Premio de Investigación SCHOT 2020: desarrollo y validación de un modelo multivariables de predicción de estadía hospitalaria en pacientes mayores de 65 años sometidos artroplastia total de cadera electiva en Chile utilizando aprendizaje de máquinas / 2020 SCHOT Research award: Development an a multivariable prediction model of hospital stay in elderly chilean patients undergoing elective total hip arthroplasty using machine learning

INTRODUCCIÓN La predicción de la estadía hospitalaria luego de una artroplastia total de cadera (ATC) electiva es crucial en la evaluación perioperatoria de los pacientes, con un rol determinante desde el punto de vista operacional y económico. Internacionalmente, se han empleado macrodatos (big data, en inglés) e inteligencia artificial para llevar a cabo evaluaciones pronósticas de este tipo. El objetivo del presente estudio es desarrollar y validar, con el empleo del aprendizaje de máquinas (machine learning, en inglés), una herramienta capaz de predecir la estadía hospitalaria de pacientes chilenos mayores de 65 años sometidos a ATC por artrosis. MATERIALES Y MÉTODOS Empleando los registros electrónicos de egresos hospitalarios anonimizados del Departamento de Estadísticas e Información de Salud (DEIS), se obtuvieron los datos de 8.970 egresos hospitalarios de pacientes sometidos a ATC por artrosis entre los años 2016 y 2018. En total, 15 variables disponibles en el DEIS, además del porcentaje de pobreza de la comuna de origen del paciente, fueron incluidos para predecir la probabilidad de que un paciente presentara una estadía acortada (< 3 días) o prolongada (> 3 días) luego de la cirugía. Utilizando técnicas de aprendizaje de máquinas, 8 algoritmos de predicción fueron entrenados con el 80% de la muestra. El 20% restante se empleó para validar las capacidades predictivas de los modelos creados a partir de los algoritmos. La métrica de optimización se evaluó y ordenó en un ranking utilizando el área bajo la curva de característica operativa del receptor (area under the receiver operating characteristic curve, AUC-ROC, en inglés), que corresponde a cuan bien un modelo puede distinguir entre dos grupos. RESULTADOS El algoritmo XGBoost obtuvo el mejor desempeño, con una AUC-ROC promedio de 0,86 (desviación estándar [DE]: 0,0087). En segundo lugar, observamos que el algoritmo lineal de máquina de vector de soporte (support vector machine, SVM, en inglés) obtuvo una AUC-ROC de 0,85 (DE: 0,0086). La importancia relativa de las variables explicativas demostró que la región de residencia, el servicio de salud, el establecimiento de salud donde se operó el paciente, y la modalidad de atención son las variables que más determinan el tiempo de estadía de un paciente. DISCUSIÓN El presente estudio desarrolló algoritmos de aprendizaje de máquinas basados en macrodatos chilenos de libre acceso, y logró desarrollar y validar una herramienta que demuestra una adecuada capacidad discriminatoria para predecir la probabilidad de estadía hospitalaria acortada versus prolongada en adultos mayores sometidos a ATC por artrosis. CONCLUSIÓN Los algoritmos creados a traves del empleo del aprendizaje de máquinas permiten predecir la estadía hospitalaria en pacientes chilenos operado de artroplastia total de cadera electiva

Introduction The prediction of the length of hospital stay after elective total hip arthroplasty (THA) is crucial in the perioperative evaluation of the patients, and it plays a decisive role from the operational and economic point of view. Internationally, big data and artificial intelligence have been used to perform prognostic evaluations of this type. The present study aims to develop and validate, through the use of artificial intelligence (machine learning), a tool capable of predicting the hospital stay of patients over 65 years of age undergoing THA for osteoarthritis. Material and Methods Using the electronic records of hospital discharges de-identified from the Department of Health Statistics and Information (Departamento de Estadísticas e Información de Salud, DEIS, in Spanish), the data of 8,970 hospital discharges of patients who had undergone THA for osteoarthritis between 2016 and 2018 were obtained. A total of 15 variables available in the DEIS registry, in addition to the poverty rate in the patient's borough of origin were included to predict the probability that a patient would have a shortened (< 3 days) or prolonged (> 3 days) stay after surgery. By using machine learning techniques, 8 prediction algorithms were trained with 80% of the sample. The remaining 20% was used to validate the predictive capabilities of the models created from the algorithms. The optimization metric was evaluated and ranked using the area under the receiver operating characteristic curve (AUC-ROC), which corresponds to how well a model can distinguish between two groups. Results The XGBoost algorithm had the best performance, with an average AUC-ROC of 0.86 (standard deviation [SD]: 0.0087). Secondly, we observed that the linear support vector machine (SVM) algorithm obtained an AUC-ROC of 0.85 (SD: 0.0086). The relative importance of the explanatory variables showed that the region of residence, the administrative health service, the hospital where the patient was operated on, and the care modality are the variables that most determine the length of stay. Discussion The present study developed machine learning algorithms based on freeaccess Chilean big data, which helped create and validate a tool that demonstrates an adequate discriminatory capacity to predict shortened versus prolonged hospital stay in elderly patients undergoing elective THA. Conclusion The algorithms created through the use of machine learning allow to predict the hospital stay in Chilean patients undergoing elective total hip arthroplasty Introduction The prediction of the length of hospital stay after elective total hip arthroplasty (THA) is crucial in the perioperative evaluation of the patients, and it plays a decisive role from the operational and economic point of view. Internationally, big data and artificial intelligence have been used to perform prognostic evaluations of this type. The present study aims to develop and validate, through the use of artificial intelligence (machine learning), a tool capable of predicting the hospital stay of patients over 65 years of age undergoing THA for osteoarthritis. Material and Methods Using the electronic records of hospital discharges de-identified from the Department of Health Statistics and Information (Departamento de Estadísticas e Información de Salud, DEIS, in Spanish), the data of 8,970 hospital discharges of patients who had undergone THA for osteoarthritis between 2016 and 2018 were obtained. A total of 15 variables available in the DEIS registry, in addition to the poverty rate in the patient's borough of origin were included to predict the probability that a patient would have a shortened (< 3 days) or prolonged (> 3 days) stay after surgery. By using machine learning techniques, 8 prediction algorithms were trained with 80% of the sample. The remaining 20% was used to validate the predictive capabilities of the models created from the algorithms. The optimization metric was evaluated and ranked using the area under the receiver operating characteristic curve (AUC-ROC), which corresponds to how well a model can distinguish between two groups. Results The XGBoost algorithm had the best performance, with an average AUC-ROC of 0.86 (standard deviation [SD]: 0.0087). Secondly, we observed that the linear

Protrusio acetabular en artroplastía total de cadera: diez tácticas para un buen resultado quirúrgico / Total hip arthroplasty in protrusio acetabuli: ten tips to improve surgical outcomes

Los pacientes candidatos a artroplastía total de cadera con protrusio acetabular asociada generan distintos desafíos en los equipos quirúrgicos. Múltiples estrategias han sido utilizadas a lo largo de los años para optimizar los resultados. Mediante una revisión de la evidencia actualizada disponible, proponemos diez tácticas a realizar en el manejo de estos pacientes que pueden mejorar y hacer predecible el tratamiento de un paciente con protrusio acetabular al que se le realiza una artroplastía total de cadera. Nivel de Evidencia V.

Patients with acetabular protrusio and osteoarthritis are a challenge for the surgical team. Many strategies have been developed to anticipate, plan and optimize the surgical results of these patients. Based on the current available clinical evidence, we propose ten tips to improve the surgical management of hip arthroplasty patients with protrusio acetabuli. Level of Evidence V.

Aloinjertos óseos y la función del banco de huesos / Bone allografts and the functions of bone banks

The use of bone grafts is a common practice in musculoskeletal surgery to provide mechanical stability where there is a defect and it allows skeletal reconstruction. Classically auto and allografts have been used. The latter are the choice in large, complex defects. Allografts can be transplanted despite cell death, have osteoconduction and osteoinduction capacity, low antigenicity and biomechanica lproperties similar to the original bone. They can be obtained from living and death donors. They are stored by cryopreservation and lyophilization in entities called bone banks. This is a review about bone allografts and the organization and function of the bone banks.