Philip C Robinson1,2, Nicola Dalbeth3,4, Peter Donovan3,4. 1. From the departments of Rheumatology and Clinical Pharmacology, Royal Brisbane and Women's Hospital; School of Medicine, University of Queensland, Brisbane, Australia; Department of Medicine, University of Auckland, Auckland, New Zealand. philip.robinson@uq.edu.au. 2. P.C. Robinson, MBChB, PhD, FRACP, Department of Rheumatology, Royal Brisbane and Women's Hospital, and School of Medicine, University of Queensland; N. Dalbeth, MBChB, MD, FRACP, Department of Medicine, University of Auckland; P. Donovan, MAppSci, MBBS, MSc, FRACP, School of Medicine, University of Queensland, and Department of Clinical Pharmacology, Royal Brisbane and Women's Hospital. philip.robinson@uq.edu.au. 3. From the departments of Rheumatology and Clinical Pharmacology, Royal Brisbane and Women's Hospital; School of Medicine, University of Queensland, Brisbane, Australia; Department of Medicine, University of Auckland, Auckland, New Zealand. 4. P.C. Robinson, MBChB, PhD, FRACP, Department of Rheumatology, Royal Brisbane and Women's Hospital, and School of Medicine, University of Queensland; N. Dalbeth, MBChB, MD, FRACP, Department of Medicine, University of Auckland; P. Donovan, MAppSci, MBBS, MSc, FRACP, School of Medicine, University of Queensland, and Department of Clinical Pharmacology, Royal Brisbane and Women's Hospital.
Abstract
OBJECTIVE: The 2012 American College of Rheumatology gout management guidelines recommend monitoring serum urate (SU) every 6 months after target SU has been achieved. Our objective was to determine through modeling whether this testing would be cost-effective, considering financial cost, quality of life, and estimated change in adherence. METHODS: A cost-utility analysis was completed with a 3-arm model: (1) no regular urate monitoring; (2) annual urate monitoring; and (3) biannual urate monitoring. Inputs to the model for health-related quality of life, flare rate, and treatment location were drawn from the medical literature and modeled over a lifetime horizon. RESULTS: No monitoring was the least costly (Australian$6974) but least effective [13.51 quality-adjusted life-yrs (QALY)], while annual urate monitoring [A$7117; 13.53 QALY; incremental cost-effectiveness ratio (ICER) A$13,678/QALY gained] and biannual monitoring [A$7298; 13.54 QALY; ICER A$15,420 per QALY gained] were both cost-effective alternatives in base case analysis. Sensitivity analysis on both an individual component level and a probabilistic sensitivity analysis (PSA) demonstrated that the result was robust to changes in input variables. An improvement in adherence of ≥ 3.5% with biannual monitoring was all that was required to demonstrate cost-effectiveness. In PSA, the probability of biannual monitoring was 78%, no monitoring was 20%, and annual monitoring was 2%. CONCLUSION: The results suggest that biannual SU monitoring after attaining target SU is the most cost-effective, compared with no testing and annual testing.
OBJECTIVE: The 2012 American College of Rheumatology gout management guidelines recommend monitoring serum urate (SU) every 6 months after target SU has been achieved. Our objective was to determine through modeling whether this testing would be cost-effective, considering financial cost, quality of life, and estimated change in adherence. METHODS: A cost-utility analysis was completed with a 3-arm model: (1) no regular urate monitoring; (2) annual urate monitoring; and (3) biannual urate monitoring. Inputs to the model for health-related quality of life, flare rate, and treatment location were drawn from the medical literature and modeled over a lifetime horizon. RESULTS: No monitoring was the least costly (Australian$6974) but least effective [13.51 quality-adjusted life-yrs (QALY)], while annual urate monitoring [A$7117; 13.53 QALY; incremental cost-effectiveness ratio (ICER) A$13,678/QALY gained] and biannual monitoring [A$7298; 13.54 QALY; ICER A$15,420 per QALY gained] were both cost-effective alternatives in base case analysis. Sensitivity analysis on both an individual component level and a probabilistic sensitivity analysis (PSA) demonstrated that the result was robust to changes in input variables. An improvement in adherence of ≥ 3.5% with biannual monitoring was all that was required to demonstrate cost-effectiveness. In PSA, the probability of biannual monitoring was 78%, no monitoring was 20%, and annual monitoring was 2%. CONCLUSION: The results suggest that biannual SU monitoring after attaining target SU is the most cost-effective, compared with no testing and annual testing.
Authors: Marc De Meulemeester; Elsa Mateus; Hilda Wieberneit-Tolman; Neil Betteridge; Lucy Ireland; Gudula Petersen; Nina Jeanette Maske; Tim L Jansen; Fernando Perez-Ruiz Journal: BJGP Open Date: 2020-05-01