CC BY-NC-ND 4.0 · Revista Chilena de Ortopedia y Traumatología 2021; 62(03): e180-e192
DOI: 10.1055/s-0041-1740232
Artículo Original | Original Article

2020 SCHOT Research Award: Development and Validation of a Multivariable Prediction Model of Hospital Stay in Elderly Chilean Patients Undergoing Elective Total Hip Arthroplasty Using Machine Learning

Article in several languages: español | English
Claudio Díaz-Ledezma
1  Unidad de Cirugía Ortopédica y Traumatología, Hospital El Carmen Dr. Luis Valentin Ferrada, Santiago, Chile
2  Departamento de Ortopedia y Traumatología, Clínica Las Condes, Santiago, Chile
David Díaz-Solís
3  Departamento de Administracion, Facultad de Economia y Negocios, Universidad de Chile, Santiago, Chile
Raúl Muñoz-Reyes
4  Data scientist, independent researcher, Santiago, Chile
Jonathan Torres Castro
5  Equipo de Cirugía de Cadera, Clínica RedSalud Santiago, Santiago, Chile
6  Equipo de Cirugía de Cadera, Instituto Traumatológico de Santiago, Santiago, Chile
› Author Affiliations


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 percentage of poverty 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 free-access 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.

Publication History

Received: 18 March 2021

Accepted: 06 August 2021

Publication Date:
22 December 2021 (online)

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