CADI, Canti, Cavi 1 (2007)

In this issue of Transplantation, Yilmaz and colleagues report that clinical risk factors, particularly donor age and acute rejection episodes, account for 60% of the variation in pathological lesions in 1-year renal protocol biopsies. They argue that protocol biopsies thereby provide prognostic information beyond that predicted by clinical risk factors. I strongly agree with their conclusion, but believe that the scoring system employed, termed Chronic Allograft Damage Index (CADI), needs substantial improvement through standardization, refinement, and mathematical validation. CADI is similar to preceding renal pathology scoring systems (1, 2) that assign numbers to the extent or severity of individual pathological features (typically 0 to 3). These individual scores are added, sometimes with weighting, to give a composite score. Correlation with outcome determines the validity of the combined scores, and further iteration can be used to select the best predictors and how they should be weighted. The sum of the six CADI scores (Table 1) as applied by the original pathologists, Eero Taskinen and Timo Paavonen, does correlate with graft survival but lacks detailed definitions and standardization with other systems, which has impaired acceptance and validation (3–5). The current most widely used definitions of pathological component grades are those of the Banff system. In 1995, some effort was made to make CADI and Banff more congruent (Solez, personal communication). Subsequently, one paper defined the CADI elements to “correspond to i, ct, cv, cg, mm and ci scores in the Banff 97 classification system,” including substitution of glomerular basement membrane duplication for glomerular sclerosis (6). However, it is not clear to this writer what the orthodox CADI system now uses for definitions. Furthermore, as also pointed out by Prof. Furness, CADI uses only basic histologic features, mixes chronic and active lesions, and has not documented the independence and strength of the component variables. This should not be a cause for dismay, but a stimulus for research, because a robust pathological measure of prognosis is sorely needed for clinical trials (7).TABLE 1: A Rosetta stone of allograft biopsy scoring systemsGenomic pathology has provided a powerful incentive to develop new mathematical algorithms to combine multiple parameters (e.g., expression of different genes) on the same sample and validate their predictive value (8). Elements can be selected by receiver operating characteristic (ROC) curves or other techniques. Each element should provide some unique contribution to the score. Individual elements should be evaluated by multivariate analysis for their relative contribution to the odds ratio of graft failure and for statistical independence from other elements. Those that have the greatest odds ratio should be given more weight, and those with no significant correlation should be discarded. If elements are strongly correlated, the most robust element with regard to observer reproducibility and size of sample should be chosen to represent the related elements. Finally, the score needs to be arithmetically proportional to some measure of outcome (7). Individual elements of the Banff score, such as inflammation (i), fibrosis (ci), transplant glomerulopathy (cg), and intimal fibrosis (cv) each correlate with outcome (9–12). However, no definitive or official combination of Banff scores has been established. One study showed that a four-component Banff Chronic Sum score (cg+ci+ct+cv) correlates with later graft failure (13). Combination of lesions may be prognostically synergistic, as shown by the adverse consequence of having both fibrosis and inflammation (9–11). A mathematical formulation that would reflect this synergy might be of value. In contrast, mesangial matrix expansion (mm) is neither reproducibly scored nor linked to any particular pathogenetic process. Tubular atrophy and interstitial fibrosis are strongly correlated and not independent variables in most, if not all studies. Using both effectively doubles the weight of that component. Different formulae need to be tested using a large database with pathology scores and outcomes. As a start, I favor using variables that reconcile CADI with Banff (ci+ct+cv+cg+i+gs), as in Table 1. This has the advantage of using standardized Banff definitions with only two minor modifications (see Table 1). Initially, the scores should be entered in raw form (%) rather than units (0 to 3) to avoid information loss and artifacts from arbitrary grade definitions. Once the data is analyzed, the cutoff for each of the grades can be decided by the ROC methods (8). A mechanism needs to be developed that would permit the addition and evaluation of molecular markers, such as C4d and gene expression data. Alternative calculations could be tested for predictive power, linearity, and robustness (e.g., ci×i+cv+cg+gs). The best formulations can then be tested on independent data sets. Finally, a caveat: quantitative scores should not be confused with diagnosis (e.g. rejection vs. calcineurin inhibitor toxicity), which likely has more significant prognostic and therapeutic implications in the individual patient.