Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England.
Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England.
复制标题
DOI：
10.2147/por.s424098
复制
发表时间：
2023
影响因子：
8.9
通讯作者：
中科院分区：
文献类型：
--
作者：
改进对哮喘恶化的准确风险评估，并通过改变哮喘患者的相关行为来减少风险，可以挽救生命并降低医疗成本。我们开发了一个简单的个性化哮喘恶化风险预测模型，使用从常规医疗数据中收集的因素，用于自动对话系统的风险建模功能。我们使用了来自英国临床实践研究数据链(CPRD)Aurum数据库的化名初级保健电子保健记录。我们使用Logistic回归将年龄、性别、种族、多次剥夺指数、地理区域和与哮喘事件相关的临床变量等变量结合起来预测哮喘加重。我们将1,203,741名患者分成三组进行时间验证：训练样本中的898,763名(74.7%)，测试样本中的226,754名(18.8%)，验证样本中的78,224名(6.5%)。全模型ROC曲线下面积(AUC)为0.72，限制性模型ROC曲线下面积为0.71。与所有患者都被视为高危患者的策略相比，使用0.1的分界点，每100名患者中约有27名临床医生对哮喘的审查将被预防。与无急性加重的患者相比，急性加重的患者年龄更大，女性更多，在过去12个月中服用SABA和ICS的次数更多，有GORD、COPD、焦虑、抑郁的病史，生活在贫困地区，疾病更严重。使用从常规收集的电子医疗记录数据中获得的信息，我们开发了一个模型，该模型具有中等能力，将自索引日期起3个月内哮喘恶化的患者与没有哮喘恶化的患者区分开来。当将该模型与带有可通过WhatsApp聊天机器人轻松自我报告的变量的简化模型进行比较时，我们已经表明该模型的预测性能并没有本质上的不同。
Improving accurate risk assessment of asthma exacerbations, and reduction via relevant behaviour change among people with asthma could save lives and reduce health care costs. We developed a simple personalised risk prediction model for asthma exacerbations using factors collected in routine healthcare data for use in a risk modelling feature for automated conversational systems. We used pseudonymised primary care electronic healthcare records from the Clinical Practice Research Datalink (CPRD) Aurum database in England. We combined variables for prediction of asthma exacerbations using logistic regression including age, gender, ethnicity, Index of Multiple Deprivation, geographical region and clinical variables related to asthma events. We included 1,203,741 patients divided into three cohorts to implement temporal validation: 898,763 (74.7%) in the training sample, 226,754 (18.8%) in the testing sample and 78,224 (6.5%) in the validation sample. The Area under the ROC curve (AUC) for the full model was 0.72 and for the restricted model was 0.71. Using a cut-off point of 0.1, approximately 27 asthma reviews by clinicians per 100 patients would be prevented compared with a strategy that all patients are regarded as high risk. Compared with patients without an exacerbation, patients who exacerbated were older, more likely to be female, prescribed more SABA and ICS in the preceding 12 months, have history of GORD, COPD, anxiety, depression, live in very deprived areas and have more severe disease. Using information available from routinely collected electronic healthcare record data, we developed a model that has moderate ability to separate patients who had an asthma exacerbation within 3 months from their index date from patients who did not. When comparing this model with a simplified model with variables that can easily be self-reported through a WhatsApp chatbot, we have shown that the predictive performance of the model is not substantially different.