Australasian ACPGBI risk prediction model for 30-day mortality after colorectal cancer surgery

Critical Analysis of the Causes of In-Hospital Mortality following Colorectal Resection: A Queensland Audit of Surgical Mortality (QASM) Registry Study

Background

Colorectal resection is a major gastrointestinal operation. Improvements in peri-operative care has led to improved outcomes; however, mortalities still occur. Using data from the Queensland Audit of Surgical Mortality (QASM), this study examines the demographic and clinical characteristics of patients who died in hospital following colorectal resection, and also reports the primary cause of death in this population.

Methods

Patients who died in hospital following colorectal resection in Queensland between January 2010 and December 2020 were identified from the QASM database.

Results

There were 755 patients who died in the 10 year study period. Pre-operatively, the risk of death as subjectively determined by operating surgeons was ‘considerable’ in 397 cases (53.0%) and ‘expected’ in 90 cases (12.0%). The patients had a mean of 2.7 (±1.5) co-morbidities, and a mean American Society of Anaesthesiologists (ASA) score of 3.6 (±0.8). Operations were categorised as emergency in 579 patients (77.2%), with 637 patients (85.0%) requiring post-operative Intensive Care Unit (ICU) support. The primary cause of death was related to a surgical cause in 395 patients (52.7%) and to a medical cause in 355 patients (47.3%). The primary causes of death were advanced surgical pathology (n=292, 38.9%), complications from surgery (n=103, 13.7%), complications arising from pre-existing medical co-morbidity (n=282, 37.6%) or new medical complications unrelated to pre-existing conditions (n=73, 9.7%).

Conclusions

Patients who died had significant co-morbidities and often presented emergently with an advanced surgical pathology. Surgical and medical causes of death both contributed equally to the mortality burden.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00268-022-06534-9.

Data analytics and artificial intelligence in predicting length of stay, readmission, and mortality: a population-based study of surgical management of colorectal cancer

Data analytics and artificial intelligence (AI) have been used to predict patient outcomes after colorectal cancer surgery. A prospectively maintained colorectal cancer database was used, covering 4336 patients who underwent colorectal cancer surgery between 2003 and 2019. The 47 patient parameters included demographics, peri- and post-operative outcomes, surgical approaches, complications, and mortality. Data analytics were used to compare the importance of each variable and AI prediction models were built for length of stay (LOS), readmission, and mortality. Accuracies of at least 80% have been achieved. The significant predictors of LOS were age, ASA grade, operative time, presence or absence of a stoma, robotic or laparoscopic approach to surgery, and complications. The model with support vector regression (SVR) algorithms predicted the LOS with an accuracy of 83% and mean absolute error (MAE) of 9.69 days. The significant predictors of readmission were age, laparoscopic procedure, stoma performed, preoperative nodal (N) stage, operation time, operation mode, previous surgery type, LOS, and the specific procedure. A BI-LSTM model predicted readmission with 87.5% accuracy, 84% sensitivity, and 90% specificity. The significant predictors of mortality were age, ASA grade, BMI, the formation of a stoma, preoperative TNM staging, neoadjuvant chemotherapy, curative resection, and LOS. Classification predictive modelling predicted three different colorectal cancer mortality measures (overall mortality, and 31- and 91-days mortality) with 80-96% accuracy, 84-93% sensitivity, and 75-100% specificity. A model using all variables performed only slightly better than one that used just the most significant ones.