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Specialist’s Decision Support System
SDSS
The SensorART Project has received funding from the European Community's
Seventh Framework Programme (FP7/2007-2013) under grant agreement n. 248763.
SDSS Scope
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To assist specialists into getting the most
imformative decision
To explore and analyse the available data
To allow for the creation of a
‘learning database’
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SDSS Objectives
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The SDSS is a Web-based application that effectively assesses and
exploits real patient data and simulated patient data, through
the following components:
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Work with
simulated data
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Work with real data
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Association Rules Tool – It enables discovery of interesting interrelations
and extraction of new knowledge from multiple and heterogeneous archived
data.
Statistics Tool – It allows analysis and interpretation of patient data and
hypothesis testing through powerful statistical techniques.
Weaning Tool – It identifies the most appropriate candidates for weaning
from the VAD.
Treatment Tool – It provides risk analysis and profiles for supporting the
effective treatment of patients.
Monitoring Tool – Based on day-to-day LVAD data it provides predicition of
adverse events
Speed Selection Tool – It proposes adjustments to pump speed
settings according to the required cardiac output and pressure perfusion
based on simulation signals. A Suction Detection Tool analyses simulation
sessions from the VAD-Heart Simulation Platform in terms of the suction
phenomenon.
SDSS in SensorART
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Association Rules Tool
Objectives – Functionality
By using this tool, the Specialist can be assisted in analysis
and research, by using data mining techniques in order to:
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Discover associations among different variables
Discover new knowledge
Associations are in the form of Rules
IF (VAR 1 > value1) AND (VAR 2 <= value2) THEN
(VAR 3> value3)
Variables in IF, THEN parts are chosen by user
Threshold values are chosen by user
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Association Rules Tool
Technical Choices
The main algorithm behind the tool is the Apriori algorithm.
It is a rule mining algorithm, which is driven by 2 main
features:
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Support, which is defined as the ratio of records in the database which
contain the features and the specific values defined from the Specialist
divided with the size of the database.
Confidence, which is defined as the ratio of records supporting the
features and the specific values defined from the Specialist divided with
the records supporting the features and the specific values defined from
the Specialist only in the if part.
Specific care is taken for missing values (2 way filtering+replacement).
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Association Rules Tool
Screenshots - Demo
The user selects
Variables (and
thresholds) for the
IF and THEN parts
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Association Rules Tool
Screenshots - Demo
By pressing the
Submit button, the
algorithm runs and
the user is
transferred to the
next screen, where
the algorithm results
are shown.
Produced rules
appear.
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Statistics Tool
Objective
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Statistic tool is a visual programming interface to bring the
power of the statistical analysis to SensorART specialists
The goal of this module is to provide access to the most of
of statistical functions without any programming expertise
2 modes available: working
either on SensorART Database
or with Excel file
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Statistics Tool on Excel file
User uploads
data from an
Excel file in
order to
compute the
most important
statistical
metrics
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Statistics Tool
User browses
through the many
different variables
which are
categorized into 3
big groups:
• Heart Related
• Sensor Related
• Laboratory Exams
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Statistics Tool
Visual representation of
Results for different
statistical tests
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Statistics Tool
Statistics tool – directly linked to Database
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Through the main
SDSS menu on the
left, the Specialist
accesses the
Statistics Tool, and
selects one of the
available
functionalities
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Statistics Tool
Statistics tool – directly linked to Database
Structured in visits
1) The population is defined
based on selection criteria:
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Gender
Age
2) The required visit is specified.
3) The variable of interest is
selected from three categories:
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Heart related
Sensor related
Laboratory related
4) The statistical method of
interest is selected.
Basic Statistics
Paired t-test
Unpaired t-test
X2 –test
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Statistics Tool
Development of an extra tool for Survival Analysis
(Kaplan Meier curve)
An input file is created:
The user:
1. Defines starting of KM analysis time
2. Defines ending time of KM analysis.
3. Chooses event:
• death,
• cerebral bleeding,
• gastrointestinal bleeding,
• ischemic stroke,
• TIA,
• thromboembolic events
Defines group based on: Sex, Age, Intermacs classification or Ethnicity
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Statistics Tool
Visual
representation of
Results for KaplanMeier Survival
Analysis
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Weaning Tool
Objectives
 To provide support in the identification of patients that can
be considered as candidates for weaning,
i.e. the selection of patients with adequate cardiac recovery
that may be removed from the VAD therapy.
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The main idea is to include in the SensorART weaning
module
(a) all state-of-the-art models presented in the literature
(b) specific models derived in SensorART
models provided by medical experts
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Weaning Tool –flow
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Weaning Tool
State-of-art
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Weaning model proposed by Dandel et al
1
M. Dandel, Y. Weng, , H. Siniawski, et al., “R., Heart failure reversal by ventricular
unloading in patients with chronic cardiomyopathy: criteria for weaning from ventricular
assist devices,” Eur Heart J, pp. 1148-1160, 2011.
1
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1Measurements
performed at rest, without any inotropic support.
Weaning Tool
State-of-art
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Weaning model proposed by Birks et al
1
Right and left heart cardiac catheterization was performed before
implantation and explantation for:
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right atrial,
pulmonary artery, and pulmonary capillary wedge (PCWP) pressures,
LV end-diastolic pressure
cardiac output (both thermodilution and Fick)
with the device on and at 6000 rpm for at least 15 minutes.
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Based on echo, 6-minute walking test, cardiopulmonary test and
right cardiac cateterization LV end-diastolic diameter (LVEDD)
measured with the pump at 6000 rpm for 15 minutes became
≤60 mm.
E. J. Birks, R. S. George, M. Hedger, et al., “Reversal of severe heart failure with a continuous-flow
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left ventricular
assist device and pharmacological therapy: a prospective study,” Circulation, pp.
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1Measurements performed at rest, without any inotropic support.
381-390, 2011.
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Weaning Tool
Fuzzy logic sub-module
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Literature models are in CRISP form.
Fuzzy logic has been applied to Weaning Models so as the models
not only to result in a wean/no wean decision but also to provide a
“qualitative” characterization of each decision (i.e. 67% wean and
33% no wean).
Fuzzy logic is the extension of the classical binary logic into a
multivariate form, thus being
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closer to the human logic
more able to deal with real world problems (noisy/imprecise data)
more flexible decision boundaries
A fuzzy model can be created by defining an initial crisp model
(set of rules) and then fuzzyfing it.
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Knowledge-based Fuzzy Models
Methodology Flowchart
Known state-of-the-art models
presented in the literature
Models defined in SensorART project
Fuzzyfication
Fuzzy
model
Optimization
Fuzzy model with initial parameters
Optimized
Fuzzy model
Data provided by Laiko Hospital / Athens University (GR)
(Prof. Nanas / Prof. Terovitis team)
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Weaning Tool
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Treatment Tool
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The Treatment Tool supports the specialists on the
identification of the most suitable treatment plan, according
to the condition/phase of the patients.
The Treatment Tool includes two functionalities.
-Risk scores calculation
-Treatment assessment based on adverse events prognosis
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Adverse events prognosis based on pre-op data
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Treatment Tool
Risk scores calculation
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Heart Failure Survival Score (HFSS) [1]
Seattle Heart Failure Model (SHFM) [2]
Model for End-Stage Liver Disease (MELD) [3]
Right Ventricular Failure Risk Score (RVFRS) [4]
Patient Selection Model [5]
Risk Stratification Model [6]
Right-to-left Ventricular end-diastolic Diameter Ratio [7]
[1] Aaronson et al. Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation. Circulation
1997;95:2660-7.
[2] Levy et al. The Seattle heart failure model: prediction of survival in heart failure. Circulation 2006;113: 1424-33.
[3] Matthews et al. Model for End-Stage Liver Disease Score Predicts Left Ventricular Assist Device Operative Transfusion Requirements, Morbidity, and Mortality. Circulation
2010;121:214-220.
[4] Matthews et al. The right ventricular failure risk score a pre-operative tool for assessing the risk of right ventricular failure in left ventricular assist device candidates. J Am
Coll Cardiol 2008;51:2163-72.
[5] Lietz et al. Outcomes of left ventricular assist device implantation as destination therapy in the post-REMATCH era: implications for patient selection. Circulation 2007;
116:497-505.
[6] Wang et al. A Classification Approach for Risk Prognosis of Patients on Mechanical Ventricular Assistance. Proc Int Conf Mach Learn Appl. , 12 December 2010, pp. 293–
298 (doi:10.1109/ICMLA.2010.50).
[7] Kukucka et al. Right-to-left ventricular end-diastolic diameter ratio and prediction of right ventricular failure with continuous-flow left ventricular assist devices, The J of
Heart and Lung Trans., 2011; 30(1): 64-69.
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Treatment Tool
Adverse events prognosis based on pre-op data
Modelling (data mining)
Pre-op data
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Adverse events
Latest Results - 77 patients (HON,LUE)
61
11
5
No adverse events
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Bleeding
Death
There are missing values, thus a replacement of missing values
technique has been employed
The number of the prototypes per category is unbalanced, thus a
resampling procedure is applied.
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Treatment Tool
Latest Results - 77 patients (HON,LEU)
Adverse events prognosis based on pre-op data
Classification Accuracy (%)
90,00
85,00
80,00
75,00
70,00
65,00
60,00
NB
kNN
DT
RF
MLP
SVM
FLR
Initial dataset
Initial Dataset with replaced missing values
Resampled dataset
Resampled dataset with replaced missing values
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Monitoring Tool
Objectives / Functionality
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Monitoring of day-to-day LVAD and patient
parameters & appearance of adverse events
Assessment for future adverse events appearance
 Adverse events
Parameters
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Death
Pump flow
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Cerebral bleeding
Pump speed
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Gastrointestinal bleeding
Pulse index
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Ischemic stroke
Pump power
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TIA (transient ischemic attack)
Temperature
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Thromboembolic events
Systolic blood pressure
Diastolic blood pressure
Pulses
Weight
INR
Anticoagulant (Warfarin/Acenocoumarin)
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With SensorART,
this process is electronically monitored
The above data are used for estimation
of next day adverse event risk
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Monitoring Tool
Results
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Results were obtained using five widely known classification
methodologies:
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naïve Bayes (NB) classifier,
k-nearest-neighbor (kNN),
decision trees (C4.5),
random forests (RF) and
multilayer perceptron (MLP) neural network
Evaluation was performed
(i) the 10-fold stratified cross validation method and
(ii) the initial dataset (before the resampling) and the respective
confusion matrices were obtained,
Metrics used: classification accuracy, sensitivity/positive predictive
value per class
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The deployed monitoring model is based on decision trees.
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Monitoring Tool
Results
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SDSS Integration with VAD-Heart simulator
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Suction Detection
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Simulation data from the hybrid model have been collected in
collaboration with CNR and IBBE PAS
The study of the simulation signals revealed no differences in
frequency domain characteristics of the signal for suction/no
suction segments.
The only significant difference was in the baseline of the signal,
which is reduced in suction segments
For this reason our first suction detector is based on the detection
of the sudden decreases in signal’s baseline
The methodology is based on online estimation of a Gaussian
mixture model (GMM) with two mixtures corresponding to nonsuction & suction classes
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Suction Detection
Remove non-suction mean
Average over 1-sec window
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Suction Detection
Evaluation
Dataset
Two different dataset are used in order to test our methodology:
 10 pump flow signals (Dataset I) with suction events
approximately 46 minutes in total duration are collected from VHSP
which enables the specialists to simulate the behaviour of a
patients circulatory system with connected a real assist device
(e.g. nonpulsatile blood pump).
 26 pump flow signals (Dataset II) approximately 20 hours, are
produced with the software from Numerator Simulator simulating
different medical cases with predefined pathologies.
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Suction Detection
Evaluation
Dataset
 A large number of realistic
cases with patient pathologies
were defined and determined
from medical partners
Hypertrophic Cardiomyopathy: HC, Ischemic Cardiomiopathy:
IC, Dilated Cardiomiopathy: DC, Right Ventricular Failure: RVF,
Interventricualr Septum Failure: ISF, Aortic Regurgitation: AR,
Aortic Stenosys: AS, Aterosclerosis: AT, Systemic
Hypertension: SH, pulmonary hypertension: PH
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Suction Detection
Results
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The results are from 10 signals with approximately 46 minutes, where GMM
online estimation was applied (Dataset I).
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Suction Detection
Results
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The results are from 26 signals with approximately 20 hours, where GMM
online estimation was applied (Dataset II).
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Pump Speed Selection
Problem Description & Motivation
As the patient recovers
& his level of activity
increases
The control problem for LVADs is
to set pump speed such that cardiac
output (pump flow) & pressure
perfusion are within acceptable
physiological ranges
Set pump speed
The body‘s demand for
cardiac output increases
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too fast
too slow
Negative pressure
in the ventricle
(i.e. suction)
Unloading of
the left ventricle
may be insufficient
Pump Speed Selection
Decision Flow for HeartMate pumps
Start
 Flowchart for the
speed selection
process currently
followed after the
operation
 The LAP check is
substitute for echo
examination, which
is used to check is the
aortic valve is opening
correctly
Speed=Initial Speed
No
Yes
LAP
Speed=Speed+200
Yes
Speed=Speed-200
Stop
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No
Suction
Pump Speed Selection
Decision Flow for CL pumps
 A flowchart for
Circulite pumps
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SDSS-VHSP Connection
Send status
Accept parameters
Run simulation
Handle que
• Send Data to VHSP
URL:
http://212.87.24.54:8080/sensorart/sdss/vhspsendrequest/
and exemplary data:
ΙΒΒΕ has created and tested two Web Services (VHSP Web Name=testuser&Text=Some
Enter
parameters
description&PumpSpeed=18000&AnotherValue=11.538
Interface):
This web service use POST method. There are 4 parameters in this
Send
parameters
Upload results
• Check availability of the VHSP
moment:
URL:
Name – login or another name of a user
http://212.87.24.54:8080/sensorart/sdss/vhspavailability
Text – just a text
this link is send by GET method and the response is a number in
PumpSpeed – speed of the pump
text/plain MIME
AnotherValue – just another double value
0 – no error, everything is ok, the VHSP is available for SDSS users.
In this web service response is the same, it means a number in
1 – warning, for future use
text/plain MIME
2 – error, in most cases it means that the VHSP is not available.
Check availability
Establish connection
0 – no error, everything is ok, the data has been successfully
written in the buffer.
1 – warning, for future use.
2 – error, in most cases it means that the data has been not
successfully written in the buffer.
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SDSS-VHSP Connection
List of parameters that a user provides to VHSP though SDSS
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LVedv: end diastolic left ventricular volume (0-400 ml)
LVesv : end systolic left ventricular volume (0 -350 ml)
PAPm: mean pulmonary arterial pressure (0-50 mmHg)
Wedge: wedge pressure (0-50 mmHg)
HR: Heart rate (0-200 beat per minute)
BP (old version ABP): Systemic arterial pressure (min-max -mean) (0-250 mmHg)
Weight: Patient weight Kg
BSA: Body Surface area m2
Valve regurgitation (severity)
CVP: Central venous pressure (0-30 mmHg)
CO: Total cardiac output (if available 0-8 l/min)
Pump Speed: (0-30000 rpm minimum pump speed 20000)
Pump flow: (0-5 l/min)
ECG times - PQ duration: (50 - 240 ms)
- QRS duration: (60 - 200 ms)
- QT duration:(250-550 ms)
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SDSS-VHSP Connection
VAD Heart Simulator is performing
simulations
Patient profile
Suggested speed.
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(suction events where estimated for higher speed)
SDSS
Technical Choices
The module is developed on well-acknowledged technologies, such
as:
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PHP, as the main language providing server-side functionalities.
JavaScript, for all client-side interactions, AJAX functionalities
and charts creation,
CSS, in order to style web components in a fixed presentation
format.
SQL Server 2008 R2, as the environment to host our database.
Java, for the development of specific algorithms (e.g. in the
extraction of the association rules).
R environment for the processing of all the statistical methods.
The module is designed to be compatible with many types of
devices (PCs, tablets, smartphones etc.) having a standard Web
browser.
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