Title: 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
Funder: Department of Health and Social Care

Abstract: Randomised controlled trials (RCTs) are the gold standard for estimating treatment effects, but their ability to answer complex questions involving multifaceted interventions or treatments that vary over time is limited. Large longitudinal observational datasets from routinely collected hospital data provide the opportunity to gain understanding of treatment effects that would not be feasible in a RCT and that reflect their impact in realistic clinical practice. However, these data present major challenges of complex confounding due to inter-relationships between time dependent measurements and treatment use, which must be addressed in the analysis to enable 'causal inferences' to be made. A specific challenge presented by hospital data is that measurements of patients' clinical status are made at high frequency, and on differing schedules for each patient dependent on their clinical status, so the timing and frequency of measurements is informative. However, many of the existing causal inference methods assume that measurements of treatments and confounders are made at regular time intervals.This 3-year PhD fellowship will evaluate, compare, and extend where necessary, statistical causal inference methods for accounting for irregularly spaced and informative measurements, to enable estimation of the impacts of both 'static' and 'dynamic' treatment regimes, on time-to-event outcomes using routinely collected hospital data. Under a static treatment regime, the treatment is the same for all patients meeting defined eligibility criteria. A dynamic treatment regime involves rules that adapt the treatment to the current status of the patient. Much of the causal inference literature focuses on static treatment regimes, and there is need to integrate and extend existing methods to provide causal inference methodology that handles irregular observation and informative monitoring of patients, under both static and dynamic treatment strategies.Over 470,000 people have been hospitalised due to COVID-19 in the UK to date, and over 36,000 of those required intensive care. 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 methods identified will be used to investigate the impact of invasive vs non-invasive ventilation on patient outcomes, including if and when patients should be transferred from non-invasive to invasive ventilation based on their current clinical status.The data source is routinely collected hospital data of all patients with COVID-19 admitted to UCLH since January 2020. Longitudinal patient data have been recorded over the course of each hospital admission episode, and include measurements of clinical biomarkers, indications of treatments, presence of comorbidities, and demographic data.This fellowship aims to inform treatment practice and health care policy for ventilation use in COVID-19 patients. The methodology developed will impact future research involving routinely collected hospital data to investigate the impacts of static and dynamic treatment regimes. By providing methods that enable best use of routinely collected data, this fellowship will contribute towards improving public confidence in allowing patient data to be used for research. Dissemination will include publications, conference presentations, and a PhD thesis, facilitated by collaboration with patient representatives, clinicians and statisticians.

Lay abstract: 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.

Research activity codes: 6.3 MEDICAL DEVICES;8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: INFECTION