Algorithms and tools to support medical decision making -Case studies-

Corresponding author: Leandro Pecchia. [email protected] 15 November 2013

AHP

Algorithms and tools to support medical decision making

-Case studies-

Leandro Pecchia

AHP

Corresponding author: Leandro Pecchia. [email protected] 2/32

RESEARCH BACKGROUNDRESEARCH BACKGROUNDMe

2005 2009 2011 2013

RF1UNINA

RF2NOTT

Ass. Prof.PHD

2007

Career path:2013-2016: Assistant Professor, University of Warwick

2011-2013: Research Fellow (RF2), University of Nottingham

2009-2011: Research Fellow (RF1), UNINA*

2005-2009: PhD in Biomedical Engineering, UNINA*

May 2005: BSc+MSc in Electronic Eng., UNINA*

*UNINA= University Federico II of Naples, Italy

Research interests: Biomedical signal processing and second level pattern recognition/data-mining

Early stage Health Technology Assessment (HTA) and User Need Elicitation methods

My main applications: active/healthy ageing: chronic cardiovascular diseases and falls in elderly

Disease Management Programs, patient pervasive monitoring and Telemedicine

Ω≈f(stress, fat, salary, ↓ free-t,…)

AHP


Contributions and outputs (1/2)Contributions and outputs (1/2)Me

Signal processing and pattern recognition for Cardiovascular disease (CVD)to identify Congestive Heart Failure (CHF) [early diagnosis]

2011, “Long-term HRV & CHF detection ”, Med & Biol Engin & Computing, 49 (1):67- 74

2011, “Short-term HRV & CHF detection”, IEEE T Inf Technolog in Biomed, 15 (1):40-46.

to manage chronic CVD monitoring its damages and severity…[early detection of risks]2012, “HRV &Organ Damage in Hypertension”, BMC Cardiovascular Disorders, 12:105

2013, “Long-term HRV & CHF severity assessment”, IEEE J. of Biom. and Health Informatics, 17(3): 727-733

…also in remote monitoring applications: [telemedicine]2011, “Remote Health Monitoring of CHF”, IEEE T Bio-Med Eng, 58 (3):800-804

2011, “A feasibility study on telemedicine”, Biomedical Engineering Online, 10: 49

Other signal processing and pattern recognition applications2011, “Nonlinear HRV for real-life stress detection”, Biomedical Engineering Online 10: 96

2012, “Pupillometric analysis for assessment of gene therapy”, Biomedical Engineering Online,11(1):40

2013, “Infant cry analysis for early detection of Autism”, ICHI2013 (+ submitting 2 journal papers)

AHP


Contributions and outputs (2/2)Contributions and outputs (2/2)Me

Medical decision making is complex and multidisciplinary and needs:Quantitative knowledge: from the best available evidence (RCT, meat-analyses, network meta-analyses)

Qualitative knowledge: to interpret the top of the EBM pyramid into everyday clinical practice

Methods for the “impact” of BME researches: quantify qualitative knowledgeUser need elicitation using the Analytic Hierarchy Process (AHP) method

2013, “User needs elicitation via AHP”, BMC Medical Informatics and Decision Making, 3(1):2

2013, “AHP & auto-injection of epinephrine”, HIS2013, 25-27 March 2013 in London.

2011, “Factors affecting wellbeing in elderly”, ISAHP 2011, Sorrento, Naples, Italy.

Health Technology Assessment (HTA), especially for early stages of technology development2013, “HTA & AHP”. In Studies in Fuzziness and Soft Computing, ed. Springer, Volume 305,

2013, “ HTA, Telemedicine, CHF”. In Telehealthcare Computing and Engineering: Principles and Design. Science Publishers. ISBN 978-1-57808-802-7 (book chapter)

2013. “Enhanced Remote Health Monitoring.. In Telehealthcare Computing and Engineering: Principles and Design, ed. Science Publishers. ISBN 978-1-57808-802-7 (book chapter)

2012, “Network meta-analysis & mini-invasive surgery”, Surgical Endoscopy, 2012 Jun 16, [Epub ahead of print]

2012, “RCT for innovative biological drug”, Hernia, 20 November 2012 Nov, [Epub ahead of print]

2011, “Meta-analysis & minimally invasive surgery”, Minimally Invasive Therapy & Allied Technologies, 21(3):150-60

June 2012, Treasurer of the HTA Division of International Federation of BME, IFMBE

Risk factors for falls in elderly home dwellingMany intrinsic risk factors are related to physiological condition that can be detected

2011, “Risk factors for falls”, Methods of Information in Medicine, 50 (5):435

2010, “Risk factors for falls”, International Journal of the Analytic Hierarchy Process, 2 (2)


AHP

-Case study 1-SHARE Project*:

home monitoring for patients suffering from Congestive Heart Failure (CHF)

Leandro Pecchia

*Smart Health and Artificial intelligence for Risk Estimation grant PON04a3_00139 to PM; 2007-2013

Italian National Operational Programme for Research and Competitiveness.

PI: Dr Paolo Melillo

AHP


INTROINTRO

• Congestive Heart Failure (CHF) is • leading cause of hospitalization among the elderly in developed countries • up to 50% of patients are rehospitalized within 3 months.• Mortality ranges from 10% in patients with mild HF to 40% in severe cases• COSTS: direct treatment costs of HF represent 2–3% of the total healthcare budget

• In literature there are three main models of care:• Usual care (UC): outpatient follow-up (GP guided) as recommended by guideline• Disease Man. Programs (DMP): UC + specialized doctors/nurses proactively at home• Home Monitoring (HM): DMP + Information and Communication Technologies (ICT)

• The goal of HM, DMP and UC for CHF is to:• ↑QoL (or at least maintain stable)• ↓mortality (for CHF and for all causes),• ↓NHS costs by: ↓ bed days (CHF/all causes); ↓readmissions (CHF/all causes)

INTROCS1

AHP


GOALSGOALS

• Not all the HM is equally important and independent contribution of ICT is unclear

• Thus, the goals of the SHARE project are:• To identify those elements that make HM more effective than DMP and UC (WP1)• To design the most effective HM program • To develop the ICT system to support such a HM• To test its cost-effectiveness with 2 clinical trials

GOALS

N° Mese 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Anno

Durata

Giu

gn

o

Lu

gli

o

Ag

ost

o

Set

tem

bre

Ott

ob

re

No

vem

bre

Dic

emb

re

Gen

nai

o

Feb

bra

io

Mar

zo

Ap

rile

Mag

gio

Giu

gn

o

Lu

gli

o

Ag

ost

o

Set

tem

bre

Ott

ob

re

No

vem

bre

Dic

emb

re

Gen

nai

o

Feb

bra

io

Mar

zo

Ap

rile

Mag

gio

Giu

gn

o

Lu

gli

o

Ag

ost

o

Set

tem

bre

Ott

ob

re

No

vem

bre

Dic

emb

re

Gen

nai

o

Feb

bra

io

Mar

zo

Ap

rile

Mag

gio

WP1 6 mesi

WP2 15 mesi

WP3 15 mesi

WP4 14 mesi

WP5 12 mesi

WP6 -

2012 2013 2014 2015

WP1: HTA of HMWP2: HM and ICT platform R&D

WP3: Algorithms R&D WP4: Observational Study

WP5: Prospective studyWP6: PM/Dissemination

CS1

AHP


METHODS 1/2METHODS 1/21. Meta-analyses were performed comparing DMPvsUC & HMvsUC

• Only well designed RCT were included• Outcome considered:

o ↑QoL (or at least maintain stable)o ↓mortality (for CHF and for all causes),o ↓ bed days (CHF/all causes); o ↓readmissions (CHF/all causes)

2. Classified all the HM RCT according to: patients’ severity and HM complexity

3. We studied the correlations between the last 6 outcomes and • patients’ severity• HM complexity

4. According to these, the clinical trial and the ICT platform were designed

5. Algorithms for early detection of CHF worsening were • Developed using public DB available• Adapted using the information collected during the WP4 [now]• Integrated into the ICT platform

[2014]

• Assessment of cost-effectiveness in WP5 [2016]

METHODSCS1

AHP


METHODS 2/2METHODS 2/2• HM RCT were classified according to patients’ severity and HM complexity

• Correlations effectiveness-severity and effectiveness-complexity were computed

METHODS

PATIENTSEVERITY

NYHA CLASSES

MEAN PATIENTS

AGE

75 -

70 -

EJECTION FRACTION

FREQUECY OF THE MONITORING

Parameters &

symptoms

HM COMPLEXITY

OnlySymptoms

Signal & Parameters

& symptoms

DATA MONITORED

CS1

AHP


RESULTSRESULTS

• Study selection:

RESULTS

314 papers

23 DMP vs UC8 HM vs UC1DMP & HM vs UC

32 papers

Full paper selection:1 Not heart failure7 Editorial or review16 not an RCT40 Other heart failure intervention or research10 Study design4 Other languages5Short Follow-up

115 papers

Title and abstract seletion:8 Not heart failure18 Invasive hemodynamic monitoring43 Editorial or review70 not an RCT41 Other heart failure intervention or research19 Study design

CS1

AHP


RESULTSRESULTSRESULTS

•There is evidence that DMP are more effective that UC•Reducing All-causes mortality•Reducing readmission (All-causes and HF-related)

•It seems, but there is not evidence that DMPs reduce bed-days

CS1

AHP


RESULTSRESULTS

• Mortality: HM vs UC

All causes HF mortality

RESULTS

The HM RCT seems (no statistically significant result) more effective than UC

CS1

AHP


RESULTSRESULTS

• Readmission: HM vs UC


RESULTS


CS1

AHP


RESULTSRESULTS

• Bad days: HM vs UC


RESULTS


CS1

AHP


RESULTSRESULTS

• HM RCT classification: Patients’ severity

RESULTS

NYHA Ejection Fraction Mean AgePatient Severity

Sherr 2008 3 <40 73 8Dendale, 2011 3 <40 76 7Dar, 2009 3 >40 72 6Koehler,2011 2&3 <35 67 5Antonicelli, 2010 2&3 <40 78 4Soran, 2008 2&3 <40 76 4Kulshreshtha, 2010 2&3 <40 68 3Giordano, 2010 2 <40 70 2Mortara, 2009 2 <40 60 1

CS1

AHP


RESULTSRESULTS

• HM RCT classification: HM protocol complexity

RESULTS

Simptomps Parameters Signals Frequency Complexity

KOEHLER, 2011 x ECG Daily 6

KULSHRESHTHA, 2010 X Daily 5

DAR, 2009 X X Daily 4

SORAN, 2008 X X 4

GELLIS, 2012 X Daily 4

SHERR, 2008 X Daily 3

DENDALE, 2011 X Daily 3

ANTONICELLI, 2010 X X ECG Weekly2

MORTARA, 2009 X X ECG Weekly2

GIORDANO, 2010 X ECG Each 15 days1

CS1

AHP

Corresponding author: Leandro Pecchia. [email protected]

RESULTSRESULTS

• HM outcomes vs Pz complexity and HM complexity:

RESULTSPA

TIEN

T SE

VERI

TYH

M

COM

PLEX

ITY

Survival Saved Bed days Reospedalization

No significant correlation



• Evidence:• HM is more effective for more severe patients

(SHARE now focus on NYHA >2)• More complex HM are more effective (SARE

is designed accordingly)17/32

CS1

significant correlation



AHP


RESULTSRESULTS

• How this informed the SHARE the clinical protocol?o Enrolling a proper number of severe cases [about 300 subjects in 12 months]o Acquiring daily useful symptoms, parameters and signalso These info will be daily reviewed to early detect patients’ worsening

• …and how the ICT platform?

• The DSS (WP3) will support clinician in modulating patient therapyo What these algorithms does and how they look like?

RESULTS

18/32

NEXUS10(4), MindMedia-sensors: - EXG: ECG, EMG, EOG, EEG- SpO2, BVP, Body Temperature, Breathing acts, GSR

-Communication: Bluetooth-Memory: Up to 7 days recording memory-Costs: from £5k to £12k, according to the sensors

BioHarmess3, Zephyr-sensors: ECG, 3axial accellerations, Breathing-up to 3 days recording memory-Communication: Bluetooth/ZigBee-Memory: Up to 7 days recording memory-Cost: £350 (≤10) or £200 (>10)

Healthcare Professionals

(App)

BIOMEDICAL SIGNALPROCESSING

(wearable devices)

REMOTE PROCESSING

(DDS)

WARNING

APPROPRIATE INTERVENTION

CS1

AHP



19/32

• Autonomous Nervous System (ANS) controls human equilibrium (homeostasis)• Normal subjects show a good degree of variability in body functionalities, reflecting a continuous state of unstable equilibrium • Unstable equilibrium is complex to control, but allows faster state changes• This allow humans to react promptly to:

– internal changes (i.e. emotions, stress)– external treads (i.e. the lion…)

• Monitoring these changes we can estimate the status of a subject and how stable it is…

CS1

AHP



20/32

Signal processing: features extractionin time-, frequency-, nonlinear- domain

Pattern recognition: signal/patient classifications“normal vs CHF”, “mild vs severe”, “damage vs sane”

Signal pre-processing: filtering, beat recognition (normal vs abnormal),…

Peculiarities of these signals:Bandwidth (0- few tens of Hz)Low S/N ratio in bandNo stationary (FFT cannot be used!)Strong non linearity and high dependence from parameters (chaos?)

Problems for pattern recognitionLimited casesNatural patterns (no human-generated)Last but not least…

…our methods/results are needed by clinicians that cold be not skilled in mathematical methods!

CS1

AHP



21/32

ECGPREPROCESSING

HRVDETECTION

HRVFEATURES EXTRAC.

CARTTRAIN/TEST

FEATURESCOMBINATION

PERFORMANCEASSESSMENT

Node 1TOTPWR<451

Node 2TOTPWR<212

Node 3LF/HF<1.35

Node 4RMSSD <12.4

Node 5LF/HF<2.44

Node 6RMSSD <16.3

Node 7RMSSD <28.9

Node 8LF/HF<1.42

Node 9HF < 115

Leaf 1Class = 1

Leaf 2Class = 1

Leaf 6Class = 1

Leaf 3Class = 1

Leaf 4Class = 0

Leaf 7Class = 0

Leaf 8Class = 0

Leaf 9Class = 1

Leaf 10Class = 0

Leaf 5Class = 0

Detection: long & short HRV(Guidelines says that 12-leads ECG is not enough to diagnoses

HF)

Severity (NYHA) assessment

HRV to identify Congestive Heart Failure (CHF)A) 2011, Med & Biol Engin & Computing 49 (1):67- 74

B) 2011, IEEE T Inf Technolog in Biomed 15 (1):40-46.

HRV to manage CHF monitoring its severity…C) IEEE J. of Biom. and Health Informatics, 17(3): 727-733

D) BMC Cardiovascular Disorders, 12:105 [organ damages]

A) B) C)

CS1

AHP


CONCLUSIONSCONCLUSIONSCONCLUSIONS

• Not all the HM strategies are equally effective:• HM is more effective on more severe patients• more complex HM interventions seems more effective than less complex ones

• However, the increased quantity of information requires:• Reliable technological solution• Smart algorithms to extract the useful information• Integrated management strategies

• The preliminary results of the algorithms developed are promising on public DB

• These SHARE trials will generate reliable databases for the adaptation of these algorithms.


AHP

-Case Study 2-A software tool to support

the Health Technology Assessment (HTA) and the user need elicitation of medical devices via the Analytic Hierarchy Process (AHP)

L. Pecchia1, F. Crispino2, S. Morgna3

1 University of Warwick, The United Kingdom2 Business Engineering, Avellino, Italy3 University of Nottingham, The United Kingdom

[email protected]

AHP


AHP for HTA & User Need Elic.AHP for HTA & User Need Elic.HTA/UNE

MULTIDIMENSIONAL EVALUATION

IDENTIFY EXISTINGTECHNOLOGIES

CLINICALEPIDEMIOLOGICAL

ECONOMICAL …ETHIC/SOCIAL

DATA ANALYSIS

RELATIVE ASSESSMENT

NEED ANALYSIS

PRIORITIZATIONINDIVIDUATION CLASSIFICATION

PERFORMANCEEFFICACY EFFICIENCY

How to prioritize the needs?

How to measure the fitting between MD performance and needs??

How to measure the MD performance in non-clinical domains?

INTRODUCTION METHOD RESUTLS COMCLUSIONS

CS2

AHP


Developing (or selecting) the health technology for a clinical problem(i.e. congestive heart failure)


25/32

↓ mortality↓ mortality ↓ worsening↓ worsening ……usabilityusability educationeducation serviceservice …… Initial costInitial cost ReadmissionC.ReadmissionC.↑ qaly↑ qaly ……

Technological domain(services/spare parts/ Human F)

[Medical Eng.]


[Medical Eng.]

Economical domain(costs)

[Hosp. Managers]


[Hosp. Managers]

AHP for HTAHierarchy via an exempla

Clinical domain(effectiveness/utility)

[clinicians/cardiologists/ger.]



ALTERNATIVE 1Disease Management Program


ALTERNATIVE 3ALTERNATIVE 3Active Implantable DeviceActive Implantable Device

ALTERNATIVE 3ALTERNATIVE 3Active Implantable DeviceActive Implantable Device

ALTERNATIVE 2ALTERNATIVE 2TelemedicineTelemedicine

ALTERNATIVE 2ALTERNATIVE 2TelemedicineTelemedicine

AHP

How important is each need for the assessment? [needs prioritization]How each alternative satisfy each factor? [MD performance]How each alternative fit with the goal? [MD/Goal fitting]


CS2

AHP


N1 N2 N3N1 1 I21 I31

N2 I12=1/I21 1 I32

N3 I13=1/I31 I23=1/I32 1

N1 N2 N3N1 1 I21 I31

N2 I12=1/I21 1 I32

N3 I13=1/I31 I23=1/I32 1

AHP method pairwise comparisons Process

Much less important

Less important

Equally important

More important

Much more

Numerical values

N1>N2 & N2>N3 =>

N1 >> N3

N1 > N3

N1 < N3

(5)

(3)

(1)

(1/3)

(1/5)

NEEDS’ INDIVIDUATION

JUDGEMENTS MATRIX (J)

TREE OF NEEDS

DATA POOLING

QUESTIONNAIRES

RELATIVE IMPORTANCE OF NEEDS

ALTERNATIVES’PERFORMANCE ASSESSMENT

ALTERNATIVES’ PRIORITIZATION

CONSISTENCY RATIO(CR)

IFCR >0.1 Eigenvector

(priorities)

Eigen value(coherence)

AHP

EXPERTS


RELATIVE IMPORTANCE OF NEEDS’ CATEGORIES

Need 1(↓mortality)

important henmuch less

lessequallymoremuch more

is :Need 3

(↑QALY)

Need 3(↑QALY)



is:Need 2

(↓Pz. worsening)

Need 2(↓Pz. worsening)



Is:Need 1

(↓mortality)

Need 1(↓mortality)



is :Need 3

(↑QALY)

Need 3(↑QALY)



is:Need 2

(↓Pz. worsening)

Need 2(↓Pz. worsening)



Is:Need 1

(↓mortality)

According to your experience, how important is each need on the left compared with each one on the right?

CS2

AHP


AHP method Analytic needs prioritization

Method

↓ worsening↓ worsening ↓ mortality↓ mortalityusabilityusability educationeducation serviceservice Initial costInitial cost ReadmissionC.ReadmissionC.↑ qaly↑ qaly




[Medical Eng.]


[Medical Eng.]


[Hosp. Managers]


[Hosp. Managers]





11

Cat

SCW1

2Cat

SCW1

3Cat

SCW

21

Cat

SCW2

2Cat

SCW2

3Cat

SCW3

1Cat

SCW3

2Cat

SCW

1Cat

CW2Cat

CW3Cat

CW

1GW 2GW 3GW 4GW 5GW 6GW 7GW 8GW

11

111

1 *

CatCatCat

SC SCWCWGWGW




ALTERNATIVE 3Active Implantable Device

ALTERNATIVE 3Active Implantable Device

ALTERNATIVE 2Telemedicine


CS2

AHP


AHP for HTAAHP

28/32

ALTERNATIVE 1DMP

ALTERNATIVE 1DMP



↓ worsening↓ worsening

↓ mortality↓ mortality

usabilityusability

educationeducation

serviceservice

Initial costInitial cost

ReadmissionC.ReadmissionC.

↑ qaly↑ qaly

ALTERNATIVE 3Active Implantable D.

ALTERNATIVE 3Active Implantable D.

Global importance

Global importance

1GW

2GW

3GW

4GW

5GW

6GW

7GW

8GW

11

AP

12AP

13AP

14AP

15AP

16AP

17AP

18AP

21

AP

22AP

23AP

24AP

25AP

26AP

27AP

28AP

31

AP

32AP

33AP

34AP

35AP

36AP

37AP

38AP

8

1

*i

Ajiij PGWeAlternativPerfomanceGlobal


CS2

AHP


• Method: AHP for HTA/User need elicitation• Applications: Publication in Healthcare whit the App• Models: to be downloaded and adapted in your study• Community: experts willing be involved

The systema web tool with App

AHP

29/32

http://www.ahpapp.net/


CS2

AHP



AHP


• Users:• The elicitor:

• design/pilot the hierarchy and the questionnaires, • invites domain experts and final responders, • pool the results; • generate the report;• publish the results on the web portal.

• The domain expert: • review the hierarchy/questionnaires, • suggest final responders or other domain experts;

• The final responder:• under invitation, • download the hierarchy• answer the questions.

CS2

AHP



AHP

• Two possible scenarios:• S1: Local

• elicitor, domain experts and the final responders in the same place • Using the APP to speed-up the process and find consensus

• S2: Remote• elicitor, domain experts and the final responders NOT in the same place, • Using the APP and the portal to cooperate to the study via the web.

• Functionalities:• Create the Hierarchy: problem definition/hierarchy draft• Download an existing hierarchy: to be used as starting model• (only S2) Invite domain experts: study piloting• (only S2) Amend the hierarchy • (only S2) Invite responders• (only S2) Participate to the study• Analyse and Pool results• Generate a report• Publish: upload on the portal hierarchy ¦¦ results ¦¦ reports¦¦papers


CS2

AHP


CONCLUSIONSCONCLUSIONSConcluding


• This is the first tool specifically designed to:• perform shared decision making in healthcare• involve lay-users into the decisional process (paramount important for HTA)• applying the AHP to the HTA/the user need elicitation in healthcare

• Medical Decision making is complex (…not necessary difficult!)

• Methods have to be:• Reliable• Well tested according to clinical practices• Intelligible (no black boxes)• Easy to use/understand for people not skilled in maths• Traceable (you may have to prove that you did the best you could after years)

AHP


Thank you!

Leandro

AHP


HTA HTA standard methods

HTA

MULTIDIMENSIONAL EVALUATIONMULTIDIMENSIONAL EVALUATION

IDENTIFY EXISTINGIDENTIFY EXISTINGTECHNOLOGIESTECHNOLOGIES

CLINICALCLINICALEPIDEMIOLOGICALEPIDEMIOLOGICALECONOMICALECONOMICAL ……ETHIC/ETHIC/

SOCIALSOCIAL

DATA ANALYSISDATA ANALYSIS

DISSEMINATION DISSEMINATION OF INFORMATIONOF INFORMATION

MONITORINGMONITORING

RELATIVE ASSESSMENT (VERSUS BENCHMARK)RELATIVE ASSESSMENT (VERSUS BENCHMARK)

NEED ANALYSISNEED ANALYSIS

SCORINGSCORINGINDIVIDUATIONINDIVIDUATION CLASSIFICATIONCLASSIFICATION

PERFORMANCEPERFORMANCEEFFICACYEFFICACY EFFICIENCYEFFICIENCY

Example 3 – not cost effectiveExample 5 – highlighting data requirementsExample 1 – cost effectiveExample 4 – price optimisationExample 4 – price optimisation

EFFECT DIFFERENCE(QALY?)

COST DIFFERENCE+

+--

red

uce

pri

ce

Reduce data uncertainty

* NHS NICE willingness-to-pay ‘threshold’ range of £20,000-£30,000 per QALY.

*

AHP


HTA HTA Limits of standard methods

HTA limits

VS

L Pecchia, MP Craven, “Early stage Health Technology Assessment (HTA) of biomedical devices. The MATCH experience” . World Congress on Medical Physics and Biomedical 2012, 26-31 May 2012, Beijing, China.

AHP


Headroom AnalysisHeadroom Analysis

Method 1

p1

30k£/QALY

QALY

p3

DEVICE Production Costs

OTHER NHS Costs

HEADROOMp2

U

C

p’2

http://www.match.ac.uk/

AHP


4 states Markov Models 4 states Markov Models disease/worsening/exacerbation/dead

MM

HAVE DISEASE(C1,U1)

DEAD(C2,U2)

1-pd

-pw-p

e

WORSE DISEASE(C3,U3)

pwpwb

↓ worsening (p’w < pw)IN

NO

VA

TE

DE

VIC

E

ped

1-peb-p

ew-ped

EXACERBATION

(C4,U4)

pwe

pwe

pe

pd

pwd

peb

HAVE DISEASE(C’1,U1’)

DEAD(C2’,U2’

)

WORSE DISEASE(C’3,U3’)

p’wpwb ped

EXACERBATION

(C4’,U4’)

pwe

P’we

p’e

pd

pwd

peb

↓ exacerbation (p’e< pe & p’we < pwe)

U

CC/U =30K/QALY

1-pw

b-pw

d-pw

e

1-pw

b-pw

d-p’w

e1-

pd-p

’w-p

’e

1-peb-p

ew-ped

37/33http://www.match.ac.uk/

AHP


Markov Models & AHPMarkov Models & AHP

MM&AHP

HAVE DISEASE(C1,U1)

DEAD(C2,U2)

1-pd

-pw-p

e

WORSE DISEASE(C3,U3)

pwpwb

INN

OV

AT

E D

EV

ICE

ped

1-peb-p

ew-ped

EXACERBATION

(C4,U4)

pwe

pwe

pe

pd

pwd

peb

HAVE DISEASE(C’1,U1’)

DEAD(C2’,U2’

)

WORSE DISEASE(C’3,U3’)

pwpwb ped

EXACERBATION

(C4’,U4’)

pwe

pwe

pe

pd ± p

pwd

peb

1-pw

b-pw

d-pw

e

1-pw

b-pw

d-pw

e1-

pd-p

w-p

e

1-peb-p

ew-ped

What if some information are missing (eHTA)?Missing data can be estimated using AHP...

…and using sensitivity analysisto estimate worst/best cases.

38/33

Documents

Algorithms and tools to support medical decision making -Case studies-