Recommendations on Monitoring and Replacement of Antithrombin, Fibrinogen, and Von Willebrand Factor in Pediatric Patients on Extracorporeal Membrane Oxygenation: The Pediatric Extracorporeal Membrane Oxygenation Anticoagulation CollaborativE Consensus Conference.

OBJECTIVES: To derive systematic review informed, modified Delphi consensus regarding monitoring and replacement of specific coagulation factors during pediatric extracorporeal membrane oxygenation (ECMO) support for the Pediatric ECMO Anticoagulation CollaborativE. DATA SOURCES: A structured literature search was performed using PubMed, Embase, and Cochrane Library (CENTRAL) databases from January 1988 to May 2020, with an update in May 2021. STUDY SELECTION: Included studies assessed monitoring and replacement of antithrombin, fibrinogen, and von Willebrand factor in pediatric ECMO support. DATA EXTRACTION: Two authors reviewed all citations independently, with conflicts resolved by a third reviewer if required. Twenty-nine references were used for data extraction and informed recommendations. Evidence tables were constructed using a standardized data extraction form. DATA SYNTHESIS: Risk of bias was assessed using the Quality in Prognosis Studies tool. The evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation system. A panel of 48 experts met over 2 years to develop evidence-based recommendations and, when evidence was lacking, expert-based consensus statements. A web-based modified Delphi process was used to build consensus via the Research And Development/University of California Appropriateness Method. Consensus was defined as greater than 80% agreement. We developed one weak recommendation and four expert consensus statements. CONCLUSIONS: There is insufficient evidence to formulate recommendations on monitoring and replacement of antithrombin, fibrinogen, and von Willebrand factor in pediatric patients on ECMO. Optimal monitoring and parameters for replacement of key hemostasis parameters is largely unknown.

Anemia From Inflammation After Intracerebral Hemorrhage and Relationships With Outcome.

BACKGROUND: Baseline anemia is associated with poor intracerebral hemorrhage (ICH) outcomes. However, underlying drivers for anemia and whether anemia development after ICH impacts clinical outcomes are unknown. We hypothesized that inflammation drives anemia development after ICH and assessed their relationship to outcomes. METHODS AND RESULTS: Patients with serial hemoglobin and iron biomarker concentrations from the HIDEF (High-Dose Deferoxamine in Intracerebral Hemorrhage) trial were analyzed. Adjusted linear mixed models assessed laboratory changes over time. Of 42 patients, significant decrements in hemoglobin occurred with anemia increasing from 19% to 45% by day 5. Anemia of inflammation iron biomarker criteria was met in 88%. A separate cohort of 521 patients with ICH with more granular serial hemoglobin and long-term neurological outcome data was also investigated. Separate regression models assessed whether (1) systemic inflammatory response syndrome (SIRS) scores related to hemoglobin changes over time and (2) hemoglobin changes related to poor 90-day outcome. In this cohort, anemia prevalence increased from 30% to 71% within 2 days of admission yet persisted beyond this time. Elevated systemic inflammatory response syndrome was associated with greater hemoglobin decrements over time (adjusted parameter estimate: -0.27 [95% CI, -0.37 to -0.17]) and greater hemoglobin decrements were associated with poor outcomes (adjusted odds ratio per 1 g/dL increase, 0.76 [95% CI, 0.62-0.93]) independent to inflammation and ICH severity. CONCLUSIONS: We identified novel findings that acute anemia development after ICH is common, rapid, and related to inflammation. Because anemia development is associated with poor outcomes, further work is required to clarify if anemia, or its underlying drivers, are modifiable treatment targets that can improve ICH outcomes. REGISTRATION: https://www.clinicaltrials.gov Unique identifier: NCT01662895.

A machine learning approach to predict mortality due to immune-mediated thrombotic thrombocytopenic purpura.

Background: Mortality due to immune-mediated thrombotic thrombocytopenic purpura (iTTP) remains significant. Predicting mortality risk may potentially help individualize treatment. The French Thrombotic Microangiopathy (TMA) Reference Score has not been externally validated in the United States. Recent advances in machine learning technology can help analyze large numbers of variables with complex interactions for the development of prediction models. Objectives: To validate the French TMA Reference Score in the United States Thrombotic Microangiopathy (USTMA) iTTP database and subsequently develop a novel mortality prediction tool, the USTMA TTP Mortality Index. Methods: We analyzed variables available at the time of initial presentation, including demographics, symptoms, and laboratory findings. We developed our model using gradient boosting machine, a machine learning ensemble method based on classification trees, implemented in the R package gbm. Results: In our cohort (n = 419), the French score predicted mortality with an area under the receiver operating characteristic curve of 0.63 (95% CI: 0.50-0.77), sensitivity of 0.35, and specificity of 0.84. Our gradient boosting machine model selected 8 variables to predict acute mortality with a cross-validated area under the receiver operating characteristic curve of 0.77 (95% CI: 0.71-0.82). The 2 cutoffs corresponded to sensitivities of 0.64 and 0.50 and specificities of 0.76 and 0.87, respectively. Conclusion: The USTMA Mortality Index was acceptable for predicting mortality due to acute iTTP in the USTMA registry, but not sensitive enough to rule out death. Identifying patients at high risk of iTTP-related mortality may help individualize care and ultimately improve iTTP survival outcomes. Further studies are needed to provide external validation. Our model is one of many recent examples where machine learning models may show promise in clinical prediction tools in healthcare.