DISCRETE EVENT SIMULATION OF WHOLE CARE PATHWAYS TO ...

Proceedings of the 2015 Winter Simulation Conference

L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, eds.

DISCRETE EVENT SIMULATION OF WHOLE CARE PATHWAYS TO ESTIMATE COST-

EFFECTIVENESS IN CLINICAL GUIDELINES

Julie Eatock Joanne Lord

Department of Computer Science Health Economics Research Group

Brunel University London

Kingston Lane


Kingston Lane

Uxbridge, UB8 3PH, UK Uxbridge, UB8 3PH, UK

Marta Trapero-Bertran Anastasia Anagnostou

Economics and Business Department; CRES Department of Computer Science

(Health Economics Research Centre)

University Pompeu Fabra


Kingston Lane

c/Ramon Trias Fargas 25-27 Uxbridge, UB8 3PH, UK

Barcelona, 08005, SPAIN

ABSTRACT

For pragmatic reasons, cost-effectiveness analyses performed for NICE Clinical Guidelines use a

piecemeal approach, evaluating only selected aspects of diagnosis, treatment or care. A Whole Pathway

approach, considering diagnosis-to-death, may provide more realistic estimates of costs and health

outcomes, taking account of the healthcare context and individual risk factors, history and choices for

patients with long-term conditions. A patient-level DES model using the characteristics of 12,766 real

patients was created to reflect the NICE guideline for Atrial Fibrillation. Of eight topics suggested for

inclusion in an update of the guideline, the model was capable of fully answering four topics, and

partially answering two topics. The remaining topics were beyond the scope of the model. The model was

used by NICE in their recent update of the Atrial Fibrillation Clinical Guidelines.

1 INTRODUCTION

In England, the National Institute for Health and Care Excellence (NICE) is responsible for assessing the

cost-effectiveness of drugs and other healthcare to be made available through the National Health Service

(NHS). NICE produce clinical guidelines recommending care pathways for certain medical conditions,

and defining criteria for access to services. This guidance is not binding and clinicians are expected to use

their judgment to provide the best care for their patients, but it does influence local decisions about how

much of which services to commission. The guidelines can be very large and complex, and need to be

updated at regular intervals to reflect changes in knowledge and technology.

Initial development and updates are led by a Guideline Development Group (GDG), which includes

clinicians and patient representatives, alongside a methodological team who are responsible for

identifying and analyzing evidence of effectiveness and cost-effectiveness. Due to time constraints, the

process starts with a scoping exercise, in which a limited number of review questions is selected. Cost-

effectiveness modeling is also restricted to selected aspects of the pathway, and therefore runs the risk of

neglecting systemic effects and interactions (Lord et al. 2013).

1447978-1-4673-9743-8/15/$31.00 ©2015 IEEE

Eatock, Lord, Trapero-Bertran, and Anagnostou

The MAPGuide (Modeling Algorithm Pathways in Guidelines) project was set up to test the

feasibility and usefulness of modeling the entire pathway, from diagnosis to death at an individual patient

level, within the context of clinical guidelines. Two exemplars, Prostate Cancer and Atrial Fibrillation,

were chosen to illustrate the approach. These topics were selected as they already had relatively well-

articulated care pathways defined in published NICE guidelines, and were due to be updated within the

timescales of the project. This provided an opportunity to develop models to reflect the existing

pathways, and then to test the models’ ability to address selected cost-effectiveness questions within the

context of guideline updates.

This paper describes the design of the MAPGuide Atrial Fibrillation model, based on the original

NICE guideline (National Collaborating Centre for Chronic Conditions 2006). The ability of this model to

assess the cost-effectiveness of potential changes to the 2006 pathway that were included in the scope for

2014 guideline update is assessed, and subsequent use of the model in the ‘live’ update process is

discussed. Finally some conclusions are drawn about the appropriateness of this type of modeling for

assessing cost-effectiveness in clinical guidelines, and areas for further research are identified.

2 ATRIAL FIBRILLATION

Atrial Fibrillation (AF) is a condition characterized by an irregular or rapid heartbeat (Camm et al. 2010).

Incidence of AF increases with age, and is correlated with comorbidities such as hypertension and

diabetes (National Collaborating Centre for Chronic Conditions 2006).

AF is often asymptomatic and intermittent, but progressive (Kirchhof et al. 2007). In paroxysmal AF

the fibrillation occurs for a short time, usually minutes or hours, but stops spontaneously within 7 days.

The frequency of episodes of paroxysmal AF can vary greatly, from several episodes per day, to months

in between (Shehadeh, Liebovitch and Wood 2002). However, the frequency generally increases as the

condition progresses, due to electrical remodeling in the heart tissue (Nattel 2002). Persistent AF is

defined by the need for medical intervention (pharmacological or electrical cardioversion) to terminate

the fibrillation within 7 days. AF is said to be permanent when it lasts for more than a year or when

cardioversion attempts have failed.

Due to the non-symptomatic nature of many AF cases, it is difficult to diagnose and to classify.

Typically, diagnosis is done through a 12 lead ECG, which is often performed for some other purpose and

for relatively short periods of time. Hence the AF may be missed if the heart is in normal sinus rhythm

during this time and, even once diagnosed, without longer monitoring it is difficult to determine the

duration of fibrillation and whether it will spontaneously terminate. Diagnosis is improved by using

Holter recorders for up to 7 days (Stahrenberg et al. 2010), but still many episodes may be missed.

There is evidence to suggest that the presence of AF increases the incidence of ischemic strokes five-

fold (Camm et al. 2010) and that AF may be the cause of up to 5% of all strokes of unknown origin

(Christensen et al. 2014). Ischemic strokes are caused when a thromboembolism (blood clot) moves and

blocks blood from reaching the brain cells. In order to reduce this risk, oral anti-coagulants are

administered. This in turn, however, increases the risk of a hemorrhagic stroke – where blood vessels in

the brain burst and damage cells. Strokes, of either type, can be catastrophic for the patient and very

expensive for the health and social care system. The risks of recurrence, mortality and disability after

stroke are also related to the type of AF (Ntaios et al. 2013). Algorithms to predict the occurrence of

thromboembolic events in AF patients have had limited success (see Lip et al. 2010 for a review).

Treatment for AF falls under the broad categories of rhythm control or rate control. Rhythm control

drugs aim to maintain sinus rhythm and therefore act to slow the progressive nature of the condition. In

the Clinical Guidelines issued in 2006 (CG36), these drugs were recommended for all patients with

paroxysmal AF, and for some with persistent AF (National Collaborating Centre for Chronic Conditions

2006). For persistent AF patients who met certain criteria and those with permanent AF, ventricular rate

control drugs were recommended. Rate control drugs do not attempt to put the heart in sinus rhythm, but

try to maintain a regular heartbeat of 60-80 beats per minute at rest. There are many classes of rhythm

1448


control drugs available that work by blocking one or more different ion channels (Savelieva and Camm

2008). The cost-effectiveness of rhythm control drugs has been called into question (Camm, Savelieva

and Lip 2007).

3 INDIVIDUAL LEVEL VS COHORT LEVEL IN CONTEXT OF ECONOMIC MODELING

3.1 Economic Modeling

The remit set for NICE by the UK government states that cost-effectiveness as well as clinical

effectiveness should be considered when developing guidance. NICE recommends a standardized set of

methods (a ‘reference case’) to be used in cost-effectiveness analysis (NICE 2013). This includes the

core concept of the quality-adjusted-life-year (QALY) as the method to value health outcomes. QALYs

are defined as value-weighted time i.e. life years weighted by their quality (Weinstein, Torrance and

McGuire 2009). A QALY is the product of a patient’s current health state (health utility) and the time

spent in that state. Health utility is measure on a sliding scale, where 0 represents a state of health

equivalent to death and 1 represents perfect health. So, one year in perfect health yields one QALY, as

would 2 years with a health utility of 0.5. It is further assumed that QALYs are additive across people, so

6 months in perfect health for each of two people also yields one QALY. NICE recommends that QALYs,

like costs, should be discounted to adjust for societal preferences over timing; thus a particular health

state is ‘worth’ more in the near future than the distant future.

The health economists who conduct cost-effectiveness analyses for NICE use decision models to

estimate the long-term costs and health outcomes (QALYs) associated with alternative treatment options

for defined patient groups (Briggs, Sculpher and Claxton 2006). Most usually these take the form of

aggregate (cohort) models, such as decision trees or state-transition Markov models. In Markov models,

distinct health states have utilities and costs attached to them. Time advances in discrete time-steps, and at

each time-step patients move to the next appropriate state, based on transition probabilities. As all patients

move in unison, costs and QALYs can be easily calculated for each time step, and accumulated over the

time horizon. This type of model is generally far quicker to run than a discrete-event simulation (DES)

model, but it has limited ability to reflect complex patterns of disease progression related to individual

characteristics and history (Karnon 2003).

3.2 Advantages of Individual Level Modeling

Clinical guidelines are intended to assist clinicians in making appropriate and cost-effective treatment

decisions given the health status, history and other relevant characteristics of individual patients.

Modeling a whole treatment pathway where decisions can be based on individuals’ characteristics

provides greater flexibility to reflect reality than a cohort-based approach, where movement through the

pathway is based only on average probabilities across a group of patients. Cohort models can be

elaborated to tailor decisions for patient characteristics – for example, by increasing the number of

branches in a decision tree, or by increasing the number of health states or introducing tunnel states in a

Markov model. However, these approaches can quickly become unwieldy if there are many

characteristics to consider. In such cases, the ability to record and update an individual’s characteristics

and to make progress through the pathway conditional on those characteristics can give a more efficient

and compact model structure, and DES is an appropriate tool to achieve this.

The ability to remember patients’ characteristics also allows cost and QALY estimates to be

individualized, which enables assessment of whether particular sub-groups of patients would benefit from

different treatment strategies, while still providing population level results from a single batch of model

runs. The conventional approach in economic decision analysis is to run a series of separate subgroup

analyses, by adjusting the model to reflect different transition probabilities or outcomes for people with

different sets of characteristics (Briggs, Sculpher and Claxton 2006). But again this can become unwieldy,

particularly when there are multiple correlated characteristics that are related to event rates or outcomes,

1449


for example key risk factors for stroke, such as high blood pressure, age and BMI (body mass index) are

highly correlated. Using a DES approach, the correlations between initial characteristics can be

automatically captured by using a real patient dataset.

3.3 Disadvantages of Individual Level Modeling

One of the most obvious disadvantages of modeling at an individual level is the data requirements. A key

input to economic modeling is clinical trial data, which is usually reported as aggregated results with

limited subgroup analysis. So for example, the effectiveness of a particular medication for patients with

paroxysmal AF may be reported, but with no indication of whether it is more or less effective for patients

with particular characteristics such as high blood pressure or BMI. It is also difficult to obtain more basic

data to characterize the incidence, progression and risks of AF over time. As outlined above, diagnosis

and monitoring of AF is problematic due to its intermittent and often asymptomatic nature. While

implanted pacemakers can record data on AF episodes over a long period of time, these are generally only

used for patients with recurrent and symptomatic disease, or who have other heart conditions. Follow up

data for patients diagnosed with AF have been used to estimate how the risk of strokes and other adverse

events are associated with patient characteristics in the short to medium term. There are also population

survey data providing a snapshot of patient characteristics at a point in time. However, to predict long

term outcomes we need to consider how these characteristics change over the patient’s lifetime. So for

example, we would need to model how blood pressure, BMI and smoking change with age, and in

response to lifestyle choices or other health issues.

Another obvious disadvantage of individual level modeling is the time it takes to develop and run the

model. While model run time is becoming less of an issue with the power of computing and the option of

using a distributed system, it takes longer to run than the decision tree and Markov models generally used

by health economists. This problem can be a significant barrier if probabilistic sensitivity analysis is to be

used to characterize uncertainty over population-level parameters, as this requires an outer loop of

iterations, multiplying the time needed to run a model by many times. Another important barrier may be

development time, as when a model becomes more complex the time to de-bug, and validate it increases

exponentially.

Patients may live with AF for many years, so the model needs to run over a long simulated time span

to capture the, possibly lifelong, costs and effects of changes to the pathway. This makes it difficult to

validate that the model is producing realistic results, as outcomes seen in reality in 20 years’ time will

differ from the results in the model in 20 simulated years, not due to model inaccuracies, but due to

technological advance and change in the population and healthcare environment.

4 DESIGN OF MODEL: THE CASE OF AF

The first stage of the project was to build a model based on the 2006 clinical guidelines for AF (CG36)

(National Collaborating Centre for Chronic Conditions 2006). The model development team started by

turning the recommendations in the guideline into a series of flowcharts, to represent the sequence of

treatment decisions and events. The modeling team consulted with clinical experts to resolve ambiguities

and uncertainties over the guideline recommendations. The flowcharts were coded for easy referral to the

specific item in guideline, and formed the basis of the discrete-event simulation model.

The model was designed for evaluating the cost-effectiveness of treatment options rather than as a

planning tool, so while costs were assigned to tests, visits and medications, resource constraints were not

included in the model. For similar reasons, the model was run as a cohort model, with all simulated

patients arriving together at the start of the run and the model terminating when the last patient died.

The sample of patients was drawn from the THIN (The Health Improvement Network) database, and

included 12,766 patients who had been diagnosed with AF. For each patient, 24 characteristics were

recorded that would be used within the model to evaluate risks, and decide treatment options. The

correlations between characteristics was therefore implicit in the data. Some characteristics, such as

1450


hypertension and history of diabetes and stroke were allowed to increase as the patients aged in the

model. However, for simplicity other characteristics such as smoking and BMI remained static.

Figure 1. The current guidelines were converted to a series of flowcharts which combined to provide a

comprehensive view of the management of AF which formed the basis of the DES model.

Table 1. Main competing event types in the model.

Main event types Event sub-types

Thromboembolic (TE) ischemic stroke (IS)

transient ischemic attack (TIA)

other thromboembolic event (OTE)

Hemorrhagic (HE) hemorrhagic stroke (HS)

major bleed

AF progression undocumented events,

self-terminating events

non-terminating events requiring cardioversion

acute arrhythmic events requiring emergency cardioversion

coronary heart disease (CHD)

diabetes

hypertension

age threshold

death

Progression of AF is difficult to characterize, and to some extent the terms paroxysmal, persistent and

permanent are simply ‘labels’ that help determine the appropriate treatment option. A flowchart of

progression and control of AF, incorporating documented and undocumented instances of AF, and

cardioversion events was designed to provide a definitive framework to capture progression. A risk

model was also developed to quantify the risks of key outcomes (as shown in Table 1) conditional on

individual characteristics and treatment effects.

STROKE RISK /ANTICOAGULATION THERAPY (R11, R20,R52, R55, R56, R57, R58) (SR)

Guideline CHAPTER 11

Aspirin (longterm) (R11) SR4

Warfarin (longterm)

(R11, R20, R52)

SR6

Warfarin or aspirin? (R11)

SR5

Moderate riskAge≥65 with no high risk factors

Age <75 with hypertension,

diabetes or vascular disease

WarfarinAspirin

Assess stroke risk

(SR)

contraindicated to warfarin/dabigatran?

SR1yes

DabigatranSR7

Which drug?SR3

dabigatran

assess stroke risk (R55, R56, R57, R58)

SR2

High risk previous ischaemic stroke/

TIA or throboembolic event

age≥75 with hypertension,

diabetes or vascular disease

valve disease or heart failure,

or impaired lef t ventricular

function on echocardiography

Treatment

stra tegy?

SR8

Rhythm control (RY)

Rate control (RA)

AF for the rest of

their lives

no

warfarin

Low risk(age<65

with no moderate

or high risk factors)

TREATMENT OPTIONS FOR PAROXYSMAL (RYpx)(R27, R28, R29, R30, R31, R32, R66)

Guideline CHAPTER 6

Rhythm control for paroxysmal

AF (RYpx)

Is NDT or PiP appropriate? RYpx1

(R33)

No drug treatment (NDT) (R27)

RYpx1b

Pill-in-the-pocket (PiP) (R27, R33)

RYpx1c

Standard beta-blockers (R28)

RYpx2

no

Treatment failure? RYpx3

no

LVD (or CHF)? (R31) RYpx4

yes

CAD? (R29) RYpx4a

1c agent (R29) RYpx5

sotalol (R29, R30)

RYpx6

amiodarone (R29, R30, R31)

RYpx9

Tertiary specialist

(R66) RYpx11

Ongoing management

(R32) (OM)

Patient choice? RYpx1a

yes




no

yesno

nono

yes

yes

no

1st line treatment

2nd line treatment

3rd line treatment

4th line treatment

yes

Pre-treatment

Treatment failure? RYpx1d

yes

no

ACUTE ONSET TREATMENT

(R35, R36, R37, R38, R39, R40. R42)

Guideline CHAPTER 9

ACUTE onset AF

(haemodynamic

instability (HI)

(A)

To check electrolytes and

review CXR. Attempt to

establish aetiology of

acute haemodynamic

instability

no yes

yes

ECG and chest

x-ray A1

Urgent

rate contro l

required?

(R37) A10

!"#$

%&'(#)'**$+,

A21

Intravenous

amiodarone

(R38) A24

Intravenous

heparin

(R39) A7

Intravenous BB

(R38) A17

Is the AF known to

be permanent? A8not known

permanent

life threatening HI?

(R42)? A2

Emergency ECV

(R35) A13

delay in organising

electrical card io-

version? A9

ECV

(R36) A14

Known Wolff-

Parkinson-White

syndrome?

A11

intravenous

amiodarone

(R36) A15

intravenous

flecainide

(R36) A16

Taking

anticoagulation?

A5

known

permanent

Contraindicated to

BB or RLCA?

(R38) A12

Intravenous RLCA

(R38) A18

!"#$%

&'(#)'**$+,

A20

yes

yes

no

heparin

contraindicated?

A6

Onset <48h?

A25

4 weeks of

warfarin

(R40) A27

Factors indicative of a

high risk recurrence?

(R40) A26

Fatal?A3

Death A4

yes

no

no

no

Cardioversion success? A19

in sinus rhythm A23

Not in sinus rhythm A22

no

yes

no

yes

yes

no

no yes

yes

yes

Classification (CL)

nono/unknown

label as persistentnot in sinus rhythm

label as persistent in sinus rhythm

yes yes

!"#$%

&'(#)'**$+,

A28

no

yes / no

no

label as permanent (onset >48h)

!"#$%&'(#)%&*+,-.+/0.1213043+"5+6!7

6#89+#:9+#;<9+#;89+#;=9+#;:9+#>;7

?@2A0B240+!C"DE(#+F

!"#$%&

'&()*&+,-.&

(R7) C1

heparin (R19) C2

warfarin (3 wks) and/or TOE

(R10, R17, R19) C3

Electrical or pharmacological

cardio? (R7) C4

Electrical cardioversion

(ECV) (R7) C7

High risk of failure C5

sotalol or amiodarone (4 wks) (R9)

C8

/$#

Onset confirmed < 48h ago? (R21) C16

warfarin (4 wks) (R18, R19) C15

Cardioversion

inmediate success

C13

Cardioversion

(C)

noyes

+ maintenance of sinus

rythm at 1 and 6 months

(R60) At the 1 month

follow-up the frequency

of subsequent reviews

should be tailored to the

individual pat ient taking

into account

comorbidities and

concomitant drug

therapies (R61) Clinician

to re-assess the need for

continued

anticoagulation (R62) ***

Cardioversion

inmediate success?

C11

assess stroke risk (SR)

CAD or LVD? (R8) C6

Intravenous class 1c

C9

Intravenous amiodarone

C10

Cardioversion

inmediate success? C12

no yes

yes yes

nono

yes

yes

no

> 2 attempts at ECV? C14

no

no

yes (not in sinus rhythm)

permanent

no

no

in sinus rhythm(persistent)

label as persistent

!"#$!%#&!'()"'"$!#'*)&!")+',"$-

,"./0'".10'"22-

345678597'*:$;!#"'<

Rate control (persistent or permanent)

(RA)

The specialist referral

should be considered

especially in those with

lone AF or ECG evidence

of an underlying

electrophysiological

disorder (eg WPW) or

where pharmacological

therapy has failed

beta-blocker (BB) (R23) RA2

Tertiary specialist

(R66) RA12

amiodarone RA10

Ongoing

management

(R32)

(OM)

rate limiting calcium antagonist (RLCA) (R23) RA3

Treatment failure?

RA4

Treatment failure?

RA5

BB+ D (R24) RA6

RLCA + D (R24) RA7

Treatment failure?

RA8

Treatment failure?

RA9

Treatment failure?

RA11

yes yes

yes yes

yes

1st line treatment

2nd line treatment

3rd line treatment

4th line treatment

no

no

no

no

Control during exercise

required? RA1

no yes

no

ONGOING MANAGEMENTGuideline CHAPTER 12

Ongoing MAnagement

(OM)

Event? OM2

Major ‘event’ TE (IS/TIA/other)

Bleed (HS/major)

Other (CHD (MI/UA)/CHF)

OM4

routine follow up

OM7

Classification of AF (CL)

Drug intolerance

OM6

Acute onset AF (A)

Wait for next event

OM1

Calculation of time

to next event

AF control

OM3

Death

New risk factorsMinor events: HT/T2D/

age

OM5

Acute onset?OM12

noGP/OP

silent(undocumented

recurrence)?OM8

no

yes

yes(emergency)

Fatal?OM9

yes

no

Drop in treatment?

OM10

yes

no

undocumentedrecurrence?

OM11

yes

no

CLASSIFICATION OF AF (CL)(R11, R12, R13)

DIAGNOSIS (D)(R2, R3)

Guideline CHAPTER 4

NON ACUTE AF presentation

Asymptomatic and symptomatic patients (NA)

Perform ECG (R2) D1

Discharge D13

Symptomatic

patients

(breathlessness/

dyspnoea;

palp ita tions;

syncope/

dizziness; chest

discomfort;

stroke/TIA); patients

with associated

medical problems

(Heart failure; stroke

or thromboembol ism)

no

Rhythm or rate? (R11)

CL1

assess stroke rIsk

(SR)

cardioversion and assess stroke risk (R13)* (C)

assess stroke risk

(SR)

Rhythm (R13)

Rate: (a) over 65; or

(b) with coronary heart disease (MI or UA); or

(c) with contraindication to ant iarrhythmic drugs; or

(d) with contraindications to ant icoagulants

(bleed); or

(e) with SHD (CAD or LVD); or

(f) history of failed attempts of cardioversion; or

(g) without CHF (R12 and R13)

paroxysmal

Symptomatic

episodes more

than 24h apart?

D7

Event recorder ECG (R3) D8

24h ambulatory ECG monitor

(R3) D9

* patients unsuitable for cardioversion

include those with:

• Contraindications to anticoagulation

• Structural heart disease that

precludes long-term maintenance of

sinus rhythm

• A long duration of AF

• A history of multiple failed attempts

at cardioversion and7or relapses,

even with concomitant use of

antiarrhytmic drugs or non-

pharmacological approaches

• An ongoing but reversible cause of

atrial fibrillat ion

permanent

yes

Symptoms

such as

having

arrhythmia

last night, or

fast heart

beat, etc.

AF for the rest of

their lives

Acute onset AF

(A)

ACUTE ONSET AF presentation (A)

yes no

TTED4

Further assessment needed? D5

TOE D6

yes

no

positive

More investigations?

D12

yes

no

Further

investigation? D3

Suspected paroxysmal?

D2negative

yes

no

* Before assess

stroke risk

Wait for next event

Confirmed paroxysmal

AF? D10

Confirmed paroxysmal

AF? D11

no no

yes


persistent


CLASSIFICATION OF AF

RHYTHM CONTROL

STRATEGY Parox & Persis AF (RY)

Guid. CHAPTERS 6 & 8

ONGOING MANAGEMENT (OM)

Guid. CHAPTER 12

RATE

CONTROL

STRATEGY (RA)

Guid. CHAPTER 7

CARDIOVERSION (C)

Guid. CHAPTER 5

STROKE RISK STRATIFICATION (SR)

Guid. CHAPTER 11

DIAGNOSIS (D)

Guid. CHAPTER 4

ECG D1

TTE D4

24hECG D9ERECG D8

Assess risk

factor? SR2

Aspirin SR4Dabigatran

SR7

Onset <48hrs

ago? C1

Heparin C23 wks

warfarin C3

4 wks sotalo l or

amiodarone C8

Electrical

cardioversion C7

Pharmacological

cardioversion

C9/C10

4 wks

warfarin C15

Schedule

next

appointment

M1

Is PiP or

antiarrhythmic

appropriate? RY1

y

No drug

treatment

RY1b

low/med med/high

n

ACUTE onset AF (A)

Guid. CHAPTER 9

ECG and

chest x-ray A1

AF control OM3

Drug introlerance

OM6

Routine

follow-up OM7

Referral from

GP (NA)

Discharge

D13

TOE D6

ECV A14

Intra flecainide

A16

Intra RLCA A18

Emergency ECV

A13

Intra amiodarone

A24

Warfarin SR6

med/high

PiP

RY1c

Amiodarone

RA10

Death M5

Wait

for

next

event

M2

Classification?

paroxysmal permanentpersistent

Contraindicated

warf/dabig? SR1

ny

Beta-blocker

(BB) RA2

Rate limiting

calcium antagonist

(RLCA) RA3

BB+D

RA6

RLCA+D

RA7

Tertiary

specialist

RA12

Standard

BB RY2

1c agent

RY5

Sotalol

RY6

Amiodarone

RY9

Tertiary

specialist

RY11

4w of warfarin

Intra amiodarone

A15

Intra BB A17

Major event

OM4

New risk factors

OM5

!"#$%&'($)#$&&%"$*+,'-./'0%+%1/0/+"'*('%"#$%&'

($)#$&&%"$*+'!"#$%&'()*+(,*-%.#/012'23+/'4556

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The model used a time to next event design, as illustrated in Figure 2. After initial diagnosis, the

model calculates the time to all the competing events (described in more detail in section 4.1), and

identifies the event that is due to occur first. The patient then waits, accumulating costs and QALYs

associated with their current health condition and treatment, until the event occurs. If this is a non-AF

related event, such as reaching an age threshold or developing diabetes, the patient’s characteristics are

updated and the risks and time to competing events re-calculated. However following an AF-related

event, such as disease progression or stroke, the patient receives short-term treatment if appropriate, their

costs and QALYs are updated, and their subsequent AF treatment is reviewed and possibly changed. This

cycle is repeated until the next event scheduled is death, when the patient exits the model.

4.1 Calculating Time to Event

Published epidemiological sources were used to estimate individuals’ risks of the included outcomes in

the absence of treatment. Rates of AF progression were based on longitudinal studies of patients with AF

in Canada and Europe (Kerr et al. 2005; Nieuwlaat et al. 2005; Nieuwlaat et al. 2008; Camm et al. 2011).

Rates of hemorrhagic and thromboembolic events were estimated from a large AF cohort in Sweden

(Friberg, Rosenqvist and Lip 2012), with rates varying according to individual risk factors as quantified

by the HAS-BLED and CHA2DS2-VASc algorithms (Lip et al. 2010; Pisters et al. 2010). Rates of

coronary heart disease, diabetes and hypertension were all based on equations from the Framingham

Heart Study (Anderson et al. 1991; Wilson et al. 2007; Parikh et al. 2008). A proportion of patients who

experienced AF-related hemorrhagic and thromboembolic events died within 30 days. Non-AF related

mortality rates came from national life table data (www.ons.gov.uk). These background risks were

modified to account for the effectiveness of the treatments where appropriate. So, for instance, an

anticoagulant decreased the risk of thromboembolic events but increased the risk of hemorrhagic events.

The relative effects of treatments were estimated from the clinical trial literature.

The chance of not having a particular event until a time t, given an adjusted hazard rate of λ is

modelled by an exponential survival function (i.e. 1-e-λt

). This assumes that the hazard remains constant

Figure 2. Overview of model structure (left) and final DES model built using Simul8 (right).

Rate Control

Diagnosis

Classification

Emergencies

Cardioversion

Stroke Risk

Rhythm Control

On-going

Management

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over the period of time modelled. However, as patients move through the model, their risk of a particular

type of event may change in response to other associated risks, or as they age. We modelled this using a

piecemeal exponential distribution, in which the hazard is constant except for changes at defined points in

time (when an event has occurred or an age-threshold has been crossed).

At model entry, each patient is assigned a random number for each of the main types of event (see

Table 1). This could be considered as a proxy for unknown factors that influence a patient’s propensity

for that particular type of event. For each of the three composite events in Table 1 (i.e. thromboembolic,

hemorrhagic, or progression), patients are assigned second random number, which determines which sub-

type of event will occur next (i.e. IS, TIA or OTE in the case of a thromboembolic event). This approach

ensures that related groups of events are not treated as independent.

Once an event has occurred, care is needed in adjusting the times for competing events. For example,

it would be counterintuitive if the predicted time to a TE event were to increase following a bleed. To

avoid this, the random numbers used to determine the time to the next events are only resampled when

that specific type of event has occurred. This approach produced a piecemeal increasing function for each

of the competing risks (as illustrated in Figure 3).

5 MODEL OUTPUTS

The model was designed to produce results at two levels, individual diaries of patients’ experiences and

interactions with the healthcare system, and aggregated cohort-level outputs for use in cost-effectiveness

analysis. The individual level diaries (see Figure 4) provided a simple but effective way for clinicians to

verify and assess the model. The diary recorded the individual’s medical history and their associated risks

when they entered the process, and their subsequent treatment and events as the patient progressed

through the model. Costs and QALYs were also recorded at each stage of the process (see Figure 4) to

enable reviewing of calculations.

The cohort-level outputs were designed for use in cost-effectiveness analyses. These recorded the

numbers of events, costs and QALYs across the patient sample. For each version of the pathway model,

results were summarized using a net benefit statistic. This is calculated by multiplying the total QALYs

by a monetary value – the cost-effectiveness threshold λ – and then subtracting total costs. We used a

threshold of £20,000 per QALY in our analyses, the lower limit of the range recommended by NICE.

Probabilistic Sensitivity Analysis (PSA) was used to account for uncertainty over the input parameters. In

PSA, the model is repeatedly run, each time drawing a set of input parameters from defined probability

distributions (using random, Monte Carlo simulation). There were therefore two nested sets of iterations:

an outer layer of PSA sampling and an inner layer of individual simulation. The output from the model

was presented as the mean and variance of the net benefits across the PSA iterations. Different versions of

the care pathway can be compared in terms of net benefit: the most cost-effective pathway (at a defined

level of λ) being that which produces the maximum net benefit.

Figure 3. Cumulative probability of three competing events over 30 years for an individual patient.

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Figure 4. Model Outputs: Patient Diary (top) and Cost and QALY reports (bottom).

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6 USE OF MODEL DURING GUIDELINE UPDATE PROCESS

The model was designed to reflect the current guideline and answer questions on cost effectiveness on

potential guideline updates. During the building of the model, the model developers were unaware of the

specific questions that would be asked as part of the update process. This ensured that the model was not

designed specifically to answer the update questions, but to determine whether a whole pathway DES

model could be utilized and adapted as part of the update process. There were 8 topics identified by the

GDG that would be considered in the update process (see Table 2).

Table 2. Update topics.

Topic Description

A Prophylaxis for the prevention of post-operative AF

B Anti-arrhythmic drugs as pharmacological cardioversion (PCV) for people with atrial fibrillation

C Rhythm versus rate control for persistent AF; subgroups including those with hypertension,

previous MI and congestive heart failure

D Treatment for maintaining Sinus Rhythm in people with AF after cardioversion.

E Alternative risk factor based scoring systems to estimate stroke and embolism risk

F Stratification tools to assess bleeding risk before prescription of antithrombotic medication

G Apixaban, rivaroxaban or Dabigatran etexilate versus warfarin as for patients at moderate or high

risk of stroke or systemic embolism

H Cather ablation for paroxysmal and persistent AF patient

The model was therefore able to answer some of the update questions but as the nature of the

questions were unknown to the modeling team during development, other update questions were

unanswerable in the time given. The GDG were unfamiliar with the simulation software, so Topics C, E

and F were included by the model development team as menu driven options from the start screen of the

model, while Topics B and G were addressed by making changes in the spreadsheet containing input data

to the model, which the GDG were able to do themselves.

An additional benefit of the process of building the model was that during development we were able

to highlight to the GDG any existing inconsistencies and ambiguities within the current guideline which

could be corrected in the update.

7 DISCUSSION

The model accurately captured the AF care pathway as described by the clinical guideline, and was able

to produce estimates of costs and health outcomes from diagnosis-to-death for the simulated patient

cohort. Using a patient level DES model allowed realistic modeling of the treatment pathway, with

decisions based on individual characteristics and history. The model used the characteristics of real

patients, which provided implicit correlations between the characteristics that would be difficult to

replicate with distribution-generated characteristics. Arguably, this individual-level simulation approach

resulted in a much more complex model with far greater data requirements than a more conventional cost-

effectiveness model.

Once the model was complete it was provided to the GDG for their clinical update of the AF

guideline. Despite the modeling team being unaware of the questions that would be raised during the

update process during the development of the model, it allowed some of the update questions to be

answered, but other questions would have required re-modeling of certain aspects of the model. As the

GDG were unfamiliar with the software, the model development team designed a user interface to enable

testing of some of the topics, while others could be tested by altering the data within the input parameter

spreadsheet. This highlighted the need to be familiar with both the software and the model itself, in order

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to use the model to address specific cost-effectiveness questions. This suggests that access to specialist

DES expertise or training for economic modelers would be necessary to implement this approach in

routine guideline development. The time taken to build the model was greater than that usually available

to the GDG, and therefore would be prohibitive. Now built, however, the model has the potential to be re-

used, with little additional development for subsequent guidelines. The model could also be extended to

include prevalent (existing) cases as well as incident (new) cases, which would help assess the budget

impact of the changes across a population.

Some members of the GDG were unfamiliar with, or did not have access to, the software and

therefore couldn’t fully review the model. The flowcharts that were created as part of the model

development process were highly accessible and understandable and linked to the existing guidelines.

These together with the individual patient diaries were used by the members to review the model design

and output, providing the development group with confidence in the model’s results. An additional

benefit of creating the flowcharts was that inconsistencies and ambiguities within the original guideline

could be addressed in the update.

8 CONCLUSION

In summary, the individual level modeling adds complexity, but allows a much more realistic

representation of the clinical pathway based on a patient’s experiences. It must be mentioned that there is

more to guideline development than cost-effectiveness analysis. The GDG are responsible for identifying

areas of clinical interest, and reviewing the available evidence for relevance and quality, and consulting

with stakeholders. Therefore the time available for cost-effectiveness analysis is extremely limited, and

this needs to be taken into account when assessing the usefulness of different modeling techniques.

ACKNOWLEDGMENTS

The MAPGuide project was funded by the Medical Research Council and the National Institute for

Health Research through the Methodology Research Programme (G0901504). Julie Eatock is currently

funded by an EPSRC grant (EP/K037145/1) Forecasting personal health in an uncertain environment.

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AUTHOR BIOGRAPHIES

JULIE EATOCK is a Research Fellow at Brunel University London. Her main area of research is

discrete event simulation modeling, and she has applied this in many different areas including information

systems and policing. In more recent times her interests have tended towards healthcare and she has

applied her skills in areas such as healthcare service provision, the impact of disruptive technology, and

cost-effectiveness. Her email address is [email protected].

JOANNE LORD is a Reader in Health Economics at Brunel University London conducting applied and

methodological research into the cost-effectiveness of health care technologies. She previously worked at

NICE, Imperial College Management School and St George’s Hospital Medical School. Her first job was

in the Operational Research Service at the Department of Health. Joanne has a BSc in Economics and

Mathematics, MSc in Operational Research and PhD in health economics. Her email address is

[email protected].

MARTA TRAPERO-BERTRAN is a Research Fellow at Universitat Pompeu Fabra (UPF) and

Universidad Castilla-La Mancha (UCLM). Her main research interests include modeling techniques for

economic evaluation, transmission of lifestyle behavior, and incorporation of external effects into

economic evaluation. She is the former President of the Spanish Association of Health Economics (AES).

She defended her PhD in Health Economics at HERG-Brunel University. Her email address is

[email protected].

ANASTASIA ANAGNOSTOU is a Research Fellow at the Department of Computer Science, Brunel

University London. She holds a PhD in Hybrid Distributed Simulation. Her research interests are related

to the application of modeling and simulation techniques in the Healthcare sector and Industry. Her email

address is [email protected].

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