Factors predicting rapid progression of kidney disease in ADPKD can be divided into genetic (non-modifiable) and clinical (modifiable) risk factors. Patients harbouring PKD1 mutations, in particular if truncating, have a more severe form of ADPKD. Clinical risk factors include decrease in glomerular filtration rate and renal blood flow at a young age; high total kidney volume; hypertension and urological complications <35 years; albuminuria/proteinuria. The renal disease is also more severe in males and in subjects with family history of ESRD <55 years. In recent years, two models for predicting progression in ADPKD have been published: the Mayo model, based on height-adjusted TKV, age and eGFR, and the Brest model, based on PKD gene mutation type, gender, and early onset of hypertension and urological complications. With the emergence of new disease-modifying therapies, prediction tools are essential. However, the high variability in ADPKD makes the predicting models difficult to apply on an individual patient basis. Thus, the above-mentioned predicting models should be viewed as complimentary to clinical evaluation and follow-up. In the future, an individual risk score linking genetic, imaging and clinical data might prove the most accurate way of predicting long-term outcome.

[ADPKD: predictors of Renal Disease progression]

SCOLARI, Francesco;DALLERA, Nadia;SALETTI, ARIANNA;TERLIZZI, VINCENZO;Izzi, Claudia
2016-01-01

Abstract

Factors predicting rapid progression of kidney disease in ADPKD can be divided into genetic (non-modifiable) and clinical (modifiable) risk factors. Patients harbouring PKD1 mutations, in particular if truncating, have a more severe form of ADPKD. Clinical risk factors include decrease in glomerular filtration rate and renal blood flow at a young age; high total kidney volume; hypertension and urological complications <35 years; albuminuria/proteinuria. The renal disease is also more severe in males and in subjects with family history of ESRD <55 years. In recent years, two models for predicting progression in ADPKD have been published: the Mayo model, based on height-adjusted TKV, age and eGFR, and the Brest model, based on PKD gene mutation type, gender, and early onset of hypertension and urological complications. With the emergence of new disease-modifying therapies, prediction tools are essential. However, the high variability in ADPKD makes the predicting models difficult to apply on an individual patient basis. Thus, the above-mentioned predicting models should be viewed as complimentary to clinical evaluation and follow-up. In the future, an individual risk score linking genetic, imaging and clinical data might prove the most accurate way of predicting long-term outcome.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11379/492202
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