Identifying and Predicting Postoperative Infections Based on Readily Available Electronic Health Record Data.

Identification of postoperative infections based on retrospective patient data is currently done using manual chart review. We used a validated, automated labelling method based on registrations and treatments to develop a high-quality prediction model (AUC 0.81) for postoperative infections.

The Added Value of Lactate and Lactate Clearance in Prediction of In-Hospital Mortality in Critically Ill Patients With Sepsis.

We investigated the added predictive value of lactate and lactate clearance to the Acute Physiology and Chronic Health Evaluation IV model for predicting in-hospital mortality in critically ill patients with sepsis. DESIGN: Retrospective observational cohort study. SETTING: Mixed ICU of Leiden University Medical Center, The Netherlands. PATIENTS: Critically ill patients adult patients with sepsis who have been admitted to the ICU of Leiden University Medical Center, The Netherlands, from 2006 to January 2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We fitted a baseline model with the Acute Physiology and Chronic Health Evaluation IV predictors and added 13 prespecified combinations of lactate and lactate clearance at 0, 6 and 24 hours after admission to create a set of extended models to compare with the baseline Acute Physiology and Chronic Health Evaluation IV model. Among 603 ICU admissions, 451 patients met the inclusion criteria. A total of 160 patients died in-hospital, of which 106 died in the ICU. Their lactate and lactate clearance measurements were higher at all time points than those of survivors. The Akaike Information Criterion score improved in 10 of 13 prespecified extended models, with best performance for models that included lactate at 24 hours, alone or in combination with lactate at admission or lactate clearance at 24 hours. We compared the observed and predicted probabilities of in-hospital mortality of the baseline Acute Physiology and Chronic Health Evaluation IV model with the best model in our data, lactate at 24 hours added to the Acute Physiology and Chronic Health Evaluation IV model. This resulted in an increase in specificity of 29.9% (95% CI, 18.9-40.9%). CONCLUSIONS: Lactate measurements at 24 hours after admission add predictive value to the prediction of mortality with Acute Physiology and Chronic Health Evaluation IV among ICU patients with sepsis. External validation is needed to develop extended prediction models.

Apoptosis and NET formation in the pathogenesis of SLE.

Systemic Lupus Erythematosus is an autoimmune disease characterized by the formation of anti-nuclear autoantibodies, particularly anti-chromatin. Although the aetiology of the disease has not yet been fully elucidated, several mechanisms have been proposed to be involved. Due to an aberrant apoptosis or decreased removal of apoptotic cells, apoptotic blebs containing chromatin are released. During apoptosis, chromatin is modified that increases its immunogenicity. Myeloid dendritic cells (myDC) can take up apoptotic blebs and stimulate autoreactive T helper cells, and subsequently the formation of autoantibodies by autoreactive B cells. Immune complexes formed by anti-chromatin autoantibodies and modified chromatin deposit on basal membranes, and incite a local inflammation, but can also stimulate plasmacytoid dendritic cells to produce IFN-α. In addition to apoptotic blebs, neutrophil extracellular traps released by dying neutrophils, in a process called NETosis, may serve as a source of autoantigens as well. In this review, we describe the role of both apoptosis and NETosis in the pathogenesis of SLE, and show how both processes may interact with each other.