Coaching your faculty and yourself.

Surgical skills vary drastically among practicing surgeons. This variation in skill has been demonstrated to translate directly into patient outcomes, highlighting the importance of skill development. Despite this, directed efforts to improve surgical skills and performance among practicing surgeons remain limited. The development of surgical coaching programs offers an exciting opportunity for surgeon performance improvement and lifelong development. In this article, we will discuss the promise of surgical coaching programs, some of the challenges met when developing a program, and future avenues and opportunities for growth within the field.

Factors Associated With Performing IPAA After Total Colectomy for Ulcerative Colitis.

BACKGROUND: IPAA is considered the procedure of choice for restorative surgery after total colectomy for ulcerative colitis. Previous studies have examined the rate of IPAA within individual states but not at the national level in the United States. OBJECTIVE: This study aimed to assess the rate of IPAA after total colectomy for ulcerative colitis in a national population and identify factors associated with IPAA. DESIGN: This was a retrospective cohort study. SETTINGS: This study was performed in the United States. PATIENTS: Patients who were aged 18 years or older and who underwent total colectomy between 2009 and 2019 for a diagnosis of ulcerative colitis were identified within a commercial database. This database excluded patients with public insurance, including all patients older than 65 years with Medicare. MAIN OUTCOME MEASURES: The primary outcome was IPAA. Multivariable logistic regression was used to assess the association between covariates and the likelihood of undergoing IPAA. RESULTS: In total, 2816 patients were included, of whom 1414 (50.2%) underwent IPAA, 928 (33.0%) underwent no further surgery, and 474 (16.8%) underwent proctectomy with end ileostomy. Younger age, lower comorbidities, elective case, and laparoscopic approach in the initial colectomy were significantly associated with IPAA but socioeconomic status was not. LIMITATIONS: This retrospective study included only patients with commercial insurance. CONCLUSIONS: A total of 50.2% of patients who had total colectomy for ulcerative colitis underwent IPAA, and younger age, lower comorbidities, and elective cases are associated with a higher rate of IPAA placement. This study emphasizes the importance of ensuring follow-up with colorectal surgeons to provide the option of restorative surgery, especially for patients undergoing urgent or emergent colectomies. See Video Abstract . FACTORES ASOCIADOS CON LA REALIZACIN DE ANASTOMOSIS ANALBOLSA ILEAL DESPUS DE UNA COLECTOMA TOTAL POR COLITIS ULCEROSA: ANTECEDENTES:La anastomosis ileo-anal se considera el procedimiento de elección para la cirugía reparadora tras la colectomía total por colitis ulcerosa. Estudios previos han examinado la tasa de anastomosis ileo-anal dentro de los estados individuales, pero no a nivel nacional en los Estados Unidos.OBJETIVO:Evaluar la tasa de anastomosis bolsa ileal-anal después de la colectomía total para la colitis ulcerosa en una población nacional e identificar los factores asociados con la anastomosis bolsa ileal-anal.DISEÑO:Se trata de un estudio de cohortes retrospectivo.LUGAR:Este estudio se realizó en los Estados Unidos.PACIENTES:Los pacientes que tenían ≥18 años de edad que se sometieron a colectomía total entre 2009 y 2019 para un diagnóstico de colitis ulcerosa fueron identificados dentro de una base de datos comercial. Esta base de datos excluyó a los pacientes con seguro público, incluidos todos los pacientes >65 años con Medicare.MEDIDAS DE RESULTADO PRINCIPALES:El resultado primario fue la anastomosis ileal bolsa-anal. Se utilizó una regresión logística multivariable para evaluar la asociación entre las covariables y la probabilidad de someterse a una anastomosis ileal.RESULTADOS:En total, se incluyeron 2.816 pacientes, de los cuales 1.414 (50,2%) se sometieron a anastomosis ileo-anal, 928 (33,0%) no se sometieron a ninguna otra intervención quirúrgica y 474 (16,8%) se sometieron a proctectomía con ileostomía terminal. La edad más joven, las comorbilidades más bajas, el caso electivo, y el abordaje laparoscópico en la colectomía inicial se asociaron significativamente con la anastomosis ileal bolsa-anal, pero no el estatus socioeconómico.LIMITACIONES:Este estudio retrospectivo incluyó sólo pacientes con seguro comercial.CONCLUSIONES:Un 50,2% de los pacientes se someten a anastomosis ileo-anal y la edad más joven, las comorbilidades más bajas y los casos electivos se asocian con una mayor tasa de colocación de anastomosis ileo-anal. Esto subraya la importancia de asegurar el seguimiento con cirujanos colorrectales para ofrecer la opción de cirugía reparadora, especialmente en pacientes sometidos a colectomías urgentes o emergentes. (Traducción-Dr. Yolanda Colorado ).

Prediction of Ureteral Injury During Colorectal Surgery Using Machine Learning.

BACKGROUND: Ureteral injury (UI) is a rare but devastating complication during colorectal surgery. Ureteral stents may reduce UI but carry risks themselves. Risk predictors for UI could help target the use of stents, but previous efforts have relied on logistic regression (LR), shown moderate accuracy, and used intraoperative variables. We sought to use an emerging approach in predictive analytics, machine learning, to create a model for UI. METHODS: Patients who underwent colorectal surgery were identified in the National Surgical Quality Improvement Program (NSQIP) database. Patients were split into training, validation, and test sets. The primary outcome was UI. Three machine learning approaches were tested including random forest (RF), gradient boosting (XGB), and neural networks (NN), and compared with traditional LR. Model performance was assessed using area under the curve (AUROC). RESULTS: The data set included 262,923 patients, of whom 1519 (.578%) experienced UI. Of the modeling techniques, XGB performed the best, with an AUROC score of .774 (95% CI .742-.807) compared with .698 (95% CI .664-.733) for LR. Random forest and NN performed similarly with scores of .738 and .763, respectively. Type of procedure, work RVUs, indication for surgery, and mechanical bowel prep showed the strongest influence on model predictions. CONCLUSIONS: Machine learning-based models significantly outperformed LR and previous models and showed high accuracy in predicting UI during colorectal surgery. With proper validation, they could be used to support decision making regarding the placement of ureteral stents preoperatively.

Improved Prediction of Surgical-Site Infection After Colorectal Surgery Using Machine Learning.

BACKGROUND: Surgical-site infection is a source of significant morbidity after colorectal surgery. Previous efforts to develop models that predict surgical-site infection have had limited accuracy. Machine learning has shown promise in predicting postoperative outcomes by identifying nonlinear patterns within large data sets. OBJECTIVE: This study aimed to seek usage of machine learning to develop a more accurate predictive model for colorectal surgical-site infections. DESIGN: Patients who underwent colorectal surgery were identified in the American College of Surgeons National Quality Improvement Program database from years 2012 to 2019 and were split into training, validation, and test sets. Machine-learning techniques included random forest, gradient boosting, and artificial neural network. A logistic regression model was also created. Model performance was assessed using area under the receiver operating characteristic curve. SETTINGS: A national, multicenter data set. PATIENTS: Patients who underwent colorectal surgery. MAIN OUTCOME MEASURES: The primary outcome (surgical-site infection) included patients who experienced superficial, deep, or organ-space surgical-site infections. RESULTS: The data set included 275,152 patients after the application of exclusion criteria. Of all patients, 10.7% experienced a surgical-site infection. Artificial neural network showed the best performance with area under the receiver operating characteristic curve of 0.769 (95% CI, 0.762-0.777), compared with 0.766 (95% CI, 0.759-0.774) for gradient boosting, 0.764 (95% CI, 0.756-0.772) for random forest, and 0.677 (95% CI, 0.669-0.685) for logistic regression. For the artificial neural network model, the strongest predictors of surgical-site infection were organ-space surgical-site infection present at time of surgery, operative time, oral antibiotic bowel preparation, and surgical approach. LIMITATIONS: Local institutional validation was not performed. CONCLUSIONS: Machine-learning techniques predict colorectal surgical-site infections with higher accuracy than logistic regression. These techniques may be used to identify patients at increased risk and to target preventive interventions for surgical-site infection. See Video Abstract at http://links.lww.com/DCR/C88 . PREDICCIN MEJORADA DE LA INFECCIN DEL SITIO QUIRRGICO DESPUS DE LA CIRUGA COLORRECTAL MEDIANTE EL APRENDIZAJE AUTOMTICO: ANTECEDENTES:La infección del sitio quirúrgico es una fuente de morbilidad significativa después de la cirugía colorrectal. Los esfuerzos anteriores para desarrollar modelos que predijeran la infección del sitio quirúrgico han tenido una precisión limitada. El aprendizaje automático se ha mostrado prometedor en la predicción de los resultados posoperatorios mediante la identificación de patrones no lineales dentro de grandes conjuntos de datos.OBJETIVO:Intentamos utilizar el aprendizaje automático para desarrollar un modelo predictivo más preciso para las infecciones del sitio quirúrgico colorrectal.DISEÑO:Los pacientes que se sometieron a cirugía colorrectal se identificaron en la base de datos del Programa Nacional de Mejoramiento de la Calidad del Colegio Estadounidense de Cirujanos de los años 2012 a 2019 y se dividieron en conjuntos de capacitación, validación y prueba. Las técnicas de aprendizaje automático incluyeron conjunto aleatorio, aumento de gradiente y red neuronal artificial. También se creó un modelo de regresión logística. El rendimiento del modelo se evaluó utilizando el área bajo la curva característica operativa del receptor.CONFIGURACIÓN:Un conjunto de datos multicéntrico nacional.PACIENTES:Pacientes intervenidos de cirugía colorrectal.PRINCIPALES MEDIDAS DE RESULTADO:El resultado primario (infección del sitio quirúrgico) incluyó pacientes que experimentaron infecciones superficiales, profundas o del espacio de órganos del sitio quirúrgico.RESULTADOS:El conjunto de datos incluyó 275.152 pacientes después de la aplicación de los criterios de exclusión. El 10,7% de los pacientes presentó infección del sitio quirúrgico. La red neuronal artificial mostró el mejor rendimiento con el área bajo la curva característica operativa del receptor de 0,769 (IC del 95 %: 0,762 - 0,777), en comparación con 0,766 (IC del 95 %: 0,759 - 0,774) para el aumento de gradiente, 0,764 (IC del 95 %: 0,756 - 0,772) para conjunto aleatorio y 0,677 (IC 95% 0,669 - 0,685) para regresión logística. Para el modelo de red neuronal artificial, los predictores más fuertes de infección del sitio quirúrgico fueron la infección del sitio quirúrgico del espacio del órgano presente en el momento de la cirugía, el tiempo operatorio, la preparación intestinal con antibióticos orales y el abordaje quirúrgico.LIMITACIONES:No se realizó validación institucional local.CONCLUSIONES:Las técnicas de aprendizaje automático predicen infecciones del sitio quirúrgico colorrectal con mayor precisión que la regresión logística. Estas técnicas se pueden usar para identificar a los pacientes con mayor riesgo y para orientar las intervenciones preventivas para la infección del sitio quirúrgico. Consulte Video Resumen en http://links.lww.com/DCR/C88 . (Traducción-Dr Yolanda Colorado ).