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Development and validation of a prognostic index for allograft outcome in kidney recipients with transplant glomerulopathy.

Patri P, Seshan SV, Matignon M, Desvaux D, Lee JR, Lee J, Dadhania DM, Serur D, Grimbert P, Hartono C, Muthukumar T - Kidney Int. (2016)

Bottom Line: Based on the Cox model, we developed a prognostic index and classified patients into risk groups.The hazard ratios were 2.18 (median survival 19 months) and 16.27 (median survival 1.6 months), respectively, for patients in the medium and high-risk groups, compared to the low-risk group (median survival 47 months).Our prognostic index model did well in measures of discrimination and calibration.

View Article: PubMed Central - PubMed

ABSTRACT
We studied 92 patients with transplant glomerulopathy to develop a prognostic index based on the risk factors for allograft failure within five years of diagnosis (Development cohort). During 60 months (median) follow-up, 64 patients developed allograft failure. A chronic-inflammation score generated by combining Banff ci, ct and ti scores, serum creatinine and proteinuria at biopsy, were independent risk factors for allograft failure. Based on the Cox model, we developed a prognostic index and classified patients into risk groups. Compared to the low-risk group (median allograft survival over 60 months from diagnosis), patients in the medium risk group had a hazard ratio of 2.83 (median survival 25 months), while those in the high-risk group had a hazard ratio of 5.96 (median survival 3.7 months). We next evaluated the performance of the prognostic index in an independent external cohort of 47 patients with transplant glomerulopathy (Validation cohort). The hazard ratios were 2.18 (median survival 19 months) and 16.27 (median survival 1.6 months), respectively, for patients in the medium and high-risk groups, compared to the low-risk group (median survival 47 months). Our prognostic index model did well in measures of discrimination and calibration. Thus, risk stratification of transplant glomerulopathy based on our prognostic index may provide informative insight for both the patient and physician regarding prognosis and treatment.

No MeSH data available.


Related in: MedlinePlus

Principal component analysis of histopathological variablesWe did Principal Component Analysis (PCA) of 14 histopathological variables. The goal here was to identify variables that were closely associated with one another, so as to combine them as a single variable. In PCA a set of few new variables called principal components (PC) are generated that still reflects a large proportion of the information contained in the original dataset. Each PC is perpendicular to one another in a multidimensional space and thus is independent and uncorrelated. We extracted the first three PC that altogether explained 54% of the total variance. A two dimensional loading plot of PC1 and PC2 is depicted. PC1 explained 26% of the total variance and PC2 explained 17% of the total variance. Variables with the highest loading on PC1 (green) were Banff ci score, ct score and ti score. The correlation coefficient between the PC1 and ci score was 0.78, ct score was 0.75 and ti score was 0.72. Variables with the highest loading on PC2 (black) were Banff i score and t score. The correlation coefficient between the PC2 and i score was 0.71, and t score was 0.70. The variables with highest loading on PC3 (blue) were Banff ah score (0.62) and cg score (0.61). Based on these results we combined ci, ct and ti scores and created a new variable (chronic-inflammation score, 0–9). We combined t and i scores and created a new variable (acute-tubulointerstitial score, 0–6). We also combined ah and cg scores and created a new variable (chronic-arteriolar score, 1–6). These three new variables were included in the multivariate Cox proportional hazard analyses. We did PCA using JMP 11.0 (SAS Institute Inc., Cary, NC) software.
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Figure 3: Principal component analysis of histopathological variablesWe did Principal Component Analysis (PCA) of 14 histopathological variables. The goal here was to identify variables that were closely associated with one another, so as to combine them as a single variable. In PCA a set of few new variables called principal components (PC) are generated that still reflects a large proportion of the information contained in the original dataset. Each PC is perpendicular to one another in a multidimensional space and thus is independent and uncorrelated. We extracted the first three PC that altogether explained 54% of the total variance. A two dimensional loading plot of PC1 and PC2 is depicted. PC1 explained 26% of the total variance and PC2 explained 17% of the total variance. Variables with the highest loading on PC1 (green) were Banff ci score, ct score and ti score. The correlation coefficient between the PC1 and ci score was 0.78, ct score was 0.75 and ti score was 0.72. Variables with the highest loading on PC2 (black) were Banff i score and t score. The correlation coefficient between the PC2 and i score was 0.71, and t score was 0.70. The variables with highest loading on PC3 (blue) were Banff ah score (0.62) and cg score (0.61). Based on these results we combined ci, ct and ti scores and created a new variable (chronic-inflammation score, 0–9). We combined t and i scores and created a new variable (acute-tubulointerstitial score, 0–6). We also combined ah and cg scores and created a new variable (chronic-arteriolar score, 1–6). These three new variables were included in the multivariate Cox proportional hazard analyses. We did PCA using JMP 11.0 (SAS Institute Inc., Cary, NC) software.

Mentions: We included 19 variables at the time of the index biopsy in a univariate Cox regression analysis to determine the association of each variable with the allograft outcome. Six variables; serum creatinine, proteinuria, Banff scores t, ci and ct, as well gs score were statistically significant at p<0.1 (Table 2). Next, as a data reduction technique, we did principal component analyses (PCA) of 14 histopathology variables (Figure 3). Banff scores ci, ct and ti were loaded heavily on principal component (PC) 1. Hence we combined them and created a new variable (chronic-inflammation score, 0–9). As Banff scores i and t were loaded heavily on PC2, we combined both and created a new variable (acute-tubulointerstitial score, 0–6). Banff scores ah and cg that loaded on PC3 was combined as a new variable (chronic-arteriolar score, 1–6).


Development and validation of a prognostic index for allograft outcome in kidney recipients with transplant glomerulopathy.

Patri P, Seshan SV, Matignon M, Desvaux D, Lee JR, Lee J, Dadhania DM, Serur D, Grimbert P, Hartono C, Muthukumar T - Kidney Int. (2016)

Principal component analysis of histopathological variablesWe did Principal Component Analysis (PCA) of 14 histopathological variables. The goal here was to identify variables that were closely associated with one another, so as to combine them as a single variable. In PCA a set of few new variables called principal components (PC) are generated that still reflects a large proportion of the information contained in the original dataset. Each PC is perpendicular to one another in a multidimensional space and thus is independent and uncorrelated. We extracted the first three PC that altogether explained 54% of the total variance. A two dimensional loading plot of PC1 and PC2 is depicted. PC1 explained 26% of the total variance and PC2 explained 17% of the total variance. Variables with the highest loading on PC1 (green) were Banff ci score, ct score and ti score. The correlation coefficient between the PC1 and ci score was 0.78, ct score was 0.75 and ti score was 0.72. Variables with the highest loading on PC2 (black) were Banff i score and t score. The correlation coefficient between the PC2 and i score was 0.71, and t score was 0.70. The variables with highest loading on PC3 (blue) were Banff ah score (0.62) and cg score (0.61). Based on these results we combined ci, ct and ti scores and created a new variable (chronic-inflammation score, 0–9). We combined t and i scores and created a new variable (acute-tubulointerstitial score, 0–6). We also combined ah and cg scores and created a new variable (chronic-arteriolar score, 1–6). These three new variables were included in the multivariate Cox proportional hazard analyses. We did PCA using JMP 11.0 (SAS Institute Inc., Cary, NC) software.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4814368&req=5

Figure 3: Principal component analysis of histopathological variablesWe did Principal Component Analysis (PCA) of 14 histopathological variables. The goal here was to identify variables that were closely associated with one another, so as to combine them as a single variable. In PCA a set of few new variables called principal components (PC) are generated that still reflects a large proportion of the information contained in the original dataset. Each PC is perpendicular to one another in a multidimensional space and thus is independent and uncorrelated. We extracted the first three PC that altogether explained 54% of the total variance. A two dimensional loading plot of PC1 and PC2 is depicted. PC1 explained 26% of the total variance and PC2 explained 17% of the total variance. Variables with the highest loading on PC1 (green) were Banff ci score, ct score and ti score. The correlation coefficient between the PC1 and ci score was 0.78, ct score was 0.75 and ti score was 0.72. Variables with the highest loading on PC2 (black) were Banff i score and t score. The correlation coefficient between the PC2 and i score was 0.71, and t score was 0.70. The variables with highest loading on PC3 (blue) were Banff ah score (0.62) and cg score (0.61). Based on these results we combined ci, ct and ti scores and created a new variable (chronic-inflammation score, 0–9). We combined t and i scores and created a new variable (acute-tubulointerstitial score, 0–6). We also combined ah and cg scores and created a new variable (chronic-arteriolar score, 1–6). These three new variables were included in the multivariate Cox proportional hazard analyses. We did PCA using JMP 11.0 (SAS Institute Inc., Cary, NC) software.
Mentions: We included 19 variables at the time of the index biopsy in a univariate Cox regression analysis to determine the association of each variable with the allograft outcome. Six variables; serum creatinine, proteinuria, Banff scores t, ci and ct, as well gs score were statistically significant at p<0.1 (Table 2). Next, as a data reduction technique, we did principal component analyses (PCA) of 14 histopathology variables (Figure 3). Banff scores ci, ct and ti were loaded heavily on principal component (PC) 1. Hence we combined them and created a new variable (chronic-inflammation score, 0–9). As Banff scores i and t were loaded heavily on PC2, we combined both and created a new variable (acute-tubulointerstitial score, 0–6). Banff scores ah and cg that loaded on PC3 was combined as a new variable (chronic-arteriolar score, 1–6).

Bottom Line: Based on the Cox model, we developed a prognostic index and classified patients into risk groups.The hazard ratios were 2.18 (median survival 19 months) and 16.27 (median survival 1.6 months), respectively, for patients in the medium and high-risk groups, compared to the low-risk group (median survival 47 months).Our prognostic index model did well in measures of discrimination and calibration.

View Article: PubMed Central - PubMed

ABSTRACT
We studied 92 patients with transplant glomerulopathy to develop a prognostic index based on the risk factors for allograft failure within five years of diagnosis (Development cohort). During 60 months (median) follow-up, 64 patients developed allograft failure. A chronic-inflammation score generated by combining Banff ci, ct and ti scores, serum creatinine and proteinuria at biopsy, were independent risk factors for allograft failure. Based on the Cox model, we developed a prognostic index and classified patients into risk groups. Compared to the low-risk group (median allograft survival over 60 months from diagnosis), patients in the medium risk group had a hazard ratio of 2.83 (median survival 25 months), while those in the high-risk group had a hazard ratio of 5.96 (median survival 3.7 months). We next evaluated the performance of the prognostic index in an independent external cohort of 47 patients with transplant glomerulopathy (Validation cohort). The hazard ratios were 2.18 (median survival 19 months) and 16.27 (median survival 1.6 months), respectively, for patients in the medium and high-risk groups, compared to the low-risk group (median survival 47 months). Our prognostic index model did well in measures of discrimination and calibration. Thus, risk stratification of transplant glomerulopathy based on our prognostic index may provide informative insight for both the patient and physician regarding prognosis and treatment.

No MeSH data available.


Related in: MedlinePlus