Frailty, ESRD, and Kidney Transplantation. Dorry Segev, MD, PhD Vice Chair for Research Department of Surgery Johns Hopkins School of Medicine
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1 Frailty, ESRD, and Kidney Transplantation Dorry Segev, MD, PhD Vice Chair for Research Department of Surgery Johns Hopkins School of Medicine
2 Funding Road AGS Jahnigen Award (07 09) NKF of Maryland Mini grant (07 09) Doris Duke Clinical Scientist Development Award (08 12) K23AG Paul Beeson Career Development Award (09 12) R21DK (10 13) R01AG (13 18, if NIH still exists then)
3 Secondary Data USRDS all patients with ESRD SRTR transplant waiting list transplant recipients and outcomes N=hundreds of thousands or millions 8 minutes to download
4 Primary Data Prevalent HD (dialysis center): 146 Prevalent HD (transplant waitlist): 2194 KT recipients: 845 N~ years to collect
5 Why Frailty and Transplantation? Older adults comprising higher proportion of: Those considering transplantation Those undergoing transplantation Even the younger adults look like older adults End stage kidney disease Multimorbidity Risk prediction is challenging in everyone
6 Clinical Decision Making Risks: Up front risk of surgery (DM, PVD, CAD, frailty, etc) Immunosuppression (infections, side effects) Work (drug levels, labs, clinic, hospitalizations) Benefits: Longer survival (less so for older adults) Quality of life / energy / etc
7 Increase over Time McAdams DeMarco/Segev, JAGS, 2014
8 Mortality over Time Kidney Transplant Recipients over 65 McAdams DeMarco/Segev, JAGS, 2014
9 Mortality over Time Kidney Transplant Recipients over 65 Almost 2x survival benefit McAdams DeMarco/Segev, JAGS, 2014
10 Death Censored Graft Loss over Time Kidney Transplant Recipients over 65 McAdams DeMarco/Segev, JAGS, 2014
11 Mortality, by Age McAdams DeMarco/Segev, JAGS, 2014
12 Death Censored Graft Loss, by Age McAdams DeMarco/Segev, JAGS, 2014
13 Mortality over Time Kidney Transplant Recipients over 65 McAdams DeMarco/Segev, JAGS, 2014
14 Mortality over Time Kidney Transplant Recipients over 65 Over 300,000 over 65 on dialysis McAdams DeMarco/Segev, JAGS, 2014
15 Kucirka/Segev, JAMA 2011
16 Kucirka/Segev, JASN 2009
17 Access to Transplantation Look at KT recipients over 65 ( ) Predict how long they survive after KT Quintiles (excellent candidates = top quintile) Go back to the ESRD patients, apply model to them (dropping those with absolute contraindications) See who from each prediction bin gets listed Grams/Segev, JAGS 2011
18 KT Outcomes in Older Adults Grams/Segev, JAGS 2011
19 Access to Transplantation Excellent candidates (3yr>87.6%) Good candidates (3yr>78.3%) Remaining candidates (3yr<78.3%) N 11,756 43,291 72,913 (%) (9.1%) (33.6%) (56.6%) Grams/Segev, JAGS 2011
20 Access to Transplantation Excellent candidates (3yr>87.6%) Good candidates (3yr>78.3%) Remaining candidates (3yr<78.3%) N 11,756 43,291 72,913 (%) (9.1%) (33.6%) (56.6%) % with access % transplanted Grams/Segev, JAGS 2011
21 Access to Transplantation Excellent candidates (3yr>87.6%) Good candidates (3yr>78.3%) Remaining candidates (3yr<78.3%) N 11,756 43,291 72,913 (%) (9.1%) (33.6%) (56.6%) % with access % transplanted % would find live donors! Grams/Segev, JAGS 2011
22
23 Frailty: General Surgery 594 adults 65yo undergoing general surgery Frailty Prevalence: 10.4% frail 31.3% intermediately frail 58.3% nonfrail Makary, Segev, JACS, 2010
24 Frailty: General Surgery 594 adults 65yo undergoing general surgery Frailty Prevalence: Complications: 10.4% frail 11.4% 43.5% 31.3% intermediately frail 7.3% 33.7% 58.3% nonfrail 3.9% 19.5% Adjusted Rate (full model): Intermediately Frail 2.06 ( ) Frail 2.54 ( ) Makary, Segev, JACS, 2010
25 Frailty: General Surgery 594 adults 65yo undergoing general surgery Frailty Prevalence: Length of Stay: 10.4% frail 1.5d 7.7d 31.3% intermediately frail 1.2d 6.2d 58.3% nonfrail 0.7d 4.2d Adjusted Rate (full model): Intermediately Frail 1.49 ( ) Frail 1.69 ( ) Makary, Segev, JACS, 2010
26 Frailty: General Surgery 594 adults 65yo undergoing general surgery Frailty Prevalence: Discharge to Non Home: 10.4% frail 17.4% 42.1% 31.3% intermediately frail 0.0% 12.2% 58.3% nonfrail 0.8% 2.9% Adjusted Rate (full model): Intermediately Frail 3.16 ( ) Frail ( ) Makary, Segev, JACS, 2010
27 Makary, Segev, JACS, 2010
28 Segev, 2006: Surviving MAFAT by doodling
29 Frailty Prevalence: By Cohort, All Ages DIALYSIS WAITLIST TRANSPLANT CHS Non Frail 9.6% 19.7% 20.1% 46.0% Intermediately Frail 44.2% 53.5% 58.4% 46.0% Frail 46.2% 26.8% 21.5% 6.9%
30 Frailty Prevalence: By Cohort, By Age 120 DIALYSIS WAITLIST TRANSPLANT CHS > > >65 >65
31 Frailty Components, Normalized 100% 90% 80% 70% 60% 50% 40% 30% Physical Activity Shrinking Exhaustion Walking Speed Grip Strength 20% 10% 0% DIALYSIS No Access WAITLIST Access TRANSPLANT Transplanted CHS
32 Frailty: Prevalent ESRD Patients 146 prevalent dialysis patients; 43.8% age 65 Frailty Prevalence: 41.8% frail 32.2% intermediately frail 26.0% nonfrail (expanded definition) McAdams DeMarco/Segev, JAGS, In Press
33 Frailty: Prevalent ESRD Patients Disability 1.4% (n=2) 5.5% (n=8) 4.8% (n=7) 4.8% (n=7) Comorbidity 8.9% (n=13) 10.3% (n=15) 20.5% (n=31) Frailty None 43.2% (n=63) McAdams DeMarco/Segev, JAGS, In Press
34 Frailty: Prevalent ESRD Patients 146 prevalent dialysis patients; 43.8% age 65 Frailty Prevalence: 3 Year Mortality: 41.8% frail 40.2% 32.2% intermediately frail 34.4% 26.0% nonfrail 16.2% McAdams DeMarco/Segev, JAGS, 2013
35 Frailty: Prevalent ESRD Patients 146 prevalent dialysis patients; 43.8% age 65 Frailty Prevalence: 3 Year Mortality: 41.8% frail 40.2% 32.2% intermediately frail 34.4% 26.0% nonfrail 16.2% Adjusted Rate (full model): Intermediately Frail 2.68 ( ) Frail 2.60 ( ) McAdams DeMarco/Segev, JAGS, 2013
36 Frailty: Prevalent ESRD Patients 146 prevalent dialysis patients; 43.8% age 65 Frailty Prevalence: Hospitalization: 41.8% frail 60.7% 32.2% intermediately frail 44.7% 26.0% nonfrail 46.2% Adjusted Rate (full model): Intermediately Frail 0.76 ( ) Frail 1.43 ( ) McAdams DeMarco/Segev, JAGS, 2013
37 Frailty: Prevalent ESRD Patients 146 prevalent dialysis patients; 43.8% age 65 Frailty Prevalence: # Falls in 6 months: 41.8% frail 32.2% intermediately frail 26.0% nonfrail Adjusted Rate (full model): Intermediately Frail 1.19 ( ) Frail 3.09 ( ) McAdams DeMarco/Segev, BMC Nephrology, 2013
38
39 Frailty: KT Recipients 183 KT recipients; 22.1% age 65 Frailty Prevalence: 25.1% frail 74.9% not frail Garonzik Wang/Segev, Archives Surgery, 2012
40 Frailty: KT Recipients 183 KT recipients; 22.1% age 65 Frailty Prevalence: DGF: 25.1% frail 30% 74.9% not frail 15% Garonzik Wang/Segev, Archives Surgery, 2012
41 Frailty: KT Recipients 183 KT recipients; 22.1% age 65 Frailty Prevalence: DGF: 25.1% frail 30% 74.9% not frail 15% Adjusted Rate (full model): Frail 1.94 ( ) Garonzik Wang/Segev, Archives Surgery, 2012
42 Frailty: KT Recipients Garonzik Wang/Segev, Archives Surgery, 2012
43 Frailty: KT Recipients 383 KT recipients Frailty Prevalence: Early Readmission: 18.8% frail 45.8% 81.2% not frail 28.0% McAdams Demarco/Segev, AJT, 2013
44 Frailty: KT Recipients 383 KT recipients Frailty Prevalence: Early Readmission: 18.8% frail 45.8% 81.2% not frail 28.0% Adjusted Rate (full model): Frail 1.61 ( ) McAdams Demarco/Segev, AJT, 2013
45 Frailty: KT Recipients Early Hospital Readmission McAdams Demarco/Segev, AJT, 2013
46 Frailty: KT Recipients Early Hospital Readmission McAdams Demarco/Segev, AJT, 2013
47 KT Outcomes, by Frailty (N=537) Nonfrail Intermediately Frail Frail Graft Loss Ref 1.91 (0.75, 4.82) 2.50* (1.00, 6.23) Mortality Ref 1.59 (0.71, 3.57) 2.19* (1.00, 4.80) All models adjusted for recipient, transplant and donor risk factors using the hybrid registry-augmented regression * P<0.05 McAdams Demarco/Segev, AJT, 2015
48 MMF Dose Reduction McAdams Demarco/Segev, Transplantation, in press
49 Frailty and Length of Stay (N=589) Ratio (95% CI) of days hospitalized OR (95% CI) of 2 week LOS Without DGF WithDGF Without DGF With DGF Frail vs. Nonfrail 1.15 ( ) 1.15 ( ) 1.67 ( ) 1.60 ( ) P value
50 Change in Frailty after KT Mean=0.4, P=0.002 Mean=0.2, P=0.09 Mean= 0.3, P=0.03 Get More Frail Get Less Frail McAdams Demarco/Segev, JAGS, in revision
51 Next Steps Better understand frailty and cognitive decline in this population Better understand frailty trajectories (on dialysis, with transplant) Integrate into clinical practice Develop a disease specific frailty measure Prehabilitation
52 Funding American Geriatrics Society NKF of Maryland Doris Duke Charitable Foundation K23AG R21DK R01AG042504
53 Epidemiology Research Group in Organ Transplantation (D Segev, Director) Core Research Group Affiliated Medicine/Surgery Epidemiology/Biostatistics Research Assistants Daniel Scharfstein, ScD Biostatistics: Coinvestigator Morgan Grams, MD PhD Nephrology Faculty; K08 Christine Durand, MD ID Faculty; R01 Pending Rebecca Craig Schapiro, MD Surgery Resident Jackie Garonzik Wang, MD PhD Surgery Resident; PhD Graduate (KL2) Allan Massie, PhD Epidemiology Faculty (K01 pending) Mara McAdams DeMarco, PhD Epidemiology Faculty (K01) Tanjala Purnell, PhD Epidemiology Faculty Abi Muzaale, MD, MHS Epidemiology Postdoc Full Time: Jennifer Alejo Amanda Brennan Ryan Brown Cassandra Delp Erika Jones Komal Kumar Katie Marks Ravi Vardhan, PhD Biostatistics: Coinvestigator Lucy Meoni, ScM iostatistics: Coinvestigator Josef Coresh, MD PhD Epidemiology: Coinvestigator Linda Kao, PhD Epidemiology: Coinvestigator Elizabeth King, MD Surgery Resident; PhD Student (F32) Babak Orandi, MD PhD MSc Surgery Resident; PhD Graduate(F32) Kyle Van Arendonk, MD PhD Surgery Resident; PhD Graduate (KL2) Medical/Graduate Students Natasha Gupta Medical Student (Doris Duke) Lauren Kucirka, ScM MD/PhD Student (F30) Young Mee Choi Epidemiology; MPH Student Megan Salter, PhD Epidemiology Postdoc (T32) Andrew Law, ScM Epidemiology Staff Xun Luo, MD ScM Analytical Staff Israel Olorunda, MBBS MPH Analytical Staff Anna Poon, MHS MS Analytical Staff Computational Science Sommer Gentry, PhD Computer Science Faculty (HRSA) Eric Chow, MHS Decision Process Programmer/Analyst Corey Wickliffe, MHS Geographic Systems Analyst Part Time: Lindsay Adam Saad Anjum Kate Appel Olivia Berman Seal Bin Han Diana Cantu Reyna Maurice Dunn Laura Grau Teal Harrison Sara Hawa Billy Kim Arnaldo Mercado Perez Ashley Millette Sachin Patel Ana Quintal Katrina Rios Michael Setteducato Chelsea Sicat Lauren Nicholas, PhD Health Policy: Coinvestigator Andrew Cameron, MD PhD Surgery: Collaborator Niraj Desai MD Surgery: Collaborator Bob Montgomery, MD PhD Surgery: Collaborator Nabil Dagher, MD Surgery: Mentee Elliott Haut, MD PhD Surgery: Mentee (KL2; PCORI) Kim Steele, MD PhD Surgery: Mentee (K23) Diane Schwartz, MD Surgery: Mentee Aliaksei Pustavoitau, MD Anesthesiology: Mentee (R03 pending)
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