ArticleHospital pediatrics2017
Development and Validation of the Pediatric Medical Complexity Algorithm (PMCA) Version 2.0.
Article in Hospital pediatrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
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- Pre-trained knowledge transfer with application in multisystem inflammatory syndrome subphenotyping in children.npj health systems · 2026Article
- Recent antibiotic exposure and response to treatment of juvenile idiopathic arthritis: a retrospective cohort study.Arthritis research & therapy · 2026Article
- Target Trial Emulation of Vaccine Effectiveness in 5- to 17-years-olds with Prior SARS-CoV-2 Infection.Nature communications · 2026Article
- Article
- High-risk medications in pediatric dentistry: Patterns, outcomes, and clinical implications.Journal of the American Dental Association (1939) · 2025Article
- Post-acute dyslipidemia and abnormal BMI in children and adolescents with COVID-19: An EHR Cohort Study from the RECOVER Initiative.medRxiv : the preprint server for health sciences · 2025Article
- Risk of neuropsychiatric and related conditions associated with SARS-CoV-2 infection: a difference-in-differences analysis.Nature communications · 2025Article
- Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program.PLOS digital health · 2025Article
- Pediatric Gastrointestinal Tract Outcomes During the Postacute Phase of COVID-19.JAMA network open · 2025Article
- Ethnic and racial differences in children and young people with respiratory and neurological post-acute sequelae of SARS-CoV-2: an electronic health record-based cohort study from the RECOVER Initiative.EClinicalMedicine · 2025Article
- New Diagnoses of Juvenile Idiopathic Arthritis Early in the COVID-19 Pandemic: Analysis of a Large United States Commercial Insurance Database.Journal of clinical rheumatology : practical reports on rheumatic & musculoskeletal diseases · 2025Article
- Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program.medRxiv : the preprint server for health sciences · 2024Article
- What do patients and families observe about pediatric safety?: A thematic analysis of real-time narratives.Journal of hospital medicine · 2024Observational
- Pediatric Gastrointestinal Outcomes During the Post-Acute Phase of COVID-19: Findings from RECOVER Initiative from 29 Hospitals in the US.medRxiv : the preprint server for health sciences · 2024Article
- Pediatric Population Management Classification for Children with Medical Complexity.Population health management · 2024Article
- Examining the economic impacts of caregiving among families of children of medical complexity: A qualitative study to inform inclusive economic models.Journal of participatory medicine · 2024Article
- Real-World Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents.Annals of internal medicine · 2024Observational
- Spectrum of severity of multisystem inflammatory syndrome in children: an EHR-based cohort study from the RECOVER program.Scientific reports · 2023Article
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Abstract
BACKGROUND AND
objectivesThe 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.
methodsPMCA 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.
resultsUsing 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.
conclusionsPMCA 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.
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