NC3Rs Maths in Medicine Case Study: Big data for Biologists
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NC3Rs Maths in Medicine Case Study:
Big data for Biologists
Manasi Nandi PhD, FBPhS, FHEA
Senior Lecturer Integrative Pharmacology
King’s College London
manasi.nandi@kcl.ac.uk
Google images used throughout
M Nandi is a co-inventor on IP presented in this presentation
The arterial pulse measured in 1738
…a horse and a glass tube
Hales, S. Haemastatics, 3rd edition pg 1. 1738
The arterial pulse measured in 1854
Sphygmograph
1831 Julius Hèrisson
1854 Karl von Vierordt
1863 Étienne-Jules Marey
Google images
Mohomed FA The physiology and clinical use of the sphygmograph Med times Gazette 1872; 1:62. M.F O’Rourke, Hypertension 1992; 19:212-217
Frederick Akbar Mahomed
Guy’s Hospital 1869
“The pulse, ranks the first among our guides; no surgeon can
despise its counsel, no physician shut his ears to its appeal.
Since, then, the information the pulse affords is of so great
importance and so often consulted, surely it must be to our
advantage to appreciate fully all it tells us, and to draw from it all
that it is capable of imparting…..
…we should study the pulse in its marvellous changes of character
and form, as recorded by the sphygmograph”
Mohomed FA The physiology and clinical use of the sphygmograph
Med times Gazette 1872; 1:62
Mohomed FA The physiology and clinical use of the sphygmograph Med times Gazette 1872; 1:62. M.F O’Rourke, Hypertension 1992; 19:212-
217
Now…we can measure almost anything, all of the time….
Big Data
125-1000Hz sampling Google images
Source: Chung, M.K., and Rich, M.W. Introduction to the cardiovascular system. Alcohol Health and Research World 14(4):269–276, 1990.
Are we missing a trick? • As individual control systems in a plane start to fail, so the plane wobbles, turns, spirals and eventually crashes…. • Similarly, in the human body, there may be subtle changes in our own control systems that are changing but by the time we have ‘diagnosed‘ a patient – they have already ‘crashed’
Sepsis
Sepsis:
Early diagnosis,
rapid treatment
(fluids, antibiotics)
Infection
Lungs, GI tract, 20% mortality
Genitourinary infection
Burns or other open wounds
Invasive surgery
Septic shock:
CVS stabilising agents
Exaggerated immune response 30-50% mortality
Blood pressure plummets
Impaired organ perfusion
Multiple organ failure
Death
40,000 deaths p.a. in UK
NHS cost £2500 per patient per bed day24 hour model – Typical experience of the animal
24 hours of sepsis in a mouse
Hypotension
Tachycardia Bradycardia
0 hrs 18-24 hrs
Metabolic acidosis
Impaired renal functionCan we predict that a patient will crash,
before they crash?
Endotoxin
Mean arterial blood pressure
Crash
TimeMacro versus Micro circulation
130/90mmHg
Mouse 1
450 bpm
Mouse 2
110/80mmHg
480 bpm Is there information
in the waveform that
predicts clinical deterioration
above and beyond the
‘set point’?
Mouse 3
120/90mmHg
420 bpm
Sand et al., J Appl Physiol . 2014Mini summary • The cardiovascular system is a complex with many homeostatic mechanisms that contribute. • Sepsis is a condition where these systems start to fail and then the patient suddenly crashes. • Data is collected at high fidelity so we collect entire waveform data…but don’t use it…. • Since 1854 scientists and doctors have considered that there is important information in the shape of the wave • How can we quantify the waveform shape?
Waveform shape 120mmHg 80mmHg
Can mathematics help?
Finding patterns in data streams
x
z
y
Time series dataLorenz attractor – a product of chaos
theoryCan we plot blood pressure data in 3D?
Floris Takens –
Mathematician
1981
Philip Aston
Mathematician
We only have 1 data stream University of Surrey
2013Jerome Di Pietro – E-learning technology
Step 1 – 3D plot with time delaysStep 2 : Rotate
Step 3 : Add density
1 140
BP (mmHg)
z
120
y
100
x
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Time (seconds)
140
2 If: 130
X = 108 mmHg z 120
y = 120 mmHg 110
Z = 138 mmHg
100
100
120
y
140
140 130 120 110 100
X
3
4 140 5 6
140
130
z 120
120
110
100 100
100 100
100
120
y 120
140 130 120 110 100
140 120
x 140AR is fundamentally to normal CV
measures….
• Data are viewed in reconstructed phase space
enabling continual analysis of lengthy data
streams (hours/days of recording).
• Baseline wander is factored out in order to
focus on subtle changes in the waveform
shape/variability – the key inventive step.
• Scalar measures from the attractor are used as
quantitative physiological readouts of change
in waveform shape and variabilityExtracting features from the attractor
What is the
angle of
How big is it?
rotation?
How wide are How dense are
the sides? the hot spots?Application to other periodic waveforms in any species
BP ECG
Pulse Oximetry Intra Cranial Pressure
Central Venous Pressure Respiratory
Gary Chaffey and Philip Aston – mathematicians, U of Surrey ; Physionet and other open access online sourcesTelemetry continuous
waveform data 1000Hz
10am-4pm naïve
Chart beat detection
10am-4pm sepsis AR coding
software
Extraction of AR
Extraction of SBP, DBP,
measures, size, form,
HR, HRV and PP
density etc.
every half hour
every half hour
Ying Huang – mathematical
Anna Starr & Claire Sand
coding
Integrative pharmacologists
University of Surrey
King’s College London‘Conventional’ ‘Attractor’
measures measures
Head to head
comparison
ROC AUC Hitesh Mistry
Statistics
ManchesterHealthy ROC AUC =1 Septic
Healthy Septic
ROC AUC ~ 0.5
ROC AUC = 1 – good discrimination between healthy and septic
ROC AUC = 0.5 – Poor discrimination/random chance‘Conventional’ ‘Attractor’
measures measures
Conventional Baseline Baseline vs. AR measure Baseline vs. Baseline vs.
measure vs. saline sepsis saline sepsis
Systolic BP 0.52 0.54 AR measure A2 0.51 0.78
Diastolic BP 0.52 0.86 AR measureA3 0.63 0.76
Pulse 0.54 0.82 AR measure A4 0.62 0.65
Pressure
AR measure A5 0.57 0.82
MABP 0.71
AR measure A6 0.59 0.64
Heart Rate 0.58 0.86
AR measure A7 0.53 0.83
Heart rate 0.53 0.84
variability AR measure A9
(HRV RR) AR measure P4 0.52 0.99
HRV SDRR 0.65 0.62
AR measure P6 0.62 0.96
HRV RMSRR 0.61 0.54Pilot studies using HESI data
Pimombendan- PDE3 inhibitor – positive ionotrope
Itraconozole- antifungal – negative ionotrope
Hypothesis: AR can be used to extract information about changes
in cardiac contractility from a peripheral BP waveform
HESI meeting, 13-15th June 2017, DublinSummary
Jerome Di Pietro Pete Charlton Claire Sand
Ying Huang KCL
KCL KCL
Surrey Sepsis models
E-learning technologist Coding developments
Carolyn Lam Coding developments Integrative pharmacologist
Clinical data
KCL Maths PhD student
Biomedical engineer
Pharmacologist;
Data processing
Philip Aston Jane Lyle
Miquel Serna Pascual Surrey
Surrey Coding developments
KCL
Mathematics Pharmacologist; Maths research PhD
Hitesh Mistry Esther Bonet Luz
Data processing student
Manchester Mathematician
Statistics, Machine learning
applied mathematics
and algorithms
Phil Chowienczyk
KCL
Clinical Pharmacology
Cardiovascular waveforms
Gary Chaffey
Surrey Ashley Noel Hirst
Jordi Alastruey Anna Starr Richard Beale
Coding developments Nuffield project student
KCL KCL KCL/GSTT
Maths research fellow In silico modelling
Biomed engineering Sepsis models and AR analysis Critical care medicine
Integrative pharmacologist Cardiovascular waveforms
Cath Williamson
Jenny Venton Guy’s & St Thomas’
KCL Women’s Health
Mathematics Duncan Mcrae and Mary Anton
Coding developments Royal Brompton
Data processing Paediatric Intensive Carehttp://ehealth.kcl.ac.uk/cardiomorph/
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