Ahmed Akl

Regulatory T cells: Potential in organ transplantation

Active regulation or suppression of donor reactive cells is emerging as a key mechanism for inducing and maintaining unresponsiveness to donor alloantigens. Accumulating evidence suggests that a balance between immunoregulation and deletion of donor alloantigen reactive T cells can provide effective control of immune responsiveness after organ or cell transplantation. In many settings, immunoregulatory activity is enriched in CD4+ T cells that express high levels of CD25, and common mechanisms appear to be responsible for the activity of regulatory T cells in both transplantation and the control of reactivity to self-antigens.

Prediction of graft survival of living-donor kidney transplantation: nomograms or artificial neural networks?

Background. An artificial neural networks (ANNs) model was developed to predict 5-year graft survival of living-donor kidney transplants. Predictions from the validated ANNs were compared with Cox regression-based nomogram. Methods. Out of 1900 patients with living-donor kidney transplant; 1581 patients were used for training of the ANNs (training group), the remainder 319 patients were used for its validation (testing group). Many variables were correlated with the graft survival by univariate analysis. Significant ones were used for ANNs construction of a predictive model. The same variables were subjected to a multivariate statistics using Cox regression model; their result was the basis of a nomogram construction. The ANNs predictive model and the nomogram were used to predict the graft survival of the testing group. The predicted probability(s) was compared with the actual survival estimates. Results. The ANNs sensitivity was 88.43% (95% confidence interval [CI] 86.4-90.3), specificity was 73.26% (95% CI 70-76.3), and predictive accuracy was 88% (95% CI 87-90) in the testing group, whereas nomogram sensitivity was 61.84% (95% CI 50-72.8) with 74.9% (95% CI 69-80.2) specificity and predictive accuracy was 72% (95% CI 67-77). The positive predictive value of graft survival was 82.1% and 43.5% for the ANNs and Cox regression-based nomogram, respectively, and the negative predictive value was 82% and 86.3% for the ANNs and Cox regression-based nomogram, respectively. Predictions by both models fitted well with the observed findings. Conclusions. These results suggest that ANNs was more accurate and sensitive than Cox regression-based nomogram in predicting 5-year graft survival.

Prediction of Survival After Radical Cystectomy for Invasive Bladder Carcinoma: Risk Group Stratification, Nomograms or Artificial Neural Networks?

PURPOSE We compared 3 predictive models for survival after radical cystectomy, risk group stratification, nomogram and artificial neural networks, in terms of their accuracy, performance and level of complexity. MATERIALS AND METHODS Between 1996 and 2002, 1,133 patients were treated with single stage radical cystectomy as monotherapy for invasive bladder cancer. A randomly selected 776 cases (70%) were used as a reference series. The remaining 357 cases (test series) were used for external validation. Survival estimates were analyzed using univariate and then multivariate appraisal. The results of multivariate analysis were used for risk group stratification and construction of a nomogram, whereas all studied variables were entered directly into the artificial neural networks. RESULTS Overall 5-year disease-free survival was 64.5% with no statistical difference between the reference and test series. Comparisons of the 3 predictive models revealed that artificial neural networks outperformed the other 2 models in terms of the value of the area under the receiver operator characteristic curve, sensitivity and specificity, as well as positive and negative predictive values. CONCLUSIONS In this study artificial neural networks outperformed the risk group stratification model and nomogram construction in predicting patient 5-year survival probability, and in terms of sensitivity and specificity.

Power doppler sonography in early renal transplantation: Does it differentiate acute graft rejection from acute tubular necrosis?

To evaluate the role of power Doppler in the identification and differentiation between acute renal transplant rejection and acute tubular necrosis (ATN), we studied 67 live donor renal transplant recipients. All patients were examined by spectral and power Doppler sonography. Assessment of cortical perfusion (CP) by power Doppler was subjective, using our grading score system: P0 (normal CP); homogenous cortical blush extending to the capsule, P1 (reduced CP); cortical vascular cut-off at interlobular level, P2 (markedly reduced CP); scattered cortical color flow at the interlobar level. Renal biopsies were performed during acute graft dysfunction. Pathological diagnoses were based on Banff classification 1997. The Mann- Whitney test was used to test the difference between CP grades with respect to serum creatinine (SCr), and resistive index (RI). For 38 episodes of acute graft rejection grade I, power Doppler showed that CP was P1 and RI ranging from 0.78 to 0.89. For 21 episodes of acute graft rejection grade II, power Doppler showed that CP was P1, with RI ranging from 0.88 to >1. Only one case of grade III rejection had a CP of P2. Twelve biopsies of ATN had CP of P0 and RI ranging from 0.80 to 0.89 There was a statistically significant correlation between CP grading and SCr (P <0.01) as well as between CP grading and RI (P <0.05). CP grading had a higher sensitivity in the detection of early acute rejection compared with RI and cross-sectional area measurements. We conclude that power Doppler is a non-invasive sensitive technique that may help in the detection and differentiation between acute renal transplant rejection and ATN, particularly in the early post-transplantation period.

Forcasting the Clinical Outcome: Artificial Neural Networks or Multivariate Statistical Models?

The field of prognostics has grown rapidly in the last decade and clinicians have been provided with numerous tools to assist with evidence-based medical decision-making. Most of these included nomograms, classification and regression tree analyses, and risk group stratification models [Grossberg JA, et al., 2006] and Artificial Neural Networks (ANN) [Djavan B, et al., 2002]. Nomograms are graphic representation of statistical model, which incorporate multiple continuous variables to predict a patient’s risk of developing a specific endpoint (recurrence, survival, complications) [Kattan MW, 2005]. Each variable is assigned a scale of points according to its prognostic significance. The total score for all the variables is converted to an estimated probability of reaching the endpoint [Akl A, et al., 2008]. Statistical approaches require guesses as to how outputs functionally depend on inputs. Artificial neural networks have been used for evaluation of clinical data to provide results similar to conventional modeling methods [Freeman RV, et al., 2002]. They do not require the articulation of such a mathematical model. ANNs are complex computational systems that can provide a nonlinear approach for data analysis. The ANNs forms a mapping from input to output nodes (simulated neurons) by extracting features from input patterns, assigning them weights, summing weights with activation functions, and propagating decisions to output nodes if activation thresholds are exceeded. Typical networks are organized into three layers of computational units (nodes) in which input/output layers are linked by hidden layers of nodes. Subject factors determine the number of input units, and the classification complexity determines the number of output units. The number of hidden units is determined by trial and error (training). Common routines start with one hidden unit and assign small arbitrary weights to all nodal connections. The network is fed sample data with known outcomes, and an error term is calculated by means of differences between known and predicted outputs. Learning consists of adjusting weights by backward pass of errors through the connections to network nodes in response to input data. Hidden units are added to achieve minimum error criteria, while constraining the number to promote generalization of input patterns and prevent overfitting (memorization). Interconnection density determines the network’s ability to correctly discriminate the outcomes. In statistical parlance, ANN models are a form of nonlinear

New Onset Diabetes Mellitus after Living Donor Renal Transplantation: A Unique Pattern in the Egyptian Population

Objectives: Our aim was to identify the diabetic risk profile of new onset diabetes after live donor renal transplantation (NODAT) and its impact on patient and graft survival in Egyptian population. Patient and methods: A retrospective review of 2019 renal allograft recipients has been performed. Risk factors, medical complications, patient and graft survival were analyzed. Results: After a mean follow up period of 8.8 ± 5.8 years, 450 (22.2%) recipients developed NODAT. A 455 post transplantation time matched control recipients without DM was selected. Time table revealed that 50% of NODAT cases discovered during the first 6 months post transplantation. The NODAT recipients were significantly older and obese with higher body mass index. Family history of DM was significantly positive among the NODAT group. Cox’s multivariate regression analysis revealed that the older age, positive family history of DM, high BMI, HCV infection and hypercholesterolemia were of significant risk factor. Medical complications were significant in the NODAT group. Patient survival was significantly lower in the NODAT group on the other hand the graft survival was comparable. Conclusion: NODAT does not statistically affect the graft survival. But, NODAT is a major problem endangers the patient life and must be minded to consider such patient as especially at higher risk for diabetic complications.

Impact of Donor Source on the Outcome of Live Donor Kidney Transplantation: A Single Center Experience

Background Renal transplantation is the ideal method for management of end-stage renal disease. The use of living donors for renal transplantation was critical for early development in the field and preceded the use of cadaveric donors. Most donors are related genetically to the recipients, like a parent, a child, or a sibling of the recipient, but there are an increasing percentage of cases where donors are genetically unrelated like spouses, friends, or altruistic individuals. Donor shortages constitute the major barrier for kidney transplantation, and much effort has been made to increase the supply of living donors. The impact of donor source on the outcome of renal transplantation is not adequately studied in our country. Objectives The aim of the study was to evaluate the impact of donor source on the outcome of live donor kidney transplantation. Patients and Methods From March 1976 to December 2013, the number of patients that underwent living renal transplantation sharing at least one HLA haplotype with their donors was 2,485. We divided these patients into two groups: (1) 2,075 kidney transplant recipients (1,554 or 74.9% male and 521 or 25.1% female) for whom the donors were living related, (2) 410 kidney transplant recipients (297 or 72.4% male and 113 or 27.6% female) for whom the donors were living unrelated. All patients received immunosuppressive therapy, consisting of a calcineurin inhibitor, mycophenolate mofetil, or azathioprine and prednisolone. We compared acute rejection and complication rates, as well as long-term graft and patient survival of both groups. Demographic characteristics were compared using the chi-square test. Graft survival and patient survival were calculated using the Kaplan-Meier method. Results The percentages of patients with acute vascular rejection were significantly higher in the unrelated group, while percentages of patients with no rejection were significantly higher in the related group, but there were no significant differences regarding patient and graft survivals between both groups. Conclusions Kidney transplant recipients who received their grafts either from live related donors or live unrelated donors had comparable patient and graft survival outcomes.

Post-Transplant Glomerulonephritis in Live-Donor Renal Transplant Recipients: Clinical Course and Risk Factors

Kidney transplantation is the treatment of choice for end-stage renal disease. It offers better quality of life and minimizes the mortality risk for patients when compared with maintenance dialysis therapy (Chailimpamontree W, et al., 2009). Little data are available concerning the impact of the post-transplantation glomerulonephritis (GN) on graft outcome (Gaston R, 2006). The post-transplant glomerulonephritis may be de novo GN or recurrence of the original kidney disease. De novo GN appear to have poorer prognosis than the recurrent type. Different types of glomerulonephritis were reported to recur in the graft with different recurrence rates (Briganti EM, et al., 2002). The recurrent glomerulonephritis was reported to be important cause of impaired graft function and consequent graft loss (Choy B. et al., 2006). Studies on recurrent disease are difficult since not all patients have undergone native kidney biopsy or it was non-representative. The reported incidence of recurrent GN is thus judged by clinical suspension and could be overor under-estimates of the true incidence (Hariharan S, 2000). Precise diagnosis of recurrent disease in view of concomitant histological features of chronic allograft nephropathy or chronic drug nephrotoxicity by calcineurine inhibitors is often difficult to be determined (Requiao-Moura LR, et al., 2007). There is accumulating evidence that recurrent GN is an important and clinically relevant cause of graft loss in the long-term follow-up of renal allograft recipients. It was reported that recurrent GN is considered to be the third most common cause for graft loss 10 years after kidney transplantation. The risk of graft loss from recurrence was found to be increased from 0.6% during the first year post-transplant to 8.4% after 10 year of follow up (Briganti EM, et al., 2002). The introduction of newer immunosuppressive agents and induction protocols improved the graft survival. The improvement of graft survival was through the direct reduction of the incidence of acute rejection. The incidence of posttransplant glomerulonephritis whether recurrence or de novo was not influenced (Hariharan S, et al., 2000). The aim of our study is to focus on the incidence, risk factors of GN after kidney transplantation and their impact on the graft function & survival. The risk for allograft loss as a result of PTGN is thus an important factor in the decision to proceed with transplantation, and an accurate understanding of the probability of PTGN is essential for the transplant team, the patient, and a potential live donor (Choy B, et al., 2006). There was a marked disparity in risk for PTGN according to histological type; the