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European Journal of Trauma and Emergency Surgery
Published in: European Journal of Trauma and Emergency Surgery 6/2022

Open Access 29-05-2022 | Original Article

Development and internal validation of a clinical prediction model using machine learning algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above

Authors: Jacobien Hillina Froukje Oosterhoff, Angelique Berit Marte Corlijn Savelberg, Aditya Vishwas Karhade, Benjamin Yaël Gravesteijn, Job Nicolaas Doornberg, Joseph Hasbrouck Schwab, Marilyn Heng

Published in: European Journal of Trauma and Emergency Surgery | Issue 6/2022

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Abstract

Purpose

Preoperative prediction of mortality in femoral neck fracture patients aged 65 years or above may be valuable in the treatment decision-making. A preoperative clinical prediction model can aid surgeons and patients in the shared decision-making process, and optimize care for elderly femoral neck fracture patients. This study aimed to develop and internally validate a clinical prediction model using machine learning (ML) algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above.

Methods

A retrospective cohort study at two trauma level I centers and three (non-level I) community hospitals was conducted to identify patients undergoing surgical fixation for a femoral neck fracture. Five different ML algorithms were developed and internally validated and assessed by discrimination, calibration, Brier score and decision curve analysis.

Results

In total, 2478 patients were included with 90 day and 2 year mortality rates of 9.1% (n = 225) and 23.5% (n = 582) respectively. The models included patient characteristics, comorbidities and laboratory values. The stochastic gradient boosting algorithm had the best performance for 90 day mortality prediction, with good discrimination (c-statistic = 0.74), calibration (intercept = − 0.05, slope = 1.11) and Brier score (0.078). The elastic-net penalized logistic regression algorithm had the best performance for 2 year mortality prediction, with good discrimination (c-statistic = 0.70), calibration (intercept = − 0.03, slope = 0.89) and Brier score (0.16). The models were incorporated into a freely available web-based application, including individual patient explanations for interpretation of the model to understand the reasoning how the model made a certain prediction: https://​sorg-apps.​shinyapps.​io/​hipfracturemorta​lity/​

Conclusions

The clinical prediction models show promise in estimating mortality prediction in elderly femoral neck fracture patients. External and prospective validation of the models may improve surgeon ability when faced with the treatment decision-making.

Level of evidence

Prognostic Level II.
Appendix
Available only for authorised users
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Metadata
Title
Development and internal validation of a clinical prediction model using machine learning algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above
Authors
Jacobien Hillina Froukje Oosterhoff
Angelique Berit Marte Corlijn Savelberg
Aditya Vishwas Karhade
Benjamin Yaël Gravesteijn
Job Nicolaas Doornberg
Joseph Hasbrouck Schwab
Marilyn Heng
Publication date
29-05-2022
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Trauma and Emergency Surgery / Issue 6/2022
Print ISSN: 1863-9933
Electronic ISSN: 1863-9941
DOI
https://doi.org/10.1007/s00068-022-01981-4

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