Academic Journal
Early prediction of functional impairment at hospital discharge in patients with osteoporotic vertebral fracture: a machine learning approach
| Title: | Early prediction of functional impairment at hospital discharge in patients with osteoporotic vertebral fracture: a machine learning approach |
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| Authors: | Soichiro Masuda, Toshiki Fukasawa, Shoichiro Inokuchi, Bungo Otsuki, Koichi Murata, Takayoshi Shimizu, Takashi Sono, Shintaro Honda, Koichiro Shima, Masaki Sakamoto, Shuichi Matsuda, Koji Kawakami |
| Source: | Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024) |
| Publisher Information: | Nature Portfolio, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Medicine LCC:Science |
| Subject Terms: | Osteoporotic vertebral fracture, OVF, Prediction model, Functional impairment, Activities of daily living, Machine learning, Medicine, Science |
| More Details: | Abstract Although conservative treatment is commonly used for osteoporotic vertebral fracture (OVF), some patients experience functional disability following OVF. This study aimed to develop prediction models for new-onset functional impairment following admission for OVF using machine learning approaches and compare their performance. Our study consisted of patients aged 65 years or older admitted for OVF using a large hospital-based database between April 2014 and December 2021. As the primary outcome, we defined new-onset functional impairment as a Barthel Index ≤ 60 at discharge. In the training dataset, we developed three machine learning models (random forest [RF], gradient-boosting decision tree [GBDT], and deep neural network [DNN]) and one conventional model (logistic regression [LR]). In the test dataset, we compared the predictive performance of these models. A total of 31,306 patients were identified as the study cohort. In the test dataset, all models showed good discriminatory ability, with an area under the curve (AUC) greater than 0.7. GBDT (AUC = 0.761) outperformed LR (0.756), followed by DNN (0.755), and RF (0.753). We successfully developed prediction models for new-onset functional impairment following admission for OVF. Our findings will contribute to effective treatment planning in this era of increasing prevalence of OVF. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-82359-x |
| Access URL: | https://doaj.org/article/f3b68075a5aa48e1b54031b177cfc69c |
| Accession Number: | edsdoj.f3b68075a5aa48e1b54031b177cfc69c |
| Database: | Directory of Open Access Journals |
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| ISSN: | 20452322 |
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| DOI: | 10.1038/s41598-024-82359-x |
| Published in: | Scientific Reports |
| Language: | English |