Neonatal networks: clinical research and quality improvement

Seminars in Fetal & Neonatal Medicine 20 (2015) 410e415

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Seminars in Fetal & Neonatal Medicine journal homepage: www.elsevier.com/locate/siny

Review

Neonatal networks: clinical research and quality improvement Jochen Profit a, b, *, Roger F. Soll c, d a Perinatal Epidemiology and Health Outcomes Research Unit, Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine and Lucile Packard Children's Hospital, Palo Alto, CA, USA b California Perinatal Quality Care Collaborative, Palo Alto, CA, USA c Vermont Oxford Network, Burlington, VT, USA d Department of Pediatrics, University of Vermont College of Medicine, Burlington, VT, USA

s u m m a r y Keywords: Infant Newborn Quality of care Collaborative quality improvement

Worldwide, neonatal networks have been formed to address both the research and quality improvement agenda of neonataleperinatal medicine. Neonatal research networks have led the way in conducting many of the most important clinical trials of the last 25 years, including studies of cooling for hypoxic eischemic encephalopathy, delivery room management with less invasive support, and oxygen saturation targeting. As we move into the future, increasing numbers of these networks are tackling quality improvement initiatives as a priority of their collaboration. Neonatal quality improvement networks have been in the forefront of the quality movement in medicine and, in the 21st century, have contributed to many of the reported improvements in care. In the coming years, building and maintaining this community of care is critical to the success of neonataleperinatal medicine. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction No neonatal intensive care unit (NICU) is an island. In the 21st century, virtually every NICU belongs to one or more “networks” of units, based on economic relationships, research or quality improvement. The following article discusses the growth of research networks, the contributions these networks make to research in neonataleperinatal medicine, and the transition of these networks from vehicles for multicenter clinical trials and research to “improvement networks”.

2. Role of research networks The first networks in neonataleperinatal medicine had their roots in a shared mission to conduct high-quality multicenter trials. Prior to creating formal networks, there were various research “consortiums” that collaborated to conduct large multicenter randomized controlled (RCTs) trials. Examples of such multicenter

* Corresponding author. Address: Perinatal Epidemiology and Health Outcomes Research Unit, Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine, MSOB Rm x115, 1265 Welch Road, Stanford, CA 94305, USA. Tel.: þ1 650 725 9933. E-mail address: profi[email protected] (J. Profit). http://dx.doi.org/10.1016/j.siny.2015.09.001 1744-165X/© 2015 Elsevier Ltd. All rights reserved.

collaborations include the large multicenter RCTs of indomethacin for the treatment of patent ductus arteriosus and of high-frequency oscillation [1,2]. These and many other large multicenter trials in neonatal perinatal medicine were funded for the purpose of conducting a specific research study and did not necessarily foster further long-term collaboration between the participating centers. Efforts such as these still exist and are responsible for some of the most influential recent trials, such as the Caffeine for Apnea of Prematurity (CAP) Trial supported by the Canadian Institutes of Health Research and the National Health and Medical Research Council of Australia [3]. One of the first successful efforts at creating a long-term neonatal research network is the National Institute of Child Health and Human Development (NICHD) Neonatal Research Network [4]. The Neonatal Research Network (NRN) was established by the NICHD in 1986 to address the need for high-quality prospectively defined data on neonatal outcomes and to create a platform to conduct large-scale high-quality RCTs. Initially, seven university centers were selected; the NRN has now grown to a network of 18 premier academic centers in the USA. Today, there are many well-established and productive neonatal networks worldwide [5]. These include the AustraliaeNew Zealand Neonatal Network (ANZNN) [6,7], the Canadian Neonatal Network [8], the Israeli Neonatal Network [9,10], the Neonatal Research Network of Japan [11,12], the Swedish Neonatal Quality Register

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[13] and the UK Neonatal Collaborative [14]. In addition, practice groups such as Pediatrix have used their extensive database to explore issues of resource allocation and dissemination of new therapies [15]. The Vermont Oxford Network (VON) is the largest of the current neonatal networks [16]. The VON is a not-for-profit organization established in the late 1980s with the goals of improving the quality and safety of medical care for newborn infants and their families through a coordinated program of research, education, and quality improvement. To date, >1000 institutions in 31 countries have joined the VON. The VON database includes records on more than two million infants accounting for >63 million patient days. Although not defined by geographic scope, the VON database includes >90% of very-low-birthweight (VLBW) infants in the USA. The California Perinatal Quality Care Collaborative (CPQCC) was established as a population-based multi-stakeholder organization, focused on improving the quality and safety of newborns across the state [55]. More than 130 NICUs and 95% of VLBW infants in the state are captured by the CPQCC. The Collaborative works closely with VON and has served in part as an innovation network, including the use of electronic data submission tools, and linkages to complementary public data sources. At the core, these networks have been established to collect data on high-risk neonates, to identify trends in outcomes (particularly for a sentinel population of VLBW infants) and to benchmark performances of their respective centers. Networks have also created focused registries to address issues relevant to rare but important neonatal populations, such as neonates with encephalopathy or surgical conditions such as diaphragmatic hernia [17,18]. 3. What have neonatal networks accomplished? Neonatal networks worldwide have made major strides in adding to the evidence base of neonatal perinatal medicine. There are many examples of the broad research agenda conducted by all of these networks. Not to slight any of the exemplary efforts of these multiple networks, we draw most of our examples from the NICHD Neonatal Network and the VON, two networks that are distinctly different in their structure and funding, but nevertheless have contributed to a broad range of research initiatives. In addition, we focus on the CPQCC and its unique linked data infrastructure.

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5. Pragmatic clinical trials Initial trials of innovative therapies such as whole-body cooling require a sophisticated research infrastructure. These are best left to well-funded networks such as the NICHD Neonatal Research Network or specifically funded multicenter collaborations such as the CAP Trial. However, there is a role for explanatory or pragmatic trials. Building on the seminal work of the National Perinatology Epidemiology Unit in Oxford, UK, the VON has conducted pragmatic trials of available therapies to evaluate clinically important outcomes. The first such trial compared two surfactant preparations for the treatment of respiratory distress syndrome (RDS) [25]. This trial enrolled >1300 VLBW infants with RDS. A previous similar but smaller trial performed by the NICHD Neonatal Research Network [26] showed similar results, demonstrating that pragmatic multicenter trials could be performed using volunteer investigators at significantly lower cost. Subsequent pragmatic trials conducted by VON have evaluated important practical issues in neonatal intensive care including early postnatal dexamethasone [27], skin care with emollient ointments [28], delivery room management with less invasive forms of respiratory support [29] and heat loss prevention using polyethylene wraps [30]. 6. Infant follow-up The NICHD Neonatal Research Network and many of the international neonatal networks have led the way in comprehensive reporting of developmental follow-up in high-risk infants. This is an important commitment of the neonatal research networks and is particularly meaningful in networks that draw from a geographically based population. Follow-up has been particularly important as we grapple with issues regarding support for many of the most preterm infants [31e34]. Comprehensive follow-up from clinical trials has been a feature of trials conducted by well-funded consortiums as well as trials conducted within the NICHD Neonatal Research Network. Whether this follow-up can be accomplished only by detailed neurodevelopmental assessment, as opposed to parental or provider questionnaires, remains an unanswered but important question due to the resources needed for these efforts [35]. In addition, tools that are widely used by the neonatal community include the Risk Assessment website that allows for understanding of infant outcomes; both in terms of short-term morbidities and long-term follow-up [36,37]. 7. Observational studies

4. Multi-center randomized controlled trials 7.1. Trends in outcome and practice Without sufficient populations on which to draw, the sample size needed to conduct multicenter randomized trials in neonatal perinatal medicine are prohibitively large and beyond the scope of individual institutions. In addition, for groundbreaking new therapies, the financial support needed for the clinical research team is formidable. Despite these constraints, the NICHD Neonatal Research Network has been at the forefront of many of the most important clinical trials of the past 25 years. Landmark studies done by the NICHD Neonatal Research Network alone or in collaboration with other neonatal research networks or consortiums include the initial studies of inhaled nitric oxide [19], prophylactic indomethacin [20], vitamin A for the prevention of chronic lung disease [21], whole-body cooling [22], and the factorial trial of delivery room nasal continuous positive airway pressure (CPAP) and oxygen targeting [23,24]. Without such a wellfunded effort, many of these studies involving breakthrough therapies could never be accomplished.

Neonatal networks have published multiple observational studies regarding trends in outcomes and practice. Although advances in neonatal care in the 20th century resulted in significant reductions in neonatal mortality and morbidity, in recent years many neonatal networks have reported meager progress or even worsening of important clinical outcomes [38e41]. This lack of progress raises questions about the effectiveness of our practices. Seminal work charting the increasing use of antenatal corticosteroids and the decreasing use of postnatal steroids, both based on strong recommendations from authoritative sources, have been reported by multiple networks [42,43]. Networks have tracked the introduction of surfactant therapy and the nuances of surfactant use [44,45]. These changes in practice undoubtedly improved neonatal outcomes. Significant changes in obstetric, delivery room and neonatal practices have occurred in the past decade [46]. Use of surfactant treatment in the delivery room has

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increased, as have less invasive methods of respiratory support, including nasal CPAP. However, these many changes in practice have not translated into significant improvement in many clinically important outcomes. 8. Studies of structure and process Databases maintained by neonatal networks can evaluate issues of structure and process that cannot be evaluated in the context of RCTs. Research has been conducted to assess the contribution of differences in the structure and organization of NICUs and variation in patient outcome. These have included studies on the effect of volume of admission and the minority-serving status of hospitals [47], the role of nursing staff and nurse-work environment [48], and the impact of reduced resident working hours and time of admission [49,50]. None of these issues can be addressed through conventional trials and all require a large number of participating centers.

steps. These data are essential to further the knowledge of how specific processes and factors may influence quality measures. Fair and useful benchmarking of quality measures and the study of variation in care is dependent on the collection of high-quality data. Data elements should be clearly defined to facilitate both coding and interpretation of the data. In addition, collection should be parsimonious, given the substantial effort associated with abstraction and transmission (automated extraction from the electronic health record may reduce the need for parsimony in the future). Finally, measures should be malleable, meaning that there is a defined pathway for improvement. In the case of CPQCC, data have been linked with several data sources, including a transport data set, California vital statistics, administrative maternal care data, and state-wide high-risk infant follow-up, providing a unique opportunity to enrich our understanding of how perinatal factors influence long-term infant outcomes for infants requiring NICU care. 12. Types of data collected by improvement networks

9. Variation Variation has been found in almost every area of medical practice and the NICU is no exception. There are several potential sources of observed variations in interventions and outcomes. These include differences in population characteristics and severity of illness (case mix), chance and differences in the processes and delivery of health care. If the differences due to case mix and chance can be adequately accounted for, the residual unexplained variation may be a valuable indicator of differences in the quality or effectiveness of care. The significant variation in care and outcomes demonstrated in the care of sick newborns [8,51e54] provides a particularly strong case for the role of quality improvement and the networks facilitating such improvement.

The main data types collected for quality improvement include structural or contextual, process, and outcome measures [56]. 12.1. Structure and institutional context Quality improvement efforts have paid relatively little attention to the structural and contextual factors that determine care and outcomes in NICUs. Consequently, relatively few data are collected and reported back to members in this regard. Most of the measures that are reported include variables that inform leadership on volume and type of patients admitted to their NICUs. However, there is an increasing appreciation that the organizational environment may be a key aspect of performance given that quality improvement activities often require changing systems of care as well as the behavior of individuals at the frontline of care.

10. Quality improvement networks 12.2. Processes Improvement networks such as VON [16] and CPQCC [55] have played a critical role in identifying and addressing shortcomings in care that can lead to potential improvements in outcome. Neonatal quality improvement networks share certain characteristics. Their primary mission is to improve care for infants, not necessarily to perform the traditional research activities of the large trials networks. These networks typically use audit and feedback of standardized case-mix adjusted performance and benchmarking reports to motivate participating centers. In addition to performance feedback, improvement networks also provide tools for improvement activities. These range from providing examples or lessons learned from other NICUs to suggestions for potentially better practices to formal evidence review and development of toolkits and to collaborative quality improvement. Networks also train their members in quality improvement methodology. 11. Routine collection of high-quality clinical data A key component of quality improvement networks is a standardized data collection system to create benchmarks for performance, both within a center over time and across centers. In order to identify opportunities for quality improvement, data are necessary to determine performance relative to current performance or peer-derived benchmarks. Once quality improvement is undertaken, data are used to track the effect of changes and interventions to assess the impact of quality improvement and to determine next

Most NICUs and improvement networks focus on processes as the key component of their quality improvement efforts. Where there is clear evidence for the link between a process and a desirable or undesirable clinical outcome, the process measure itself can serve as a quality measure. For example, given the evidence for the clinical impact of antenatal corticosteroids, utilization of antenatal steroids in and of itself represents a key process change worthy of quality improvement efforts [57e59]. 12.3. Outcomes Ultimately, outcomes are of key interest in all quality improvement activities. Pragmatically, outcomes are negative events such as death and morbidity, and quality is inferred on the basis of lower-than-expected negative event rates. It is important to record a wide spectrum of outcomes, but for an outcome to serve as an effective quality indicator, evidence must be strong that variations in process or structure can change its incidence. A widely used outcome measure is nosocomial infection, or the more specific subset of central-line-associated infections. These infections are an important quality indicator because they cause significant morbidity, and incorporating certain processes of patient care (e.g. hand hygiene and intravenous line care) has been shown to reduce their incidence [60]. Studies of nosocomial infections in the NICU have shown wide variation by hospital, signifying opportunities for improvement, leading to successful efforts to reduce infections at

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collaborative levels, and identification of high performers who can serve as role models for other NICUs [61e66]. 13. Risk adjustment/fair comparisons As previously discussed, variation in outcome may be due to chance or case mix, not necessarily reflecting quality of care. As we move into the future, issues such as setting benchmarks, pay-forperformance or public reporting, require further statistical, methodological considerations. The incorporation of patient characteristics including both maternal socio-demographic and clinical factors, as well as infant factors into risk adjustment allows for fairer comparisons by accounting for expected rates of positive or negative outcomes. In considering what variables to use in risk adjustment, there are two essential requirements. First, the variable should be a risk factor for the outcome of interest. Second, the variable should not be under the control of the entity being evaluated. Birthweight, gestational age, plurality, intrauterine growth, birth defects, and sex are risk factors that are commonly used as risk adjusters to control for institutional differences in case mix in order to make fair comparisons across NICUs [67,68]. Using this approach, the expected number of deaths (or adverse outcomes) at each NICU can be determined based on the characteristics (risk adjustment variables) of infants treated at that NICU. The ratio or a difference between the observed number of events and the expected number of events can then be calculated. It is important to recognize that all estimates of risk-adjusted performance must be interpreted carefully and that these estimates are only the first step in assessing the quality of care [51,69,70]. 14. Limitations of registry data Despite the substantial benefit NICUs can derive from tracking their performance over time and from comparing themselves with other NICUs, it is important to note several limitations of registry data. First, as alluded to earlier, manual data abstraction is labor intensive and requires substantial support, expertise, and motivation on behalf of the member NICUs as well as infrastructure, training and back-office support by the network headquarters. Automated extraction from electronic health records may provide some relief but there remain numerous barriers to ensure that such data are reliable and valid. Second, each clinical outcome is produced by numerous processes, but collecting data on all of these would be too laborious. Thus, registry data lack important granularity. However, this granularity can and must be added during specific improvement initiatives. Third, assigning an outcome to individual NICUs for infants that are transferred can be difficult; it may be unclear where an event occurred. Even if it is clear where an event occurred, it is difficult to assign responsibility because, by the time of transfer, a patient's degree of illness may place him at high risk for an adverse outcome at the receiving hospital. 15. Collaborative improvement Collaborative improvement is a core activity of improvement networks. Evidence is accumulating that these initiatives are quite successful. One of the first quality improvement collaboratives was undertaken by the VON NIC/Q Project [65,71]. Ten self-selected NICUs received the intervention. They formed two subgroups (six NICUs working on infection, four NICUs working on chronic lung disease). Sixty-six other NICUs served as a contemporaneous comparison group. NICUs formed multi-disciplinary teams that worked together under the direction of a trained facilitator over a

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three-year period beginning in January 1995. They received instruction in quality improvement, reviewed performance data, identified common improvement goals, and implemented “potentially better practices” developed through analysis of the processes of care, literature review, and site visits. NICUs participating in this initiative demonstrated decrease in nosocomial infection, chronic lung disease, and costs. The CPQCC has conducted several collaboratives throughout California which have shown sustained improvements in primary outcomes (antenatal steroids, breast milk feeding at discharge, and hypothermia) [58,64,72]. The broader literature also shows a benefit of collaborative improvement work [73]. Improvement networks offer several types of collaboratives. Some offer in-depth partnering with other NICUs and face-to-face meetings, others are conducted via the web, or simply offer toolkits for local implementation. Some evidence exists correlating the degree of intensity of engagement with effectiveness of the collaborative [72]. Both the CPQCC and the VON collaboratives are based on the Model for Improvement developed by Langley and Nolan [74] and advocated by the Institute for Healthcare Improvement [75,76]. However, other effective methodologies exist (Lean, Six Sigma, Deming, etc.). Although meaningful in their differences, their common thread lies in the centrality of managing work processes while optimizing the care delivery environment. Organizational goals for high-quality care delivery and patient safety are undergirded by strategies that encourage continuous monitoring of processes and outcomes, facilitate continuous learning, and minimize unwarranted waste and process variation. Paramount to all is the concept that changes must not destroy productivity. To accomplish this, changes must be integrated into the workflow and allow the desired care to be provided more efficiently. These aspects are critical to the sustainability of change. 16. New frontiers for neonatal quality networks The neonatal networks focusing on quality improvement represent the vanguard of large-scale quality improvement efforts in medicine. Preterm infants have experienced substantial improvements in certain (but not all) morbidities over the last decade [40]. Improvement networks have also made substantial contributions to quality science using innovative trial designs for applied research settings [76]. Nevertheless, many challenges remain to be addressed in next generation networks. First, improvement efforts have largely targeted specific processes or outcomes. Whereas this is important, there is an increasing recognition that the context of care is critical to sustained quality improvement success. The VON has been innovative in experimenting with addressing care context using the Model for Understanding Success in Quality (MUSIQ) [68]; additional such efforts may consider specifically addressing safety culture, teamwork, leadership, or caregiver resilience. Process improvement could be combined with efforts to improve aspects of care context. Second, improvement networks should continue to advance quality science. The optimal method for continuous improvement is not known. In fact, different problems are likely to require different methods. In addition, NICUs differ in their quality capacity. Therefore, improvement networks should not depend solely on the Model for Improvement. The Joint Commission has been supporting the use of more advanced methods such as Lean or Six Sigma [77]. Using practical trial designs (stepped wedge or adaptive), collaboratives could formally study various approaches to identify what works best in what situation. Third, collaborative improvement efforts tend to attract the same NICUs over time. To spread improvement across provider

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networks, collaboratives could be organized specifically to include perinatal centers and their respective referral NICUs. A highpriority clinical topic that spreads across different types of NICUs could be chosen to facilitate such improvement. Given the current changes in health care reimbursement towards budgeted payments for care of populations, improvement at the network level will be critical to the financial health of individual institutions. Fourth, current retrospective feedback for NICUs, even though provided in real time, is akin to navigating through the rear-view mirror. Potentially, networks could take on a more active role as an early warning center. Using data-forecasting methods, networks could provide early alerts to NICUs when their computed quality rates exceed certain control limits. Both positive and negative feedback could be provided. Finally, neonatal networks may have a responsibility to extend their knowledge and capacity beyond their borders to include middle- and low-income country settings. Innovative, low-cost avenues should be explored to facilitate training in quality improvement, routine capture of data, and improvement collaboratives for these members addressing their specific needs and barriers. These countries are uniquely poised to benefit from quality improvement know-how and engagement with a global community of peers because much of the quality work is low cost. Networks such as the VON, which already has member NICUs from such nations, can provide a valuable platform for knowledge diffusion, experimentation, and multi-directional learning.

17. Conclusion Neonatal networks have a lot to be proud of. The neonatal research networks have led the way in conducting many of the most important trials of the last 25 years. The safe introduction of practices such as inhaled nitric oxide for critically ill term neonates with hypoxic respiratory failure or cooling for term infants with moderate-to-severe hypoxiceischemic encephalopathy would not be part of our routine practice today without these sophisticated networks. Both research networks and improvement networks have contributed invaluably in charting the progress (or lack thereof) in the care of critically ill neonates and following dissemination of care and changes in practice. Neonatal quality improvement networks have been at the forefront of the quality movement in medicine. Participation of NICUs in the networks and in continuous quality improvement is testament to the professionalism that motivates providers and to the value that improvement networks provide to their members. Countless infants, families, and providers have benefited from these efforts. Building on this foundation, there are exciting opportunities for improvement networks to make their work even more impactful.

Conflict of interest statement Dr Soll is President and Director of Clinical Trials and Follow up at the Vermont Oxford Network. Dr Profit is Director of Perinatal Health Systems Research at Stanford University and a member of the Executive Steering Committee at the California Perinatal Quality Care Collaborative.

Funding source Dr Profit's contribution is supported in part by a Stanford Child Health Research Institute grant (1111239-285-JHACT).

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