To investigate the predict effect of machine learning on POI underwent laparoscopic colorectal cancer surgery, we conducted a retrospective observational study at Nanjing Medical University Affiliated Suzhou Hospital.
We performed a retrospective analysis of consecutive patients aged 18 years or older who underwent laparoscopic colorectal surgery for malignant lesions from April 2016 to January 2017. Exclusion criteria were patients who underwent surgery other than laparoscopic colorectal surgery, converted to open surgery, robot-assisted laparoscopic colorectal surgery, and parenteral nutrition surgery. POI was defined as flatulence and/or fecal pass delay or oral intake intolerance on the third day after surgery and confirmed with radiographs that small and/or large intestinal dilatation on abdominal X-ray films.
Anesthesia and operation management
The surgeries were performed by six different surgeons, each with more than 200 cases of surgical experiences in laparoscopic colorectal surgery. Laparoscopic surgery includes single incision and conventional laparoscopic colorectal surgery. Anesthesia techniques were similar in all cases. There was no thoracic epidural analgesia. Intravenous midazolam，sufentanil, propofol, and rocuronium were applied for induction of anesthesia, providing neuromuscular blockage for endotracheal intubation. Anesthesia was maintained with propofol, remifentanil, and sevoflurane. Opioids were routinely administered for postoperative pain 30 minutes prior to the end of surgery.
Several studies have shown that the risk factors for development of POI in patients who underwent colorectal surgery including the age, open approach, difficulty in operation, operative duration more than 3 hours, American Society of Anesthesiologists scores (ASA) 3 to 4, low-hematocrit and transfusion . Therefore, we collected clinical data on 27 variables included the categories of demographics, social habits, comorbidities, intraoperative situation and postoperative management (Table 1). All doses of opioids were converted to equivalent intravenous morphine. The age-adjusted Charlson Comorbidity Index (ACCI), which has better predictive effects on hospital mortality and adverse events than other versions, was used to assess comorbidity. Events such as postoperative wound dehiscence were also recorded. The types of variables are shown in Table 2.
Four different algorithms were considered: logistic regression, decision tree, random forest, gradient boosting decision tree (Parameters see in Table 3). 637 cases were randomly split into a training (80%) and a testing (20%) data sets. The 20 times repeated to find the optimal hyper-parameters with a bootstrap procedure was performed in the training data set. The reason for using this method was its very low variance which appropriate to choose the goal between models . Then models with the optimal hyper-parameters were run in the training set and used to predict the risk of cases in the testing data set.
The missing data were pre-processed using a nonparametric imputation method based on random forest that is good at coping with non-linear relations and complex interactions . Meanwhile the categorical variables (ASA, type of surgery, operator, type of anesthesia) were transform into dummy variables.
The predictive performance was based on the area under ROC (receiver operator characteristic) curve (AUC), precision, recall and F1-score in testing data set. The drawing of AUC takes the false positive rate as x-axis and true positive rate as y-axis. Precision indicates the probability of the correctly predicted positive samples among all the samples predicted as positive samples. Recall indicates the probability of correctly predicted positive samples among all the original positive samples. And F1-score is the harmonic average of precision and recall, F1 = 2rp / (r + p), where r is recall and p is precision. Finally, the variable importance was calculated for each risk factor by the optimal model. The flowchart of this study for prediction of risk factors of POI is showed in Figure 1.