Development of a pain intensity estimation model in breast cancer survivors using quantile regression: A nationwide...

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.

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.

In the mild pain group (Q1), age (≥ 50 years; β_Q1 = 0.5803) and BMI (≥ 25; β_Q1 = 0.7062) -affected the pain estimate. In the moderate-pain group (Q2), age (≥ 50 years; β_Q2 = 1.0380), BMI (≥ 25; β_Q2 = 0.9011), CCI (≥ 3; β_Q2 = 0.6106) and radiation therapy (yes; β_Q2 = 0.5652) affected pain intensity. In the severe-pain group (Q4), age (≥ 50 years; β_Q4 = 7.002), BMI (≥ 25; β_Q4 = 6.2800), CCI (≥ 3; β_Q4 = 3.4480), lymph node dissection (yes; β_Q4 = 2.4420), and lymphedema (yes; β_Q4 = 4.6580) affected pain estimate.

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.