Prediction of Heart-failure and Mortality by Echocardiographic Parameters and Machine Learning in Individuals With Left Bundle Branch Block
Conditions: Left Bundle-Branch Block
Sponsor: University Hospital of North Norway

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Research protocol; Research project in cardiovascular medicine  
Project leader: Assami Rösner, MD, PhD Cardiological department, University Hospital of North 
Norway and UiT The Arctic University. 
Prediction of heart failure and mortality by echocardiographic parameters 
and artificial intelligence in individuals with left bundle branch block -
“Echo LBBB” 
 
Assami Rösner1,2, Henrik Schirmer2,3,4, Harald Kjekshus3,4, Espen Holte5,6, Helge Skulstad7, Mai-Tone 
Lønnebakken8, Lasse Løvstakken6, Knut Tore Lappegård2,9, Tatiana Kouznetsova10, Siri Malm11, Tor 
Bosch12and Tove Aminda Hanssen13 
1Department of Cardiology, Division of Cardiothoracic and Respiratory Medicine, University Hospital of North Norway 
2Department of Clinical medicine, University in Tromsø, the Arctic University, Tromsø 
3Department of Clinical medicine, University in Oslo 
4Department of Cardiology, Division of Medicine, Akershus University Hospital, Lørenskog, Norway 
5Department of Cardiology, St. Olav’s University Hospital, Trondheim, Norway 
6Department of Circulation and Medical Imaging, Norwegian University of Science and 
Technology, Trondheim, Norway 
7Department of Cardiology, Oslo University Hospital, Rikshospitalet 
8Department of Cardiology, Haukeland University Hospital, Bergen, University of Bergen 
9Department of medicine, Nordlands Hospital, Bodø 
10Hypertension and Cardiovascular Epidemiology, Kaholieke Universiteit (KU) Leuven, Belgium 
11Department of medicine, University Hospital of North Norway, Harstad 
12LHL Landsforening for hjerte lunge syke 
13Institute for Health and Caring Sciences, University in Tromsø, the Arctic University, Tromsø 
 
 

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Contents 
 .................................................................................................................................................... 1 
1 
Background ......................................................................................................................... 3 
1.1 
Epidemiology and etiology .......................................................................................... 5 
1.2 
Prognosis ..................................................................................................................... 5 
1.3 
Pathophysiology .......................................................................................................... 6 
1.4 
Specific therapy ........................................................................................................... 6 
1.5 
“Novel echocardiographic indices” ............................................................................. 7 
1.6 
Integration of multifaceted data by artificial intelligence ........................................... 8 
1.7 
Clinical significance: ................................................................................................... 9 
2 
Hypotheses, aims and objectives: ..................................................................................... 10 
2.1 
Primary hypotheses .................................................................................................... 10 
2.2 
Secondary hypotheses ................................................................................................ 10 
2.3 
Primary objectives: .................................................................................................... 11 
2.4 
Secondary objectives: ................................................................................................ 11 
3 
Project arrangements, method selection and analyses ...................................................... 11 
3.1 
Patients ....................................................................................................................... 11 
3.2 
Population-based studies: .......................................................................................... 12 
3.3 
Planned inclusion of participants of Tromsø 7 .......................................................... 12 
3.4 
Primary outcome ........................................................................................................ 13 
3.5 
Secondary outcomes .................................................................................................. 13 
3.6 
Inclusion criteria ........................................................................................................ 13 
3.7 
Exclusion criteria ....................................................................................................... 13 
3.8 
Echocardiography ...................................................................................................... 14 
3.9 
Artificial intelligence ................................................................................................. 14 
3.10 
Statistical power ..................................................................................................... 14 
3.11 
Statistics ................................................................................................................. 15 
3.12 
Data management ................................................................................................... 15 
3.13 
Participants, organization and collaborations ........................................................ 16 
4 
Plan for activities, visibility and dissemination ................................................................ 16 
4.1 
Communication/planned publications ....................................................................... 16 
4.2 
Plan for implementation ............................................................................................ 17 
4.3 
User involvement ....................................................................................................... 17 
5 
Ethical considerations ....................................................................................................... 17 
5.1 
Intellectual property rights: ....................................................................................... 18 
6 
References ......................................................................................................................... 18 
 

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1 Background 
This study is a cardiovascular study, especially relevant for the elderly population, aiming to identify 
individuals with left bundle branch block (LBBB) who are at high risk for developing heart failure and 
increased risk for cardiovascular morbidity and mortality. In patients with LBBB, cardiac 
resynchronization therapy (CRT) has the ability to reverse heart failure and reduce morbidity and 
mortality with high effectivity (1,2). However, CRT is an invasive procedure with inherent short- and 
long-term complications and a substantial rate of non-responders. Improved patient selection will be 
needed for better outcome of an already well-established therapy. Low-risk patients with stable 
conditions need to be identified to be assigned to an individually adjusted follow-up. Thus, careful 
selection of potential CRT responders at the appropriate time-point is crucial for optimal patient 
treatment. For the time being, the identification of high and low-risk patients is the most important step 
before new randomized controlled trials for CRT treatment should be conducted. 
LBBB is relatively common in the cardiovascular patient population and is known to be associated with 
increased risk for the development of congestive heart failure and risk for mortality (3). However, 
asymptomatic individuals with structurally normal hearts can also have LBBB.  Prognosis of LBBB in 
population-based studies is highly dependent on age and comorbidity (4-8). Both, population based, and 
clinical studies demonstrate that electrocardiogram (ECG) criteria and clinical findings alone are not 
specific enough to develop risk assessment algorithms for cardiovascular morbidity and mortality in the 
LBBB population, thus the number of publications of the natural history of LBBB has rapidly decreased 
since 2005. However, recent experimental and clinical studies using modern echocardiographic tools as 
well as computer models for regional deformation imaging of the heart have given new insights into the 
pathophysiology of LBBB and the interplay between delayed electrical activation of parts of the left 
List of abbreviations: 
 
 
 
LBBB = left bundle branch block  
CAD = coronary artery disease 
ECG = electrocardiogram 
 
LV = left ventricle/left ventricular 
PVP = predominant ventricular pacing CRT = cardiac resynchronization therapy 
PM = pacemaker 
 
 
BP = blood pressure 
AI = artificial intelligence 
 
HBP = his bundle pacing 
NPR = Norwegian Patient Registry 
EF = ejection fraction 
RBBB = right bundle branch block 
MACCE = major adverse cardiovascular and cerebral 
t

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ventricle (LV), and the presence of myocardial scars. Only recently the equal effect of loading and QRS 
on dyssynchrony has been better understood. Artificial intelligence (AI) can use information from strain-
curve analysis together with other echocardiographic and clinical information to identify patients at risk 
and treatment responders (9). Based on these new insights, this study aims to investigate the potential 
of new echocardiographic and AI methods, in combination with clinical information and afterload 
assessment (ventricular dimensions and geometry combined with blood pressure (BP)). These easily 
obtainable parameters might hopefully become a new cornerstone for development of risk-assessment 
algorithms as a basis for individual treatment recommendation.   
The recent development of ultrasound-based quantification of dyssynchrony seems to be a promising 
tool for individual risk-stratification(10-12). Patient at risk might undergo timely CRT treatment before 
heart failure is fully established or the mortality risk increases. To the best of our knowledge this will 
be the first longitudinal study on echocardiographic functional markers for dyssynchrony in the general 
LBBB population. Additionally, AI algorithms will hopefully improve identification of patients at risk 
for heart failure development. The aim is to identify the time-point when a stable state with LBBB turns 
into deteriorating heart failure, which is probably the best time-point when specific treatment needs to 
be applied and heart failure can be reversed. 
This study will include prospectively clinical patients from the cardiological outpatients’ clinics, and 
the cardiology wards in University Hospital of North Norway Tromsø and Harstad, Akershus University 
Hospital, Lørenskog, Oslo University Hospital, Rikshospitalet, Haukeland University Hospital, Bergen 
and Nordlands Hospital, Bodø. Furthermore, we intend to use retrospectively collected data of 
population-based study from the Katholieke Universiteit (KU) Leuven, Belgium and invite participants 
with LBBB or predominant ventricular pacing (PVP)  from the Tromsø 7 study for the prospective study. 
The study is furthermore open to include retrospectively datasets where subjects with LBBB or PVP 
can be identified and echocardiographies are taken (e.g. St. Olav Hospital, Trondheim). Prospectively 
included patients will be followed by a repeated echocardiography study after one year and access to 
patient registries through 5 years (eventually up to 15 years). All studies abroad will follow their own 
study protocol and follow-up.  

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1.1 Epidemiology and etiology 
LBBB occurs frequently in various cardiac diseases of differing etiology. The presence of LBBB has 
been reported to adversely affect prognosis even in individuals who have no symptoms or known 
cardiovascular disorders and LBBB has been an incidental finding on ECG. Thus, individuals with 
LBBB are reported to have increased risk of death or heart failure (13,14).  
LBBB almost never occurs before 35 years of age, suggesting it could be an acquired condition (15). In 
population-based studies the prevalence of LBBB ranges between 0.1 and 0.8% (3,7,16). The prevalence 
of LBBB strongly correlates with age with an average age at LBBB diagnosis being 70 ± 10 years in 
men and 68 ± 11 years in women (17). Proportion of those with LBBB increases progressively from 
<1% at age of 50 to 6% by 80 years (17,18). Factors found to be associated with its development 
included arterial hypertension, coronary artery disease (CAD), valvular heart disease, cardiomyopathies, 
myocarditis which all are states with known myocardial macro- or micro fibrosis (15,17). Recent human 
and animal studies suggest gene expression of connexin 40 and 43 to be involved in the development of 
LBBB and cardiomyopathy (19,20).  
1.2 Prognosis 
To date there is no consensus on LBBB-related prognosis as study results are clearly influenced by study 
design, population size, selection criteria and age (21). In 1979 the Framingham Study (4209 subjects, 
55 with LBBB) (6) showed a clear association between LBBB and main cardiovascular diseases, such 
as hypertension, cardiac enlargement and CAD. Coincident with or subsequent to the detection of 
LBBB, 48% of these individuals developed congestive heart failure. Within 10 years from LBBB 
detection, cardiovascular mortality was 50% and at 18 years follow-up only 11% of subjects with LBBB 
remained free of detectable cardiovascular abnormalities. Several studies showed that once a LBBB is 
established, the prognosis worsens with age, established CAD or congestive heart failure (3,5,7,22,23). 
In patients with acute myocardial infarctions with LBBB, 30 days mortality, in-hospital death and 1 
year’s mortality were reported to be substantially increased (3,22,24,25).   

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1.3 Pathophysiology  
In addition to patients with “spontaneous” LBBB, pacemaker (PM)- patients with PVP often develop 
conduction disturbances resembling an electro-mechanical activation pattern of a LBBB. LBBB or PVP 
result in regionally delayed electrical activation with comparable hemodynamic effect (26).  
Individuals with LBBB or PVP develop delayed electrical activation of the lateral wall of the LV (27). 
During the septal contraction, the intraventricular pressure rise is low. The delayed lateral wall 
contraction leads to stretching of the septum during the ejection phase, which does not contribute to 
further pressure generation or stroke volume. Consequently, the regional work load of the septum is 
reduced, while the work load of the lateral wall increases due to initial pre-stretch. Perfusion imaging 
shows relative septal hypoperfusion (28,29). Animal-models have shown an unfavorable effect on LV 
remodeling including LV dilatation, asymmetric hypertrophy and decreased pump function (29), altered 
cellular Ca ++ transport and a pro-arrhythmic state (15). Deformation of the mitral valve apparatus with 
annular dilatation and dyssynchronous papillary muscle contraction lead to development and 
progression of functional mitral regurgitation (30,31) which aggravates heart failure development. 
However, the presence of LBBB alone seems not to cause ventricular dysfunction and heart failure. 
First, when the LV starts to become dyssynchronous and inefficient (32,33) a vicious cycle starts with 
progressive LV failure and progression of the conduction abnormality as well (34,35).  
Recent computational, experimental and clinical studies showed the deleterious role of increased 
afterload, causing higher wall stress, when either the ventricle is dilated, or intraventricular pressure is 
high. These conditions seem to influence the degree of dyssynchrony and seem to contribute 
substantially to reduced cardiac performance when delayed electrical activation is present (36-38). 
These recent publications seem to be the missing link to be able to predict the prognosis on individual 
basis.  
1.4 Specific therapy 
Development of heart failure might be disrupted by specific treatment such CRT where two ventricular 
PM leads are implanted at two opposite sites. This type of cardiac pacing provides resynchronized 
electrical activation of the LV. CRT has become one cornerstone of heart failure treatment of patients 

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with LBBB. According to current guidelines, only patients with established heart failure are being 
assigned to CRT (39).  
Recent studies have focused on predicting response to CRT when heart failure is established 
(10,16,26,40). Dependent on criteria used for implantation of CRT devices, 30% and 50% are non-
responders to CRT (41). This high number indicates either inappropriate patient-selection or irreversibly 
decreased ventricular function. Based on response defined by echocardiographic measures of volume or 
ejection fraction modern imaging modalities have been developed in order to predict outcome-prediction 
for CRT. Additionally, these modalities have shown to be of incremental value for outcome-prediction 
when a CRT is implanted (26,40). 
1.5 “Novel echocardiographic indices” 
Modern echocardiographic modalities like segmental strain imaging allows to visualize and quantify the 
typical early shortening of the septum followed by the delayed contraction of the lateral wall (10). This 
pattern has an important prognostic value in patients with established heart failure as its absence was 
shown to be associated with unfavorable outcome after CRT implantation with increased risk of death 
or transplantation (40). Computer models could show that the typical strain-patterns changed in 
dependence on global regional contractility of the septum and lateral wall. 
  
Patients responded to CRT treatment when typical patterns for electro-mechanical dyssynchrony were 
present. These patterns can be quantitatively assessed by newly developed algorithms (42). In addition 
to qualitative and quantitative strain-curve analysis “LV workload quantification” and “analysis of 
energy loss and waste of myocardial work” have been recently introduced as new echocardiographic 
measures(43) also applicable in the presence of dyssynchrony (12). These are based on strain imaging 
combined with BP measurements, where strain loop areas can be derived. It has been demonstrated that 
Strain-patterns of different segments
A: normal synchonous contraction pattern.  
B: regionally reduced myocardial function.  
C: CPD (classic pattern dyssynchrony)            
D: delayed electrical activation with synchronous 
late-systolic contraction.

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contraction of the septum in patients with LBBB and chronic heart failure perform a net negative work 
(systolic lengthening) while after CRT positive work (systolic shortening) increases dramatically (11). 
In small patient populations myocardial work showed to be a good indicator for CRT response(44).  
 
Figure 2: Pressure strain curves in simulations with narrow QRS complex and with LBBB, and changes with 
afterload. The area under the curve expresses segmental myocardial work in the septum (upper row) and lateral 
wall (lower row) (F.Prinzen, E.Willem, J. Lumens JACC-CVI 2019) 
1.6 Integration of multifaceted data by artificial intelligence  
All these new echocardiography-based measures have significantly improved our understanding of the 
nature of LBBB, its cardio-mechanic effect and the hemodynamic consequences of abnormal electrical 
activation. Thus, these new measures alone have high potential to improve the selection of adequate 
treatment strategy. Furthermore, it had been shown that the presence of LBBB morphology on ECG is 
not always associated with typical mechanical patterns. 30% of patients with LBBB on ECG selected 
for CRT did not have typical contraction patterns as indicated by speckle tracking strain 
echocardiography (40). The mismatch between ECG and myocardial mechanics was independently 
associated with increased risk of adverse outcome. Conversely typical dyssynchrony patterns were 
observed in 20-26% of patients without typical LBBB morphology at ECG, where the majority 
responded to CRT (26). All these findings clearly demonstrate that echocardiography may have high 
potential for accurately selecting patients needing CRT independently of ECG morphology.  
As a result of computer-models (38), experimental and clinical studies (36), the interplay of delayed 
electrical activation, afterload, global and regional myocardial function and the influence on ventricular 
dyssynchrony or inefficient myocardial work is much better understood. In addition to ECG criteria and 

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assessment of ejection fraction (EF), echocardiography renders useful information about ventricular 
loading and function. However, to date CRT management is only based on EF, QRS length, LBBB 
morphology and NYHA class with a failure rate of still >30% (41). Several echocardiographic studies 
on CRT response have shown that the failure rate would be expected to be dramatically reduced if 
imaging would be involved (40,41,45,46).  
The development of heart failure and its reversibility is dependent on multiple factors. Unsupervised AI 
algorithms are recently developed tools being able to integrate individual phenotypic subgroups and 
identifying isolated characteristics based on strain-curves, echocardiographic phenotypes, flow 
characteristics, genetics and co-morbidities. A recent important publication has re-analyzed the 
randomized MADIT CRT study population using AI and were able to identify subgroups with 
substantially better CRT-outcome in a AI selected patient group with a HR of 0,31 on mortality and 
adverse cardiovascular events compared to ICD treated patients (9), while the original outcome of the 
study did not show significant risk reduction by CRT treatment overall.   
According to these recent publications, the present study intends to use AI algorithms including evolved 
echocardiography-based imaging modalities like strain imaging and regional work assessment. We aim 
to show that these are promising tools to improve assessment of patients with LBBB to identify patients 
at risk for development of heart failure that might respond to early medical treatment preventing the 
development of heart failure or identifying patients early who will respond to CRT treatment.   
1.7 Clinical significance: 
LBBB is highly prevalent among patients with chronic heart failure (25%). Morbidity and mortality are 
high among patients with LBBB, but ECG detected LBBB has not been shown to be a sufficiently 
accurate predictor of outcome. Early medical treatment or CRT for carefully selected individuals might 
help to lower morbidity and mortality among individuals with LBBB or PVP. However, criteria for an 
optimal time-point and patient characteristics for CRT responders are still missing.  
In conclusion, the study aims to identify new predictors for the appropriate time point for initiating 
specific treatment, as well as specific patient characteristics for treatment response based on AI and 
novel echocardiographic measures. Risk- algorithms for heart failure and increased mortality are 

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urgently needed in this large group of cardiological patients (25% of heart failure patients). Studies on 
patients with CRT have demonstrated high specificity of echocardiographic parameters when heart 
failure is established. Other newer parameters like “septal and lateral work” and as well as integration 
of all accessible features by AI have been recently introduced and shown to provide a powerful tool for 
differentiating degrees of dyssynchrony in patients with LBBB. This study aims to contribute to a more 
specific patient selection for specific heart failure treatment, when LBBB or PVP is present. Patients 
with His bundle pacing (HBP) might provide synchronous pacing and are also included as a special 
control-group for patients with permanent ventricular pacing.  
2 Hypotheses, aims and objectives:  
2.1 Primary hypotheses 
1. Typical strain-pattern and myocardial work assessment improve substantially prediction of heart 
failure development, hospital admission, death and adverse cardiovascular events and can help to 
establish more effective patient selection criteria for CRT or medical heart failure treatment. 
2. AI, incorporating strain imaging and myocardial work assessment as well as conventional clinical 
patient characteristics is able to improve additionally risk assessment and treatment response in the 
LBBB population 
2.2 Secondary hypotheses 
1. Patients with high afterload have typical dyssynchrony patterns with earlier deterioration of cardiac 
function over time. 
2. Repetition of echocardiographic assessment and clinical parameters increases accuracies of 
outcome-prediction. 
3. Predictors for major adverse cardiovascular and cerebrovascular events (MACCE) indicate also 
CRT- or medical treatment- response. These factors might be useful indicators for risk reduction 
through treatment of carefully selected patient-groups 
4. Patients showing ability to increase or sustain myocardial work during a stress test with no classic 
pattern of dyssynchrony have a good long-term prognosis.  
5. HBP can prevent development of heart failure compared to usual ventricular pacing. 

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2.3 Primary objectives: 
1. To identify patients with LBBB eligible for timely CRT treatment by evaluating myocardial work 
by echocardiography as predictors in for a) deterioration of myocardial function, b) risk for 
mortality, c) heart failure development and hospital admissions d) risk for MACCE 
2. To investigate the ability of AI to improve risk assessment and treatment response in the LBBB 
population 
2.4 Secondary objectives: 
1. To investigate the dependency of strain-patterns on afterload and ventricular geometry 
2. To investigate the predictive value of repeated echocardiographic measures after one year 
3. To compare indicators for increased morbidity and mortality risk with indicators for CRT or medical 
treatment response 
4. To assess development of strain-patterns for dyssynchrony, ventricular volume and clinical and 
echocardiographic parameters for systolic function over time in dependency on myocardial work 
during stress testing 
5. To compare outcomes between usual ventricular pacing and HBP. 
 
3 Project arrangements, method selection and analyses  
3.1 Patients 
This is a longitudinal prospective cohort-study intending to include at least 1000 patients with LBBB 
from University Hospital of North Norway Tromsø and Harstad, Akershus University Hospital, 
Lørenskog, Oslo University Hospital, Rikshospitalet, Haukeland University Hospital, Bergen and 
Nordlands Hospital, Bodø, over a 2-year inclusion period.  
Information about present and previous diseases, medication, surgical- or invasive treatment, PM, ICD 
or CRT implantation, clinical examination and present and previous echocardiographic imaging and 
blood samples (Hb, Thrombocytes, CRP, Kreatinine, eGFR, Urate, HbA1C, Cholesterol, ProBNP, 
Troponins, ALAT and Ferritin) will be accessed by the patient`s journal.  

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All prospective study participants will answer one customized and two standardized (EQ5D and 
HeartQoL) questionnaires. They will undergo general clinical investigation comprising height and 
weight measurements, baseline ECG, blood samples when not found in the patient`s journal, 
standardized BP measurements and echocardiography with a 3 minutes- hand-grip test at baseline and 
in a 18 month follow-up study.  
Clinical outcome data including death and hospital admissions due to heart failure and CRT implantation 
will be assessed after 3 and 5 years (eventually after 10 and 15 years) by linkage to data from the 
patient`s clinical records through access to Norwegian Patient Registry (Norsk pasientregister (NPR)), 
Norwegian Causes of Death Registry (Dødsårsaksregisteret) and Norwegian Cardiovascular Disease 
Registry (Hjerte- og karregistereret). 
St Olavs Hospital, Trondheim, might provide retrospective patient data with echocardiography loops, 
where outcome-data will be extracted from the same registries. 
3.2 Population-based studies:  
In addition to the prospective study, we intend to include retrospective data from a population-based 
study with echocardiography from Katholieke Universiteit Leuven, Belgium. This study has already 
been approved for baseline echocardiography and follow-up assessment. Participants with LBBB or 
PVP of the Tromsø 7 study will be invited to be prospectively included into the study-population from 
University Hospital of North Norway, Tromsø .  
3.3 Planned inclusion of participants of Tromsø 7  
The main focus of this study is to identify individuals with LBBB or ventricular pacing who have 
characteristics for stable disease or those who are soon developing clinical heart failure. 
Asymptomatic Individuals with LBBB are usually not referred to the cardiologist. In order to create a 
suitable control-algorithm for these stable diseases and to identify individuals who present with 
subclinical heart failure who are in need of either medical treatment or even CRT it will be important 
to include the broad spectra of patients and “healthy” participants of epidemiological studies. ECG of 
the participants of Tromsø 7 have been analyzed and we wish to include all participants with increased 
QRS width >130ms and LBBB configuration or predominant ventricular pacing, excluding those with 

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RBBB configuration. Echocardiography in individuals with LBBB and predominant ventricular 
pacing is clinically indicated.  
3.4 Primary outcome  
Deterioration, stability or improvement of markers for myocardial function over time. Outcome-
variables will be measured by deterioration or non-deterioration of LV function by defining deterioration 
of ventricular function by using either echocardiographic parameters that are classically used for the 
definition of CRT responders: EF decrease by >5% with EF initially <55% or >10% with EF initially ≥ 
55%, LV diastolic or systolic volume enlargement by 10%, NYHA class deterioration or proBNP 
increase by >10%. 
3.5 Secondary outcomes 
Hospital admissions, adverse cardiovascular events, mortality, CRT implantation and initiation of 
medical treatment for heart failure. 
3.6 Inclusion criteria  
Patients with typical LBBB, thus QRS complex >130 ms and R-wave duration in V6 >70 ms or atypical 
LBBB or patients with predominantly ventricular pacing will be included. Atypical LBBB is present, 
when the Minnesota code criteria for typical LBBB are not met, at a QRS of >130ms and the absence 
of right bundle branch block (RBBB). Several subgroups will be defined as follows: 1. PM dependent 
patients with ventricular pacing over 50% including new implanted PM with AV block grade 3 or in 
need of heavy rate reduction (His bundle ablation), 2. Bundle branch block following acute myocardial 
infarction. 3. Baseline investigation of patients with CRT indication will be performed shortly before 
CRT implantation. In case of already implanted CRT, in addition to the inclusion to the prospective 
study, previous echocardiograms will be retrospectively analyzed for strain-patterns, ventricular 
geometric properties and clinical assessment from the patient`s records. Patients with HBP will be 
included as a control-group.  
3.7 Exclusion criteria 
Typical RBBB. No ability to give informed consent, non-cardiovascular co-morbidities with reduced 
life-expectancy < 1 year or patients with complex congenital heart disease. 

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In-hospital patients will be included during the in-hospital period and the baseline echocardiogram and 
BP monitoring will be performed as part of the routine clinical investigations. From the “Out-patients’ 
clinics the patients will be recruited after assessing ECGs from patient`s records.  
In a Pilot-study feasibility of patient recruitment will be tested. The incidence of heart failure and 
deterioration of ventricular function in the first 100 included patients will be assessed in order to define 
the power of the final study-population size.  
3.8 Echocardiography 
Participants will be examined with transthoracic echocardiography using a commercially available 
system (Vingmed Vivid E9 or E95 with M4S-RS Sector Probe, General Electric Healthcare, Horten, 
Norway). The general echocardiography protocol, optimizing imaging loops for two-dimensional 
speckle tracking analyses and myocardial work analysis and the generation of wall stress-strain loops.  
3.9 Artificial intelligence 
An unsupervised AI algorithm (Multiple Kernel Learning and K-means clustering) will be used to 
categorize subjects by similarities in clinical parameters, LV volume, wall stress and deformation traces 
at rest and after hand-grip test. All available input data will be converted into a compact representation 
space where subjects are positioned according to their similarity. Afterwards subjects will be clustered 
with K-means algorithm to identify phenotypically-distinct categories patients with adverse outcomes.  
3.10 Statistical power 
It is unknown how high the percentage in this study population will be who suffer from deterioration 
of LV function. Therefore, a pilot-study on the first 100 study-subject of the different subgroups will 
be performed in order to calculate the power of the final population size.  Pilot participants will be 
included into the final study population. Assuming different proportions of 5%, 10%, 20% and 30% of 
patients with deteriorating ventricular function, a difference in septal or global strain between at 5% at 
a SD of ±6% the sample size needed for hypothesis 1 in each subgroup will be 220, 129, 73 or 57, 
respectively for a two-sided test and a Type I error of 0.05.  
For calculation of hazard-ratios between patients with against without deterioration of LV function for 
the primary outcome variable: hospital admission due to CVD, assumed that 5% of all patients either 

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deteriorate function and the presence of decreased segmental work indicates at least a relative risk of 
1.8, at an alpha of 0.05 with 500 patients included, the statistical power will be 82%.  
3.11 Statistics 
Patients with deteriorating myocardial function will be identified by increasing proBNP by more than 
10%, increasing NYHA class, increasing diastolic volume by more than 10% and/or decreasing EF by 
more than 5%. ANOVA will be used to compare parameters between patients with deteriorating 
myocardial function and those with unchanged clinical condition after one year. ROC curve analysis 
will serve to find the ideal cut-off values for continuous variables and assessing sensitivity and 
specificity of the tests. Univariate and multivariate logistic regression tests will be performed in order 
to identify the most powerful parameters predicting development of heart-failure with hospital 
admissions as primary outcome and predicting death (or transplantation) as secondary outcome.  
3.12 Data management 
Data from patient`s visit and patient`s journal will be stored as de-identified data in an a password 
secured internet-based database (Viedoc). Viedoc is approved by The Norwegian Data Protection 
Authority for use in scientific studies. Echocardiographic data will be stored as de-identified DICOM 
files including locally performed measurements, labeled by the patient`s study ID, all ECGs will be 
taken in digitalized format and be stored as de-identified image files as well as DICOM files. All de-
identified files will be locally stored at a dedicated server.  Key files coupling personal patient 
information to ID`s will be stored locally on a server with restricted access. All de-identified ECGs, 
echocardiography-studies and key-files will be centrally collected at University Hospital of North 
Norway, Tromsø, latest after end of the second patient`s visit. Echocardiographic data will be analyzed 
in the core-lab at University Hospital of North Norway, Tromsø. Readers of the strain-data will be 
blinded for the outcome-data. Other researchers assess outcome-data from NPR and The Norwegian 
Cause of Death Registry will be added to the database. The de-identified dataset and DICOM images 
from echocardiography and ECG will also be transferred to NTNU Trondheim for automated reading 
of the echocardiographic imaging and AI purposes. 

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3.13 Participants, organization and collaborations 
The following Co-workers and institutes are involved:  
The main-supervisor Assami Rösner, cardiologist, consultant, and associate professor is expert in strain 
imaging. Henrik Schirmer is the supervisor`s main co-worker at UiT The Arctic University of Norway 
and has been involved in project-planning and writing. Helge Skulstad is also specialist on strain-
imaging and has been involved in development of myocardial work assessment. Espen Holte is strain-
expert and also involved in AI processes performed in NTNU, Mai-Tone Lønnebakken, Harald 
Kjekshus, Knut Tore Lappegård and Siri Malm will be local project leaders. Lasse Løvstakken is 
professor in engineering, conducting the AI process in his institute, NTNU Trondheim. Tatiana 
Kouznetsova is specialist in strain-imaging in context with epidemiologic research and is one of the first 
who implemented myocardial work into her epidemiological studies. Tove Aminda Hanssen will lead 
research about quality of life assessment.  
4 Plan for activities, visibility and dissemination 
Data collection will start after final Regional Ethics Committee (REK) and Data Protection Officer 
(PVO) approval. A long-term follow-up of at least 5 years, possibly 10 to 15 years is planned after an 
inclusion period of at least 2 years plus one-year clinical control. A core-lab will be established in 
Tromsø for analyses of all echocardiographic measurements and post-processing. University Hospital 
of North Norway, Tromsø  will coordinate and assess the quality of data acquisition and database from 
the different centers. All registry-data from NPR and The Norwegian Causes of Death Registry as 
follow-up data will be retrieved by a dedicated research administrator.  
4.1 Communication/planned publications 
At least three contributing centers will form a central steering-committee to coordinate planned 
publications. Upon study completion and finalization of the study report the results of this study will be 
submitted for publication and posted in a publicly accessible database of clinical study results. The 
results of this study will also be submitted to the Competent Authority and the Ethics Committee 
according to EU and national regulations. Personnel who have contributed significantly with the 

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planning and performance of the study may be included in the list of authors (Vancover convention 
1988).  
4.2 Plan for implementation 
The strategy of the cardio-vascular research group of Institute of Clinical Medicine, UiT The Arctic 
University of Norway and the Heart-lung clinic, University Hospital of North Norway, Tromsø,  
supports the topic of cardiovascular imaging, including improvement and implementation of new 
cardiac imaging modalities, thus also strain-imaging. Morbidity and mortality in the heart failure 
population is high and often associated with the presence of LBBB (25%) in the heart failure population. 
This project has high potential to render results being implemented into clinical use with facilitating 
patient-selection for early and successful treatment, thus preventing further disease-development and 
hospital-admissions. Thus, the study is central in improving patient care, reducing progress of chronic 
cardiac disease and reduction of hospital-admissions. We hope that the results of the study help to 
establish a substantially improved patient selection for CRT treatment. 
4.3 User involvement 
Tor Bosch from Landsforening for hjerte lunge syke (LHL) is the patient-representative in the Heart-
lung clinic, University Hospital of North Norway, Tromsø.  
He has been an active part in planning of the study, he has initially worked out the patient information 
and consent and given shared considerations about patient recruitment. He or other patient-represents 
from LHL will be active in distributing the results to potential users.  
5 Ethical considerations 
Data collection for the current study is approved by the Ethics Committee “Regional committee for 
medical and health research ethics (REK2019/134)”. All data collection and storage have been done in 
accordance with the regulations for data protection including the appropriate use of written informed 
consent by study participants. Data made available for scientific analysis is de-identified and excludes 
personal identifiers. All patients have signed a written informed consent. The transfer of data between 
institutions is de-identified, while the key files of all Norwegian hospitals will be transferred to 
University Hospital of North Norway, Tromsø . 

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5.1 Intellectual property rights:  
The project is not confounded with intellectual property rights issues that would cause restrictions 
regarding the dissemination and use of the results. 
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