Conventional statistics are based on a simple cause-and-effect principle. Postoperative complications, however, have a multifactorial and interrelated etiology. The application of artificial intelligence might be more accurate to predict postoperative outcomes. The objective of this study was to determine the current quality of studies describing the use of artificial intelligence in predicting complications in patients undergoing major abdominal surgery. A literature search was performed in PubMed, Embase, and Web of Science. Inclusion criteria were (1) empirical studies including patients undergoing (2) any type of gastrointestinal surgery, including hepatopancreaticobiliary surgery, whose (3) complications or mortality were predicted with the use of (4) any artificial intelligence system. Studies were screened for description of method of validation and testing in methodology. Outcome measurements were sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. From a total of 1,537 identified articles, 15 were included for the review. Among a large variety of algorithms used by the included studies, sensitivity was between 0.06 and 0.96, specificity was between 0.61 and 0.98, accuracy was between 0.78 and 0.95, and area under the receiver operating characteristic curve varied between 0.50 and 0.96. Artificial intelligence algorithms have the ability to accurately predict postoperative complications. Nevertheless, algorithms should be properly tested and validated, both internally and externally. Furthermore, a complete database and the absence of unsampled imbalanced data are absolute prerequisites for algorithms to predict accurately.

Introduction: The variety of robotic surgery systems, training modalities, and assessment tools within robotic surgery training is extensive. This systematic review aimed to comprehensively overview different training modalities and assessment methods for teaching and assessing surgical skills in robotic surgery, with a specific focus on comparing objective and subjective assessment methods. Methods: A systematic review was conducted following the PRISMA guidelines. The electronic databases Pubmed, EMBASE, and Cochrane were searched from inception until February 1, 2022. Included studies consisted of robotic-assisted surgery training (e.g., box training, virtual reality training, cadaver training and animal tissue training) with an assessment method (objective or subjective), such as assessment forms, virtual reality scores, peer-to-peer feedback or time recording. Results: The search identified 1591 studies. After abstract screening and full-texts examination, 209 studies were identified that focused on robotic surgery training and included an assessment tool. The majority of the studies utilized the da Vinci Surgical System, with dry lab training being the most common approach, followed by the da Vinci Surgical Skills Simulator. The most frequently used assessment methods included simulator scoring system (e.g., dVSS score), and assessment forms (e.g., GEARS and OSATS). Conclusion: This systematic review provides an overview of training modalities and assessment methods in robotic-assisted surgery. Dry lab training on the da Vinci Surgical System and training on the da Vinci Skills Simulator are the predominant approaches. However, focused training on tissue handling, manipulation, and force interaction is lacking, despite the absence of haptic feedback. Future research should focus on developing universal objective assessment and feedback methods to address these limitations as the field continues to evolve.

The International Study Group of Pancreatic Surgery 4-tier (ie, A-D) risk classification for postoperative pancreatic fistula grade B/C is based on pancreatic texture and pancreatic duct size: A (not-soft texture and pancreatic duct >3 mm), B (not-soft texture and pancreatic duct ≤3 mm), C (soft texture and pancreatic duct >3 mm), and D (soft texture and pancreatic duct ≤3 mm). This study aimed to validate the International Study Group of Pancreatic Surgery risk classification for postoperative pancreatic fistula after pancreatoduodenectomy. Consecutive patients after pancreatoduodenectomy for all indications (2014-2021) were included from the nationwide, mandatory Dutch Pancreatic Cancer Audit. The rate of postoperative pancreatic fistula grade B/C (according to the International Study Group of Pancreatic Surgery 2016 definition) was calculated per risk category. Model performance was assessed using the area under the receiver operating curve (discrimination) and calibration plots. Overall, 3,900 patients were included in risk categories: A (n = 1,046), B (n = 498), C (n = 963), and D (n = 1,393) with corresponding postoperative pancreatic fistula grade B/C rates of 3.8%, 12.2%, 15.6%, and 29.6%. Per category, the in-hospital mortality rates were 1.3%, 3.4%, 2.9%, and 4.1%, P = .001. There was no difference in the rate of postoperative pancreatic fistula between risk categories B and C (12.2% vs 15.6%, P = .101). When simplifying the classification system to a 3-tier classification system (based on 0, 1, and 2 risk factors), the discrimination was not significantly different (area under the receiver operating curve 0.697 vs area under the receiver operating curve 0.701, P = .077). This validation of the 4-tier International Study Group of Pancreatic Surgery risk classification for postoperative pancreatic fistula after pancreatoduodenectomy confirmed its predictive value. However, as the 2 middle risk categories provide no added predictive value, a simplified 3-tier classification with comparable predictive value is proposed and should be validated in future prospective studies.

Distinction of pancreatic ductal adenocarcinoma (PDAC) in the head of the pancreas, distal cholangiocarcinoma (dCCA), and benign periampullary conditions, is complex as they often share similar clinical symptoms. However, these diseases require specific management strategies, urging improvement of non-invasive tools for accurate diagnosis. Recent evidence has shown that the ratio between CA19-9 and bilirubin levels supports diagnostic distinction of benign or malignant hepatopancreaticobiliary diseases. Here, we investigate the diagnostic value of this ratio in PDAC, dCCA and benign diseases of the periampullary region in a novel fashion. To address this aim, we enrolled 265 patients with hepatopancreaticobiliary diseases and constructed four logistic regression models on a subset of patients (n = 232) based on CA19-9, bilirubin and the ratio of both values: CA19-9/(bilirubin⁻¹). Non-linearity was investigated using restricted cubic splines and a final model, the ‘Model Ratio’, based on these three variables was fitted using multivariable fractional polynomials. The performance of this model was consistently superior in terms of discrimination and calibration compared to models based on CA19-9 combined with bilirubin and CA19-9 or bilirubin alone. The ‘Model Ratio’ accurately distinguished between malignant and benign disease (AUC [95% CI], 0.91 [0.86–0.95]), PDAC and benign disease (AUC 0.91 [0.87–0.96]) and PDAC and dCCA (AUC 0.83 [0.74–0.92]) which was confirmed by internal validation using 1000 bootstrap replicates. These findings provide a foundation to improve minimally-invasive diagnostic procedures, ultimately ameliorating effective therapy for PDAC and dCCA.

Machine learning is increasingly advocated to develop prediction models for postoperative complications. It is, however, unclear if machine learning is superior to logistic regression when using structured clinical data. Postoperative pancreatic fistula and delayed gastric emptying are the two most common complications with the biggest impact on patient condition and length of hospital stay after pancreatoduodenectomy. This study aimed to compare the performance of machine learning and logistic regression in predicting pancreatic fistula and delayed gastric emptying after pancreatoduodenectomy. This retrospective observational study used nationwide data from 16 centers in the Dutch Pancreatic Cancer Audit between January 2014 and January 2021. The area under the curve of a machine learning and logistic regression model for clinically relevant postoperative pancreatic fistula and delayed gastric emptying were compared. Overall, 799 (16.3%) patients developed a postoperative pancreatic fistula, and 943 developed (19.2%) delayed gastric emptying. For postoperative pancreatic fistula, the area under the curve of the machine learning model was 0.74, and the area under the curve of the logistic regression model was 0.73. For delayed gastric emptying, the area under the curve of the machine learning model and logistic regression was 0.59. Machine learning did not outperform logistic regression modeling in predicting postoperative complications after pancreatoduodenectomy.

Perioperative care in colorectal surgery is systematically defined in the Enhanced Recovery After Surgery (ERAS) protocol. The ERAS protocol improves perioperative care in a multimodal way to enhance early and safe release from the hospital. Adequate compliance to the elements of the ERAS protocol is multifactorial. There are still opportunities to improve compliance of the protocol by actively involving the patient. The main objective of this study is to investigate whether compliance of selected items in the ERAS protocol can be improved through actively involving patients in the ERAS care pathway through the use of a patient-centred mobile application. A multicentre randomized controlled trial will be conducted. Patients undergoing elective colorectal surgery, who are 18 years or older and in possession of an eligible smartphone, will be included. Patients assigned to the intervention group will install a patient-centred mobile application to be guided through the ERAS care pathway. Patients in the control group will receive care as usual. Both groups will wear an activity tracker. The primary outcome is overall compliance to selected active elements of the ERAS protocol, as registered by the patient. Secondary outcomes include Patient Reported Outcome Measures (PROMs) such as health-related quality of life, physical activity, and patient satisfaction of received care. Care-related outcomes, such as length of hospital stay, number of complications, re-intervention, and readmission rates, will also be assessed. The enrolment of patients will start in the second quarter of 2019. Data collection had not begun by the time this protocol was submitted. We hypothesize that by providing patients with a patient-centred mobile application, compliance to the active elements of ERAS protocol can be improved, resulting in an increased health-related quality of life, physical activity, and patient satisfaction. Netherlands Trial Register, NTR7314 , prospectively registered on the 9th of November 2017 ( http://www.trialregister.nl ).