Development and validation of artificial intelligence models for early detection of postoperative infections (PERISCOPE): a multicentre study using electronic health record data.

Background: Postoperative infections significantly impact patient outcomes and costs, exacerbated by late diagnoses, yet early reliable predictors are scarce. Existing artificial intelligence (AI) models for postoperative infection prediction often lack external validation or perform poorly in local settings when validated. We aimed to develop locally valid models as part of the PERISCOPE AI system to enable early detection, safer discharge, and more timely treatment of patients.

Systematic evaluation of machine learning models for postoperative surgical site infection prediction.

Background: Surgical site infections (SSIs) lead to increased mortality and morbidity, as well as increased healthcare costs. Multiple models for the prediction of this serious surgical complication have been developed, with an increasing use of machine learning (ML) tools.

Objective: The aim of this systematic review was to assess the performance as well as the methodological quality of validated ML models for the prediction of SSIs.

Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review.

Background: Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice.

Objective: This scoping review aimed to explore methods for identifying patients with postoperative infections using electronic health record (EHR) data to go beyond the reference standard of manual chart review.

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.

Unlabelled: 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.

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.