1 / 47

Overview of Ageing: a How Can We Optimize Care in the Context of Multimorbidity?

Overview of Ageing: a How Can We Optimize Care in the Context of Multimorbidity?. Amy C. Justice, MD, PhD Professor, Yale University Schools of Medicine and Public Health Section Chief, General Internal Medicine VA Connecticut Healthcare System. Who is Ageing with HIV?.

spiro
Download Presentation

Overview of Ageing: a How Can We Optimize Care in the Context of Multimorbidity?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Overview of Ageing: aHow Can We Optimize Care in the Context of Multimorbidity? Amy C. Justice, MD, PhD Professor, Yale University Schools of Medicine and Public Health Section Chief, General Internal Medicine VA Connecticut Healthcare System

  2. Who is Ageing with HIV? Everyone with access to ART and those who contract HIV at older ages.

  3. In US: More People Living with HIV Infection Every Year (+38K/yr*) Each year: 56K new infections-18K deaths=38K* http://www.cdc.gov/hiv/topics/surveillance/resources/slides/index.htm

  4. Projected *Data from 2009, onward projected based on 2001-20078 trends (calculated by author), 2001-20078 data from CDC Surveillance Reports 2009. New York and San Francisco data from Departments of Public Health

  5. In New York City HIV Epidemiology & Field Services Semiannual Report, NYCDOH. April 2010

  6. Africa is No Exception • An estimated 14% of adults with HIV infection in Sub Saharan Africa are >50 years • AIDS is leading cause of death among >50 yrs. in Nyanza Providence, Western Kenya Negin J. Bull World Health Organ 2010 Nov 1;88(11):847-853

  7. Projected HIV Prevalence by Age in Hlabisa Sub-district of KwaZulu-Natal, South Africa Hontelez J. Ageing with HIV in South Africa. AIDS 2011 25:1665-73

  8. Sex is Not Only for the Young Proportion reporting sex in last 12 months Lindau ST, et al. NEJM. 2007;357:762-774.

  9. Sexual Risks Among Older Adults • Newly single (widowed/divorced) status • Ratio of men to women increasingly skewed • Less likely to use condoms • Postmenopausal women--pregnancy no longer possible • Men may have erectile dysfunction complicating condom use • Lower estrogen leads to vaginal dryness and likely increases risk of viral transmission

  10. Among HIV+ on ART,What Drives Morbidity and Mortality? Multi morbidity define as co occurrence of health conditions that cannot be cured and likely interact, but require ongoing monitoring and treatment.

  11. Delayed Presentation By Age (NA ACCORD) Altoff K. et al. JAIDS 2011

  12. AIDS Events Increasingly Rare ART-CC, Archives Int Med 2005: 165 416-423

  13. AIDS Events Variably Associated with CD4 and Survival By Median (IQR) CD4 By Relative Hazard of Death ART-CC, CID 2009;48:1138-51

  14. >50% of Deaths Attributed to Non-AIDS Events Cumulative Mortality by COD Among Those on cART (1996-2006) ART-CC, CID 2010: 1387-1396

  15. Death Rate Disparities by HIV, Race/Ethnicity and Age HIV Epidemiology & Field Services Semiannual Report, NYCDOH. April 2010

  16. Strategies for Management of ART (SMART) *More AIDS and “Non-AIDS” Events Among Rx. Sparing Arm (HR 1.7 in SMART) NEJM 2006;355:2283-96

  17. HIV Associated Non AIDS(HANA) Conditions • After adjustment for established risk factors, association with HIV remains • Compare to demographically and behaviorally similar uninfected controls • Weaker (<2 fold) associations may be due to inadequate adjustment for risk factors • May be due to HIV, ART, or both • Not necessarily closely tied to CD4 count

  18. Premature or Accentuated Aging??? • Some studies suggest HANA conditions occur 20-30 years earlier than expected among HIV+ • Most are not adjusted for differences in the underlying age distribution • Others are not adjusted for differences in established risk factors (smoking, alcohol, drug use, or hepatitis C co-infection)

  19. Premature or Accentuated Cancer? A. Premature cancer : cancer occurs earlier among those with HIV than uninfected comparators. B. Accentuated risk: cancer could occur at the same ages but more often than among comparators. Shiels MS. Ann Intern Med 2010:153:452-460.

  20. Multimorbidityin HIV • In North America and Europe • HCV co infection, alcohol, tobacco, and opioid abuse • In Africa • Tuberculosis, malaria, obstructive lung disease (smoke inhalation) and alcohol abuse • Among all those ageing: HANA conditions • Vascular disease, liver disease, renal disease, osteoporosis, and specific cancers

  21. Justice AC. HIV and Aging: time for a new paradigm. Curr HIV/AIDS Rep 2010: &:69-76

  22. What are the Implications of Multimorbidity?

  23. In the US General Population • Screening and Treatment Guidelines do not consider it (RCTs exclude multimorbidity) • 50% of >65 years have >3 comorbid conditions • A disconnect between healthcare focusing on individual patient vs. individual disease • Multimorbidity represents the next frontier in the evolution of Evidence Based Medicine Campbell-Scherer D. Multimorbidity: a challenge for EBM. Evid Based Med 2010: 15:165-166

  24. Guidelines do not Consider • Harms from polypharmacy • Interactions with substance use or depression • Hepatitis B or C • Social issues which compete with ability to adhere to complex treatment regimens

  25. Guideline Overload • Considered guidelines for 10 chronic diseases to a panel of 2500 with age, sex, and chronic disease prevalence matched to US • Did not allow for new patients • Estimated MD time required assuming • All stable (3.5 hours/day) • Some active disease (10.6 hours/day) • Did not allow for new problems Ostbye T, Ann Fam Med 2005;3:209-14

  26. Multimorbidityis a Game Changer • Increases treatment benefit if condition interacts with other conditions (e.g. HCV) • Decreases time to benefit from screening (e.g. cancer screening) • Increases risk of toxicity • Creates competing demands: there isn’t time to address HIV and primary care guidelines and adequately care for active problems

  27. We Need a New Paradigm and a New Approach to Measuring Disease to Guide Us

  28. We Need to Prioritize Synergies • Hypertension causes cardiovascular disease, stroke, and renal disease • Smoking increases risk of cardio- vascular disease, stroke, lung disease, and cancer • Alcohol causes microbial translocation, elevates bp, speeds HCV progression, causes liver cirrhosis and cancer, impedes adherence, and may substantially contribute to vascular disease

  29. And to Tailor Screening and Treatment to Individual Risk • Use prediction tools to estimate net benefit • Rather than relative benefit • Account for treatment disutilities • Requires two inputs: • Accurate estimation of risk • Risk reduction associated with interventions Hayward RA. et al. Optimizing Statin Treatment for Primary Prevention of CAD. Ann Int Med 2010:152:69-77 Eddy DM. et al. Individualized Guidelines: The Potential for Increasing Quality and Reducing Costs Ann Intern Med 2011;154:627-634.

  30. An index composed of routinely collected laboratory values that accurately predicts all cause mortality among those with HIV infection Veterans Aging Cohort Study Risk Index (VACS Index) Justice, AC. et. al, HIV Med. 2010 Feb;11(2):143-51. Epub 2009 Sep 14.

  31. The Veterans Aging Cohort Study (VACS) • Well characterized NIAAA cohort • >40,000 HIV+ matched to >80,000 HIV- • Matched on age, race/ethnicity, region • All HIV+ entering care since 1998 • Controls had to be seen in VA in same year • ~10 yrs. of longitudinal data • Clinically arbitrated endpoints for MI, stroke, cancer, pneumonia, and cirrhosis • Nested in-depth cohort of >7,000 (half HIV+)

  32. Validated in Cross Cohort Collaborations • Collaborations • ART-CC: Largely European, 19 cohorts • NA-ACCORD: North American, 21cohorts • VA mortality rates are somewhat higher and population is older and more likely to be male • Associations with outcomes very consistent

  33. Veterans Aging Cohort Study Risk Index (VACS Index) • Composed of age and laboratory tests currently recommended for clinical management • HIV Biomarkers: HIV-1 RNA and CD4 Count • “non HIV Biomarkers”: Hemoglobin, hepatitis C, composite markers for liver and renal injury

  34. AGE * AST FIB 4 = PLT * sqrt(ALT ) -1.154 -0.203 eGFR = 186.3 * CREAT * AGE * FEM_VAL * BLACK_VAL FEM_VAL = 0.742 if female, 1 if male BLACK_VAL = 1.21 if black, 1 otherwise Composite Biomarkers 34

  35. VACS Index Thresholds and Weights Age HIV Specific Biomarkers Biomarkers of General Organ System Injury Tate J. et al. IDSA 2010 Vancouver, BC October 21-24th. Poster 1136

  36. VACS Index Highly Predictive of Long Term (5 Year) All Cause Mortality Aggregated Scores Individual Scores Justice, AC. et. al, HIV Med. 2010 Feb;11(2):143-51. Epub 2009 Sep 14. Justice AC. HIV and Aging: Time for a New Paradigm. Curr HIV/AIDS Rep. 2010 May;7(2):69-76

  37. Discrimination of VACS vs. Restricted Index Justice AC. et al. A Prognostic Index for those Aging with HIV. CROI 2011 Poster # 793

  38. Calibration of VACS vs. Restricted Index (5 Year Mortality) Justice AC. et al. A Prognostic Index for those Aging with HIV. CROI 2011 Poster # 793

  39. VACS Index Response to 1st Year of cART (+/- 80% adherence) Solid lines indicate >80% adherence Tate J. et al. IDSA 2010 Vancouver, BC October 21-24th. Poster 1136

  40. VACS Index Correlated with Biomarkers of Inflammation Justice AC et al,“Biomarkers of Inflammation, Coagulation, and Monocyte Activation are Strongly Associated with the VACS Index among Veterans on cART” CROI 2011 Poster # 796

  41. VACS Vs. Restricted Index Summary • More accurately predicts mortality among patients in North America and Europe • More responsive to antiretroviral treatment • More strongly correlated with markers of hyper-coagulability, microbial translocation, and inflammation

  42. Why Should Clinicians Care? • Uses lab tests currently part of routine care • Identifies modifiable risk at lower test thresholds • Incorporates age, and effects of HANA and toxicity • Computation easy, can be included in lab reports and available through websites/apps • Offers approach that incorporates multifaceted HIV effects, multimorbidity, and toxicity

  43. Case • HIV+ 45 yrold man. After 1 yr. of ART, CD4 count is 500 cells/mm3, HIV-1 RNA undetectable. HCV+ and has a FIB-4 >3.25. • Restricted Index • Score=0 • Expected 5 yr mortality 2% • VACS Index • Score=30 (5 pts HCV ;25 pts FIB-4) • Expected 5 yrmortality 12%

  44. Case Continued • Just as Framingham charts CVD risk over time the VACS Index can chart overall health over time • For this patient, we would target sources of liver injury: HCV, alcohol, toxic medications, and obesity • If we achieve a SVR and his FIB-4 normalizes score drops to 0; new 5 yrmortality 2% • If we decrease his FIB-4 from “high” to “moderate” his score would drop to 11; new 5 yr mortality 3-fold lower (from 12% to 4%)

  45. Future Work • Informatics: tools to calculate index, counsel on risk, identify modifiable risk, and suggest patient and provider action • Observational Analyses: estimate likely effect size for potential interventions: eg, alcohol cessation, HCV treatment, adherence, etc. • RCT: compare VACS Index guided management to usual care among multimorbid HIV+ patients • Possible outcomes: hospitalization, MICU admission, nursing home placement, or death

  46. National VACS Project Team 2010

  47. Veterans Aging Cohort Study • PI and Co-PI: AC Justice, DA Fiellin • Scientific Officer (NIAAA): K Bryant • Participating VA Medical Centers: Atlanta (D. Rimland), Baltimore (KA Oursler, R Titanji), Bronx (S Brown, S Garrison), Houston (M Rodriguez-Barradas, N Masozera), Los Angeles (M Goetz, D Leaf), Manhattan-Brooklyn (M Simberkoff, D Blumenthal, H Leaf, J Leung), Pittsburgh (A Butt, E Hoffman), and Washington DC (C Gibert, R Peck) • Core Faculty: K Akgun, S Braithwaite, C Brandt, K Bryant, R Cook, K Crothers, J Chang, S Crystal, N Day, R Dubrow, M Duggal, J Erdos, M Freiberg, M Gaziano, M Gerschenson, A Gordon, J Goulet, N Kim, M Kozal, K Kraemer, V LoRe, S Maisto, K Mattocks, P Miller, P O’Connor, C Parikh, C Rinaldo, J Samet • Staff: H Bathulapalli, T Bohan, D Cohen, A Consorte, P Cunningham, A Dinh, C Frank, K Gordon, J Huston, F Kidwai, F Levin, K McGinnis, L Park, C Rogina, J Rogers, L Sacchetti, M Skanderson, J Tate, E Williams • Major Collaborators: VA Public Health Strategic Healthcare Group, VA Pharmacy Benefits Management, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Yale Center for Interdisciplinary Research on AIDS (CIRA), Center for Health Equity Research and Promotion (CHERP), ART-CC, NA-ACCORD, HIV-Causal • Major Funding by: National Institutes of Health: NIAAA (U10-AA13566), NIA (R01-AG029154), NHLBI (R01-HL095136; R01-HL090342; RCI-HL100347) , NIAID (U01-A1069918), NIMH (P30-MH062294), and the Veterans Health Administration Office of Research and Development (VA REA 08-266) and Office of Academic Affiliations (Medical Informatics Fellowship).

More Related