National Institute for Health Research (NIHR) Cardiovascular Health Informatics Collaborative
Conditions: Troponin, Age, Mortality, Acute Coronary Syndrome
Sponsor: Imperial College London

Unique Protocol ID:  174052 
 
Brief Title:  National Institute for Health Research (NIHR) Health 
Informatics Collaborative Troponin Study (NHIC-Troponin)   
 
Official Title:  National Institute for Health Research (NIHR) Health 
Informatics Collaborative Cardiovascular Project   
 
 
DATA ACQUISITION AND ANALYSIS PLAN 
 
Author: Amit Kaura 
Approvers: Jamil Mayet / Darrel Francis 
Date: 07/03/2017 
Updated: 04/07/2024 
DATABASE PROTOCOL  
Introduction 
This document outlines the specifications and procedures for the NIHR Health 
Informatics Collaborative (NIHR HIC) Cardiovascular research database and defines 
the processes for the collection of the NIHR HIC Cardiovascular research data and 
onward sharing with researchers.  The central research database is held within 
Imperial College Healthcare NHS Trust (ICHNT).  
Data are collected locally at the following trusts data for all patients receiving a 
troponin test and submitted pseudonymously to ICHNT: 
• Imperial College Healthcare NHS Trust 
• University College London Hospitals NHS Foundation Trust  
• Oxford University Hospitals NHS Foundation Trust  
• Kings College Hospital NHS Foundation Trust  
• Guys and St Thomas’ Hospital NHS Foundation Trust  
This document covers the processes for local collection of data at all sites. The main 
database and systems are hosted at ICHNT as the lead organisation for the 
Cardiovascular Theme. 
 
NIHR HIC Cardiovascular Database Definitions  
Local data store 
Each trust will have a local store of NIHR HIC Cardiovascular data; this collects their 
information in an identifiable form from clinical systems, the data will then be de-
identified within the local NHS Trust. These de-identified data will be passed to the 
central research database. 
 

 
 
Research database  
The research database will contain only de-identified information. This database 
combines the data from each site (including ICHNT) and will be used for the TROP-
RISK study.  
 
Procedures for local data collection into local stores  
Data will be collected automatically from primary clinical systems within each Trust. 
NHS staff will enter data into clinical systems during routine clinical care of patients, 
or data will be generated as a result of clinical tests. Data in the primary clinical 
systems will be processed in accordance with each Trusts clinical guidelines and 
subject to local quality and governance procedures. Data will be extracted 
automatically and validated processes for data extraction, transformation and 
loading (ETL) have been designed to import the data into the local secure data 
stores. 
 
Access to Local data stores 
Access to identified data will be limited to those justified and approved by the local 
information governance teams and will always be NHS staff in accordance with the 
duty of confidentiality required by law. Anonymisation procedures will be 
automated after implementation. 
Local data stores will be the only areas that hold any patient identifiers. De-
identification is completed at this stage prior to passing data into any research 
databases and datasets.  
 
De-identification  
The data will be pseudonymised locally within each Trust; anonymisation processes 
will be automated and set up by NHS staff in accordance with: 
• advice from local information governance procedures  
• the HIC Standard Operating Procedure (SOP) for data sharing and 
anonymisation  
• the Clinical data transfer policy 
The data items summarised in Table 1 will be anonymised. Anonymisation will be 
approved locally by information governance teams before data are sent externally to 
ICHNT. De-identified data are then shared in accordance with the overarching data 
sharing agreement. 
Each site will hold two versions of the database, one identifiable and one with de-
identified, pseudonymised data. The de-identified version is for use in research and 
shared with the central research database at ICHNT. The identifiable database is 
held so that if necessary patients can be re-identified if it is of importance to re-
contact the patient via their care team (please refer to ‘re-contacting patients’ 
section.  
NHS numbers and hospital numbers are pseudonymised using locally approved 
procedures. Names are removed from the dataset and date of birth is transformed 
to year of birth. Date of death is shared, however is converted in to relevant survival 
rates on provision of data to researchers. Researchers will never see the full date of 

 
 
death or be able to calculate it from other information (all dates are provided as 
delta for first troponin). The provision of date of death has been agreed by each of 
the information governance offices at each of the sites in the following de-
identification and anonymisation protocol for the TROP-RISK STUDY: 
• The exact date of death is required to evaluate mortality after diagnosis.  
• Patients presenting with acute coronary syndromes have a high frequency of 
cardiac events and a high short-term mortality rate. An accurate measure of 
death is therefore required to fully evaluate this.  
• To redact the date of death to year only would misrepresent the survival of 
these patients, particularly for those who survive for less than one year.  The 
clinical leads at our BRCs have underlined the importance of having the date 
in full for the TROP-RISK study.    
 
Demographic 
Anonymisation  
Local 
Identifier 
Provided if NHS number is missing 
NHS Number 
Use local pseudonymisation algorithm 
(key to be retained at source)  
Rename field to subject ID  
Family Name 
To be removed – LOCAL use 
Given Name 
To be removed – LOCAL use 
Date of Birth  
YYYY 
Date of Death 
DD-MM-YYYY 
 
Table 1. Anonymisation of data elements  
 
Data validity and quality 
Prior to pseudonymisation within the clinical systems, NHS number, Date of Birth 
and patient names will be automatically checked to remove duplication of patients. 
Samples of data will be clinically validated by members of the clinical team to ensure 
that the transformation process is correct and data are attributed to the correct 
patients prior to pseudonymisation. After clinical validation is complete, the data 
will be transformed to a standardised XML format and validated (Figure 1). 
 

 
 
 
Figure 1. Data import and validation process.  
 
Data sharing between Trusts  
Data are shared in accordance with the SOP for data sharing, as outlined in Section 
1.3.2. Data are encrypted in transit via sFTP on the N3 network (Figure 1). The sFTP 
is set up and hosted by ICHNT.  
 
Procedures for secure research database 
Data validity and quality 
ICHNT validate the data to ensure that the structure, data items, units and data 
types are in accordance with the standardised data model. Data will be rejected if 
validation fails. On rejection, the files will be archived and the data manager will 
contact the data provider to review the submission and resend once corrected.  
Once imported into the secure research database, data are subject to clinical 
validation by Cardiovascular clinical experts; these validations will be completed by 
the clinical researchers using de-identified data. Data will be reviewed for data 
completeness, spread and actual data point values. If data appears invalid it will be 
rejected.  
Data quality reports will be generated and provided to the research team and local 
data provider after each submission. These will be reviewed after each submission 
to ensure all areas are populated.    
 
Research Database software  
The database is built using Microsoft SQL server 2014, Microsoft’s principle database 
management system software. The installation of the software was carried out by 
certified technical consultants and tested by the trust ICT team and data warehouse 
team in accordance with Trust ICT procedures and policies.  
 
Database management 
The database is fully backed up on a daily basis. The back-ups are standardised for all 
Trust databases within the Trust data warehouse. Backups can be restored at any 

 
 
point by warehouse staff. Data are secondary copies from clinical systems; at any 
point the participating Trusts can re-extract the data from primary sources.  
Each site will submit data on a quarterly basis to the database. Data integrity checks 
will be completed to ensure correct structure is maintained and duplicates are not 
present.  
Once entered on the system, data will not be changed. All access will be ‘read only’ 
except via exception, approved by the research Informatics Programme Manager 
and Clinical Leads group.  
The database will be managed by the data manager (Ben Glampson) and developer 
(Abdul Mulla). Database changes are controlled by the research informatics 
Programme manager (Ben Glampson), and are sanctioned by the NIHR HIC 
Cardiovascular scientific steering committee (Chaired by Jamil Mayet). All staff are 
substantive NHS employees at ICHNT.   
Data extracts taken for research will be stored within the data warehouse and 
retained for the period specified in the data request. All information pertinent to the 
request will be retained and tracked by the data manager. 
 
Data access for research 
TROP-RISK STUDY dataset  
All analyses for the TROP-RISK STRUDY will be completed on fully de-identified data. 
This includes further de-identification to remove dates.  
A designated clinical researcher (Vas) will freeze a copy of the database on 1st April 
2017, so a static dataset can be used for analysis. This will be retained separately 
from the live database to allow reproducible analyses.  
The study dataset will comprise all patients who had a troponin measured at each of 
the five academic centres between 2010 (2008 for University College Hospital) and 
2017. 
Dates will be converted to delta dates, with date zero being the date of the first 
troponin test. All further dates are provided as number of days from date zero. Age 
will be provided in years, at the time of the first troponin test.  
Date of death will be converted to the number of days since date zero. All patients 
will be retrospectively followed up, using routinely collected data on the NHS Spine 
Application, Summary Care Record, until death or censoring on 1st April 2017. 
 
Data elements 
The database model will include 156 data points, grouped into demographics, 
emergency department attendance and inpatient episodes, biochemistry, diagnosis, 
angiography, revascularization, echocardiography and mortality. Diagnostic data will 
be based on International Statistical Classification of Diseases and Related Health 
Problems (ICD) discharge codes.  
 
 

 
 
STATISTICAL ANALYSIS 
Outcomes 
Primary outcome   
The primary outcome will be all-cause mortality. The nature if the data sources 
means that this is the outcome that will be available and it will be available with high 
fidelity.  
 
Secondary outcomes   
The secondary outcomes will be: 
• Acute coronary syndrome (ACS) diagnosis (unstable angina, non-ST segment 
elevation myocardial infarction (NSTEMI), ST-segment elevation myocardial 
infarction (STEMI) 
• Non-ACS diagnosis  
• Angiography 
• Revascularisation (coronary artery bypass grafting CABG), percutaneous 
coronary intervention (PCI)) 
 
Statistical Methods 
Baseline data  
Baseline and demographic characteristics will be summarised by standard 
descriptive summaries: 
• means (standard deviation) for continuous variables which are normally 
distributed 
• median (interquartile range) for continuous variables which are not normally 
distributed  
• number (percentage) for categorical variables 
 
Age distribution  
The age distribution of patients who have a troponin measured will be compared 
with that of the general population using the World Health Organisation (WHO) 
population estimates for the UK. 
 
Troponin data 
Assay performance  
Each Trust measures either troponin I or T using either contemporary or high-
sensitivity assays. These tests yield results in different measurable ranges with 
unique cut-off points for the 99th percentile of the upper limit of normal (ULN). A 
summary table of the performance of each assay will be assembled.   
 

 
 
Handling of troponin data   
The study dataset will comprise all patients who have had a troponin measured at 
each of the five academic centres between 2010 (2008 for University College 
Hospital) and 1st April 2017.  
In clinical practice, troponin levels are frequently dichotomised into “positive” 
(meaning above ULN) or “negative”. Troponin levels may have a progressive 
relationship with prognosis, too, but the shape of this relationship is not known 
across the full spectrum of values and making the assumption of a linear relationship 
of mortality with troponin (or log troponin) may not be secure.  
For these reasons, we will treat the data in two ways.  
1. We will dichotomise the peak troponin level as being either positive or 
negative based on the ULN for each troponin assay. This makes no 
assumption of the shape of the relationship. 
 
2. To standardise the many troponin assays, we will scale the results using the 
ratio of the observed troponin value divided by the ULN for that particular 
troponin assay. For example, a patient with a troponin value of 96 using an 
assay which has an ULN of 40 would have a scaled result of 96/40 = 2.4 xULN.  
 
In some hospitals and some assays, troponin values above a certain level are 
systematically reported as “more than X”. According to what the hospitals 
have reported so far, the threshold at which the system gives results like this 
is in all cases very high. In no case does his truncation of this results occur 
below 10,000 xULN. We therefore plan to set the uppermost band to be 
“>10,000 x ULN”. This ensures that the unavailability of an explicit value does 
not affect the troponin band into which any result is allocated.  
 
Similarly, no hospital has reported truncating lower values of troponin at a 
threshold that is higher than ½ xULN. Therefore, we plan for the lowermost 
band to be “<½ xULN”.  
 
Troponin has a marked positive skew. Therefore, we plan for the bands to be 
spaced not arithmetically but exponentially, with narrow spacing nearer low 
values, where there is a higher frequency density of troponin values.     
 
Adopting this approach for analysis has the following advantages: 
1. It allows the shape of the relationship to be explored without making 
assumptions about the shape in advance.  
2. It allows values documented in the hospital databases as “less than X” 
or “more than X” to be used correctly in the analysis rather than 
omitted or have imputed values.  
3. The resulting display of the mortality relationship should be easily 
interpreted and applied by clinicians.  
All analyses on troponin will be performed using the peak troponin level. For 
patients who have a single troponin measurement, the peak troponin will be based 
on this measurement. In the remainder of the patients who have more than one 

 
 
troponin test in the same hospital episode, the peak troponin value will be defined 
as the highest of all measurements.  
 
Relationship between troponin positivity, age and mortality 
To assess the association of dichotomous troponin level (above ULN or not) with all-
cause mortality, Cox proportional hazards regression modelling will be used. Hazard 
ratios will be determined for the following age groups: 18-29, 30-39, 40-49, 50-59, 
60-69, 70-79, 80-89 and 90+ years. We will also stratify the hazard ratios for 
troponin positive versus negative groups across these age groups by troponin assay 
(contemporary versus high-sensitivity troponin assay).  
The Kaplan-Meier method will be used to calculate and display cumulative mortality, 
with positive and negative troponin bands compared using the log-rank statistic.  
 
Relationship between troponin level and mortality 
The main analysis of the association of progressively higher troponins with all-cause 
mortality will be performed without making assumptions about the shape of the 
relationship, i.e. with Cox proportional hazards regression modelling. 
The Kaplan-Meier method will be used to calculate and display cumulative mortality, 
with troponin bands compared using the log-rank statistic. The results will be 
stratified according to ACS diagnosis and revascularisation status. 
 
ACS diagnosis  
The ICD-10 codes in Table 2 will be used to indicate an ACS diagnosis. 
 
ICD-10 Code 
Category  
I20.0 
Unstable angina 
I21.0 
Acute transmural myocardial infarction of anterior wall 
I21.1 
Acute transmural myocardial infarction of inferior wall 
I21.2 
Acute transmural myocardial infarction of other sites 
I21.3 
Acute transmural myocardial infarction of unspecified site 
I21.4 
Acute subendocardial myocardial infarction 
I21.9 
Acute myocardial infarction, unspecified 
I22.0 
Subsequent myocardial infarction of anterior wall 
I22.1 
Subsequent myocardial infarction of inferior wall 
I22.8 
Subsequent myocardial infarction of other sites 
I22.9 
Subsequent myocardial infarction of unspecified site 
I24.8 
Other forms of acute ischaemic heart disease 
I24.9 
Acute ischaemic heart disease, unspecified 
 
Table 2. ICD-10 codes used to indicate an ACS diagnosis 
 
Revascularisation status   
Acute revascularisation will be defined as having PCI or CABG in the time window 
between 48 hours before and 3 months after the first troponin measurement. This 
will account for patients who had revascularisation, in particular PCI, as an 

 
 
emergency prior to their first troponin blood test and to capture revascularisation, in 
particular CABG, performed as an outpatient following their index admission.    
 
Statistical package 
Statistical analyses will be performed using SPSS software version 24 (SPSS Inc., 
Chicago, Illinois, United States) and the results verified by replicate analysis using R 
3.3.2 statistical package (the R Core Team, Vienna, Austria).  
 
Statistical significance 
All hypothesis tests will be 2-tailed. A p-value of <0.05 will be considered statistically 
significant. No correction will be implemented for multiple testing.