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Aging and HIV: Prognostication, personalization, and prevention. R Scott Braithwaite, MD, MS, FACP Chief, Section of Value and Comparative Effectiveness New York University School of Medicine; NY; U.S.A. . Personalizing screening recommendations for HIV-infected. - PowerPoint PPT Presentation
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Aging and HIV: Prognostication, personalization, and prevention
• R Scott Braithwaite, MD, MS, FACPChief, Section of Value and Comparative EffectivenessNew York University School of Medicine; NY; U.S.A.
Personalizing screening recommendations for HIV-infected
• HIV-infected population is aging• More screening recommendations applicable
– Cancer, other• Increasing emphasis on personalized medicine
– Health information technology – Personalize algorithms at point-of-care
• How should screening recommendations be personalized for HIV-infected individuals?
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
17%19% 21% 22%
25%27% 27% 29%
33%35%
37%39%
41%44%
45%47%
50%
Projected Proportion of those 50+ Years of Age* Living With HIV in
United States 2001-2017
NY City
US VA in 2003
As of 2008:San Francisco
*Data from 2008, onward projected based on 2001-2007 trends (calculated by author), 2001-2007 data from CDC Surveillance Reports 2007. New York and San Francisco data from their Departments of Public Health.
Projected
Personalizing screening recommendations for HIV-infected
• HIV is now chronic disease• Framework for personalizing screening
• Braithwaite RS et al, 2009, Medical Care• Braithwaite RS et al, 2011, Med Decis Making
– Estimate benefit/harm ratio based on personalized benefits, harms, and competing risks• If benefit/harm more favorable, then earlier and/or
more frequent screening favored• If benefit/harm less favorable, then later and/or less
frequent screening favored• If harms > benefits considering competing risks
screening not recommended
Illustrative cases: Screen for colorectal cancer?
• Case 1: 62 year-old male, CD4 590, undetectable viral load, first-line ARV, no major comorbidities
• Case 2: 62 year-old male, CD4 46, viral load 3,500; 3rd line ARV, atrial fibrillation (on coumadin), Hep C, mild anemia
Personalizing screening
• Estimate benefit/harm ratio based on personalized benefits, harms, and competing risks– If benefit/harm more favorable, then earlier and/or
more frequent screening favored– If benefit/harm less favorable, then later and/or less
frequent screening favored– If harms > benefits considering competing risks
screening not recommended
Braithwaite RS et al, Medical Care, 2009
Personalizing benefits of colorectal cancer screening
• HIV increases risk for CR cancer by RR 2.3• Therefore potential benefit from screening
increased by RR 2.3• But need to also consider other chronic
diseases, medications, and risk factors
Characteristic Impact on benefits (RR)
Impact on harms (RR)
Smoking17*† 1.8 Unknown‡ Obesity 18,19* 1.5 Unknown‡Heavy alcohol 20§ 1.3 Unknown‡Diabetes 21*§ 1.3 Unknown‡Aspirin (regular use) 22 § 0.8 Unknown‡NSAID (regular use) 22 § 0.7 Unknown‡Hormone replacement therapy23§ 0.6 Unknown‡║Coumadin 14§ Unknown¶ 4.0American Anesthesiology Society Class 1
(“normal healthy patient”) 14§Unknown¶ 0.7
American Anesthesiology Society Class 3 (“severe systemic disease”) 14§
Unknown¶ 4.3
1st degree relative with CRC, age unknown24†
2.3 Unknown‡
1st degree relative with CRC, age<4524† 3.9 Unknown‡>1 1st degree relative with CRC24† 4.3 Unknown‡
Personalizing benefits
• Case 1– Healthy 62 year-old well controlled HIV – Benefit 2.3 X greater than typical person because
of HIV• Case 2
– 62 year old poorly controlled HIV and other chronic diseases
– Benefit 2.3 X greater than typical person because of HIV
Personalizing screening
• Estimate benefit/harm ratio based on personalized benefits, harms, and competing risks– If benefit/harm more favorable, then earlier and/or
more frequent screening favored– If benefit/harm less favorable, then later and/or less
frequent screening favored– If harms > benefits considering competing risks
screening not recommended
Braithwaite RS et al, Medical Care, 2009
Personalizing harms of colorectal cancer screening
• HIV itself not known to impact harms• But need to consider other chronic diseases,
medications, and risk factors
Characteristic Impact on benefits (RR)
Impact on harms (RR)
Smoking17*† 1.8 Unknown‡ Obesity 18,19* 1.5 Unknown‡Heavy alcohol 20§ 1.3 Unknown‡Diabetes 21*§ 1.3 Unknown‡Aspirin (regular use) 22 § 0.8 Unknown‡NSAID (regular use) 22 § 0.7 Unknown‡Hormone replacement therapy23§ 0.6 Unknown‡║Coumadin 14§ Unknown¶ 4.0American Anesthesiology Society Class 1
(“normal healthy patient”) 14§Unknown¶ 0.7
American Anesthesiology Society Class 3 (“severe systemic disease”) 14§
Unknown¶ 4.3
1st degree relative with CRC, age unknown24†
2.3 Unknown‡
1st degree relative with CRC, age<4524† 3.9 Unknown‡>1 1st degree relative with CRC24† 4.3 Unknown‡
Personalizing harms
• Case 1 – Healthy 62 year-old well controlled HIV– Harm unchanged from typical person
• Case 2 – 62 year-old poorly controlled HIV and other
chronic diseases– Harm 4.0 X that of typical person because of
coumadin
Personalizing screening
• Estimate benefit/harm ratio based on personalized benefits, harms, and competing risks– If benefit/harm more favorable, then earlier and/or
more frequent screening favored– If benefit/harm less favorable, then later and/or less
frequent screening favored– If harms > benefits considering competing risks
screening not recommended
Braithwaite RS et al, Medical Care, 2009
Personalizing competing risks
• HIV: little effect if well controlled, large effect if poorly controlled
• Need to consider other chronic diseases, medications, and risk factors
• Instruments for quantification include– VACS index– Computer simulation
Veterans Aging Cohort Study Risk Index (VACS Index)
• Composed of age and laboratory tests currently recommended for clinical management
– HIV Biomarkers: HIV-1 RNA, CD4+ count, AIDS defining conditions
– “Non-HIV Biomarkers”: Hemoglobin, hepatitis C, composite markers for liver and renal injury
• Developed in US veterans, validated in Europe and North America
17Justice AC. HIV and Aging: Time for a New Paradigm. Curr HIV/AIDS Rep. 2010 May;7(2):69-76
y = 0.0091x - 0.0318R2 = 0.9916
0%
20%
40%
60%
80%
100%
0 20 40 60 80 100Risk Score
Mor
talit
y
Justice, AC. et. al, HIV Med. 2010 Feb;11(2):143-51. Epub 2009 Sep 14.
VACS Index Highly Predictive of Long Term (5 Year) All Cause Mortality
VACS Index in OPTIMA
Brown S.T. et al. Poster Presentation, Abstract #16436 International AIDS Conference 2010
VACS Index Response to 1st Year of cART (+/- 80% adherence)
19
Solid lines indicate >80% adherence
Tate J. et al. Change in a prognostic index for survival in HIV infection after one year on cART by level of adherence. IDSA 2010. Poster # 1136
Computer Simulation• Widely published, calibrated and validated
– Braithwaite RS et al, Am J Med, 2005– Braithwaite RS et al, J Antimicrob Chemother 2006– Braithwaite RS et al, Value in Health, 2007– Braithwaite RS et al, Annals Intern Med, 2008– Braithwaite RS et al, Clin Infectious Dis 2009– Braithwaite RS et al, Med Care, 2010
• Mechanistic, represents reasons for failing ARV– Nonadherence to ARV– Resistance accumulation
• Estimates life expectancy based on age, sex, baseline CD4, baseline viral load, baseline resistance, ART adherence, ART initiating criteria, switching criteria, and sequencing
Viral Replication
HIV Mutations
CART Resistance
CART Adherence
CART Toxicity
CART effectiveness
Viral Load CD4 Count
DEATH FROM HIV/AIDS DEATH FROM OTHER CAUSES
Patient Characteristics
Unobserved or rarely observed characteristics
Time (in year)
Kaplan-Meier estimates Simulation
0 1 2 3 4 5 6
0
.2
.4
.6
.8
1
Pro
porti
on re
mai
ning
on
third
ther
apy
roun
d
CHORUS
Simulation
Years
Time (in year)
Kaplan-Meier estimates Simulation
0 1 2 3 4 5 6 7
.8
.85
.9
.95
1
Pro
porti
on re
mai
ning
aliv
e
CHORUS
Simulation
Years
Time (in year)
Kaplan-Meier estimates Simulation
0 1 2 3 4 5 6
0
.2
.4
.6
.8
1
Pro
porti
on re
mai
ning
on
seco
nd th
erap
y ro
und
CHORUS
Simulation
Years
Time (in year)
Kaplan-Meier estimates Simulation
0 1 2 3 4 5 6 0
.2
.4
.6
.8
1
Pro
porti
on re
mai
ning
on
first
ther
apy
roun
d
CHORUS
Simulation
Years
Calibration
Validation
0
2
4
6
8
10
12
14
16
18
20
>5 <5 >5 <5 >5 <5 >5 <5 >5 <5 >5 <5 >5 <5 >5 <5 >5 <5 >5 <5>50 >50 <50 <50 >50 >50 <50 <50 >50 >50 <50 <50 >50 >50 <50 <50 >50 >50 <50 <50
Log Viral LoadAge (Years)
CD4<50 cells/m3 CD4 50-99 cells/m3 CD4 100-199 cells/m3 CD4 200-349 cells/m3 CD4350 cells/m3
3 ye
ar m
orta
lity
(%)
Personalizing screening
• Estimate benefit/harm ratio based on personalized benefits, harms, and competing risks– If benefit/harm more favorable, then earlier and/or
more frequent screening favored– If benefit/harm less favorable, then later and/or less
frequent screening favored– If harms > benefits considering competing risks
screening not recommended
Braithwaite RS et al, Medical Care, 2009
Personalizing competing risks
• Case 1 – Healthy 62 year-old well controlled HIV – VACS index: Life Expectancy >>10 years– Simulation: Life Expectancy >>10 years
• Case 2– 62 year-old poorly controlled HIV and other
chronic diseases– VACS Index: Life Expectancy 4.1 years– Simulation: Life Expectancy 5.1 years
Case 1: Personalized harm/benefit of colorectal cancer screening
• Benefits increased by 2.3-fold• Harms unchanged
– Therefore personalized benefit/harm ratio = 2.3• Competing risks minimally affected
– HIV well controlled and does not add clinically significant mortality burden
– Therefore Life Expectancy >> 10 years using either VACS index or computer simulation
Life expectancy needed for benefits from CR screening to exceed harms
Benefit-to-harm ratio
Age 40 Age 50 Age 60 Age 70
M F M F M F M F
0.1 >10 >10 >10 >10 9.7 >10 7.5 8.6
0.2 >10 >10 >10 >10 7.3 8.7 6.2 6.7
0.5 >10 >10 7.4 8.7 6.0 6.5 5.5 5.8
1 9.0 10.0 6.2 6.8 5.4 5.7 5.2 5.3
2 7.0 7.6 5.6 5.9 5.3 5.4 5.1 5.2
4 6.0 6.3 5.3 5.5 5.1 5.2 5.0 5.1
10 5.4 5.5 5.1 5.2 5.0 5.1 5.0 5.0
Braithwaite et al, Medical Care, 2009
Case 1: Personalized harm/benefit of colorectal cancer screening
• Since Case 1 would require 5.3 years to have benefits exceed harms and is expected to live >> 10 years, Case 1 would benefit from colorectal cancer screening more than typical person
• Raises question of whether screening should begin at earlier age or with greater frequency
Case 2: Personalized harm/benefit of colorectal cancer screening
• Benefits increased by 2.3-fold• Harms increased by 4.0-fold
– Therefore personalized benefit/harm ratio = 0.6• Competing risks increased greatly
– VACS index: Life Expectancy 4.1 years– Simulation: Life Expectancy: 5.1 years
Life expectancy needed for benefits from CR screening to exceed harms
Benefit-to-harm ratio
Age 40 Age 50 Age 60 Age 70
M F M F M F M F
0.1 >10 >10 >10 >10 9.7 >10 7.5 8.6
0.2 >10 >10 >10 >10 7.3 8.7 6.2 6.7
0.5 >10 >10 7.4 8.7 6.0 6.5 5.5 5.8
1 9.0 10.0 6.2 6.8 5.4 5.7 5.2 5.3
2 7.0 7.6 5.6 5.9 5.3 5.4 5.1 5.2
4 6.0 6.3 5.3 5.5 5.1 5.2 5.0 5.1
10 5.4 5.5 5.1 5.2 5.0 5.1 5.0 5.0
Braithwaite et al, Medical Care, 2009
Case 2: Personalized harm/benefit of colorectal cancer screening
• Since Case 2 would require 6.0 years to have benefits exceed harms and has life expectancy of only 4.1 years (VACS index) or 5.1 years (Computer Simulation), Case 2 would not benefit from colorectal cancer screening– Benefit exceeds harms– Screening should not be part of “denominator” for
quality measure or P4P
Conclusions• HIV-infected persons may benefit from
personalized screening recommendations– Sometimes favor more aggressive screening– Sometimes favor less aggressive or no screening
• Personalization occurs by considering effects of HIV, other chronic diseases, and risk factors– Screening-attributable benefits– Screening-attributable harms– Competing risks
• Personalizing may be facilitated by HIT and EMRs