Valdez Ashley R, Hancock Elizabeth E, Adebayo Seyi, Kiernicki David J, Proskauer Daniel, Attewell John R, Bateman Lucinda, DeMaria Alfred, Lapp Charles W, Rowe Peter C, Proskauer Charmian
Optum Enterprise Analytics, UnitedHealth Group, Minneapolis, MN, United States.
Optum Technology, UnitedHealth Group, Minneapolis, MN, United States.
Front Pediatr. 2019 Jan 8;6:412. doi: 10.3389/fped.2018.00412. eCollection 2018.
Techniques of data mining and machine learning were applied to a large database of medical and facility claims from commercially insured patients to determine the prevalence, gender demographics, and costs for individuals with provider-assigned diagnosis codes for myalgic encephalomyelitis (ME) or chronic fatigue syndrome (CFS). The frequency of diagnosis was 519-1,038/100,000 with the relative risk of females being diagnosed with ME or CFS compared to males 1.238 and 1.178, respectively. While the percentage of women diagnosed with ME/CFS is higher than the percentage of men, ME/CFS is not a "women's disease." Thirty-five to forty percent of diagnosed patients are men. Extrapolating from this frequency of diagnosis and based on the estimated 2017 population of the United States, a rough estimate for the number of patients who may be diagnosed with ME or CFS in the U.S. is 1.7 million to 3.38 million. Patients diagnosed with CFS appear to represent a more heterogeneous group than those diagnosed with ME. A machine learning model based on characteristics of individuals diagnosed with ME was developed and applied, resulting in a predicted prevalence of 857/100,000 ( > 0.01), or roughly 2.8 million in the U.S. Average annual costs for individuals with a diagnosis of ME or CFS were compared with those for lupus (all categories) and multiple sclerosis (MS), and found to be 50% higher for ME and CFS than for lupus or MS, and three to four times higher than for the general insured population. A separate aspect of the study attempted to determine if a diagnosis of ME or CFS could be predicted based on symptom codes in the insurance claims records. Due to the absence of specific codes for some core symptoms, we were unable to validate that the information in insurance claims records is sufficient to identify diagnosed patients or suggest that a diagnosis of ME or CFS should be considered based solely on looking for presence of those symptoms. These results show that a prevalence rate of 857/100,000 for ME/CFS is not unreasonable; therefore, it is not a rare disease, but in fact a relatively common one.
数据挖掘和机器学习技术被应用于一个来自商业保险患者的大型医疗和设施索赔数据库,以确定被医生诊断为肌痛性脑脊髓炎(ME)或慢性疲劳综合征(CFS)的个体的患病率、性别分布和费用。诊断频率为519 - 1038/10万,女性被诊断为ME或CFS相对于男性的相对风险分别为1.238和1.178。虽然被诊断为ME/CFS的女性比例高于男性,但ME/CFS并非“女性疾病”。35%至40%的确诊患者为男性。根据这一诊断频率并基于2017年美国的估计人口进行推断,美国可能被诊断为ME或CFS的患者数量粗略估计为170万至338万。与被诊断为ME的患者相比,被诊断为CFS的患者似乎代表了一个更具异质性的群体。基于被诊断为ME的个体特征开发并应用了一个机器学习模型,得出预测患病率为857/10万(>0.01),在美国约为280万。将被诊断为ME或CFS的个体的年均费用与狼疮(所有类别)和多发性硬化症(MS)的年均费用进行比较,发现ME和CFS患者的费用比狼疮或MS患者高50%,比一般参保人群高3至4倍。该研究的另一个方面试图根据保险索赔记录中的症状代码来确定是否可以预测ME或CFS的诊断。由于某些核心症状没有特定代码,我们无法验证保险索赔记录中的信息是否足以识别确诊患者,也无法表明仅根据这些症状的存在就应考虑诊断为ME或CFS。这些结果表明,ME/CFS的患病率为857/10万并非不合理;因此,它不是一种罕见疾病,实际上是一种相对常见的疾病。