Easix before Conditioning Therapy Predicts Sepsis after Allogeneic Stem Cell Transplantation

Purpose: Endothelial complications are principal causes of non-relapse mortality (NRM) after allogeneic stem cell transplantation (alloSCT). Sepsis is a dysfunctional endothelial response to harmful microorganisms with increased risk of microvascular damage and organ failure. We hypothesized that the endothelial activation and stress index (EASIX) predicts risk of sepsis after alloSCT. Methods: In this retrospective evaluation, 1290 patients (random 1:1 allocation into a training cohort and a validation cohort while balancing for sepsis events) were assessed for presence of neutropenic fever, sepsis and infectious pathogens within 50 days after alloSCT. Sepsis and septic shock were defined according to the modified Sepsis-3 guidelines by the Infectious Diseases Working Party (AGIHO) and Intensive Care Working Party (iCHOP) of the German Society of Hematology and Medical Oncology (DGHO) for neutropenic cancer patients. EASIX and additional serum markers were assessed longitudinally before and after transplantation and correlated with outcome. Established clinical risk scores (EBMT, HCT-CI and VOD-CIBMTR) were raised and compared to EASIX. Results: Within the full cohort of 1290 patients transplanted since 01/2004, neutropenic fever episodes until day+50 after alloSCT were reported in 989 (76.7%), sepsis in 93 (7.2%), catecholamine use in 35 (2.7%), and mechanical ventilation in 31 (2.4%) patients. Patients who developed sepsis until day+50 differed from patients without early sepsis in more frequently having a higher disease score (58% vs 33%, p<0.001), more often receiving ATG prophylaxis (77% vs 64%, p=0.007), myeloablative or aplasia conditioning (44% vs 20%, p<0.001), and less likely receiving MTX prophylaxis (33% vs 43%, p=0.06). Prior to conditioning therapy, EASIX(-pre) was the only marker to predict the hazard of early sepsis irrespective of pathogen detection in time-dependent ROC (AUC 0.85 at day 50) and multivariable cause-specific Cox regression analyses (HR per two-fold increase 2.3 (1.9-2.8, p<0.001). The prognostic value of uni- and multivariable models were evaluated in the second cohort. An optimized cutoff for EASIX defined in the training cohort (2.32) strongly predicted sepsis in multivariable cause-specific Cox regression in the second cohort (HR 16.3 (7.0-37.5), p<0.001). Although Ferritin significantly predicted time to death without sepsis (HR per two-fold increase 1.11 (1.03-1.19), p=0.008), EASIX performed superior to other scores in univariate analysis when predicting time to early sepsis with respect to predictive accuracy measured by the time-dependent Brier score (Figure 1). Accordingly, EASIX was the strongest pre-transplantation score to predict early non-relapse mortality (NRM) within 6 months after alloSCT. Pre-conditioning leukocyte counts, CRP or endothelial serum markers did not associate with early sepsis. After transplantation starting with day 0, sepsis associated with EASIX at any time point until day+28, and in addition with suppressor of tumorigenicity (ST)2 and interleukin-18. Conclusions: EASIX is a powerful predictor of early sepsis and 6-months NRM, irrespective of detected pathogens. Patients with EASIX-pre>2.32 represent a high-risk cohort requiring intensive prophylaxis and endothelial protective treatment strategies. Figure 1 Figure 1. Disclosures Schönland: Pfizer: Honoraria; Janssen: Honoraria, Other: Travel grants, Research Funding; Prothena: Honoraria, Other: Travel grants; Sanofi: Research Funding; Takeda: Honoraria, Other: Travel grants. Hegenbart: Pfizer: Consultancy, Honoraria; Janssen: Consultancy, Research Funding; Alnylam: Honoraria; Akcea: Honoraria; Prothena: Research Funding. Schmitt: Apogenix: Research Funding; Hexal: Other: Travel grants, Research Funding; Novartis: Other: Travel grants, Research Funding; Kite Gilead: Other: Travel grants; Bluebird Bio: Other: Travel grants; MSD: Membership on an entity's Board of Directors or advisory committees; TolerogenixX: Current holder of individual stocks in a privately-held company. Dreger: Novartis: Consultancy, Speakers Bureau; Bluebird Bio: Consultancy; Janssen: Consultancy; Gilead Sciences: Consultancy, Speakers Bureau; BMS: Consultancy; AbbVie: Consultancy, Speakers Bureau; AstraZeneca: Consultancy, Speakers Bureau; Riemser: Consultancy, Research Funding, Speakers Bureau; Roche: Consultancy, Speakers Bureau.

Early Prediction of Sepsis Based on Machine Learning Algorithm

Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, aiming to predict early sepsis 6 hours in advance. The feature generation methods are constructed by combining different features, including statistical strength features, window features, and medical features. Miceforest multiple interpolation method is applied to tackle large missing data problems. Results show that the feature generation method outperforms the mean processing method. XGBoost and LightGBM algorithms are both excellent in prediction performance (AUC: 0.910∼0.979), among which LightGBM boasts a faster running speed and is stronger in generalization ability especially on multidimensional data, with AUC reaching 0.979 in the feature generation method. PTT, WBC, and platelets are the key risk factors to predict early sepsis.