Doctor of Philosophy

dissertationAlthough renal transplant is the preferred modality for end-stage renal disease, it brings with it a number of challenges primarily associated with lack of individualized approach. The goals of the present project were: (1) to determine the most significant and clinically practical predictors of kidney transplant outcomes (patient survival, allograft survival, posttransplant complications) using United States Renal Data System (USRDS) data; (2) based on the selected predictors, to generate prediction models of renal transplant outcomes. Our initial study developed prediction models using logistic regression and treebased algorithms derived from data provided by the United Network of Organ Sharing (UNOS). A series of follow-up projects, using data supplied by the United States Renal Data System (USRDS), was performed. We were able to capture significant associations between donor, recipient, and transplant procedure variables (that could not be derived from UNOS data) and the allograph and recipient survival. Among our important findings, compared to peritoneal dialysis (PD), hemodialysis is associated with increased risk of graft failure and recipient death; preemptive retransplantation is associated with an increased risk of graft failure; increased time on dialysis between transplants is associated with a negative effect upon graft and recipient survival in most patient subgroups; short-term (6 months or less) dialysis had no negative effect on graft survival compared to preemptive transplants; certain socioeconomic factors, such as higher education level, citizenship, and type of insurance coverage, influenced graft and recipient outcomes, independent of racial differences; and that one particular iv immunosuppressive medication regimen was superior to others in prolonging graft and recipient survival. Based on these results, we developed a more comprehensive prediction model of the graft outcome using URSDS data using logistic regression and tree-based models. The new models included both deceased and living donor graft recipients, was based on the longer list of pertinent predictors while still being practical in the clinical setting, and addressed the probability of graft failure at five different time points (1, 3, 5, 7, and 10- year allograft survival). The models have been validated on the independent dataset and demonstrated performance suggesting implementation in the clinical decision support system

Single and multiple time-point prediction models in kidney transplant outcomes

abstractThis study predicted graft and recipient survival in kidney transplantation based on the USRDS dataset by regression models and artificial neural networks (ANNs). We examined single time-point models (logistic regression and single-output ANNs) versus multiple time-point models (Cox models and multiple-output ANNs). These models in general achieved good prediction discrimination (AUC up to 0.82) and model calibration. This study found that: (1) Single time-point and multiple time-point models can achieve comparable AUC, except for multiple-output ANNs, which may perform poorly when a large proportion of observations are censored, (2) Logistic regression is able to achieve comparable performance as ANNs if there are no strong interactions or non-linear relationships among the predictors and the outcomes, (3) Time-varying effects must be modeled explicitly in Cox models when predictors have significantly different effects on short-term versus long-term survival, and (4) Appropriate baseline survivor function should be specified for Cox models to achieve good model calibration, especially when clinical decision support is designed to provide exact predicted survival rates

Prediction Model and Risk Stratification Tool for Survival in Patients With CKD

Introduction: Because chronic kidney disease (CKD) adversely affects survival, prediction of mortality risk should help to identify individuals requiring therapeutic intervention. The goal of this project was to construct and to validate a risk scoring system and prediction model of the probability of 2-year mortality in a CKD population. Methods: We applied the Woodpecker approach to develop prediction equations using linear, exponential, and combined models. A risk indicator R on a scale of 0 to 10 was calculated as follows: starting with 0, add 0.048 for each year of age above 20, 0.45 for male sex, 0.49 for each stage of CKD over stage 2, 1.04 for proteinuria, 0.72 for smoking history, and 0.49 for each significant comorbidity up to 5. Results: Using R to predict 2-year mortality, the model yielded an area under the receiver operating characterisic curve of 0.83 (95% confidence interval = 0.81−0.86) with 5062 subjects with CKD ≥stage 2 from a National Health and Nutrition Examination Survey cohort (1999−2004) having a 3.2% 2-year mortality. The combined expression offered results closest to most actual outcomes for the entire population and for each CKD stage. For those patients with higher risk (R ≥ 4−5, >5−6, and >6), the predicted 2-year mortality rates were 3.8%, 6.4%, and 13.0%, respectively, compared to observed mortality rates of 2.7%, 4.5%, and 13.3%. Conclusion: The risk stratification tool and prediction model of 2-year mortality demonstrated good performance and may be used in clinical practice to quantify the risk of death for individual patients with CKD

Pre-transplant Social Adaptability Index and clinical outcomes in renal transplantation - The Swiss Transplant Cohort Study

The impact of pre-transplant social determinants of health on post-transplant outcomes remains understudied. In the US, poor clinical outcomes are associated with underprivileged status, as assessed by the Social Adaptability Index (SAI), a composite score of education, employment status, marital status, household income, and substance abuse. Using data from the Swiss Transplant Cohort Study (STCS), we determined the SAI's predictive value regarding two post-transplant outcomes: all-cause mortality and return to dialysis.; Between 2012 and 2018, we included adult renal transplant patients (aged ≥18 years) with pre-transplant assessment SAI scores, calculated from a STCS Psychosocial Questionnaire. Time to all-cause mortality and return to dialysis were predicted using Cox regression.; Of 1238 included patients (mean age: 53.8±13.2 years; 37.9% female; median follow-up time: 4.4 years (IQR: 2.7)), 93 (7.5%) died and 57 (4.6%) returned to dialysis. The SAI's hazard ratio was 0.94 (95%CI: 0.88-1.01; p=0.09) for mortality and 0.93 (95%CI: 0.85-1.02; p=0.15) for return to dialysis.; In contrast to most published studies on social deprivation, analysis of this Swiss sample detected no significant association between SAI score and mortality or return to dialysis

Pre‐transplant Social Adaptability Index and clinical outcomes in renal transplantation – The Swiss Transplant Cohort Study

Background The impact of pre‐transplant social determinants of health on post‐transplant outcomes remains understudied. In the US, poor clinical outcomes are associated with underprivileged status, as assessed by the Social Adaptability Index (SAI), a composite score of education, employment status, marital status, household income, and substance abuse. Using data from the Swiss Transplant Cohort Study (STCS), we determined the SAI’s predictive value regarding two post‐transplant outcomes: all‐cause mortality and return to dialysis. Methods Between 2012 and 2018, we included adult renal transplant patients (aged ≥18 years) with pre‐transplant assessment SAI scores, calculated from a STCS Psychosocial Questionnaire. Time to all‐cause mortality and return to dialysis were predicted using Cox regression. Results Of 1238 included patients (mean age: 53.8±13.2 years; 37.9% female; median follow‐up time: 4.4 years (IQR: 2.7)), 93 (7.5%) died and 57 (4.6%) returned to dialysis. The SAI’s hazard ratio was 0.94 (95%CI: 0.88‐1.01; p=0.09) for mortality and 0.93 (95%CI: 0.85‐1.02; p=0.15) for return to dialysis. Conclusions In contrast to most published studies on social deprivation, analysis of this Swiss sample detected no significant association between SAI score and mortality or return to dialysis