Validation of the MIRACLE Score for Prognostication After Out-of-hospital Cardiac Arrest.

Background: Out-of-hospital cardiac arrest (OHCA) is associated with very poor clinical outcomes. An optimal pathway of care is yet to be defined, but prognostication is likely to assist in the challenging decision-making required for treatment of this high-risk patient cohort. The MIRACLE score provides a simple method of neuro-prognostication but as yet it has not been externally validated.

Explainable hierarchical clustering for patient subtyping and risk prediction.

We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely collected hospital data, such as those used in the UK's National Early Warning Score 2 (NEWS2). An iterative, hierarchical clustering process was used to identify the minimum set of relevant features for cluster separation.

Requirements for a Bespoke Intensive Care Unit Dashboard in Response to the COVID-19 Pandemic: Semistructured Interview Study.

Background: Intensive care units (ICUs) around the world are in high demand due to patients with COVID-19 requiring hospitalization. As researchers at the University of Bristol, we were approached to develop a bespoke data visualization dashboard to assist two local ICUs during the pandemic that will centralize disparate data sources in the ICU to help reduce the cognitive load on busy ICU staff in the ever-evolving pandemic.

Objective: The aim of this study was to conduct interviews with ICU staff in University Hospitals Bristol and Weston National Health Service Foundation Trust to elicit requirements for a bespoke dashboard to monitor the high volume of patients, particularly during the COVID-19 pandemic.

The Value of Triage during Periods of Intense COVID-19 Demand: Simulation Modeling Study.

Background: During the COVID-19 pandemic, many intensive care units have been overwhelmed by unprecedented levels of demand. Notwithstanding ethical considerations, the prioritization of patients with better prognoses may support a more effective use of available capacity in maximizing aggregate outcomes. This has prompted various proposed triage criteria, although in none of these has an objective assessment been made in terms of impact on number of lives and life-years saved.

Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study.

Background: The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification.

Methods: Training cohorts comprised 1276 patients admitted to King's College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020.

Impact of a computerized decision support tool deployed in two intensive care units on acute kidney injury progression and guideline compliance: a prospective observational study.

Background: Acute kidney injury (AKI) affects a large proportion of the critically ill and is associated with worse patient outcomes. Early identification of AKI can lead to earlier initiation of supportive therapy and better management. In this study, we evaluate the impact of computerized AKI decision support tool integrated with the critical care clinical information system (CCIS) on patient outcomes.

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care.

Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these 'capacity-dependent' deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients.

Curation of an intensive care research dataset from routinely collected patient data in an NHS trust.

In this data note we provide the details of a research database of 4831 adult intensive care patients who were treated in the Bristol Royal Infirmary, UK between 2015 and 2019. The purposes of this publication are to describe the dataset for external researchers who may be interested in making use of it, and to detail the methods used to curate the dataset in order to help other intensive care units make secondary use of their routinely collected data. The curation involves linkage between two critical care datasets within our hospital and the accompanying code is available online.

Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK.

Objective: The primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.

Design: We used two datasets of routinely collected patient data to test and improve on a set of previously proposed discharge criteria.

Setting: Bristol Royal Infirmary general intensive care unit (GICU).

Protective mechanical ventilation in United Kingdom critical care units: A multicentre audit.

Lung protective ventilation is becoming increasingly used for all critically ill patients being mechanically ventilated on a mandatory ventilator mode. Compliance with the universal application of this ventilation strategy in intensive care units in the United Kingdom is unknown. This 24-h audit of ventilation practice took place in 16 intensive care units in two regions of the United Kingdom.

Increasing compliance with low tidal volume ventilation in the ICU with two nudge-based interventions: evaluation through intervention time-series analyses.

Objectives: Low tidal volume (TVe) ventilation improves outcomes for ventilated patients, and the majority of clinicians state they implement it. Unfortunately, most patients never receive low TVes. 'Nudges' influence decision-making with subtle cognitive mechanisms and are effective in many contexts.

Using 'nudge' principles for order set design: a before and after evaluation of an electronic prescribing template in critical care.

Objective: Computerised order sets have the potential to reduce clinical variation and improve patient safety but the effect is variable. We sought to evaluate the impact of changes to the design of an order set on the delivery of chlorhexidine mouthwash and hydroxyethyl starch (HES) to patients in the intensive care unit.

Methods: The study was conducted at University Hospitals Bristol NHS Foundation Trust, UK.