Radiomics for the prediction of a postoperative pancreatic fistula following a pancreatoduodenectomy: A systematic review and radiomic score quality assessment.

BACKGROUND: Postoperative pancreatic fistula (POPF) is a severe complication following a pancreatoduodenectomy. An accurate prediction of POPF could assist the surgeon in offering tailor-made treatment decisions. The use of radiomic features has been introduced to predict POPF. A systematic review was conducted to evaluate the performance of models predicting POPF using radiomic features and to systematically evaluate the methodological quality. METHODS: Studies with patients undergoing a pancreatoduodenectomy and radiomics analysis on computed tomography or magnetic resonance imaging were included. Methodological quality was assessed using the Radiomics Quality Score (RQS) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. RESULTS: Seven studies were included in this systematic review, comprising 1300 patients, of whom 364 patients (28 %) developed POPF. The area under the curve (AUC) of the included studies ranged from 0.76 to 0.95. Only one study externally validated the model, showing an AUC of 0.89 on this dataset. Overall adherence to the RQS (31 %) and TRIPOD guidelines (54 %) was poor. CONCLUSION: This systematic review showed that high predictive power was reported of studies using radiomic features to predict POPF. However, the quality of most studies was poor. Future studies need to standardize the methodology. REGISTRATION: not registered.

Radiomics preoperative-Fistula Risk Score (RAD-FRS) for pancreatoduodenectomy: development and external validation.

BACKGROUND: Accurately predicting the risk of clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy before surgery may assist surgeons in making more informed treatment decisions and improved patient counselling. The aim was to evaluate the predictive accuracy of a radiomics-based preoperative-Fistula Risk Score (RAD-FRS) for clinically relevant postoperative pancreatic fistula. METHODS: Radiomic features were derived from preoperative CT scans from adult patients after pancreatoduodenectomy at a single centre in the Netherlands (Amsterdam, 2013-2018) to develop the radiomics-based preoperative-Fistula Risk Score. Extracted radiomic features were analysed with four machine learning classifiers. The model was externally validated in a single centre in Italy (Verona, 2020-2021). The radiomics-based preoperative-Fistula Risk Score was compared with the Fistula Risk Score and the updated alternative Fistula Risk Score. RESULTS: Overall, 359 patients underwent a pancreatoduodenectomy, of whom 89 (25 per cent) developed a clinically relevant postoperative pancreatic fistula. The radiomics-based preoperative-Fistula Risk Score model was developed using CT scans of 118 patients, of which three radiomic features were included in the random forest model, and externally validated in 57 patients. The model performed well with an area under the curve of 0.90 (95 per cent c.i. 0.71 to 0.99) and 0.81 (95 per cent c.i. 0.69 to 0.92) in the Amsterdam test set and Verona data set respectively. The radiomics-based preoperative-Fistula Risk Score performed similarly to the Fistula Risk Score (area under the curve 0.79) and updated alternative Fistula Risk Score (area under the curve 0.79). CONCLUSION: The radiomics-based preoperative-Fistula Risk Score, which uses only preoperative CT features, is a new and promising radiomics-based score that has the potential to be integrated with hospital CT report systems and improve patient counselling before surgery. The model with underlying code is readily available via www.pancreascalculator.com and www.github.com/PHAIR-Consortium/POPF-predictor.

[Artificial Intelligence: applications for the operating room]. / Artificiële intelligentie in de operatiekamer.

The operating room nowadays is a data-rich environment to which Artificial Intelligence (AI) can respond. Current AI applications mainly focus on supporting perioperative decision-making and on improving surgical skills and safety. Specific steps need to be taken to advance the implementation of AI. Further studies are needed that focus on external validation and standardization of data and monitoring of the implementation process, as well as consensus on ethical and legal issues. In conclusion, much is expected from AI in making surgical care more efficient and safer.

Fluid management and vasopressor use during colorectal surgery: the search for the optimal balance.

BACKGROUND: Although it is known that excessive intraoperative fluid and vasopressor agents are detrimental for anastomotic healing, optimal anesthesiology protocols for colorectal surgery are currently lacking. OBJECTIVE: To scrutinize the current hemodynamic practice and vasopressor use and their relation to colorectal anastomotic leakage. DESIGN: A secondary analysis of a previously published prospective observational study: the LekCheck study. STUDY SETTING: Adult patients undergoing a colorectal resection with the creation of a primary anastomosis. OUTCOME MEASURES: Colorectal anastomotic leakage (CAL) within 30 days postoperatively, hospital length of stay and 30-day mortality. RESULTS: Of the 1548 patients, 579 (37%) received vasopressor agents during surgery. Of these, 201 were treated with solely noradrenaline, 349 were treated with phenylephrine, and 29 received ephedrine. CAL rate significantly differed between the patients receiving vasopressor agents during surgery compared to patients without (11.8% vs 6.3%, p < 0.001). CAL was significantly higher in the group receiving phenylephrine compared to noradrenaline (14.3% vs 6%, p < 0.001). Vasopressor agents were used more often in patients treated with Goal Directed Therapy (47% vs 34.6%, p < 0.001). There was a higher mortality rate in patients with vasopressors compared to the group without (2.8% vs 0.4%, p = 0.01, OR 3.8). Mortality was higher in the noradrenaline group compared to the phenylephrine and those without vasopressors (5% vs. 0.4% and 1.7%, respectively, p < 0.001). In multivariable analysis, patients with intraoperative vasopressor agents had an increased risk to develop CAL (OR 2.1, CI 1.3-3.2, p = 0.001). CONCLUSION: The present study contributes to the evidence that intraoperative use of vasopressor agents is associated with a higher rate of CAL. This study helps to create awareness on the (necessity to) use of vasopressor agents in colorectal surgery patients in striving for successful anastomotic wound healing. Future research will be required to balance vasopressor agent dosage in view of colorectal anastomotic leakage.

Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: Machine learning did not outperform logistic regression modeling in predicting postoperative complications after pancreatoduodenectomy.

Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery.

Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery.

One Decade of Declining Use of Defunctioning Stomas After Rectal Cancer Surgery in the Netherlands: Are We on the Right Track?

BACKGROUND: The beneficial effect of a defunctioning stoma in mitigating the consequences of anastomotic leakage after rectal cancer surgery is still debated. OBJECTIVE: This study aims to reflect on a decade of rectal cancer surgery in terms of stoma construction and anastomotic leakage. DESIGN: Retrospective observational study. SETTING: This study used data from the Dutch Colorectal Audit from 2011 to 2020. PATIENTS: Patients undergoing rectal cancer surgery with a primary anastomosis. MAIN OUTCOME MEASURES: Primary outcome was anastomotic leakage. Secondary outcomes were minor complications, admission to intensive care, length of stay, readmission, and patient death. RESULTS: A total of 13,263 patients were included in this study. A defunctioning stoma was constructed in 7106 patients (53.6%). Patients with a defunctioning stoma were less likely to develop anastomotic leakage (7.9% vs 13.0%), and if anastomotic leakage occurred, fewer patients needed surgical reintervention (37.7% vs 81.1%). An annual decrease in the construction of a defunctioning stoma was seen (69.8% in 2011 vs 51.8% in 2015 vs 29.7% in 2020), accompanied by a 5% increase in anastomotic leakage (9.1% in 2011 vs 14.1% in 2020). A defunctioning stoma was associated with a higher occurrence of minor complications, increased admissions to the intensive care unit, longer length of stay, and more readmissions within 90 days. LIMITATION: This retrospective study is susceptible to confounders by indications, and there could be risk factors for anastomotic leakage and the use of a stoma that were not regarded. CONCLUSIONS: The reduction in defunctioning stomas is paralleled with an increase in anastomotic leakage. However, patients with a defunctioning stoma also showed more minor complications, a prolonged length of stay, more intensive care admissions, and more readmissions. In our opinion, the trade-offs of selective use should be individually considered. See Video Abstract at http://links.lww.com/DCR/C137 . UNA DCADA DISMINUYENDO EL USO DE ESTOMAS DISFUNCIONANTES EN LOS CASOS DE CNCER DE RECTO EN HOLANDA ESTAMOS HACIENDO LO CORRECTO: ANTECEDENTES:Aún se debate el efecto benéfico de la confección de un estoma disfuncionante para limitar las consecuencias de la fuga anastomótica en los casos de cirugía por cáncer de recto.OBJETIVO:Reflexiones sobre una década de cirugía por cáncer de recto en términos de confección de estomas y de fugas anastomóticas.DISEÑO:Estudio retrospectivo y observacional.AJUSTE:El presente estudio utilizó datos de la Auditoría Colorectal Holandesa entre 2011 y 2020.PACIENTES:Todos aquellos intervenidos por cáncer de recto con anastomosis primaria.PRINCIPALES MEDIDAS DE RESULTADO:El resultado primario fue evaluar la fuga anastomótica. Los resultados secundarios fueron las complicaciones menores, la permanencia en cuidados intensivos, la duración de la hospitalización, las rehospitalizaciones y las causas de muerte en los pacientes.RESULTADOS:Un total de 13.263 pacientes fueron incluidos en el presente estudio. Se confeccionó un estoma disfuncionante en 7.106 (53,6%) pacientes. Aquellos portadores de un estoma disfuncionante tenían menos probabilidades de desarrollar una fuga anastomótica (7,9 % frente a 13,0 %) y, si ocurría una fuga anastomótica, menos pacientes necesitaban reintervención quirúrgica (37,7 % frente a 81,1 %). Se observó una disminución anual en la confección de un estoma disfuncionante (69,8 % en 2011 frente a 51,8 % en 2015 frente a 29,7 % en 2020), acompañada de un aumento del 5 % en la fuga anastomótica (9,1 % en 2011 frente a 14,1 % en 2020). Un estoma disfuncionante se asoció con una mayor incidencia de complicaciones menores, permanencia en la unidad de cuidados intensivos, una estadía más prolongada y más rehospitalizaciones dentro de los 90 días.LIMITACIÓN:Estudio retrospectivo susceptible de factores de confusión según las indicaciones, donde podrían no haber sido considerados ciertos factores de riesgo con relación a la fuga anastomótica y a la confección de un estoma disfuncionante.CONCLUSIÓN:La reducción de estomas disfuncionantes es paralela con el aumento de la fuga anastomótica. Sin embargo, los pacientes con un estoma disfuncionante también mostraron más complicaciones menores, una estadía prolongada, más admisiones a cuidados intensivos y más rehospitalizaciones. En nuestra opinión, las ventajas y desventajas del uso selectivo de estomas disfuncionantes deben ser consideradas caso por caso. Consulte Video Resumen en https://links.lww.com/DCR/C137 . (Traducción-Dr. Xavier Delgadillo ).

The prediction of surgical complications using artificial intelligence in patients undergoing major abdominal surgery: A systematic review.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.