Development and Validation of the Pediatric Medical Complexity Algorithm (PMCA) Version 3.0

Simon, Tamara D.; Haaland, Wren; Hawley, Katherine; Lambka, Karen; Mangione-Smith, Rita

doi:10.1016/j.acap.2018.02.010

Cited by 163 publications

(112 citation statements)

References 20 publications

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“…Analysis was also adjusted for influenza season (November through March) and patient level of medical complexity by using the previously validated Pediatric Medical Complexity Algorithm version 3.0. 24,25 In reporting the results, we converted the logistic regression odds ratios into adjusted rate ratios (aRRs) to facilitate interpretation and describe these results as increases or decreases in the likelihoods of antibiotic prescribing during ARTI visits. 26,27 On the basis of the clusterrandomized design, all preintervention ARTI visits serve as controls.…”

Section: Discussionmentioning

confidence: 99%

Reducing Antibiotic Prescribing in Primary Care for Respiratory Illness

et al. 2020

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BACKGROUND: One-third of outpatient antibiotic prescriptions for pediatric acute respiratory tract infections (ARTIs) are inappropriate. We evaluated a distance learning program's effectiveness for reducing outpatient antibiotic prescribing for ARTI visits. METHODS: In this stepped-wedge clinical trial run from November 2015 to June 2018, we randomly assigned 19 pediatric practices belonging to the Pediatric Research in Office Settings Network or the NorthShore University HealthSystem to 4 wedges. Visits for acute otitis media, bronchitis, pharyngitis, sinusitis, and upper respiratory infection for children 6 months to ,11 years old without recent antibiotic use were included. Clinicians received the intervention as 3 program modules containing online tutorials and webinars on evidencebased communication strategies and antibiotic prescribing, booster video vignettes, and individualized antibiotic prescribing feedback reports over 11 months. The primary outcome was overall antibiotic prescribing rates for all ARTI visits. Mixed-effects logistic regression compared prescribing rates during each program module and a postintervention period to a baseline control period. Odds ratios were converted to adjusted rate ratios (aRRs) for interpretability. RESULTS: Among 72 723 ARTI visits by 29 762 patients, intention-to-treat analyses revealed a 7% decrease in the probability of antibiotic prescribing for ARTI overall between the baseline and postintervention periods (aRR 0.93; 95% confidence interval [CI], 0.90-0.96). Second-line antibiotic prescribing decreased for streptococcal pharyngitis (aRR 0.66; 95% CI, 0.50-0.87) and sinusitis (aRR 0.59; 95% CI, 0.44-0.77) but not for acute otitis media (aRR 0.93; 95% CI, 0.83-1.03). Any antibiotic prescribing decreased for viral ARTIs (aRR 0.60; 95% CI, 0.51-0.70). CONCLUSIONS: This program reduced antibiotic prescribing during outpatient ARTI visits; broader dissemination may be beneficial. WHAT'S KNOWN ON THIS SUBJECT: Behavioral interventions including individualized clinician prescribing feedback can reduce inappropriate antibiotic prescribing in ambulatory settings. These interventions have not been previously paired with communication training and evidence-based education on antibiotic prescribing for childhood acute respiratory tract infections. WHAT THIS STUDY ADDS: In this multisite stepped-wedge cluster-randomized trial, the Dialogue Around Respiratory Illness Treatment intervention combined communication training, evidence-based antibiotic prescribing education, and individualized prescribing feedback, producing a 7% sustained reduction in the probability of antibiotic prescribing for acute respiratory infection visits.

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Section: Discussionmentioning

confidence: 99%

Reducing Antibiotic Prescribing in Primary Care for Respiratory Illness

et al. 2020

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“…Additional outcomes were healthcare service usage and in-hospital mortality. To investigate the impacts of patient characteristics and complexity of comorbid conditions on the group of 3GCs-resistant participants, the Pediatric Medical Complexity Algorithm (PMCA) [25,26] was employed and compared between sensitive and resistant groups (Supplementary Materials Table S1). To understand the contribution of an E. coli bloodstream infection around the time of an episode of E. coli CA-UTI, we investigated the discharge diagnosis of bacterial bloodstream infections from the index hospitalization.…”

Section: Outcome Measurementsmentioning

confidence: 99%

Trends in Antimicrobial Susceptibility of Escherichia coli Isolates in a Taiwanese Child Cohort with Urinary Tract Infections between 2004 and 2018

et al. 2020

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The aim of this study was to investigate the annual incidence of Escherichia coli isolates in urinary tract infections (UTIs) and the antimicrobial resistance of the third-generation cephalosporin (3GCs) to E. coli, including the factors associated with the resistance in hospitalized children in Taiwan. A large electronic database of medical records combining hospital admission and microbiological data during 2004–2018 was used to study childhood UTIs in Taiwan. Annual incidence rate ratios (IRR) of E. coli in children with UTIs and its resistant rate to the 3GCs and other antibiotics were estimated by linear Poisson regression. Factors associated with E. coli resistance to 3GCs were assessed through multivariable logistic regression analysis. E. coli UTIs occurred in 10,756 unique individuals among 41,879 hospitalized children, with 92.58% being community associated based on urine culture results reported within four days after the hospitalization. The overall IRR E. coli UTI was 1.01 (95% confidence interval (CI) 0.99–1.02) in community-associated (CA) and 0.96 (0.90–1.02) in healthcare-associated infections. The trend in 3GCs against E. coli increased (IRR 1.18, 95% CI 1.13–1.24) over time in CA-UTIs. Complex chronic disease (adjusted odds ratio (aOR), 2.04; 95% CI, 1.47–2.83) and antibiotics therapy ≤ 3 months prior (aOR, 1.49; 95% CI, 1.15–1.94) were associated with increased risk of 3GCs resistance to E. coli. The study results suggested little or no change in the trend of E. coli UTIs in Taiwanese youths over the past 15 years. Nevertheless, the increase in 3GCs-resistant E. coli was substantial. Interventions for children with complex chronic comorbidities and prior antibiotic treatment could be effective in reducing the incidence of 3GCs-resistant E. coli in CA-UTIs in this region and more generally.

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“…Notably, 47% of infants required medical equipment. All infants had chronic disease as classified by the Pediatric Medical Complexity Algorithm (PMCA) and 67% were noted to have medical complexity [5]. Details of diagnoses and characterization are included as Additional file 2: Table S1.…”

Section: Demographicsmentioning

confidence: 99%

“…These infants often join the population of children with "medical complexity" [3,4]. Medical complexity has been classified using the Pediatric Medical Complexity Algorithm (PMCA) into 3 groups, "complex chronic disease, noncomplex chronic disease and not chronic disease" [5]. Families caring for high-risk infants face multiple challenges in meeting their child's health needs, which are pronounced in underserved populations.…”

Section: Introductionmentioning

confidence: 99%

Viewpoints from families for improving transition from NICU-to-home for infants with medical complexity at a safety net hospital: a qualitative study

et al. 2019

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Background: We have limited information on families' experiences during transition and after discharge from the neonatal intensive care unit. Methods: Open-ended semi-structured interviews were conducted with English or Spanish-speaking families enrolled in Medicaid in an urban high-risk infant follow up clinic at a safety-net center, which serves preterm and high-risk term infants. We generated salient themes using inductive-deductive thematic analysis. Results: Twenty-one participants completed the study. The infant's median (IQR) birth weight was 1750 (1305, 2641) grams; 71% were Hispanic and 10% were Black non-Hispanic; 62% reported living in a neighborhood with 3-4th quartile economic hardship. All were classified as having chronic disease per the Pediatric Medical Complexity Algorithm and 67% had medical complexity. A conceptual model was constructed and the analysis revealed major themes describing families' challenges and ideas to support transition centered on the parent-child role and parent self-efficacy. The challenges were: (1) comparison to normal babies, (2) caregiver mental health, (3) need for information. Ideas to support transition included, (1) support systems, (2) interventions using mobile health technology (3) improved communication to the primary care provider and (4) information regarding financial assistance programs. Specific subthemes differed in frequency counts between infants with and without medical complexity. Conclusions: Families often compare their preterm or high-risk infant to their peers and mothers feel great anxiety and stress. However, families often found hope and resilience in peer support and cited that in addition to information needs, interventions using mobile health technology and transition and financial systems could better support families after discharge.

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Development and Validation of the Pediatric Medical Complexity Algorithm (PMCA) Version 3.0

Cited by 163 publications

References 20 publications

Reducing Antibiotic Prescribing in Primary Care for Respiratory Illness

Reducing Antibiotic Prescribing in Primary Care for Respiratory Illness

Trends in Antimicrobial Susceptibility of Escherichia coli Isolates in a Taiwanese Child Cohort with Urinary Tract Infections between 2004 and 2018

Viewpoints from families for improving transition from NICU-to-home for infants with medical complexity at a safety net hospital: a qualitative study

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