Development of a pain intensity estimation model in breast cancer survivors using quantile regression: A nationwide claims-based cohort study.

OBJECTIVE

In patients in whom pain cannot be objectively evaluated, achieving high compliance with symptom management is challenging. This study aimed to estimate pain-related factors and pain intensity in patients with breast cancer according to prescription history of analgesics.

MATERIALS AND

METHODS

Pain intensity was rated according to low, moderate, and high cumulative analgesic consumption score (CACS). CACS-based quantile regression analysis was performed considering sociodemographic (age, income, and disease duration), index-related (body mass index (BMI) and Charlson Comorbidity Index (CCI)), and surgery- and treatment-related variables.

RESULTS

In the mild pain group (Q1), age (≥ 50 years; βQ1 = 0.5803) and BMI (≥ 25; βQ1 = 0.7062) -affected the pain estimate. In the moderate-pain group (Q2), age (≥ 50 years; βQ2 = 1.0380), BMI (≥ 25; βQ2 = 0.9011), CCI (≥ 3; βQ2 = 0.6106) and radiation therapy (yes; βQ2 = 0.5652) affected pain intensity. In the severe-pain group (Q4), age (≥ 50 years; βQ4 = 7.002), BMI (≥ 25; βQ4 = 6.2800), CCI (≥ 3; βQ4 = 3.4480), lymph node dissection (yes; βQ4 = 2.4420), and lymphedema (yes; βQ4 = 4.6580) affected pain estimate.

CONCLUSION

Among patients with breast cancer, older age, longer disease duration, higher BMI and CCI, prior lymph node dissection, and lymphedema presence may contribute to more severe pain symptoms. These findings may pave the way for the development of preemptive pain intervention for patients with breast cancer.