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

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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/hipfracturemortality/ 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.

Original languageEnglish
Pages (from-to)4669-4682
Number of pages14
JournalEuropean Journal of Trauma and Emergency Surgery
Volume48
Issue number6
Early online date29 May 2022
DOIs
Publication statusPublished - Dec 2022

Bibliographical note

Funding:
This research did not receive grants from any funding agency
in the public, commercial or not-for-proft sectors

Publisher Copyright:
© 2022, The Author(s).

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