The document examines the comparative effectiveness of ACE inhibitors (ACEI) and angiotensin receptor blockers (ARB) in reducing the risk of dementia in elderly patients with type 2 diabetes and hypertension. It presents a newly developed RXDX risk index aimed at predicting dementia, alongside a comparison of existing comorbidity indices. The study highlights the importance of proper confounding control and provides a methodological framework for future research into dementia risk factors.

1. Hemalkumar B. Mehta, MS
PhD Candidate in Pharmaceutical Health Outcomes and Policy
Comparative Effectiveness of
ACEI and ARB for the Risk of Dementia in Elderly
Patients with Diabetes and Hypertension
1

2. Outline
2
Introduction
Objectives
Methods
Results
Conclusions

3. Why elderly patients with type 2 diabetes and hypertension?
Why dementia outcome?
Why compare ACEI and ARB?
How to properly account for confounding?
Introduction
3

4. HypertensionType 2 Diabetes
Two Major Public Health Problems
1 CDC, 2011; 2 Zhang, 2010; 3DIabetes Atlas, 2013; 4Wang, 2004; 5Yoon, 2012; 6Heidenreich, 2011
4
Prevalence
• Adults: 60%
• Elderly: 88%
Cost
• $131 billion (2010)
• $389 billion (2030)
Prevalence
• All age: 8.3%
• Elderly: 26.9%
Cost
• $198 billion (2010)
• $264 billion (2030)

5. 1 + 1 = 3
7Deedwania, 2005 5
HypertensionType 2 Diabetes
Diabetes + Hypertension = “Deadly Duet”

6. Elderly Patients with “Deadly Duet”
Conditions are At High Risk of Dementia
8Plassman, 2007; 9Hendrie, 2007; 10Fillit, 2012; 11Cukierman, 2005 6
96% of dementia patients
≥65 years of age
Diabetes patients are twice
as likely to develop
dementia
Hypertension is an
independent risk factor for
dementia

7. Burden of Dementia
12Alzheimer's disease facts and figures, 2013 7

8. Treatment of Patients with Type 2
Diabetes and Hypertension
13ADA, 2011 8
ADA Guideline
“Should be with a regimen
that includes either an
ACEI or an ARB”
ACEI: Captopril, enalapril, lisinopril, ramipril
ARB: Losartan, valsartan, irbesartan, telmisartan

9. ACEI, ARB and Cognitive Function
14
9

10.  ONTARGET clinical trial
 ARB (Telmisartan) vs. ACE (Ramipril) - Secondary endpoints
 Cognitive impairment: OR = 0.90 (95%CI: 0.80-1.01)
 Epidemiological Studies
ACEI, ARB and the Risk of Dementia
15 Anderson, 2011; 16 Johnson, 2012; 17Li, 2010; 18 Davies, 2012; 19Yasar, 2013 10
Study Drug treatment HR, 95% CI
Johnson et al., 2012 ARB versus non-users 0.78 (0.71–0.85)
Li et al., 2010 ARB versus Lisinopril 0.81 (0.68-0.96)
Davies et al., 2011
ARB versus other antihypertensive
ACEI versus other antihypertensive
0.47 (0.37-0.58)
0.76 (0.69-0.84)
Yasar et al., 2013
ARB versus none
ACEI versus none
ARB versus ACEI
0.31 (0.14-0.68)
0.50 (0.29-0.83)
0.62 (0.27-1.40)

11.  Baby boomers
 By 2050, 88.5 million older Americans (20.2%) of total population
 ↑ Incidence of type 2 diabetes and hypertension
 Greater risk for dementia
 Treatment that can delay dementia onset by few years
 Major public health implication
 Blood Pressure – time varying confounder affected by
previous treatment history
 Prior studies did not properly account for it
Significance: CER of ACEI and ARB
11

12. CER of ACEI versus ARB for the Risk of
Dementia
12
ACEI
ARB

13. 13

14. Controlling Confounding in
Observational Studies
14
ACEI
ARB
C

15. 1. Categorical variables for comorbidities
2. Charlson comorbidity score (CCS) – 1987
 Diagnosis based score – 17 diseases
 Adaptation in administrative claims data – ICD-9-CM algorithms
3. Chronic disease score (CDS) – 1995
 Rx based score – 29 disease categories
 Drugs and drug classes - American Hospital Formulary system
How to Control for Confounding?
20Charlson, 1987; 21Clark, 1995; 22Austin, 2013 15

16. Including CCS and CDS Improves
Confounding Control
16
ACEI
ARB
CCS
CDS

17. Including CCS and CDS improves
Confounding Control
23Schneeweiss, 2001; 24Mehta, 2013; 25Mehta, 2013; 26Mcgregor, 2006 17

18. Including CCS and CDS improves
Confounding Control
18

19. Including CCS and CDS improves
Confounding Control
19

20. Including CCS and CDS improves
Confounding Control
20

21. Including CCS and CDS improves
Confounding Control
21

22. Issues with Existing Dementia Specific
Risk Indices
27Exalto, 2013; 28Barnes, 2009; 29Barnes, 2010; 30Reitz, 2010; 31Kivipelto, 2006; 32Mehta, 2012; 33Exalto, 2013 22
Issues
Use of genetic, MRI or MMSE
information
Use of lab values – How to deal
with missing values
No index included Rx medications
as risk factors
Why two separate indices based
on Diagnosis and Prescription
drug use
Dementia Risk Index
Mid-life dementia risk
Late-life dementia risk index
Brief dementia risk index
Summary risk score for
Alzheimer’s disease
Late-life dementia risk index – GE
data
Diabetes-specific dementia risk
score (DSDRS)

23.  Why to include two separate variables in the model?
 Charlson comorbidity score
 Chronic disease score
 Why not combine two informations in a single summary
score?
RxDx risk index
 Conditions will be identified from
Bright !dea
23
Rx Dx

24.  First study to combine diagnosis and prescription drug
information in one risk index
 Easily applicable to claims and EMR data
 Diagnosis
 Prescription
 New tool for confounding control
 Useful for identifying patients at risk
 Steps can be taken to target modifiable risk factors for dementia
Significance: RxDx Risk Index
24

25. Summary
25
ACEI
ARB
RxDx
Risk
Index

26. Objectives
26
Aim 1
• Develop RxDx risk index to predict dementia in
patients with type 2 diabetes mellitus and
hypertension
Aim 2
• Compare RxDx risk index with Charlson
comorbidity index and Chronic disease score to
predict dementia
Aim 3
• To compare ACE versus ARB for the risk of
dementia in patients with type 2 diabetes
mellitus and hypertension

27. Data Source
Study Cohort
Variables
Statistical Analysis
Methods
27

28.  Electronic medical record data, United Kingdom (UK)
 Information entered by general practitioner (GPs)
 12 million patients - 593 general practices
 Anonymized longitudinal clinical and prescribing information
 Clinical diagnoses are recorded using medcodes
 High validity (Median = 89%)
 Prescription information is well documented
 Computerized system generates each prescription
 Clinically Rich
 >450 lab measures
Clinical Practice
Research Data
34 Williams, 2012; 35Lewis, 2002 28

29.  Elderly (age ≥ 60 years)
 Diagnosed with type 2 diabetes
 Diagnosis or (abnormal lab value + oral hypoglycemic agent)
 Diagnosed with hypertension
 Diagnosis or (abnormal lab value + antihypertensive agent)
 Prevalent dementia cases were excluded
Study Cohort
29

30.  Covariates
 Age
 Gender
 Risk indices
 Lab measures
 Blood pressure
 HbA1c
 Body mass index (BMI)
 Smoking status
 Alcohol consumption
 Albumin
 Serum creatinine
 Platelets count
 Pottasium
 Total white blood cell count
 Cholesterol, HDL and LDL
 Exposure
 ACEI / ARB
Variables
30
 Outcome
 Time to dementia

31.  29 disease categories based
on Rx drug use
 Developed algorithm to map
CDS to multiples Rx coding
system
 Two sets of weights
Chronic disease score
 17 disease categories
 The CCS has been adapted to
CPRD data medcodes system
 Three sets of weights
Charlson comorbidity index
Risk Indices
20Charlson, 1987; 36Schneeweiss, 2003; 37Quan, 201; 21Clark, 1995; 38Johnson, 2006 31

32.  29 disease categories based
on Rx drug use
 Developed algorithm to map
CDS to multiples Rx coding
system
 Two sets of weights
Chronic disease score
 17 disease categories
 The CCS has been adapted to
CPRD data medcodes system
 Three sets of weights
Charlson comorbidity index
Risk Indices
32

33.  29 disease categories based
on Rx drug use
 Developed algorithm to map
CDS to multiples Rx coding
system
 Two sets of weights
Chronic disease score
 17 disease categories
 The CCS has been adapted to
CPRD data medcodes system
 Three sets of weights
Charlson comorbidity index
Risk Indices
33

34.  RxDx risk index – 31 disease conditions
 Few disease conditions were merged
 CDS: Pain/Inflammation and Pain are separate conditions
 Not possible to identify separately using diagnosis
 CCS: Renal disease and ESRD
 Not possible to identify using Rx drugs
RxDx-Dementia Risk Index
34
Rx Dx

35. Statistical Analysis – Aim 1
35
Jan 1, 2003
Index date
One year
prior
Jan 1, 2002 Dec 31, 2012
Baseline year to
construct risk index
Index date: Date when patient was diagnosed with
type 2 diabetes mellitus and hypertension

36.  Cox proportional hazards regression
 Dependent variable – Time to dementia
 Independent variables
 Age and gender
 RxDx disease conditions
 Censoring – discontinue, died, combination, end of study
 Weights were assigned based on beta coefficient
 Rule: Beta * 10  round it to the nearest integer
 Discrimination: c-statistics
 Validation: 10 cross-fold validation
 Calibration: Hosmer-Lemeshow calibration
Statistical Analysis – Aim 1
39Harrell, 2001 36

37.  Discrimination
 C-statistics (0.7-0.8 = acceptable; 0.8-0.9 = excellent)
 Net reclassification index (NRI)
 Assesses risk reclassification and is the sum of improvement in
cases and in controls
 Positive NRI indicates “new” model is better compared to “old”
 Integrated discrimination index (IDI)
 Difference in discrimination slopes between two models
 Positive IDI indicates “new” model is better compared to “old”
Statistical Analysis – Aim 2
39Harrell, 2001; 40Cook, 2009 37

38. Statistical Analysis – Aim 3
38
Jan 1, 2003
Index date
One year
prior
Jan 1, 2002 Dec 31, 2012
Baseline year to
construct
covariates
Index date: Date when patient started taking ACEI or ARB
New users study design

39.  Descriptive Statistics
 Multivariable time-dependent Cox model
 Dependent variable – Time to dementia
 Independent variables
 Drug exposure (quarterly)
 Demographics, comorbidities and clinical measures
 Censoring – discontinue, died, combination, end of study
Statistical Analysis – Aim 3
39

40.  Marginal Structural Model (MSM)
 Blood Pressure (BP) - time dependent confounder affected by
previous treatment history
 Time dependent Cox model – biased estimate
 MSM – unbiased estimate – IPTW estimator
Statistical Analysis – Aim 3
41Robins, 2000; 42Hernan, 2000; 43Delaney, 2009; 44Gerhard, 2012 40
BPt0 BPt1
Et0 Et1
Fixed confounders

41. Results | Discussion
41

42. Objectives
42
Aim 1
• Develop RxDx risk index to predict dementia in
patients with type 2 diabetes mellitus and
hypertension
Aim 2
• Compare RxDx score with Charlson comorbidity
index and Chronic disease score to predict
dementia
Aim 3
• To compare ACE versus ARB for the risk of
dementia in patients with type 2 diabetes
mellitus and hypertension

43.  Cohort included 133,176 patients
 Means age = 72.12 years (SD =8.04)
 52% of patients were male
 Incidence of dementia = 3.42%
Aim 1: Descriptive Statistics
43
Disease categories
identified using
diagnosis
n, (%)
Disease categories
identified using
prescription drug
use (n, %)
Disease categories
identified using
diagnosis or
prescription drug
use (n, %)
Chronic pulmonary
disease
3,501 (2.63) 27,154 (20.39) 27,583 (20.71)
Rheumatologic disease 1,443 (1.08) 13,154 (9.88) 13,426 (10.08)
Peptic ulcer disease 332 (0.25) 42,474 (31.89) 42,521 (31.93)

44. Aim 1 - Deriving Points for RxDX
Conditions
44
Beta Estimates
(p-value)
Hazard Ratio
(95% CI)
Points
Myocardial infarction -0.16 (0.21) 0.85 (0.65-1.10) -2
Congestive heart failure -0.07 (0.02) 0.93 (0.87-0.99) -1
Coronary and peripheral
vascular disease
0.15 (<0.001) 1.16 (1.07-1.26) 1
Cerebrovascular disease 0.36 (<0.001) 1.43 (1.23-1.67) 4
Epilepsy 0.33 (<0.001) 1.39 (1.22-1.58) 3
Hyperlipidemia 0.11 (<0.001) 1.11 (1.05-1.19) 1
Parkinson’s disease 0.78 (<0.001) 2.19 (1.81-2.64) 8
Depression 0.61 (<0.001) 1.84 (1.71-1.97) 6
Psychotic illness 0.63 (<0.001) 1.88 (1.71-2.06) 6

45. CalibrationCross - Validation
Aim 1: Cross-Validation | Calibration
45
RxDx-Dementia
Risk Index
C-statistics 0.806 (0.798 – 0.814)
Ten-fold
cross-validation
C-statistics
0.806 (0.800 – 0.813)
1 2 3 4 5 6 7 8 9 10PercentageofDementiacases
RxDx risk index deciles
Observed % Expectd %
* Likelihood-ratio Chi-square (df = 9) = 217.65;
P-value = <0.0001

46.  First study to combine diagnosis and prescription
information in a single summary risk index
 RxDx-Dementia Risk Index
 Control confounding in observational studies
 Prognostic tool
 Identify patients who are at high risk
 Interventions can be focused on modifiable risk factors for dementia
 Can be used in EMR as well as claims data
 Diagnosis and prescription data are available
 No issue of missing values
Discussion for RxDx-Dementia
46

47. Objectives
47
Aim 1
• Develop RxDx risk index to predict dementia in
patients with type 2 diabetes mellitus and
hypertension
Aim 2
• Compare RxDx score with Charlson comorbidity
index and Chronic disease score to predict
dementia
Aim 3
• To compare ACE versus ARB for the risk of
dementia in patients with type 2 diabetes
mellitus and hypertension

48. Aim 2: RxDX-Dementia Risk Index Performance
Model Risk Index C-Statistics (95% CI) NRI, % (P-value) IDI, % (P-value)
1 Baseline model (Age + Gender) 0.779 (0.773-0.786) 5.63 (<0.001) 1.93 (0.52)
RxDx-Dementia risk index
2 RxDx-Dementia risk index 0.806 (0.798-0.814)
Reference
(New Model)
Reference
(New Model)
3 RxDx risk index categorical* 0.807 (0.801-0.813) 0.21 (0.38) -0.06 (0.95)
Charlson comorbidity score (CCS)
4 Charlson original 0.782 (0.775-0.788) 6.47 (<0.001) 1.91 (0.52)
5 Charlson/Schneeweiss 0.782 (0.776-0.788) 6.50 (<0.001) 1.92 (0.52)
6 Charlson/Quan 0.781 (0.775-0.788) 5.63 (<0.001) 1.93 (0.52)
7 Charlson categorical† 0.783 (0.777-0.789) 6.13 (<0.001) 1.84 (0.53)
Chronic disease Score (CDS)
8 CDS original 0.789 (0.782-0.795) 5.90 (<0.001) 1.67 (0.56)
9 CDS/RxRisk – V 0.787 (0.779-0.794) 6.59 (<0.001) 1.80 (0.54)
10 CDS categorical‡ 0.805 (0.798-0.812) 0.86 (0.02) 0.09 (0.96)

49. Model Risk Index C-Statistics (95% CI) NRI, % (P-value) IDI, % (P-value)
RxDx-Dementia risk index
2 RxDx-Dementia risk index 0.806 (0.798-0.814) Reference Reference
3 RxDx risk index categorical* 0.807 (0.801-0.813) 0.21 (0.38) -0.06 (0.95)
Combined comorbidity score
(CCS+CDS)
12
Charlson original + CDS
original
0.789 (0.783-0.795) 5.88 (<0.001) 1.67 (0.56)
13
Charlson/Schneeweiss + CDS
original
0.789 (0.783-0.795) 5.94 (<0.001) 1.67 (0.56)
14 Charlson/Quan + CDS original 0.789 (0.782-0.796) 5.98 (<0.001) 1.68 (0.56)
15
Charlson original + CDS/RxRisk
– V
0.787 (0.781-0.793) 6.06 (<0.001) 1.79 (0.54)
16
Charlson/Schneeweiss + CDS/
RxRisk-V
0.787 (0.780-0.794) 6.11 (<0.001) 1.79 (0.54)
17
Charlson/Quan + CDS/RxRisk –
V
0.787 (0.781-0.793) 6.44 (<0.001) 1.80 (0.54)
Aim 2: RxDX-Dementia Risk Index Performance

50.  Performed superior
compared to CCS, CDS or
CCS + CDS
 Performance is superior or
comparable to existing
dementia-specific risk
indices
 Future studies
 RxDxClin-Risk Index
Risk Index C-Stat
RxDx – Dementia 0.81
Mid-life dementia risk 0.75
Late-life dementia risk index 0.81
Brief dementia risk index 0.77
Summary risk score for
Alzheimer’s disease
0.79
Late-life dementia risk index –
GE data
0.72
Diabetes-specific dementia
risk score (DSDRS)
0.75
Discussion for RxDX-Dementia
Performance
27Exalto, 2013; 28Barnes, 2009; 29Barnes, 2010; 30Reitz, 2010; 31Kivipelto, 2006; 32Mehta, 2012; 33Exalto, 2013 50

51. Objectives
51
Aim 1
• Develop RxDx risk index to predict dementia in
patients with type 2 diabetes mellitus and
hypertension
Aim 2
• Compare RxDx score with Charlson comorbidity
index and Chronic disease score to predict
dementia
Aim 3
• To compare ACE versus ARB for the risk of
dementia in patients with type 2 diabetes
mellitus and hypertension

52. Aim 3: Descriptive Statistics
52
Characteristics
Total*
n = 32,856; n (%)
ACE users
n = 28,562; n
(%)
ARB Users
n =3,943; n (%)
P-value
Age, mean (SD) 71.55 (7.86) 71.52 (7.88) 71.79 (7.77) 0.04
Age Categories
60-64 years 7,490 (22.80) 6,572 (23.01) 836 (21.20) 0.03
65-69 years 7,220 (21.97) 6,275 (21.97) 878 (22.27)
70-74 years 6,891 (20.97) 5,986 (20.96) 821 (20.82)
75-79 years 5,517 (16.79) 4,730 (16.56) 721 (18.29)
80-84 years 3,515 (10.70) 3,047 (10.67) 431 (10.93)
>85 years 2,223 (6.77) 1,952 (6.83) 256 (6.49)
Male, n (%) 17,600 (53.57) 15,642 (54.77) 1,810 (45.90) <0.0001
RxDx-Dementia risk
index
0.71 (3.74) 0.72 (3.75) 0.66 (3.67) 0.31

53. Aim 3: CER of ACEI and ARB
53
Treatment Risk Ratio
95% Confidence
Interval
ARB vs. ACEI
Unadjusted 0.86 0.73 – 1.01
Time-dependent Cox
regression
0.88 0.75 – 1.04
Marginal structural Cox
model
0.61 0.50 – 0.77

54.  Current study
 ARB offers 39% reduction in the risk of dementia
 Previous epidemiological studies
 Estimates ranged from 19% to 69%
 What is the true estimate?
 Prior studies showed MSM estimates ~ true estimates
 All prior studies
 Standard regression methods
 Shorter follow-up
 Time varying effect of blood pressure not considered
Discussion for CER of ARB versus ACEI
41Robins, 2000; 42Hernan, 2000; 43Delaney, 2009 54

55. Strengths | Limitations
55
- Created new RxDx-Dementia risk
index
- 10 years of longitudinal follow-up
- Causal effect of ACEI and ARB on
dementia (use of MSM)
- Cannot ascertain that
prescriptions are always filled or
taken by patients
- Missing data imputation

56. Conclusions
56

57. Conclusions
57
Aim 1
• Successfully developed and validated
RxDx-Dementia Risk Index
Aim 2
• RxDx-Dementia Risk Index
outperformed all other risk indices
Aim 3
• ARB may offer protective effect on the
risk of dementia compared to ACEI

58.  RxDx-Dementia Risk Index
 Prognosis of dementia
 Control of confounding
 Protective effect of ARB compared to ACEI
 Proper adjustment of time dependent confounding is important
in estimating causal effect
 Use of ARB will increase due to generics
 Losartan (2010); Irbesartan (2012)
 Delay the onset of dementia
 Reduce healthcare expenditure, improve patient quality of life and have
public health implications
Implications
58

59. 59

60. 60