Personalised Simulation Technologies for Optimising Treatment in the Intensive Care Unit
Conditions: Respiratory Distress Syndrome, Adult, Respiratory Insufficiency
Sponsor: Guy's and St Thomas' NHS Foundation Trust

 
 
 
1. Protocol details 
1.1 PROTOCOL TITLE: 
Personalised simulation technologies for optimising treatment in the inte
1.2 Names (titles), roles and contact details of: 
Sponsor 
Guy’s and St Thomas’ NHS Foundation Trust 
Elizabeth Bruna  
16th floor Tower Wing 
Guy’s Hospital 
Great Maze Pond 
London 
SE1 9RT 
r&d@gstt.nhs.uk 
02071887188 
Chief Investigator  
Dr Luigi Camporota 
 
Intensive Care Research Office 
Ground Floor South Wing Block C, 
Lane Fox Unit, 
St Thomas’ Hospital, 
Westminster Bridge Road, 
London, 
United Kingdom 
SE1 7EH 
 
luigi.camporota@gstt.nhs.uk 
020 7188 3046 

 
 
 
1.3 Protocol details 
Version number 1.0 
Draft 
Date 05/02/2020 
 
 

 
 
 
2 Signature Page 
 
The Chief Investigator and the R&D (sponsor office) have discussed this
agree to perform the investigations and to abide by this protocol  
The investigator agrees to conduct the trial in compliance with the approv
Data Protection Act (1998), the Trust Information Governance Policy (or
Research Governance Framework (2005’ 2nd Edition; as amended), the
regulatory requirements as amended. 
 
Chief investigator 
Dr Luigi Camporota 
 
 
Signature 
Sponsor Representative 
R&D to Add  
 
GSTFT 
Signature 
 
 
 

 
 
 
Contents Page 
 
1. 
Protocol details .............................................................................
1.1 
PROTOCOL TITLE: ...................................................................
1.2 
Names (titles), roles and contact details of: .............................
1.3 
Protocol details......................................................................
2 
Signature Page ..............................................................................
Contents Page ......................................................................................
3 
List of Abbreviations and Definitions ..............................................
4 
Summary/Synopsis ........................................................................
5 
Introduction..................................................................................
6 
Study objectives and purpose ........................................................
7 
Study design & Flowchart ..............................................................
7.1 
Study Design..........................................................................
7.2 
Flowchart ..............................................................................
8 
Subject selection ...........................................................................
8.1 
Inclusion Criteria ....................................................................
8.2 
Exclusion Criteria ...................................................................
9 
Study procedures ..........................................................................
9.1 
Subject recruitment and screening..........................................

 
 
 
11.1.1 
Demographics ................................................................
11.1.2 
Physiological ..................................................................
11.1.3 
Ventilator.......................................................................
11.1.4 
Blood Gas Analysis ..........................................................
11.1.1 
ECMO ............................................................................
11.2 
Format and Scale of Data .......................................................
11.3 
Methodologies of Data Collection and Generation ...................
11.4 
Data Quality and Standards ....................................................
11.5 
Data handling and record keeping...........................................
11.5.1 
GSTFT ............................................................................
11.5.2 
Data Repository ..............................................................
11.5.3 
Academic Partners..........................................................
11.6 
Metadata standards and data documentation .........................
11.6.1 
Data Set Directory Structures and Filenames ....................
11.6.2 
Metadata Descriptive Files ..............................................
11.7 
Data preservation strategy and standards ...............................
11.7.1 
GSTFT ............................................................................
11.7.2 
Data Repository ..............................................................
11.7.3 
Academic Collaborators ..................................................
11 8
Data Security and Confidentiality of Potentially Disclosive Perso

 
 
 
14 
Financing and Insurance.............................................................
15 
Reporting and dissemination ......................................................
References ...........................................................................................
 
 

 
 
 
3 List of Abbreviations and Definitions 
 
GSTFT  
 
Guy’s and St Thomas’ NHS Foundation Trust 
UON 
 
 
University of Nottingham 
UWAR  
 
University of Warwick  
 
 
 

 
 
 
4 Summary/Synopsis 
 
Title  
 
 
Personalised simulation technolo
intensive care unit  
Protocol Short Title/Acronym 
 
PSTOTICU 
Protocol Version number and Date 
 
0.1.4  21/08/2019 
IRAS Number 
 
266780 
REC Reference 
 
NA 
Study Duration 
 
2 years 
Sponsor name 
 
Guy’s and St Thomas’ NHS Found
Chief Investigator 
 
Dr Luigi Camporota 
Funder 
 
Engineering and Physical Science
Medical condition or disease under 
investigation 
 
Respiratory Failure 
Purpose of research  
 
This study aims to further dev
models, by the fitting of those mo
data of critical care patients. 
Primary objective 
 
To further develop our simulation
of recently proposed therapeutic
Secondary objective (s) 
 
To integrate data-streams and co
our existing modelling technolo
individual patients and treatmen
imminent problems. 
To develop mathematical met
explore the "design space" of po
individual patient in simulation, 
can be suggested for evaluation 
Number of Episodes 
 
5,000 
Study Type 
 
Observational 
Endpoints 
 
Not Applicable 
Main Inclusion Criteria 
 
This project will include criti

 
 
 
5 Introduction 
 
This project aims to further develop and refine in silico physiological mo
models to the physiological and treatment data of critical care patients. O
development is fitting the model to, and its description of, the physiolog
specific interventions. An example would be how a patients physiologica
increase in positive end expiratory pressure (PEEP). Data for this exa
physiological and treatment data pre and post ventilator changes (e.g., PE
the clinical team, for a clinically appropriate time period. 
Computer simulation offers a new approach to traditional medical resea
suited to investigating treatment of critical illness. Critically ill patients
great detail, providing extensive, high quality data streams for model 
patient-matching. Models based on this data can incorporate very compl
be validated against responses of individual patients, for use as investigat
In contrast to trials on animal models and humans, in silico models o
disease pathology are completely configurable, reproducible and reusab
combinations of treatments, can be applied to the same patient or subse
to understand mode of action, quantitatively compare effectiveness in var
interventions for particular clinical objectives and particular patient grou
It is widely accepted that modification of existing mechanical ventilation
novel physical treatment strategies could significantly reduce pulmo
impairment, and mortality rates, particularly if combined with novel phar
To achieve this we are undertaking a collaborative project between three
different and complimentary areas of expertise. 
The three institutions involved in this project are: 
 

 
 
 
6 Study objectives and purpose 
 
This project is a collaborative endeavour between Guy’s and St Thomas
academic collaborators at the University of Nottingham and the Unive
further develop and refine in silico physiological models, by the fitti
physiological and treatment data of critical care patients. 
The project’s primary objective is to further develop our simulation 
simulation of commonly used therapeutic interventions within the int
amenable interventions include prone positioning, optimising the co
breathing effort with mechanical ventilation, application of recruitment 
cause and maintain an "open lung", and Extra-corporeal membrane o
interventions have been chosen on the basis of amenability to an
optimisation, possessing a strong potential for providing improvement in 
current usage in many ICU's worldwide. 
Integration of available data within the ICU with our existing modelling t
real time simulation of individual patients and treatments. Sufficient
models would enable systematic and efficient exploration of the possib
for an individual patient in simulation, allowing optimised treatment str
evaluation by the clinician. Possible therapeutic interventions to be expl
designed pulmonary recruitment (alveolar re-opening) manoeuvres, ven
(e.g. airway-pressure release ventilation low tidal volume, high positiv
inspiratory flow waveform modification, and closed-loop ventilatory cont
To facilitate the further development of the simulation platform requires 
curated data sets representative of the therapeutic interventions of intere
will be created to catalogue and share collated data sets between co
repository will enforce the oversight and governance ensuring the requ
protection against unlawful or unauthorised processing, access, loss, dest
At this stage of the project, the simulation platform will not be accessib

 
 
 
7 Study design & Flowchart 
7.1 Study Design 
This study aims to further develop and refine in silico physiological mo
models to the physiological and treatment data of critical care patients.  
Of specific interest to model development is fitting the model to, 
physiological response of patients to specific interventions. An example
physiological parameters respond to an increase in PEEP. Data for this ex
physiological and treatment data pre and post ventilator changes (e.g., PE
the clinical team, for a clinically appropriate time period. This would com
Data required for this study will therefore be truncated case report data
short time periods, on the order of 12 hours duration. The exact length
determined by the clinical scenario, but would be for a clinically appropri
No attempt will be made to capture data from entire patient cohorts or t
There is no intention to implement any study specific interventions or tre
be retrospective, pragmatic and limited to that data recorded throug
routinely provided to all patients. 
Within this project, there is no intention for physiological models develope
Clinicians will have no access or exposure to the simulations and predictio
by the models that this project aims to develop. 
For the purposes of this document, a data set is defined as a collated set
subject and one intervention. 
7.2 Flowchart 
The collaborative nature of this study requires that data be managed a
environments: 
GSTFT

 
 
 

 
 
 
8 Subject selection 
 
Subjects will be drawn from the adult critical care population at GSTFT. S
receiving mechanical ventilation. 
It is anticipated that this project will require several iterations of 
development, with each iteration examining a different treatment interv
will inform the exact nature of data collection in the subsequent iteratio
and analysis iterations that will be required is dependent on the
development. 
As noted in section 7.1 collated data sets will episodic in nature with ex
the order of twelve hours. It is entirely possible that the admission and c
several episodes of interest, or the patient may receive a similar interven
episodes may be therefore be contributed under differing study ids to t
catalogued in the data repository. 
8.1 Inclusion Criteria 
 At least 18 years of age 
 Patients admitted to GSTFT Intensive Care between th
31/03/2019 
 Receiving mechanical ventilation 
 
8.2 Exclusion Criteria 
 Pregnancy or lactation. 
 
 

 
 
 
 
9 Study procedures 
9.1 Subject recruitment and screening 
Episodes of interest will be identified retrospectively by querying existing d
attributes. As an example, patient treatment data could be queried for epi
setting is increased from 5 to 10.  
Data subjects will not be approached as data is collected retrospectively 
existing data sources. We will not be requested any additional data or res
Consent will not be sought from individual patients due to the non-inter
nature of the data collection. 
Data collection will be limited to that data recorded as part of routin
01/01/2010 and 31/03/2019 
9.2 Schedule of assessments for each visit  
Data is available from the electronic health records and the data export
technology employed in the routine care of critically ill patients. 
No schedule of assessment is planned due to the retrospective nature of 
health care records. 
9.3 Follow up Procedures 
There are no follow up procedures planned for this study. 
9.4 End of Study Definition  
Completion of the project is defined as the time point five years after co
activities. 

 
 
 
10  Assessment of Safety  
Study procedures for this study consist of data collection from the electro
in the routine care provided to patients. It is anticipated that this study p
patient safety than that caused by their routine clinical care. 
10.1 Study Steering Committee 
There will be no monitoring, steering or safety committees set up for this
10.2 Ethics & Regulatory Approvals 
Appropriate HRA and REC will be in place before the study commences. 
 
 

 
 
 
11 Data 
11.1 Data to be collected 
Data will be collected within four domains: 
 Demographics 
 Physiological 
 Ventilation 
 Blood gas analysis 
 
Demographic data will be collected from the health care record, and is of
Physiological data is continuously captured by monitoring equipment (Phi
within ICU, and recorded on a regular basis into an electronic health rec
Data can also be captured remotely from the continuous stream captured
Ventilator parameters and patient ventilatory parameters are measured 
at the bedside in the ICU by the patient ventilators (Drager V500). This d
basis into an electronic health record system by clinical staff. Data can 
from the ventilators.  
Blood gas analysis results are collected routinely as part of the care of ICU p
their electronic health record. Data will be collected retrospectively from
Data points that will be collected from each domain are detailed below. 
identify specific data fields, alongside a short description of that data 
realistic that only subsets of the entire list of potential data fields will
present in individual data sets, with availability dependent on differing pa
 
11.1.1 Demographics 
STUDY_ID 
 
 
 
Unique study data set identifier  

 
 
 
CENTRAL_TEMP_MONITOR_C    
Central temperature measureme
PERIPHERAL_TEMP_MONITOR_C   
Peripheral temperature measure
ECG_HEART_RATE_MIN    
 
ECG Heart Rate 
HEART_RATE_MONITOR_MIN    
 Monitor Heart Rate 
ECG_RHYTHM_MONITOR 
   
 ECG cardiac rhythm 
PVC_RATE_MONITOR_MIN 
    
Premature Ventricular Contractio
ECTOPIC_BEAT_MONITOR_STR    
Premature Ventricular Contractio
ECT_RATE_MONITOR_MIN 
   
ECG Ectopic Rate 
SPO2_MONITOR 
             
Photoplethysmogram - Non-in
monitoring 
RESP_RATE_MONITOR_MIN 
    
Respiratory Rate 
NIBP_SYS_MMHG 
             
Non-invasive systolic blood press
NIBP_DIA_MMHG 
 
    
Non-invasive diastolic blood pres
NIBP_MEAN_MMHG 
            
Non-invasive mean blood pressu
ABP_SYS_MMHG 
 
   
Invasive systolic blood pressure m
ABP_DIA_MMHG 
 
   
Invasive diastolic blood pressure
ABP_MEAN_MMHG 
 
    
Invasive mean blood pressure m
ART_SYS_MMHG 
 
    
Invasive systolic blood pressure m
ART_DIA_MMHG 
 
    
Invasive diastolic blood pressure
ART_MEAN_MMHG 
 
    
Invasive mean blood pressure m

 
 
 
CENTRAL_TEMP_ICIP_C     
 
Central temperature measureme
PERIPHERAL_TEMP_ICIP_C 
    
Peripheral temperature measure
HEART_RATE_ICIP 
             
Heart Rate 
CARDIAC_RHYTHM_ICIP    
 
Cardiac Rhythm 
SPO2_ICIP 
             
 
Photoplethysmogram 
NIBP_SYS_ICIP_MMHG     
 
Non-invasive systolic blood press
NIBP_DIA_ICIP_MMHG     
 
Non-invasive diastolic blood pres
NIBP_MEAN_ICIP_MMHG 
    
Non-invasive mean blood pressu
ABP_SYS_ICIP_MMHG     
 
Invasive systolic blood pressure m
ABP_DIA_ICIP_MMHG     
 
Invasive diastolic blood pressure
ABP_MEAN_ICIP_MMHG 
    
Invasive mean blood pressure m
ART_SYS_ICIP_MMHG     
 
Invasive systolic blood pressure m
ART_DIA_ICIP_MMHG     
 
Invasive diastolic blood pressure
ART_MEAN_ICIP_MMHG 
    
Invasive mean blood pressure m
PABP_SYS_ICIP_MMHG     
 
Systolic pulmonary artery blood 
PABP_DIA_ICIP_MMHG     
 
Diastolic pulmonary artery blood
PABP_MEAN_ICIP_MMHG 
    
Mean pulmonary artery blood pr
CVP_MEAN_ICIP_MMHG 
    
Invasive central venous pressure
CARDIAC_OUTPUT_ICIP_L_MIN   
Cardiac Output 
CARDIAC INDEX ICIP L MIN M2
Cardiac Index

 
 
 
P_LOW_CMH2O 
 
    
Pressure Low 
P_HIGH_CMH2O 
 
    
Pressure High 
P_MEAN_CMH2O 
 
    
Pressure mean 
P_MIN_CMH2O 
    
 
Pressure minimum 
P_PEAK_CMH2O 
 
   
Pressure peak inspiratory 
P_PLATEAU_CMH2O 
 
    
Pressure plateau 
MIN_VOL_EXP_L_MIN     
 
Expired minute volume 
MIN_VOL_EXP_SPON_L_MIN 
    
Expired minute volume - spontan
MIN_VOL_EXP_MAND_L_MIN     
Expired minute volume - mandat
MIN_VOL_LEAK_L_MIN     
 
Minute volume leak  
TIDAL_VOLUME_MLS 
    
 
Tidal Volume 
TIDAL_VOLUME_INSP_MLS 
    
Tidal Volume Inspired 
TIDAL_VOLUME_EXP_MLS 
    
Tidal Volume Expired 
TIDAL_VOLUME_SPON_MLS 
    
Tidal Volume Spontaneous 
TIDAL_VOLUME_INSP_SPON_MLS  
Tidal Volume Inspired Spontaneo
TIDAL_VOLUME_EXP_SPON_MLS   
Tidal Volume Expired Spontaneo
TIDAL_VOLUME_MAND_MLS 
    
Tidal Volume Mandatory 
TIDAL_VOLUME_INSP_MAND_MLS  
Tidal Volume Inspired Mandator
TIDAL_VOLUME_EXP_MAND_MLS   
Tidal Volume Expired Mandatory
RESISTANCE CMH2O L S
Resistance

 
 
 
E_IE 
  
    
    
 
Expiratory component IE ratio 
I_IE_SPON 
 
    
 
Inspiratory component IE ratio - 
E_IE_SPON 
 
    
 
Expiratory component IE ratio – 
ELASTANCE_CMH2O_L     
 
Elastance 
RSB_S_L 
 
 
 
Rapid shallow breathing index 
COMP_DYN_MLS_CMH2O 
   
Dynamic compliance 
COMP_20_MLS_CMH2O 
    
Compliance 20 
TIME_CONSTANT_S 
             
Time constant 
TIME_CONSTANT_EXPIRED_S 
    
Time constant expired 
P01_CMH2O 
 
    
 
Airway occlusion pressure 
NIF_CMH2O 
 
    
 
Negative Inspiratory Force 
EIP_CMH2O 
 
    
 
End inspiratory Pressure 
FIO2_PC 
 
    
    
Fractional Inspired Oxygen 
VTCO2_MLS 
 
    
 
Tidal CO2 production 
V_CO2_MLS_MIN 
             
CO2 Production 
VOLUME_DS_MLS 
 
    
Deadspace Volume 
ET_CO2_KPA 
 
    
 
End tidal CO2 kPa 
ET_CO2_VOLPC  
    
 
End tidal CO2 vol% 
CO2_SLOPE_KPA_L 
 
    
CO2 Slope 
VENTILATOR MODE
Ventilator Mode

 
 
 
T_LOW_SETTING_S 
             
Time low - Ventilator Setting 
T_INSP_MAX_SETTING_S 
    
Maximum Inspiratory Time - Ven
T_LOW_MAX_SETTING_S 
    
Maximum time low - Ventilator s
SLOPE_SETTING_S 
 
    
Slope setting - Ventilator Setting
FLOW_SETTING_L_MIN     
 
Flow setting - Ventilator Setting 
CONST_FLOW_L_MIN     
 
Constant flow - Ventilator setting
RESPIRATORY_RATE_SETTING_MIN 
Respiratory rate - Ventilator Sett
P_SUPPORT_SETTING_CMH2O   
Pressure support - Ventilator Set
TIDAL_VOLUME_SETTING_MLS  
Tidal volume setting - Ventilator 
FLOW_TRIGGER_SETTING_L_MIN 
Flow trigger - Ventilator Setting 
ATC_COMPENSATION_SETTING_PC 
ATC compensation - Ventilator S
FIO2_SETTING_PC 
    
    
Fractional Inspired Oxygen - Ven
INSP_TERM_SETTING_PIF_PC 
 
Inspiratory term % Peak Inspirato
EXP_TERM_SETTING_PIF_PC 
 
Expiratory term % Peak Expirator
 
11.1.4 Blood Gas Analysis 
STUDY_ID 
 
 
 
Unique study data set identifier  
DATETIME 
 
   
  
ISO 8601 Date & Time 
BLOOD_GAS_TYPE 
 
    
Blood gas type 
PCO2 KPA
Partial Pressure CO2

 
 
 
NA_MMOL_L 
    
    
 
Sodium concentration 
CL_MMOL_L 
    
    
 
Chloride concentration 
HAEMOGLOBIN_G_L 
    
    
Haemoglobin concentration 
OXY_HAEMOGLOBIN_PC 
 
Oxyhaemoglobin 
CARBOXY_HAEMOGLOBIN_PC    
Carboxyhaemoglobin 
H_HAEMOGLOBIN_PC     
 
Deoxyhaemoglobin 
METHE_HAEMOGLOBIN_PC 
    
Methehaemoglobin 
GLUCOSE_MMOL_L 
             
Glucose concentration 
LACTATE_MMOL_L 
    
    
Lactate concentration 
TEMP_BLOOD_GAS_C            
 
Temperature blood gas 
FIO2_BLOOD_GAS_F 
    
 
FiO2 blood gas 
R_BLOOD_GAS     
    
 
Respiratory quotient 
P50_BLOOD_GAS_KPA           
 
P50 
BASE_EXCESS_MMOL_L    
              Base Excess 
BASE_EXCESS_ACT_MMOL_L 
    
Actual Base Excess 
HCO3_MMOL_L    
      
              Bicarbonate concentration 
HCO3_STAND_MMOL_L 
    
Standardised bicarbonate concen
 
11.1.1 ECMO 
STUDY ID
Unique study data set identifier

 
 
 
SWEEP_FO2_ECMO_ICIP_F 
 
Sweep Gas Fractional O2 concen
SWEEP_FLOW_ECMO_ICIP_L_MIN 
Sweep gas flow  
P_ACCESS_ECMO_CHELP_MMHG 
Access pressure - Cardiohelp dat
P_PREOXY_ECMO_CHELP_MMHG 
Pre membrane pressure - Cardio
P_POSTOXY_ECMO_CHELP_MMHG 
Post membrane pressure - Cardi
P_TMP_ECMO_CHELP_MMHG  
Transmembrane pressure - Card
Q_BLOOD_ECMO_CHELP_L_MIN 
ECMO Blood Flow - Cardiohelp d
PUMP_ECMO_CHELP_RPM 
 
ECMO Pump RPM - Cardiohelp d
SVO2_ECMO_CHELP_PC 
 
Pre Membrane Haemoglobin sat
 
11.2 Format and Scale of Data 
The requirements and nature of this project mean that all study data will
Data will be collated to comma separated value (CSV) format. CSV is
accessible by all major data processing software platforms e.g. spreads
software. The vast majority of modern programming languages possess t
format files. 
The near universal interoperability of the CSV format ensures ease of dat
validity of collated data sets. 
It is anticipated that this project will require several iterations of 
development, with each iteration examining a different treatment interv
will inform the exact nature of data collection in the subsequent iteration
limited to the scope defined in the preceding section, 11.1 

 
 
 
Collated data will then be subject to the defined anonymisation proce
anonymisation process, data will be made available to academic rese
repository.  
11.4 Data Quality and Standards 
Data retrieved from the EHR has been validated by a qualified clinician at t
be considered representative of patient condition and treatment. 
Data retrieved from medical equipment, e.g. ventilators, is not validated
data domains exhibit different qualities in this regard. Lab value
measurements are made on equipment that is calibrated on a basis appro
high quality and safe care. Data from sources such as ventilators o
standardised in that the equipment models are standardised across GSTF
11.5 Data handling and record keeping 
The collaborative nature of this study requires that data be managed a
environments: 
• 
GSTFT 
• 
Data Repository 
• 
Academic partners 
 
Please note the data pathway defined in section 7.2 of this document. 
Each environment differs in available data management facilities a
practices and compliance that must be observed. Each environment w
separately. 
11.5.1 GSTFT 
Data available at GSTFT can be of a personal and confidential nature, an
project where the highest risk of breaching patient confidentiality exists. 

 
 
 
• 
Confidentiality & Data Protection Policy (Version 2) 
• 
Information Security Policy (Version 4) 
• 
Information security (Principle f) 
 
11.5.2 Data Repository 
This project will set up a central data repository for data storage and dist
Clinical investigators at GSTFT will upload data sets to the data repository. 
for ensuring the local and data repository indexes are maintained and up
GSTFT operates a SharePoint site, to which read only access can be given
GSTFT. 
The duplication between data sets stored within IT systems at GSTFT and
as an extra layer of redundant storage and mutual back up. 
Data storage, management and security practices for the data repository
Operating Procedures (SOP): 
• PSTOTICU_SOP_003 - Standard Operating Procedure 
Administrator 
 
11.5.3 Academic Partners 
The University of Warwick (UWAR) and the University of Nottingham (UON
of this study and are the end recipients of compiled data sets. 
Data storage and management practices for study participants at UWAR
Standard Operating Procedure (SOP): 
 PSTOTICU_SOP_002 - Data Storage and Management by Ac
 
11 6 Metadata standards and data documentation

 
 
 
A log of study subjects linked to unique study data set identifiers 
investigators at GSTFT. This record will be securely stored within the site fi
form within GSTFT. This log will not be shared to the central repository o
blank log is provided by study document: 
• 
PSTOTICU_SD_004 – Subject Log 
 
A full index of the collated subject data set files will be maintained by si
local GSTFT copy will be kept, and duplicated within the central data repo
index is included in Error! Reference source not found., and a blan
document: 
• 
PSTOTICU_SD_005 – Data Set Index 
 
11.6.2 Metadata Descriptive Files 
To provide context for the CSV files of a data set, text files will be g
describing the intervention captured. These descriptions will be entirely li
intervention and provide no data which might provide an increased r
subjects. File format will be of a txt format, and thus universally readab
textual descriptions will be apparent on examination of the quantitativ
provide a more accessible context for the data. 
Example descriptions: 
• 
‘PEEP increased from 5cmH2O to 10cmH2O’ 
• 
‘250ml Crystalloid Bolus given at 02:00’ * 
• 
‘Prone positioning started 02:00. Prone positioning disco
 
* - Dates and times are subject to anonymisation as per PSTOTICU_SO
Procedure (SOP) Data De-Identification 
11.7 Data preservation strategy and standards 

 
 
 
11.7.2 Data Repository 
The data repository will be maintained for a five year period after da
completed.  After this period, access will be revoked for those users with 
will be retained as part of the study archival process at GSTFT and duplica
no longer required. 
11.7.3 Academic Collaborators 
Upon completion of data collection activities, it is anticipated that academ
extended period. Data will be available to project collaborators for five
activities are completed. 
11.8 Data Security and Confidentiality of Potentially
Information 
The level of data security is dependent upon the nature of the data – per
higher levels of security. 
Data collected for this study will be subjected to an anonymisation proces
a negligible risk of subject re-identification. 
Despite the low risk of subject identification, health related data is s
appropriate to ensure study data is handled in a way that ensures se
against unlawful or unauthorised processing, access, loss, destruction or 
11.8.1 Data Anonymisation 
The aim of anonymisation, is to ensure that the risk of potential re-ident
minimised to negligible levels.  
Clinical data will be collected by investigators at GSTFT and subje
anonymisation process, before being made available to academic partn
anonymisation process employed is defined in the SOP: 
• 
PSTOTICU_SOP_004 – Standard Operating Procedure (S
and De Identification

 
 
 
11.8.2.1 GSTFT 
Data will be stored on GSTFT network drives within a proje
S:\\IntensiveCare\Research folder. This ensures that access to data is 
Intensive Care clinical research team at GSTFT. Access is authoris
authorisation system administered by GSTFT IT services and therefore to t
the security of clinical services and data. This method of project specif
across the research portfolio of this research team. 
Usage of GSTFT network drives also ensures the durability and availabi
same standard as that provided for clinical services and data.  
GSTFT data management practice and information governance policy will 
is resident within the GSTFT environment. 
11.8.2.2 Data Repository 
Access to the data repository requires a username and password, and ex
an additional one time pass code is required. One time pass codes are
authentication app set up at the time of account registration. 
Authorisation and permissions are described in the Standard Operating P
• 
PSTOTICU_SOP_003 - Standard Operating Procedure
Administrator 
 
The clinical investigator team at GSTFT will have permissions to upload and
collaborators will be granted read only permissions to the repository, al
download data sets and indexes only.  
Auditing of all file operations and specifically downloads will be implem
trail of repository activity is available. This will be collated, reviewed and
monthly basis.  
Data sets stored in the repository will be duplicates of data stored within

 
 
 
Data will only be processed on specific computing devices maintained with
Storage on project computers will be encrypted with BitLocker software a
over an encrypted transport layer. Access to project computers will be lim
Potential loss of data from the computing environments of study collab
duplication of data within the repository and GSTFT environments. 
11.9 Data Collection and Processing Responsibilities 
The chief investigator for this project has ultimate responsibility for ens
administered in accordance with the processes outlined by this SOP. 
The chief investigator may delegate responsibility for management of th
clinical investigators. A record of delegated responsibilities will be mainta
per the study document: 
 PSTOTICU_SD_008 – Staff Delegation Log 
 
 
 

 
 
 
12 Statistical considerations 
12.1  Sample size calculation  
The subjects will comprise a convenience sample only. This study is not s
size or incidence, and so a formal sample size calculation to ensure
inappropriate. Power calculation is not meaningful in this context, sin
prospective matching technology rather than statistical comparison. 
As described in section 8, the admission of one patient may contribute
catalogue of data sets within the repository. 
The iterative nature of model development was described previously in se
that there will a large but variable number of episodes, fitting the require
iteration, available from the electronic health record.  
Although the precise number of iterations or available episodes cannot 
anticipate 20 development iterations for which a maximum of 250 appro
We thus put an upper bound of collected episodes at 5,000 episodes.  
12.2  Statistical analysis 
The core models have been designed to represent a dynamic in vivo card
The pulmonary model is comprised of conducting airways and a resp
alveolar compartments, with each compartment having a corresponding s
for stiffness, threshold opening pressures and extrinsic pressures as wel
vascular resistances. This allows for a wide spectrum of ventilation 
replicated. The model includes inherent physiological reflex mechanism
vasoconstriction.  
The cardiovascular model consists of 19 vascular compartments, each
momentary pressure and volume and its non-linear compliance. Ventricu
using a time-dependent elastance, which implements pulsatile blood flo
is variably transmitted to all intrathoracic compartments, including the 

 
 
 
13 Ethical considerations 
HRA approval will be sought for this study. Local R&D approval will also b
14 Financing and Insurance 
Finance for this project is provided through an EPSRC grant, reference nu
Insurance is provided by the sponsor, Guys & St Thomas’ NHS Foundation
15 Reporting and dissemination 
Progress of the study will be disseminated and discussed at collaborator 
The findings will be presented at national and international conferences a
journals. 
 
 
 
 
 

 
 
 
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