Simon Tamara D, Cawthon Mary Lawrence, Popalisky Jean, Mangione-Smith Rita
Department of Pediatrics, University of Washington/Seattle Children's Hospital, Seattle, Washington;
Seattle Children's Research Institute, Seattle, Washington; and.
Hosp Pediatr. 2017 Jul;7(7):373-377. doi: 10.1542/hpeds.2016-0173. Epub 2017 Jun 20.
The Pediatric Medical Complexity Algorithm (PMCA) was developed to stratify children by level of medical complexity. We sought to refine PMCA and evaluate its performance based on the duration of eligibility and completeness of Medicaid data.
PMCA version 1.0 was applied to a cohort of 299 children insured by Washington State Medicaid with ≥1 Seattle Children's Hospital outpatient, emergency department, and/or inpatient encounter in 2012. Blinded assessment of the validation cohort's PMCA category was performed by using medical records. In-depth review of discrepant cases was performed and informed the development of PMCA version 2.0. The sensitivity and specificity of PMCA version 2.0 were assessed.
Using Medicaid data, the sensitivity of PMCA version 2.0 was 74% for complex chronic disease (C-CD), 60% for noncomplex chronic disease (NC-CD), and 87% for those without chronic disease (CD). Specificity was 84% to 91% in Medicaid data for all 3 groups. Medicaid data were most complete for children that had primarily fee-for-service claims and were less complete for those with some managed care encounter data. PMCA version 2.0 performed optimally when children had a longer duration of coverage (25 to 36 months) with fee-for-service reimbursement, identifying children with C-CD with 85% sensitivity and 75% specificity, children with NC-CD with 55% sensitivity and 88% specificity, and children without CD with 100% sensitivity and 97% specificity.
PMCA version 2.0 identifies children with C-CD with good sensitivity and very good specificity when applied to Medicaid data. Data quality is a critical consideration when using PMCA.
儿科医疗复杂性算法（PMCA）旨在根据医疗复杂性水平对儿童进行分层。我们试图完善PMCA，并根据资格期限和医疗补助数据的完整性评估其性能。
将PMCA 1.0版本应用于2012年由华盛顿州医疗补助计划承保、在西雅图儿童医院有≥1次门诊、急诊科和/或住院治疗经历的299名儿童队列。通过查阅病历对验证队列的PMCA类别进行盲法评估。对存在差异的病例进行深入审查，并为PMCA 2.0版本的开发提供依据。评估了PMCA 2.0版本的敏感性和特异性。
使用医疗补助数据时，PMCA 2.0版本对复杂慢性病（C-CD）的敏感性为74%，对非复杂慢性病（NC-CD）的敏感性为60%，对无慢性病（CD）儿童的敏感性为87%。在医疗补助数据中，所有3组的特异性为84%至91%。主要有按服务付费索赔的儿童的医疗补助数据最为完整，而有一些管理式医疗就诊数据的儿童的数据完整性较差。当儿童有较长的按服务付费报销覆盖期（25至36个月）时，PMCA 2.0版本的表现最佳，识别C-CD儿童的敏感性为85%，特异性为75%，识别NC-CD儿童的敏感性为55%，特异性为88%，识别无CD儿童的敏感性为100%，特异性为97%。
当应用于医疗补助数据时，PMCA 2.0版本能够以良好的敏感性和非常好的特异性识别C-CD儿童。使用PMCA时，数据质量是一个关键考虑因素。
