Artificial Intelligence Based Timing, Infarct Size and Outcomes in Acute Coronary Occlusion Myocardial Infarction Without ST Elevation
Conditions: Coronary Arterial Disease (CAD), Acute Coronary Syndrome (ACS) Undergoing Percutaneous Coronary Intervention (PCI)
Sponsor: Azienda Ospedaliera di Bolzano

ANOMI-Study Intent Letter
2024
AI-BASED ECG OMI DETECTION IMPROVEMENT AND
ITS IMPLICATIONS
ON REPERFUSION TIMING AND INFARCT SIZE
Oﬃcial Study Title
NCT Number
NCT06910436
Document Date
Bolzano, March 2024
Document Type
Study Intent Letter / Protocol Synopsis
Principal Investigator
Assoc. Prof. Dr. Dr. Matthias Unterhuber
Department of Cardiology
Regional Hospital of Bolzano
Bolzano, Italy
This document is provided for submission to ClinicalTrials.gov (NCT06910436).
The original scientiﬁc content (pages 2–7) remains unchanged.
Artiﬁcial Intelligence Based Timing, Infarct Size and Outcomes in Acute Coronary Occlusion 
Myocardial Infarction
Artiﬁcial Intelligence Based Timing, Infarct Size and Outcomes in Acute 
Coronary Occlusion Myocardial Infarction

ANOMI-Study Intent Letter
Artiﬁcial Intelligence Based Timing, Infarct Size aN d Outcomes in Acute
Coronary Occlusion M yocardial I nfarction
2024
AI-based ECG OMI detection improvement and its
implications on reperfusion timing and infarct size
Principal investigator
Assoc. Prof. Dr. Dr. Matthias Unterhuber

INDICE
ANOMI Study Intent Letter
Indice
1
Introduction
2
2
The ANOMI study
2
3
Primary outcomes
3
4
Secondary outcomes
3
5
Sample size and statistical analysis
4
6
Study collaborators
4
1

Introduction
ANOMI Study Intent Letter
1
Introduction
The 12-lead electrocardiogram (ECG) is the most widely used initial diagno-
stic tool to guide the management of patients with suspected acute coronary
syndrome (ACS). At present, ACS is divided into ST-segment elevation myo-
cardial infarction (STEMI) and non-ST-segment elevation acute coronary
syndrome (NSTE-ACS), with diﬀerent treatment protocols. However, some
patients with NSTE-ACS have an acute coronary occlusion (OMI) and may
beneﬁt from immediate reperfusion by percutaneous coronary intervention
(PCI), but are often treated late. ECG signs suggestive of OMI have been
described, but their visual interpretation by experts is variable and subop-
timal. Recent studies have shown that artiﬁcial intelligence (AI) models for
ECG analysis can outperform clinicians in the detection of OMI, suggesting
the potential use of AI to improve triage and timely access to PCI.
2
The ANOMI study
This prospective observational study aims to assess the ability of an AI mo-
del to identify OMI in patients with suspected ACS without ST-segment
elevation and to analyze the impact of the time elapsed between patient pre-
sentation and percutaneous revascularization on infarct size, as measured by
peak troponin levels, echocardiography, and / or cardiac magnetic resonance
imaging.
The study will be conducted at the Department of Cardiology of the Re-
gional Hospital of Bolzano and will enroll adult patients (>18 years) with an
initial diagnosis of NSTE-ACS. Patients with STEMI, ventricular arrhyth-
mias, poor-quality ECGs, or inadequate digitization will be excluded. The
ECGs of included patients will be analyzed (without the addition of sensi-
tive data, using only the electrical waveforms) and classiﬁed by AI as OMI
or non-OMI. This result will be compared with the coronary angiography
2

Primary outcomes
ANOMI Study Intent Letter
ﬁndings. Clinical practice will not be altered; only the potential diﬀerence
between the two classiﬁcations will be observed. The collected data will allow
evaluation of:
• The feasibility of AI-ECG interpretation in patients with suspected
ACS without ST-segment elevation
• The accuracy of AI in the diagnosis of OMI compared with conventional
criteria (ROC-AUC)
• The correlation between infarct size and the time elapsed between pre-
sentation and percutaneous revascularization, in order to assess the
potential beneﬁt of a change in clinical practice for patients with OMI
identiﬁed by the algorithm.
3
Primary outcomes
Cardiovascular Mortality at 12 months
Determination of whether there is a diﬀerence in myocardial infarct size
(ventricular function on follow-up echocardiography and/or cardiac magnetic
resonance imaging) according to AI categorization (OMI or non-OMI)
4
Secondary outcomes
Sensitivity and speciﬁcity of the PMCardio AI-ECG app for the diagnosis of
OMI compared with expert clinical decision-making.
• Pre-intervention peak troponin values in patients with OMI undergoing
PCI according to OMI / non-OMI categorization
• Left ventricular ejection fraction (LV-EF) according to categorization
• Major adverse cardiovascular events (MACE)
3

Sample size and statistical analysis
ANOMI Study Intent Letter
5
Sample size and statistical analysis
For the infarct size analysis, peak troponin levels will be considered as a proxy
for myocardial damage as a function of time to percutaneous revasculariza-
tion, with a minimum of 176 patients required to ensure adequate statistical
power (80% with α = 0.05) to detect a statistically signiﬁcant diﬀerence in
peak troponin of 500 ± 200 ng/ml. A statistically signiﬁcant diﬀerence in
infarct size will also be assessed in terms of left ventricular ejection fraction
on echocardiography or late gadolinium enhancement on cardiac magnetic
resonance imaging. A diﬀerence of 10% (eﬀect size) with a standard devia-
tion of 10% can be detected with 80% statistical power and an alpha of 0.05
if the study sample includes n=15 individuals per arm; therefore, 30 patients
will be suﬃcient to detect statistically signiﬁcant diﬀerences in ventricular
infarct size. Thus, once the above-mentioned 176 patients have been reached,
a statistically signiﬁcant diﬀerence with adequate power in terms of infarct
size will also be ensured.
6
Study collaborators
• Unterhuber Matthias
• Donazzan Luca
• Rainer Oberhollenzer
The ANOMI study will provide crucial data to better understand the
role of AI in the early diagnosis of OMI and its potential impact on the
clinical management of patients with ACS. Furthermore, it is certiﬁed that
no sensitive data other than the ECG tracing alone will be analyzed by the
artiﬁcial intelligence system; therefore, no exchange of sensitive data with
third parties will take place.
4

Study collaborators
ANOMI Study Intent Letter
The duration of the main study ends once the above-mentioned sample
size has been reached and after at least one year of follow-up.
5

Study collaborators
ANOMI Study Intent Letter
Thanking you for your kind attention,
best regards
Bolzano, March 2024
Assoc. Prof. Dr. Dr. Matthias Unterhuber
6