Association between body mass index and outcomes after autologous breast reconstruction: a nationwide inpatient database study in Japan

BREAST CANCER RESEARCH AND TREATMENT(2023)

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摘要
Background Although autologous reconstruction following breast cancer surgery is common, little is known about the association between preoperative body mass index (BMI) and short-term surgical outcomes. This study investigated the association between BMI and short-term surgical outcomes in autologous breast reconstruction using a nationwide Japanese inpatient database. Methods We retrospectively identified female patients with breast cancer who underwent breast reconstruction using a pedicled flap or free flap from July 2010 to March 2020. Multivariable regression analyses and restricted cubic spline analyses were conducted to investigate the associations between BMI and short-term outcomes with adjustment for demographic and clinical backgrounds. Results Of the 13,734 eligible patients, 7.1% and 22.2% had a BMI of < 18.5 kg/m 2 and > 25 kg/m 2 , respectively. Compared with BMI of 18.5–21.9 kg/m 2 , overweight (25.0–29.9 kg/m 2 ) and obese (≥ 30.0 kg/m 2 ) were significantly associated with higher occurrences of takebacks (odds ratio, 1.74 [95% confidence interval, 1.28–2.38] and 2.89 [1.88–4.43], respectively) and overall complications (1.37 [1.20–1.57] and 1.77 [1.42–2.20], respectively). In the restricted cubic spline analyses, BMI showed J-shaped associations with takebacks, overall complications, local complications, and wound dehiscence. BMI also demonstrated linear associations with postoperative surgical site infection, duration of anesthesia, duration of drainage, length of stay, and hospitalization costs. Conclusion In autologous breast reconstruction following breast cancer surgery, a higher BMI was associated with takebacks, morbidity, a longer hospital stay, and higher total costs, whereas a lower BMI was associated with fewer surgical site infections, a shorter hospital stay, and lower total costs.
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关键词
Breast reconstruction,BMI,Big data
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