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Summary <p>Acute phase reaction (APR) after zoledronic acid infusion impairs adherence and treatment outcomes. Using smartphone-based ePRO monitoring, we developed a simple prediction model for severe APR incorporating age, sex, and day-1 symptoms. This algorithm showed good discrimination and may enable early identification and personalized prevention of severe APR.</p> Purpose <p>Zoledronic acid (ZOL) is an effective antiresorptive therapy for osteoporosis, but acute phase reaction (APR) after initial infusion may impair adherence and outcomes. Identifying individuals at high risk for severe APR is essential for personalized prevention.</p> Methods <p>This multicenter, prospective observational study collected electronic patient-reported outcome (ePRO) data via a smartphone application after a single 5-mg ZOL infusion. Adults aged ≥ 19&#xa0;years reported the severity of nine APR symptoms daily for four days using a 5-point scale. Daily scores were summed to calculate a total APR score. Severity was categorized as mild-to-moderate or severe based on the median of total APR score. A prediction algorithm for severe APR was developed using age, sex, and day-1 symptoms.</p> Results <p>Among 685 participants (mean age 70.6&#xa0;years; 83.7% women), 222 (32.4%) experienced APR, and 118 (17.2%) developed severe APR. APR scores peaked on day 2 and declined by day 4. Severe APR showed earlier onset, longer duration, and greater symptom burden than mild-to-moderate APR. Younger age and female sex were associated with severe APR. Five day-1 symptoms (myalgia, arthralgia, chills, headache, and back pain) were identified as core predictors. A compact model incorporating age, sex, and the number of day-1 core symptoms demonstrated good discrimination for severe APR (AUROC 0.81).</p> Conclusion <p>Smartphone-based ePRO effectively captured APR severity following ZOL. A simple clinical algorithm using age, sex, and day-1 symptoms accurately identified individuals at high risk for severe APR, enabling personalized preventive strategies.</p>

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Smartphone-based patient-reported outcome of acute phase reaction after zoledronic acid infusion and prediction algorithm: a multicenter, prospective cohort study

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Sung Joon Cho,
  • Sang Wouk Cho,
  • Min Heui Yu,
  • Seng Chan You,
  • Seong Hee Ahn,
  • Yumie Rhee,
  • Namki Hong

摘要

Summary

Acute phase reaction (APR) after zoledronic acid infusion impairs adherence and treatment outcomes. Using smartphone-based ePRO monitoring, we developed a simple prediction model for severe APR incorporating age, sex, and day-1 symptoms. This algorithm showed good discrimination and may enable early identification and personalized prevention of severe APR.

Purpose

Zoledronic acid (ZOL) is an effective antiresorptive therapy for osteoporosis, but acute phase reaction (APR) after initial infusion may impair adherence and outcomes. Identifying individuals at high risk for severe APR is essential for personalized prevention.

Methods

This multicenter, prospective observational study collected electronic patient-reported outcome (ePRO) data via a smartphone application after a single 5-mg ZOL infusion. Adults aged ≥ 19 years reported the severity of nine APR symptoms daily for four days using a 5-point scale. Daily scores were summed to calculate a total APR score. Severity was categorized as mild-to-moderate or severe based on the median of total APR score. A prediction algorithm for severe APR was developed using age, sex, and day-1 symptoms.

Results

Among 685 participants (mean age 70.6 years; 83.7% women), 222 (32.4%) experienced APR, and 118 (17.2%) developed severe APR. APR scores peaked on day 2 and declined by day 4. Severe APR showed earlier onset, longer duration, and greater symptom burden than mild-to-moderate APR. Younger age and female sex were associated with severe APR. Five day-1 symptoms (myalgia, arthralgia, chills, headache, and back pain) were identified as core predictors. A compact model incorporating age, sex, and the number of day-1 core symptoms demonstrated good discrimination for severe APR (AUROC 0.81).

Conclusion

Smartphone-based ePRO effectively captured APR severity following ZOL. A simple clinical algorithm using age, sex, and day-1 symptoms accurately identified individuals at high risk for severe APR, enabling personalized preventive strategies.