Roger Bedimo, MD; Song Zhang, PhD; Henning Drechsler, MD; Pablo Tebas, MD; Naim Maalouf, MD

Post on 23-Feb-2016

42 views 0 download

description

Risk of Osteoporotic Fractures Associated with Cumulative Exposure to Tenofovir and Other Antiretroviral Agents. Roger Bedimo, MD; Song Zhang, PhD; Henning Drechsler, MD; Pablo Tebas, MD; Naim Maalouf, MD. Osteoporotic Fractures among HIV-Infected Patients: Role of ART. - PowerPoint PPT Presentation

Transcript of Roger Bedimo, MD; Song Zhang, PhD; Henning Drechsler, MD; Pablo Tebas, MD; Naim Maalouf, MD

Risk of Osteoporotic Fractures Associated with Cumulative Exposure to

Tenofovir and Other Antiretroviral Agents

Roger Bedimo, MD; Song Zhang, PhD; Henning Drechsler, MD; Pablo Tebas,

MD; Naim Maalouf, MD

Osteoporotic Fractures among HIV-Infected Patients: Role of

ART Decreased bone mineral density is increasingly

reported in the aging HIV-positive population. Odds of osteoporosis are elevated in HIV-infected vs.

uninfected1,2, and in ART users vs. non-users1 Tenofovir exposure was shown to be associated

with a significantly greater decrease in bone mineral density than stavudine3, and abacavir4

The risk of osteoporotic fractures (OF) associated with cumulative (PY) exposure to tenofovir vs. other antiretroviral agents has never been explored1Brown and Qaqish AIDS 2006,20:2165-2174.; 2Triant et al, JCEM. 2008,93:3499-3504

3Gallant et al., JAMA. 2004;292(2):191-201. 4McComsey G. JID 2011;203(12):1791-801

Methods: Data Source, Predictors and Outcome Measures

• Data Source: Veterans Affairs’ Clinical Case Registry; HIV patients in pre-HAART (’88-’95) and HAART eras (’96-’09).

• Predictors: – Antiretroviral exposure: PY of exposure to NRTIs (TDF,

ABC, AZT or D4T), NNRTI, boosted PI.– Age, Race, Smoking, BMI, type 2 diabetes, HCV co-

infection (by ICD-9 codes or antibody +), Chronic kidney disease: Estimated GFR<60 by MDRD• Gender not included in the model; The population is >98%

male. • Outcome: Incident osteoporotic fracture defined as

any:– Vertebral fractures (ICD-9 codes 805.2 through

805.7), Hip fractures (820.0 through 820.9), or Wrist fractures (814.0, 814.1, 813.4 and 813.5)

Results: Study Population, Treatment Exposure and

Events• Two separate analyses were conducted, including:

1. All patients enrolled in CCR from 1988 to 2009.2. Patients enrolled in the HAART era only: from 1996 to

2009.• Cumulative exposure to each separate ARV or ARV

class, from first administration to censure date:– 1) development of the first OF episode ; 2)

discontinuation of the ARV; 3) last recorded patient encounter; 4) December 31st, 2009 (date of censure of the dataset).

• Cox survival models of association of ARV exposure & OF: – 1) Univariate analysis; – 2) MV Model 1: Controlling for CKD, age, race, tobacco

use, DM, BMI & HCV; 3) MV Model 2: Controlling for Model 1 variables + concomitant exposure to other ARVs.

Results: Study Population, Follow-Up Time and Events

Entire Period: 1988 - 2009 (n =56,660)

HAART Era Only: 1996-2009

(n =32,439)Number of Patients with ART Exposure (%)

39,277 (69.4%) 27,107 (83.6%)

Total PY of Observation 305,237 191,258Total PY of ART Exposure 164,414 122,364

Vertebral Fractures 124 77Wrist Fractures 486 296

Hip Fractures 341 200

All Osteoporotic Fractures*

951 572*defined for this study as first vertebral, wrist or hip fracture during follow-up

Risk Factors among Patients with and without Osteoporotic

FractureBaseline

CharacteristicsTotal

(n =56,660)

Fracture (n =951)

No Fracture(n =55,709)

P-value

Age in Yrs; median (SD)

44 (10) 46 (10) 44 (10) P<0.0001

% Male 98 98 98 P=0.91% Whites 45 57 45 P<0.0001% Smokers 33 56 32 P<0.0001% Diabetes* 15 25 15 P<0.0001% BMI<20 33 49 33 P<0.0001% HCV Positive 31 51 31 P<0.0001

*Classified only by ICD-9 codes. Laboratory values not extracted

Age-adjusted Rates of Osteoporotic Fractures (Entire Cohort)

0

1

2

3

4

5

6

7

8

18-29 30-39 40-49 50-59 60-69 ≥70Age at Cohort Entry (Years)

Frac

ture

Rat

e (p

er 1

,000

pat

ient

-yea

rs)

Vertebral

Hip

Wrist

Total

General population1

1Data from Triant V, et al., JCEM 2008;93: 3499–3504

Factors Predicting Osteoporotic Fracture in HIV Patients

Factors Hazard Ratio (95% Confidence Interval; p value)

  Univariate Analysis Multi-variable AnalysisCumulative ART Use (per year) 1.05 (1.01 – 1.10; p=0.02) 0.99 (0.95 – 1.04; p=0.77)

CKD (eGFR <60) 1.48 (1.04 – 2.09; p=0.03) 1.05 (0.72 – 1.53; p = 0.79)

White Race 1.76 (1.46 – 2.13; p < 0.0001)

1.88 (1.54 – 2.30; p< 0.0001)

Age (per 10 year increase)

1.51 (1.39 – 1.63; p <0.0001)

1.50 (1.37 – 1.64; p< 0.0001)

Tobacco Use 1.25 (1.06 – 1.47; p=0.01) 1.31 (1.09 – 1.56; p=0.003)

Diabetes 1.27 (1.05 – 1.53; p=0.01) 1.10 (0.90 – 1.34; p=0.34)

BMI < 20 1.61 (1.29 – 2.00; p<0.0001)

1.48 (1.18 – 1.87; p=0.007)

HCV Co-infection 1.43 (1.21 – 1.69; p<0.0001)

1.49 (1.25 – 1.77; p< 0.0001)

Antiretroviral Exposure and Risk of Osteoporotic Fractures: 1988-

2009

MV Model 1: Controlling for CKD, age, race, tobacco use, diabetes and BMI; MV Model 2: Controlling for Model 1 variables + concomitant exposure to other ARVs.

Drug or Drug Category

PY of Exposure

Hazard Ratio per Year of Exposure (95% Confidence Interval; p value)

    Univariate Analysis Multi-variable Model 1

Multi-variable Model 2

Tenofovir (TDF) 46,062 1.08 (1.02-1.15;

<0.001) 1.06 (0.99-1.12; 0.079)

1.06 (0.99-1.14; 0.106)

Abacavir (ABC) 24,251 0.99 (0.93-1.05;

0.989) 0.96 (0.90-1.03; 0.245)

0.96 (0.90-1.03; 0.224)

Thymidines (AZT or D4T) 94,595 1.02 (0.99-1.05;

0.199) 0.96 (0.95-1.02; 0.311)

0.99 (0.95-1.02; 0.520)

boosted PI (rPI) 41,336 1.06 (1.01-1.12;

0.015) 1.04 (0.99-1.10; 0.142)

1.03 (0.97-1.09; 0.349)

NNRTI 59,857 0.99 (0.95-1.03; 0.655)

0.96 (0.92-1.01; 0.094)

0.96 (0.92-1.01; 0.112)

Antiretroviral Exposure and Risk of Osteoporotic Fractures: 1988-

2009

MV Model 1: Controlling for CKD, age, race, tobacco use, diabetes and BMI; MV Model 2: Controlling for Model 1 variables + concomitant exposure to other ARVs.

TDFTD

F1TD

F2 ABCAB

C1AB

C2

AZT/D

4T

AZT/D

4T1

AZT/D

4T2

NNRTI

NNRTI1

NNRTI2 rPI rPI1

rPI2

0.8

0.9

1.0

1.1

1.2

Haz

ard

Rat

io

What Happened in the HAART Era?

• Higher % of patients on ARVs, low viremia.• Increased survival (and time at risk) and

increased fracture rates

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

00.5

11.5

22.5

33.5

44.5

Fracture Rate by Year

Year of Fracture Diagnosis

Frac

ture

Rat

e pe

r 10

00 P

atie

nt-

Year

s

Pre-HAART Era:1.61 Events/1000 PY

HAART Era:4.09 Events/1000 PY

Antiretroviral Exposure and Risk of Osteoporotic Fractures: HAART

Era

MV Model 1: Controlling for CKD, age, race, tobacco use, diabetes and BMI; MV Model 2: Controlling for Model 1 variables + concomitant exposure to other ARVs.

Drug or Drug Category

PY of Exposure

Hazard Ratio per Year of Exposure (95% Confidence Interval; p value)

    Univariate Analysis Multi-variable Model 1

Multi-variable Model 2

Tenofovir (TDF) 38,009 1.16 (1.08-1.24;

<0.0001)1.13 (1.05-1.21; 0.001)

1.12 (1.03-1.21; 0.011)

Abacavir (ABC) 18,885 0.99 (0.92-1.07;

0.842)0.96 (0.88-1.04; 0.313)

0.95 (0.87 -1.03; 0.194)

AZT or D4T 68,376 1.02 (0.97-1.06; 0.489)

0.98 (0.93-1.02; 0.289)

0.99 (0.94-1.04; 0.600)

boosted PI (rPI) 32,109 1.11 (1.05-1.18;

0.001)1.08 (1.01-1.15; 0.026)

1.05 (0.97-1.13; 0.237)

NNRTI 48,943 1.01 (0.96-1.06; 0.771)

0.98 (0.93-1.03; 0.409)

0.98 (0.92-1.03; 0.386)

Antiretroviral Exposure and Risk of Osteoporotic Fractures:

HAART Era

MV Model 1: Controlling for CKD, age, race, tobacco use, diabetes and BMI; MV Model 2: Controlling for Model 1 variables + concomitant exposure to other ARVs.

TDFTD

F1TD

F2 ABC

ABC1

ABC2

AZT/D

4T

AZT/D

4T1

AZT/D

4T2

NNRTI

NNRTI1

NNRTI2 rPI rPI1

rPI2

0.8

0.9

1.0

1.1

1.2

1.3

Haz

ard

Rat

io

Interaction Between TDF and PI Exposure for OF Risk: HAART

Era• Concomitant exposure to both TDF and rPI associated

with a greater OF risk than exposure to either TDF without rPI or rPI without TDF

TDF rPI TDF + rPI0.6

0.8

1.0

1.2

1.4

Haz

ard

Rat

io

Exposure to Specific Protease Inhibitors and OF Risk: HAART

Era

MV Model 1: Controlling for CKD, age, race, tobacco use, diabetes and BMI; MV Model 2: Controlling for Model 1 variables + concomitant exposure to other ARVs.

Drug PY of Exposure Hazard Ratio per Year of Exposure (95% Confidence Interval; p value)

    Univariate Analysis Multi-variable Model 1 Multi-variable Model 2

IDV 12,124 1.00 (0.93 - 1.07; 0.947)

0.98 (0.91 - 1.05; 0.579)

0.99 (0.92 - 1.07; 0.755)

ATV 12,685 1.12 (0.98 - 1.27; 0.097)

1.08 (0.95 - 1.24; 0.233)

1.03 (0.89 - 1.18; 0.713)

NFV 14,356 1.00 (0.93 - 1.07; 0.977)

0.98 (0.91 -1.05; 0.509)

0.98 (0.91 - 1.05; 0.512)

LPV/RTV 15,319 1.17 (1.08 - 1.26; <0.0001)

1.13 (1.04 - 1.22; 0.005)

1.09 (1.00 -1.20; 0.051)

Exposure to Specific Protease Inhibitors and OF Risk: HAART

Era

MV Model 1: Controlling for CKD, age, race, tobacco use, diabetes and BMI; MV Model 2: Controlling for Model 1 variables + concomitant exposure to other ARVs.

Drug PY of Exposure Hazard Ratio per Year of Exposure (95% Confidence Interval; p value)

    Univariate Analysis Multi-variable Model 1 Multi-variable Model 2

IDV 12,124 1.00 (0.93 - 1.07; 0.947)

0.98 (0.91 - 1.05; 0.579)

0.99 (0.92 - 1.07; 0.755)

ATV 12,685 1.12 (0.98 - 1.27; 0.097)

1.08 (0.95 - 1.24; 0.233)

1.03 (0.89 - 1.18; 0.713)

NFV 14,356 1.00 (0.93 - 1.07; 0.977)

0.98 (0.91 -1.05; 0.509)

0.98 (0.91 - 1.05; 0.512)

LPV/RTV 15,319 1.17 (1.08 - 1.26; <0.0001)

1.13 (1.04 - 1.22; 0.005)

1.09 (1.00 -1.20; 0.051)

RTV 18,691 1.06 (0.97 - 1.15; 0.2) 1.04 (0.96 - 1.14; 0.349)

1.01 (0.92 - 1.11; 0.79)

ATV/RTV 9546 1.11 (0.95 - 1.31; 0.18)

1.08 (0.91 - 1.27; 0.378)

0.99 (0.84 - 1.18; 0.946)

Exposure to Specific Protease Inhibitors and OF Risk: HAART

Era

MV Model 1: Controlling for CKD, age, race, tobacco use, diabetes and BMI; MV Model 2: Controlling for Model 1 variables + concomitant exposure to other ARVs.

IDVID

V1ID

V2ATV

ATV1ATV

2LP

VLP

V1LP

V2RTV

RTV1RTV

2ATV

/r

ATV/r1

ATV/r2

0.6

0.8

1.0

1.2

1.4

Haz

ard

Rat

io

Discussion – Entire Study Period

• Overall, antiretroviral exposure is associated with a non-significant OF risk after controlling for OF risk factors.– HR for cumulative ART exposure is modest compared to

other fracture risk factors: White race, advancing age and smoking

• Cumulative exposure to TDF and boosted PI are each associated with modest increase in fracture risk in univariate analysis, but not after controlling for fracture risk factors.

• Significant increase in fracture rates in the HAART era– Cumulative ART exposure likely does not account for the

increased risk in the HAART era

Discussion – HAART Era - I• Fracture risk associated with cumulative exposure

to TDF remains significant after controlling for other OF risk factors and concomitant ARV used.

• Cumulative exposure to boosted PI is also associated with increased OF risk after controlling for other OF risk factors, but not after controlling for concomitant ARVs.– There was an interaction between TDF and boosted PI

use.• Greater fracture rates, higher (significant) HR for

TDF and rPI in the HAART era could be due to longer survival, and exclusion of most patients with no Rx, mono-dual Rx

Discussion – HAART Era - II• Among PIs, LPV/RTV is associated with an

increased OF risk. Exposure to ATV, NFV or IDV were not associated with increased OF risk.– While these could be explained by concomitant use of

RTV with LPV, neither RTV alone nor boosted ATV or IDV were associated with increased risk.

Strengths and Limitations• Large sample size (more than 56,000 patients; more

than 900 with fracture events)• Uniform data collection on exposures and outcomes

across VA system, including pre-HAART and HAART eras.

• Our study is a retrospective cohort study. • Osteoporotic fracture events not ascertained (only

ICD-9 code used – validated in other VA studies) • Bone mineral density is not evaluated. Fractures

cannot be proven to be osteoporotic in nature.

Acknowledgements• Study funded by VA MERIT grant I01

CX000418-01A1• Thanks to the VA Center for Quality

Management for access to CCR data and material support

• Thanks to IAS for giving us the opportunity to share our work