Clinical Sciences, Liverpool School of Tropical Medicine, and Respiratory Medicine, Royal Liverpool Hospital, Liverpool, United Kingdom.
Academic Primary Care, University of Aberdeen, Aberdeen, United Kingdom; Observational and Pragmatic Research Institute Pte Ltd, Singapore.
J Allergy Clin Immunol Pract. 2017 Jul-Aug;5(4):1015-1024.e8. doi: 10.1016/j.jaip.2016.11.007. Epub 2016 Dec 22.
Asthma attacks are common, serious, and costly. Individual factors associated with attacks, such as poor symptom control, are not robust predictors.
We investigated whether the rich data available in UK electronic medical records could identify patients at risk of recurrent attacks.
We analyzed anonymized, longitudinal medical records of 118,981 patients with actively treated asthma (ages 12-80 years) and 3 or more years of data. Potential risk factors during 1 baseline year were evaluated using univariable (simple) logistic regression for outcomes of 2 or more and 4 or more attacks during the following 2-year period. Predictors with significant univariable association (P < .05) were entered into multiple logistic regression analysis with backward stepwise selection of the model including all significant independent predictors. The predictive accuracy of the multivariable models was assessed.
Independent predictors associated with future attacks included baseline-year markers of attacks (acute oral corticosteroid courses, emergency visits), more frequent reliever use and health care utilization, worse lung function, current smoking, blood eosinophilia, rhinitis, nasal polyps, eczema, gastroesophageal reflux disease, obesity, older age, and being female. The number of oral corticosteroid courses had the strongest association. The final cross-validated models incorporated 19 and 16 risk factors for 2 or more and 4 or more attacks over 2 years, respectively, with areas under the curve of 0.785 (95% CI, 0.780-0.789) and 0.867 (95% CI, 0.860-0.873), respectively.
Routinely collected data could be used proactively via automated searches to identify individuals at risk of recurrent asthma attacks. Further research is needed to assess the impact of such knowledge on clinical prognosis.
哮喘发作较为常见，且较为严重、花费较高。发作相关的个体因素，如症状控制不佳，不能很好地预测发作。
我们研究英国电子病历中丰富的数据是否能识别出有复发性发作风险的患者。
我们分析了 118981 例有积极治疗的哮喘（年龄 12-80 岁）且有 3 年以上数据的患者匿名、纵向的医疗记录。使用单变量（简单）逻辑回归分析在基线 1 年内的潜在危险因素，评估以下 2 年期间发生 2 次或以上及 4 次或以上发作的结局。单变量分析中与结果有显著关联（P<.05）的预测因子被纳入多变量逻辑回归分析，模型采用向后逐步选择，包括所有显著的独立预测因子。评估多变量模型的预测准确性。
与未来发作相关的独立预测因子包括基线年发作的标志物（急性口服皮质类固醇疗程、急诊就诊）、更频繁地使用缓解药物和卫生保健资源、更差的肺功能、当前吸烟、血嗜酸性粒细胞增多、鼻炎、鼻息肉、湿疹、胃食管反流病、肥胖、年龄较大和女性。口服皮质类固醇疗程数与发作的关联最强。最终的交叉验证模型分别纳入了 19 个和 16 个 2 年期间发生 2 次或以上及 4 次或以上发作的风险因素，曲线下面积分别为 0.785（95%CI，0.780-0.789）和 0.867（95%CI，0.860-0.873）。
可通过自动搜索，使用常规收集的数据积极识别有复发性哮喘发作风险的个体。需要进一步研究评估此类知识对临床预后的影响。
