Research Projects
Clinical phenotypes
Most adults who are admitted to ICU have at least one if not more chronic conditions, yet historically researchers have failed to take this into account when designing trials. Treatments that work in one group of patients may cause harmful side-effects in another. We will explore the relationship between chronic conditions and acute illness in critically unwell patients, along with further information that we acquire during their stay to understand how these impact on organ dysfunction and longer-term outcomes. We will use cutting edge causal inference techniques that will help us explore whether the outcome is caused by the exposure, or if an apparent association is caused by a different exposure.
Detecting myocardial infarction
When high-risk patients are admitted into ICU, they have a 1 in 4 chance of having a heart attack during their admission. Yet 95% of these heart attacks are missed — partly because critically ill patients do not have symptoms typical of heart attacks, and partly because diagnostic tests are only done intermittently, typically daily. For the first time using continuous ICU monitoring, our aim is to identify heart attacks systematically.
Haemodynamic instability
We are working to quantify haemodynamic instability (insufficient bloodflow in critically ill patients) in combination with other patient factors to predict routinely collected organ-specific, major adverse cardiac events, and non-organ-specific (death, critical care support, longer term healthcare trajectories) outcomes in critically unwell patients. Following on from this we will identify groups of patients that are at higher risk of the above outcomes, and consider strategies to mitigate these risks.
Target trial emulation
Critically ill patients often become anaemic, increasing the likelihood of adverse outcomes such as
myocardial infarction or death. This can be managed by giving blood transfusions, though these also carry
risks. While randomised trials have shown no benefit to liberal transfusion strategies compared to more
restrictive ones, these were in general critical care populations. The balance of risks may differ
among patients with cardiovascular disease, however trial evidence in this patient group is limited.
We are using novel methods aimed at gaining causal insights from observational data to compare the effect
of different transfusion strategies on mortality among critically ill patients with cardiovascular disease.
While these methods are increasingly popular, it is important to disentangle hyperbole from genuine
benefit. In addition, Stella is building on her prior training in philosophy as well as data science,
to conduct a critical ethical analysis of the ways that causal methodology are “sold”
in academic literature. These strands of work are interrelated, with ethical and technical analyses
informing one another.
Sociotechnical analysis
The sociotechnical analysis work aims to explore and understand the baseline work processes in critical care and the needs of ICU clinicians, as well as facilitating collaboration amongst stakeholders through understanding the different components and requirements of each stakeholder. We are involving a wide range of stakeholders to ensure valuable insights and perspectives are known and acknowledged through the development process. Having multiple disciplines involved throughout will facilitate relationships and build a foundation of trust amongst stakeholders.
TRAITS
TRAITS (Time-critical precision medicine for acute critical illness using treatable trait principles; PI Prof Manu Shankar-Hari) is a multicentre trial across Scottish ICUs that aims to improve outcomes for critically ill patients through a precision medicine approach. The TRAITS Data Team links national healthcare datasets with trial data , repurposing routinely collected ICU data to reduce the burden of manual data entry. Its objective is to validate routinely collected data against manually entered trial records, supporting future automated data capture. This approach has the potential to reduce costs and workload while enabling more scalable research.
Infrastructure
NHS Lothian Critical Care data
We are supporting the provision of
research datasets
from NHS Lothian Critical Care via DataLoch (the NHS Lothian safe haven). This consists of hourly physiology data along with medications and fluids
from the Phillips ICCA system, as well as high frequency (about 250Hz) continuous waveform monitoring from the patient bedside (ECG, blood pressure etc.)
We have developed a pipeline
for processing waveform data and storing it in a compressed AtriumDB database.
Pandemic preparedness
We are leading a project funded by the Scottish Pandemic Sciences Partnership entitled 'Sentinel ICUs for Severe Acute Respiratory Infection: a national real-time surveillance and research platform'. We are using routinely collected, high-frequency ICU data to deliver near real-time national intelligence on the burden, severity, trajectory, management and outcomes of severe acute respiratory infections, supporting public health decision-making while reducing reliance on staff-intensive enhanced surveillance.