Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using administrative claims data
Development and validation of a predictive model to identify patients at risk of severe COPD exacerbations using administrative claims data
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DOI：
10.2147/copd.s155773
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发表时间：
2018-01-01
影响因子：
2.8
通讯作者：
Kaila, Shuchita
中科院分区：
文献类型：
--
作者：
Annavarapu, Srinivas;Goldfarb, Seth;Kaila, Shuchita
背景：慢性阻塞性肺病患者经常会出现严重的病情恶化，需要住院治疗，从而加速肺功能下降并降低生活质量。本研究旨在开发和验证预测模型，以使用行政索赔数据识别有严重 COPD 恶化风险的患者，以促进适当的疾病管理计划。 方法：使用 Humana 的索赔数据在 2010 年 7 月 1 日至 2013 年 6 月 30 日期间确定的年龄为 5589 岁的 COPD 患者回顾性队列开发了预测模型。基线期为诊断后 12 个月，预测期涵盖 1224 个月。对预测期内有和没有严重加重的患者进行比较，以确定与 COPD 严重加重相关的特征。使用逐步逻辑回归开发模型，并选择最终模型来优化敏感性、特异性、阳性预测值 (PPV) 和阴性 PV (NPV)。 结果：在 45,722 名患者中，5,317 名患者在预测期内出现严重恶化。与基线期间没有严重恶化的患者相比，严重恶化的患者有显着更高的合并症负担、呼吸药物的使用和戒烟咨询。预测模型包括 29 个与严重急性加重显着相关的变量。最强的预测因素是既往严重急性加重和较高的 DeyoCharlson 合并症评分（OR 分别为 1.50 和 1.47）。表现最佳的预测模型的曲线下面积为 0.77。选择0.4的接受者操作特征截止值来优化PPV，模型的敏感性为17%，特异性为98%，PPV为48%，NPV为90%。结论：本研究发现，在预测模型确定的每两名存在严重急性加重风险的患者中，就有一名患者可能出现严重急性加重。一旦识别出高危患者，适当的维持药物、疾病管理计划的实施和教育可以防止未来病情恶化。
Background: Patients with COPD often experience severe exacerbations involving hospitalization, which accelerate lung function decline and reduce quality of life. This study aimed to develop and validate a predictive model to identify patients at risk of developing severe COPD exacerbations using administrative claims data, to facilitate appropriate disease management programs.Methods: A predictive model was developed using a retrospective cohort of COPD patients aged 5589 years identified between July 1, 2010 and June 30, 2013 using Humana's claims data. The baseline period was 12 months postdiagnosis, and the prediction period covered months 1224. Patients with and without severe exacerbations in the prediction period were compared to identify characteristics associated with severe COPD exacerbations. Models were developed using stepwise logistic regression, and a final model was chosen to optimize sensitivity, specificity, positive predictive value (PPV), and negative PV (NPV).Results: Of 45,722 patients, 5,317 had severe exacerbations in the prediction period. Patients with severe exacerbations had significantly higher comorbidity burden, use of respiratory medications, and tobaccocessation counseling compared to those without severe exacerbations in the baseline period. The predictive model included 29 variables that were significantly associated with severe exacerbations. The strongest predictors were prior severe exacerbations and higher DeyoCharlson comorbidity score (OR 1.50 and 1.47, respectively). The bestperforming predictive model had an area under the curve of 0.77. A receiver operating characteristic cutoff of 0.4 was chosen to optimize PPV, and the model had sensitivity of 17%, specificity of 98%, PPV of 48%, and NPV of 90%.Conclusion: This study found that of every two patients identified by the predictive model to be at risk of severe exacerbation, one patient may have a severe exacerbation. Once atrisk patients are identified, appropriate maintenance medication, implementation of diseasemanagement programs, and education may prevent future exacerbations.