The efficacy of the American College of Surgeons Surgical Risk Calculator in the prediction of postoperative complications in oncogeriatric patients after curative surgery for abdominal tumors

Assessment of the American College of Surgeons Surgical Risk Calculator (ACS-SRC) for Prediction of Early Postoperative Complications in Patients Undergoing Cytoreductive Surgery for Ovarian Peritoneal Carcinomatosis

Ovarian cancer (OC) is diagnosed at a locally advanced stage in two-thirds of cases. The first line of treatment consists of cytoreductive surgery (CRS) combined with neoadjuvant and/or adjuvant chemotherapy. However, CRS can be associated with high rates of postoperative complications (POCs), and detection of fragile patients at high risk of POCs is important. The American College of Surgeons Surgical Risk Calculator (ACS-SRC) provides a predictive model for early POCs (30 days) for any given surgical procedure. This study aimed to evaluate the performance of the ACS-SRC in predicting the occurrence of early POCs for patients undergoing CRS for OC. This was a retrospective study that included patients undergoing CRS for advanced OC between January 2010 and December 2022. Early POCs were reviewed, and the rate of POCs was compared with those predicted by the ACS-SRC to evaluate its accuracy (i.e., discrimination and calibration). A total of 218 patients were included, 112 of whom underwent extensive surgery/resection. A total of 94 complications were recorded. This cohort demonstrated correct calibration of the ACS-SRC for the prediction of surgical site infection, readmission, and the need for nursing care post-discharge (NCPD; transfer to revalidation center or need for nursing care at home). Using both the discrimination and calibration methods, the score only predicted NCPD. In this study, the ACS-SRC was shown to be of little value for patients undergoing cytoreductive surgery for ovarian peritoneal carcinomatosis, as it only accurately predicted NCPD.

Explainable Machine Learning Model to Preoperatively Predict Postoperative Complications in Inpatients With Cancer Undergoing Major Operations

PURPOSE

Preoperative prediction of postoperative complications (PCs) in inpatients with cancer is challenging. We developed an explainable machine learning (ML) model to predict PCs in a heterogenous population of inpatients with cancer undergoing same-hospitalization major operations.

METHODS

Consecutive inpatients who underwent same-hospitalization operations from December 2017 to June 2021 at a single institution were retrospectively reviewed. The ML model was developed and tested using electronic health record (EHR) data to predict 30-day PCs for patients with Clavien-Dindo grade 3 or higher (CD 3+) per the CD classification system. Model performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), and calibration plots. Model explanation was performed using the Shapley additive explanations (SHAP) method at cohort and individual operation levels.

RESULTS

A total of 988 operations in 827 inpatients were included. The ML model was trained using 788 operations and tested using a holdout set of 200 operations. The CD 3+ complication rates were 28.6% and 27.5% in the training and holdout test sets, respectively. Training and holdout test sets' model performance in predicting CD 3+ complications yielded an AUROC of 0.77 and 0.73 and an AUPRC of 0.56 and 0.52, respectively. Calibration plots demonstrated good reliability. The SHAP method identified features and the contributions of the features to the risk of PCs.

CONCLUSION

We trained and tested an explainable ML model to predict the risk of developing PCs in patients with cancer. Using patient-specific EHR data, the ML model accurately discriminated the risk of developing CD 3+ complications and displayed top features at the individual operation and cohort level.