Department of Public Health Sciences, Seoul National University, Seoul, Korea.
Department of Orthopedic Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Clin Orthop Surg. 2023 Aug;15(4):678-689. doi: 10.4055/cios22240. Epub 2023 May 26.
Nonsteroidal anti-inflammatory drugs (NSAID) are currently among the most prescribed medications worldwide to relieve pain and reduce inflammation, especially in patients suffering osteoarthritis (OA). However, NSAIDs are known to have adverse effects on the gastrointestinal system. If a gastric ulcer occurs, planned OA treatment needs to be changed, incurring additional treatment costs and causing discomfort for both patients and clinicians. Therefore, it is necessary to create a gastric ulcer prediction model that can reflect the detailed health status of each individual and to use it when making treatment plans.
Using sample cohort data from 2008 to 2013 from the National Health Insurance Service in South Korea, we developed a prediction model for NSAID-induced gastric ulcers using machine-learning algorithms and investigated new risk factors associated with medication and comorbidities.
The population of the study consisted of 30,808 patients with OA who were treated with NSAIDs between 2008 and 2013. After a 2-year follow-up, these patients were divided into two groups: without gastric ulcer (n=29,579) and with gastric ulcer (n=1,229). Five machine-learning algorithms were used to develop the prediction model, and a gradient boosting machine (GBM) was selected as the model with the best performance (area under the curve, 0.896; 95% confidence interval, 0.883-0.909). The GBM identified 5 medications (loxoprofen, aceclofenac, talniflumate, meloxicam, and dexibuprofen) and 2 comorbidities (acute upper respiratory tract infection [AURI] and gastroesophageal reflux disease) as important features. AURI did not have a dose-response relationship, so it could not be interpreted as a significant risk factor even though it was initially detected as an important feature and improved the prediction performance.
We obtained a prediction model for NSAID-induced gastric ulcers using the GBM method. Since personal prescription period and the severity of comorbidities were considered numerically, individual patients' risk could be well reflected. The prediction model showed high performance and interpretability, so it is meaningful to both clinicians and NSAID users.
非甾体抗炎药（NSAIDs）目前是全球应用最广泛的缓解疼痛和减轻炎症的药物之一，尤其是在患有骨关节炎（OA）的患者中。然而，NSAIDs 已知对胃肠道系统有不良反应。如果发生胃溃疡，计划的 OA 治疗需要改变，这将导致额外的治疗费用，并给患者和临床医生带来不适。因此，有必要创建一个能够反映每个人详细健康状况的胃溃疡预测模型，并在制定治疗计划时使用它。
使用韩国国家健康保险服务局 2008 年至 2013 年的样本队列数据，我们使用机器学习算法为 NSAID 诱导的胃溃疡开发了一个预测模型，并研究了与药物和合并症相关的新风险因素。
该研究人群由 2008 年至 2013 年接受 NSAIDs 治疗的 30808 名 OA 患者组成。经过 2 年的随访，这些患者被分为两组：无胃溃疡（n=29579）和有胃溃疡（n=1229）。使用了 5 种机器学习算法来开发预测模型，梯度提升机（GBM）被选为性能最佳的模型（曲线下面积，0.896；95%置信区间，0.883-0.909）。GBM 确定了 5 种药物（洛索洛芬、醋氯芬酸、托那氟酯、美洛昔康和右旋布洛芬）和 2 种合并症（急性上呼吸道感染[AURI]和胃食管反流病）作为重要特征。AURI 没有剂量反应关系，因此尽管最初被检测为重要特征并提高了预测性能，但不能将其解释为显著的风险因素。
我们使用 GBM 方法获得了 NSAID 诱导的胃溃疡预测模型。由于考虑了个人处方期和合并症的严重程度，因此可以很好地反映个体患者的风险。预测模型表现出较高的性能和可解释性，因此对临床医生和 NSAID 用户都具有重要意义。
