1 University of Maryland School of Medicine, Baltimore.
2 University of Maryland School of Pharmacy, Baltimore.
J Manag Care Spec Pharm. 2018 Nov;24(11):1138-1145. doi: 10.18553/jmcp.2018.24.11.1138.
Predictive models for earlier diagnosis of Alzheimer's disease and related dementias (ADRD) that rely on variables requiring assessment during an office visit, such as cognitive function, body mass index, or lifestyle factors, may not be broadly applicable, since that level of data may be inaccessible or inefficient.
To build a predictive model for earlier diagnosis of ADRD using only administrative claims data to enhance applicability at the health care-system level. Building on the strength of this approach and knowledge that health care utilization (HCU) is increased before dementia diagnosis, it was hypothesized that previous HCU history would improve predictive ability of the model.
We conducted a case-control study using data from the OptumLabs Data Warehouse. ADRD was defined using ICD-9-CM codes and prescription fills for antidementia medications. We included individuals with mild cognitive impairment. Cases aged ≥ 18 years with a diagnosis between 2011-2014 were matched to controls without ADRD. HCU variables were incorporated into regression models along with comorbidities and symptoms.
The derivation cohort comprised 24,521 cases and 95,464 controls. Final adjusted models were stratified by age. We obtained moderate accuracy (c-statistic = 0.76) for the model among younger (aged < 65 years) adults and poor discriminatory ability (c-statistic = 0.63) for the model among older adults (aged ≥ 65 years). Neurological and psychological disorders had the largest effect estimates.
We created age-stratified predictive models for earlier diagnosis of dementia using information available in administrative claims. These models could be used in decision support systems to promote targeted cognitive screening and earlier dementia recognition for individuals aged < 65 years. These models should be validated in other cohorts.
This research was supported by AstraZeneca, Global CEO Initiative, Janssen, OptumLabs, and Roche. Albrecht was supported by Agency for Healthcare Quality and Research grant K01HS024560. Perfetto is employed by the National Health Council, which accepts membership dues and sponsorships from a variety of organizations and companies. The authors declare no other potential conflicts of interest.
基于在就诊时需要评估的变量（如认知功能、体重指数或生活方式因素）预测阿尔茨海默病和相关痴呆症（ADRD）的更早诊断的模型，可能无法广泛应用，因为可能无法获得或无法有效利用这种程度的数据。
仅使用行政索赔数据构建用于 ADRD 更早诊断的预测模型，以增强在医疗保健系统层面的适用性。基于该方法的优势以及认知到在痴呆症诊断之前医疗保健利用率（HCU）增加的知识，假设先前的 HCU 历史将提高模型的预测能力。
我们使用 OptumLabs 数据仓库中的数据进行了病例对照研究。ADRD 使用 ICD-9-CM 代码和抗痴呆药物的处方来定义。我们纳入了有轻度认知障碍的患者。年龄≥18 岁，2011-2014 年间诊断为病例，与无 ADRD 的对照组匹配。HCU 变量与合并症和症状一起纳入回归模型。
在推导队列中，有 24521 例病例和 95464 例对照。最终调整后的模型按年龄分层。我们在年龄较小（<65 岁）的成年人中获得了中等准确性（c 统计量=0.76），而在年龄较大（≥65 岁）的成年人中模型的区分能力较差（c 统计量=0.63）。神经和心理障碍的影响估计值最大。
我们使用行政索赔中可用的信息创建了用于痴呆症早期诊断的分层预测模型。这些模型可用于决策支持系统，以促进针对年龄<65 岁的个体的有针对性的认知筛查和更早的痴呆症识别。这些模型应在其他队列中进行验证。
这项研究得到了 AstraZeneca、全球首席执行官倡议、Janssen、OptumLabs 和 Roche 的支持。Albrecht 得到了美国医疗保健质量与研究局 K01HS024560 拨款的支持。 Perfetto 受国家健康理事会雇佣，该理事会接受各种组织和公司的会费和赞助。作者声明没有其他潜在的利益冲突。
