Emergency Call versus General Practitioner Requested Ambulances - Patient Mortality, Disease Severity and Pattern.

Introduction: Ambulance requests by general practitioners for primary care patients (GP-requested) are often omitted in studies on increased demand within emergency care but may comprise a substantial patient group. We aimed to assess acute severity, intensive care unit (ICU) admission, and diagnostic pattern, including comorbidity, and mortality among GP-requested ambulance patients, compared to emergency call ambulance patients. Our hypothesis was that emergency call patients had more severe health issues than GP-requested ambulance patients.

Prehospital Early Warning Scores to Predict Mortality in Patients Using Ambulances.

Importance: Early warning scores (EWSs) are designed for in-hospital use but are widely used in the prehospital field, especially in select groups of patients potentially at high risk. To be useful for paramedics in daily prehospital clinical practice, evaluations are needed of the predictive value of EWSs based on first measured vital signs on scene in large cohorts covering unselected patients using ambulance services.

Objective: To validate EWSs' ability to predict mortality and intensive care unit (ICU) stay in an unselected cohort of adult patients who used ambulances.

Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data.

Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting.

Machine-Learning Model for Mortality Prediction in Patients With Community-Acquired Pneumonia: Development and Validation Study.

Background: Artificial intelligence tools and techniques such as machine learning (ML) are increasingly seen as a suitable manner in which to increase the prediction capacity of currently available clinical tools, including prognostic scores. However, studies evaluating the efficacy of ML methods in enhancing the predictive capacity of existing scores for community-acquired pneumonia (CAP) are limited. We aimed to apply and validate a causal probabilistic network (CPN) model to predict mortality in patients with CAP.

Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data.

Background: Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards.

Methods: A rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013.

The effect of tissue elastic properties and surfactant on alveolar stability.

This paper presents a novel mathematical model of alveoli, which simulates the effects of tissue elasticity and surfactant on the stability of human alveoli. The model incorporates a spherical approximation to the alveolar geometry, the hysteretic behavior of pulmonary surfactant and tissue elasticity. The model shows that the alveolus without surfactant and the elastic properties of the lung tissue are always at an unstable equilibrium, with the capability both to collapse irreversibly and to open with infinite volume when the alveolus has small opening radii.

A physiological model of the effect of hypoglycemia on plasma potassium.

Background: Adrenaline release and excess insulin during hypoglycemia stimulate the uptake of potassium from the bloodstream, causing low plasma potassium (hypokalemia). Hypokalemia has a profound effect on the heart and is associated with an increased risk of malignant cardiac arrhythmias. It is the aim of this study to develop a physiological model of potassium changes during hypoglycemia to better understand the effect of hypoglycemia on plasma potassium.