GFR Decline as an Endpoint in Clinical Trials of CKD · • CKD is defined as abnormalities of...
Transcript of GFR Decline as an Endpoint in Clinical Trials of CKD · • CKD is defined as abnormalities of...
Included participantsNot included
GFR Decline as an Endpoint in Clinical Trials of CKD
Josef Coresh, MD, PhDG.W. Comstock Professor
of Epidemiology, Biostatistics & Medicine Johns Hopkins University
1
Josef Coresh, MD, PhD & Morgan Grams, MD, PhDCo-Principal Investigators (~70 cohorts including ~10 Million participants)
1. Criteria for assessing a surrogate outcomes in CKD
2. Using data to make progress Observational studies Clinical Trials Simulations
3. 2012 FDA Workshop Conclusions Using eGFR change as a surrogate outcome (57% reduction 30-40% reduction) with a major caveat (acute effects can nullify the paradigm)
4. Related work – 2018 FDA Workshop on change in albuminuria and GFR slopes
Outline
Am J Kidney Dis. 2014; 64(6):821-835
DEFINITION (since 2002)• CKD is defined as abnormalities of kidney structure or function, present for 3+ monthsSTAGING (CGA since 2013)• by cause (C), GFR category (G), and albuminuria category (A)
Progression data were limited• Decline in GFR category
• Certain drop (25+%) • Rapid 5+ ml/min/y• Confidence # yrs, # measures
• ACR small fluctuations are common
KDIGO 2012 – Evaluation & Mangement of CKD
• Biologic plausibility: whether surrogate is on pathophysiologic pathway leading to clinical outcome of interest (causal? necessary intermediate?)
• Strength and consistency of epidemiologic data supporting relationship between surrogate and clinical outcome of interest
• Prediction of treatment effects on surrogate from treatment effects on clinical outcome of interest (with drugs in the same/related pharmacologic class? with drugs from distinct pharmacologic classes/ regardless of the mechanism of the intervention?)
Assessing a Candidate Surrogate Endpoint
At present, no FDA guidance document contains a detailed discussion of the evidence needed to establish a “validated surrogate endpoint” supporting traditional approval, however FDA has stated that the standard is high.
Chronic Kidney Disease Prognosis Consortium~70 cohorts, ~10 million participants
Josef Coresh, MD, PhD & Morgan Grams, MD, PhDCo-PIs, CKD Prognosis Consortium
http://www.jhsph.edu/ckdpc
Included participantsNot included
AichiHiroshi YatsuyaKentaro YamashitaHideaki ToyoshimaKoji Tamakoshi
AKDN:Marcello TonelliBrenda HemmelgarnMatthew James Tanvir C Turin
ARIC:Josef CoreshKunihiro Matsushita Morgan GramsYingying Sang
AusDiab: Robert C Atkins Kevan R PolkinghorneSteven Chadban
Beaver Dam: Ronald Klein Barbara EK KleinKristine E Lee
Beijing:HaiYan WangLuxia Zhang Fang WangLi Zuo
China National Survey:Luxia ZhangLisheng LiuMinghui ZhaoFang WangJinwei Wang
CHS:Michael ShlipakCarmen PeraltaRonit Katz
CIRCS:Hiroyasu IsoAkihiko KitamuraTetsuya OhiraKazumasa Yamagishi
COBRA:Tazeen JafarMuhammad Islam Juanita Hatcher Neil PoulterNish Chaturvedi
ESTHER:Dietrich RothenbacherHermann Brenner Heiko MüllerBen Schöttker
Framingham:Caroline S Fox Shih-Jen Hwang James B MeigsAshish Uphadhay
Gubbio:Massimo Cirillo
HUNT: Stein HallanKnut AasarødCecilia M ØienMarie Radtke
Ibaraki:Fujiko IrieHiroyasu IsoToshimi SairenchiKazumasa Yamagishi
Jackson Heart Study:Adolfo CorreaCasey RebholzEbony BoulwareBessie Young
Japan Health Checkup:Tsuyoshi WatanabeKunihiro YamagataKunitoshi IsekiKouichi Asahi
JMS:Shizukiyo IshikawaYuichiro Yano
Korean Heart Study: Sun Ha JeeHeejin KimmYejin Mok
KSHS:Eliseo GuallarSeungho RyuYoosoo ChangJuhee ChoHocheol Shin
Maccabi:Gabriel ChodickVarda ShalevYair C BirnbaumAnat Bet-Or
MESA:Michael ShlipakMark J SarnakCarmen PeraltaRonit Katz Holly J Kramer
MRC Older People:Paul Roderick Dorothea Nitsch Astrid Fletcher Christopher Bulpitt
Mt. Sinai BioMe:Erwin BottingerGirish Nadkarni
NHANES III:Brad Astor Josef Coresh Kunihiro Matsushita
NIPPON:Hirotsugu UeshimaAkira OkayamaKatsuyuki MiuraSachiko Tanaka
Ohasama:Takayoshi Ohkubo Hirohito MetokiMasaaki Nakayama Masahiro KikuyaYutaka Imai
Okinawa 83 & 93:Kunitoshi Iseki
Ontario ICES-KDT:Amit GargEric McArthur
PREVEND:Ron T Gansevoort Paul E de Jong Hans Hillege
Rancho Bernardo:Simerjot K JassalElizabeth Barrett-Connor Jaclyn Bergstrom
REGARDS:David G Warnock Paul MuntnerSuzanne Judd Orlando Gutierrez
CKD-PC (GP cohorts)Rotterdam:
Sanaz SedaghatM Arfan IkramEwout J HoornAbbas Dehghan
SCREAM:Juan-Jesus CarreroAbdul Rashid Qureshi
SEED:Tien Yin WongCharumathi SabanyagamChing-Yu ChengWan Gen Yip
Taiwan:Chi-Pang Wen Sung-Feng Wen Chwen-Keng TsaoMin-Kuang Tsai
Takahata:Tsuneo KontaAtsushi HirayamaKazunobu Ichikawa
TLGS Iran:Farzad Hadaegh, Mohammadhassan MirboloukFereidoun Azizi
Tromsø:Marit D SolbuBjørn O Eriksen
ULSAM:Johan ÄrnlövLars LannfeltAnders Larsson
ADVANCE:Mark Woodward John Chalmers Stephen MacMahonHisatomi Arima
CARE:Marcello TonellFrank Sacks Gary Curhan
KEEP: Allan J CollinsJoseph A VassalottiSuying Li Shu-Cheng Chen
KP Hawaii:Brian J Lee
MRFIT:Areef IshaniJames Neaton
NZDCS:C Raina ElleyTim KenealySimon MoyesJohn CollinsPaul Drury
Pima Indian:Robert G NelsonWilliam C Knowler
SMART:Frank Visseren
ZODIAC:Henk J BiloHanneke JoostenNanne KleefstraKlaas H GroenierIefke Drion
AASK:Jackson Wright Lawrence Appel Tom GreeneBrad C Astor
British Columbia CKD: Adeera Levin Ognjenka Djurdjev
CanPREDDICTAdeera LevinOgnjenka Djurdjev
CARE FOR HOMeGunnar HeineSarah SeilerAdam Sawada
CCF:Sankar NavaneethanJoseph NallyJesse Schold
CKD-JAC:Masafumi FukagawaShoichi MaruyamaTakayuki HamanoTakeshi HasegawaNaohiko Fujii
CRIB:David C Wheeler Martin J Landray Jonathan N TownendJonathan Emberson
CRIC:Lawrence AppelHarold FeldmanChi-Yuan Hsu
CRISIS:Philip A KalraJames RitchieRaman MaharajanHelen AldersonBeverly Lane
GCKD:Kai-Uwe EckardtAnna KottgenStephanie Titze
GeisingerAlex R ChangRobert PerkinsH Les Kirchner
GLOMMS 2:Corri BlackAngharad Marks Nicholas FluckGordon Prescott
Gonryo CKDSadayoshi ItoMasaaki NakayamaMariko Miyazaki
Hong Kong CKD:Angela Yee Moon WangSharon CheungSharon WongJessie ChuHenry Wu
KP Northwest: David H Smith Eric S Johnson Micah L ThorpJessica Weinstein
MASTERPLAN: Jack F WetzelsPeter J Blankestijn Arjan D van Zuilen
MDRD: Mark SarnakAndrew S LeveyLesley InkerVandana Menon
MMKD:Florian KronenbergBarbara KolleritsEberhard Ritz
Nanjing CKD:Haitao Zhang
Nefrona:Jose M ValdivielsoElvira FernandezAngels BetriuMarcelino Bermudez-Lopez
NephroTest:Marc Froissart Benedicte Stengel Marie Metzger Jean-Philippe HaymannPascal HouillierMartin Flamant
NRHP-URU:Pablo RiosNelson MazzuchiLiliana GadolaVerónica LamadridLaura Sola
CKD-PC (HR/CKD cohorts)PSPA:
Olivier MoranneCecile CouchoudCecile Vigneau
PSP-CKD:Nigel BrunskilRupert Major
RENAAL: Hiddo J L HeerspinkBarry Brenner Dick de Zeeuw
SRR-CKDMarie Evans
STENO CKD: Peter RossingHans-Henrik Parving
Sunnybrook:David NaimarkNavdeep Tangri
VA/RCAV:Csaba P KovesdyKamyar Kalantar-Zadeh
West of Scotland CKD: Patrick B MarkJamie P TraynorColin C GeddesPeter C Thomson
CKD Progression: A Focus on Future Risk
Adjust for first eGFR
First eGFR
8
Baselineperiod
Follow-up for ESRD
150
120
90
60
30
eGFR
ml/
min
/1.7
3m2
(A)
150
120
90
60
30 (B)
Last eGFR
45
Clinical Trial Perspective:Equal starting point
• Question: Is more rapid CKD progression (B vs. A) associated with higher subsequent risk of ESRD?
• Analyze each participating cohort: model % change in eGFR using a spline• Meta-analyze across cohorts: random effects meta-analysis• Examine heterogeneity: Forest Plots and Meta-Regression
Adjust for last eGFR
First eGFR
Last eGFR
Baselineperiod
Follow-up for ESRD
150
120
90
60
30 (A)
150
120
90
60
30
(B)
45eGFR
ml/
min
/1.7
3m2
Clinician Perspective: at last visit extrapolate past history
ESRD
45
ESRD
Am J Kidney Dis. 2014; 64(6):821-835
Participating Cohorts: Description by Baseline Kidney Function
During the baseline period After the baseline period
Participants,N
Serum creatinine,
n (IQR)
ESRD events, n
Follow-up Mean (SD),
yearsBaseline eGFR <60
Baseline eGFR 60+
Baseline eGFR <60
Baseline eGFR 60+
Baseline 19 cohorts 9 cohorts 19 cohorts 9 cohorts1 year 487,213 1,043,401 2 (2-3) 11,236 1,109 3.12 years 383,216 957,949 3 (3-5) 7,548 992 2.43 years 255,795 824,405 5 (5-5) 4,087 1,085 2.0
Variable Mean MeanBaseline eGFR 48 90Age 74 52% Female 23 52% Black 6 1Total Cholesterol 5 5Systolic BP 135 132% DM 38 16% Hx of CVD 33 6
Adjustment variables at baseline
JAMA 2014;311: 2518-31
31.4 (21.6, 45.7)
10.1 (8.1, 12.5)5.3 (4.5, 6.2)
2.9 (2.5, 3.3)
Ref.
0.30.30.30.30.30.30.30.30.30.3 0.70.70.70.70.70.70.70.70.70.7 1.51.51.51.51.51.51.51.51.51.5 3.43.43.43.43.43.43.43.43.43.46.96.96.96.96.96.96.96.96.96.9
12121212121212121212
25252525252525252525
1616161616161616161612121212121212121212 10101010101010101010
4.84.84.84.84.84.84.84.84.84.8
-4-202468
1012
.2
1
5
25
125
625
PAR
, %
Adj
uste
d H
R
-70 -60 -50 -40 -30 -20 -10 0 10 20 30 40Percent change of eGFR
55.7 (22.7, 136.7)
15.1 (8.7, 26.3)
6.6 (3.8, 11.5)
2.8 (1.8, 4.3)
Ref.
0.10.10.10.10.10.10.10.10.10.1 0.20.20.20.20.20.20.20.20.20.2 0.40.40.40.40.40.40.40.40.40.4 1.21.21.21.21.21.21.21.21.21.2 3.23.23.23.23.23.23.23.23.23.2
12121212121212121212
2020202020202020202015151515151515151515
8.68.68.68.68.68.68.68.68.68.63.73.73.73.73.73.73.73.73.73.7
34343434343434343434
0
10
20
30
40
Perc
enta
ge o
f pop
ulat
ion
-70 -60 -50 -40 -30 -20 -10 0 10 20 30 40Percent change of eGFR
Adjusted Hazard Ratio (HR) of ESRD and Subsequent to % Change in eGFR during a 2-year baseline period
Doub
ling
of se
rum
cr
eatin
ine
Doub
ling
of se
rum
cr
eatin
ine
-30% eGFR
eGFR<60 eGFR≥60
JAMA 2014;311: 2518-31
31.4 (21.6, 45.7)
10.1 (8.1, 12.5)5.3 (4.5, 6.2)
2.9 (2.5, 3.3)
Ref.
0.30.30.30.30.30.30.30.30.30.3 0.70.70.70.70.70.70.70.70.70.7 1.51.51.51.51.51.51.51.51.51.5 3.43.43.43.43.43.43.43.43.43.46.96.96.96.96.96.96.96.96.96.9
12121212121212121212
25252525252525252525
1616161616161616161612121212121212121212 10101010101010101010
4.84.84.84.84.84.84.84.84.84.8
-4-202468
1012
.2
1
5
25
125
625
PAR
, %
Adj
uste
d H
R
-70 -60 -50 -40 -30 -20 -10 0 10 20 30 40Percent change of eGFR
55.7 (22.7, 136.7)
15.1 (8.7, 26.3)
6.6 (3.8, 11.5)
2.8 (1.8, 4.3)
Ref.
0.10.10.10.10.10.10.10.10.10.1 0.20.20.20.20.20.20.20.20.20.2 0.40.40.40.40.40.40.40.40.40.4 1.21.21.21.21.21.21.21.21.21.2 3.23.23.23.23.23.23.23.23.23.2
12121212121212121212
2020202020202020202015151515151515151515
8.68.68.68.68.68.68.68.68.68.63.73.73.73.73.73.73.73.73.73.7
34343434343434343434
0
10
20
30
40
Perc
enta
ge o
f pop
ulat
ion
-70 -60 -50 -40 -30 -20 -10 0 10 20 30 40Percent change of eGFR
Adjusted Hazard Ratio (HR) of ESRD and Subsequent to % Change in eGFR during a 2-year baseline period
Doub
ling
of se
rum
cr
eatin
ine
Doub
ling
of se
rum
cr
eatin
ine
-30% eGFR
eGFR<6012% 39%
% population attributable risk eGFR≥607% 23%
JAMA 2014;311: 2518-31
NOTE: Weights are from random effects analysis
Overall (I-squared = 79.9%, p = 0.000)
Study
Maccabi
NZDCS
MRFIT
ID
Sunnybrook
VA_CKD
Pima
AKDN_dipstick
ADVANCE
BC_CKD
6.61 (3.74, 11.68)
Hazard
20.49 (11.32, 37.08)
9.87 (5.43, 17.96)
1.53 (0.81, 2.90)
Ratio (95% CI)
3.32 (0.72, 15.25)
10.12 (6.00, 17.07)
9.13 (3.95, 21.12)
6.47 (2.83, 14.76)
6.07 (1.73, 21.26)
4.34 (1.77, 10.63)
100.00
%
12.72
12.69
12.44
Weight
7.17
13.13
11.18
11.26
8.60
10.81
6.61 (3.74, 11.68)
Hazard
20.49 (11.32, 37.08)
9.87 (5.43, 17.96)
1.53 (0.81, 2.90)
Ratio (95% CI)
3.32 (0.72, 15.25)
10.12 (6.00, 17.07)
9.13 (3.95, 21.12)
6.47 (2.83, 14.76)
6.07 (1.73, 21.26)
4.34 (1.77, 10.63)
100.00
%
12.72
12.69
12.44
Weight
7.17
13.13
11.18
11.26
8.60
10.81
1.5 1 2 4 8 16 32First eGFR60
for 30% decline in eGFR in 2 yearsRelative risk of End-Stage Renal Disease
NOTE: Weights are from random effects analysis
Overall (I-squared = 62.8%, p = 0.000)
CCF
MDRDMRFIT
ID
RENAAL
VA_CKD
CRIB
AASK
MASTERPLAN
KPNW
BC_CKD
Geisinger
NZDCS
GLOMMS1
NephroTest
Sunnybrook
ADVANCEAKDN_dipstick
KP Hawaii
Maccabi
Study
5.32 (4.52, 6.26)
5.36 (3.53, 8.12)
3.48 (2.66, 4.55)9.52 (1.54, 59.04)
Ratio (95% CI)
4.25 (1.42, 12.70)
6.40 (5.75, 7.13)
9.45 (3.62, 24.70)
4.99 (3.43, 7.27)
9.37 (4.84, 18.13)
23.94 (5.49, 104.38)
4.36 (3.64, 5.22)
5.22 (3.56, 7.66)
2.79 (1.73, 4.49)
4.27 (1.73, 10.52)
5.76 (3.05, 10.88)
6.23 (3.44, 11.29)
4.21 (0.80, 22.17)4.67 (3.04, 7.16)
25.94 (4.04, 166.37)
6.76 (5.37, 8.51)
Hazard
100.00
6.89
9.340.75
Weight
1.88
11.89
2.34
7.52
4.10
1.11
10.84
7.39
6.04
2.58
4.31
4.69
0.896.71
0.72
10.03
%
5.32 (4.52, 6.26)
5.36 (3.53, 8.12)
3.48 (2.66, 4.55)9.52 (1.54, 59.04)
Ratio (95% CI)
4.25 (1.42, 12.70)
6.40 (5.75, 7.13)
9.45 (3.62, 24.70)
4.99 (3.43, 7.27)
9.37 (4.84, 18.13)
23.94 (5.49, 104.38)
4.36 (3.64, 5.22)
5.22 (3.56, 7.66)
2.79 (1.73, 4.49)
4.27 (1.73, 10.52)
5.76 (3.05, 10.88)
6.23 (3.44, 11.29)
4.21 (0.80, 22.17)4.67 (3.04, 7.16)
25.94 (4.04, 166.37)
6.76 (5.37, 8.51)
Hazard
100.00
6.89
9.340.75
Weight
1.88
11.89
2.34
7.52
4.10
1.11
10.84
7.39
6.04
2.58
4.31
4.69
0.896.71
0.72
10.03
%
1.5 1 2 4 8 16 32First eGFR<60
for 30% decline in eGFR in 2 yearsRelative risk of End-Stage Renal Disease
12
19 studies with eGFR<60 9 studies with eGFR 60+
Adj. Hazard Ratio of ESRD after a 30% Decline in eGFR over 2-yearsForest Plot Showing Consistency Across Studies
5.32 (4.52, 6.26) 6.61 (3.74, 11.68)
2014 Publications Series for CKD Clinical Trials
Am J Kidney Dis. 2014; 64(6):848-859
Am J Kidney Dis. 2014; 64(6):860-866
Am J Kidney Dis. 2014; 64(6):867-879
JAMA 2014;311: 2518-31
Cohorts
Simulations
Clinical Trials – Intention to Treat
Clinical Trials - Observational
Am J Kidney Dis. 2014; 64(6):821-835
Conference Report - SUMMARY
Clinical Trials – Support using 30-40% Decline 37 randomized trials BUT Data are Limited
• Nice correlation across studies between: Hazard ratio alternativeoutcome (for 30-40% eGFR decline) hazard ratio for established outcome (doubling of serum creatinine or ESRD)
Am J Kidney Dis. 2014; 64(6):848-859
Simulations – Support using 30-40% Decline BUT Warn about Acute Effects
Am J Kidney Dis. 2014; 64(6): 821-835
Inflated Type 1 Error – False Positive Benefit
Inflated Type 1 Error – False Positive Harm
Type 1 Error Acceptable and Power Improved
Acute Effects
Summary of Evidence from All SourcesObservational Studies, Trials, Simulations
• Based on a series of meta-analyses of cohorts and clinical trials and simulations of trial designs and analytic methods, the workshop concluded that a confirmed decline in estimated GFR of 30% over 2 to 3 years may be an acceptable surrogate end point in some circumstances, but the pattern of treatment effects on GFR must be examined, specifically acute effects on estimated GFR. – An estimated GFR decline of 40% may be more broadly acceptable
than a 30% decline across a wider range of baseline GFRs and patterns of treatment effects on GFR.
– However, there are other circumstances in which these end points could lead to a reduction in statistical power or erroneous conclusions regarding benefits or harms of interventions.
– We encourage careful consideration of these alternative end points in the design of future clinical trials.
Summary of 5 papers : Am J Kidney Dis. 2014; 64(6):821-835Favorable editorials by FDA & EMA
Conclusions
• Meaningful CKD progression can be understood in the context of its prediction of future risk & surrogacy– eGFR decline of 30%-40% is a useful outcomes and surrogate for
CKD progression (with caveats)
• Ongoing work (FDA meeting 2018: data + discussion papers)
• Surrogacy speaks only to efficacy not safety which may relate to off-target effects
Acknowledgements
• CKD-EPI Collaboration (eGFR & trials)
• CKD Prognosis Consortium (NKF & KDIGO) • Consortium - a group formed to undertake an enterprise beyond the resources of any one member
• NKF, FDA and Workshop Attendees• International collaborations• Johns Hopkins co-investigators & staff
Thank you!
EXTRA SLIDES
NKF Workshop Report
2018 Preliminary Datato be presented at ASN
Figures
GFR Slope ESKD Risk Associations(ESKD HR and CI for 0.75 ml/min/1.73 m2 per year difference)
Least Square Mean Regression Linear Mixed Models RegressionBaseline eGFR <60
1-year 0.88 (0.86, 0.91) 0.80 (0.77, 0.83)2-year 0.80 (0.78, 0.82) 0.71 (0.69, 0.74)3-year 0.71 (0.69, 0.74) 0.64 (0.60, 0.67)
Baseline eGFR >601-year 0.93 (0.92, 0.94) 0.77 (0.74, 0.80)2-year 0.85 (0.83, 0.87) 0.70 (0.68, 0.73)3-year 0.77 (0.74, 0.80) 0.67 (0.64, 0.69)
*LSM; empirical: beta coefficient from linear regression of eGFR on time. LMM; best linear unbiased prediction from linear mixed models. All eGFR values within a given observation period (1-, 2-, 3- years +/- 30%) were used to estimate slope coefficient.
Treatment Effect on the Clinical vs. Slope Surrogate: Meta-Regression of 47 trial interventions
.06 .12 .25 .5 1 2 4 8 16ACR fold change
.06 .12 .25 .5 1 2 4 8 16PCR fold change
Albuminuria Change ESKD Risk Associations(ESKD HR and CI for 30% ACR or PCR Decrease)
Empiric* Adjusted for Regression Dilution (Median Reliability)**
ACR1-year 0.82 (0.74-0.91) 0.75 (0.64-0.87)2-year 0.83 (0.74-0.94) 0.78 (0.66-0.92)3-year 0.80 (0.71-0.90) 0.76 (0.65-0.87)
PCR1-year 0.86 (0.76-0.97) 0.80 (0.67-0.95)2-year 0.77 (0.68-0.87) 0.69 (0.58-0.83)3-year 0.74 (0.61-0.89) 0.68 (0.54-0.86)
*Adjusted for age, sex, race/ethnicity (blacks vs. non-blacks), systolic blood pressure, total cholesterol, diabetes, history of cardiovascular disease, current smoking, former smoking, and first eGFR and albuminuria. **Based on estimates for ACR and PCR in 19 studies. Median (IQR 25th to 75th percentile) reliability estimates (λ) for 1, 2 and 3 year change were 0.677 (0.533-0.770), 0.721 (0.650-0.808) and 0.789 (0.713-0.852). The same reliability estimates were used for ACR and PCR.
Treatment Effect on the Clinical vs. Slope Surrogate: Meta-Regression of 39 trial interventions
FDA meeting 2018: Data + discussion ASN 2018 + papers in progress
• ACR change can be a reasonably likely surrogate endpoint for kidney disease progression in many Phase III RCTs and can be a valid surrogate endpoint in some Phase III RCTs, but its appropriateness varies by disease and by intervention. It is most appropriate for diseases characterized by albuminuria and for interventions in which reducing albuminuria is hypothesized to be one of the mechanisms of action. A large treatment effect (a 20-30% reduction in GMR ACR) is likely to be necessary to assure a significant treatment effect on the clinical endpoint
• eGFR slopes are impressively predictive and correlated with clinical trial outcomes meeting criteria for a valid surrogate (with the caveat that acute effects complicate the analysis and interpretation)
Planning Committee
Advisors StakeholdersIncluding patients
Workshop Attendees
Operations Committee
Analytic Group
SponsorsNKF, FDA, EMA
GFR Slopes - Trials• Acute treatment effects are common and
vary by intervention.• Treatment effects on total and chronic
slope are less precise at shorter follow up intervals.
• At 3 years, treatment effects on chronic slope approach effect on total slope.
• Strong relationships of treatment effect on the clinical endpoint and treatment effect on slope at 3 years. Stronger for total slope than chronic slope.
• Consistency across baseline eGFR, ACR, disease and intervention, but insufficient power for definitive evaluation.
• Relationships weaken but persist at 2 years, but deteriorate greatly at 1 year
• Slope reduction of 0.5-1.0 ml/min/1.73 m2 per year has HR of 0.6-0.7 for treatment effect on clinical endpoint and PPV of 97.5% for HR <1.0.
Albuminuria Change – Trials (1)
• Moderately strong relationship.• Relationship is stronger for the
subgroup with ACR > 30 mg/g, but limited power to assess statistical significance.
• Consistency across baseline eGFR, ACR, disease and intervention, but insufficient power for definitive evaluation.
• 30-40% ACR reduction is associated with HR of 0.6-0.7 for treatment effect on clinical endpoint and PPV of 97.5% for HR <1.0.
Utility vs. ValidityHigher power (smaller
sample size, shorter follow up) compared
to Scrdoubling
Strong biologic
plausibility, strong
associations with clinical
endpoint, preserves low type 1 error
Both criteria depend on nature of treatment effect, duration of follow up, baseline GFR, size and direction of acute effects and trial duration.
Circumstances in which the proposed alternative surrogates may not be applicable (1)
• Effects of the interventions on non-GFR determinants of serum creatinine– Measure other filtration markers (cystatin C, etc)– Measure GFR
• Acute effects– No easy answer, will require modifications to clinical trial
design, generally on a case-by-case basis– Omission of pre-randomization GFR (inclusion of only on-
treatment GFR) would be a violation of RCT intention-to-treat analysis; justification should be specific to the intervention and disease
Relationship between Decline in eGFR andRise in Serum Creatinine or Cystatin C
• Mathematical transformations based on Scr or Scys coefficient in GFR estimating equations
• For CKD-EPI equations: Scr -1.209 (above the knots, Scr0.7 in women and Scr 0.9 in men)Scys -1.328 (above the knot, Scys0.8 in women and men)
eGFRdecline
x↑Scr x↑Scys
75% 3.15 2.8467% 2.50 2.3057% 2.01 1.8950% 1.77 1.6940% 1.53 1.4730% 1.34 1.3125% 1.27 1.2420% 1.20 1.1810% 1.09 1.08
Scorecard for eGFR decline >30% and >40%Analysis Results Comment
Cohorts
Relative risk for ESRD is strong (HR >4) Yes Consistent across cohorts and baseline eGFR
Excess risk is substantial (>10-50% for 30% decline in eGFR)
Yes Varies by baseline eGFR and follow-up interval
Trials
Relative risk for ESRD is strong (HR >4) Yes Consistent among trials
Treatment effect precision is greater than for Scr doubling
Yes Greater with longer duration of follow up
Treatment effect HR (with confirmation) is is similar to Scr doubling (within 10%)
Yes Consistent among interventions except diet (non-GFR effect) and except RASB vs. CCB with 30% (acute effects)
Simulations
Acceptable type 1 errors in simulations with null treatment effects
Yes Substantial savings for shorter trials and high baseline GFR. Inflated type I error in some settings with moderate and large acute effects(more with 30%)
Power stronger than Scr doubling (smaller samples size or shorter follow-up)