Value of adding the renal pathological score to the kidney failure risk equation in advanced diabetic nephropathy
Value of adding the renal pathological score to the kidney failure risk equation in advanced diabetic nephropathy
Masayuki Yamanouchi 0 1 2
Junichi Hoshino 1 2
Yoshifumi Ubara 1 2
Kenmei Takaichi 1 2
Keiichi Kinowaki 1 2
Takeshi Fujii 1 2
Kenichi Ohashi 1 2
Koki Mise 1 2
Tadashi Toyama 1 2
Akinori Hara 1 2
Kiyoki Kitagawa 1 2
Miho Shimizu 1 2
Kengo Furuichi 1 2
Takashi Wada 0 1 2
0 Department of Nephrology and Laboratory Medicine, Faculty of Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan, 2 Nephrology Center, Toranomon Hospital , Tokyo , Japan , 3 Nephrology Center, Toranomon Hospital Kajigaya, Kanagawa, Japan, 4 Okinaka Memorial Institute for Medical Research, Tokyo, Japan, 5 Department of Pathology, Toranomon Hospital , Tokyo , Japan , 6 Department of Pathology, Yokohama City University Graduate School of Medicine, Kanagawa, Japan, 7 Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences , Okayama , Japan , 8 Division of Nephrology, Kanazawa University Hospital, Kanazawa, Japan, 9 Division of Internal Medicine, National Hospital Organization Kanazawa Medical Center , Kanazawa , Japan
1 Editor: Hideharu Abe, Tokushima University Graduate School , JAPAN
2 Toranomon Hospital:
Data Availability Statement: Our data cannot be
made publicly available for ethical reasons; public
availability would compromise patient
confidentiality and the local rules of the institutional
review boards of our hospitals' committees.
However, the data will be available upon request
with the permission of the institutional review
boards of Toranomon Hospital, Toranomon
Hospital Kajigaya, Kanazawa University Hospital,
and Kanazawa Medical Center. Each governing
body or institution and its contact information are
as follows: Clinical Research Committee,
There have been a limited number of biopsy-based studies on diabetic nephropathy, and
therefore the clinical importance of renal biopsy in patients with diabetes in late-stage
chronic kidney disease (CKD) is still debated. We aimed to clarify the renal prognostic value
of pathological information to clinical information in patients with diabetes and advanced
We retrospectively assessed 493 type 2 diabetics with biopsy-proven diabetic
nephropathy in four centers in Japan. 296 patients with stage 3±5 CKD at the time of biopsy were
identified and assigned two risk prediction scores for end-stage renal disease (ESRD):
the Kidney Failure Risk Equation (KFRE, a score composed of clinical parameters)
and the Diabetic Nephropathy Score (D-score, a score integrated pathological parameters
of the Diabetic Nephropathy Classification by the Renal Pathology Society (RPS DN
Classification)). They were randomized 2:1 to development and validation cohort.
Hazard Ratios (HR) of incident ESRD were reported with 95% confidence interval (CI)
of the KFRE, D-score and KFRE+D-score in Cox regression model. Improvement of risk
prediction with the addition of D-score to the KFRE was assessed using c-statistics,
continuous net reclassification improvement (NRI), and integrated discrimination
Funding: This work was partly supported by a
Grant-in-Aid for Practical Research Projects for
Renal Diseases from the Japan Agency for Medical
Research and Development (grant no:
15ek0310003h0001). The funding source had no
role in study design or execution, data analysis,
manuscript writing, or manuscript submission.
Competing interests: The authors have declared
that no competing interests exist.
During median follow-up of 1.9 years, 194 patients developed ESRD. The cox regression
analysis showed that the KFRE,D-score and KFRE+D-score were significant predictors of
ESRD both in the development cohort and in the validation cohort. The c-statistics of the
Dscore was 0.67. The c-statistics of the KFRE was good, but its predictive value was weaker
than that in the miscellaneous CKD cohort originally reported (c-statistics, 0.78 vs. 0.90) and
was not significantly improved by adding the D-score (0.78 vs. 0.79, p = 0.83). Only
continuous NRI was positive after adding the D-score to the KFRE (0.4%; CI: 0.0±0.8%).
We found that the predict values of the KFRE and the D-score were not as good as reported,
and combining the D-score with the KFRE did not significantly improve prediction of the
risk of ESRD in advanced diabetic nephropathy. To improve prediction of renal prognosis
for advanced diabetic nephropathy may require different approaches with combining
clinical and pathological parameters that were not measured in the KFRE and the RPS DN
Despite advances over the past 20 years in delaying the progression of diabetic nephropathy, it
is still a leading cause of end-stage renal disease (ESRD) worldwide and imposes a heavy
burden not only on individual patients but also on society [
]. Therefore, it is important to be able
to predict the risk of developing ESRD in patients with diabetes and chronic kidney disease
(CKD) since it could facilitate earlier intervention and better allocation of medical resources to
Tangri et al. developed the kidney failure risk equation (KFRE) for patients with stage 3 to 5
CKD to identify those at high risk of developing ESRD based on demographic, clinical, and
laboratory variables [
]. Without considering the etiology of CKD, the simple version of KFRE
uses only four clinical variables (age, gender, estimated glomerular filtration rate (eGFR), and
urine albumin/creatinine ratio [ACR]) to identify patients at high risk of developing ESRD
with a c-statistic > 0.90 (95% CI, 0.894±0.926; P<0.001) and has been validated in more than
30 countries with very similar c-statistics [
]. Use of the KFRE in clinical practice could
facilitate decision-making for patients with late-stage CKD.
Hoshino et al., on the other hand, developed a pathological score for diabetic nephropathy
(D-score), based on a pathological classification of diabetic nephropathy by the Renal
Pathology Society (RPS DN Classification) [
], to predict ESRD in patients with diabetic
]. Its predictive value of the 10-year risk of ESRD was good with a c-statistics of 0.931
(95% CI: 0.898±0.965).
It has been unclear whether the KFRE can identify ESRD in patients with diabetic nephrop
athy who have a much higher risk of end-stage disease than other CKD patients, and whether
there is any value in combining pathological information on diabetic nephropathy with the
KFRE. Therefore, we performed the present study to assess the performance of the KFRE for predicting ESRD in patients with biopsy proven diabetic nephropathy, to investigate the incremental value of combining the D-score with the KFRE, and to validate a new combined model (KFRE + D-score) for predicting ESRD in these patients.
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Materials and methods
To identify patients with type 2 diabetes, we retrospectively reviewed all renal biopsies per
formed from 1985 to 2013 at the following four nephrology centers: Toranomon Hospital
(Tokyo, Japan), Toranomon Hospital Kajigaya (Kanagawa, Japan), Kanazawa University
Hospital (Kanazawa, Japan), and Kanazawa Medical Center (Kanazawa, Japan). The indica
tions for biopsy were (i) renal impairment, (ii) urinary abnormalities such as albuminuria,
proteinuria, hematuria, or casts, and (iii) suspected concomitant renal disease with diabetic
nephropathy. Patients were excluded if they had protocol renal transplant biopsy, confirmed
concomitant renal disease (except for nephrosclerosis), or inadequate tissue for diagnosis.
Among 521 biopsies, 493 were performed in patients with diabetic nephropathy. Among them, 296 patients with stage 3 to 5 CKD at the time of biopsy were followed up for at least three months, and these patients were enrolled.
This study was approved by the institutional review boards of Toranomon Hospital, Tora
nomon Hospital Kajigaya, Kanazawa University Hospital, and Kanazawa Medical Center. The
study design, clinical setting, eligibility criteria, variables investigated, and statistical analysis
were in conformity with the STROBE Statement [
]. This work was partly supported by a
Grant-in-Aid for Practical Research Projects for Renal Diseases from the Japan Agency for
Medical Research and Development (grant no: 15ek0310003h0001). The funding source had no role in study design or execution, data analysis, manuscript writing, or manuscript submission. The authors have no conflicts of interest to disclose.
Clinical characteristics, laboratory data, and pathological classification
Clinical characteristics at the time of biopsy, such as the age, gender, duration of diabetes,
BMI, hypertension, and dyslipidemia, were ascertained from the medical records.
Laboratory data at the time of biopsy were also obtained from the medical records,
including hemoglobin A1c, serum creatinine, eGFR (calculated by the Modified Diet in
Renal Disease study equation for Japanese ), and ACR or urine protein/creatinine ratio (PCR).
Renal biopsy specimens were processed for light microscopy, immunofluorescence, and
electron microscopy. All biopsies were evaluated with three pathologists according to the RPS
DN Classification . If all three pathologists did not make the same evaluation, discussion
was held until consensus was reached. Diabetic nephropathy was classified as follows. Class I
was glomerular basement thickening and only mild, nonspecific changes on light microscopy.
Class II was mild (IIa) or severe (IIb) mesangial expansion without either nodular lesions
or global sclerosis in >50% of the glomeruli. Class III was nodular lesions without global
sclerosis in >50% of the glomeruli. Class IV was global sclerosis in >50% of the glomeruli. Other
pathological findings evaluated were interstitial lesions (interstitial fibrosis & tubular atrophy
[grades 0±3] and interstitial inflammation [grades 0±2]) and vascular lesions (arteriolar
hyalinosis [grades 0±2] and arteriosclerosis [grades 0±2]).
The primary endpoint of this study was ESRD, which was defined as initiation of hemodialysis
or peritoneal dialysis, or renal transplantation, or death from uremia. Death unrelated to
ESRD was also extracted and loss to follow-up was considered a censoring event.
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There were no missing data for age, gender, eGFR, and the D-score. However, 207 of 493
patients had missing ACR data but had PCR data. In these patients, we converted PCR to ACR
using a converting formula: ln(ACR) = 1.32 × ln(PCR) − 2.64 [
]. Differences of clinical,
laboratory, and pathological variables between the development and validation cohorts were
analyzed by Student's t-test or the Wilcoxon test for continuous variables, while the chi-square
test or Fisher's exact test was used for categorical variables.
The 296 patients were randomized 2:1 to the development cohort of 198 and validation
cohort of 98 patients. In the development cohort, the KFRE index, the D-score, and the
incremental value of the D-score were assessed with using Cox proportional hazard models to test
whether predictors were associated with the primary outcome. Model performance was also
assessed with calculating the global chi-square, Akaike information criterion, and Harrell's
cstatistics. Furthermore, the area under the receiver operating characteristic curve, net
reclassification improvement, integrated discrimination improvement, integrated sensitivity, and
integrated specificity for the 3-year risk of ESRD were investigated in the development cohort.
To assess incremental values, we followed the guidelines of Kerr et al . Then the perfor
mance of the two models (KFRE index, D-score, and KFRE index + D-score) was determined
in the validation cohort.
Results are expressed as the mean with standard deviation or the median with interquartile
range for continuous data and as percentages for categorical data.
Statistical tests were considered significant at p<0.05 (two-sided). All statistical analyses
were conducted using Stata version 14.1 (StataCorp LLC, College Station, TX).
The study group comprised four independent cohorts of patients (recruited at Toranomon
Hospital, Toranomon Hospital Kajigaya, Kanazawa University Hospital, and Kanazawa
Medical Center) with type 2 diabetes and advanced CKD (eGFR <60 mL/min/m2) who all had
biopsy proven diabetic nephropathy without other renal diseases. A total of 296 patients
followed for at least three months were randomized at a 2:1 ratio to two groups, which were a
development cohort (n = 198) and a validation cohort (n = 98). The median follow-up period
(25th-75th percentiles) was similar in both cohorts, being 1.8 (1.0±5.0) years in the development
cohort and 2.0 (1.0±3.8) years in the validation cohort (p = 0.89). Table 1 shows the baseline
characteristics of the development and validation cohorts at the time of renal biopsy. The two
cohorts showed no significant differences of clinical variables such as the age, gender, body
mass index (BMI), systolic blood pressure, hemoglobin A1c, total cholesterol, estimated
glomerular filtration rate, and ACR.
Development of equations for the KFRE index and D-score
In the original paper by Tangri et al. , the KFRE is based on four variables, which are the age
(per 10 years), gender, baseline eGFR (per 5 mL/min/1.73 m2), and logarithmic urine ACR
(mg/g). In this study, we used the KFRE index reported by Lennartz et al [
]., with hazard
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BMI, body mass index; sBP, systolic blood pressure; dBP, diastolic blood pressure; HbA1c, hemoglobin A1c; TCho; total cholesterol; eGFR, estimated glomerular
filtration rate; ACR, albumin/creatinine ratio.
CKD stages 3, 4, and 5 correspond to eGFR of 30±59, 15±29, and <15 mL/min/1.73 m2, respectively.
Normoalbuminuria, microalbuminuria, and macroalbuminuria correspond to an ACR of <30, 30±299, and 300 mg/g, respectively.
Data are expressed as the mean (standard deviation), median (25th, 75th percentiles), or percentage (number).
ratios obtained from the development cohort:
where gender = 1 for male and 0 for female and ln = natural logarithm.
Based on the Pathological Classification of Diabetic Nephropathy by Tervaert et al. [
pathological risk score for predicting ESRD (D-score [
]) was calculated for each subject. By
summing the products of the beta coefficient and bootstrap-inclusion fraction of clinical
parameters and the pathological classification of diabetic nephropathy in Cox proportional
hazards analysis, the D-scores for glomerular classes I, IIa, IIb, III, and IV were calculated to
be 0, 3, 4, 6, and 6, respectively. In addition, the D-scores for interstitial fibrosis & tubular
atrophy of grades 0, 1, 2, and 3 were 0, 7, 9, and 11, respectively, while the scores for interstitial
inflammation of grades 0, 1, and 2 were 0, 3, and 4, respectively. Furthermore, the D-scores for
arteriolar hyalinosis of grades 0, 1, and 2 were 0, 0, and 3, respectively, while the scores for
arteriosclerosis of grades 0, 1, and 2 were 0, 0, and 1, respectively.
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Performance of the KFRE and KFRE+D-score
Predicted risk of ESRD after three years
The risk of ESRD was calculated according to the KFRE as reported in the original article2:
Predictedrisk of ESRD 1
where 0.535 is the three-year survival rate of an individual with average covariates in the
development cohort, 0.989 is the beta coefficient of the KFRE according to the Cox proportional
hazards model, and -2.74 is the average KFRE value in the development cohort.
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where 0.535 is the three-year survival rate of an individual with the average covariates in the
development cohort, 0.910 is the beta coefficient of the KFRE according to the Cox
proportional hazards model, 0.068 is the beta coefficient of the D-score according to the Cox
proportional hazards model, -2.74 is the average KFRE value in the development cohort, and 19.5 is
the average D-score in the development cohort.
Incremental value of the D-score for predicting ESRD at three years in the
We assessed the incremental value of the D-score for predicting ESRD at three years in the
development cohort from the two-category threshold at a prevalence of 46.5% based on the
percentage of patients with events. As shown in Table 4, there was no significant change of net
reclassification improvement (NRI) when comparing the KFRE model and the KFRE+D-score
model. We also investigated the incremental value of the D-score by using free cut-points.
Although NRInonevents showed no significant change, there was a significant change of NRIe
vents that resulted in a positive overall NRI (0.4%; CI, 0.0 to 0.9). The change of integrated
discrimination improvement was similar to that of NRI, but was also not significant (0.02; CI:
-0.009 to 0.04). Integrated sensitivity was improved in the KFRE+D-score model, whereas
integrated specificity was not improved in the KFRE+D-score model. The actual differences from
the KFRE model were small.
Fig 1 displays risk assessment plots for the KFRE and the KFRE+D-score. When the
incremental value of adding the D-score to the KFRE was assessed, improvement in predicting the
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risk of ESRD after three years was noted above 0.65 for patients with events and below 0.15 for
patients without events.
This study demonstrated that the KFRE and the D-score were independent predictors of
ESRD in patients with advanced diabetic nephropathy. However, its predict values were not as good as reported, and combining the KFRE with the D-score did not significantly improve prediction of the risk of ESRD.
A number of authors have proposed prognostic scores for ESRD in patients with advanced
CKD, but none of these methods have been widely accepted [11±16]. For more accurate
prediction of the risk of ESRD in the clinical setting, Tangri et al. developed the KFRE to identify
patients with stage 3 to 5 CKD at high risk of progressing to ESRD based on demographic,
clinical, and laboratory variables [
]. Without considering the etiology of CKD, the simple
version of KFRE employs four clinical variables (age, gender, eGFR, and urine ACR) to identify
patients at high risk of ESRD and it has been validated in over 30 countries [
]. However, it
has been unclear whether the KFRE performs well in patients who have specific renal diseases
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Fig 1. Risk assessment plots with the KFRE model and KFRE+D-score model for predicting ESRD at three years in the development cohort. KFRE model
(dashed lines), KFRE+D-score model (solid lines). Black lines indicate sensitivity versus predicted risk. Gray lines represent 1-specificity versus predicted risk.
associated with a very high risk of ESRD such as diabetic nephropathy. In the present study,
we found that the c-statistic of the KFRE was still good, but its predictive value was weaker
than that in the miscellaneous CKD cohort originally reported by Tangri et al. (c-statistics,
0.78 vs. 0.90). The following points can be given as this reason. First, the KFRE may not be
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able to capture a phenotype of diabetic nephropathyÐnonproteinurics. The simple version of
KFRE consists of only four variablesÐage, sex, estimated GFR, and ACRÐand from their
hazard ratios we see ACR is the strongest predictor of ESRD among them. However, in our cohort
population, we see an increasing frequency of impaired renal function with
normoalbuminuria or with microalbuminuriaÐACR<300 mg/g (113/296 = 38%)Ðof our study group. We
think this population is a different phenotype of diabetic nephropathy and they are not well
captured by KFRE. Second, diabetic nephropathy has a lot of different risk factors of ESRD
compared to other CKDs. Other than four variables of KFRE, we have other known risk factors
of developing diabetic nephropathyÐpoor glycemic control, duration of diabetes,
hypertension, dislypidemiaÐand unknown risk factors of developing diabetic nephropathyÐrace,
genetics, and novel biomarkers. Combining these factors may improve prediction of ESRD in
patients with diabetic nephropathy. Unfortunately, adding known risk factors did not improve
prediction of ESRD with KFRE in our cohort.
For many years, diabetic nephropathy was clinically diagnosed from the presence of
macroalbuminuria and renal impairment in patients with diabetes and so there have been a limited
number of biopsy-based studies on diabetic nephropathy. Even after Tarvaert et al. developed
a consensus classification of diabetic nephropathy on behalf of the Renal Pathology Society [
only a few studies have assessed the prognostic value of this classification [17±19] and none of
them investigated the incremental prognostic value of adding renal pathological information
to common clinical parameters. The clinical importance of renal biopsy in patients with
diabetes, especially in the late-stage CKD, is still debated. In this situation, this study was prepared
specifically to clarify the prognostic aspect of renal biopsy in combination with clinical data in
advanced diabetic nephropathy. It is important to address this issue because (i) it may change
our clinical practice on diabetic nephropathyÐwe may reduce unnecessary renal biopsy since
renal biopsy is an invasive, time-consuming, and costly diagnostic procedure, and (ii) the
Dscore based on the RPS DN Classification may not include important prognostic pathological
parameters and we may be better to seek another unmeasured pathological parameters or
clinical parameters that could improve prediction of ESRD in diabetic nephropathy. We found
that adding the D-score to the KFRE did not lead to significant improvement in predicting
the risk of ESRD. Our findings suggest that we may not perform renal biopsies just for
anticipating additional prognostic information from renal pathology based on the RPS DN
Classification and that alternatively, to improve prediction of renal prognosis for advanced diabetic
nephropathy may require different approaches with combining unmeasured clinical and
Despite this result, the main clinical importance of renal biopsy in patients with diabetes is
probably related to differentiating diabetic nephropathy from other renal diseases or
categorizing concomitant renal pathology. Indeed, several studies have indicated that renal biopsy is
useful for differentiating pure diabetic nephropathy from pure non-diabetic nephropathy or
combined states since the renal prognosis is different [20±23]. We also think that patients with
diabetes and early CKD may gain more benefit from renal biopsy than those with late CKD,
since a certain percentage of them develop diabetic nephropathy before renal disease is
clinically evident and risk equations like the KFRE based on clinical variables cannot capture this
population. This concept was endorsed by Klessens et al. [
] who found biopsy proven
diabetic nephropathy at autopsy in 106 out of 168 diabetic patients without a clinical diagnosis of
diabetic nephropathy, suggesting that it is considerably underestimated.
Our study had a number of strengths. It was based on a large multicenter cohort of patients with biopsy proven diabetic nephropathy recruited from across Japan. In addition, follow-up was adequate to assess renal outcomes, enabling robust survival analysis of the incremental value combining of renal biopsy data with clinical variables.
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However, a number of limitations of this study should be considered. First, there may be a
possibility of confounding by indicationÐthe study population was potentially biased by
nephrologists who were interested in diabetic nephropathy, since usually it was diagnosed
clinically from overt albuminuria and renal impairment in patients with diabetes. Second, the
Dscore was based on the Pathologic Classification of Diabetic Nephropathy, in which certain
pathological changes such as exudative lesions or mesangiolysis, which Furuichi et al. [
reported to be strong predictors of ESRD, were not included. It is possible that adding such
pathological information may have improved prediction of ESRD with the KFRE. Third,
approximately 40% of data for ACR were not directly measured. They were converted from
PCR, using a converting formula for Japanese CKD patients . However, this formula was
developed from Japanese CKD cohort and we believe that applying this formula to our
Japanese cohort sounds natural. Finally, our study population was limited to Asian patients with
type 2 diabetes and advanced CKD, so our findings may not be widely generalizable.
In conclusion, the kidney failure risk equation was a good instrument for identifying a high
risk of progression to ESRD among patients with diabetic nephropathy and advanced CKD.
However, its predictive value was weaker than in the miscellaneous CKD cohort originally
reported by Tangri et al and adding pathological information based on the Diabetic
Nephropathy Classification by the Renal Pathology Society to the KFRE did not significantly improve
prediction of ESRD. Accordingly, to improve prediction of renal prognosis for advanced
diabetic nephropathy may require different approaches with combining unmeasured clinical or
T.W. is the guarantor of this work and, as such, had full access to all the data in the study and
takes responsibility for the integrity of the data and the accuracy of the data analysis. M.Y., K.
F., and T.W. designed the study protocol, researched data, contributed to discussion, wrote the
manuscript, and reviewed and edited the manuscript. J.H., Y.U., K.T., K.Kin., T.F., K.O., K.M.,
T.T., A.H., K.Kit., and M.S. researched data, contributed to discussion, and reviewed and edited the manuscript.
Conceptualization: Takashi Wada.
Data curation: Junichi Hoshino, Keiichi Kinowaki, Takeshi Fujii, Kenichi Ohashi, Koki Mise,
Tadashi Toyama, Akinori Hara, Kiyoki Kitagawa, Miho Shimizu, Kengo Furuichi.
Formal analysis: Masayuki Yamanouchi, Junichi Hoshino.
Investigation: Masayuki Yamanouchi.
Methodology: Masayuki Yamanouchi, Junichi Hoshino, Kengo Furuichi.
Project administration: Takashi Wada.
Supervision: Junichi Hoshino, Yoshifumi Ubara, Kenmei Takaichi, Miho Shimizu, Kengo
Furuichi, Takashi Wada.
Validation: Junichi Hoshino.
Writing ± original draft: Masayuki Yamanouchi.
Writing ± review & editing: Yoshifumi Ubara, Kenmei Takaichi, Miho Shimizu, Kengo Furuichi, Takashi Wada.
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