Ground truth labels challenge the validity of sepsis consensus definitions in critical illness.

BACKGROUND: Sepsis is the leading cause of death in the intensive care unit (ICU). Expediting its diagnosis, largely determined by clinical assessment, improves survival. Predictive and explanatory modelling of sepsis in the critically ill commonly bases both outcome definition and predictions on clinical criteria for consensus definitions of sepsis, leading to circularity. As a remedy, we collected ground truth labels for sepsis. METHODS: In the Ground Truth for Sepsis Questionnaire (GTSQ), senior attending physicians in the ICU documented daily their opinion on each patient's condition regarding sepsis as a five-category working diagnosis and nine related items. Working diagnosis groups were described and compared and their SOFA-scores analyzed with a generalized linear mixed model. Agreement and discriminatory performance measures for clinical criteria of sepsis and GTSQ labels as reference class were derived. RESULTS: We analyzed 7291 questionnaires and 761 complete encounters from the first survey year. Editing rates for all items were > 90%, and responses were consistent with current understanding of critical illness pathophysiology, including sepsis pathogenesis. Interrater agreement for presence and absence of sepsis was almost perfect but only slight for suspected infection. ICU mortality was 19.5% in encounters with SIRS as the "worst" working diagnosis compared to 5.9% with sepsis and 5.9% with severe sepsis without differences in admission and maximum SOFA. Compared to sepsis, proportions of GTSQs with SIRS plus acute organ dysfunction were equal and macrocirculatory abnormalities higher (p < 0.0001). SIRS proportionally ranked above sepsis in daily assessment of illness severity (p < 0.0001). Separate analyses of neurosurgical referrals revealed similar differences. Discriminatory performance of Sepsis-1/2 and Sepsis-3 compared to GTSQ labels was similar with sensitivities around 70% and specificities 92%. Essentially no difference between the prevalence of SIRS and SOFA ≥ 2 yielded sensitivities and specificities for detecting sepsis onset close to 55% and 83%, respectively. CONCLUSIONS: GTSQ labels are a valid measure of sepsis in the ICU. They reveal suspicion of infection as an unclear clinical concept and refute an illness severity hierarchy in the SIRS-sepsis-severe sepsis spectrum. Ground truth challenges the accuracy of Sepsis-1/2 and Sepsis-3 in detecting sepsis onset. It is an indispensable intermediate step towards advancing diagnosis and therapy in the ICU and, potentially, other health care settings.

Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction.

Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early prediction of sepsis a key task in machine learning for healthcare. Previous approaches have achieved high AUROC by learning from electronic health records where sepsis labels were defined automatically following established clinical criteria. We argue that the practice of incorporating the clinical criteria that are used to automatically define ground truth sepsis labels as features of severity scoring models is inherently circular and compromises the validity of the proposed approaches. We propose to create an independent ground truth for sepsis research by exploiting implicit knowledge of clinical practitioners via an electronic questionnaire which records attending physicians' daily judgements of patients' sepsis status. We show that despite its small size, our dataset allows to achieve state-of-the-art AUROC scores. An inspection of learned weights for standardized features of the linear model lets us infer potentially surprising feature contributions and allows to interpret seemingly counterintuitive findings.

Multidrug-Resistant Bacteria and Disease Progression in Patients with End-Stage Liver Disease and after Liver Transplantation.

BACKGROUND: Multidrug-resistant (MDR) pathogens represent an emerging challenge in end-stage liver disease and in liver transplant recipients. METHODS: We evaluated the impact of MDR bacteria upon clinical outcomes in patients with end-stage liver disease (n = 777) at the time of enrollment on the liver transplant (LTx) waiting list, after first LTx (n = 645), and after second LTx (n = 128). RESULTS: Colonization/infection with MDR bacteria was present in 72/777 patients on the waiting list, in 98/645 patients at first LTx, and in 46/128 patients at second LTx. While on the LTx waiting list, the time until first hydropic decompensation (p = 0.021), hepatic encephalopathy (p < 0.001) and hepatorenal syndrome (p < 0.001) was reduced in the presence of MDR bacteria, which remained an independent risk factor of poor survival in multivariate analysis (p < 0.001). Following first and second liver transplant, MDR bacteria were associated with an increased risk of infection-related deaths (first LTx: p < 0.001; second LTx: p = 0.037) and reduced actuarial survival (first LTx: p < 0.001; second LTx: p = 0.046). CONCLUSIONS: We showed that MDR pathogens are associated with poor outcomes before, after first and after recurrent LTx.