Complication Burden and Demographic Trends in Autologous Breast Reconstruction Outcomes: Insights from the NIH All of Us Program.

BACKGROUND

While the risks and benefits of autologous breast reconstruction have been widely examined, nationally representative, longitudinal data on complication burden, flap utilization trends, and patient-level risk factors remain limited. The NIH All of Us Research Program provides an opportunity to address these gaps using a diverse, population-scale cohort.

METHODS

We identified 260 patients who underwent autologous breast reconstruction using current procedural terminology codes within the All of Us Registered Tier Dataset (1995-2025). Complications were tracked at 30 days and 1 year postoperatively. Logistic, multivariate regressions, and Kaplan-Meier analyses evaluated predictors and timing of complications. Unsupervised machine learning via k-means clustering was utilized to uncover phenotypic subgroups by age and body mass index (BMI).

RESULTS

Deep inferior epigastric perforator flap utilization increased over time, particularly among younger patients. Complication rates did not significantly differ across flap types. BMI > 32.7 kg/m2 was associated with increased 30-day complications, while age and race were not independent predictors. Chronic pain and persistent postoperative pain were the most common 1-year complications. Flap failure occurred in fewer than 2% of cases. Clustering revealed three patient subgroups with distinct complication profiles; older patients and those with higher BMI experienced greater morbidity but maintained high flap success rates.

CONCLUSION

Autologous breast reconstruction is broadly effective across diverse patient populations. Complication risk is more strongly influenced by BMI than by age or race. Chronic pain emerged as a common long-term morbidity, underscoring the need for improved detection and management efforts. The diversity, depth, and follow-up available through All of Us enable nuanced insights into reconstructive outcomes not possible with traditional datasets.