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

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