Palliative pelvic exenteration: A systematic review of patient-centered outcomes

Objective: Palliative pelvic exenteration (PPE) is a technically complex operation with high morbidity and mortality rates, considered in patients with limited life expectancy. There is little evidence to guide practice. We performed a systematic review to evaluate the impact of PPE on symptom relief and quality of life (QoL). Methods: A systematic review was conducted according to the PRISMA guidelines using Ovid MEDLINE, EMBASe, and PubMed databases for studies reporting on outcomes of PPE for symptom relief or QoL. Descriptive statistics were used on pooled patient cohorts. Results: Twenty-three historical cohorts and case series were included, comprising 509 patients. No comparative studies were found. Most malignancies were of colorectal, gynaecological and urological origin. Common indications for PPE were pain, symptomatic fistula, bleeding, malodour, obstruction and pelvic sepsis. The pooled median postoperative morbidity rate was 53.6% (13\u2013100%), the median in-hospital mortality was 6.3% (0\u201366.7%), and median OS was 14 months (4\u201340 months). Some symptom relief was reported in a median of 79% (50\u2013100%) of the patients, although the magnitude of effect was poorly measured. Data for QoL measures were inconclusive. Five studies discouraged performing PPE in any patient, while 18 studies concluded that the procedure can be considered in highly selected patients. Conclusion: Available evidence on PPE is of low-quality. Morbidity and mortality rates are high with a short median OS interval. While some symptom relief may be afforded by this procedure, evidence for improvement in QoL is limited. A highly selective individualised approach is required to optimise the risk:benefit equation

Predicting outcomes of pelvic exenteration using machine learning

Aim: We aim to compare machine learning with neural network performance in predicting R0 resection (R0), length of stay > 14 days (LOS), major complication rates at 30 days postoperatively (COMP) and survival greater than 1 year (SURV) for patients having pelvic exenteration for locally advanced and recurrent rectal cancer. Method: A deep learning computer was built and the programming environment was established. The PelvEx Collaborative database was used which contains anonymized data on patients who underwent pelvic exenteration for locally advanced or locally recurrent colorectal cancer between 2004 and 2014. Logistic regression, a support vector machine and an artificial neural network (ANN) were trained. Twenty per cent of the data were used as a test set for calculating prediction accuracy for R0, LOS, COMP and SURV. Model performance was measured by plotting receiver operating characteristic (ROC) curves and calculating the area under the ROC curve (AUROC). Results: Machine learning models and ANNs were trained on 1147 cases. The AUROC for all outcome predictions ranged from 0.608 to 0.793 indicating modest to moderate predictive ability. The models performed best at predicting LOS > 14 days with an AUROC of 0.793 using preoperative and operative data. Visualized logistic regression model weights indicate a varying impact of variables on the outcome in question. Conclusion: This paper highlights the potential for predictive modelling of large international databases. Current data allow moderate predictive ability of both complex ANNs and more classic methods