Scoring Systems in the Intensive Care Unit and Time Series Preprocessing
Orhan Konak
Data Management for Digital Health
Winter 2018
Where are we?
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DataSources
DataFormats
Oncology Nephrology Intensive CareAdditional
Topics
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BiologyRecap
BusinessProcesses
Processingand Analysis
Recap
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ICU
• Patients with life threatening illness
• Supporting failing organ systems
• Highly specialized environment
Equipment
• Organ support equipment
• Intravenous lines, feeding, suction, drains, catheter
• Constant monitoring of bodily functions
Monitoring
• Observing vital signs
• Key to improve patients survival
• Generates data like waveforms
http://telemedicinamorsch.blogspot.com/2018/11/como-colocar-eletrodos-no-paciente-para.html
Patient Data Management System (PDMS)
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■ Document vital parameters sampled by monitors
■ Demands on PDMS have increased immensely
■ PDMS are currently expected to assist clinicians at every level of
intensive care, e.g.
□ Strategic level of physician orders and prescriptions
□ Operational level
□ Administrative level
https://www.getinge.com/de/Produktkatalog/metavision-perfusion/
Does Introduction of a PDMS Improve the ICU?
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■ PDMS implementation costs range from 15k to 20 kEUR per
bed
■ Costs and revenues increased continuously over the years
■ No clear evidence for cost savings after the PDMS introduction
■ PDMS has resulted in better patient outcomes
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3847636/
https://www.imd-soft.com/
PDMSIT Infrastructure
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Patient Data Management System
HIS
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Scoring Systems in the ICU
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■ Scoring system as clinical decision support
■ Severity scales important to predict
□ Patient outcome,
□ Comparing quality-of-care, and
□ Stratification for clinical trials.
■ Essential part of improvement in clinical decisions and in
identifying patients with unexpected outcomes
■ Using logistic regression models
https://www.digitalhealth.net/2017/02/papworth-hospital-goes-paperless-icu-metavision/
Scoring Systems in the ICU
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■ Scoring system usually comprises of two parts
□ a score (a number assigned to disease severity) and
□ a probability model (equation giving the probability of
hospital death of the patients)
Number
Probability model
Score
P 𝑨
A =10
Types of Scoring SystemsCommonly Used Adult ICU Scoring Systems
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■ First-day scoring systems
□ Acute Physiology and Chronic Health Evaluation (APACHE)
□ Simplified Acute Physiology Score (SAPS)
□ Mortality Prediction Model (MPM)
■ Repetitive scoring systems
□ Organ System Failure (OSF)
□ Sequential Organ Failure Assessment (SOFA)
□ Organ Dysfunction and Infection System (ODIN)
□ Multiple Organ Dysfunction Score (MODS)
□ Logistic Organ Dysfunction (LOD)
http://scoringexpert.pl/2017/01/01/model-scoringowy-troche-teorii/
Severity Scores in Medical & Surgical ICUTimeline
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•APACHE
•SAPS
•APACHE II
1980-85
•SAPS II
•MPM
1986-90•APACHE III
•MODS
•MPM II
•ODIN
1990-95
•SOFA
•CIS
1996-2000•SAPS III
•APACHE IV
2000-current
Glasgow Coma Score (GCS)Let’s Take a Closer Look at One Score
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■ Neurological scale
■ Give a reliable and objective way of recording the conscious
■ Initially used to assess a person's level of consciousness after a head
injury
■ Now used by first responders, EMS, nurses, and doctors
■ Part of several ICU scoring systems, including APACHE II, SAPS II, and
SOFA https://nurse.org/articles/glasgow-coma-scale/
GCSCalculation
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Eye Opening Response
•4 Spontaneously
•3 To speech
•2 To pain
•1 No response
Verbal Response
•5 Oriented to time, person and place
•4 Confused
•3 Inappropriate words
•2 Incomprehensible sounds
•1 No response
Motor Response
•6 Obeys command
•5 Moves to localized pain
•4 Flex to withdraw from pain
•3 Abnormal flexion
•2 Abnormal extension
•1 No response
Behavior Response
Total Score
Mild 13 – 15
Moderate 9 – 12
Severe 3 – 8
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GCSCalculation
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https://codehealth.io/library/article-1/glasgow-coma-scale/
E
V
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GCSExample 1
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■ Infant, moves spontaneously towards objects and follows them, smiling and orienting
towards interesting sounds. The infant opens the eyes spontaneously.
E V M
4 5 6 15 https://www.thompsons-scotland.co.uk/serious-head-and-brain-injury/brain-injury-solicitors-scotland/brain-injury-claims-and-the-glasgow-coma-scale
GCSExample 2
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■ Adult, moves the hand away when applying pressure on the nail bed. The patient can make
words but not form sentences. The patient opens the eyes to pain, but not to speech.
E V M
2 3 4 9 https://www.thompsons-scotland.co.uk/serious-head-and-brain-injury/brain-injury-solicitors-scotland/brain-injury-claims-and-the-glasgow-coma-scale
GCSExample 3
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■ Adult, moves hand towards head when applying pressure above the eye socket. The patient
is disoriented but able to form sentences. The patient opens the eyes in response to speech.
E V M
3 4 5 12 https://www.thompsons-scotland.co.uk/serious-head-and-brain-injury/brain-injury-solicitors-scotland/brain-injury-claims-and-the-glasgow-coma-scale
Acute Physiology And Chronic Health Evaluation II (APACHE II)Calculation - Patient’s Age and 12 Routine Physiological Measurements
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17http://www.scymed.com/en/smnxpw/pwfbd770.htm
AaDO2 or PaO2
(depending on FiO2)
Temperature (rectal)
Mean arterial pressure
pH arterial Heart rate
Respiratory rate
Sodium (serum)
Potassium (serum)
Creatinine
Hematocrit
White blood cell count
Glasgow Coma Scale
Comparison of ICU Scoring
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ICU Scoring System Timing of data collected
Physiological values
Other required data Total data elements required
Original reported mortality prediction performance
SAPS III Prior to and within 1 hour of ICU admission
10 Age, six chronic health variables, ICU admission diagnosis, ICU admission source, LOS prior to ICU admission, emergency surgery, infection on admission, four variables for surgery type
26 AUC = 84.8% (n=16,784)
APACHE IV First ICU day (16-32 h depending on time of admission)
17 Age, six chronic health variables, ICU admission diagnosis, ICU admission source, LOS prior to ICU admission, emergency surgery, thrombolytic therapy, Fio2, mechanical ventilation
32 AUC = 88.0% (n=52,647)
MPM III Prior to and within 1 hour of ICU admission
3 Age, three chronic health variables, five acute diagnosis variables, admission type (e.g., medical-surgical) and emergency surgery, CPR within 1 h of ICU admission, mechanical ventilation, code status
16 AUC = 82.3% (n=50,307)
https://www.researchgate.net/figure/A-comparison-of-intensive-care-unit-ICU-scoring-systems-from-47-with-permission_tbl1_273059579
Which Score to Use?
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■ APACHE, SAPS, MPM → only of historic significance
■ APACHE II →most widely used in USA
■ SAPS II → commonly used in Europe
■ APACHE III → not in public domain
■ SAPS III, APACHE IV → better design
■ MODS, LODS → uncommonly used ?
The Ideal Scoring System
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■ On the basis of easily/routinely recordable variables
■ Well calibrated
■ Applicable to all patient populations
■ Can be used in different countries
■ The ability to predict functional status or quality of life after ICU discharge
No scoring system currently incorporates all these features
Taking Off the Physician’s Glasses
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Everything’s kind of blurry!
But I'm seeing things very clearly!
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https://www.brillenecke.eu/sitemap/
Selected Data SourceElectrocardiography (ECG)
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Hospital Intensive Care Unit Monitoring
ECG Signal
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https://en.wikipedia.org/wiki/Electrocardiography
Function of ECG
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https://en.wikipedia.org/wiki/Electrocardiography
■ Process of recording the electrical activity of the heart
■ Electrodes placed over the skin
■ Electrodes detect the tiny electrical changes on the skin
■ Commonly performed to detect any cardiac problems
https://www.philips.de/healthcare/product/
Simplified ECG Signal Chain
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Amplifier FilterAnalog-to-digital
converter
https://de.banggood.com/AD8232-Measurement-Pulse-Heart-Monitoring-Hearbeat-Sensor-Module-for-Arduino-Monitor-Devices-p-1402651.html?akmClientCountry=DE&gmcCountry=DE¤cy=EUR&createTmp=1&utm_source=googleshopping&utm_medium=cpc_bgcs&utm_content=zouzou&utm_campaign=pla-deg-label3-0-30-pc&ad_id=324664290179&cur_warehouse=CN
https://www.boehm-elektromedizin-gmbh.de/shop/neue-produkte/philips-intellivue-mx550/
Amplifier
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25https://www.adinstruments.com/tips/data-quality
■ Electronic device that increases the power of a signal. It does this by taking energy
from a power supply and controlling the output to match the input signal shape but
with a larger amplitude
https://www.conrad.de/de/conrad-components-stereo-verstaerker-bausatz-9-vdc-12-vdc-18-vdc-20-w-2-115592.html
Electronic Filter
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26https://www.allaboutcircuits.com/technical-articles/an-introduction-to-filters/
■ Circuits which perform signal processing functions
■ Remove unwanted frequency components from the signal
■ Include the low-pass filter, the high-pass filter, the band-pass filter, and
the notch filter (or the band-reject or band-stop filter)
https://de.banggood.com/MAX262-Programmable-Filter-Bandpass-Band-Resistant-All-Pass-Low-Pass-High-Pass-p-1382154.html?akmClientCountry=DE&gmcCountry=DE¤cy=EUR&createTmp=1&utm_source=googleshopping&utm_medium=cpc_bgcs&utm_content=zouzou&utm_campaign=pla-deg-ele-pc&cur_warehouse=CN
Analog-to-Digital Converter (ADC)
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http://qqtrading.com.my/electrocardiogram-sensor-ecg-heart-rate-monitor-ad8232
■ Converts an analog voltage to a digital number
■ Converts the output data into a series of digital values by
approximating the signal with fixed precision
■ Detecting binary signals: is the button pressed or not? These
are digital signals
■ Converts voltage as a binary – 0 or 1
Sampling RateNyquist–Shannon Sampling Theorem
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https://www.adinstruments.com/tips/data-quality
■ The minimum rate at which digital
sampling can accurately record an analog
signal is known as the Nyquist Frequency,
which is double the highest expected
signal frequency
■ Nyquist frequency = 2 x highest expected
frequency
■ E.g. you are recording ECG in humans that
has components which can reach up to 50
Hz as their highest expected frequency, so
the minimum sampling rate should be 100
Hz
Data Acquisition
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29http://www.buykorea.org/product-details/SimDAQ-KIT--Biosignal-DAQ-8-channel-24bit-ADC-Isolation-DAQ-USB-DAQ--3107900.html
■ Biological signals recorded via a data acquisition unit (DAQ)
■ Converted to a digital signal by DAQ unit
■ Resulting digital signal is sampled at regular intervals by analysis
software
■ Data stored and displayed on computer
What is a Time Series?
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■ A time series is a collection of observations made sequentially in time
15:51:00 1 25.175015:51:01 1 25.225015:51:02 2 25.250015:51:03 3 25.250015:51:04 4 25.275015:51:05 5 25.325015:51:06 6 25.350015:51:07 7 25.350015:51:08 8 25.350015:51:09 9 25.3500
…
…
15:52:40 100 24.750015:52:41 101 24.755015:52:42 102 24.760015:52:43 103 24.770015:52:44 104 24.760015:52:45 105 24.5500
http://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/time_series_2017.pdf
Time
Time series ID
Value
Time Series?
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Time Series are Ubiquitous!
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■ Everything which is on the monitoring
screen (ECG, BP, RR, …)
■ Angela Merkel’s popularity rating
■ The weather in Berlin
■ German Stock Index DAX
People measure things …
… and things change over timehtt
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Time Series
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■ 1 hour of ECG data: 1 GB
■ Typical weblog: 5 GB per week
■ Space Shuttle database: 200 GB and growing
■ Since most of the data lives on disk (or tape), we need
a representation of the data we can efficiently
manipulate
Why is working with time series so difficult?
How do we work with very large databases?
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■ Time-series data applications are proliferating
■ As a result time-series databases are in fashion
■ Most adopt a NoSQL model
■ Developers preferred NoSQL to relational databases
for time-series data by over 2:1
■ Reason for adopting NoSQL time-series databases
comes down to scale
Time Series Database
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https://www.percona.com/blog/2017/02/10/percona-blog-poll-database-engine-using-store-time-series-data/
Time Series
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■ Differing data formats
■ Differing sampling rates
■ Noise, missing values, etc.
Why is working with time series so difficult?
Miscellaneous data handling problems
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Time SeriesLet’s Compare ECG Signals
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35htt
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I’m comparing the curves and
try to find similarities, respectively
abnormalities.
What are you doing there?
Let me show you how to do it.
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Euclidean Distance MetricComparing to Time Series
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■ Let’s assume we want to compare two time series
About 80% of published work in data mining uses
Euclidean distance
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http://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/time_series_2017.pdf
Preprocessing the Data Before Distance Calculations
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■ 4 most common distortions
□ Offset Translation
□ Amplitude Scaling
□ Linear Trends
□ Noise
Euclidean distance is very sensitive to some “distortions”
in the data. For most problems
these distortions are not meaningful → should remove
them
If we naively try to measure the distance between two
“raw” time series, we may get very unintuitive results
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Preprocessing the DataOffset Translation
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http://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/time_series_2017.pdf
Preprocessing the DataAmplitude Scaling
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■ Zero-mean
■ Unit-variance
■ Widely used for normalization in many machine learning algorithms
http://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/time_series_2017.pdf
Preprocessing the DataOffset Translation
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■ Removing linear trend:
□ Fit the best fitting straight line to the
time series, then
□ subtract that line from the time
■ Remove linear trend
■ Removed offset translation
■ Removed amplitude scaling
http://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/time_series_2017.pdf
Preprocessing the DataNoise
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The intuition behind removing noise is
…
Average each data points value with
its neighborshttp://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/time_series_2017.pdf
Software Filter
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Low Pass Filter High Pass Filter
https://www.adinstruments.com/tips/data-quality
■ Fourier showed that any periodic signal s(t) can be written as a sum of sine waves with
various amplitudes, frequencies and phases
■ For example, the Fourier expansion of a square wave can be written as
Discrete Fourier Transform
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43http://mriquestions.com/fourier-transform-ft.html
Discrete Fourier Transform
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https://de.wikipedia.org/wiki/Joseph_Fourier
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Fourier series in 1822
Discrete Fourier Transform
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■ Important signal processing tool
■ Used to decompose a signal into its sine and cosine
components
■ Output of the transformation represents the signal in the
Fourier or frequency domain
■ Apply mathematical operations to eliminate certain
frequency domains very easily
■ Applying the inverse Fourier transform to recover the
original time signal
https://slideplayer.com/slide/4173668/
Summary of Preprocessing
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The “raw” time series may have distortions which we
should remove before clustering, classification
etc.
Of course, sometimes the distortions are the most interesting thing about the data, the above is only a general
rule
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What Do We Want to Do with the Time Series Data?
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47http://didawiki.cli.di.unipi.it/lib/exe/fetch.php/dm/time_series_2017.pdfhttp://amid.fish/anomaly-detection-with-k-means-clustering
What to take home?
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■ Patient Data Management Systems are currently expected to assist clinicians at every level
of intensive care
■ Currently, decision support via ICU scoring systems
■ Data is generated by sensors
■ Preprocessing of time series
■ Chance to improve decision support with the help of machine learning