Background Fractures as a result of osteoporosis and low bone mass are common and give rise to significant clinical, personal, and economic burden. Even after a fracture occurs, high fracture risk remains widely underdiagnosed and undertreated. Common fracture risk assessment tools utilize a subset of clinical risk factors for prediction, and often require manual data entry. Furthermore, these tools predict risk over the long term and do not explicitly provide short-term risk estimates necessary to identify patients likely to experience a fracture in the next 1-2 years. Objective The goal of this study was to develop and evaluate an algorithm for the identification of patients at risk of fracture in a subsequent 1- to 2-year period. In order to address the aforementioned limitations of current prediction tools, this approach focused on a short-term timeframe, automated data entry, and the use of longitudinal data to inform the predictions. Methods Using retrospective electronic health record data from over 1,000,000 patients, we developed Crystal Bone, an algorithm that applies machine learning techniques from natural language processing to the temporal nature of patient histories to generate short-term fracture risk predictions. Similar to how language models predict the next word in a given sentence or the topic of a document, Crystal Bone predicts whether a patient’s future trajectory might contain a fracture event, or whether the signature of the patient’s journey is similar to that of a typical future fracture patient. A holdout set with 192,590 patients was used to validate accuracy. Experimental baseline models and human-level performance were used for comparison. Results The model accurately predicted 1- to 2-year fracture risk for patients aged over 50 years (area under the receiver operating characteristics curve [AUROC] 0.81). These algorithms outperformed the experimental baselines (AUROC 0.67) and showed meaningful improvements when compared to retrospective approximation of human-level performance by correctly identifying 9649 of 13,765 (70%) at-risk patients who did not receive any preventative bone-health-related medical interventions from their physicians. Conclusions These findings indicate that it is possible to use a patient’s unique medical history as it changes over time to predict the risk of short-term fracture. Validating and applying such a tool within the health care system could enable automated and widespread prediction of this risk and may help with identification of patients at very high risk of fracture.
BACKGROUND Fractures due to osteoporosis and low bone mass are common and give rise to significant clinical, personal, and economic burden. Even after a fracture occurs, high fracture risk remains widely underdiagnosed and undertreated. Common fracture risk assessment tools utilize a subset of clinical risk factors for prediction, and often require manual data entry. Furthermore, these tools predict risk over the long term, and do not explicitly provide short-term risk estimates necessary to identify patients likely to experience a fracture in the next 1-2 years. OBJECTIVE The goal of this study was to develop and evaluate an algorithmic approach to the identification of patients at risk of fracture in the next 1-2 years. In order to address the aforementioned limitations of current prediction tools, this approach focuses on a short-term timeframe, automated data entry, and the use of longitudinal data to inform the predictions. METHODS Using electronic health record (EHR) data, we developed Crystal Bone, a method that applies machine learning techniques from Natural Language Processing to the temporal nature of patient histories to generate short-term fracture risk predictions. Similar to how language models predict the next word in a given sentence or the topic of a document, Crystal Bone predicts whether a patient’s future trajectory might contain a fracture event, or whether the “signature” of the patient’s journey is similar to that of a typical future fracture patient. RESULTS The proposed models accurately predict fracture risk in the next 1-2 years for patients aged over 50 years (area under the receiver operating characteristics curve [AUROC]=0.81). These algorithms outperform the experimental baselines (AUROC=0.67) and have shown meaningful improvements when compared to a retrospective approximation of human-level performance, such as correctly identifying 70% of at-risk patients who did not receive any preventative bone-health-related medical interventions from their physicians. CONCLUSIONS These findings indicate that it is possible to use a patient’s unique medical history as it changes over time to predict the risk of short-term fracture. Validating and applying such a tool within the healthcare system could enable automated and widespread prediction of this risk and may help with identification of patients at very high risk of fracture.
Fragility fractures due to osteoporosis are common and are associated with significant clinical, personal, and economic burden. Even after a fragility fracture, osteoporosis remains widely underdiagnosed and undertreated. Common fracture risk assessment tools, such as FRAX1 and Garvan,2 confer risk over the long term but do not provide short-term risk estimates necessary to identify very high-risk patients likely to fracture in the next 1–2 years. Furthermore, these tools utilize cross-sectional data representing a subset of all available clinical risk factors for risk prediction. Thus, these methods are generalized across patient populations and may not fully utilize patient histories commonly found in electronic health records (EHRs) that contain temporal information for thousands of unique features. The Optum® de-identified EHR dataset (2007–2018) provides an opportunity to use historical medical data to generate short-term, personalized fracture risk predictions for individual patients. We used the Optum® dataset to develop Crystal Bone, a method that applies machine learning techniques commonly used in natural language processing to the temporal nature of patient histories in order to predict fracture risk over a 1- to 2-year timeframe. Specifically, we repurposed deep-learning models typically applied to language-based prediction tasks in which the goal is to learn the meanings of words and sentences to classify them. Crystal Bone uses context-based embedding techniques to learn an equivalent “semantic” meaning of various medical events. Similar to how language models predict the next word in a given sentence or the topic of an overall document, Crystal Bone can predict that a patient’s future trajectory may contain a fracture or that the “signature” of the patient’s overall journey is similar to that of a typical fracture patient. We applied Crystal Bone to two datasets, one enriched for fracture patients and one representative of a typical hospital system. In both datasets, when predicting likelihood of fracture in the next 1–2 years, Crystal Bone had an area under the receiver operating characteristic (AUROC) score ranging from 72% to 83% on a test (hold-out) dataset. These results suggest performance similar to that of FRAX and Garvan, which have 10-year fracture risk prediction AUROC scores of 64.4% +/- 3.7%.3 Our results suggest that it is possible to use each patient’s unique medical history as it changes over time to predict patients at risk for fracture in 1–2 years. Furthermore, it is theoretically possible to integrate a model like Crystal Bone directly into an EHR system, enabling “hands-off” fracture risk prediction, which could lead to improved identification of patients at very high risk for fracture. 1Kanis JA, Osteoporos Int. 2012;23:2239–56. 2Rubin KH, J Bone Miner Res. 2013;28:1701–17. 3Leslie WD, Osteoporos Int. 2014;25:1–21.
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