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D. Serón Nephrology Department Hospital Vall d’Hebron Barcelona

Interpretation of sequential protocol biopsies in terms of prognosis and clinical implications. D. Serón Nephrology Department Hospital Vall d’Hebron Barcelona. Biopsy. SCr m mol/l. 300 250 200 150 100 50 0. 1 2 3 4 5 years. Lesions are too advanced

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D. Serón Nephrology Department Hospital Vall d’Hebron Barcelona

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  1. Interpretation of sequential protocol biopsies in terms of prognosis and clinical implications D. Serón Nephrology Department Hospital Vall d’Hebron Barcelona

  2. Biopsy SCr mmol/l 300 250 200 150 100 50 0 1 2 3 4 5 years

  3. Lesions are too advanced The biopsy was done too late There is nothing we can do

  4. The assumption that renal allograft histology should be perfectly normal during quiescence has not been adequately investigated Burdick JF et al, Transplantation 1984; 38: 679

  5. Characteristics of early routine renal allograft biopsies Protocol biopsies done at 1-4 weeks Quantification of interstitial infiltrates with a morphometric technique in HE stained biopsies Diagnosis N cel/mm2 interstitium __________________________________________ ATN in native kidney 9 451  101 Stable function 4 1290  179 Post-transplant ATN 7 1335  182 Acute rejection 5 2269  215* __________________________________________ Burdick JF et al, Transplantation 1984; 38: 679

  6. Protocol biopsies Are lesions observed in protocol biopsies relevant from the clinical point of view?

  7. CAN in (2y) protocol biopsies predictsrenal function deterioration N = 94 patients Stable Deteriorated _________________________________________ CADI 1.79  1.89 5.67 2.94 <0.0001 _________________________________________ Graft function deterioration: SCr >20% at 2-4y CADI: interstitial inflammation & fibrosis + glomerular sclerosis +mesangial matrix increase + vascular intimal proliferation + tubular atrophy Isoniemi H, Transplantation 1994

  8. Chronic lesions at 6m and graft survival N = 89 patients %graft survival 100 CGD<6 (n=54) 80 CGD>6 (n=35) 60 40 p=0.0009 20 0 years 0 1 2 3 Dimény E, Clin Transplantation 1995; 58(11): 1195

  9. % graft survival N=94 patients 100 Normal=53 80 CAN=41 60 40 p=0.024 20 0 0 1 2 3 4 5 6 7 years IF/TA is an independent predictor of graft survival RR 95% CI _____________________________ SCr 1.026 (1.005-1.0047) (mol/l) CAN 5.98 (1.15-31.25) (yes vs. no) _____________________________ Serón D, Kidney Int 1997: 51:310

  10. Sirius red derived VIntFib and time to graft failure Grimm PC et al, J Am Soc Nephrol 2003

  11. CAN, transplant vasculopathy and survival3 m protocol Bx, n=282 % deatt censored graft survival 100 Univariate Multivariate Variable RR 95%CI RR 95%CI __________________________________ SCr (mol/l) 1.009 (1.001-1.016) - Prot (g/l) 1.002 (1.001-1.004) - IF/TA 4.64 (1.44-14.95) 4.53(1.39-14.82) IF/TA (cv-score) 13.61(3.73-49.62) 9.45 (2.32-38.41) 80 Normal IF/TA 60 IF/TA (cv-score 1 40 20 p < 0.001 0 meses 0 20 40 60 80 100 120 Serón D, Transplantation 2000; 69: 1849

  12. SCR + IF/TA and graft survival95 pediatric recipients from a living donor 1 year protocol Bx Normal IF/TA without SCR IF/TA with SCR Shishido et al, JASN 2003; 14: 1046

  13. Predicting decline in allograft functionBiopsy at 1 year (living 69%), Tx 1998-2001,n=292Primary endpoint: death censored graft loss or > 50%  GFR beyond 1y Cosio FG et al, Am J Transplant 2005

  14. 1 Normal=186 SCR=74 .75 IF/TA=110 .5 IF/TA+SCR=65 .25 months 0 50 100 150 200 SCR, CAN and graft survivalProtocol Bx < 6m; n=435 Moreso F et al Am J Transplant 2006; 6: 747

  15. Function and structure are independent predictors of outcome Moreso F et al. AJT 2006; 6: 747

  16. Predictive value of clinical variables and different histological patterns on 7 y death censored graft survivaln=361 pts, protocol Bx before 6 m, follow up > 7y Surrogate Category Accuracy Sensitivity Specificity ______________________________________________________________ Acute rejection yes 72% 30% 80% 3-month SCr >1.8 mg/dl 73% 58% 76% Protocol biopsy IF/TA 67% 65% 67% Protocol biopsy IF/TA + cv-score 1 81% 21% 92% Protocol biopsy IF/TA + SCR 78% 31% 86% ______________________________________________________________ Seron D & Moreso F. Kidney Int 200; 72:690

  17. Poor predictive value ofserum creatinine for renal allograft loss 1st RT > 17y, 1988-1999, at least 2y follow up SCr > 1.8 mg/dl Variable Follow up Obs %Failed OR CI AUC _____________________________________________________________________________ SCr at 1y 2y 74480 7.2 2.22 2.13-2.31 0.627 SCr at 1y 7y 35255 45.2 2.4 2.31-2.50 0.624 _____________________________________________________________________________ While renal function is a strong risk factor and highly correlated with graft failure, the utility of renal function as a predictive tool for graft loss is limited Kaplan B et al. AJT 2003; 3: 1560

  18. GFR ROC AUC for graft failure at 5 yn=430 early protocol BX Banff score AUC = 0.679 (0.581 - 0.777); p=0.001 AUC = 0.685 (0.598 - 0.771); p=0.001 Unpublishedobservation

  19. Histology is not only a predictive variable but a surrogate variable

  20. SRL and CsA withdrawal SRL > 5 ng/mL CsA 150 - 400 ng/mL Steroids + + N = 525 Randomization 3m: n = 430 SRL+CsA, n = 215 SRL, n = 215

  21. Mota A et al., AJT 2004; 4: 953 Oberbauer R, Transpl Int 2005; 1: 22 A reduction in CADI score is associated with improved survival

  22. Risk Benefit

  23. Questions How much contributes one protocol biopsy to predict outcome? Two sequential protocol biopsies improve the predictive value of histology

  24. Questions How much contributes a protocol biopsy to predict outcome? Two Sequential protocol biopsies improve the predictive value of histology?

  25. Inclusion criteria EARLY Prot Bx LATE Prot Bx Protocol Bx < 6m GFR (MDRD4) > 30 ml/min/1.73 m2 Proteinuria < 1g/day Stable function > 5 years of follow up Protocol Bx > 12-24 m

  26. Patients and biopsies june 88-december 2003 Bx < 6m 458 Bx 12-24m 250 Bx < 6m 430 with tissue Bx 12-24m 231 with tissue

  27. PREDICTIVE VALUE OFONE BIOPSY n=430

  28. Statistical approachCox proportional hazard model a.) Predictive clinical variables b.) Predictive clinical and histologicalvariables

  29. Characteristics of patients n=430 Donor age 37±17 Donor sex (%male) 70% Recipient age 46±14 Recipient sex (%male) 63% PRA (%) 7.5±19 HLA DR mm 0.63±0.58 CIT (h) 22±6 Retransplantation 64/430 (17.5%) VHC 16% DGF 17% Acute rejection 19% Graft loss 146 (33.2%) Death censored graft loss 104 (24.2%) Time of biopsy (months) 4.3±1.7 GFR ml/min/1.73m2 53±14 Proteinuria g/d 0.30±0.21

  30. Histological data at the time of biopsy n=430 ______________________________ N glomeruli 13±8 N arteries 5±4 g 0.15±0.48 i 0.58±0.68 t 0.38±0.61 v 0.01±0.11 ah 0.16±0.45 Acute score 1.13±1.31 cg 0.13±0.34 ci 0.46±0.64 ct 0.45±0.62 cv 0.20±0.54 mm 0.25±0.45 Chronic score 1.24±1.65 ______________________________

  31. Clinical variables and death censored graft survival

  32. Histological diagnosis and graft survival No SCR - no IF/TA P = 0.037 1 SCR - no IF/TA No SCR - IF/TA ,9 SCR - IF/TA ,8 no SCR - IF/TA Cum. Survival ,7 SCR - IF/TA ,6 ,5 0 50 100 150 200 250 Time (months)

  33. Clinical variables and death censored graft survival

  34. Is it worth to include histology in multivariate models to predict graft survival?

  35. The contribution of histology to predict death-censored graft failure Model 1 Model 2 Clinical variables Clinical + histological variables Donorage Recipientage PRA GFR Histology Donor age Recipient age, PRA, GFR

  36. The contribution of histology to predict death-censored graft failure Model 1 Model 2 Clinical variables Clinical + histological variables Donorage Recipientage PRA GFR Histology Donor age Recipient age, PRA, GFR

  37. The contribution of histology to predict death-censored graft failure Donor age years Recipient age years PRA % GFR ml/min/1.73m2 Histology yes/no

  38. First classification of acute rejction

  39. The contribution of histology to predict death-censored graft failure Donor age years Recipient age years PARA % GFR ml/min Histology yes/no risk

  40. Beta coefficient of Cox regression model to calculate risk scores H(t)=H0(t) x exp (β1x1+ β2x2+ β3x3+…+ βkxk) Variable β coefficientβ coefficient without histology with histology ___________________________________________________ Donor age (year) +0.020 (+2.0) +0.020 (+2.0) Patient age (year) -0.035 (-3.5) -0.035 (-3.5) GFR (ml/min) -0.024 (-2.4) -0.022 (-2.2) PARA (%) +0.011 (+1.1) +0.011 (+1.1) SCR&IF/TA n.a. +0.559 (+55.9) ____________________________________________________

  41. Beta coefficient of Cox regression model to calculate risk scores Risk score without histology=(2*Donor age)+ (-3.5*patient age)+ (-2.4*GFR)+ (1.1*PRA) Risk score with histology=(2*Donor age)+ (-3.5*patient age)+ (-2.2*GFR)+(1.1*PRA)+(55.9*SCR&IF/TA)

  42. Classification of patients according to risk scores Q3 Q4 Q2 Q1 Risk score

  43. With histology Without histology p<0.0001

  44. Changes in quartile classification due to inclusion of histology in the statistical model: 15%

  45. 1 Q1 ,8 Q2 ,6 Cum. Survival Q3 ,4 Q4 ,2 0 0 50 100 150 200 250 Time Death censored graft failure using quartiles of risk scores Without histology With histology 1 Q1 ,8 Q2 ,6 Cum. Survival Q3 ,4 Q4 ,2 0 0 50 100 150 200 250 Time months months

  46. Validation Modelling sample Testing sample

  47. TWO BIOPSIES<6m and 12-24mN=231

  48. Two sequential biopsies N=231 6m 12-24m _____________________________________________ Time of biopsy (M) 4.3±1.7 16.5±6.0 GFR ml/min/1.73m2 53±14 52±15 ns Proteinuria g/d 0.30±0.21 0.37±0.49 0.01 ______________________________________________

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