Development and validation of machine learning models to predict perioperative transfusion risk for hip fractures in the elderly.
Ann Med
; 56(1): 2357225, 2024 Dec.
Article
en En
| MEDLINE
| ID: mdl-38902847
ABSTRACT
BACKGROUND:
Patients with hip fractures frequently need to receive perioperative transfusions of concentrated red blood cells due to preoperative anemia or surgical blood loss. However, the use of perioperative blood products increases the risk of adverse events, and the shortage of blood products is prompting us to minimize blood transfusion. Our study aimed to construct a machine learning algorithm predictive model to identify patients at high risk for perioperative transfusion early in hospital admission and to manage their patient blood to reduce transfusion requirements.METHODS:
This study collected patients hospitalized for hip fractures at a university hospital from May 2016 to November 2022. All patients included in the analysis were randomly divided into a training set and validation set according to 7030. Eight machine learning algorithms, CART, GBM, KNN, LR, NNet, RF, SVM, and XGBoost, were used to construct the prediction models. The models were evaluated for discrimination, calibration, and clinical utility, and the best prediction model was selected.RESULTS:
A total of 805 patients were included in the study, of whom 306 received transfusions during the perioperative period. We screened eight features used to construct the prediction model age, fracture time, fracture type, hemoglobin, albumin, creatinine, calcium ion, and activated partial thromboplastin time. After evaluating and comparing the performance of each of the eight models, the model constructed by the XGBoost algorithm had the best performance, with MCC values of 0.828 and 0.939 in the training and validation sets, respectively. In addition, it had good calibration and clinical utility in both the training and validation sets.CONCLUSION:
The model constructed by the XGBoost algorithm has the best performance, using this model to identify patients at high risk for transfusion early in their admission and promptly incorporating them into a patient blood management plan can help reduce the risk of transfusion.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Transfusión Sanguínea
/
Aprendizaje Automático
/
Fracturas de Cadera
Límite:
Aged
/
Aged80
/
Female
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Humans
/
Male
Idioma:
En
Revista:
Ann Med
Asunto de la revista:
MEDICINA
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Reino Unido