Trial Review

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Trial registered on ANZCTR


Registration number
ACTRN12616001441404
Ethics application status
Not required
Date submitted
20/09/2016
Date registered
14/10/2016
Date last updated
3/05/2017
Type of registration
Retrospectively registered

Titles & IDs
Public title
Development and validation of a tool to rule out heart attacks
Scientific title
Development and validation of a multivariable prediction model to risk stratify patients being investigated for possible Acute Myocardial Infarction in the Emergency Department.
Secondary ID [1] 290111 0
Nil known
Universal Trial Number (UTN)
Trial acronym
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Acute Myocardial Infarction 300210 0
Acute Coronary Syndrome 300211 0
Condition category
Condition code
Cardiovascular 300090 300090 0 0
Coronary heart disease
Cardiovascular 300091 300091 0 0
Diseases of the vasculature and circulation including the lymphatic system

Intervention/exposure
Study type
Observational
Patient registry
False
Target follow-up duration
Target follow-up type
Description of intervention(s) / exposure
Observational study: Patients presenting to Emergency Departments with symptoms suggestive of Acute Coronary Syndrome are assessed for possible Type I Acute Myocardial Infarction
A retrospective analysis of previously collected data. Patients were participating in studies designed to assess the effectiveness of methodologies and biochemistry to rule-out and rule-in acute myocardial infarction.
The MI3 tool is a an algorithm developed from Age, Sex, serial troponin measurements, and the time between the troponin measurements. It is designed to provide clinicians with an indication of the likelihood of an acute myocardial infarction. It was applied only retrospectively on the validation cohort.

Derivation Cohorts
BACC (Hamburg): July 2013 to December 2014
HighSTEACS (Edinburgh): NCT01852123 June 2013 to January 2014
Machine learning was used to derive the MI3 tool to predict events as recorded in the medical records (and previous studies).

Validation cohorts
UTROPIA (Minneapolis)
APACE (Basal): April 2006 to June 2013
ADAPT-Brisbane (Brisbane): ACTRN12611001069943 November 2007 to February 2011
IMPACT (Brisbane): February 2011 to March 2014
ADAPT-Christchurch (Christchurch): ACTRN12611001069943 November 2007 to February 2011
TIMIRCT (Christchurch): ACTRN12610000766011 October 2010 to July 2012
EDACSRCT (Christchurch): ACTRN12613000745741 June 2013 to July 2014

The MI3 tool was applied in the validation cohort to assess its ability to predict outcomes recorded in medical records/previous studies.
Intervention code [1] 295864 0
Early Detection / Screening
Comparator / control treatment
No control group
Control group
Uncontrolled

Outcomes
Primary outcome [1] 299567 0
Probability of Type I AMI as assessed by the MI3 tool
Timepoint [1] 299567 0
index presentation
Secondary outcome [1] 327540 0
Probability of Type I AMI as assessed by the MI3 tool
Timepoint [1] 327540 0
within 30 days of presentation to the ED

Eligibility
Key inclusion criteria
Patients investigated for possible ACS in the cohorts described ( BACC (Hamburg, HighSTEACS (Edinburgh), UTROPIA (Minneapolis), APACE (Basal), ADAPT-Brisbane, IMPACT (Brisbane, ADAPT-Christchurch, TIMIRCT, EDACSRCT (Christchurch))

The MI3 tool was applied in the validation cohort to assess its ability to predict outcomes recorded in medical records/previous studies.
Serial (two) hs-cTnI troponin measurements. The first made on arrival in the Emergency Department.
Minimum age
18 Years
Maximum age
No limit
Sex
Both males and females
Can healthy volunteers participate?
No
Key exclusion criteria
STEMI diagnosed within the ED
Missing data on sex, age, timing of two consecutive blood samples, high sensitivity cardiac troponin I.
The universal definition not used to adjudicate for AMI
Cohorts without adjudicated outcomes

Study design
Purpose
Screening
Duration
Cross-sectional
Selection
Defined population
Timing
Retrospective
Statistical methods / analysis
A multi-centre international diagnostic biomarker development and validation study using a secondary analysis of data prospectively collected from multiple cohorts.

The overall project objective:
To develop and validate an assessment algorithm that allows:
1. The early rule in of AMI for as many patients was possible with a low false positive rate.
2. The early rule out of acute myocardial infarction (AMI) for a large proportion of patients with a very low false negative rate

The overall aims are:
1. To derive and validate the Myocardial Ischaemic Injury Index (MI3) for identification of patients with acute myocardial infarction amongst patients being investigated for possible acute coronary syndrome.

2. To derive and validate Index Value thresholds for optimal (i) rule-out and (ii) rule-in of acute myocardial infarction with the MI3 algorithm for rapid assessment of patients being investigated for possible acute coronary syndrome.

DERIVATION
The Boosted Decision Trees (also called Additive Logistic Model) is to be applied to a derivation cohort using initial hsTnI value, hsTnI change rate, gender, age, subjects’ MI status.
Thresholds with 95% confidence intervals of the resulting index value (IV) will be derived using bootstrapping methods for (i) sensitivity >=99.0%, (ii) NPV>=99.5%, (iii) Specificity >=90.0%, (iv) PPV>=75%.

VALIDATION
In a separate cohort the sensitivity, NPV, specificity, PPV will be calculated at the IV thresholds identified in the development cohort.

No formal power calculation for sample size was done.

Recruitment
Recruitment status
Completed
Date of first participant enrolment
Anticipated
Actual
1/04/2006
Date of last participant enrolment
Anticipated
Actual
31/12/2014
Date of last data collection
Anticipated
Actual
30/01/2015
Sample size
Target
5000
Accrual to date
Final
101540
Recruitment in Australia
Recruitment state(s)
QLD
Recruitment hospital [1] 6631 0
Royal Brisbane & Womens Hospital - Herston
Recruitment postcode(s) [1] 14252 0
4029 - Herston
Recruitment outside Australia
Country [1] 8209 0
New Zealand
State/province [1] 8209 0
Canterbury
Country [2] 8210 0
United Kingdom
State/province [2] 8210 0
Edinburgh
Country [3] 8211 0
Germany
State/province [3] 8211 0
Hamburg
Country [4] 8212 0
United States of America
State/province [4] 8212 0
Minnesota
Country [5] 8303 0
Switzerland
State/province [5] 8303 0
Basal

Funding & Sponsors
Funding source category [1] 294510 0
Charities/Societies/Foundations
Name [1] 294510 0
Emergency Care Foundation
Country [1] 294510 0
New Zealand
Funding source category [2] 294543 0
Commercial sector/Industry
Name [2] 294543 0
Abbott Diagnostics
Country [2] 294543 0
United States of America
Primary sponsor type
Individual
Name
Dr Martin Than
Address
Emergency Department
Christchurch Hospital
2a Riccarton Ave
Private Bag 4710
Christchurch 8140
Country
New Zealand
Secondary sponsor category [1] 293414 0
None
Name [1] 293414 0
none
Address [1] 293414 0
na
Country [1] 293414 0

Ethics approval
Ethics application status
Not required

Summary
Brief summary
Trial website
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 68898 0
Dr Martin Than
Address 68898 0
Emergency Department
Christchurch Hospital
2a Riccarton Ave
Private Bag 4170
Christchurch 8140
Country 68898 0
New Zealand
Phone 68898 0
+64 3 364 0640
Fax 68898 0
Email 68898 0
[email protected]
Contact person for public queries
Name 68899 0
Martin Than
Address 68899 0
Emergency Department
Christchurch Hospital
2a Riccarton Ave
Private Bag 4170
Christchurch 8140
Country 68899 0
New Zealand
Phone 68899 0
+64 3 364 0640
Fax 68899 0
Email 68899 0
[email protected]
Contact person for scientific queries
Name 68900 0
Martin Than
Address 68900 0
Emergency Department
Christchurch Hospital
2a Riccarton Ave
Private Bag 4170
Christchurch 8140
Country 68900 0
New Zealand
Phone 68900 0
+64 3 364 0640
Fax 68900 0
Email 68900 0
[email protected]

No information has been provided regarding IPD availability


What supporting documents are/will be available?

No Supporting Document Provided



Results publications and other study-related documents

Documents added manually
No documents have been uploaded by study researchers.

Documents added automatically
SourceTitleYear of PublicationDOI
Dimensions AIMachine Learning to Predict the Likelihood of Acute Myocardial Infarction2019https://doi.org/10.1161/circulationaha.119.041980
N.B. These documents automatically identified may not have been verified by the study sponsor.