Safety of the Living Kidney Donor - The German National Register.

Safety of the Living Kidney Donor - The German National Register. - SOLKID-GNR

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00023532

Enrollment

2000

Registered

2020-12-10

Start date

2020-01-31

Completion date

Unknown

Last updated

2025-04-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Z52.4

Interventions

Group 1: Live kidney donors are surveyed to improve the assessment of the medical and psychosocial donor risks in the long-term through systematic and prospective data collection, including by means o

Eligibility

Sex/Gender

All

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: Live kidney donors, Of legal age, Sufficient knowledge of German to answer the questionnaire, Informed consent

Exclusion criteria

Exclusion criteria: Minor at donation, Insufficient knowledge of German to answer the questionnaire., no Informed Consent

Design outcomes

Primary

Measure	Time frame
In the primary statistical analysis, the effect of living donation on the outcomes somatization (PHQ-15), fatigue (MFI-20), quality of life (SF-12), kidney function (creatinine, eGFR, albuminuria) and blood pressure (hypertension, antihypertensive drugs) examined. For each of the target variables, the distribution at times T0-Pre and T1 is compared with the help of a non-parametric Wilcoxon rank sum test. The first type of error is controlled in the form of the “comparison-wise type-I error rate” instead of the “experiment-wise error rate”. For each of the primary outcomes described above, the local bilateral significance level is set to 5%. For the power calculation it was assumed that there is a positive correlation r=0 between the target value at time T0-Pre and at time T1 for all target values. With a sample size of 2000-2500 register participants, a power of 90% can reveal a difference between the times T0-Pre and T1, if the expected values ??for T0-Pre and T1 are 0.1 times the standard deviation of the corresponding Differentiate target size. In subgroup analyzes carried out if necessary, a clinically relevant difference with a power of 80% can be revealed with a number of 200 study participants if the expected values ??for T0-Pre and T1 differ by 0.3 times the standard deviation of the respective target variable. In addition to comparing the T0-Pre and T1 times, the course of the primary outcome measures at all follow-up times is examined. For this purpose, a non-parametric Friedman test is carried out at a significance level of 5%. Then, if necessary, post-hoc comparisons of the individual points in time are carried out in pairs. To study the rate of surgical complications over time, a mixed generalized linear model with logistic link function and random effects (see below) is used. In the primary analyzes described above, no further prognostic factors or confounders are used for adjustment. In order to ensure the highest possible quality of the results, the	—

Measure

Time frame

In the primary statistical analysis, the effect of living donation on the outcomes somatization (PHQ-15), fatigue (MFI-20), quality of life (SF-12), kidney function (creatinine, eGFR, albuminuria) and blood pressure (hypertension, antihypertensive drugs) examined. For each of the target variables, the distribution at times T0-Pre and T1 is compared with the help of a non-parametric Wilcoxon rank sum test. The first type of error is controlled in the form of the “comparison-wise type-I error rate” instead of the “experiment-wise error rate”. For each of the primary outcomes described above, the local bilateral significance level is set to 5%. For the power calculation it was assumed that there is a positive correlation r=0 between the target value at time T0-Pre and at time T1 for all target values. With a sample size of 2000-2500 register participants, a power of 90% can reveal a difference between the times T0-Pre and T1, if the expected values ??for T0-Pre and T1 are 0.1 times the standard deviation of the corresponding Differentiate target size. In subgroup analyzes carried out if necessary, a clinically relevant difference with a power of 80% can be revealed with a number of 200 study participants if the expected values ??for T0-Pre and T1 differ by 0.3 times the standard deviation of the respective target variable. In addition to comparing the T0-Pre and T1 times, the course of the primary outcome measures at all follow-up times is examined. For this purpose, a non-parametric Friedman test is carried out at a significance level of 5%. Then, if necessary, post-hoc comparisons of the individual points in time are carried out in pairs. To study the rate of surgical complications over time, a mixed generalized linear model with logistic link function and random effects (see below) is used. In the primary analyzes described above, no further prognostic factors or confounders are used for adjustment. In order to ensure the highest possible quality of the results, the

—

Secondary

Measure	Time frame
The effect of living donation on the outcomes listed under Hypothesis 2 is examined using multivariate mixed linear models. Both fixed and random effects are included in the models. The random effects include, on the one hand, the transplant center and, on the other hand, a subject-specific effect that corresponds to the registry participant. The subject-specific effect is necessary in order to adequately take into account the repeated measurements at the different times for each registry participant. The possible prognostic factors and disturbance variables are included in the model as fixed effects. If it proves to be appropriate, the respective target value at the time T0-Pre is also included in the model as a prognostic factor. The model formation and selection is carried out with statistical standard methods according to the "state of the art". A step-by-step selection of variables based on Akaike's information criterion (AIC) is carried out in order to identify relevant prognostic factors. The parameter estimation is carried out using the restricted maximum likelihood method. The underlying assumptions of the mixed linear model, including the assumption of the normal distribution of the residuals, are checked with graphical methods (histogram, QQ plots). If necessary, continuous target values ??are suitably transformed using a logarithmic or Box-Cox transformation. Further statistical analyzes are carried out in order to develop suitable risk scores for the psychosocial and / or physical negative outcomes. For this purpose, as described above, multivariate mixed linear models with a logistic link function, which contain both fixed and random effects, are created in order to identify risk factors for a negative outcome of living donation. With the final model, the individual probability of experiencing a negative outcome can be predicted for each donor. The development of the algorithm for the prediction of negative outcomes includes a detailed examination of	—

Measure

Time frame

The effect of living donation on the outcomes listed under Hypothesis 2 is examined using multivariate mixed linear models. Both fixed and random effects are included in the models. The random effects include, on the one hand, the transplant center and, on the other hand, a subject-specific effect that corresponds to the registry participant. The subject-specific effect is necessary in order to adequately take into account the repeated measurements at the different times for each registry participant. The possible prognostic factors and disturbance variables are included in the model as fixed effects. If it proves to be appropriate, the respective target value at the time T0-Pre is also included in the model as a prognostic factor. The model formation and selection is carried out with statistical standard methods according to the "state of the art". A step-by-step selection of variables based on Akaike's information criterion (AIC) is carried out in order to identify relevant prognostic factors. The parameter estimation is carried out using the restricted maximum likelihood method. The underlying assumptions of the mixed linear model, including the assumption of the normal distribution of the residuals, are checked with graphical methods (histogram, QQ plots). If necessary, continuous target values ??are suitably transformed using a logarithmic or Box-Cox transformation. Further statistical analyzes are carried out in order to develop suitable risk scores for the psychosocial and / or physical negative outcomes. For this purpose, as described above, multivariate mixed linear models with a logistic link function, which contain both fixed and random effects, are created in order to identify risk factors for a negative outcome of living donation. With the final model, the individual probability of experiencing a negative outcome can be predicted for each donor. The development of the algorithm for the prediction of negative outcomes includes a detailed examination of

—

Countries

Germany

Contacts

Public ContactJeannine Wegner

Universitätsklinikum Münster Deutsches Lebendspenderegister SOLKID-GNR

jeannine.wegner@ukmuenster.de02518351454

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 4, 2026