Background: To increase the diagnostic rate of primary hyperoxaluria type 1 (PH1) in the adult dialysis setting, a prediction model based on five readily available clinical parameters was recently developed and validated in an adult hemodialysis population. To further test the prediction model in clinical practice, this case series describes the retrospective application of the diagnostic algorithm in a group of adult dialysis patients with PH1 treated at different Italian nephrology centers. Methods: Between January and May 2023, adult patients (≥ 18 years) undergoing chronic hemodialysis with a genetic diagnosis of PH1, followed at 14 Italian nephrology centers, were selected for the retrospective application of the prediction model. Results: The presence of at least one red flag of the diagnostic algorithm was reported in most patients (14 out of 15; 93%), two red flags were present in four patients (27%), and three red flags in two patients (13%). A history of active nephrolithiasis was the most common clinical feature (87% of patients), followed by early dialysis initiation, nephrocalcinosis and a family history of CKD (20–27%). Conclusions: Our study provides further evidence on the real-world application of a simple algorithm, implemented by easily accessible clinical parameters, to be used as a screening tool for diagnosing PH1 in adult patients undergoing dialysis. The successful implementation of this prediction model has the potential to facilitate timely diagnosis, improve patient outcomes, and inform targeted therapeutic interventions in this patient setting.

Primary hyperoxaluria type 1 diagnosis in adult dialysis patients: prediction model assessment in a group of Italian patients / Ferraro, Pietro Manuel; Arbustini, Eloisa; Bellino, Diego; Caletti, Chiara; Capelli, Irene; Capolongo, Giovanna; Caruso, Maria Rosa; Cianfrone, Paola; D'Alessandro, Maria Michela; Di Luca, Marina; Gambaro, Giovanni; Palmisano, Alessandra; Ranghino, Andrea; Santagostino Barbone, Gaia; Viazzi, Francesca; Zambianchi, Loretta; Mandrile, Giorgia. - In: JN. JOURNAL OF NEPHROLOGY. - ISSN 1724-6059. - (2025). [Epub ahead of print] [10.1007/s40620-025-02243-3]

Primary hyperoxaluria type 1 diagnosis in adult dialysis patients: prediction model assessment in a group of Italian patients

Ranghino, Andrea;
2025-01-01

Abstract

Background: To increase the diagnostic rate of primary hyperoxaluria type 1 (PH1) in the adult dialysis setting, a prediction model based on five readily available clinical parameters was recently developed and validated in an adult hemodialysis population. To further test the prediction model in clinical practice, this case series describes the retrospective application of the diagnostic algorithm in a group of adult dialysis patients with PH1 treated at different Italian nephrology centers. Methods: Between January and May 2023, adult patients (≥ 18 years) undergoing chronic hemodialysis with a genetic diagnosis of PH1, followed at 14 Italian nephrology centers, were selected for the retrospective application of the prediction model. Results: The presence of at least one red flag of the diagnostic algorithm was reported in most patients (14 out of 15; 93%), two red flags were present in four patients (27%), and three red flags in two patients (13%). A history of active nephrolithiasis was the most common clinical feature (87% of patients), followed by early dialysis initiation, nephrocalcinosis and a family history of CKD (20–27%). Conclusions: Our study provides further evidence on the real-world application of a simple algorithm, implemented by easily accessible clinical parameters, to be used as a screening tool for diagnosing PH1 in adult patients undergoing dialysis. The successful implementation of this prediction model has the potential to facilitate timely diagnosis, improve patient outcomes, and inform targeted therapeutic interventions in this patient setting.
2025
Diagnostic algorithm; PH1; Primary hyperoxaluria; Red flags
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Descrizione: Primary hyperoxaluria type 1 diagnosis in adult dialysis patients: prediction model assessment in a group of Italian patients
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/345375
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