Potential Predictors for Deterioration of Renal Function After Transfusion.

BACKGROUND: Transfusion of packed red blood cells (pRBCs) is still associated with risks. This study aims to determine whether renal function deterioration in the context of individual transfusions in individual patients can be predicted using machine learning. Recipient and donor characteristics linked to increased risk are identified. METHODS: This study was registered at ClinicalTrials.gov (NCT05466370) and was conducted after local ethics committee approval. We evaluated 3366 transfusion episodes from a university hospital between October 31, 2016, and August 31, 2020. Random forest models were tuned and trained via Python auto-sklearn package to predict acute kidney injury (AKI). The models included recipients' and donors' demographic parameters and laboratory values, donor questionnaire results, and the age of the pRBCs. Bootstrapping on the test dataset was used to calculate the means and standard deviations of various performance metrics. RESULTS: AKI as defined by a modified Kidney Disease Improving Global Outcomes (KDIGO) criterion developed after 17.4% transfusion episodes (base rate). AKI could be predicted with an area under the curve of the receiver operating characteristic (AUC-ROC) of 0.73 ± 0.02. The negative (NPV) and positive (PPV) predictive values were 0.90 ± 0.02 and 0.32 ± 0.03, respectively. Feature importance and relative risk analyses revealed that donor features were far less important than recipient features for predicting posttransfusion AKI. CONCLUSIONS: Surprisingly, only the recipients' characteristics played a decisive role in AKI prediction. Based on this result, we speculate that the selection of a specific pRBC may have less influence than recipient characteristics.

Implementation of a Bleeding Management Algorithm in Liver Transplantation: A Pilot Study.

Objectives: The aims of the study were to compare the consumption of blood products before and after the implementation of a bleeding management algorithm in patients undergoing liver transplantation and to determine the feasibility of a multicentre, randomized study. Background: Liver transplantation remains the only curative therapy for patients with end-stage liver disease, but it carries a high risk of surgical bleeding. Materials and Methods: Retrospective study of patients treated before (group 1) and after (group 2) implementation of a haemostatic algorithm guided by viscoelastic testing, including use of lyophilized coagulation factor concentrates (prothrombin complex and fibrinogen concentrates). Primary outcome was the number of units of blood products transfused in 24 h after surgery. Secondary outcomes included hospital stay, mortality, and cost. Results: Data from 30 consecutive patients was analysed; 14 in group 1 and 16 in group 2. Baseline data were similar between groups. Median total blood product consumption 24 h after surgery was 33 U (IQR: 11-57) in group 1 and 1.5 (0-23.5) in group 2 (p = 0.028). Significantly fewer units of red blood cells, fresh frozen plasma, and cryoprecipitate were transfused in group 2 versus group 1. There was no significant difference in complications, hospital stay, or in-hospital mortality between groups. The cost of haemostatic therapy was non-significantly lower in group 2 versus group 1 (7,400 vs. 15,500 USD; p = 0.454). Conclusion: The haemostatic management algorithm was associated with a significant reduction in blood product use during 24 h after liver transplantation. This study demonstrated the feasibility and provided a sample size calculation for a larger, randomized study.

Recommendations From the International Consensus Conference on Anemia Management in Surgical Patients (ICCAMS).

BACKGROUND: Perioperative anemia has been associated with increased risk of red blood cell transfusion and increased morbidity and mortality after surgery. The optimal approach to the diagnosis and management of perioperative anemia is not fully established. OBJECTIVE: To develop consensus recommendations for anemia management in surgical patients. METHODS: An international expert panel reviewed the current evidence and developed recommendations using modified RAND Delphi methodology. RESULTS: The panel recommends that all patients except those undergoing minor procedures be screened for anemia before surgery. Appropriate therapy for anemia should be guided by an accurate diagnosis of the etiology. The need to proceed with surgery in some patients with anemia is expected to persist. However, early identification and effective treatment of anemia has the potential to reduce the risks associated with surgery and improve clinical outcomes. As with preoperative anemia, postoperative anemia should be treated in the perioperative period. CONCLUSIONS: Early identification and effective treatment of anemia has the potential to improve clinical outcomes in surgical patients.

Management of severe peri-operative bleeding: Guidelines from the European Society of Anaesthesiology and Intensive Care: Second update 2022.

BACKGROUND: Management of peri-operative bleeding is complex and involves multiple assessment tools and strategies to ensure optimal patient care with the goal of reducing morbidity and mortality. These updated guidelines from the European Society of Anaesthesiology and Intensive Care (ESAIC) aim to provide an evidence-based set of recommendations for healthcare professionals to help ensure improved clinical management. DESIGN: A systematic literature search from 2015 to 2021 of several electronic databases was performed without language restrictions. Grading of Recommendations, Assessment, Development and Evaluation (GRADE) was used to assess the methodological quality of the included studies and to formulate recommendations. A Delphi methodology was used to prepare a clinical practice guideline. RESULTS: These searches identified 137 999 articles. All articles were assessed, and the existing 2017 guidelines were revised to incorporate new evidence. Sixteen recommendations derived from the systematic literature search, and four clinical guidances retained from previous ESAIC guidelines were formulated. Using the Delphi process on 253 sentences of guidance, strong consensus (>90% agreement) was achieved in 97% and consensus (75 to 90% agreement) in 3%. DISCUSSION: Peri-operative bleeding management encompasses the patient's journey from the pre-operative state through the postoperative period. Along this journey, many features of the patient's pre-operative coagulation status, underlying comorbidities, general health and the procedures that they are undergoing need to be taken into account. Due to the many important aspects in peri-operative nontrauma bleeding management, guidance as to how best approach and treat each individual patient are key. Understanding which therapeutic approaches are most valuable at each timepoint can only enhance patient care, ensuring the best outcomes by reducing blood loss and, therefore, overall morbidity and mortality. CONCLUSION: All healthcare professionals involved in the management of patients at risk for surgical bleeding should be aware of the current therapeutic options and approaches that are available to them. These guidelines aim to provide specific guidance for bleeding management in a variety of clinical situations.

Red Blood Cell Transfusion in the Intensive Care Unit.

Importance: Red blood cell (RBC) transfusion is common among patients admitted to the intensive care unit (ICU). Despite multiple randomized clinical trials of hemoglobin (Hb) thresholds for transfusion, little is known about how these thresholds are incorporated into current practice. Objective: To evaluate and describe ICU RBC transfusion practices worldwide. Design, Setting, and Participants: International, prospective, cohort study that involved 3643 adult patients from 233 ICUs in 30 countries on 6 continents from March 2019 to October 2022 with data collection in prespecified weeks. Exposure: ICU stay. Main Outcomes and Measures: The primary outcome was the occurrence of RBC transfusion during ICU stay. Additional outcomes included the indication(s) for RBC transfusion (consisting of clinical reasons and physiological triggers), the stated Hb threshold and actual measured Hb values before and after an RBC transfusion, and the number of units transfused. Results: Among 3908 potentially eligible patients, 3643 were included across 233 ICUs (median of 11 patients per ICU [IQR, 5-20]) in 30 countries on 6 continents. Among the participants, the mean (SD) age was 61 (16) years, 62% were male (2267/3643), and the median Sequential Organ Failure Assessment score was 3.2 (IQR, 1.5-6.0). A total of 894 patients (25%) received 1 or more RBC transfusions during their ICU stay, with a median total of 2 units per patient (IQR, 1-4). The proportion of patients who received a transfusion ranged from 0% to 100% across centers, from 0% to 80% across countries, and from 19% to 45% across continents. Among the patients who received a transfusion, a total of 1727 RBC transfusions were administered, wherein the most common clinical indications were low Hb value (n = 1412 [81.8%]; mean [SD] lowest Hb before transfusion, 7.4 [1.2] g/dL), active bleeding (n = 479; 27.7%), and hemodynamic instability (n = 406 [23.5%]). Among the events with a stated physiological trigger, the most frequently stated triggers were hypotension (n = 728 [42.2%]), tachycardia (n = 474 [27.4%]), and increased lactate levels (n = 308 [17.8%]). The median lowest Hb level on days with an RBC transfusion ranged from 5.2 g/dL to 13.1 g/dL across centers, from 5.3 g/dL to 9.1 g/dL across countries, and from 7.2 g/dL to 8.7 g/dL across continents. Approximately 84% of ICUs administered transfusions to patients at a median Hb level greater than 7 g/dL. Conclusions and Relevance: RBC transfusion was common in patients admitted to ICUs worldwide between 2019 and 2022, with high variability across centers in transfusion practices.

[Risk Factors for COVID-19 Mortality]. / Risikofaktoren für COVID-19-Mortalität.

The COVID-19 pandemic has changed the world significantly within the last two years and has put a major burden on health care systems worldwide. Due to the imbalance between the number of patients requiring treatment and the shortage of necessary healthcare resources, a new mode of triage had to be established. The allocation of resources and definition of treatment priorities could be supported by taking the actual short-term mortality risk of patients with COVID-19 into account. We therefore analyzed the current literature for criteria to predict mortality in COVID-19.

Artificial Intelligence and Machine Learning in Patient Blood Management: A Scoping Review.

Machine learning (ML) and artificial intelligence (AI) are widely used in many different fields of modern medicine. This narrative review gives, in the first part, a brief overview of the methods of ML and AI used in patient blood management (PBM) and, in the second part, aims at describing which fields have been analyzed using these methods so far. A total of 442 articles were identified by a literature search, and 47 of them were judged as qualified articles that applied ML and AI techniques in PBM. We assembled the eligible articles to provide insights into the areas of application, quality measures of these studies, and treatment outcomes that can pave the way for further adoption of this promising technology and its possible use in routine clinical decision making. The topics that have been investigated most often were the prediction of transfusion (30%), bleeding (28%), and laboratory studies (15%). Although in the last 3 years a constantly increasing number of questions of ML in PBM have been investigated, there is a vast scientific potential for further application of ML and AI in other fields of PBM.

Machine learning-based prediction of fainting during blood donations using donor properties and weather data as features.

BACKGROUND AND OBJECTIVES: Fainting is a well-known side effect of blood donation. Such adverse experiences can diminish the return rate for further blood donations. Identifying factors associated with fainting could help prevent adverse incidents during blood donation. MATERIALS AND METHODS: Data of 85,040 blood donations from whole blood and apheresis donors within four consecutive years were included in this retrospective study. Seven different machine learning models (random forests, artificial neural networks, XGradient Boosting, AdaBoost, logistic regression, K nearest neighbors, and support vector machines) for predicting fainting during blood donation were established. The used features derived from the data obtained from the questionnaire every donor has to fill in before the donation and weather data of the day of the donation. RESULTS: One thousand seven hundred fifteen fainting reactions were observed in 228 846 blood donations from 88,003 donors over a study period of 48 months. Similar values for all machine learning algorithms investigated for NPV, PPV, AUC, and F1-score were obtained. In general, NPV was above 0.996, whereas PPV was below 0.03. AUC and F1-score were close to 0.9 for all models. Essential features predicting fainting during blood donation were systolic and diastolic blood pressure and ambient temperature, humidity, and barometric pressure. CONCLUSION: Machine-learning algorithms can establish prediction models of fainting in blood donors. These new tools can reduce adverse reactions during blood donation and improve donor safety and minimize negative associations relating to blood donation.

Machine learning-based prediction of massive perioperative allogeneic blood transfusion in cardiac surgery.

BACKGROUND: Massive perioperative allogeneic blood transfusion, that is, perioperative transfusion of more than 10 units of packed red blood cells (pRBC), is one of the main contributors to perioperative morbidity and mortality in cardiac surgery. Prediction of perioperative blood transfusion might enable preemptive treatment strategies to reduce risk and improve patient outcomes while reducing resource utilisation. We, therefore, investigated the precision of five different machine learning algorithms to predict the occurrence of massive perioperative allogeneic blood transfusion in cardiac surgery at our centre. OBJECTIVE: Is it possible to predict massive perioperative allogeneic blood transfusion using machine learning? DESIGN: Retrospective, observational study. SETTING: Single adult cardiac surgery centre in Austria between 01 January 2010 and 31 December 2019. PATIENTS: Patients undergoing cardiac surgery. MAIN OUTCOME MEASURES: Primary outcome measures were the number of patients receiving at least 10 units pRBC, the area under the curve for the receiver operating characteristics curve, the F1 score, and the negative-predictive (NPV) and positive-predictive values (PPV) of the five machine learning algorithms used to predict massive perioperative allogeneic blood transfusion. RESULTS: A total of 3782 (1124 female:) patients were enrolled and 139 received at least 10 pRBC units. Using all features available at hospital admission, massive perioperative allogeneic blood transfusion could be excluded rather accurately. The best area under the curve was achieved by Random Forests: 0.810 (0.76 to 0.86) with high NPV of 0.99). This was still true using only the eight most important features [area under the curve 0.800 (0.75 to 0.85)]. CONCLUSION: Machine learning models may provide clinical decision support as to which patients to focus on for perioperative preventive treatment in order to preemptively reduce massive perioperative allogeneic blood transfusion by predicting, which patients are not at risk. TRIAL REGISTRATION: Johannes Kepler University Ethics Committee Study Number 1091/2021, Clinicaltrials.gov identifier NCT04856618.

Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests.

Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.

qSOFA score poorly predicts critical progression in COVID-19 patients.

BACKGROUND: In December 2019, the new virus infection coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged. Simple clinical risk scores may improve the management of COVID-19 patients. Therefore, the aim of this pilot study was to evaluate the quick Sequential Organ Failure Assessment (qSOFA) score, which is well established for other diseases, as an early risk assessment tool predicting a severe course of COVID-19. METHODS: We retrospectively analyzed data from adult COVID-19 patients hospitalized between March and July 2020. A critical disease progress was defined as admission to intensive care unit (ICU) or death. RESULTS: Of 64 COVID-19 patients, 33% (21/64) had a critical disease progression from which 13 patients had to be transferred to ICU. The COVID-19-associated mortality rate was 20%, increasing to 39% after ICU admission. All patients without a critical progress had a qSOFA score ≤ 1 at admission. Patients with a critical progress had in only 14% (3/21) and in 20% (3/15) of cases a qSOFA score ≥ 2 at admission (p = 0.023) or when measured directly before critical progression, respectively, while 95% (20/21) of patients with critical progress had an impairment oxygen saturation (SO2) at admission time requiring oxygen supplementation. CONCLUSION: A low qSOFA score cannot be used to assume short-term stable or noncritical disease status in COVID-19.

Machine learning-based prediction of transfusion.

BACKGROUND: The ability to predict transfusions arising during hospital admission might enable economized blood supply management and might furthermore increase patient safety by ensuring a sufficient stock of red blood cells (RBCs) for a specific patient. We therefore investigated the precision of four different machine learning-based prediction algorithms to predict transfusion, massive transfusion, and the number of transfusions in patients admitted to a hospital. STUDY DESIGN AND METHODS: This was a retrospective, observational study in three adult tertiary care hospitals in Western Australia between January 2008 and June 2017. Primary outcome measures for the classification tasks were the area under the curve for the receiver operating characteristics curve, the F1 score, and the average precision of the four machine learning algorithms used: neural networks (NNs), logistic regression (LR), random forests (RFs), and gradient boosting (GB) trees. RESULTS: Using our four predictive models, transfusion of at least 1 unit of RBCs could be predicted rather accurately (sensitivity for NN, LR, RF, and GB: 0.898, 0.894, 0.584, and 0.872, respectively; specificity: 0.958, 0.966, 0.964, 0.965). Using the four methods for prediction of massive transfusion was less successful (sensitivity for NN, LR, RF, and GB: 0.780, 0.721, 0.002, and 0.797, respectively; specificity: 0.994, 0.995, 0.993, 0.995). As a consequence, prediction of the total number of packed RBCs transfused was also rather inaccurate. CONCLUSION: This study demonstrates that the necessity for intrahospital transfusion can be forecasted reliably, however the amount of RBC units transfused during a hospital stay is more difficult to predict.

Essential Role of Patient Blood Management in a Pandemic: A Call for Action.

The World Health Organization (WHO) has declared coronavirus disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a pandemic. Global health care now faces unprecedented challenges with widespread and rapid human-to-human transmission of SARS-CoV-2 and high morbidity and mortality with COVID-19 worldwide. Across the world, medical care is hampered by a critical shortage of not only hand sanitizers, personal protective equipment, ventilators, and hospital beds, but also impediments to the blood supply. Blood donation centers in many areas around the globe have mostly closed. Donors, practicing social distancing, some either with illness or undergoing self-quarantine, are quickly diminishing. Drastic public health initiatives have focused on containment and "flattening the curve" while invaluable resources are being depleted. In some countries, the point has been reached at which the demand for such resources, including donor blood, outstrips the supply. Questions as to the safety of blood persist. Although it does not appear very likely that the virus can be transmitted through allogeneic blood transfusion, this still remains to be fully determined. As options dwindle, we must enact regional and national shortage plans worldwide and more vitally disseminate the knowledge of and immediately implement patient blood management (PBM). PBM is an evidence-based bundle of care to optimize medical and surgical patient outcomes by clinically managing and preserving a patient's own blood. This multinational and diverse group of authors issue this "Call to Action" underscoring "The Essential Role of Patient Blood Management in the Management of Pandemics" and urging all stakeholders and providers to implement the practical and commonsense principles of PBM and its multiprofessional and multimodality approaches.

A comparative study on adequate anesthesia depth: clinical judgement and the Narcotrend® measurement.

PURPOSE: To compare the clinical judgement of electroencephalogram (EEG)-naïve anesthesiologists with an EEG-based measurement of anesthetic depth (AD) using the Narcotrend® monitor. METHODS: In this prospective cohort study including 600 patients, AD during stable anesthesia was assessed by clinical judgement of the attending, EEG-blinded anesthesiologist (using a scale staging the AD as mid-adequate, adequate but fairly deep, or adequate but fairly light) and by simultaneously recorded Narcotrend measurements. RESULTS: In 42% of patients (n = 250), the anesthesiologist's clinical judgement was in agreement with anesthetic levels as measured by the Narcotrend monitor. In 46% of patients (n = 274), the anesthesiologist's judgement and the Narcotrend monitor differed by one AD level (minor discordance). Major discordance was observed in 76 (13%) measurements (judged deeper than measured, n = 29 [5%]; judged lighter than measured, n = 47 [8%]). In 7% of patients (n = 44), the Narcotrend index was outside the limits of adequate AD (too deep, n = 28 [5%]; too superficial, n = 16 [3%]). The overall level of agreement between the anesthesiologist's judgement and the Narcotrend monitor was not statistically significant (Cohen's kappa, -0.039; P = 0.17). Using a random forests algorithm, age, mean blood pressure, the American Society of Anesthesiologists classification, body mass index, and frailty were the variables with the highest relative feature importance to predict the level of agreement. CONCLUSION: These results suggest that clinical judgement of AD during stable anesthesia was not in agreement with EEG-based assessment of anesthetic depth in 58% of cases. Nevertheless, this finding could be influenced by the lack of validated scales to clinically judge AD. TRIAL REGISTRATION: www.clinicaltrials.gov (NCT02766894); registered 10 May, 2016.

RéSUMé: OBJECTIF: Notre objectif était de comparer le jugement clinique d'anesthésiologistes n'ayant pas accès à un électroencéphalogramme (EEG) à une mesure de la profondeur anesthésique (PA) fondée sur l'EEG utilisant le moniteur Narcotrend®. MéTHODE: Dans cette étude de cohorte prospective de 600 patients, la PA a été évaluée pendant la phase de maintien stable de l'anesthésie selon le jugement clinique de l'anesthésiologiste traitant, qui n'avait pas accès à l'EEG (sur une échelle évaluant la PA comme étant adéquate, adéquate mais relativement profonde ou adéquate mais relativement légère) et par des mesures simultanément enregistrées par le Narcotrend. RéSULTATS: Chez 42 % des patients (n = 250), le jugement clinique de l'anesthésiologiste concordait aux niveaux anesthésiques tels que mesurés par le moniteur Narcotrend. Chez 46 % des patients (n = 274), le jugement de l'anesthésiologiste et le moniteur Narcotrend différaient d'un niveau de PA (discordance mineure). Une discordance majeure a été observée dans 76 (13 %) mesures (jugées plus profondes que mesurées, n = 29 [5 %], jugées plus légères que mesurées, n = 47 [8 %]). Chez 7 % des patients (n = 44), l'indice Narcotrend était situé au-delà des limites d'une PA adéquate (trop profond, n = 28 [5 %]; trop superficiel, n = 16 [3 %]). Le niveau global de concordance entre le jugement de l'anesthésiologiste et le moniteur Narcotrend n'était pas significatif d'un point de vue statistique (kappa de Cohen, -0,039; P = 0,17). En se fondant sur un algorithme de forêt d'arbres décisionnels (random forests algorithm), l'âge, la tension artérielle moyenne, la classification selon l'American Society of Anesthesiologists, l'indice de masse corporelle et l'index de fragilité ont été identifiés comme les variables ayant la plus grande importance relative pour prédire le niveau de concordance. CONCLUSION: Ces résultats suggèrent que, dans 58 % des cas, le jugement clinique de la PA ne concordait pas à l'évaluation par EEG de la profondeur anesthésique pendant une phase de maintien stable de l'anesthésie. Toutefois, ces résultats pourraient être influencés par l'absence d'échelles validées pour juger la PA d'un point de vue clinique. ENREGISTREMENT DE L'éTUDE: www.clinicaltrials.gov (NCT02766894); enregistrée le 10 mai 2016.

Standards and Best Practice for Acute Normovolemic Hemodilution: Evidence-based Consensus Recommendations.

OBJECTIVE: To develop a standardized approach to the implementation and performance of acute normovolemic hemodilution (ANH) in order to reduce the incidence of bleeding and allogeneic blood transfusion in high-risk surgical bleeding-related cardiac surgery with cardiopulmonary bypass (CPB). DESIGN: A 2-round modified RAND-Delphi consensus process. PARTICIPANTS: Seven physicians from multiple geographic locations and clinical disciplines including anesthesiology and cardiac surgery and 1 cardiac surgery perfusionist participated in the survey. One registered nurse, specializing in Patient Blood Management, participated in the discussion but did not participate in the survey. METHODS: A modified RAND-Delphi method was utilized that integrated evidence review with a face-to-face expert multidisciplinary panel meeting, followed by repeated scoring using a 9-point Likert scale. Consensus was determined as a result from the second round survey, as follows: median rating of 1-3: ANH acceptable; median rating of 7-9: ANH not acceptable; median rating of 4-6: use clinical judgment. RESULTS: Evidentiary review identified 18 key peer-reviewed manuscripts for discussion. Through the consensus-building process, 39 statements including 26 contraindications to ANH and 10 CPB patient variables were assessed. In total, 22 statements were accepted or modified for the second scoring round. CONCLUSIONS: Consensus was reached on 6 conditions in which ANH would or would not be acceptable, showing that development of a standardized approach for the use of ANH in high-risk surgical bleeding and allogeneic blood transfusion is clearly possible. The recommendations developed by this expert panel may help guide the management and inclusion of ANH as an evidence and consensus-based blood conservation modality.

RBC Transfusion Triggers: Is There Anything New?

For many years, in daily clinical practice, the traditional 10/30 rule (hemoglobin 10 g/dL - hematocrit 30%) has been the most commonly used trigger for blood transfusions. Over the years, this approach is believed to have contributed to a countless number of unnecessary transfusions and an unknown number of overtransfusion-related deaths. Recent studies have shown that lower hemoglobin levels can safely be accepted, even in critically ill patients. However, even these new transfusion thresholds are far beyond the theoretical limits of individual anemia tolerance. For this reason, almost all publications addressing the limits of acute anemia recommend physiological transfusion triggers to indicate the transfusion of erythrocyte concentrates as an alternative. Although this concept appears intuitive at first glance, no solid scientific evidence supports the safety and benefit of physiological transfusion triggers to indicate the optimal time point for transfusion of allogeneic blood. It is therefore imperative to continue searching for the most sensitive and specific parameters that can guide the clinician when to transfuse in order to avoid anemia-induced organ dysfunction while avoiding overtransfusion-related adverse effects. This narrative review discusses the concept of anemia tolerance and critically compares hemoglobin-based triggers with physiological transfusion for various clinical indications.

Inflammatory response in trauma patients: are there ways to decrease the inflammatory reaction?

PURPOSE OF REVIEW: Trauma patients are considered a complex population of patients in emergency medicine and need extensive, specialized therapy. One major part is the prevention and treatment of the inflammatory response, which occurs in patients after severe injury resulting in complications like endotheliopathy. Likely as a consequence, coagulopathy occurs. Sterile inflammation is hard to address, especially because of the lack of a single activator. Moreover, it is a complex composition of factors that lead to a pathologic immune response. Our understanding of these patterns is increasing, but the complete pathophysiologic changes have yet to be investigated. Therefore, there is no specific target to treat inflammatory response in trauma patients at the moment. RECENT FINDINGS: There is increasing knowledge of the pathways and mediators that are responsible for the inflammatory response in patients after severe trauma. The endothelial glycocalyx has been identified to be an integral part of these mechanisms. There have been several new therapeutic approaches to diminish the inflammatory response. SUMMARY: Our increasing understanding of the immune system have led to new potential therapeutic perspectives. All of these approaches need further research to be validated. As the current therapies are based on empirical strategies and have not changed much over the years, new treatment options would be an important progress.

A Machine Learning-Based Triage Tool for Children With Acute Infection in a Low Resource Setting.

OBJECTIVES: To deploy machine learning tools (random forests) to develop a model that reliably predicts hospital mortality in children with acute infections residing in low- and middle-income countries, using age and other variables collected at hospital admission. DESIGN: Post hoc analysis of a single-center, prospective, before-and-after feasibility trial. SETTING: Rural district hospital in Rwanda, a low-income country in Sub-Sahara Africa. PATIENTS: Infants and children greater than 28 days and less than 18 years of life hospitalized because of an acute infection. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Age, vital signs (heart rate, respiratory rate, and temperature) capillary refill time, altered mental state collected at hospital admission, as well as survival status at hospital discharge were extracted from the trial database. This information was collected for 1,579 adult and pediatric patients admitted to a regional referral hospital with an acute infection in rural Rwanda. Nine-hundred forty-nine children were included in this analysis. We predicted survival in study subjects using random forests, a machine learning algorithm. Five prediction models, all including age plus two to five other variables, were tested. Three distinct optimization criteria of the algorithm were then compared. The in-hospital mortality was 1.5% (n = 14). All five models could predict in-hospital mortality with an area under the receiver operating characteristic curve ranging between 0.69 and 0.8. The model including age, respiratory rate, capillary refill time, altered mental state exhibited the highest predictive value area under the receiver operating characteristic curve 0.8 (95% CI, 0.78-0.8) with the lowest possible number of variables. CONCLUSIONS: A machine learning-based algorithm could reliably predict hospital mortality in a Sub-Sahara African population of 949 children with an acute infection using easily collected information at admission which includes age, respiratory rate, capillary refill time, and altered mental state. Future studies need to evaluate and strengthen this algorithm in larger pediatric populations, both in high- and low-/middle-income countries.

Patient Blood Management for Neonates and Children Undergoing Cardiac Surgery: 2019 NATA Guidelines.

Pediatric cardiac surgery is associated with a substantial risk of bleeding, frequently requiring the administration of allogeneic blood products. Efforts to optimize preoperative hemoglobin, limit blood sampling, improve hemostasis, reduce bleeding, correct coagulopathy, and incorporate blood sparing techniques (including restrictive transfusion practices) are key elements of patient blood management (PBM) programs, and should be applied to the pediatric cardiac surgical population as across other disciplines. Many guidelines for implementation of PBM in adults undergoing cardiac surgery are available, but evidence regarding the implementation of PBM in children is limited to systematic reviews and specific guidelines for the pediatric cardiac population are missing. The objective of the task force from the Network for the Advancement of Patient Blood Management, Haemostasis and Thrombosis (NATA, www.nataonline.com) is to provide evidence-based recommendations regarding anemia management and blood transfusion practices in the perioperative care of neonates and children undergoing cardiac surgery, and to highlight potential areas where additional research is urgently required.

[Trauma-Induced Coagulopathy: Pathophysiology and Management]. / Traumaassoziierte Koagulopathie: Pathophysiologie und Therapie.

Persistent, uncontrolled bleeding after trauma is one of the leading causes of fatalities in patients with severe injuries. 40% of trauma deaths are associated with massive haemorrhage. Hypoperfusion due to major loss of blood volume leads to tissue damage. In combination with acidosis and hypothermia, a generalized immune response with activation of coagulation is triggered. This leads to trauma-induced coagulopathy. A suitable, early treatment might lead to a significant reduction in morbidity and mortality.