Digital Health & Innovation, Amgen Inc, Thousand Oaks, CA, United States.
Global Medical Operations, Amgen Inc, Thousand Oaks, CA, United States.
J Med Internet Res. 2020 Oct 16;22(10):e22550. doi: 10.2196/22550.
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.
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.
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.
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.
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.
骨质疏松症和低骨量导致的骨折很常见，会给患者带来显著的临床、个人和经济负担。即使发生了骨折，高骨折风险仍然广泛未被诊断和治疗不足。常见的骨折风险评估工具利用了临床风险因素的子集进行预测，并且通常需要手动输入数据。此外，这些工具预测的是长期风险，并且不能明确提供识别未来 1-2 年内可能发生骨折的患者所需的短期风险估计。
本研究旨在开发和评估一种用于识别随后 1-2 年内骨折风险患者的算法。为了解决当前预测工具的上述局限性，该方法侧重于短期时间范围、自动化数据输入以及使用纵向数据为预测提供信息。
使用来自超过 100 万患者的回顾性电子健康记录数据，我们开发了 Crystal Bone 算法，该算法应用自然语言处理技术从患者病史的时间性质来生成短期骨折风险预测。类似于语言模型预测给定句子中的下一个单词或文档的主题，Crystal Bone 预测患者的未来轨迹是否可能包含骨折事件，或者患者的就诊轨迹是否与典型的未来骨折患者相似。使用包含 192590 名患者的验证集来验证准确性。实验基线模型和人类水平表现用于比较。
该模型准确预测了年龄在 50 岁以上的患者 1-2 年内的骨折风险（受试者工作特征曲线下面积 [AUROC] 0.81）。这些算法优于实验基线（AUROC 0.67），并且与通过正确识别未接受任何预防骨骼健康相关医疗干预的医生的 13765 名高危患者中的 9649 名（70%）相比，显示出了有意义的改进。
这些发现表明，使用患者随时间变化的独特病史来预测短期骨折风险是可能的。在医疗保健系统中验证和应用这样的工具可以实现这种风险的自动和广泛预测，并可能有助于识别骨折风险极高的患者。
