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Open Access 01-12-2022 | Research

Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture

Authors: Nitchanant Kitcharanant, Pojchong Chotiyarnwong, Thiraphat Tanphiriyakun, Ekasame Vanitcharoenkul, Chantas Mahaisavariya, Wichian Boonyaprapa, Aasis Unnanuntana

Published in: BMC Geriatrics | Issue 1/2022

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Abstract

Background

Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture.

Methods

This retrospective study included patients with fragility hip fractures from a single center (Siriraj Hospital, Bangkok, Thailand) from July 2016 to October 2018. A total of 492 patients were enrolled. They were randomly categorized into a training group (344 cases, 70%) or a testing group (148 cases, 30%). Various machine learning techniques were used: the Gradient Boosting Classifier (GB), Random Forests Classifier (RF), Artificial Neural Network Classifier (ANN), Logistic Regression Classifier (LR), Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), and K-Nearest Neighbors Classifier (KNN). All models were internally validated by evaluating their performance and the area under a receiver operating characteristic curve (AUC).

Results

For the testing dataset, the accuracies were GB model = 0.93, RF model = 0.95, ANN model = 0.94, LR model = 0.91, NB model = 0.89, SVM model = 0.90, and KNN model = 0.90. All models achieved high AUCs that ranged between 0.81 and 0.99. The RF model also provided a negative predictive value of 0.96, a positive predictive value of 0.93, a specificity of 0.99, and a sensitivity of 0.68.

Conclusions

Our machine learning approach facilitated the successful development of an accurate model to predict 1-year mortality after fragility hip fracture. Several machine learning algorithms (eg, Gradient Boosting and Random Forest) had the potential to provide high predictive performance based on the clinical parameters of each patient. The web application is available at www.​hipprediction.​com. External validation in a larger group of patients or in different hospital settings is warranted to evaluate the clinical utility of this tool.

Trial registration

Thai Clinical Trials Registry (22 February 2021; reg. no. TCTR20210222003).
Appendix
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Literature
10.
go back to reference Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, NY: Springer; 2009.CrossRef Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, NY: Springer; 2009.CrossRef
23.
go back to reference Lo C-L, Yang Y-H, Hsu C-J, Chen C-Y, Huang W-C, Tang P-L, et al. Development of a Mortality Risk Model in Elderly Hip Fracture Patients by Different Analytical Approaches. Appl Sci. 2020;10:6787.CrossRef Lo C-L, Yang Y-H, Hsu C-J, Chen C-Y, Huang W-C, Tang P-L, et al. Development of a Mortality Risk Model in Elderly Hip Fracture Patients by Different Analytical Approaches. Appl Sci. 2020;10:6787.CrossRef
29.
go back to reference White BL, Fisher WD, Laurin CA. Rate of mortality for elderly patients after fracture of the hip in the 1980’s. J Bone Joint Surg Am. 1987;69:1335–40.CrossRef White BL, Fisher WD, Laurin CA. Rate of mortality for elderly patients after fracture of the hip in the 1980’s. J Bone Joint Surg Am. 1987;69:1335–40.CrossRef
33.
go back to reference Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011;12:2825–30. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011;12:2825–30.
34.
go back to reference Bergstra J, Bengio Y. Random Search for Hyper-Parameter Optimization. J Mach Learn Res. 2012;13:281–305. Bergstra J, Bengio Y. Random Search for Hyper-Parameter Optimization. J Mach Learn Res. 2012;13:281–305.
39.
go back to reference Handoll HH, Farrar MJ, McBirnie J, Tytherleigh-Strong G, Milne AA, Gillespie WJ. Heparin, low molecular weight heparin and physical methods for preventing deep vein thrombosis and pulmonary embolism following surgery for hip fractures. Cochrane Database Syst Rev. 2002:Cd000305. https://doi.org/10.1002/14651858.cd000305. Handoll HH, Farrar MJ, McBirnie J, Tytherleigh-Strong G, Milne AA, Gillespie WJ. Heparin, low molecular weight heparin and physical methods for preventing deep vein thrombosis and pulmonary embolism following surgery for hip fractures. Cochrane Database Syst Rev. 2002:Cd000305. https://​doi.​org/​10.​1002/​14651858.​cd000305.
45.
go back to reference Malmgen H, Borga M, Niklasson L. Artificial Neural Networks in Medicine and Biology. New York, NY: Springer; 2000.CrossRef Malmgen H, Borga M, Niklasson L. Artificial Neural Networks in Medicine and Biology. New York, NY: Springer; 2000.CrossRef
52.
go back to reference Webb GI. Naive Bayes. In: Sammut C, Webb GI, editors. Encyclopedia of Machine Learning. Boston: Springer; 2010. p. 713–4. Webb GI. Naive Bayes. In: Sammut C, Webb GI, editors. Encyclopedia of Machine Learning. Boston: Springer; 2010. p. 713–4.
54.
go back to reference Vapnik V. The Nature of Statistical Learning Theory. 2nd ed. Red Bank: Springer; 2000.CrossRef Vapnik V. The Nature of Statistical Learning Theory. 2nd ed. Red Bank: Springer; 2000.CrossRef
55.
go back to reference Raghavendra NS, Deka PC. Support vector machine applications in the field of hydrology: a review. Appl Soft Comput. 2014;19:372–86.CrossRef Raghavendra NS, Deka PC. Support vector machine applications in the field of hydrology: a review. Appl Soft Comput. 2014;19:372–86.CrossRef
Metadata
Title
Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture
Authors
Nitchanant Kitcharanant
Pojchong Chotiyarnwong
Thiraphat Tanphiriyakun
Ekasame Vanitcharoenkul
Chantas Mahaisavariya
Wichian Boonyaprapa
Aasis Unnanuntana
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Geriatrics / Issue 1/2022
Electronic ISSN: 1471-2318
DOI
https://doi.org/10.1186/s12877-022-03152-x

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