Reproducibility of the Oxford classification of immunoglobulin A nephropathy, impact of biopsy scoring on treatment allocation and clinical relevance of disagreements: Evidence from the VALidation of IGA study cohort

Background: The VALidation of IGA (VALIGA) study investigated the utility of the Oxford Classification of immunoglobulin A nephropathy (IgAN) in 1147 patients from 13 European countries. Methods. Biopsies were scored by local pathologists followed by central review in Oxford. We had two distinct objectives: to assess how closely pathology findings were associated with the decision to give corticosteroid/immunosuppressive (CS/IS) treatments, and to determine the impact of differences in MEST-C scoring between central and local pathologists on the clinical value of the Oxford Classification. We tested for each lesion the associations between the type of agreement (local and central pathologists scoring absent, local present and central absent, local absent and central present, both scoring present) with the initial clinical assessment, as well as long-term outcomes in those patients who did not receive CS/IS. Results: All glomerular lesions (M, E, C and S) assessed by local pathologists were independently associated with the decision to administer CS/IS therapy, while the severity of tubulointerstitial lesions was not. Reproducibility between local and central pathologists was moderate for S (segmental sclerosis) and T (tubular atrophy/interstitial fibrosis), and poor for M (mesangial hypercellularity), E (endocapillary hypercellularity) and C (crescents). Local pathologists found statistically more of each lesion, except for the S lesion, which was more frequent with central review. Disagreements were more likely to occur when the proportion of glomeruli affected was low. The M lesion, assessed by central pathologists, correlated better with the severity of the disease at presentation and discriminated better with outcomes. In contrast, the E lesion, evaluated by local pathologists, correlated better with the clinical presentation and outcomes when compared with central review. Both C and S lesions, when discordant between local and central pathologists, had a clinical phenotype intermediate to double absent lesions (milder disease) and double present (more severe). Conclusion: We conclude that differences in the scoring of MEST-C criteria between local pathologists and a central reviewer have a significant impact on the prognostic value of the Oxford Classification. Since the decision to offer immunosuppressive therapy in this cohort was intimately associated with the MEST-C score, this study indicates a need for a more detailed guidance for pathologists in the scoring of IgAN biopsies

Validation of the Oxford classification of IgA nephropathy in cohorts with different presentations and treatments

The Oxford Classification of IgA Nephropathy (IgAN) identified mesangial hypercellularity (M), endocapillary proliferation (E), segmental glomerulosclerosis (S), and tubular atrophy/interstitial fibrosis (T) as independent predictors of outcome. Whether it applies to individuals excluded from the original study and how therapy influences the predictive value of pathology remain uncertain. The VALIGA study examined 1147 patients from 13 European countries that encompassed the whole spectrum of IgAN. Over a median follow-up of 4.7 years, 86% received renin-angiotensin system blockade and 42% glucocorticoid/immunosuppressive drugs. M, S, and T lesions independently predicted the loss of estimated glomerular filtration rate (eGFR) and a lower renal survival. Their value was also assessed in patients not represented in the Oxford cohort. In individuals with eGFR less than 30 ml/min per 1.73 m(2), the M and T lesions independently predicted a poor survival. In those with proteinuria under 0.5 g/day, both M and E lesions were associated with a rise in proteinuria to 1 or 2 g/day or more. The addition of M, S, and T lesions to clinical variables significantly enhanced the ability to predict progression only in those who did not receive immunosuppression (net reclassification index 11.5%). The VALIGA study provides a validation of the Oxford classification in a large European cohort of IgAN patients across the whole spectrum of the disease. The independent predictive value of pathology MEST score is reduced by glucocorticoid/immunosuppressive therapy

Development and testing of an artificial intelligence tool for predicting end-stage kidney disease in patients with immunoglobulin A nephropathy

We have developed an artificial neural network prediction model for end-stage kidney disease (ESKD) in patients with primary immunoglobulin A nephropathy (IgAN) using a retrospective cohort of 948 patients with IgAN. Our tool is based on a two-step procedure of a classifier model that predicts ESKD, and a regression model that predicts development of ESKD over time. The classifier model showed a performance value of 0.82 (area under the receiver operating characteristic curve) in patients with a follow-up of five years, which improved to 0.89 at the ten-year follow-up. Both models had a higher recall rate, which indicated the practicality of the tool. The regression model showed a mean absolute error of 1.78 years and a root mean square error of 2.15 years. Testing in an independent cohort of 167patients with IgAN found successful results for 91% of the patients. Comparison of our system with other mathematical models showed the highest discriminant Harrell C index at five- and ten-years follow-up (81% and 86%, respectively), paralleling the lowest Akaike information criterion values (355.01 and 269.56, respectively). Moreover, our system was the best calibrated model indicating that the predicted and observed outcome probabilities did not significantly differ. Finally, the dynamic discrimination indexes of our artificial neural network, expressed as the weighted average of time-dependent areas under the curve calculated at one and two years, were 0.80 and 0.79, respectively. Similar results were observed over a 25-year follow-up period. Thus, our tool identified individuals who were at a high risk of developing ESKD due to IgAN and predicted the time-to-event endpoint. Accurate prediction is an important step toward introduction of a therapeutic strategy for improving clinical outcomes

Development and testing of an artificial intelligence tool for predicting end-stage kidney disease in patients with immunoglobulin A nephropathy

We have developed an artificial neural network prediction model for end-stage kidney disease (ESKD) in patients with primary immunoglobulin A nephropathy (IgAN) using a retrospective cohort of 948 patients with IgAN. Our tool is based on a two-step procedure of a classifier model that predicts ESKD, and a regression model that predicts development of ESKD over time. The classifier model showed a performance value of 0.82 (area under the receiver operating characteristic curve) in patients with a follow-up of five years, which improved to 0.89 at the ten-year follow-up. Both models had a higher recall rate, which indicated the practicality of the tool. The regression model showed a mean absolute error of 1.78 years and a root mean square error of 2.15 years. Testing in an independent cohort of 167patients with IgAN found successful results for 91% of the patients. Comparison of our system with other mathematical models showed the highest discriminant Harrell C index at five- and ten-years follow-up (81% and 86%, respectively), paralleling the lowest Akaike information criterion values (355.01 and 269.56, respectively). Moreover, our system was the best calibrated model indicating that the predicted and observed outcome probabilities did not significantly differ. Finally, the dynamic discrimination indexes of our artificial neural network, expressed as the weighted average of time-dependent areas under the curve calculated at one and two years, were 0.80 and 0.79, respectively. Similar results were observed over a 25-year follow-up period. Thus, our tool identified individuals who were at a high risk of developing ESKD due to IgAN and predicted the time-to-event endpoint. Accurate prediction is an important step toward introduction of a therapeutic strategy for improving clinical outcomes

β-Carboline alkaloids in Peganum harmala and inhibition of human monoamine oxidase (MAO)

Peganum harmala L. is a multipurpose medicinal plant increasingly used for psychoactive recreational purposes (Ayahuasca analog). Harmaline, harmine, harmalol, harmol and tetrahydroharmine were identified and quantified as the main β-carboline alkaloids in P. harmala extracts. Seeds and roots contained the highest levels of alkaloids with low levels in stems and leaves, and absence in flowers. Harmine and harmaline accumulated in dry seeds at 4.3% and 5.6% (w/w), respectively, harmalol at 0.6%, and tetrahydroharmine at 0.1% (w/w). Roots contained harmine and harmol with 2.0% and 1.4% (w/w), respectively. Seed extracts were potent reversible and competitive inhibitors of human monoamine oxidase (MAO-A) with an IC50 of 27 μg/l whereas root extracts strongly inhibited MAO-A with an IC50 of 159 μg/l. In contrast, they were poor inhibitors of MAO-B. Inhibition of MAO-A by seed extracts was quantitatively attributed to harmaline and harmine whereas inhibition by root extracts came from harmine with no additional interferences. Stems and leaves extracts were poor inhibitors of MAO. The potent inhibition of MAO-A by seed and root extracts of P. harmala containing β-carbolines should contribute to the psychopharmacological and toxicological effects of this plant and could be the basis for its purported antidepressant actions. © 2010 Elsevier Ltd. All rights reserved.Peer Reviewe