate.

DISCUSSION

Clinicians should consider quantitative risks of important transfusion-related harms, such as TACO, when making the decision to transfuse.

CE: Recognizing Transfusion-Associated Circulatory Overload

ABSTRACT

Transfusion-associated circulatory overload (TACO) is the leading cause of transfusion-related deaths in the United States, accounting for more than 30% of fatalities reported to the Food and Drug Administration between 2016 and 2020. However, TACO is widely considered to be an underdiagnosed and underreported complication of blood transfusions, and its exact incidence is unknown. One of the reasons for this is a lack of recognition of TACO and its signs and symptoms, especially as the definition of TACO has been updated twice since 2018 without full dissemination to nurses, who are responsible for bedside care of patients during and following blood transfusions. This article seeks to bridge this gap by discussing the updated definitions and signs and symptoms of TACO, as well as the management of this treatable blood transfusion reaction.

A Hybrid Water Balance Machine Learning Model to Estimate Inter-Annual Rainfall-Runoff

Watershed climatic diversity poses a hard problem when it comes to finding suitable models to estimate inter-annual rainfall runoff (IARR). In this work, a hybrid model (dubbed MR-CART) is proposed, based on a combination of MR (multiple regression) and CART (classification and regression tree) machine-learning methods, applied to an IARR predicted data series obtained from a set of non-parametric and empirical water balance models in five climatic floors of northern Algeria between 1960 and 2020. A comparative analysis showed that the Yang, Sharif, and Zhang’s models were reliable for estimating input data of the hybrid model in all climatic classes. In addition, Schreiber’s model was more efficient in very humid, humid, and semi-humid areas. A set of performance and distribution statistical tests were applied to the estimated IARR data series to show the reliability and dynamicity of each model in all study areas. The results showed that our hybrid model provided the best performance and data distribution, where the R2Adj and p-values obtained in each case were between (0.793, 0.989), and (0.773, 0.939), respectively. The MR model showed good data distribution compared to the CART method, where p-values obtained by signtest and WSR test were (0.773, 0.705), and (0.326, 0.335), respectively.

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is a clinical syndrome that encompasses a heterogeneous population. Management of such heterogeneous patient population is a big challenge for clinicians. With accumulating ALI datasets being publicly available, more knowledge could be discovered with sophisticated analytics. We reviewed literatures with big data analytics to understand the role of AI for improving the caring of patients with ALI/ARDS. Many studies have utilized the electronic medical records (EMR) data for the identification and prognostication of ARDS patients. As increasing number of ARDS clinical trials data is open to public, secondary analysis on these combined datasets provide a powerful way of finding solution to clinical questions with a new perspective. AI techniques such as Classification and Regression Tree (CART) and artificial neural networks (ANN) have also been successfully used in the investigation of ARDS problems. Individualized treatment of ARDS could be implemented with a support from AI as we are now able to classify ARDS into many subphenotypes by unsupervised machine learning algorithms. Interestingly, these subphenotypes show different responses to a certain intervention. However, current analytics involving ARDS have not fully incorporated information from omics such as transcriptome, proteomics, daily activities and environmental conditions. AI technology is assisting us to interpret complex data of ARDS patients and enable us to further improve the management of ARDS patients in future with individual treatment plans.

Transfusion-related Acute Lung Injury in the Perioperative Patient

Transfusion-related acute lung injury is a leading cause of death associated with the use of blood products. Transfusion-related acute lung injury is a diagnosis of exclusion which can be difficult to identify during surgery amid the various physiologic and pathophysiologic changes associated with the perioperative period. As anesthesiologists supervise delivery of a large portion of inpatient prescribed blood products, and since the incidence of transfusion-related acute lung injury in the perioperative patient is higher than in nonsurgical patients, anesthesiologists need to consider transfusion-related acute lung injury in the perioperative setting, identify at-risk patients, recognize early signs of transfusion-related acute lung injury, and have established strategies for its prevention and treatment.

CE: A Review of Current Practice in Transfusion Therapy

: In the United States, roughly 4.5 million patients per year receive transfusions of various blood products. Despite the lifesaving benefits of transfusion therapy, it is an independent risk factor for infection, morbidity, and death in critically ill patients. It's important for nurses to understand the potential complications patients face when blood products are administered and to recognize that patients who have received blood products in the past remain at risk for delayed reactions, including immune compromise and infection. Here, the authors review the blood products that are commonly transfused; discuss potential complications of transfusion, as well as their associated signs and symptoms; and outline current recommendations for transfusion therapy that are widely supported in the medical and nursing literature.

Rotational Thromboelastometry Helps to Reduce Blood Product Consumption in Critically Ill Patients during Small Surgical Procedures at the Intensive Care Unit - a Retrospective Clinical Analysis and Literature Search

Background

Patients at intensive care units (ICUs) are often transfused to correct increased coagulation parameters (prothrombin time and activated partial thromboplastine time) and/or low platelet count. Thromboelastometry using whole blood is considered to be superior to these tests. In clinical praxis, prolonged standard tests are seen but thromboelastometry values are normal. The objective was to compare the blood product consumptions before and after the introduction of thromboelastometry assays into the treatment protocol during small surgical procedures at our mixed ICU.

Methods

We analyzed 1,879 patients treated at our ICU who underwent small interventions. We compared the fresh frozen plasma and platelet consumption before and after the introduction of rotational thromboelastometry into the routine use. The obtained data were compared to relevant research results from the PubMed database, the MeSH index in the Medline database, and Google Scholar using key words 'tromboelastometry', 'fresh frozen plasma' and 'platelets'.

Results

Annual fresh frozen plasma and platelet consumptions were significantly decreased following thromboelastometry introduction. The number of patients and procedures did not differ significantly during the periods analyzed.

Conclusion

Routine thromboelastometry assays can enable significant reduction of blood product consumption in critically ill patients undergoing small surgery without any bleeding complications.