Journal of plastic, reconstructive & aesthetic surgery : JPRAS · 2025
Artificial intelligence prediction model for readmission after DIEP flap breast reconstruction based on NSQIP data.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Ozmen Berk B, Phuyal Diwakar, Berber Ibrahim, Schwarz Graham S
Аннотация
BACKGROUND: Readmissions following deep inferior epigastric perforator (DIEP) flap breast reconstruction represent a significant healthcare burden, yet current risk prediction methods lack precision in identifying high-risk patients. We developed a machine learning model to predict 30-day readmission risk using a large national surgical quality database.
METHODS: This retrospective analysis examined 13,312 DIEP flap procedures from the American College of Surgeons National Surgical Quality Improvement Program database (2016-2022). A stacked machine learning model was developed incorporating patient demographics, comorbidities, operative characteristics, and laboratory values. Model performance was assessed using accuracy, precision, recall, and F1 score.
RESULTS: The overall readmission rate was 5.42% (723 patients). The stacked model achieved 88% accuracy and 79% recall for readmission prediction with an area under the receiver operating characteristic curve of 0.8921 (95% CI: 0.853-0.927) on the test set. Key predictors included days from operation until superficial incisional surgical site infection complications, operative time, body mass index, and preoperative albumin.
CONCLUSION: This stacked machine learning approach demonstrates strong predictive capability for post-DIEP flap readmissions, with high sensitivity for identifying at-risk patients. The model's performance suggests clinical utility in preoperative risk stratification and resource allocation. Implementation could enable targeted intervention strategies to potentially reduce readmission rates in high-risk populations.
METHODS: This retrospective analysis examined 13,312 DIEP flap procedures from the American College of Surgeons National Surgical Quality Improvement Program database (2016-2022). A stacked machine learning model was developed incorporating patient demographics, comorbidities, operative characteristics, and laboratory values. Model performance was assessed using accuracy, precision, recall, and F1 score.
RESULTS: The overall readmission rate was 5.42% (723 patients). The stacked model achieved 88% accuracy and 79% recall for readmission prediction with an area under the receiver operating characteristic curve of 0.8921 (95% CI: 0.853-0.927) on the test set. Key predictors included days from operation until superficial incisional surgical site infection complications, operative time, body mass index, and preoperative albumin.
CONCLUSION: This stacked machine learning approach demonstrates strong predictive capability for post-DIEP flap readmissions, with high sensitivity for identifying at-risk patients. The model's performance suggests clinical utility in preoperative risk stratification and resource allocation. Implementation could enable targeted intervention strategies to potentially reduce readmission rates in high-risk populations.