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Predictive model for a second hip fracture occurrence using natural language processing and machine learning on electronic health records
Larrainzar-Garijo, Ricardo Fernández-Tormos, Esther Collado-Escudero, Carlos Alberto Alcantud Ibáñez, María Oñorbe-San Francisco, Fernando Marin-Corral, Judith Casadevall, David Donaire-Gonzalez, David Martínez-Sanchez, Luisa Cabal-Hierro, Lucia
Larrainzar-Garijo, Ricardo
Fernández-Tormos, Esther
Collado-Escudero, Carlos Alberto
Alcantud Ibáñez, María
Oñorbe-San Francisco, Fernando
Marin-Corral, Judith
Casadevall, David
Donaire-Gonzalez, David
Martínez-Sanchez, Luisa
Cabal-Hierro, Lucia
Abstract
Hip fractures (HFx) are associated with a higher morbidity and mortality rates, leading to a significant reduction in life quality and in limitation of patient´s mobility. The present study aimed to obtain real-world evidence on the clinical characteristics of patients with an initial and a second hip fracture (HFx) and develop a predictive model for second HFx using artificial intelligence. Electronic health records from one hospital centre in Spain from January 2011 to December 2019 were analysed using EHRead® technology, based on natural language processing and machine learning. A total of 1,960 patients with HFx were finally included during the study period after meeting all inclusion and exclusion criteria. From this total, 1835 (93.6%) patients were included in the HFx subgroup, while 124 (6.4%) were admitted to the second HFx (2HFx) subgroup. The mean age of the participants was 84 years and 75.5% were female. Most of comorbidities were more frequently identified in the HFx group, including hypertension (72.0% vs. 67.2%), cognitive impairment (33.0% vs. 31.2%), diabetes mellitus (28.7% vs. 24.8%), heart failure (27.6% vs. 22.4%) and chronic kidney disease (26.9% vs. 16.0%). Based on clinical criteria, 26 features were selected as potential prediction factors. From there, 16 demographics and clinical characteristics such as comorbidities, medications, measures of disabilities for ambulation and type of refracture were selected for development of a competitive risk model. Specifically, those predictors with different associated risk ratios, sorted from higher to lower risk relevance were visual deficit, malnutrition, walking assistance, hypothyroidism, female sex, osteoporosis treatment, pertrochanteric fracture, dementia, age at index, osteoporosis, renal failure, stroke, COPD, heart disease, anaemia, and asthma. This model showed good performance (dependent AUC: 0.69; apparent performance: 0.75) and could help the identification of patients with higher risk of developing a second HFx, allowing preventive measures. This study expands the current available information of HFx patients in Spain and identifies factors that exhibit potential in predicting a second HFx among older patients.
Keywords
Date
2024
Type
Journal article
Journal
Scientific Reports
Book
Volume
14
Issue
1
Page Range
1-10
Article Number
Article 532
ACU Department
Mary MacKillop Institute for Health Research
Faculty of Health Sciences
Faculty of Health Sciences
Collections
Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open
Notes
© The Author(s) 2024.
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