Using routinely collected hospital data to investigate the impacts of static and dynamic treatment regimes, with an application to ventilation strategies for COVID-19 patients

Over 470,000 people have been hospitalised due to COVID-19 in the UK to date. 36,000 of those required intensive care, of which the majority received breathing support, known as ventilation. Ventilation can be 'invasive' or 'non-invasive'. Invasive ventilation is where a machine does the breathing for a patient, who must be sedated and have a breathing tube put into their windpipe. Non-invasive ventilation is where a patient receives air and oxygen via a tightly-fitting mask at high pressure. Around 40% of COVID-19 patients admitted to intensive care have received invasive ventilation, however, there is debate over whether and when to introduce invasive ventilation in COVID-19 patients. The ideal way to study whether one treatment is more effective than another is through a randomised controlled trial (RCT). This involves randomly choosing half of a group of patients to receive one treatment and half to receive another, enabling a fair comparison between treatments. However, RCTs are limited because they are generally not designed to look at multiple treatments or treatments that vary over time. Furthermore, RCTs may not be possible due to financial, logistical or ethical constraints. Meanwhile, there is a growing availability of 'observational' data routinely collected when patients are admitted to hospital or attend their GP, known as electronic health records. These data are often available on a large number of people and reflect how treatments are used in day-to-day care, providing a useful resource for examining the effects of different treatments. Using such data has challenges as the treatments are not randomly allocated. The data have complicated features that must be dealt with using tailored statistical methods, and in recent years there have been many advances in such methods. In this fellowship I will use and compare existing statistical methods and develop new methods to evaluate the effects of treatments using routinely collected data. I will focus on two key statistical challenges. First, the fact that measurements on patients' clinical status are collected at irregular time intervals, and on differing schedules for each patient. This presents a challenge as most of the statistical methods assume measurements are made at regular time intervals. Second, we may want to examine the effects of both 'dynamic' and 'static' treatment strategies. A 'static' treatment strategy is where the treatment received is the same for all patients meeting defined criteria, e.g. 'all patients receive invasive ventilation within 24 hours of admission'. A 'dynamic' treatment strategy is where there are rules that adapt the treatment to the current status of the patient, e.g. 'transfer patient from non-invasive to invasive ventilation when breathing rate exceeds X'. It is more complicated to evaluate dynamic treatment strategies. I will apply the methods identified to investigate the impact of invasive vs non-invasive ventilation on outcomes of patients with COVID-19. I will (i) compare the impact of invasive vs non-invasive ventilation ('static treatment strategy') and (ii) identify if and when patients should be transferred from non-invasive to invasive ventilation based on their current status ('dynamic treatment strategy'). The data source is routinely collected hospital data of all patients with COVID-19 admitted to University College London Hospital since January 2020. I will seek involvement from patients who have experienced critical illness and ventilation to understand their experiences and what outcomes are important to them, and to seek advice on communicating the outcomes of my research. This work will give patients, doctors and policymakers more information about ventilation strategies, allowing them to make more informed decisions about the best ventilation strategy tailored to the patient. It will impact future research by providing methods that enable best use of routinely collected hospital data.