Predicting AF After Cardiac Surgery - the PARADISE Score. A Clinical Prediction Rule for Post-operative Atrial Fibrillation in Patients Undergoing Cardiac Surgery
Conditions: Atrial Fibrillation New Onset
Sponsor: University of Oxford

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Study Title:  Predicting AF after Cardiac Surgery - the PARADISE Score. A Clinical Prediction Rule for Post-
operative Atrial Fibrillation in Patients Undergoing Cardiac Surgery 
Internal Reference Number / Short title: PARADISE 
Ethics Ref: 21/EE/0166 
CAG Ref: 21/CAG/0097 
IRAS Project ID: 296508 
Date and Version No: 03/07/2023 Version 5 
Joint Research Office Ref: PID15669 
ClinicalTrials.gov: NCT05255224  
HTA Project: NIHR131227 
 
Chief Investigator: 
Professor Peter Watkinson, University of Oxford  
 
Lead Investigator: 
Professor Benjamin O'Brien, Perioperative Medicine Barts Heart Centre 
(Joint Lead Applicant) 
Investigators:  
Dr Rui Providencia, Clinical Scientist, University College London 
Professor Gregory Lip, Price-Evans Chair of Cardiovascular Medicine, 
Liverpool Heart and Chest Hospital NHS Foundation Trust 
Professor Jochen Muehlschlegel, Distinguished Chair of Anesthesiology, 
Brigham and Women’s Hospital, Associate Professor, Harvard Medical 
School 
Professor Gary Collins, Professor of Medical Statistics University of 
Oxford 
Professor David Clifton, Professor of Clinical Machine Learning, 
University of Oxford 
Dr Oliver C Redfern, Clinical Researcher, University of Oxford 
Dr Jonathan Bedford, NIHR Doctoral Fellow, University of Oxford 
 
Sponsor:  
University of Oxford  

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Clinical Trials & Research Governance,  
Boundary Brook House,   
Oxford,  
OX3 7GB,  
United Kingdom 
Funder: 
NIHR National Institute of Health Research, Health Technology 
Assessment Programme HTA Project: NIHR131227 
Chief Investigator Signature:   
 
Statistician Signature:   
 
Conflicts of interest: 
BOB has received funding from the British Heart Foundation for the Tight K trial Grant Number: 
CS/18/3/34063 clinicaltrials.gov: NCT04053816 
GYHL: Consultant for Bayer/Janssen, BMS/Pfizer, Medtronic, Boehringer Ingelheim, Novartis, Verseon 
and Daiichi-Sankyo. Speaker for Bayer, BMS/Pfizer, Medtronic, Boehringer Ingelheim, and Daiichi-
Sankyo. No fees are directly received personally. 
PW has received funding from the NIHR for the CAFE study and the RRAM study. He was previously Chief 
Medical officer for Sensyne Health and has shares in the company. Sensyne Health have provided 
research support to his university department. 
GSC was a member of the NIHR HTA commissioning board from 2016 to 2020. 
Confidentiality Statement 
This document contains confidential information that must not be disclosed to anyone other than the 
Sponsor, the Investigator Team, HRA, host organisation, and members of the Research Ethics Committee, 
unless authorised to do so. 
 

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TABLE OF CONTENTS 
 
1 
KEY CONTACTS ................................................................................................................................... 6 
2 
LAY SUMMARY................................................................................................................................... 7 
3 
SYNOPSIS ........................................................................................................................................... 7 
4 
ABBREVIATIONS ................................................................................................................................. 9 
5 
BACKGROUND AND RATIONALE ...................................................................................................... 10 
5.1 
Background .............................................................................................................................. 10 
5.2 
Aim ........................................................................................................................................... 12 
6 
OBJECTIVES AND OUTCOME MEASURES ......................................................................................... 12 
7 
STUDY DESIGN ................................................................................................................................. 13 
7.1 
Overview .................................................................................................................................. 13 
7.1.1 
Project flowchart ................................................................................................................. 13 
7.2 
Setting and data collection ...................................................................................................... 14 
7.2.1 
Databases ............................................................................................................................ 14 
7.2.2 
Trial data sets ...................................................................................................................... 14 
7.3 
Identifying long-list of risk factors for AFACS .......................................................................... 15 
7.3.1 
Literature – systematic literature review ............................................................................ 15 
7.3.2 
Data-driven identification – CALIBER database ................................................................... 16 
7.3.3 
Expert panel – modified Delphi process .............................................................................. 16 
7.3.4 
Machine learning methods –  exploring combinations of risk factors in CALIBER and PRD 16 
7.3.5 
Expert panel – consolidating risk factors to be used in model development ..................... 16 
7.4 
Model development – PARADISE-1 and PARADISE-2 .............................................................. 16 
7.5 
Model validation – PARADISE-1 and PARADISE-2 ................................................................... 17 
8 
PARTICIPANT IDENTIFICATION ........................................................................................................ 17 
8.1 
Study Participants .................................................................................................................... 17 
8.2 
Inclusion Criteria ...................................................................................................................... 17 
8.3 
Exclusion Criteria ..................................................................................................................... 18 
9 
PROTOCOL PROCEDURES ................................................................................................................ 18 
9.1 
Recruitment ............................................................................................................................. 18 
9.2 
Screening and Eligibility Assessment ....................................................................................... 18 
9.3 
Informed Consent .................................................................................................................... 18 
9.3.1 
Opt-out ................................................................................................................................ 18 

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9.4 
Enrolment ................................................................................................................................ 18 
9.5 
Description of study intervention(s), comparators and study procedures (clinical) ............... 18 
9.6 
Baseline Assessments .............................................................................................................. 19 
9.7 
Subsequent Visits .................................................................................................................... 19 
9.8 
Sample Handling ...................................................................................................................... 19 
9.9 
Early Discontinuation/Withdrawal of Participants .................................................................. 19 
9.10 
Definition of End of Study ....................................................................................................... 19 
10 
SAFETY REPORTING ......................................................................................................................... 19 
11 
STATISTICS AND ANALYSIS ............................................................................................................... 19 
11.1 
Statistical Analysis Plan (SAP) .................................................................................................. 19 
11.2 
Description of the Statistical Methods .................................................................................... 19 
11.2.1 
Model development ............................................................................................................ 19 
11.2.2 
Model performance and validation ..................................................................................... 20 
11.3 
Sample Size Determination ..................................................................................................... 21 
11.4 
Analysis populations ................................................................................................................ 21 
11.5 
Decision points ........................................................................................................................ 21 
11.6 
Stopping rules .......................................................................................................................... 21 
11.7 
The Level of Statistical Significance ......................................................................................... 21 
11.8 
Procedure for Accounting for Missing, Unused, and Spurious Data. ...................................... 21 
11.9 
Procedures for Reporting any Deviation(s) from the Original Statistical Plan ........................ 22 
12 
DATA MANAGEMENT ...................................................................................................................... 22 
12.1 
Source Data ............................................................................................................................. 22 
12.1.1 
Databases ............................................................................................................................ 22 
12.1.2 
Trial data sets ...................................................................................................................... 23 
12.2 
Access to Data ......................................................................................................................... 23 
12.3 
Data Recording and Record Keeping ....................................................................................... 23 
13 
QUALITY ASSURANCE PROCEDURES ............................................................................................... 24 
13.1 
Risk assessment ....................................................................................................................... 24 
13.2 
Study monitoring ..................................................................................................................... 24 
13.3 
Study Committees ................................................................................................................... 24 
13.3.1 
Study management group (SMG) ........................................................................................ 24 
13.3.2 
Project Oversight Group (POG) ........................................................................................... 25 
14 
PROTOCOL DEVIATIONS .................................................................................................................. 25 
15 
SERIOUS BREACHES ......................................................................................................................... 25 

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16 
ETHICAL AND REGULATORY CONSIDERATIONS ............................................................................... 25 
16.1 
Declaration of Helsinki............................................................................................................. 25 
16.2 
Guidelines for Good Clinical Practice ...................................................................................... 25 
16.3 
Approvals ................................................................................................................................. 25 
16.4 
Other Ethical Considerations ................................................................................................... 26 
16.5 
Reporting ................................................................................................................................. 26 
16.6 
Transparency in Research ........................................................................................................ 26 
16.7 
Participant Confidentiality ....................................................................................................... 26 
16.8 
Expenses and Benefits ............................................................................................................. 26 
17 
FINANCE AND INSURANCE .............................................................................................................. 26 
17.1 
Funding .................................................................................................................................... 26 
17.2 
Insurance ................................................................................................................................. 26 
17.3 
Contractual arrangements ...................................................................................................... 27 
18 
PUBLICATION POLICY ....................................................................................................................... 27 
19. DEVELOPMENT OF A NEW PRODUCT/ PROCESS OR THE GENERATION OF INTELLECTUAL PROPERTY 27 
19 
ARCHIVING....................................................................................................................................... 27 
20 
REFERENCES .................................................................................................................................... 27 
21 
APPENDIX A:  AMENDMENT HISTORY ............................................................................................. 33 
 
 
 

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1 
KEY CONTACTS 
 
Chief Investigator 
Professor Peter Watkinson 
Kadoorie Centre, University of Oxford, Level 3, John Radcliffe Hospital, 
Headley Way, Oxford, OX3 9DU 
01865 857613 
peter.watkinson@ndcn.ox.ac.uk 
Sponsor 
University of Oxford 
Clinical Trials & Research Governance,  
Boundary Brook House,   
Oxford,  
OX3 7GB,  
United Kingdom 
Telephone: +44 1865 616480 
Email: ctrg@admin.ox.ac.uk 
   
Funder(s) 
NIHR National Institute of Health Research, Health Technology 
Assessment Programme 
Clinical Trials Unit 
Oxford Clinical Trials Research Unit  
Botnar Research Centre  
University of Oxford 
 
Lead Statistician 
Professor Gary Collins 
Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, 
Rheumatology and Musculoskeletal sciences 
01865 223463 
gary.collins@csm.ox.ac.uk 
Committees 
Project Oversight Group  

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The project oversight group will comprise the CI (Peter Watkinson) and 
co-lead investigator (Ben O’Brien), two PPI members, the Founder & 
CEO of the Atrial Fibrillation Association, a Consultant Cardiologist, and 
Professor of Cardiovascular Medicine, from the East and North 
Hertfordshire NHS Trust and University of Hertfordshire and a 
Professor of Stroke and Older People’s Care at the University of Central 
Lancashire. The final member is still to be recruited.  
 
2 
LAY SUMMARY  
Atrial Fibrillation (AF) is a common abnormal heart rhythm. AF causes the heart to beat irregularly and 
sometimes very rapidly. About 30-50% of patients develop AF after heart surgery. These patients stay 
longer on the Intensive Care Unit (ICU) after surgery, are more likely to develop complications and have a 
higher risk of dying. Avoiding AF is important. 
Some drugs, including beta blockers and amiodarone may help prevent AF if given after surgery. 
However, these may also lead to complications (such as lung damage). It is therefore important to 
identify which patients are most likely to benefit from these treatments (i.e., where the benefits 
outweigh the risks). There are existing tools designed to predict the risk of suffering AF after heart 
surgery. However, they are unreliable and therefore not used in clinical practice. A modern, reliable risk 
prediction tool is needed.  
The PARADISE study will develop and test new prediction tools to identify which patients are most at risk 
of developing AF after heart surgery. We will focus our tools on those patients who most commonly 
develop AF, such as those who have had surgery to repair a valve or blood vessel in their heart. 
To do this we will: 
• 
Review the medical literature and assemble a panel of medical experts to create a list of known 
factors that affect patients’ risk of AF after heart surgery 
• 
Use a large UK general practice database (CALIBER)  to see whether we can find new risk factors.  
• 
Ask the expert panel to agree a list of known and new risks factors to be included in the 
prediction tool. 
• 
Develop two new prediction tools using an existing American cardiac surgery database (the 
Partners research Database). The first will be used before surgery, the second immediately 
following surgery. Two models are needed as events during surgery may alter the risk of AF. 
• 
Test how reliably our new tools predict which patients suffer AF after surgery, with data from 
large UK NHS heart centres, one US Hospital (Brigham) and a UK clinical trial (Tight-K). 
• 
We will work with two charities (AF Alliance and StopAfib) to share our results with patients and 
the wider public. 
3 
SYNOPSIS 
 

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Study Title 
Predicting AF after Cardiac Surgery - the PARADISE Score. A Clinical Prediction 
Rule for Post-operative Atrial Fibrillation in Patients Undergoing Cardiac 
Surgery 
Internal ref. no. / short 
title 
PARADISE 
Study registration 
ClinicalTrials.gov: NCT05255224 
Sponsor  
University of Oxford  
Clinical Trials & Research Governance,  
Boundary Brook House,   
Oxford,  
OX3 7GB,  
United Kingdom 
Funder  
NIHR National Institute of Health Research, Health Technology Assessment 
Programme (NIHR131227) 
Study Design 
Longitudinal cohort study 
Study Participants 
Patients who have undergone cardiac surgery  
Sample Size 
Retrospective: 38, 000 
Prospective: 13, 684 
Planned Study Period 
Funded study period: 1st February 2021 to 30th June 2024 
Prospective sub-study period: 1st October 2021 to 31st July 2023 
Planned Recruitment 
period 
Retrospective: 1st January 1998 to 31st December 2020 
Prospective: 1st October 2021 to 31st July 2023 
 
Objectives 
Outcome Measures 
Timepoint(s) 
Primary 
 
Primary Objective 
To develop and externally 
validate two prognostic 
models to predict post-
operative atrial fibrillation 
after cardiac surgery using 
data available in the pre-
operative assessment 
clinic or on admission for 
surgery (PARADISE-1) and 
on arrival in the post-
operative care unit 
(PARADISE-2); and 
compare their 
performance to other 
published models 
Primary outcome 
Discrimination (c-statistic) 
and calibration (intercept 
and slope) in external 
dataset 
 
 
Secondary outcomes 
Positive and negative 
predictive values, sensitivity 
and specificity 
7 days after 
cardiac surgery 
 
 
 
 
 
 
 
Secondary 
 
1. Systematic 
literature review 
2. Analysis of 
CALIBER database 
using statistical 
and machine 
learning methods 
Candidate risk factors for 
inclusion in new onset atrial 
fibrillation prognostic 
models 
For pre-operative 
model, factors up 
to surgery, for 
post-operative 
model factors up 
to 12 hours post 
surgery. 

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3. Modified Delphi 
Process 
4. Exploring 
combinations of 
risk factors in 
CALIBER and PRD 
5. Expert panel – 
consolidating risk 
factors to be used 
in model 
development  
 
 
 
 
4 
ABBREVIATIONS 
 
ACE 
Angiotensin-Converting Enzyme 
AF 
Atrial Fibrillation 
AFACS 
Atrial Fibrillation after cardiac surgery 
AUROC 
Area Under Receiver-Operator Curve 
BHC 
Barts Heart Centre 
BHF 
British Heart Foundation 
CABG 
Coronary Artery Bypass Graft 
CAG 
Confidentiality Advisory Group 
CALIBER 
CArdiovascular disease research using LInked Bespoke studies and Electronic health Records 
CENTRAL 
Cochrane Central Register of Controlled Trials 
CHARMS 
Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies 
CI 
Chief Investigator 
CINAHL 
Cumulative Index to Nursing and Allied Health Literature 
CRF 
Case Report Form 
CRN 
Clinical Research Nurse 
CTRG 
Clinical Trials & Research Governance, University of Oxford 
GCP 
Good Clinical Practice 
 
UK GDPR 
UK General Data Protection Regulation 
GP 
General Practitioner 
HRA 
Health Research Authority 
ICU 
Intensive Care Unit 

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LASSO 
Least Absolute Shrinkage and Selection Operator 
LHCH 
Liverpool Heart & Chest Hospital 
MEDLINE 
Medical Literature Analysis and Retrieval System Online 
MeSH 
Medical Subject Headings 
ML 
Machine Learning 
NHS 
National Health Service 
NICOR  
National Institute for Cardiovascular Outcomes Research 
OCTRU 
Oxford Clinical Trial Research Unit 
OPCS-4 
Office of Population Censuses and Surveys Classification of Surgical Operations and 
Procedures (4th revision) 
OpenSIGLE 
Open System for Information on Grey Literature in Europe 
PROBAST 
Prediction model Risk Of Bias ASsessment Tool 
PRISMA-P 
Preferred Reporting Items for Systematic review and Meta-Analysis Protocols 
PROSPERO 
International Prospective Register of Systematic Reviews 
QUIPS 
Quality In Prognosis Studies 
PI 
Principal Investigator 
PIL 
Participant/ Patient Information Leaflet 
POG 
Project Oversight Group 
PRD 
Partners Research Database 
R&D 
NHS Trust R&D Department 
REC 
Research Ethics Committee 
RES 
Research Ethics Service 
SOP 
Standard Operating Procedure 
SMG 
Study Management Group 
TIGHT-K 
TIGHT-K trial 
UK 
United Kingdom 
US 
United States 
 
5 
BACKGROUND AND RATIONALE 
5.1 
Background 
Atrial Fibrillation after cardiac surgery (AFACS) is the most common complication following cardiac 
surgery, with an incidence between 30% and 50%. The risk is higher for patients having either isolated 
mitral valve or combined mitral valve and coronary artery bypass graft surgery (1–3). Around 35,000 
patients undergoing cardiac surgery in the UK every year (4). In the United States, around 500,000 

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patients undergo cardiac surgery annually (5). AFACS is strongly associated with adverse patient 
outcomes, longer hospital and ICU stays, increased risk of stroke, increased risk of developing long-term 
AF (with associated complications and need for anticoagulation), and increased all-cause 30-day and 6-
month mortality (6,7,16,8–15). In the US alone, this translates to an additional $10,000–20,000 in 
hospital costs for each patient who suffers AFACS, resulting in an estimated $1 billion in overall 
healthcare costs per year (2). Interventions that reduce the incidence of AFACS would have a substantial 
impact both on patient outcomes and cost.  
Even though AFACS can be transient and patients are often discharged from hospital in normal sinus 
rhythm, patients with new-onset AFACS have a 5-fold increased risk of developing long-term AF (1). A 
number of risk factors have been identified, including (older age, obesity) (17,18), comorbidities (e.g. 
prior AF, hypertension, chronic kidney disease, obstructive sleep apnoea) (19), surgical features (e.g. 
valve surgery, increased aortic cross-clamp time, acute kidney injury) (20,21) and the failure or inability 
to (re-)introduce Beta-Blockers. However, each of these factors only accounts for part of the overall risk 
(22). Importantly, even when controlling for these confounding variables are, long-duration AFACS (> 2 
days) is independently associated with decreased survival (23), as well as stroke and respiratory 
complications (24). Current evidence therefore indicates that AFACS itself contributes to poor patient 
outcomes following cardiac surgery, and that tools to predict, prevent and guide treatment of AFACS are 
needed.  
Currently, there is no widely accepted prediction model that reliably allows clinicians to determine the 
risk of a patient developing AFACS, despite multiple efforts over the past 15 years to develop one (25–
31). Published AFACS prediction models are limited by small sample sizes, a failure to include modern 
variables and a lack of external prospective validation (26,27,29,32–36). Moreover, our team and others 
have previously developed AFACS risk prediction models that suffer from limited generalizability, over-
simplification, and the fact that they often use information from variables that occur after surgery, or 
even after onset of AFACS (22,35,37). A recent analysis showed the best area under the receiver 
operator characteristic curve (AUROC) for any of the published prediction models was 0.68 (95% 
confidence interval [CI]: 0.67–0.69) (38,39), just marginally better than a model which only included age 
(0.66, 95% CI: 0.65–0.68) (22). The work emphasised the importance of using representative and detailed 
multicentre data sets to develop prediction models and prospectively validating these on multiple 
cohorts. 
The lack of effective pre and immediate post-operative prediction models for estimating AFACS risk has 
prevented the implementation of AF prophylaxis protocols (38). Interventions to prevent AFACS lead to 
decreased hospital length of stay, lower costs of hospital treatment, and decreased risk of post-operative 
stroke (40). Pharmacological (e.g., Amiodarone, beta blockers, electrolyte supplementation) and non-
pharmacological (e.g., atrial pacing, posterior pericardiotomy, botulinum toxin) therapies provide 
practical methods to significantly reduce AFACS incidence (2). In selected patients, some peri-operative 
prophylactic therapies reduce the risk of AFACS (41,42), and have been included in guidelines (43–46). 
However, these interventions are not effective in all patients and some have a high risk of adverse side 
effects. Therefore, it is crucial to use clinical information to target interventions to those individuals at 
highest risk of developing AFACS, who will more likely benefit from prophylaxis. Furthermore, monitoring 
patients after hospital discharge shows significant numbers of early asymptomatic recurrences. These 
patients are often at high risk of stroke and may benefit from anticoagulation for stroke prevention. 

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Improved prediction of AFACS could facilitate targeted monitoring that is more likely to identify AFACS 
recurrence (1). 
This study will develop and validate two different scores as they apply to different situations, in the pre-
operative assessment clinic (PARADISE 1) and on arrival in the post-operative care unit (PARADISE 2), 
have different input variables (the latter including intra-operative and immediately available post-
operative variables) and different time horizons for prediction (from weeks before the operation and 
from immediately post-operative in the post-operative care unit). These two models will have two 
distinct use cases. For example, PARADISE-1 could be used to start prophylactic medication in the pre-
operative assessment clinic, whereas PARADISE 2 will be used to adjust risk on admission to the post-
operative care unit. Of the patients who develop AFACS, 70% do so before post-operative day 4 and 94% 
by post-operative day 7 (8,47). Therefore, both models will be optimised to predict AFACS within the first 
week of the initial post-operative hospital stay. This ensures we address the unmet need for prediction 
of AF following cardiac surgery, rather than AF in the context of prolonged hospitalisation or critical 
illness. 
5.2 
Aim 
The study aims to develop and validate two clinical prediction models to estimate the risk of a patient 
developing AF in the seven days following cardiac surgery, using data available: 
• 
In the pre-operative assessment clinic or on admission for surgery (PARADISE-1) 
• 
On arrival in the post-operative care unit (PARADISE-2) 
6 
OBJECTIVES AND OUTCOME MEASURES 
 
Objectives 
Outcome Measures  
Timepoint(s) 
of 
evaluation of this 
outcome measure 
(if applicable) 
Primary Objective 
To develop and externally validate 
two prognostic models to predict 
post-operative atrial fibrillation 
after cardiac surgery using data 
available in the pre-operative 
assessment clinic or on admission 
for surgery (PARADISE-1) and on 
arrival in the post-operative care 
unit (PARADISE-2); and compare 
their performance to other 
published models  
Primary outcome 
Discrimination (c-statistic) and calibration 
(intercept and slope) in external dataset 
 
Secondary outcomes 
Positive and negative predictive values, 
sensitivity and specificity  
 
7 days after cardiac 
surgery 
 
 
 
 
 
 

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Secondary Objectives 
1. Systematic literature 
review 
2. Analysis of CALIBER 
database using statistical 
and machine learning 
methods 
3. Modified Delphi Process 
4. Exploring combinations of 
risk factors in CALIBER and 
PRD 
5. Expert panel – 
consolidating risk factors to 
be used in model 
development  
 
Candidate risk factors for inclusion in new 
onset atrial fibrillation prognostic models 
 
7 days after cardiac 
surgery 
 
 
 
7 
STUDY DESIGN 
7.1 
Overview 
This is an international, multi-centre longitudinal cohort study of patients who have undergone cardiac 
surgery. The study will use retrospective data to develop clinical prediction models to estimate the risk of 
developing atrial fibrillation in the seven days following surgery. We will develop two models: PARADISE-
1 and PARADISE-2. PARADISE-1 will only use patient data collected in pre-operative period (i.e. in the 
pre-operative assessment clinic or on admission for surgery). PARADISE-2 will include additional data 
available on arrival in the post-operative care unit. Both models will then be externally validated on 
prospectively collected data from large UK centres and one UK clinical trial. 
7.1.1 
Project flowchart 
Prediction models will be developed and validated as follows: 
1. Identify a long-list of candidate risk factors for AFACS  
2. Model development – PARADISE-1 and PARADISE-2  
3. Model validation – PARADISE-1 and PARADISE-2 

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7.2 
Setting and data collection 
The study will use data collected from patients who have undergone cardiac surgery in specialist centres 
in the United States and United Kingdom. There are four databases and a prospective trial data set. 
7.2.1 
Databases 
1. CALIBER: The Health Data Research UK CALIBER research platform contains variables extracted 
from linked electronic health records from primary care, hospital records, social deprivation 
information and cause-specific mortality data in England. This dataset includes electronic health 
records from 6.5 million individuals between 1998 and 2016.  
2. PRD: The Partners Research Database (PRD) contains detailed modern data for over 30,000 
individuals undergoing cardiac surgery from 2 centres in the US between 2001-2020. PRD 
includes pre-operative echocardiographic measurements.  
Note: During the study, we will expand this database to include pre-and peri-operative variables 
from the clinical data warehouses of the two centres. It will be used for model development. 
3. Brigham: The Brigham and Women’s CABG Genomics Database is a prospective single centre 
detailed research database comprising 3000 individuals from 2001-2016. This dataset contains 
over 1,700 prospectively collected and curated pre, intra, and post-operative variables. It will be 
used for external US retrospective model validation. 
4. NHS Trusts: The study will prospectively collect data from the UK NHS Trusts which will allow us 
to undertake external validation of PARADISE-1/PARADISE-2 with a total of approximately 6000 
patients (2000 AFACS events) per annum. The NHS Trusts where data will be extracted from are: 
a. Liverpool Heart & Chest NHS Foundation Trust 
b. Barts Heart Centre (Barts Health NHS Trust) 
c. Oxford University Hospitals NHS Foundation Trust 
7.2.2 
Trial data sets 
              
           
              
                            
                   
                         
                  
                            
              
          
             
                        
                      
            
                        
              
                              
             
                                               
              
                           
                             
                          
                  
                            
                  
                               
  months
   months
   months
         

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1. Tight K study: (BHF-funded CS/18/3/34063, ClinicalTrials.gov Identifier: NCT04053816). Tight K is 
a 22-centre clinical trial that will include 1684 patients, randomised within this non-inferiority 
trial to investigate whether maintaining serum potassium ≥ . mEq/L is equivalent to maintaining 
levels at 4.5- 5.5mEq/L, with a primary endpoint of development of AFACS using precise clinical 
definitions and 5-day Holter monitoring. The trial is already designed to collect detailed data on 
AFACS risk factors, minimising any additional burden of PARADISE. It is restricted to patients 
undergoing isolated coronary artery bypass grafting. This has the strength that detailed data are 
being collected in a well-defined cohort, but means we need additional prospective data to 
assess the performance of the PARADISE scores in valve surgery.  
7.3 
Identifying long-list of risk factors for AFACS 
Prior to model development, we will use a diverse range of approaches to identify a comprehensive list 
of potential risk factors for AFACS: 
• 
Literature – systematic literature review 
• 
Data-driven identification – CALIBER database 
• 
Expert panel – modified Delphi process 
• 
Machine learning methods –  exploring combinations of risk factors in the CALIBER and PRD 
databases 
• 
Expert panel – consolidating risk factors to be used in model development  
7.3.1 
Literature – systematic literature review 
We will perform a systematic review and critical appraisal of pre-operative variables predictive of AFACS. 
We will include existing prediction models and associated publications, both for AFACS and other AF 
substrates, such as primary AF (not after intervention), AF in critically ill patients and post-operative AF 
after non-cardiac surgery.  
An initial search of MEDLINE, Embase, and Web of Science using MeSH and free-text search terms, 
retrieved 3,170 papers. We will expand our search to include:  CINAHL, Conference Proceedings Citation 
Index: Science, OpenSIGLE, the Cochrane Database of Systematic Reviews, and the Cochrane Central 
Register of Controlled Trials (CENTRAL). We estimate 300-450 full papers will need to be assessed. We 
will incorporate findings from our published work in general ICU populations (NIHR 17/71/04) and follow 
guidance by the Cochrane Prognosis Methods Group (48,49). We will snowball to identify any further 
relevant studies. Searches will be performed by a critical care information specialist, without date or 
language restrictions. Data will be extracted in duplicate. Authors will be contacted by e-mail where 
additional information is required. 
As most studies identified in our initial search were non-randomised, we are likely to evaluate how tools 
such as QUIPS for prognostic factors and PROBAST for prediction models will inform our evidence 
assessment (50,51). The data extraction form will follow the Critical Appraisal and Data Extraction for 
Systematic Reviews of Prediction Modelling Studies (CHARMS) guidance (52). We will synthesise 
available data using a semi-quantitative method previously described by Zaal et al. (53) and adapted by 
Dettmer et al (54). 
We will register and publish the systematic review protocol on PROSPERO following PRISMA-P guidance 
(55).  

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7.3.2 
Data-driven identification – CALIBER database 
We will use patient data within the CALIBER research platform to identify risk factors (comorbidities, 
medical conditions, and medications) prior to cardiac surgery associated with incident AF. 
CALIBER links primary care and hospital records, allowing us to construct a detailed medical history for 
each patient prior to cardiac surgery. We will use OPCS-4 codes to identify patients who have undergone 
cardiac surgery. We will identify patients’ comorbidities from both primary and secondary care 
diagnostic coding. Medications will be grouped into broad classes (e.g. ACE inhibitors) using standard 
ontologies. For medications we will also explore any associations with the duration of exposure to the 
drug. We will explore univariate relationships between candidate variables and AFACS will be explored 
using standard statistical Cox models and machine-learning techniques (see Section 10 for further 
details) 
7.3.3 
Expert panel – modified Delphi process 
We will select a panel of international experts from relevant publications identified in our systematic 
review. The panel will undertake a modified Delphi process to identify a list of potential risk factors for 
AFACS. 
Stage 1 of the Delphi process will involve panel members suggesting all risk factors they feel may be 
predictive of AFACS. These candidate variables will be collated and passed back to the panel in Stage 2. 
The panel will review all variables generated during stage 1, selecting those they judge to be most 
important. Variables meeting a consensus agreement of 60% will be accepted (56,57); those <20% will be 
rejected. In stage 3, variables with 20-60% agreement will be presented to the panel, along with their 
respective percentage agreement, for a final round of voting. Variables that achieve at least 60% 
agreement will be added to those previously accepted in Stage 2 to form the final list of candidate 
variables from the Delphi process.  
7.3.4 
Machine learning methods –  exploring combinations of risk factors in CALIBER and PRD  
We will combine all candidate variables identified from the systematic review, CALIBER analysis and 
Delphi into a combined “long-list”. Any variables missing from the PRD (to be used for model 
development) will be extracted from the electronic patient record. We will then apply machine learning 
methods (see Section 10) to the CALIBER and PRD data sets to derive combinations of risk factors (i.e. 
interactions) that should be considered for inclusion in model development.  
7.3.5 
Expert panel – consolidating risk factors to be used in model development  
At their final meeting, the expert panel will review the final long-list of candidate variables (and their 
combinations). Any variables judged to be redundant or likely to represent spurious correlations will be 
removed. 
7.4 
Model development – PARADISE-1 and PARADISE-2 
Using the PRD data set, we will develop two prognostic models (PARADISE-1 and PARADISE-2) to predict 
AFACS using the long-list of candidate variables identified above.  
PARADISE-1 will only use patient data collected in pre-operative period (i.e. in the pre-operative 
assessment clinic or on admission for surgery).  
PARADISE-2 will include additional data available on arrival in the post-operative care unit.  

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In developing both models, we will consider standard statistical approaches (e.g. logistic regression) and 
machine learning methods (e.g. random forests, deep neural networks). Model performance will be 
assessed using standard metrics (e.g. calibration, discrimination) and for optimism using internal 
validation. Further details are given in Section 10. 
7.5 
Model validation – PARADISE-1 and PARADISE-2 
Evaluating the prediction models using the same data used to develop them will lead to optimistic 
estimations of performance. Therefore, we will externally validate (assessing calibration and 
discrimination) both models using: 
• 
Retrospective data from one US hospital (Brigham) 
• 
Externally validate models on prospective data from UK NHS Trusts  
• 
Externally validate models on prospective data from a clinical trial data set (TIGHT-K) 
Retrospective external validation on the Brigham data set will comprise 3000 individuals (≈900 AFACS 
events, much larger than the minimum recommended sample size of 100 events (58). 
Prospective external validation will be performed on four cohorts. Data collected within clinical trials and 
“real-world” data from patients receiving routine clinical care offer different strengths and weaknesses 
when validating a prediction models (59). We will therefore undertake prospective external validations in 
both trial patients (using data from the Tight K study CS/18/3/34063 and PROPHESY studies (BHF pilot 
PG/17/82/33368) and in a prospective cohort of ≈6000 patients developed from enhanced routine data 
collection at NHS trusts. Data from the Tight K and PROPHESY studies bring the advantages of multiple 
centres and study-grade data collection, but in relatively small numbers – we estimate between one half 
and two thirds of the total intended recruitment will be available for model validation, totalling over 
1000 trial patients. Data from the NHS Trusts provide a large “real-world” prospective validation, but will 
likely contain more missing data (60). 
Ethical approvals for enhanced data collection will be obtained prior to analysis. 
As part of the external validation, we will assess the need for recalibration and use decision curve 
analysis to identify appropriate cut-offs for risk stratification (61). Further details are given in Section 10. 
We will compare our prediction models to those identified in the systematic review. These will include 
the CHA2DS2VASc score (originally designed to predict stroke in AF) which has also been used to predict 
AFACS (62,63) 
8 
PARTICIPANT IDENTIFICATION 
 
8.1 
Study Participants 
The study will include all adult patients who underwent or are about to undergo cardiac surgery in any of 
the study databases or clinical trial data sets. 
8.2 
Inclusion Criteria 

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• 
Patients 18 years or over 
• 
Admitted to hospital for any cardiac surgery 
8.3 
Exclusion Criteria 
• 
Patients who have requested that their data not be used for research (e.g. NHS Opt-out, see 
Section 9.3.1) 
9 
PROTOCOL PROCEDURES  
9.1 
Recruitment 
Participants will be recruited from the study databases and trial data sets, as described in section 7.2. 
9.2 
Screening and Eligibility Assessment 
9.3 
Informed Consent 
Existing ethical approvals are in place for the CALIBER (UK), PRD (US) and Brigham (US) databases. 
For the remaining data sets (including the TIGHT-K trial) used for external validation, we will apply for 
Health Research Approval (HRA), under advice from the Research Ethics Committee and the 
Confidentiality Advisory Group (CAG) to allow access to confidential medical records without specific 
written consent (Section 251 support). 
9.3.1 
Opt-out 
For data sets that include UK participants, the study will fully support and implement the NHS National 
Opt-out. We will use our link to NHS Digital asking them to inform us which patients have opted out and 
that will be a continuous process from enrolment until the study ends.  Records that are already flagged 
within each site as meeting the Opt-out will not be extracted. 
If we are informed by NHS Digital of patients completing the Opt-out after data extraction, these data 
will be purged and future records relating to these patients will not be extracted. 
We will also purge records from the study (in the same manner) if patients contact the study team 
directly. 
We will clearly display on the website links to register for the opt-out, as well as contact details for the 
study team. 
9.4 
Enrolment 
This is a non-randomised observational study. Enrolment of participants will occur through their data 
being present within the participating databases or prior enrolment into the Tight-K study. 
9.5 
Description of study intervention(s), comparators and study procedures (clinical) 
This is a non-interventional study.  

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9.6 
Baseline Assessments 
Not applicable 
9.7 
Subsequent Visits 
Not applicable 
9.8 
Sample Handling  
No additional samples will be taken during this observational study. 
9.9 
Early Discontinuation/Withdrawal of Participants 
This is an observational study. Participants can request their data to be deleted at any time in accordance 
with GDPR and the study privacy policy. 
9.10 Definition of End of Study 
The end of the study will be when all prospective data sets have been collected and used to externally 
validate both prediction models (PARADISE-1, PARADISE-2). 
10 SAFETY REPORTING  
This is an observational study, so safety reporting is not applicable. 
11 STATISTICS AND ANALYSIS 
11.1 Statistical Analysis Plan (SAP) 
The statistical aspects of the study are summarised here, with details fully described in a statistical 
analysis plan that will be available before any analysis takes place. 
11.2 Description of the Statistical Methods  
11.2.1 Model development 
In the PRD, we anticipate that around one third of patients will experience AFACS (10,000 events), so the 
issue of overfitting is negligible. Using logistic regression, we will select variables using least angle 
selection and shrinkage operator (LASSO) penalties (64). Continuous predictors exhibiting a nonlinear 
relationship with AFACS will be modelled using fractional polynomials. 
Heterogeneity in model performance will be explored over different hospitals using internal-external 
cross-validation (65). We will overcome challenges arising from the use of treatments during the study in 
two ways: Treatments at or before baseline will be handled by including treatments as predictors (66). 
The impact of any treatments on the absolute risk of AFACS during follow-up will be investigated using 
inverse probability weighting (59). 

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In parallel we will investigate machine learning (ML) approaches to developing a prediction model, 
including methods such as random forests, deep neural networks, and Bayesian Gaussian processes. 
Such models allow consideration of interactions between available variables providing “latent variables” 
(complex, non-linear transformations of the original input variables) that may improve prediction 
performance over the non-transformed input variables. We will prioritise principled, probabilistic 
methods that permit the incorporation of prior clinical knowledge, such that results are “interpretable”, 
avoiding the “black box” nature of much ML-oriented research in this area (67,68). We will exploit the 
ability of the complementary modern ML fields of (i) Bayesian non-parametric modelling (which provides 
a principled means of coping with artefact and measurement noise in the clinical data) and (ii) deep 
learning, which permits the fusion of large quantities of (potentially time-varying) clinical variables. 
These models include components (“attention mechanisms”) that allow the resulting risk estimations to 
be interpretable for clinicians. Importantly, these models permit the model to quantify its uncertainty in 
its score prediction – whereas conventional medical statistical methods always output a score, ML 
models have the opportunity to quantity probabilistically the certainty with which a score is produced; 
this offers advantages in applications in which classes are substantially overlapped, as often occurs in 
complex, real-world clinical problems, where there is imperfect class separation. Accuracy of such 
models can often be substantially improved by permitting a model not to make a classification if the 
score is highly uncertain; such approaches are used in many application areas of critical systems to 
improve acceptability with users.  
Additionally, we will investigate the joint construction of phenotypical clustering models (which identify 
reproducible phenotypes in the physiological data) with predictive models specific to each phenotypical 
group – this novel approach constructs an ensemble of predictive models that are phenotype-specific, 
thereby improving predictive accuracy with respect to a single “one-size-fits-all” generic algorithm 
applied to the entire population. 
11.2.2 Model performance and validation 
The performance of a prediction model is typically assessed by its calibration and discrimination.  
Calibration, which reflects how close the predictions from the model are to the observed outcome 
frequencies will be assessed graphically, using a calibration plot, plotting observed outcomes against 
predictions using smoothing techniques. The plot will also be supplemented with results for individuals 
grouped by similar probabilities (tenths) comparing the mean predicted probability to the mean 
observed outcome. Calibration will also be quantified by calculating the calibration slope and intercept.  
The discrimination of the prediction models will be summarised with the concordance index (equivalent 
to the Area Under Receiver Operating Characteristic curve) with 95% confidence interval. 
An important goal of a prediction model is to classify patients into risk groups. Both models will produce 
a risk score (probability) for each patient, based on their own predictor values. We will then identify a 
cut-off value to decide when the risk is high (such that we predict poor outcome) and when it is low 
(such that we predict a good outcome). The cut-off value (or range of), will be examined using decision 
curve analysis, which calculates the net benefit of using the model (compared to not using the model). 
The net benefit of a model is the difference between the proportion of true positives and the proportion 
of false positives weighted by the odds of the selected threshold for high-risk designation (61). 

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The internal validity of the final models will also be assessed by the bootstrap re-sampling technique to 
adjust for over-optimism in the estimation of model performance. ML models will also be additionally 
validated via k-fold cross-validation. The internal validation will quantify and be used to adjust the 
performance measures (e.g. discrimination, calibration) for any optimism. 
To account for potential differences in case-mix (distribution of patient characteristics and prevalence of 
the outcome) between the US data used to develop the model and the prospective external validation, 
we will investigate whether recalibration is needed to update the model to better fit the UK population 
(60). 
Finally, we will compare our prediction models to those identified in the systematic review.  
11.3 Sample Size Determination  
The model development data set (PRD) contains detailed data for >30,000 individuals undergoing cardiac 
surgery from 2 centres, 2001-2020. Of these, we expect around one third of patients to have 
experienced AFACS (10,000 events) 
Retrospective external validation on the Brigham data set will comprise 3000 individuals (≈900 AFACS 
events, much larger than the minimum recommended sample size of 100 events (58). 
Data from patients within trials and “real-world” data from patients receiving routine clinical care offer 
different strengths and weaknesses when validating a prediction rule (59). We will therefore undertake 
prospective external validations in both trial patients (using data from the ongoing British Heart 
Foundation (BHF) funded Tight K study CS/18/3/34063 and in a prospective cohort of ≈6000 patients 
developed from enhanced routine data collection at NHS Trusts.  
11.4 Analysis populations 
Our analysis populations will include all eligible participants in each study data set, as described above. 
11.5 Decision points  
Not applicable 
11.6 Stopping rules 
Not applicable 
11.7 The Level of Statistical Significance 
The level of statistical significance will be set at p<0.05. 
11.8 Procedure for Accounting for Missing, Unused, and Spurious Data. 
To avoid excluding patients when developing and validating our models, we will use multiple imputation 
to impute missing values, under a missing at random assumption. Identifying the true underlying missing 
data mechanism from the available is rarely possible. Assumptions need to be made on the plausible 
mechanism, and approaches needed to be used. Under a missing completely at random mechanism. 

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(MCAR), the missingness mechanism does not depend on unobserved (unseen) data. Carrying out a 
complete case analysis will produce unbiased estimates (but with a loss precision if full data are 
observed). Under the missing at random (MAR) assumption, the missingness after conditioning on the 
observed data does not depend on the unobserved (unseen) data. Under this approach, we can apply 
approaches such as multiple imputation, by fitting a joint model to the observed data and impute the 
missing data, taking account of the uncertainty in the estimated parameters of this joint model. We feel 
this, MAR, approach makes a less strong and more realistic assumption than the MCAR approach. The 
MAR imputation model will include all variables considered for the multivariable model building, the 
outcome and any auxiliary variables that will help explain the missingness. The assumption of a missing 
not at random (MNAR) approach whilst not implausible is considerably more complex to investigate – 
there is a dearth of research investigating MNAR in the context of prediction model research.  We will 
nevertheless explore whether the MAR assumption holds by comparing the imputed values (after 
accounting for the observed values) and the missing values to identify if there are any systematic 
differences to suggest a MNAR assumption.   
Data from the NHS Trusts provide a large “real-world” prospective validation, but is likely to contain 
more missingness (60). 
11.9 Procedures for Reporting any Deviation(s) from the Original Statistical Plan 
Any deviation from the statistical analysis plan will be described and justified in all study publications. 
12 DATA MANAGEMENT 
The plan for the data management of the study is outlined below. There is not a separate Data 
Management document in use for the study.  
12.1 Source Data 
The study will use data collected from patients who have undergone cardiac surgery in the specialist 
centres in the United States and United Kingdom. There are four databases and one prospective trial 
data set. All data will be anonymised and assigned a pseudonymous “study key” prior to analysis. 
Creation of the NHS Trust data set requires accessing and linking confidential health care records. Access 
to these data will be subject to approval from a REC and Section 251 support from the CAG. 
All pseudonymised data transferred to the Sponsor for analysis will be held within a secure “Data Safe 
Haven”, which is owned and maintained by the  Critical Care Research Group (University of Oxford). The 
“Data Safe Haven” is a computing environment which has been designed to store and analyse large 
datasets in a manner that is safe and secure. It conforms to NHS Digital Security Toolkit and Cyber 
Essentials Plus accreditation. The environment is designed to prevent patient level data leaving this 
environment. 
12.1.1 Databases 
1. CALIBER 
2. PRD 
3. Brigham 
4. NHS Trusts 

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12.1.2 Trial data sets 
1. Tight K study: (BHF-funded CS/18/3/34063, ClinicalTrials.gov Identifier: NCT04053816, REC: 
19/LO/1064).  
12.2 Access to Data 
Creation of the NHS Trusts data set requires accessing and linking confidential health care records. 
Where variables (e.g. ECHO findings) have not been transferred into a structured data source, research 
nurses from the study team will manually extract them from participants’ electronic records. All 
members of the study team involved in this process will undergo appropriate local governance training. 
Pseudonymised data set will be accessed by authorised members of the study team at the University of 
Oxford for analysis purposes. At the University of Oxford, access will be via the Data Safe Haven subject 
to compliance with local information governance policies.  
12.3 Data Recording and Record Keeping 
All study records will be electronic – an overview of their generation is detailed above. All records will be 
subject to quality assurance polices both at the University and research group level. These are designed 
to guarantee the accuracy and validity of the study data. 
The participants will be identified by a unique study number (pseudonymous study key). 
We will use the CALIBER and PRD data sources to validate known (and potentially identify novel) risk 
factors for AF after cardiac surgery. A complete list of risk factors will not be known a priori, as it 
depends on the results of the systematic review and Delphi process. However, likely required data will be 
contained within following categories: 
• 
Demographics (e.g., age, sex, ethnicity) 
• 
Diagnostic coding (e.g., known chronic conditions) 
• 
Limited pre- and post-operative medications (e.g., beta-blockers, vasoactive drugs) 
• 
Limited surgical procedures and intraoperative findings (e.g., type of cardiac surgery, duration of 
bypass, blood transfusion) 
• 
laboratory blood tests (including blood gases) 
• 
vital signs (e.g., heart rate, blood pressure) 
• 
Limited Echocardiogram findings  
• 
Limited electrocardiogram (ECG) findings 
Excluding age, sex and ethnicity we will not be accessing data on protected characteristics (e.g., religion, 
civil status, sexual orientation). 
The expert panel will agree a final list of risk factors, which will be used to develop the two prediction 
models. 
The models will be validated in one US data set (Brigham), one clinical trial data set (Tight-K) and 
prospective data collected from UK cardiac surgery centres (NHS Trusts). Only data from the above 
categories included in the candidate risk factor list agreed by the expert panel will be extracted from the 
UK cardiac centres along with the required outcome data for model assessment (including in-hospital 

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mortality and episodes of atrial fibrillation). Tight-K is an ongoing clinical trial with its own approvals. 
Data for the NHS Trust data sets will be acquired from the individual Trusts patient records in 
collaboration with the respective organisations. 
13 QUALITY ASSURANCE PROCEDURES 
All research team members will be trained in Information Governance, data protection and 
confidentiality. The study may be monitored, or audited in accordance with the current approved 
protocol, GCP, relevant regulations and standard operating procedures. 
13.1 Risk assessment  
This is a retrospective observational study, where researchers will not interact directly with patients or 
intervene in their care. We will undertake and document a formal risk assessment of the project. 
This study requires access to confidential patient records. Eligible patient records will be identified by the 
participating organisations. Directly identifiable data will only be required for record linkage and will not 
be available to the Sponsor. 
To mitigate the risk of reidentification of participants and the risk of data loss we will undertake the 
following: 
• 
All records will be accessed and de-identified at each participating site, using a dedicated 
computer that will conform to NHS information security standards.  
• 
Only pseudonymous personal data will be transferred via secure/encrypted protocols to the 
coordinating centre (Critical Care Research Group (CCRG), Nuffield Department of Clinical 
Neurosciences, Oxford University). 
• 
Only pseudonymous personal data will be held by the CCRG. 
• 
Pseudonymous personal data will be held inside the Sponsors “Data Safe Haven” which conforms 
to the same NHS standards of information security and cyber security. 
13.2 Study monitoring  
All research team members will be fully trained in Information Governance, data protection and 
confidentiality. The study may be monitored, or audited in accordance with the current approved 
protocol, GCP, relevant regulations and standard operating procedures. 
13.3 Study Committees  
13.3.1 Study management group (SMG) 
The study management group will consist of the Chief Investigator and the named investigators listed 
under Key Contacts, and individuals directly funded by the project. The study management group will be 
primarily responsible for the running and conduct of the study. They will be responsible for ensuring that 
standard operating procedures are followed and that regulations are adhered to. Where appropriate 
public patient involvement will be gained in any changes or amendments that are needed during the 
study. 

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13.3.2 Project Oversight Group (POG) 
The project oversight group, is made up of the CI (Peter Watkinson) and co-lead investigator (Ben 
O’Brien), two PPI members, the Founder & CEO of the Atrial Fibrillation Association, a Consultant 
Cardiologist, and Professor of Cardiovascular Medicine, from the East and North Hertfordshire NHS Trust 
and University of Hertfordshire, a Professor of Stroke and Older People’s Care at the University of Central 
Lancashire, a Professor of Evidence Synthesis at the University of York; the Director of Nursing & 
Midwifery for the National Institute for Health Research (NIHR) and a Professor of Medical Statistics, 
from the University of Leicester. 
14 PROTOCOL DEVIATIONS  
Any deviations from the protocol will be documented in a protocol deviation form and filed in the study 
master file. 
15 SERIOUS BREACHES 
A “serious breach” is a breach of the protocol or of the conditions or principles of Good Clinical Practice 
which is likely to affect to a significant degree – 
 (a) the safety or physical or mental integrity of the trial subjects; or 
(b) the scientific value of the research. 
This is a non-interventional study and will not interact directly with its participants, only their data. The 
risk of a serious breach is therefore extremely low. However, the study will process directly identifiable 
patient data at the NHS Trusts to create an anonymised data for model validation. This present potential 
for a data breach. In the event that a data breach is suspected, the study team will follow local hospital 
policies relating to GDPR.  
The Sponsor will also be contacted within 1 working day. In collaboration with the CI, the serious breach 
will be reviewed by the Sponsor and, if appropriate, the Sponsor will report it to the approving REC/CAG 
committees and the relevant NHS host organisation within seven calendar days.  
16 ETHICAL AND REGULATORY CONSIDERATIONS 
16.1 Declaration of Helsinki 
The Investigator will ensure that this study is conducted in accordance with the principles of the 
Declaration of Helsinki. 
16.2 Guidelines for Good Clinical Practice 
The Investigator will ensure that this study is conducted in accordance with relevant regulations and with 
Good Clinical Practice. 
16.3 Approvals 

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Following Sponsor approval, the protocol, and any associated documentation will be submitted to an 
appropriate Research Ethics Committee (REC), CAG and HRA (where required) and host institutions for 
written approval. 
The Investigator will submit and, where necessary, obtain approval from the above parties for all 
substantial amendments to the original approved documents. 
16.4 Other Ethical Considerations 
The CALIBER research platform has existing research ethics approval (09/H0810/16) and ECC approval 
(ECC 2-06(b)/2009 CALIBER dataset). The PRD has existing research ethics approval (Ref:2016P001986) 
from the Mass General Brigham Institutional Review Board. 
16.5 Reporting 
The CI shall submit once a year throughout the study, or on request, an Annual Progress report to the 
REC Committee, CAG, HRA (where required) host organisation, Sponsor, and funder (where required). In 
addition, an End of Study notification and final report will be submitted to the same parties.  
16.6 Transparency in Research  
Not applicable as the research is non-interventional. 
16.7 Participant Confidentiality 
The study will comply with the UK General Data Protection Regulation (UK GDPR) and Data Protection 
Act 2018, which require data to be de-identified as soon as it is practical to do so. The processing of the 
personal data of participants will be minimised by making use of a unique participant study number only 
on all study documents and any electronic database(s). All documents will be stored securely and only 
accessible by study staff and authorised personnel. The study staff will safeguard the privacy of 
participants’ personal data. 
16.8 Expenses and Benefits 
No payments or any other benefits will be provided to participants. 
17 FINANCE AND INSURANCE 
17.1 Funding 
The study is funded by the NIHR National Institute of Health Research, Health Technology Assessment 
Programme HTA Project: NIHR131227. 
17.2 Insurance 

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The University has a specialist insurance policy in place which would operate in the event of any 
participant suffering harm as a result of their involvement in the research (Newline Underwriting 
Management Ltd, at Lloyd’s of London).   
17.3 Contractual arrangements  
Appropriate contractual arrangements will be put in place with all third parties.  
18 PUBLICATION POLICY 
The Investigators will be involved in reviewing drafts of the manuscripts, abstracts, press releases and 
any other publications arising from the study.  Authors will acknowledge that the study was funded by 
the NIHR National Institute of Health Research, Health Technology Assessment Programme HTA Project: 
NIHR131227. Authorship will be determined in accordance with the ICMJE guidelines and other 
contributors will be acknowledged. 
19. DEVELOPMENT OF A NEW PRODUCT/ PROCESS OR THE GENERATION OF INTELLECTUAL 
PROPERTY  
Ownership of IP generated by employees of the University of Oxford vests in the University.  The 
University will ensure appropriate arrangements are in place as regards any new IP arising from the 
study.  
19 ARCHIVING 
All pseudonymised data collected as part of this study will be stored securely within the CCRG Data Safe 
Haven for a minimum of five years, in keeping with the MRC Retention Framework for Research Data and 
Records. The CCRG Data Safe Haven is a secure environment run by the Critical Care Research Group 
(University of Oxford), which conforms to Data Security and Protection Toolkit standards. Data will not 
be used or released from this environment and it will be deleted at the end of its retention period. 
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21 APPENDIX A:  AMENDMENT HISTORY 
 
Amendment 
No. 
Protocol Version No. 
Date issued 
Author(s) of 
changes 
Details of 
Changes made 
 
1 
01/06/2021 
Peter 
Watkinson and 
study team 
N/A 
 
2 
03/08/2021 
Peter 
Watkinson and 
study team 
Changed study 
start date to 
1/10/21; 
Clarification of 
primary 
outcome; 
Addition of 
secondary 
outcomes 
measure to 
primary 
objective; 
Revision of 
Plain English 
Summary; 
Details of 
model variables 
added to 
Section 12.3; 
Clarification of 
Project 
Oversight 
group in 
section 13.3.2 
 
 
 
3 
04/03/2022 
Peter 
Watkinson and 
study team 
Addition of 
clinical 
trials.gov 
registration; 
Specific 
references to 
The Liverpool 
Heart & Chest 
Hospital (LHCH) 
and Barts Heart 
Centre (BHC) 
have been 
replaced with 
“NHS Trusts” to 

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© Copyright: The University of Oxford and Oxford University Hospitals NHS Foundation Trust 2019 
 
Page 34 of 34 
cover 
additional 
study sites 
 
4 
14/06/2022 
Peter 
Watkinson and 
study team 
Study period 
separated into 
“Funded” and 
“Prospective” 
to clarify NIHR 
funding period.  
 
5 
03/07/2023 
Peter 
Watkinson and 
study team 
Funded study 
end date 
changed to 30 
June 2024 in 
Section 3, as 
agreed with the 
funder 
(National 
Institute for 
Health and 
Care Research, 
NIHR). The CAG 
reference 
number has 
been corrected 
to 
21/CAG/0097 
in the study 
documentation. 
 
List details of all protocol amendments here whenever a new version of the protocol is produced.   
Protocol amendments must be submitted to the Sponsor for approval prior to submission to the REC 
committee and HRA (where required).