Publication

ADPKD: Risk Prediction for Treatment Selection

Messchendorp, A. L., 2019, [Groningen]: Rijksuniversiteit Groningen. 292 p.

Research output: ThesisThesis fully internal (DIV)Academic

APA

Messchendorp, A. L. (2019). ADPKD: Risk Prediction for Treatment Selection. [Groningen]: Rijksuniversiteit Groningen.

Author

Messchendorp, Annemarie Lianne. / ADPKD : Risk Prediction for Treatment Selection. [Groningen] : Rijksuniversiteit Groningen, 2019. 292 p.

Harvard

Messchendorp, AL 2019, 'ADPKD: Risk Prediction for Treatment Selection', Doctor of Philosophy, University of Groningen, [Groningen].

Standard

ADPKD : Risk Prediction for Treatment Selection. / Messchendorp, Annemarie Lianne.

[Groningen] : Rijksuniversiteit Groningen, 2019. 292 p.

Research output: ThesisThesis fully internal (DIV)Academic

Vancouver

Messchendorp AL. ADPKD: Risk Prediction for Treatment Selection. [Groningen]: Rijksuniversiteit Groningen, 2019. 292 p.


BibTeX

@phdthesis{77323d8c98c24a3784f73a17e3df1a65,
title = "ADPKD: Risk Prediction for Treatment Selection",
abstract = "Autosomal dominant polycystic kidney disease (ADPKD) is the most common inheritable kidney disease. It is characterized by progressive kidney cyst formation throughout life, which can lead to kidney failure. However, only 70{\%} of patients will develop kidney failure and the age at which patients develop kidney failure shows a large interindividual variability. It is therefore difficult to predict the rate of disease progression in an individual patient. Obviously, the ability to predict the rate of disease progression in patients with ADPKD would help patients and caregivers alike in treatment related decisions. Patients with a higher rate of disease progression will probably benefit the most from therapy, since in these patients the benefit to risk ratio of treatment is expected to be better, especially when treatment is started early. Currently, there are several variables used to predict the rate of disease progression in ADPKD like kidney volume and the DNA mutation which underlies the disease. However, these variables are expensive and laborious to assess, less sensitive at an individual patient level and not always available in routine clinical care. The aim of this thesis was therefore to study if current used variables could be improved and to search for new variables that predict disease progression in ADPKD. The studies described in this thesis bring us closer to the answer to the question how we can identify patients in a clinically applicable way with rapidly progressive ADPKD, who are eligible for treatment.",
author = "Messchendorp, {Annemarie Lianne}",
year = "2019",
language = "English",
isbn = "978-94-6375-264-0",
publisher = "Rijksuniversiteit Groningen",
school = "University of Groningen",

}

RIS

TY - THES

T1 - ADPKD

T2 - Risk Prediction for Treatment Selection

AU - Messchendorp, Annemarie Lianne

PY - 2019

Y1 - 2019

N2 - Autosomal dominant polycystic kidney disease (ADPKD) is the most common inheritable kidney disease. It is characterized by progressive kidney cyst formation throughout life, which can lead to kidney failure. However, only 70% of patients will develop kidney failure and the age at which patients develop kidney failure shows a large interindividual variability. It is therefore difficult to predict the rate of disease progression in an individual patient. Obviously, the ability to predict the rate of disease progression in patients with ADPKD would help patients and caregivers alike in treatment related decisions. Patients with a higher rate of disease progression will probably benefit the most from therapy, since in these patients the benefit to risk ratio of treatment is expected to be better, especially when treatment is started early. Currently, there are several variables used to predict the rate of disease progression in ADPKD like kidney volume and the DNA mutation which underlies the disease. However, these variables are expensive and laborious to assess, less sensitive at an individual patient level and not always available in routine clinical care. The aim of this thesis was therefore to study if current used variables could be improved and to search for new variables that predict disease progression in ADPKD. The studies described in this thesis bring us closer to the answer to the question how we can identify patients in a clinically applicable way with rapidly progressive ADPKD, who are eligible for treatment.

AB - Autosomal dominant polycystic kidney disease (ADPKD) is the most common inheritable kidney disease. It is characterized by progressive kidney cyst formation throughout life, which can lead to kidney failure. However, only 70% of patients will develop kidney failure and the age at which patients develop kidney failure shows a large interindividual variability. It is therefore difficult to predict the rate of disease progression in an individual patient. Obviously, the ability to predict the rate of disease progression in patients with ADPKD would help patients and caregivers alike in treatment related decisions. Patients with a higher rate of disease progression will probably benefit the most from therapy, since in these patients the benefit to risk ratio of treatment is expected to be better, especially when treatment is started early. Currently, there are several variables used to predict the rate of disease progression in ADPKD like kidney volume and the DNA mutation which underlies the disease. However, these variables are expensive and laborious to assess, less sensitive at an individual patient level and not always available in routine clinical care. The aim of this thesis was therefore to study if current used variables could be improved and to search for new variables that predict disease progression in ADPKD. The studies described in this thesis bring us closer to the answer to the question how we can identify patients in a clinically applicable way with rapidly progressive ADPKD, who are eligible for treatment.

M3 - Thesis fully internal (DIV)

SN - 978-94-6375-264-0

PB - Rijksuniversiteit Groningen

CY - [Groningen]

ER -

ID: 75440764