Rheumatology (Oxford, England) · 2026
Development of a time-dependent prediction model for the risk of Pneumocystis jirovecii pneumonia in ANCA-associated vasculitis.
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Takada Toshihiko, Hoogland Jeroen, Anan Keisuke, Shimizu Sayaka, Sada Ken-Ei, Yajima Nobuyuki, Fukuhara Shunichi
Аннотация
OBJECTIVES: In patients with ANCA-associated vasculitis (AAV), Pneumocystis jirovecii pneumonia (PJP) is a potentially fatal complication. As treatment intensity typically changes over time, PJP risk also changes, making fixed prophylaxis strategies suboptimal. We aimed to develop a time-dependent prediction model to guide individualized decisions on PJP prophylaxis.
METHODS: We conducted a retrospective cohort study using a nationwide claims database. Patients newly diagnosed with AAV between 2015 and 2024 were included. Candidate predictors included demographics, comorbidities and monthly updated medication use. A flexible parametric survival model was developed to predict the risk of PJP. Discrimination, calibration and clinical utility were evaluated by internal-external cross-validation (IECV) to investigate model's generalizability.
RESULTS: Among 15 649 036 individuals in the database, 2751 patients with AAV were included. Of them, 170 (6.2%) developed PJP. The final model included age, monthly maximum glucocorticoid dose, use of rituximab, CYC and PJP prophylaxis. In the IECV, the time-dependent C-index was 0.70 (95% CI 0.60-0.79) at 2 months and 0.66 (95% CI 0.61-0.71) at 12 months; calibration was generally acceptable. In the clinical utility analysis, per 1000 patients with AAV including 61 PJP cases, 948 would be recommended prophylaxis with a monthly risk threshold of 1.0%. Of them, 643 had not received it, with 34 potentially preventable PJP cases, and 336 could have discontinued prophylaxis during follow-up.
CONCLUSION: We developed a time-dependent model to predict the risk of PJP in patients with AAV. The model may support more appropriate, individualized prophylaxis decisions.
METHODS: We conducted a retrospective cohort study using a nationwide claims database. Patients newly diagnosed with AAV between 2015 and 2024 were included. Candidate predictors included demographics, comorbidities and monthly updated medication use. A flexible parametric survival model was developed to predict the risk of PJP. Discrimination, calibration and clinical utility were evaluated by internal-external cross-validation (IECV) to investigate model's generalizability.
RESULTS: Among 15 649 036 individuals in the database, 2751 patients with AAV were included. Of them, 170 (6.2%) developed PJP. The final model included age, monthly maximum glucocorticoid dose, use of rituximab, CYC and PJP prophylaxis. In the IECV, the time-dependent C-index was 0.70 (95% CI 0.60-0.79) at 2 months and 0.66 (95% CI 0.61-0.71) at 12 months; calibration was generally acceptable. In the clinical utility analysis, per 1000 patients with AAV including 61 PJP cases, 948 would be recommended prophylaxis with a monthly risk threshold of 1.0%. Of them, 643 had not received it, with 34 potentially preventable PJP cases, and 336 could have discontinued prophylaxis during follow-up.
CONCLUSION: We developed a time-dependent model to predict the risk of PJP in patients with AAV. The model may support more appropriate, individualized prophylaxis decisions.