NC3Rs Maths in Medicine Case Study: Big data for Biologists

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NC3Rs Maths in Medicine Case Study: Big data for Biologists
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
NC3Rs Maths in Medicine Case Study: Big data for Biologists
The arterial pulse measured in 1738
              …a horse and a glass tube

Hales, S. Haemastatics, 3rd edition pg 1. 1738
NC3Rs Maths in Medicine Case Study: Big data for Biologists
The arterial pulse measured in 1854
                  Sphygmograph

 1831 Julius Hèrisson
 1854 Karl von Vierordt
 1863 Étienne-Jules Marey
Google images
NC3Rs Maths in Medicine Case Study: Big data for Biologists
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
NC3Rs Maths in Medicine Case Study: Big data for Biologists
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
NC3Rs Maths in Medicine Case Study: Big data for Biologists
Now…we can measure almost anything, all of the time….
                    Big Data

125-1000Hz sampling                             Google images
NC3Rs Maths in Medicine Case Study: Big data for Biologists
Source: Chung, M.K., and Rich, M.W. Introduction to the cardiovascular system. Alcohol Health and Research World
14(4):269–276, 1990.
NC3Rs Maths in Medicine Case Study: Big data for Biologists
NC3Rs Maths in Medicine Case Study: Big data for Biologists
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’
NC3Rs Maths in Medicine Case Study: Big data for Biologists
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 day
24 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 function
Can we predict that a patient will crash,
         before they crash?
                                Endotoxin
 Mean arterial blood pressure

                                            Crash

                                              Time
Macro 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 . 2014
Mini 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 data
Lorenz attractor – a product of chaos
               theory
Can we plot blood pressure data in 3D?
                             Floris Takens –
                             Mathematician
                             1981

                             Philip Aston
                             Mathematician
We only have 1 data stream   University of Surrey
                             2013
Jerome Di Pietro – E-learning technology

           Step 1 – 3D plot with time delays
Step 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                                               140
AR 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 variability
Extracting 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 sources
Telemetry 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
                                   Manchester
Healthy      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          0.54
Pilot 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, Dublin
Summary
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 Care
http://ehealth.kcl.ac.uk/cardiomorph/
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