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Keywords:

  • Kidney transplantation;
  • prediction models;
  • risk factors;
  • outcomes

Although randomized controlled trials (RCT) are the gold standard for establishing causation in clinical research, their aggregated results can be misleading when applied to individual patients. A treatment may be beneficial in some patients, but its harms may outweigh benefits in others. While conventional one-variable-at-a-time subgroup analyses have well-known limitations, multivariable risk-based analyses can help uncover clinically significant heterogeneity in treatment effects that may be otherwise obscured. Trials in kidney transplantation have yielded the finding that a reduction in acute rejection does not translate into a similar benefit in prolonging graft survival and improving graft function. This paradox might be explained by the variation in risk for acute rejection among included kidney transplant recipients varying the likelihood of benefit or harm from intense immunosuppressive regimens. Analyses that stratify patients by their immunological risk may resolve these otherwise puzzling results. Reliable risk models should be developed to investigate benefits and harms in rationally designed risk-based subgroups of patients in existing RCT data sets. These risk strata would need to be validated in future prospective clinical trials examining long-term effects on patient and graft survival. This approach may allow better individualized treatment choices for kidney transplant recipients.