Exploring the Relationship Between Continuously Monitored Vital Signs, Clinical Deterioration, and Clinical Actions.

Continuous monitoring on the general ward leads to more and earlier interventions to prevent clinical deterioration. These clinical actions influence outcomes and may serve as an indicator of impending deterioration. This study aims to correlate clinical actions with clinical endpoints and deviating vital signs.

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.

Neutrophil extracellular traps and oxidative stress in systemic lupus erythematosus patients with and without renal involvement.

Objectives: To investigate the levels of plasma neutrophil extracellular traps (NETs) and free thiols, the latter reflecting systemic oxidative stress (OS), and to explore the relationship between NETs and OS in quiescent systemic lupus erythematosus (SLE) patients with and without renal involvement.

Methods: Plasma levels of NETs and free thiols were measured cross-sectionally in 100 SLE patients with low disease activity (SLEDAI < 5), of whom 73 patients had no renal involvement (non-LN) and 27 patients had lupus nephritis (LN). Additionally, 22 healthy controls (HCs) were included.

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.

Pulmonary and Physical Virtual Reality Exercises for Patients With Blunt Chest Trauma: Randomized Clinical Trial.

Background: Adequate pain relief, early restoration of breathing, and rapid mobilization pose a clinical challenge in patients with blunt chest trauma. Virtual reality (VR) has the potential to achieve these 3 interrelated treatment objectives with enhanced self-efficacy and autonomy of patients and limited support by clinicians.

Objective: This study aimed to assess the effectivity of breathing and physical exercises using VR on the pulmonary recovery of patients with blunt chest trauma at the ward.