Challenges of Modeling Outcomes for Surgical Infections: A Word of Caution

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.

Artificial Intelligence Applications in Space Medicine

INTRODUCTION:During future interplanetary space missions, a number of health conditions may arise, owing to the hostile environment of space and the myriad of stressors experienced by the crew. When managing these conditions, crews will be required to make accurate, timely clinical decisions at a high level of autonomy, as telecommunication delays and increasing distances restrict real-time support from the ground. On Earth, artificial intelligence (AI) has proven successful in healthcare, augmenting expert clinical decision-making or enhancing medical knowledge where it is lacking. Similarly, deploying AI tools in the context of a space mission could improve crew self-reliance and healthcare delivery.METHODS: We conducted a narrative review to discuss existing AI applications that could improve the prevention, recognition, evaluation, and management of the most mission-critical conditions, including psychological and mental health, acute radiation sickness, surgical emergencies, spaceflight-associated neuro-ocular syndrome, infections, and cardiovascular deconditioning.RESULTS: Some examples of the applications we identified include AI chatbots designed to prevent and mitigate psychological and mental health conditions, automated medical imaging analysis, and closed-loop systems for hemodynamic optimization. We also discuss at length gaps in current technologies, as well as the key challenges and limitations of developing and deploying AI for space medicine to inform future research and innovation. Indeed, shifts in patient cohorts, space-induced physiological changes, limited size and breadth of space biomedical datasets, and changes in disease characteristics may render the models invalid when transferred from ground settings into space.Cheung HC, De Louche C, Komorowski M. Artificial intelligence applications in space medicine. Aerosp Med Hum Perform. 2023; 94(8):610-622.

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 ).

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.