ObjectiveTo develop and validate an integrative system to predict long term kidney allograft failure.DesignInternational cohort study.SettingThree cohorts including kidney transplant recipients from 10 academic medical centres from Europe and the United States.ParticipantsDerivation cohort: 4000 consecutive kidney recipients prospectively recruited in four French centres between 2005 and 2014. Validation cohorts: 2129 kidney recipients from three centres in Europe and 1428 from three centres in North America, recruited between 2002 and 2014. Additional validation in three randomised controlled trials (NCT01079143, EudraCT 2007-003213-13, and NCT01873157).Main outcome measureAllograft failure (return to dialysis or pre-emptive retransplantation). 32 candidate prognostic factors for kidney allograft survival were assessed.ResultsAmong the 7557 kidney transplant recipients included, 1067 (14.1%) allografts failed after a median post-transplant follow-up time of 7.12 (interquartile range 3.51-8.77) years. In the derivation cohort, eight functional, histological, and immunological prognostic factors were independently associated with allograft failure and were then combined into a risk prediction score (iBox). This score showed accurate calibration and discrimination (C index 0.81, 95% confidence interval 0.79 to 0.83). The performance of the iBox was also confirmed in the validation cohorts from Europe (C index 0.81, 0.78 to 0.84) and the US (0.80, 0.76 to 0.84). The iBox system showed accuracy when assessed at different times of evaluation post-transplant, was validated in different clinical scenarios including type of immunosuppressive regimen used and response to rejection therapy, and outperformed previous risk prediction scores as well as a risk score based solely on functional parameters including estimated glomerular filtration rate and proteinuria. Finally, the accuracy of the iBox risk score in predicting long term allograft loss was confirmed in the three randomised controlled trials.ConclusionAn integrative, accurate, and readily implementable risk prediction score for kidney allograft failure has been developed, which shows generalisability across centres worldwide and common clinical scenarios. The iBox risk prediction score may help to guide monitoring of patients and further improve the design and development of a valid and early surrogate endpoint for clinical trials.Trial registrationClinicaltrials.gov NCT03474003.

Antibodies may have different pathogenicities according to IgG subclass. We investigated the association between IgG subclasses of circulating anti-human HLA antibodies and antibody-mediated kidney allograft injury. Among 635 consecutive kidney transplantations performed between 2008 and 2010, we enrolled 125 patients with donorspecific anti-human HLA antibodies (DSA) detected in the first year post-transplant. We assessed DSA characteristics, including specificity, HLA class specificity, mean fluorescence intensity (MFI), C1q-binding, and IgG subclass, and graft injury phenotype at the time of sera evaluation. Overall, 51 (40.8%) patients had acute antibody-mediated rejection (aABMR), 36 (28.8%) patients had subclinical ABMR (sABMR), and 38 (30.4%) patients were ABMR-free. The MFI of the immunodominant DSA (iDSA, the DSA with the highest MFI level) was 67246464, and 41.6% of patients had iDSA showing C1q positivity. The distribution of iDSA IgG1-4 subclasses among the population was 75.2%, 44.0%, 28.0%, and 26.4%, respectively. An unsupervised principal component analysis integrating iDSA IgG subclasses revealed aABMR was mainly driven by IgG3 iDSA, whereas sABMR was driven by IgG4 iDSA. IgG3 iDSA was associated with a shorter time to rejection (P,0.001), increased microcirculation injury (P=0.002), and C4d capillary deposition (P,0.001). IgG4 iDSA was associated with later allograft injury with increased allograft glomerulopathy and interstitial fibrosis/tubular atrophy lesions (P,0.001 for all comparisons). Integrating iDSA HLA class specificity, MFI level, C1q-binding status, and IgG subclasses in a Cox survival model revealed IgG3 iDSA and C1q-binding iDSA were strongly and independently associated with allograft failure. These results suggest IgG iDSA subclasses identify distinct phenotypes of kidney allograft antibody-mediated injury.

Antibody-mediated rejection (ABMR) can occur in patients with preexisting anti-HLA donor-specific antibodies (DSA) or in patients who develop DSA. However, how these processes compare in terms of allograft injury and outcome has not been addressed. From a cohort of 771 kidney biopsy specimens from two North American and five European centers, we performed a systematic assessment of clinical and biologic parameters, histopathology, circulating DSA, and allograft gene expression for all patients with ABMR (=205). Overall, 103 (50%) patients had preexisting DSA and 102 (50%) had DSA. Compared with patients with preexisting DSA ABMR, patients with DSA ABMR displayed increased proteinuria, more transplant glomerulopathy lesions, and lower glomerulitis, but similar levels of peritubular capillaritis and C4d deposition. DSA ABMR was characterized by increased expression of IFN-inducible, natural killer cell, and T cell transcripts, but less expression of AKI transcripts compared with preexisting DSA ABMR. The preexisting DSA ABMR had superior graft survival compared with the DSA ABMR (63% versus 34% at 8 years after rejection, respectively;<0.001). After adjusting for clinical, histologic, and immunologic characteristics and treatment, we identified DSA ABMR (hazard ratio [HR], 1.82 compared with preexisting DSA ABMR; 95% confidence interval [95% CI], 1.07 to 3.08;=0.03); low eGFR (<30 ml/min per 1.73 m) at diagnosis (HR, 3.27; 95% CI, 1.48 to 7.23; <0.001); ≥0.30 g/g urine protein-to-creatinine ratio (HR, 2.44; 95% CI, 1.47 to 4.09; <0.001); and presence of cg lesions (HR, 2.25; 95% CI, 1.34 to 3.79; =0.002) as the main independent determinants of allograft loss. Our findings support the transplant of kidneys into highly sensitized patients and should encourage efforts to monitor patients for DSA.