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The UKPDS Outcomes model:The UKPDS Outcomes model:A detailed overview A detailed overview

Dr Philip Clarke

Health Economics Research Centre & Diabetes Trial Unit, University of Oxford, UK

The UKPDS Outcomes Model Workshop 25th February 2005

Christ Church, Oxford

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• Objective & Methods

• Epidemiological module

–Forecasting a profile of diabetes-related complications based on patient’s characteristics

• Risk factor progression module

–Model estimated and user defined time paths for risk factors (e.g. HbA1c)

• Quality of life module

–Assumptions regarding utility decrements caused by events

• Costs module

Overview of the UKPDS Outcomes modelOverview of the UKPDS Outcomes model

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• To construct a comprehensive model for estimating health outcomes for people with type 2 diabetes, that:–uses long-term (median 10.3 years) follow-up data–predicts risk of major diabetes-related complications–captures time varying risk factors such as HbA1c and history

of previous complications–estimates lifetime health outcomes of alternative

management strategies in terms of:• event rates• life expectancy & quality adjusted life expectancy

Objective Objective

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MethodsMethods

• A probabilistic discrete-time model with annual cycles

• Uses an integrated set of parametric proportional hazard

models to predict absolute risk of first occurrence of seven

major diabetes-related complications, based on:– patients’ characteristics (e.g. age and sex)

– time varying risk factors (e.g. HbA1c)

• Patients start with specified health status & can have one or

more non-fatal complications or die in any model cycle

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Variable Mean (SD) unless stated otherwise

Age (years) 53 (8)

Proportion male (%) 60

Ethnicity (% white/Asian/Indian/Afro-Caribbean/other) 82/10/8/0

Body mass index (kg/m2) 27.7 (5.3)

Fasting plasma glucose (mmol/l)* 7.9 (6.6-10.0)

Haemoglobin A1c (%) 7.1 (1.8)

Systolic blood pressure (mm Hg) 135 (19)

Low density lipoprotein cholesterol (mmol/l) 3.5 (1.0)

High density lipoprotein cholesterol (mmol/l) 1.06 (0.24)

* Median (interquartile range)

(n = 3642 newly diagnosed patients, median follow(n = 3642 newly diagnosed patients, median follow--up = 10.3 years)up = 10.3 years)

Characteristics of the populationCharacteristics of the population

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Epidemiological moduleEpidemiological module

UKPDS Outcomes model

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EventEvent--related dependenciesrelated dependencies

• Need to account for event-related dependence between some

complications

–Event-related dependencies were estimated empirically

during equation-fitting procedure

–Included if p<0.05

–All significant dependencies were checked against the

known epidemiology

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Summary of model equationsSummary of model equations

DIABETES MORTALITY(In subsequent years)

Ln (AGE_EVENT) 113.40TOTAL:HDL 1.12MI_EVENT 51.38 MI_POST 3.06STROKE_EVENT 16.56STROKE_POST 1.00CHF 1.00AMP 2.81RENAL 4.88

Ischaemic Heart Disease (IHD)

AGE 1.03FEMALE 0.62HbA1c 1.13SBP 1.10Ln (TOTAL:HDL) 4.47

(Eq.1, 231 events)

Heart failure (CHF)AGE 1.10HbA1c 1.17SBP 1.12BMI 1.07

(Eq. 3, n = 97)

Blindness (BLIND)AGE 1.07HbA1c 1.25

(Eq. 6, 104 events)

Fatal and non-fatal myocardial infarction (MI)

AGE 1.06FEMALE 0.44AC 0.27SMOK 1.41HbA1c 1.13SBP 1.11Ln (TOTAL:HDL) 3.29IHD 2.49CHF 4.75

(Eq. 2, n = 495)

STROKEAGE 1.09FEMALE 0.60SMOK 1.43ATRFIB 4.17HbA1c 1.12 SBP 1.32TOTAL:HDL 1.12CHF 5.71

(Eq. 4, n = 157)

Amputation (AMP)PVD 11.42HbA1c 1.55SBP 1.26BLIND 6.12

(Eq. 5, 40 events )

Renal failure (RENAL)SBP 1.50BLIND 8.02

(Eq. 7, 24 events)

OTHER DEATH(In force at all times)

AGE × FEMALE 1.08AGE × (1-FEMALE) 1.11SMOK 1.36

(Eq. 10, 250 deaths)

EVENT FATALITY (odds ratios)(In year of first event)Ln (AGE_EVENT) 16.00HbA1c 1.12MI_EVENT 14.01STROKE 2.85RENAL 1.00AMP 1.00CHF 1.00

Diabetes related mortality

(Eq. 8, 717 deaths) (Eq. 9, 100 deaths)

DIABETES MORTALITY(In subsequent years)

Ln (AGE_EVENT) 113.40TOTAL:HDL 1.12MI_EVENT 51.38 MI_POST 3.06STROKE_EVENT 16.56STROKE_POST 1.00CHF 1.00AMP 2.81RENAL 4.88

Ischaemic Heart Disease (IHD)

AGE 1.03FEMALE 0.62HbA1c 1.13SBP 1.10Ln (TOTAL:HDL) 4.47

(Eq.1, 231 events)

Heart failure (CHF)AGE 1.10HbA1c 1.17SBP 1.12BMI 1.07

(Eq. 3, n = 97)

Blindness (BLIND)AGE 1.07HbA1c 1.25

(Eq. 6, 104 events)

Fatal and non-fatal myocardial infarction (MI)

AGE 1.06FEMALE 0.44AC 0.27SMOK 1.41HbA1c 1.13SBP 1.11Ln (TOTAL:HDL) 3.29IHD 2.49CHF 4.75

(Eq. 2, n = 495)

STROKEAGE 1.09FEMALE 0.60SMOK 1.43ATRFIB 4.17HbA1c 1.12 SBP 1.32TOTAL:HDL 1.12CHF 5.71

(Eq. 4, n = 157)

Amputation (AMP)PVD 11.42HbA1c 1.55SBP 1.26BLIND 6.12

(Eq. 5, 40 events )

Renal failure (RENAL)SBP 1.50BLIND 8.02

(Eq. 7, 24 events)

OTHER DEATH(In force at all times)

AGE × FEMALE 1.08AGE × (1-FEMALE) 1.11SMOK 1.36

(Eq. 10, 250 deaths)

EVENT FATALITY (odds ratios)(In year of first event)Ln (AGE_EVENT) 16.00HbA1c 1.12MI_EVENT 14.01STROKE 2.85RENAL 1.00AMP 1.00CHF 1.00

Diabetes related mortality

(Eq. 8, 717 deaths) (Eq. 9, 100 deaths)

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Why do we “link” the equations?Why do we “link” the equations?

Jonathon Brown has noted that: “The difficulty is not just that there are so many potential events, but the occurrence of one event substantially increases the likelihood of the other events…Under these circumstances it can be catastrophically inaccurate to simulate each kind of event as if it were statistically independent” (Brown et. al. The global diabetes model: user friendly version 3.0, Diabetes Research and Clinical Practice, 50 Suppl 3(2000) S15-S46.)

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Algorithm of model process Algorithm of model process Start: define the following characteristics for each patient in the simulation:Age at diagnosis, Ethnicity, Sex, BMI, HbA1c, Total:HDL(Lipids), Blood pressure, Smoking status, Atrial fibrillation at diagnosis, peripheral vascular disease (PVD) at diagnosisHistory of diabetes-related events: Ischaemic Heart Disease (IHD), Congestive Heart Failure (CHF), Blindness, Amputation, Renal failure, Myocardial infarction (MI), Stroke

Randomly order and run event equations:Ischaemic Heart Disease (IHD) Eq. 1Myocardial infarction (MI) Eq. 2Congestive Heart Failure (CHF) Eq. 3Stroke Eq. 4Amputation Eq. 5Blindness Eq. 6Renal failure Eq. 7Diabetes-related mortality (conditional on CHF, amputation,renal failure, MI or stroke having occurred) Eq. 8&9Other mortality Eq. 10

Update patient risk factors using risk factor equations:HbA1c Eq. 11Blood pressure Eq. 12Total:HDL Eq. 13Smoking Eq. 14Update history of diabetes-related events

Calculate lifeyears & QALYs

Commence yearly model cycle

Dead?Update history of

diabetes-related events

yes no

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Probabilities and timing Probabilities and timing

DEATH

CHF

IHD

STROKE

MI

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

1 2 3 4 5 6 7

Years (since diagnosis)

Prob

abili

ty

IHDMICHFStroke

Simulation period

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Internal validation (I)Internal validation (I)

• Consistency of the forecast cumulative incidence

–Number of events predicted from the second to the twelfth year after diagnosis

–Using information from the first year after diagnosis for the 3642 patients in the trial

• Number of simulated patients experiencing each event is initially based on baseline levels of risk factors and on their time paths

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Internal validation II (MI)Internal validation II (MI)

0

0.05

0.1

0.15

0.2

1 2 3 4 5 6 7 8 9 10 11 12Years since diagnosis

Cum

ulat

ive

inci

denc

e

ObservedEstimatedUpper 95% CILower 95% CI

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Internal validation III (Death)Internal validation III (Death)

0

0.05

0.1

0.15

0.2

1 2 3 4 5 6 7 8 9 10 11 12Years from diagnosis

Cum

ulat

ive

inci

denc

e

ObservedUpper 95% CIEstimatedLower 95% CI

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Risk factor Progression moduleRisk factor Progression module

UKPDS Outcomes model

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•Separate equations to represent the time paths from diagnosis ofdiabetes of four risk factors :

•HbA1c

•Systolic Blood Pressure

•Ratio of Total: HDL

•Smoking status

•e.g. Current HbA1c is determined by the last value of HbA1c, value at diagnosis, years from diagnosis and an indicator variable to capture the decline in HbA1c following the initial period of treatment

Risk factor progressionRisk factor progression

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Time path of HbATime path of HbA1c1c

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Years from diagnosis

HbA

1c(%

)

5%7%12%Mean

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Quality of Life moduleQuality of Life module

UKPDS Outcomes model

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Quality of lifeQuality of life

• The potential impact of complications on quality of life is important

• Quality of life survey was administered in 1996-7 to 3,667 UKPDS patients

• EQ-5D survey completed by the patient when they attended UKPDS clinics

• 3,302 survey forms returned of which 3,192 were fully completed

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EQEQ--5D Survey5D Survey

• A simple generic measure which assesses health status in terms of five dimensions (domains)

• Produces a single summary score or 0-1 index of health status that is used to quality adjusted life years.

• Patients also rate current overall health status on a visual analogue scale (VAS)

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EQEQ--5D: Self description5D: Self description

• Five domains–Mobility –Self care–Usual activities–Pain/ discomfort–Anxiety/ depression

• Three levels for each domain:–No problem–Some, or moderate problems–Unable, or extreme problems

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Assumptions regarding Assumptions regarding QALYsQALYs

Complication Effect on utility

MI -0.055 (-0.042, -0.067)IHD (angina) -0.090 (-0.054,-0.126)Stroke -0.164 (-0.105, -0.222)Heart Failure -0.108 (-0.048, -0.169)Amputation -0.280 (-0.170, -0.389)Loss of sight in one eye -0.074 (-0.025,-0.124)Source: Clarke PM, Gray AM, Holman R. Estimating Utility Values for Health States of Type 2 Diabetic Patients Using the EQ-5D,Medical Decision Making 2002; 22(4):340-349

No complications 0.785

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A schematic quality of life profileA schematic quality of life profile

Time (Years)

Stroke

Amputation

Death

Quality of life scale (0-1)

0

0.78

8

No complications1.00

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Costs moduleCosts module

UKPDS Outcomes model

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Methods and dataMethods and data• Regression based methods to estimate the immediate and long-term impact

of seven diabetes related complications on health care costs

• Data available for all UKPDS patients on:

– occurrence and timing of pre-defined diabetes related complications

– all hospital inpatient episodes with associated specialities, lengths of stay, and procedures

– non-inpatient health care use from cross-sectional survey of all patients in study in 1996 (n = 3,488)

• Hospital inpatient costs calculated for each recorded episode using national average specialty-specific costs per in-patient day

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£0

£2,000

£4,000

£6,000

£8,000

£10,000

£12,000

£14,000

No com

plica

tion

Fatal M

INon

fatal

MI

Fatal S

troke

Non fa

tal S

troke

Angina

(IHD)

Heart f

ailure

Blindn

ess i

n one

eye

Amputat

ion

Catarac

t extr

actio

nExpected mean hospital inpatient costs of Expected mean hospital inpatient costs of

complications (probability x conditional cost) complications (probability x conditional cost) (£s 1999) (£s 1999) –– short termshort term

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Expected mean hospital inpatient costs of Expected mean hospital inpatient costs of complications (probability x conditional cost) complications (probability x conditional cost)

(£s 1999) (£s 1999) –– long termlong term

£0

£200

£400

£600

£800

1,000

Non fatalMI

Non fatalstroke

Angina(IHD)

HeartFailure

Blindnessin one eye

Amputation Cataractextraction

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Schematic representation of resultsSchematic representation of results

Year2 64

Ann

ual c

ost £

s (no

t to

scal

e)

Patient 2: non fatal MI in year 4 with long term impact

Patient 1: no complications

1 3 5 7 8

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ReferencesReferences

• Clarke PM, Gray AM, Holman R. Estimating Utility Values for Health States of Type 2 Diabetic Patients Using the EQ-5D, Medical Decision Making 2002; 22(4):340-349.

• Clarke PM et al. The impact of diabetes-related complications on healthcare costs: results from the United Kingdom Prospective Diabetes Study (UKPDS Study No. 65). Diabetic Medicine 2003; 20(6):442-50.

• Clarke PM et al. A model to estimate the lifetime health outcomes of patients with Type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes M d l (UKPDS 68) Di b l i 2004 4 1 4 1 9