Sepsis and septic shock after breast cancer surgery: risk modeling and machine learning validation across 328

Sepsis and septic shock after breast cancer surgery: risk modeling and machine learning validation across 328,292 cases. Download PDF Abstract Postoperative sepsis is a rare but life-threatening complication after breast cancer surgery. Although infection risk in breast surgery is generally low, cancer-related immune dysregulation combined with surgical stress may increase susceptibility, yet large-scale data on incidence, risk factors, and outcomes remain limited.

We conducted a retrospective analysis of the ACS-NSQIP database (2008–2022). Adult female patients undergoing breast cancer surgery were included. The primary outcome was ACS-NSQIP-coded 30-day postoperative sepsis; septic shock was analyzed separately and descriptively.

Multivariable logistic regression was used to evaluate factors associated with postoperative sepsis. Model performance was assessed by area under the receiver operating characteristic curve (AUC) with bootstrap correction for optimism and compared with machine-learning approaches trained with explicit handling of class imbalance. Among 328,292 patients (47.8% partial, 38.6% simple, 13.6% radical mastectomy), sepsis occurred in 838 (0.26%), with incidence increasing across procedure categories (0.08% vs.

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