Award Title: Transforming outcome measures in plastic surgery through computer science
Funder: Department of Health and Social Care

Abstract: Background Cleft lip and/or palate (CL/P) affects 1/700 births and has lifelong sequelae even after treatment. There is limited high-quality evidence underpinning CL/P management, and the Eurocleft study showed undesirable treatment variation across Europe. An international consensus-based core outcome set (COS) has been proposed to evaluate CL/P treatments' effectiveness and value. Subscales of CLEFT-Q, a patient-reported outcome measure (PROM) are central to the COS. CLEFT-Q contains 110 questions within 12 subscales, plus a nine-question checklist. Our PPI team felt this is too burdensome, as the PROM is intended for children as young as eight.The rating of facial photographs by a panel of surgeons is considered a robust method of assessing aesthetic outcomes of CL/P surgery in patients too young to complete PROMs. However, this method is expensive and impractical.Research Question/Aim: To improve outcome measurement in CL/P using contemporary psychometrics and computer science. Objectives	To develop computerised adaptive testing (CAT) software for CLEFT-Q that accurately predicts respondents' scores from fewer questions 	To evaluate machine learning models for autonomously grading photographic outcomes of CL/P surgery Methods	I have already programmed a pilot unidimensional CAT that reduces the total number of questions from 110 to a mean of 43, predicting scores with a 97% accuracy. Using the international CLEFT-Q field test data, I will further study the structural validity of CLEFT-Q to inform the development of more sophisticated item response theory models (bifactor and second order structural equation models) that could improve these results. Once these models have been optimised, I will evaluate them in simulated trials that compare the number of questions administered in the full-length CLEFT-Q and its CAT counterpart. User interfaces will be developed in Concerto: an open-source CAT testing platform. 	In a PPI-co-developed workstream, I will use an existing platform (Face++) to process 150,000 pre and post-operative facial photographs of patients with CL/P, which have previously been rated by surgeons. This will return numerical values for each photograph, representing features such as symmetry and distances between facial landmarks. I will analyse this dataset with regularised regression models and test these models' ability to predict surgeon ratings using independent variables derived from photographs.Impact This project will deliver an optimised CLEFT-Q CAT and a platform to run it, which will support the uptake of CLEFT-Q in research and clinical practice, as recommended in the COS, within 5 years. This is expected to raise standards of patient-centred care, research and clinical commissioning internationally. Automating the assessment of facial photographs could provide a cheap and consistent means of assessing surgical outcomes in children too young to complete CLEFT-Q CAT.Dissemination Dissemination will be through scientific meetings and journals, including co-presentation with PPI representatives. Collaboration with ICHOM will support the deployment of our instruments on a global scale. Patients and the public will be updated via the Q-portfolio website, social media and charity newsletters. Our experience of working with patients and the public will be presented at scientific meetings and published on the NIHR INVOLVE website.

Lay Abstract: Aim The aim of this project is to improve how we measure the outcomes of treatments for cleft lip and/or palate. Background One in 700 babies are born with a gap in their lip and/or the roof of their mouth. This is called cleft lip and/or palate (CL/P). It can cause long-term difficulties with feeding, hearing, speaking, appearance and emotional wellbeing. Clefts are treated in many ways. This typically includes a series of operations as the child grows. Different surgeons recommend different operations, but there is little evidence to tell us which operations are more successful than others. It is important to measure how successful operations are to know which ones work best. So far, this has involved doctors' opinions, which are inconsistent. It can also involve looking at scans and other test results. But this isn't necessarily what matters most to people born with CL/P. Recently, a team of researchers asked people born with CL/P what matters most to them. From this, they made a questionnaire to score these things. It is called CLEFT-Q.Overall, CLEFT-Q has 119 questions. These are arranged into groups, e.g. questions about 'lips' 'speech' 'mood' etc. Some groups of questions are not always suitable. For example, questions about lips are skipped for somebody with only a cleft palate. We discussed this with our research partners (children and adults with CL/P, parents of children with CL/P, and members of the public). They felt CLEFT-Q was still too long. Children under eight are too young to use CLEFT-Q. Instead, their outcomes are often scored by groups of doctors looking at photographs of their face before and after surgery. This is expensive and not always practical. Methods I am making computer programs that shorten CLEFT-Q. These are called computerised adaptive tests (CATs). The programs pick only relevant questions for each person, adjusting based on the answers they've already given. A CAT can predict someone's final CLEFT-Q scores without asking all the questions. This would make CLEFT-Q easier to complete. To make and test the CAT we will study patients' CLEFT-Q answers that were collected around the world by different research teams. I am also making an 'artificial intelligence' computer program that can measure the outcome of surgery by looking at photographs. I think this will be cheaper, quicker and more consistent than doctors' measurements. To do this I will use 150,000 photos of patients with CL/P before and after they had surgery. The photographs have already been studied by surgeons who rated how well they think the surgery went. My program will learn what a good or bad outcome is by measuring different parts of the face and working out patterns that lead to an outcome being considered good or bad.Patient and public involvement My research partners have already been involved in the project design and helped to write this application. I will invite them to contribute to all further stages of this research. This includes making decisions about what I should work on and how I should do it. They will ensure we make the programs as user-friendly as possible. To make this easier for them, most of our meetings will be done over video-calls or at charity events. We will write up our experience of working together. This will be published on the NIHR INVOLVE website, and I will invite research partners to join me in presenting this at relevant meetings. Dissemination I will keep the CL/P community updated through the CLEFT-Q website, social media and charity newsletters. I will also present our findings at scientific meetings and publish the results in scientific journals.

Research Activity Codes: 6.4 SURGERY;8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health Categories: ORAL AND GASTROINTESTINAL