BACKGROUND:Respiratory illness is consistently the leading cause of death and hospitalization in severe cerebral palsy (CP). Respiratory Exacerbations-Plan for Action and Care Transitions (RE-PACT) is a just-in-time adaptive intervention to prevent respiratory illness in severe CP. RE-PACT combines early illness detection with rapid clinical response to address varying causes of respiratory illness early enough to modify illness trajectory. This study's objective was to determine RE-PACT's feasibility, acceptability, fidelity, and estimated effect size. METHODS:This two-site randomized controlled trial occurred from April 2022-February 2024 in demographically and geographically distinct locations. Caregiver-child pairs were recruited from complex care programs, and children had both gross motor function classification system level 4-5 CP and either pulmonologist care or daily respiratory treatments. Children were randomized to usual care or RE-PACT for six months. Primary outcomes were feasibility, acceptability, and fidelity measures having a priori definitions of success. The primary clinical outcome was the severe respiratory illness (SRI) event rate, defined as hospitalizations due to respiratory diagnoses. Clinicaltrials.gov registration is NCT05292365. RESULTS:Sixty children were enrolled, of which 26 were randomized into RE-PACT. Measures confirmed RE-PACT's feasibility, acceptability, and fidelity, e.g., text message response rates were 97.5%, and no action planning or clinical responder activities were missed. System usability scale scores were "good to excellent" (mean [SD], 79.5 [11.7]). The RE-PACT SRI event rate (95% confidence interval, CI) was 0.71 (0.36-1.14) per person-year compared to the usual care event rate 1.08 (0.61-1.91) per person-year, a risk ratio of 0.66 (0.28-1.56). Secondary outcomes and qualitative data reinforced RE-PACT's positive impact. CONCLUSIONS:RE-PACT is a feasible, acceptable intervention that can be delivered with high fidelity to diverse families caring for children with severe CP. These data inform the sample and design characteristics needed for efficacy testing of RE-PACT's ability to prevent severe respiratory illness.

BackgroundThis study will pilot-test an innovative just-in-time adaptive intervention to reduce severe respiratory illness among children with severe cerebral palsy (CP). Our intervention program, Respiratory Exacerbation–Plans for Action and Care Transitions (RE-PACT), delivers timely customized action planning and rapid clinical response when hospitalization risk is elevated. ObjectiveThis study aims to establish RE-PACT’s feasibility, acceptability, and fidelity in up to 90 children with severe CP. An additional aim is to preliminarily estimate RE-PACT’s effect size. MethodsThe study will recruit up to 90 caregivers of children with severe CP aged 0 to 17 years who are cared for by a respiratory specialist or are receiving daily respiratory treatments. Participants will be recruited from pediatric complex care programs at the University of Wisconsin–Madison (UW) and the University of California, Los Angeles (UCLA). Study participants will be randomly assigned to receive usual care through the complex care clinical program at UW or UCLA or the study intervention, RE-PACT. The intervention involves action planning, rapid clinical response to prevent and manage respiratory illness, and weekly SMS text messaging surveillance of caregiver confidence for their child to avoid hospitalization. RE-PACT will be run through 3 successively larger 6-month trial waves, allowing ongoing protocol refinement according to prespecified definitions of success for measures of feasibility, acceptability, and fidelity. The feasibility measures include recruitment and intervention time. The acceptability measures include recruitment and completion rates as well as intervention satisfaction. The fidelity measures include observed versus expected rates of intervention and data collection activities. The primary clinical outcome is a severe respiratory illness, defined as a respiratory diagnosis requiring hospitalization. The secondary clinical outcomes include hospital days and emergency department visits, systemic steroid courses, systemic antibiotic courses, and death from severe respiratory illness. ResultsThe recruitment of the first wave began on April 27, 2022. To date, we have enrolled 30 (33%) out of 90 participants, as projected. The final wave of recruitment will end by October 31, 2023, and the final participant will complete the study by April 30, 2024. We will start analyzing the complete responses by April 30, 2024, and the publication of results is expected at the end of 2024. ConclusionsThis pilot intervention, using adaptive just-in-time strategies, represents a novel approach to reducing the incidence of significant respiratory illness for children with severe CP. This protocol may be helpful to other researchers and health care providers caring for patients at high risk for acute severe illness exacerbations. Trial RegistrationClinicalTrials.gov NCT05292365; https://clinicaltrials.gov/study/NCT05292365 International Registered Report Identifier (IRRID)DERR1-10.2196/49705

ObjectiveTo understand caregiver, healthcare professional and national expert perspectives on implementation of a just-in-time adaptive intervention, RE-PACT (Respiratory Exacerbation-Plans for Action and Care Transitions) to prevent respiratory crises in severe cerebral palsy. DesignQualitative research study. SettingPaediatric complex care programmes at two academic medical institutions. ParticipantsA total of n=4 focus groups were conducted with caregivers of children with severe cerebral palsy and chronic respiratory illness, n=4 with healthcare professionals, and n=1 with national experts. MethodsParticipants viewed a video summarising RE-PACT, which includes action planning, mobile health surveillance of parent confidence to avoid hospitalisation and rapid clinical response at times of low confidence. Moderated discussion elicited challenges and benefits of RE-PACT's design, and inductive thematic analysis elicited implementation barriers and facilitators. ResultsOf the 19 caregivers recruited, nearly half reported at least one hospitalisation for their child in the prior year. Healthcare professionals and national experts (n=26) included physicians, nurses, respiratory therapists, social workers and researchers. Four overarching themes and their barriers/facilitators emphasised the importance of design and interpersonal relationships balanced against health system infrastructure constraints. Intervention usefulness in crisis scenarios relies on designing action plans for intuitiveness and accuracy, and mobile health surveillance tools for integration into daily life. Trust, knowledge, empathy and adequate clinician capacity are essential components of clinical responder-caregiver relationships. ConclusionsRE-PACT's identified barriers are addressable. Just-in-time adaptive interventions for cerebral palsy appear well-suited to address families' need to tailor intervention content to levels of experience, preference and competing demands.

Objective To test associations between parent-reported confidence to avoid hospitalization and caregiving strain, activation, and health-related quality of life (HRQOL). Study design In this prospective cohort study, enrolled parents of children with medical complexity (n = 75) from 3 complex care programs received text messages (at random times every 2 weeks for 3 months) asking them to rate their confidence to avoid hospitalization in the next month. Low confidence, as measured on a 10-point Likert scale (1 = not confident; 10 = fully confident), was defined as a mean rating <5. Caregiving measures included the Caregiver Strain Questionnaire, Family Caregiver Activation in Transition (FCAT), and caregiver HRQOL (Medical Outcomes Study Short Form 12 [SF12]). Relationships between caregiving and confidence were assessed with a hierarchical logistic regression and classification and regression trees (CART) model. Results The parents were mostly mothers (77%) and were linguistically diverse (20% spoke Spanish as their primary language), and 18% had low confidence on average. Demographic and clinical variables had weaker associations with confidence. In regression models, low confidence was associated with higher caregiver strain (aOR, 3.52; 95% CI, 1.45-8.54). Better mental HRQOL was associated with lower likelihood of low confidence (aOR, 0.89; 95% CI, 0.80-0.97). In the CART model, higher strain similarly identified parents with lower confidence. In all models, low confidence was not associated with caregiver activation (FCAT) or physical HRQOL (SF12) scores. Conclusions Parents of children with medical complexity with high strain and low mental HRQOL had low confidence in the range in which intervention to avoid hospitalization would be warranted. Future work could determine how adaptive interventions to improve confidence and prevent hospitalizations should account for strain and low mental HRQOL.

OBJECTIVE:To evaluate the associations between parent confidence in avoiding hospitalization and subsequent hospitalization in children with medical complexity (CMC); and feasibility/acceptability of a texting platform, Assessing Confidence at Times of Increased Vulnerability (ACTIV), to collect repeated measures of parent confidence. STUDY DESIGN:This prospective cohort study purposively sampled parent-child dyads (n = 75) in 1 of 3 complex care programs for demographic diversity to pilot test ACTIV for 3 months. At random days/times every 2 weeks, parents received text messages asking them to rate confidence in their child avoiding hospitalization in the next month, from 1 (not confident) to 10 (fully confident). Unadjusted and adjusted generalized estimating equations with repeated measures evaluated associations between confidence and hospitalization in the next 14 days. Post-study questionnaires and focus groups assessed ACTIV's feasibility/acceptability. RESULTS:Parents were 77.3% mothers and 20% Spanish-speaking. Texting response rate was 95.6%. Eighteen hospitalizations occurred within 14 days after texting, median (IQR) 8 (2-10) days. When confidence was <5 vs ≥5, adjusted odds (95% CI) of hospitalization within 2 weeks were 4.02 (1.20-13.51) times greater. Almost all (96.8%) reported no burden texting, one-third desired more frequent texts, and 93.7% were very likely to continue texting. Focus groups explored the meaning of responses and suggested ACTIV improvements. CONCLUSIONS:In this demographically diverse multicenter pilot, low parent confidence predicted impending CMC hospitalization. Text messaging was feasible and acceptable. Future work will test efficacy of real-time interventions triggered by parent-reported low confidence.

Introduction Despite the significant healthcare policy and program implications, a summary measure of health for children with medical complexity (CMC) has not been identified. It is unclear whether existing population health approaches apply to CMC. We conducted a systematic review of the existing peer-reviewed research literature on CMC to describe the health outcomes currently measured for CMC. Methods We searched MEDLINE and PsycINFO by linking combinations of key words from three groups of concepts: (1) pediatric, (2) medical complexity, and (3) chronicity or severity. Study eligibility criteria were research studies including CMC with any outcome reported. Data on the outcomes were systematically extracted. Iterative content analysis organized outcomes into conceptual domains and sub-domains. Results Our search yielded 3853 articles. After exclusion criteria were applied, 517 articles remained for data extraction. Five distinct outcome domains and twenty-four sub-domains emerged. Specifically, 50% of the articles studied healthcare access and use; 43% family well-being; 39% child health and well-being; 38% healthcare quality; and 25% adaptive functioning. Notably lacking were articles examining routine child health promotion as well as child mental health and outcomes related to family functioning. Conclusions Key health domains for CMC exist. Adaptations of existing sets of metrics and additional tools are needed to fully represent and measure population health for CMC. This approach may guide policies and programs to improve care for CMC.

BACKGROUND AND OBJECTIVES: Defining and measuring health for children with medical complexity (CMC) is poorly understood. We engaged a diverse national sample of stakeholder experts to generate and then synthesize a comprehensive list of health outcomes for CMC. METHODS: With national snowball sampling of CMC caregiver, advocate, provider, researcher, and policy or health systems experts, we identified 182 invitees for group concept mapping (GCM), a rigorous mixed-methods approach. Respondents (n = 125) first completed Internet-based idea generation by providing unlimited short, free-text responses to the focus prompt, "A healthy life for a child or youth with medical complexity includes: _." The resulting 707 statements were reduced to 77 unique ideas. Participants sorted the ideas into clusters based on conceptual similarity and rated items on perceived importance and measurement feasibility. Responses were analyzed and mapped via GCM software. RESULTS: The cluster map best fitting the data had 10 outcome domains: (1) basic needs, (2) inclusive education, (3) child social integration, (4) current child health-related quality of life, (5) long-term child and family self-sufficiency, (6) family social integration, (7) community system supports, (8) health care system supports, (9) a high-quality patient-centered medical home, and (10) family-centered care. Seventeen outcomes representing 8 of the 10 domains were rated as both important and feasible to measure (go zone). CONCLUSIONS: GCM identified a rich set of CMC outcome domains. Go-zone items provide an opportunity to test and implement measures that align with a broad view of health for CMC and potentially all children.

OBJECTIVES: We sought to examine the effect of a caregiver coaching intervention, Plans for Action and Care Transitions (PACT), on hospital use among children with medical complexity (CMC) within a complex care medical home at an urban tertiary medical center. METHODS: PACT was an 18-month caregiver coaching intervention designed to influence key drivers of hospitalizations: (1) recognizing critical symptoms and conducting crisis plans and (2) supporting comprehensive hospital transitions. Usual care was within a complex care medical home. Primary outcomes included hospitalizations and 30-day readmissions. Secondary outcomes included total charges and mortality. Intervention effects were examined with bivariate and multivariate analyses. RESULTS: From December 2014 to September 2016, 147 English- and Spanish-speaking CMC <18 years old and their caregivers were randomly assigned to PACT (n = 77) or usual care (n = 70). Most patients were Hispanic, Spanish-speaking, and publicly insured. Although in unadjusted intent-to-treat analyses, only charges were significantly reduced, both hospitalizations and charges were lower in adjusted analyses. Hospitalization rates (per 100 child-years) were 81 for PACT vs 101 for usual care (adjusted incident rate ratio: 0.61 [95% confidence interval 0.38–0.97]). Adjusted mean charges per patient were $14 206 lower in PACT. There were 0 deaths in PACT vs 4 in usual care (log-rank P = .04). CONCLUSIONS: Among CMC within a complex care program, a health coaching intervention designed to identify, prevent, and manage patient-specific crises and postdischarge transitions appears to lower hospitalizations and charges. Future research should confirm findings in broader populations and care models.

OBJECTIVE:Because children with medical complexity (CMC) display very different health trajectories, needs, and resource utilization than other children, it is unclear how well traditional conceptions of population health apply to CMC. We sought to identify key health outcome domains for CMC as a step toward determining core health metrics for this distinct population of children.METHODS:We conducted and analyzed interviews with 23 diverse national experts on CMC to better understand population health for CMC. Interviewees included child and family advocates, health and social service providers, and research, health systems, and policy leaders. We performed thematic content analyses to identify emergent themes regarding population health for CMC.RESULTS:Overall, interviewees conveyed that defining and measuring population health for CMC is an achievable, worthwhile goal. Qualitative themes from interviews included: 1) CMC share unifying characteristics that could serve as the basis for population health outcomes; 2) optimal health for CMC is child specific and dynamic; 3) health of CMC is intertwined with health of families; 4) social determinants of health are especially important for CMC; and 5) measuring population health for CMC faces serious conceptual and logistical challenges.CONCLUSIONS:Experts have taken initial steps in defining the population health of CMC. Population health for CMC involves a dynamic concept of health that is attuned to individual, health-related goals for each child. We propose a framework that can guide the identification and development of population health metrics for CMC.

BACKGROUND: Improvement in hospital transitional care has become a major national priority, although the impact on children's postdischarge outcomes is unclear.OBJECTIVE: To characterize common handoff practices between hospital and primary care providers (PCPs), and test the hypothesis that common handoff practices would be associated with fewer unplanned readmissions.DESIGN, SETTING, AND PATIENTS: This prospective cohort study enrolled randomly selected pediatric patients during an acute hospitalization at a tertiary children's hospital in 2012-2014.MEASUREMENTS: Primary care and patient data were abstracted from administrative, caregiver, and PCP questionnaires on admission through 30 days postdischarge. The primary outcome was 30-day unplanned readmission to any hospital. Logistic regression assessed relationships between readmissions and 11 handoff communication practices.RESULTS: We enrolled 701 children, from which 685 identified PCPs. Complete data were collected from 84% of PCPs. Communication practices varied widely-verbal handoffs occurred rarely (10.7%); PCP notification of admission occurred for 50.8%. Caregiver experience scores, using an adapted Care Transitions Measure-3, were high but were unrelated to readmissions. Thirty-day unplanned readmissions to any hospital were unrelated to most handoff practices. Having PCP follow-up appointments scheduled prior to discharge was associated with more readmissions (adjusted odds ratio, 2.20; 95% confidence interval, 1.08-4.46).CONCLUSION: Despite their presumed value, common handoff practices between hospital providers and PCPs may not lead to reductions in postdischarge utilization for children. Addressing broader constructs like caregiver self-efficacy or social determinants is likely necessary. (C) 2017 Society of Hospital Medicine

The medical home has been widely promoted as a model of primary care with the potential to transform the health care delivery system. Although this model was initially focused on children with chronic conditions, the American Academy of Pediatrics has endorsed a generalization of the model, promoting the statement, “Every child deserves a medical home.” Recently, other major professional and governmental organizations have embraced this more inclusive vision, and the medical home concept has been promoted in provisions of the Affordable Care Act. Yet, rigorous evaluations of the value of the medical home, within pediatrics and beyond, have been limited, and the results have been mixed. Early results from large demonstration projects in adults have generally noted modest improvements in quality without accompanying reductions in cost. At this critical period in health care, with widespread interest in health care delivery and payment reform, these results present a potential threat to the medical home. Understanding possible reasons for these early findings is crucial to sustaining the spread of the medical home beyond its first 50 years. With this aim, we review the history of the medical home and trends in child health, and we explore the concepts of value and complexity as they pertain to pediatric health care delivery. We propose that, because of the demographic characteristics and economics of child health and current policy imperatives with regard to health care, a strong value proposition for the medical home in pediatrics involves children with medical complexity.

OBJECTIVE: Interventions to reduce disproportionate hospital use among children with medical complexity (CMC) are needed. We conducted a rigorous, structured process to develop intervention strategies aiming to reduce hospitalizations within a complex care program population.METHODS: A complex care medical home program used 1) semistructured interviews of caregivers of CMC experiencing acute, unscheduled hospitalizations and 2) literature review on preventing hospitalizations among CMC to develop key drivers for lowering hospital utilization and link them with intervention strategies. Using an adapted version of the RAND/UCLA Appropriateness Method, an expert panel rated each model for effectiveness at impacting each key driver and ultimately reducing hospitalizations. The complex care program applied these findings to select a final set of feasible intervention strategies for implementation.RESULTS: Intervention strategies focused on expanding access to familiar providers, enhancing general or technical caregiver knowledge and skill, creating specific and proactive crisis or contingency plans, and improving transitions between hospital and home. Activities aimed to facilitate family-centered, flexible implementation and consideration of all of the child's environments, including school and while traveling. Tailored activities and special attention to the highest utilizing subset of CMC were also critical for these interventions.CONCLUSIONS: A set of intervention strategies to reduce hospitalizations among CMC, informed by key drivers, can be created through a structured, reproducible process. Both this process and the results may be relevant to clinical programs and researchers aiming to reduce hospital utilization through the medical home for CMC.

OBJECTIVE: Children with medical complexity (CMC) are a small group that utilizes large amounts of health care resources. Although parents are the primary healthcare decision-makers for their children, little is known from their perspective about why CMC are hospitalized. We sought to understand what parents think about factors leading to hospitalization and whether any recent hospitalizations might have been avoidable.METHODS: We conducted qualitative, semistructured interviews with 35 parents of hospitalized CMC who receive care in the Pediatric Medical Home Program, a complex care program at University of California, Los Angeles. Interviews were conducted in English and in Spanish, audio-recorded, transcribed and translated, then coded in ATLAS.ti (Scientific Software Development Gmbh, Berlin, Germany) for qualitative analysis. We sorted qualitative codes into groups with shared concepts, to generate emergent themes.RESULTS: Parents described their experiences leading up to their children's hospitalization, but no one suggested that the hospitalization was potentially avoidable. Most parents perceived their children as having higher susceptibility because of underlying conditions, perceived the symptoms they observed as high-risk, and described seeking emergent care only when they no longer were comfortable at home. Decisions about where to seek care were influenced by health care system factors such as accessibility and continuity of care. Most parents expressed a desire to learn more about their children's conditions and how best to care for them at home.CONCLUSIONS: Parents of CMC believe that hospitalizations are largely unavoidable because of higher susceptibility and higher risk. Increasing parents' self-efficacy in caring for children at home might influence their decisions to seek emergent care.

HomeCirculation: Cardiovascular Quality and OutcomesVol. 8, No. 4Improvement in Interstage Survival in a National Pediatric Cardiology Learning Network Free AccessResearch ArticlePDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissionsDownload Articles + Supplements ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toSupplemental MaterialFree AccessResearch ArticlePDF/EPUBImprovement in Interstage Survival in a National Pediatric Cardiology Learning Network Jeffrey B. Anderson, MD, MPH, MBA, Robert H. BeekmanIII, MD, John D. Kugler, MD, Geoffrey L. Rosenthal, MD, PhD, Kathy J. Jenkins, MD, Thomas S. Klitzner, MD, PhD, Gerard R. Martin, MD, Steven R. Neish, MD, David W. Brown, MD, Colleen Mangeot, MS, Eileen King, PhD, Laura E. Peterson, BSN, SM, Lloyd Provost, MS and Carole Lannon, MD, MPHfor the National Pediatric Cardiology Quality Improvement Collaborative Jeffrey B. AndersonJeffrey B. Anderson From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). , Robert H. BeekmanIIIRobert H. BeekmanIII From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). , John D. KuglerJohn D. Kugler From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). , Geoffrey L. RosenthalGeoffrey L. Rosenthal From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). , Kathy J. JenkinsKathy J. Jenkins From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). , Thomas S. KlitznerThomas S. Klitzner From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). , Gerard R. MartinGerard R. Martin From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). , Steven R. NeishSteven R. Neish From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). , David W. BrownDavid W. Brown From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). , Colleen MangeotColleen Mangeot From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). , Eileen KingEileen King From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). , Laura E. PetersonLaura E. Peterson From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). , Lloyd ProvostLloyd Provost From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). and Carole LannonCarole Lannon From the Heart Institute, Cincinnati Children's Hospital Medical Center, OH (J.B.A., R.H.B.); Children's Hospital & Medical Center, Omaha, NE (J.D.K.); University of Maryland School of Medicine, Baltimore (G.L.R.); Boston Children's Hospital Medical Center, Boston, MA (K.J.J., D.W.B.); Mattel Children's Hospital at University of California, Los Angeles (T.S.K.); Children's National Medical Center, Washington, DC (G.R.M.); University of Texas Health Center, San Antonio (S.R.N.); Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.M., E.K.); Health Care Consultant, Boston, MA (L.E.P.); Associates in Process Improvement, Austin, TX (L.P.); and The James M. Anderson Center for Clinical Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH (C.L.). and for the National Pediatric Cardiology Quality Improvement Collaborative Originally published9 Jun 2015https://doi.org/10.1161/CIRCOUTCOMES.115.001956Circulation: Cardiovascular Quality and Outcomes. 2015;8:428–436Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: January 1, 2015: Previous Version 1 Goals and Vision of the ProgramInfants with univentricular congenital heart disease (CHD), including those with hypoplastic left heart syndrome (HLHS), regularly pose dilemmas in decision-making because their anatomy and physiology are often unique and variable. The typical staged surgical course for infants with complex univentricular anatomy with systemic outflow obstruction begins with the Norwood (stage 1) operation or variant shortly after birth, followed several months later by superior cavopulmonary anastomosis (stage 2 palliation) with an ultimate goal of a Fontan-type operation several years later.1–3 Improvement in surgical and postoperative management has led to considerable improvement in early post-Norwood survival in the recent era.4–7 However, after the Norwood procedure and before stage 2 palliation, a high-risk time period termed interstage, mortality has been previously been reported at 10% to 15%.8–10 The rare nature of this disorder has limited robust learning about successful strategies to improve survival undertaken by single-surgical centers, and a gap exists in our ability to further improve mortality in this population.The National Pediatric Cardiology Quality Improvement Collaborative (NPC-QIC), the first multicenter learning network within pediatric cardiology,11 was established with the goal of improving care and outcomes for children with univentricular heart after the Norwood operation and specifically to (1) improve interstage mortality, (2) decrease interstage growth failure, and (3) reduce interstage hospital readmissions for major medical events.Local Challenges in ImplementationThere were several perceived challenges to success in changing clinical outcomes before starting the NPC-QIC collaborative. A primary challenge in collaboration among multiple sites can be agreement on best practices that should be implemented. This is especially true for rare diseases, such as univentricular heart disease, where evidence-based clinical guidelines are not available to clinicians. As noted above, major variation persists in management practices among individuals and institutions caring for children with HLHS and other forms of univentricular CHD.8,12–16 Although NPC was designed as a learning collaborative, it was unclear whether teams of caregivers would be willing or interested in changing practices or whether there would continue to be persistent variation. A second challenge was the linkage between process measures and clinical outcomes. Although expert opinion and literature (where available) were used to design clinical practices expected to be related to reduction in mortality, it was unclear at the onset of the collaborative, whether adherence to these specific practices would indeed move mortality. We expected challenges in measuring adherence to processes and measuring outcomes in a learning network that would have rolling enrollment with new centers joining each year. Finally, it was felt to be essential that clinical care teams actively participate in the collaborative, including attending face-to-face NPC-QIC meetings. There was some concern that it would be difficult to gain institutional buy-in locally, so clinical teams would have support to do this work. The challenges of team buy-in and implementation of change practices were primarily addressed building the program with engagement of national leaders in the field of congential heart disease. We addressed the measurement challenges by working closely with experts in statistical process control (SPC) from the James M. Anderson Center for Health Systems Excellence at Cincinnati Children's Hospital Medical Center and the consultant firm Associates in Process Improvement.Design of the InitiativeConceptual ModelNPC-QIC is a longitudinal learning community modeled after the Institute of Medicine learning healthcare system framework.17 These networks are multisite collaborations that focus on both improvement and research and engage patients, families, clinicians, and researchers in working together to improve outcomes. They provide a resource for understanding variation in clinical care and opportunities to test changes in clinical practice to improve care. Large networks with registries provide the infrastructure to gather information on patients across treatment centers and to understand differences in care processes and clinical outcomes and to reduce unnecessary variation.18–20 Learning networks may be especially useful in rare medical problems, such as complex CHD, where no one center is able to care for enough cases to learn about potential optimal practices. Regional and national networks and databases have also been established to better understand care of pediatric cancer, inflammatory bowel disease, neonatal management, and cystic fibrosis.21–24Improvement MethodologyNPC-QIC's improvement method is based on an adapted Institute for Healthcare Improvement's Breakthrough Series Model, which incorporates knowledge about dissemination and behavior change to support practice change.25 Pediatric cardiology centers participate through local teams comprised of a physician champion, nursing, nutrition, and family representatives. Each month, teams submit data on patient status and care processes; postreports of their progress; participate in webinars and a listserv; and test changes to improve their systems. Teams receive monthly reports from NPC-QIC demonstrating results of their local clinical processes and outcomes, as well as those of the entire network for benchmarking (Table 1). Semiannual learning session workshops bring teams and parents together to share lessons learned about clinical process changes.Table 1. Key Metrics Reported on Each Patient in the NPC-QIC RegistryMeasureGoalData DefinitionsMortality0%Numerator: cumulative no. of deaths between discharge after Norwood repair and completion of stage 2 repair (ie, the interstage)Denominator: cumulative no. of parents who had a Glenn, died, or had a heart transplantMortality G·chart***No. of patients who were admitted for glenn surgery between patients who diedMajor event readmission0%Numerator: no. of interstage readmissions for a major eventDenominator: no. of patients who had at least 1 interstage day n the month/100Major event G chart***No. of patients who were admitted for Glenn surgery between patients who had a major event readmissionAverage daily weight gain90%Numerator: number achieving a minimum age-appropriate daily weight gain between Norwood discharge and Glenn admissionDenominator: no. of patients admitted for Glenn surgeryWeight for length achievement***No. of patients with adequate growth between patients with growth failure events. A growth failure event is defined as decreasing ≥2Weight for length percentile bands over the course of the interstage (Norwood discharge to Glenn admission)Proportion of discharges with identified discharge coordinator100%Numerator: no. of discharges with identified discharge coordinatorDenominator: no. of patients discharged alive after NorwoodProportion of discharges with complete preventative care plan100%Numerator: no. of discharges with documented immunization status at discharge, and plan for RSV and influenza prevention discussedDenominator: no. of patients discharged alive after Norwood where site indicates that routine immunizations are recommended during the interstageProportion of discharges with written medication list100%Numerator: no. of discharges with written medication list providedDenominator: no. of patients discharged alive after NorwoodProportion of discharges with written Red-Flag Action Plan100%Numerator: no. of discharges with written red-flag action plan provided to familiesDenominator: no. of patients discharged alive after NorwoodProportion of discharges with written nutrition plan100%Numerator: no. of discharges with written nutrition plan provided to familiesDenominator: no. of patients discharged alive after NorwoodProportion of discharges with follow-up plan with PCP and primary cardiologist100%Numerator: no. of discharges with identified PCP, identified primary cardiologist, and scheduled appointments or contact information for self-schedulingDenominator: no. of patients discharged alive after NorwoodProportion of clinic visits with identified postclinic care coordinator100%Numerator: no. of clinic visits with an individual or group identified for coordinating the outpatient management of the patientDenominator: total no. of clinic visits for interstage population that monthProportion of clinic visits with updated preventative care plan100%Numerator: no. of clinic visits with documented immunization status and plan for RSV and influenza prevention discussedDenominator: no. of clinic visits during the month where site indicates that routine immunizations are recommended during the interstageProportion of clinic visits with updated written medication list100%Numerator: no. of clinic visits with updated written medication list providedDenominator: total no. of clinic visits for interstage population that monthProportion of clinic visits with updated written red-flag action plan100%Numerator: no. of clinic visits with updated red-flag action plan providedDenominator: total no. of clinic visits for interstage population that monthProportion of clinic visits with growth parameter documentation100%Numerator: no. of clinic visits where weight, weight for age percentile, average daily weight gain and current caloric intake is documentedDenominator: total no. of clinic visits for interstage population that monthProportion of clinic visits with updated nutrition plan100%Numerator: no. of clinic visits with updated nutrition plan providedDenominator: total no. of clinic visits for interstage population that monthProportion of clinic visits where clinic visit information was communicated to PCP100%Numerator: no. of clinic visits with documented communication to PCP of clinic visit informationDenominator: total no. of clinic visits for interstage population that monthPCP indicates primary care physician; and RSV, respiratory syncytial virus.Theory for Mortality ImprovementEvidence (literature, where available) and expert opinion were used to identify clinical practices expected to be related to improvement in interstage mortality (key driver diagram: Figure 1). These care processes are grouped into 4 domains or key drivers: (1) care coordination, (2) care transitions, (3) interstage growth, and (4) engaging families. Example processes include applying standard Norwood discharge procedures, providing families with a written action plan for acting on clinical Red Flags that may arise in the interstage and communicating the care plan to the infant's primary care physician at the time of discharge after stage 1 palliation and when updated at interstage clinic visits.Download figureDownload PowerPointFigure 1. National Pediatric Cardiology Quality Improvement Collaborative (NPC-QIC) key driver diagram.To address concerns for buy-in from local teams and institutions, several steps were taken. The leadership of NPC-QIC was made up of a group of national experts in CHD. The Joint Council on Congenital Heart Disease (JCCHD) was formed in 2003 as an alliance between pediatric cardiologists, congenital cardiothoracic surgeons, and adult CHD specialists. This group founded NPC-QIC in 2006. This leadership at the national level gave instant clinical credibility to the collaborative. At the local team level, NPC-QIC leadership made a point to encourage local teams to include clinician and parent involvement, allowing parental voice to push teams for improvement. NPC-QIC leadership also worked with US News and World Report to add involvement in NPC-QIC as a line item point on the scoring system for Cardiac and Cardiothoracic Surgery Programs, increasing the value to institutions. In addition, the American Board of Pediatrics Maintenance of Certification Part 4 credit was made available to participating physicians. The engagement of clinical leaders and parents, as well as the alignment with US News and the American Board of Pediatrics, helped drive the involvement of institutions and teams and buy-in to quality improvement activities.Data CollectionThe NPC-QIC registry captures information about infants with a univentricular CHD who undergo a Norwood procedure or variant with ultimate plan for a stage 2 palliation. Institutional Review Boards at all participating sites approved their participation. Infants become eligible for registry inclusion when they are discharged home from their Norwood surgery; patients who are eligible and consented are enrolled in the registry. Patients who spend their entire interstage hospitalized are not eligible and are not included in the registry. At the time of this discharge, data from their surgery and initial hospitalization are captured. Additional clinical information is then collected from each outpatient visit and readmission to the hospital during the interstage and information about each interstage transplantation or mortality. Finally, data are collected on admission for stage 2 surgical palliation and the hospitalization that follows this surgery. Data are collected at the site level and entered into an electronic registry using the Research Electronic Data Capture system.Statistical MethodologyNPC-QIC uses SPC methods and charts to measure and report progress. This is a novel approach in the fields of pediatric cardiology and cardiac surgery. As with all rare diseases, the low incidence of HLHS presents a challenge to the ability to measure changes in care process or outcome performance using traditional statistical methods.26 Combining data from individual sites improves the statistical power to measure differences and the effects of changes over time. SPC methods combine rigorous time series analysis methods with graphical presentation of data, allowing meaningful interpretation of data despite the relatively small numbers of patients in the population of interest.27–29 SPC charts document statistical changes to a system using control limits, which define 3 SDs above and below the mean. Statistical rules determine when there has been a significant change to the system and identify measurement points that fall outside the statistical control limits. Several SPC charts are used by NPC-QIC to identify system changes. These include G charts, P charts, and cumulative sum (CuSum) charts. G charts track time and distance between rare events. In this case, NPC-QIC tracks the number of infants who successfully complete the interstage between mortalities. P charts document percentage of events, here tracking the percentage of mortalities per interstage patient for the collaborative on a monthly basis. Finally, a CuSum chart determines the accumulation of small changes to a system over time.Implementation of the InitiativeThe NPC-QIC interstage project was implemented in 2008 with a group of 6 pilot sites. Since then, the network has grown to 55 sites (Figure 2; Appendix A in the Data Supplement) with a rolling onboarding system, and now, it includes the majority of centers that perform staged palliation for univentricular CHD in the United States. Since 2008, there has been steady growth of the number of infants enrolled in the registry. However, since October 2012 when the 50th surgical site joined the network, only 4 additional surgical sites have been added, leading to a fairly stable system of surgical centers since late 2012. Self-audits by participating sites twice yearly indicate that >95% of eligible infants at participating centers are consented and included in the registry. Over half (54%) of centers report regularly involving parents of HLHS patients in their local improvement work. We have had few barriers in engaging care teams at local sites, but it has been a challenge to get each team to find and engage parents in the teams in a meaningful way. We have worked closely with a parent group, Sisters By Heart, to identify parents that would like to be involved at each center. However, working with parents on this type of project is not something that many clinicians have done before. However, with education and shared practices among sites, we have had steady improvement in parent involvement at the local level over time.Download figureDownload PowerPointFigure 2. Growth of National Pediatric Cardiology Quality Improvement Collaborative (NPC-QIC). Growth in number of NPC-QIC teams (red) and patients enrolle

OBJECTIVE:To identify subgroups of U.S. children with special health care needs (CSHCN) and characterize key outcomes.DATA SOURCE:Secondary analysis of 2009-2010 National Survey of CSHCN.STUDY DESIGN:Latent class analysis grouped individuals into substantively meaningful classes empirically derived from measures of pediatric medical complexity. Outcomes were compared among latent classes with weighted logistic or negative binomial regression.PRINCIPAL FINDINGS:LCA identified four unique CSHCN subgroups: broad functional impairment (physical, cognitive, and mental health) with extensive health care (Class 1), broad functional impairment alone (Class 2), predominant physical impairment requiring family-delivered care (Class 3), and physical impairment alone (Class 4). CSHCN from Class 1 had the highest ED visit rates (IRR 3.3, p < .001) and hospitalization odds (AOR: 12.0, p < .001) and lowest odds of a medical home (AOR: 0.17, p < .001). CSHCN in Class 3, despite experiencing more shared decision making and medical home attributes, had more ED visits and missed school than CSHCN in Class 2 (p < .001); the latter, however, experienced more cost-related difficulties, care delays, and parents having to stop work (p < .001).CONCLUSIONS:Recognizing distinct impacts of cognitive and mental health impairments and health care delivery needs on CSHCN outcomes may better direct future intervention efforts.

BACKGROUND AND OBJECTIVE: Despite considerable attention, little is known about the degree to which primary care medical homes influence early postdischarge utilization. We sought to test the hypothesis that patients with medical homes are less likely to have early postdischarge hospital or emergency department (ED) encounters.METHODS: This prospective cohort study enrolled randomly selected patients during an acute hospitalization at a children's hospital during 2012 to 2014. Demographic and clinical data were abstracted from administrative sources and caregiver questionnaires on admission through 30 days postdischarge. Medical home experience was assessed by using Maternal and Child Health Bureau definitions. Primary outcomes were 30-day unplanned readmission and 7-day ED visits to any hospital. Logistic regression explored relationships between outcomes and medical home experiences.RESULTS: We followed 701 patients, 97% with complete data. Thirty-day unplanned readmission and 7-day ED revisit rates were 12.4% and 5.6%, respectively. More than 65% did not have a medical home. In adjusted models, those with medical home component "having a usual source of sick and well care" had fewer readmissions than those without (adjusted odds ratio 0.54, 95% confidence interval 0.30-0.96). Readmissions were higher among those with less parent confidence in avoiding a readmission, subspecialist primary care providers, longer length of index stay, and more hospitalizations in the past year. ED visits were associated with lack of parent confidence but not medical home components.CONCLUSIONS: Lacking a usual source for care was associated with readmissions. Lack of parent confidence was associated with readmissions and ED visits. This information may be used to target interventions or identify high-risk patients before discharge.

The objective of this study is to identify predictors of prolonged intensive care unit (ICU) length of stay (LOS) for single ventricle patients following Stage I palliation. We hypothesize that peri-operative factors contribute to prolonged ICU stay among children with hypoplastic left heart syndrome (HLHS) and its variants. In 2008, as a part of the Joint Council on Congenital Heart Disease initiative, the National Pediatric Cardiology-Quality Improvement Collaborative established a data registry for patients with HLHS and its variants undergoing staged palliation. Between July 2008 and August 2011, 33 sites across the United States submitted discharge data essential to this analysis. Data describing the patients, their procedures, and their hospital experience were entered. LOS estimates were generated. Prolonged LOS in the ICU was defined as stay greater than or equal to 26 days (i.e., 75th percentile). Statistical analyses were carried out to identify pre-operative, operative, and post-operative predictors of prolonged LOS in the ICU. The number of patients with complete discharge data was 303, and these subjects were included in the analysis. Univariate and multivariate analyses were performed. Multivariate analysis revealed that lower number of enrolled participants (e.g., 1–10) per site, the presence of pre-operative acidosis, increased circulatory arrest time, the occurrence of a central line infection, and the development of respiratory insufficiency requiring re-intubation were associated with prolonged LOS in the ICU. Prolonged LOS in the ICU following Stage I palliation in patients with HLHS and HLHS variant anatomy is associated with site enrollment, circulatory arrest time, pre-operative acidosis, and some post-operative complications, including central line infection and re-intubation. Further study of these associations may reveal strategies for reducing LOS in the ICU following the Norwood and Norwood-variant surgeries.

BACKGROUND AND OBJECTIVES: Children with medical complexity (CMC) account for disproportionately high hospital use, and it is unknown if hospitalizations may be prevented. Our objective was to summarize evidence from (1) studies characterizing potentially preventable hospitalizations in CMC and (2) interventions aiming to reduce such hospitalizations. METHODS: Our data sources include Medline, Cochrane Central Register of Controlled Trials, Web of Science, and Cumulative Index to Nursing and Allied Health Literature databases from their originations, and hand search of article bibliographies. Observational studies (n = 13) characterized potentially preventable hospitalizations, and experimental studies (n = 4) evaluated the efficacy of interventions to reduce them. Data were extracted on patient and family characteristics, medical complexity and preventable hospitalization indicators, hospitalization rates, costs, and days. Results of interventions were summarized by their effect on changes in hospital use. RESULTS: Preventable hospitalizations were measured in 3 ways: ambulatory care sensitive conditions, readmissions, or investigator-defined criteria. Postsurgical patients, those with neurologic disorders, and those with medical devices had higher preventable hospitalization rates, as did those with public insurance and nonwhite race/ethnicity. Passive smoke exposure, nonadherence to medications, and lack of follow-up after discharge were additional risks. Hospitalizations for ambulatory care sensitive conditions were less common in more complex patients. Patients receiving home visits, care coordination, chronic care-management, and continuity across settings had fewer preventable hospitalizations. Conclusions: There were a limited number of published studies. Measures for CMC and preventable hospitalizations were heterogeneous. Risk of bias was moderate due primarily to limited controlled experimental designs. Reductions in hospital use among CMC might be possible. Strategies should target primary drivers of preventable hospitalizations.