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Measuring quality and outcomes in pediatric cardiac critical care
Progress in Pediatric Cardiology 33 (2012) 33–36
Contents lists available at SciVerse ScienceDirect
Progress in Pediatric Cardiology journal homepage: www.elsevier.com/locate/ppedcard
Measuring quality and outcomes in pediatric cardiac critical care☆ Michael G. Gaies a,⁎, Howard E. Jeffries b, Jeffrey P. Jacobs c, Peter C. Laussen d a
Division of Cardiology, Department of Pediatrics and Communicable Diseases, University of Michigan Medical School, C.S. Mott Children's Hospital, Ann Arbor, MI, United States Division of Critical Care Medicine, Department of Pediatrics, University of Washington School of Medicine, Seattle Children's Hospital, Seattle, WA, United States c Department of Surgery, University of South Florida, All Children's Hospital, St. Petersburg, FL, United States d Division of Cardiovascular Critical Care, Department of Cardiology, Children's Hospital Boston, Boston, MA, United States b
a r t i c l e
i n f o
Keywords: Critical care Patient outcomes Virtual PICU system Quality-of-care metrics
a b s t r a c t Measuring quality and outcomes in the pediatric cardiac critical care environment is challenging due to many inherent obstacles. These include a diverse patient mix, difficulty in determining how the care of the intensive care unit team contributes to outcomes and lack of an adequate risk-adjustment method for pediatric cardiac critical care patients. Despite these barriers, new solutions are emerging that capitalizes on lessons learned from other quality improvement initiatives and provide opportunities to build on those successes. The infrastructure is in place to develop robust quality metrics, create benchmarks for patient outcomes and to determine the structures and processes that drive variation in outcomes in the pediatric cardiac critical care setting. © 2011 Elsevier Ireland Ltd. All rights reserved.
1. Introduction The improvement in morbidity and mortality after pediatric and congenital heart surgery observed over the last three decades [1] can be attributed in part to the growth and development of pediatric cardiac critical care as a specialty. The understanding of how intensive care interventions and morbidities impact short- and long-term patient outcomes in pediatric surgical and non-surgical patients with critical cardiovascular disease continues to evolve. In order to continue the progress made to-date, pediatric cardiac critical care practitioners and investigators will need robust methods for measuring cardiac critical care outcomes, assessing quality of care, and testing new interventions. The pediatric cardiac critical care community faces inherent challenges to achieving these goals, but current efforts within the group of multidisciplinary specialists who practice here – including pediatric critical care physicians, cardiothoracic surgeons, cardiologists, anesthesiologists, nurses, perfusionists, respiratory therapists, and others – offer the possibility of solutions. 2. Challenges to measuring outcomes and quality in pediatric cardiac critical care 2.1. Heterogeneous patient populations Pediatric cardiac critical care units are populated by patients, from birth to adulthood, with a wide variety of clinical conditions and ☆ All authors report no financial disclosures related to the content of the article. ⁎ Corresponding author at: U of M Congenital Heart Center, C.S. Mott Children's Hospital, 1540 E. Hospital Drive, Ann Arbor, MI 48109-4204, United States. Tel.: +1 734 936 3770. E-mail address: [email protected] (M.G. Gaies). 1058-9813/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.ppedcard.2011.12.006
pathophysiology. The most basic distinction is between patients admitted to the intensive care unit (ICU) directly from the operating room after palliative or corrective surgery for structural heart disease and those admitted for medical conditions including, but not limited to, pre-operative care, congestive heart failure exacerbation, arrhythmia management, or after cardiac arrest. It is clear that one metric could not “fit all,” and separate outcomes must be tracked for surgical vs. non-surgical patients. Though certain metrics like hospitalacquired infection rate, development of acute kidney injury, and rate of new neurologic injury are important to measure in both populations, sub-analysis of outcomes within each group is mandatory. Finally, especially when considering the measurement of long-term outcomes, like neurocognitive development and functional status, the spectrum of patient age necessitates separate strategies and test batteries. 2.2. Defining critical care patient outcomes The ideal independent outcome measure for the pediatric cardiac critical care environment would be one that reflects the competence and quality of care provided by the ICU team, and is unaffected by care prior or subsequent to the ICU admission. However, robust metrics to allow such an assessment have not yet been developed. This is perhaps most apparent when considering post-operative care: it is extremely difficult to separate the contribution to outcomes of care received in the ICU from the impact of surgical care. For example, average length of mechanical ventilation after a particular operative procedure will depend on the ventilation strategy and weaning practices of an ICU team, but will also be influenced by the frequency of residual anatomic defects, anesthetic practices and complicating surgical morbidities (e.g. phrenic nerve injury). Propensity to develop acute kidney injury will reflect not just the fluid and hemodynamic
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M.G. Gaies et al. / Progress in Pediatric Cardiology 33 (2012) 33–36
management in the ICU, but will also depend on the preoperative condition of the patient, surgical results, postoperative ventricular function and cardiac output, and strategies on cardiopulmonary bypass for organ protection and mitigating the inflammatory response. While the search to define an “ideal” pediatric cardiac critical care outcome measure in post-operative patients continues, it is probably necessary in the short-term to view outcome measurement as an assessment of an entire pediatric cardiac surgical program. While the outcomes of medical patients with cardiovascular disease treated in intensive care units are influenced by their preadmission condition, they are also more likely to be influenced by the interventions and care of the CICU team. Measurement of outcomes and application of validated risk-adjustment strategies employed in the general pediatric critical care setting (see below) would appear to be more straightforward in these patients than in a post-surgical population. However, it will be important to determine whether benchmarks for certain outcomes (i.e. catheter-associated bloodstream infections, resource use, frequency of cardiac arrest) are applicable to a group of patients with unique pathophysiology compared to the general pediatric intensive care patient. Accurate measurement and comparison of clinical outcomes is dependent on a common nomenclature and standardized data collection. Perhaps reflecting the diversity of practitioners who participate in pediatric cardiac critical care, until recently there have been no standard definitions for events or complications that occur commonly in the pediatric cardiac intensive care unit. However, as a result of the efforts of The International Society for Nomenclature of Paediatric and Congenital Heart Disease (ISNPCHD) [http://www.ipccc.net/] and the Multi-Societal Database Committee for Pediatric and Congenital Heart Disease (MSDC), a consensus-based, comprehensive nomenclature now exists for the diagnosis, procedures, and complications associated with the treatment of patients with pediatric and congenital cardiac disease [2]. This nomenclature has been adopted by several clinical databases, including most notably: • The Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database, • The European Association for Cardio-Thoracic Surgery (EACTS) Congenital Heart Surgery Database, • The IMPACT Interventional Cardiology Registry™ (IMproving Pediatric and Adult Congenital Treatment) of the National Cardiovascular Data Registry R of The American College of Cardiology Foundation R and The Society for Cardiovascular Angiography and Interventions (SCAI), • The Joint Congenital Cardiac Anesthesia Society–Society of Thoracic Surgeons Congenital Cardiac Anesthesia Database, and • The Virtual PICU System (VPS). In the future, it will be imperative for the pediatric cardiac critical care community to capitalize on the accomplishments of the ISNPCHD and MSDC to-date, and to mandate standardization within and between database projects to promote data sharing and robust analyses of large amounts of data (see below). 2.3. Defining predictive intermediate outcome variables While mortality and specific post-operative morbidities constitute some of the most important metrics in the pediatric cardiac critical care setting, these variables can by definition only be collected after they occur. Another important class of outcome variables is one which would be termed “intermediate predictors”; clinical, physiologic, and other data variables that demonstrate a strong correlation with eventual clinical outcome. An ideal intermediate predictor variable to inform clinical practice should be one that is clinically important, relevant to the question asked, frequently occurring, and easily measurable without need for prolonged follow-up [3]. Clinicians managing patients with critical cardiovascular disease currently lack
good evidence to estimate the likelihood of eventual morbidity and mortality based on a patient's post-operative physiology and measures of organ dysfunction. Despite the large volume of clinical and physiologic data obtained at the time of admission and on subsequent days in the ICU, previous studies either do not address or have failed to show an association between these data and eventual clinical outcomes. Lack of these data limits clinicians' ability to adjust treatment and counsel families based on a child's likelihood of recovery. The lack of verified intermediate predictors of outcome also complicates the design of clinical trials in the ICU. It is difficult for investigators to choose primary outcomes that accurately assess the efficacy of interventions designed to improve myocardial and multiorgan recovery after admission to the ICU with critical cardiovascular disease. Mortality alone is an inadequate endpoint because fatality rates are now so low outside of patients admitted after single ventricle palliation; thus, required study sample sizes based on mortality are prohibitively large to conduct in single-institution cardiac critical care populations. It will be important for pediatric cardiac critical care researchers to elucidate and develop robust intermediate predictors to improve clinical care and clinical research in our environment. 2.4. Defining quality-of-care metrics The Institute of Medicine has defined quality health care as that which is safe, effective, efficient, timely, patient-centered, and equitable [4], highlighting many potential targets for quality metrics in pediatric cardiac critical care. Donabedian's structure–process–outcome framework has long served as the model for measurement of health care quality [5,6]. While structure and process measures may serve as important first steps in pediatric cardiac critical care quality assessment, selection of relevant and easily measured outcomes as quality metrics remains the goal; in Donabedian's own words, “outcomes… remain the ultimate validators of the effectiveness and quality of medical care” [6]. A series of quality measures for congenital and pediatric cardiac surgery based on Donabedian's triad of structure, process, and outcome was recently published [7]. As previously described, an ideal independent quality metric would be one that isolates the effect of ICU management on patient outcome, but this has proved elusive. In 2008, the American College of Cardiology (ACC) Adult Congenital/Pediatric Cardiology Section convened several sub-specialty working groups charged with developing quality metrics suitable for national implementation. Three authors on this manuscript (MGG, HEJ, PCL) have participated on the cardiac intensive care subcommittee since its inception. Several potential quality metrics were discussed in subcommittee meetings: rate of acute kidney injury, rate of cardiac arrest, rate of surgical site infection, and standardized mortality ratio using the Pediatric Index of Mortality-2 [8]. However, all of these metrics failed to win approval based on either relevancy (e.g. metric too dependent on surgical care factors), the perceived inability to accurately measure the outcome (e.g. metric currently not well-defined in a cardiac intensive care population), or both. A general metric related to the frequency of catheter-associated blood stream infections was considered, recognizing that this is not specific to pediatric cardiac intensive care practice. Definition of pediatric cardiac critical care-specific quality metrics, establishment of national benchmarks, and advancement of those proposed metrics through organizations like the ACC and the National Quality Forum for widespread adoption remain high priorities in the field. 2.5. Measures of illness severity and risk adjustment Adequate risk adjustment methods will be necessary in order to correctly interpret the to-be-developed patient outcomes and performance on quality metrics for pediatric cardiac critical care programs. Risk stratification systems like the Risk Adjustment for Congenital
M.G. Gaies et al. / Progress in Pediatric Cardiology 33 (2012) 33–36
Heart Surgery-1 (RACHS-1) method [9–12], the Aristotle methodology [11–13], and the STS-EACTS Mortality Score and Categories have been shown to predict mortality after congenital and pediatric heart surgery, but they do not account for physiologic differences after admission to the ICU. For example, RACHS-1, Aristotle, and the STSEACTS Complexity Categories can predict that a patient undergoing a Norwood (Stage 1) operation is more likely to die or have morbidity than a patient undergoing repair of tetralogy of Fallot. However, a good system is not available to predict who is more likely to die or have morbidity when comparing two patients undergoing a Norwood (Stage 1) operation or two patients undergoing repair of tetralogy of Fallot. Measuring performance in the ICU should take this physiologic variability and severity of illness into account; this is another example of attempting to separate and analyze the impact of pediatric cardiac critical care on eventual patient outcome. Furthermore, the procedure-based adjustment methods do not apply to non-surgical patients with critical cardiovascular disease. Thus, other tools are needed to provide adequate risk adjustment in the pediatric cardiac ICU. The previously mentioned PIM-2 is a physiology-based risk prediction scale developed from and subsequently validated in a general mix of ICU patients, including cardiac surgery patients, from Australia, New Zealand, and the United Kingdom [8]. Assessment of performance of PIM-2 in the original validation study demonstrated reasonable discrimination and calibration in patients with critical cardiac disease. However, previous literature demonstrates that heavy shifts in the patient mix of a cohort, as may be seen in specialized ICUs, may decrease the accuracy of a risk model's predictive ability [14–17]. In a recent study by Czaja and colleagues, the performance of PIM-2 was assessed in a cohort composed of only cardiac surgical patients [14]. The authors demonstrated the relatively poor predictive ability of PIM-2 in this patient population, particularly with regards to expected mortality in the highest-risk surgical patients. Similarly poor performance in pediatric cardiac surgical patients has been reported for the Pediatric Risk of Mortality (PRISM) [18]. Currently, both scores are applied shortly after admission for patients treated in the ICU pre-operatively, and they are not re-tabulated after surgery; as such important pieces of data are missing. While these scales may perform better in cardiac medical patients, the imperfections of the current risk-adjustment systems used for general pediatric critical care units highlight the need to develop a robust method that is validated primarily in cardiac critical care patients specifically for application within our population. Only then will the pediatric cardiac critical care community have the appropriate methods for measuring outcomes and quality.
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The Pediatric Cardiac Critical Care Consortium (PC4) now includes nine participating institutions, with data collection beginning in the 4th quarter of 2011. PC4 is committed to partnering with professional organizations across geographic and subspecialty boundaries, to integrate with existing databases, and to harmonize the common efforts to define quality for patients with critical cardiovascular disease. The stated goals of the consortium are to 1) measure outcomes and describe the variability in outcomes between institutions, 2) determine the specific structures and processes that differentiate high-performing centers, and 3) to disseminate those findings throughout the pediatric cardiac critical care community. To accomplish these goals, it will be necessary to develop a risk-adjustment method suitable for this highlyspecialized clinical environment, and this will be a focus of early research within the consortium. Finally, after establishing appropriate, risk-adjusted outcome benchmarks, PC4 will, in partnership with other major professional organizations, be able to propose robust quality metrics to be tracked by institutions, payors, and governing bodies. 3.2. Potential quality metric: failure-to-rescue Failure-to-rescue (FTR) rate is defined as the rate of in-hospital mortality after developing a complication. This metric has been used to explain a mechanism for variation between hospitals in mortality after adult inpatient surgery [19–21]; low-and high-performing hospitals in terms of overall adjusted mortality rates have similar rates of complications after surgical procedures, but low-performing hospitals have significantly higher FTR rates. In a landmark study, Ghaferi and colleagues showed that among Medicare patients undergoing inpatient general or vascular surgical procedures, those treated at highmortality hospitals were almost twice as likely to die after a complication as those treated at low-mortality hospitals despite equivalent rates of major complications [20]. These authors and others have identified the crucial role of the ICU in a hospital's response to complications, suggesting that timely recognition of a complication and rapid, expert treatment are likely characteristics of high-performing hospitals with lower FTR rates. FTR may be a highly-valuable metric for assessing the quality of care given in pediatric cardiac critical care units as well. In fact, the STS Congenital Heart Surgery Database Task Force is involved with preliminary analyses that examine FTR in the domain of pediatric cardiac surgery [22]. PC 4, in partnership with VPS and the STS, is ideally positioned to define, collect and analyze data related to specific ICU complications, and thus to define FTR among pediatric cardiac surgical centers and critical care units. 3.3. Integrating databases
3. Steps toward solutions 3.1. The pediatric critical care consortium (PC 4) Recognizing the challenges described above, new approaches are necessary to improve the measurement of outcomes and quality in the pediatric cardiac critical care setting. In 2009, investigators from C.S. Mott Children's Hospital, Children's Hospital Boston, University of California-San Francisco, Lucille Packard Children's Hospital, Children's Hospital of Wisconsin, and Seattle Children's Hospital formed a consortium of pediatric cardiac intensive care units dedicated to standardized data collection and performance of multi-institutional research to track outcomes and quality metrics. Initially funded by the NIH and the Clinical and Translational Science Awards program, the consortium created, in partnership with VPS, LLC, a pediatric cardiac critical care registry module to complement data currently entered into the existing VPS critical care and STS congenital databases. This module focuses on data collection for patients admitted to intensive care units after surgery, with variables collected in the pre- and post-operative periods covering the domains of critical care practices, physiologic/laboratory data, and outcomes.
The clinical care of patients with pediatric and congenital cardiac disease requires true multi-disciplinary collaboration, and this holds true for the process of measuring outcomes and quality. Platforms must be created to allow linkage of existing and future multiinstitutional subspecialty databases to promote seamless sharing of longitudinal data across temporal, geographical, and subspecialty boundaries [23–25]. To perform meaningful multi-institutional outcomes analyses and quality improvement, any database must incorporate the following seven essential elements: (1) Use of a common language and nomenclature. (2) Use of a database with an established uniform core dataset for collection of information. (3) Incorporation of a mechanism to evaluate the complexity of cases. (4) Implementation of a mechanism to assure and verify the completeness and accuracy of the data collected. (5) Collaboration between medical and surgical subspecialties. (6) Standardization of protocols for life-long longitudinal followup.
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(7) Implementation of strategies to use the data from quality improvement initiatives. Description of outcomes requires input from surgeons, cardiologists, anesthesiologists, intensivists, perfusionists, neurologists, educators, primary care physicians, nurses, respiratory therapists, and physical therapists, among others. Outcomes should inform current practice, but with therapeutic decision-making shifting toward maximization of long-term benefits there is an increased onus to monitor outcomes much later in life. Measurement has shifted beyond recording 30-day or hospital mortality, and must encompass longer-term follow-up, including cardiac and non-cardiac morbidities, and importantly, functional status and health-related quality of life. Methodologies must be implemented in databases to facilitate uniform, protocol-driven, and meaningful long-term follow-up [26,27]. The relatively small numbers of patients with critical cardiovascular disease requires multi-institutional, multi-disciplinary cooperation to accomplish these goals. By creating a platform that links extant databases from pediatric cardiology, pediatric cardiac surgery, pediatric cardiac anesthesia, and pediatric critical care, opportunities will be plentiful to improve patient care, research, and education related to patients with congenital and pediatric cardiac disease.
References [1] Boneva RS, Botto LD, Moore CA, et al. Mortality associated with congenital heart defects in the United States: trends and racial disparities, 1979–1997. Circulation 2001;103(19):2376–81. [2] Jacobs JP. Introduction—databases and the assessment of complications associated with the treatment of patients with congenital cardiac disease. Cardiol Young 2008;18(Suppl 2):1–37. [3] Scheurer MA, Thiagarajan RR. Vasoactive-inotropic score as a measure of pediatric cardiac surgical outcomes. Pediatr Crit Care Med 2010;11(2):307–8. [4] Institute of Medicine (U.S.), Committee on Quality of Health Care in America. Crossing the quality chasm : a new health system for the 21st century. Washington, D.C: National Academy Press; 2001. [5] Donabedian A. The quality of medical care. Science 1978;200(4344):856–64. [6] Donabedian A. Evaluating the quality of medical care. Milbank Mem Fund Q 1966;44(3):166–206 Suppl. [7] Jacobs JP JM, Austin EH, Mavroudis M, Pasquali SK, Lacour-Gayet FG, Tchervenkov CI, Walters III HW, Bacha EA, del Nido PJ, Fraser CD, Gaynor JW, Hirsch JC, Morales DLS, Pourmoghadam KK, Tweddell JT, Prager RL, Mayer JE. Quality Measures for Congenital and Pediatric Cardiac Surgery. The World Journal for Pediatric and Congenital Heart Surgery in press:3(1). [8] Slater A, Shann F, Pearson G, et al. PIM2: a revised version of the Paediatric Index of Mortality. Intensive Care Med 2003;29(2):278–85. [9] Jenkins KJ, Gauvreau K. Center-specific differences in mortality: preliminary analyses using the Risk Adjustment in Congenital Heart Surgery (RACHS-1) method. J Thorac Cardiovasc Surg 2002;124(1):97–104.
[10] Jenkins KJ, Gauvreau K, Newburger JW, et al. Consensus-based method for risk adjustment for surgery for congenital heart disease. J Thorac Cardiovasc Surg 2002;123(1):110–8. [11] Jacobs JP, Jacobs ML, Lacour-Gayet FG, et al. Stratification of complexity improves the utility and accuracy of outcomes analysis in a Multi-Institutional Congenital Heart Surgery Database: application of the Risk Adjustment in Congenital Heart Surgery (RACHS-1) and Aristotle Systems in the Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database. Pediatr Cardiol 2009;30(8):1117–30. [12] Jacobs ML, Jacobs JP, Jenkins KJ, et al. Stratification of complexity: the Risk Adjustment for Congenital Heart Surgery-1 method and the Aristotle Complexity Score—past, present, and future. Cardiol Young 2008;18(Suppl 2):163–8. [13] Jacobs JP, Lacour-Gayet FG, Jacobs ML, et al. Initial application in the STS congenital database of complexity adjustment to evaluate surgical case mix and results. Ann Thorac Surg 2005;79(5):1635–49 discussion 1635–1649. [14] Czaja AS, Scanlon MC, Kuhn EM, et al. Performance of the Pediatric Index of Mortality 2 for pediatric cardiac surgery patients. Pediatr Crit Care Med 2011;12(2): 184–9. [15] Metnitz PG, Lang T, Vesely H, et al. Ratios of observed to expected mortality are affected by differences in case mix and quality of care. Intensive Care Med 2000;26(10):1466–72. [16] Patel PA, Grant BJ. Application of mortality prediction systems to individual intensive care units. Intensive Care Med 1999;25(9):977–82. [17] Murphy-Filkins R, Teres D, Lemeshow S, et al. Effect of changing patient mix on the performance of an intensive care unit severity-of-illness model: how to distinguish a general from a specialty intensive care unit. Crit Care Med 1996;24(12): 1968–73. [18] Mildh L, Pettila V, Sairanen H, et al. Predictive value of paediatric risk of mortality score and risk adjustment for congenital heart surgery score after paediatric open-heart surgery. Interact Cardiovasc Thorac Surg 2007;6(5):628–31. [19] Ghaferi AA, Birkmeyer JD, Dimick JB. Complications, failure to rescue, and mortality with major inpatient surgery in medicare patients. Ann Surg 2009;250(6):1029–34. [20] Ghaferi AA, Birkmeyer JD, Dimick JB. Variation in hospital mortality associated with inpatient surgery. N Engl J Med 2009;361(14):1368–75. [21] Ghaferi AA, Birkmeyer JD, Dimick JB. Hospital volume and failure to rescue with high-risk surgery. Med Care 2011;49(12):1076–81. [22] Pasquali SK JJ, He X, Jacobs ML, O'Brien SM, Gaynor JW. Evaluation of Failure to Rescue as a Quality Metric in Pediatric Heart Surgery. In: Proceedings of the 2012 Annual Meeting of The Society of Thoracic Surgeons, Ft. Lauderdale, FL, 2012. [23] Jacobs JP, Jacobs ML, Mavroudis C, et al. Nomenclature and databases for the surgical treatment of congenital cardiac disease—an updated primer and an analysis of opportunities for improvement. Cardiol Young 2008;18(Suppl 2): 38–62. [24] Jacobs JP, Mavroudis C, Jacobs ML, et al. Nomenclature and databases—the past, the present, and the future : a primer for the congenital heart surgeon. Pediatr Cardiol 2007;28(2):105–15. [25] Jacobs JP, Maruszewski B, Kurosawa H, et al. Congenital heart surgery databases around the world: do we need a global database? Semin Thorac Cardiovasc Surg Pediatr Card Surg Annu 2010;13(1):3–19. [26] Jacobs JP, Barnett-Jones S, Cooper DC, Vincent R. Outcomes analysis and quality improvement for the treatment of patients with pediatric and congenital cardiac disease. The World Journal for Pediatric and Congenital Heart Surgery 2011;2(4): 620–33. [27] Jacobs JP, Gaynor JW. Invited commentary: the assessment of outcomes and the improvement of quality of the treatment of patients with congenital and pediatric cardiac disease. World J Pediatr Congenit Heart Surg 2011;2(4):597–602.
Contents lists available at SciVerse ScienceDirect
Progress in Pediatric Cardiology journal homepage: www.elsevier.com/locate/ppedcard
Measuring quality and outcomes in pediatric cardiac critical care☆ Michael G. Gaies a,⁎, Howard E. Jeffries b, Jeffrey P. Jacobs c, Peter C. Laussen d a
Division of Cardiology, Department of Pediatrics and Communicable Diseases, University of Michigan Medical School, C.S. Mott Children's Hospital, Ann Arbor, MI, United States Division of Critical Care Medicine, Department of Pediatrics, University of Washington School of Medicine, Seattle Children's Hospital, Seattle, WA, United States c Department of Surgery, University of South Florida, All Children's Hospital, St. Petersburg, FL, United States d Division of Cardiovascular Critical Care, Department of Cardiology, Children's Hospital Boston, Boston, MA, United States b
a r t i c l e
i n f o
Keywords: Critical care Patient outcomes Virtual PICU system Quality-of-care metrics
a b s t r a c t Measuring quality and outcomes in the pediatric cardiac critical care environment is challenging due to many inherent obstacles. These include a diverse patient mix, difficulty in determining how the care of the intensive care unit team contributes to outcomes and lack of an adequate risk-adjustment method for pediatric cardiac critical care patients. Despite these barriers, new solutions are emerging that capitalizes on lessons learned from other quality improvement initiatives and provide opportunities to build on those successes. The infrastructure is in place to develop robust quality metrics, create benchmarks for patient outcomes and to determine the structures and processes that drive variation in outcomes in the pediatric cardiac critical care setting. © 2011 Elsevier Ireland Ltd. All rights reserved.
1. Introduction The improvement in morbidity and mortality after pediatric and congenital heart surgery observed over the last three decades [1] can be attributed in part to the growth and development of pediatric cardiac critical care as a specialty. The understanding of how intensive care interventions and morbidities impact short- and long-term patient outcomes in pediatric surgical and non-surgical patients with critical cardiovascular disease continues to evolve. In order to continue the progress made to-date, pediatric cardiac critical care practitioners and investigators will need robust methods for measuring cardiac critical care outcomes, assessing quality of care, and testing new interventions. The pediatric cardiac critical care community faces inherent challenges to achieving these goals, but current efforts within the group of multidisciplinary specialists who practice here – including pediatric critical care physicians, cardiothoracic surgeons, cardiologists, anesthesiologists, nurses, perfusionists, respiratory therapists, and others – offer the possibility of solutions. 2. Challenges to measuring outcomes and quality in pediatric cardiac critical care 2.1. Heterogeneous patient populations Pediatric cardiac critical care units are populated by patients, from birth to adulthood, with a wide variety of clinical conditions and ☆ All authors report no financial disclosures related to the content of the article. ⁎ Corresponding author at: U of M Congenital Heart Center, C.S. Mott Children's Hospital, 1540 E. Hospital Drive, Ann Arbor, MI 48109-4204, United States. Tel.: +1 734 936 3770. E-mail address: [email protected] (M.G. Gaies). 1058-9813/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.ppedcard.2011.12.006
pathophysiology. The most basic distinction is between patients admitted to the intensive care unit (ICU) directly from the operating room after palliative or corrective surgery for structural heart disease and those admitted for medical conditions including, but not limited to, pre-operative care, congestive heart failure exacerbation, arrhythmia management, or after cardiac arrest. It is clear that one metric could not “fit all,” and separate outcomes must be tracked for surgical vs. non-surgical patients. Though certain metrics like hospitalacquired infection rate, development of acute kidney injury, and rate of new neurologic injury are important to measure in both populations, sub-analysis of outcomes within each group is mandatory. Finally, especially when considering the measurement of long-term outcomes, like neurocognitive development and functional status, the spectrum of patient age necessitates separate strategies and test batteries. 2.2. Defining critical care patient outcomes The ideal independent outcome measure for the pediatric cardiac critical care environment would be one that reflects the competence and quality of care provided by the ICU team, and is unaffected by care prior or subsequent to the ICU admission. However, robust metrics to allow such an assessment have not yet been developed. This is perhaps most apparent when considering post-operative care: it is extremely difficult to separate the contribution to outcomes of care received in the ICU from the impact of surgical care. For example, average length of mechanical ventilation after a particular operative procedure will depend on the ventilation strategy and weaning practices of an ICU team, but will also be influenced by the frequency of residual anatomic defects, anesthetic practices and complicating surgical morbidities (e.g. phrenic nerve injury). Propensity to develop acute kidney injury will reflect not just the fluid and hemodynamic
34
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management in the ICU, but will also depend on the preoperative condition of the patient, surgical results, postoperative ventricular function and cardiac output, and strategies on cardiopulmonary bypass for organ protection and mitigating the inflammatory response. While the search to define an “ideal” pediatric cardiac critical care outcome measure in post-operative patients continues, it is probably necessary in the short-term to view outcome measurement as an assessment of an entire pediatric cardiac surgical program. While the outcomes of medical patients with cardiovascular disease treated in intensive care units are influenced by their preadmission condition, they are also more likely to be influenced by the interventions and care of the CICU team. Measurement of outcomes and application of validated risk-adjustment strategies employed in the general pediatric critical care setting (see below) would appear to be more straightforward in these patients than in a post-surgical population. However, it will be important to determine whether benchmarks for certain outcomes (i.e. catheter-associated bloodstream infections, resource use, frequency of cardiac arrest) are applicable to a group of patients with unique pathophysiology compared to the general pediatric intensive care patient. Accurate measurement and comparison of clinical outcomes is dependent on a common nomenclature and standardized data collection. Perhaps reflecting the diversity of practitioners who participate in pediatric cardiac critical care, until recently there have been no standard definitions for events or complications that occur commonly in the pediatric cardiac intensive care unit. However, as a result of the efforts of The International Society for Nomenclature of Paediatric and Congenital Heart Disease (ISNPCHD) [http://www.ipccc.net/] and the Multi-Societal Database Committee for Pediatric and Congenital Heart Disease (MSDC), a consensus-based, comprehensive nomenclature now exists for the diagnosis, procedures, and complications associated with the treatment of patients with pediatric and congenital cardiac disease [2]. This nomenclature has been adopted by several clinical databases, including most notably: • The Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database, • The European Association for Cardio-Thoracic Surgery (EACTS) Congenital Heart Surgery Database, • The IMPACT Interventional Cardiology Registry™ (IMproving Pediatric and Adult Congenital Treatment) of the National Cardiovascular Data Registry R of The American College of Cardiology Foundation R and The Society for Cardiovascular Angiography and Interventions (SCAI), • The Joint Congenital Cardiac Anesthesia Society–Society of Thoracic Surgeons Congenital Cardiac Anesthesia Database, and • The Virtual PICU System (VPS). In the future, it will be imperative for the pediatric cardiac critical care community to capitalize on the accomplishments of the ISNPCHD and MSDC to-date, and to mandate standardization within and between database projects to promote data sharing and robust analyses of large amounts of data (see below). 2.3. Defining predictive intermediate outcome variables While mortality and specific post-operative morbidities constitute some of the most important metrics in the pediatric cardiac critical care setting, these variables can by definition only be collected after they occur. Another important class of outcome variables is one which would be termed “intermediate predictors”; clinical, physiologic, and other data variables that demonstrate a strong correlation with eventual clinical outcome. An ideal intermediate predictor variable to inform clinical practice should be one that is clinically important, relevant to the question asked, frequently occurring, and easily measurable without need for prolonged follow-up [3]. Clinicians managing patients with critical cardiovascular disease currently lack
good evidence to estimate the likelihood of eventual morbidity and mortality based on a patient's post-operative physiology and measures of organ dysfunction. Despite the large volume of clinical and physiologic data obtained at the time of admission and on subsequent days in the ICU, previous studies either do not address or have failed to show an association between these data and eventual clinical outcomes. Lack of these data limits clinicians' ability to adjust treatment and counsel families based on a child's likelihood of recovery. The lack of verified intermediate predictors of outcome also complicates the design of clinical trials in the ICU. It is difficult for investigators to choose primary outcomes that accurately assess the efficacy of interventions designed to improve myocardial and multiorgan recovery after admission to the ICU with critical cardiovascular disease. Mortality alone is an inadequate endpoint because fatality rates are now so low outside of patients admitted after single ventricle palliation; thus, required study sample sizes based on mortality are prohibitively large to conduct in single-institution cardiac critical care populations. It will be important for pediatric cardiac critical care researchers to elucidate and develop robust intermediate predictors to improve clinical care and clinical research in our environment. 2.4. Defining quality-of-care metrics The Institute of Medicine has defined quality health care as that which is safe, effective, efficient, timely, patient-centered, and equitable [4], highlighting many potential targets for quality metrics in pediatric cardiac critical care. Donabedian's structure–process–outcome framework has long served as the model for measurement of health care quality [5,6]. While structure and process measures may serve as important first steps in pediatric cardiac critical care quality assessment, selection of relevant and easily measured outcomes as quality metrics remains the goal; in Donabedian's own words, “outcomes… remain the ultimate validators of the effectiveness and quality of medical care” [6]. A series of quality measures for congenital and pediatric cardiac surgery based on Donabedian's triad of structure, process, and outcome was recently published [7]. As previously described, an ideal independent quality metric would be one that isolates the effect of ICU management on patient outcome, but this has proved elusive. In 2008, the American College of Cardiology (ACC) Adult Congenital/Pediatric Cardiology Section convened several sub-specialty working groups charged with developing quality metrics suitable for national implementation. Three authors on this manuscript (MGG, HEJ, PCL) have participated on the cardiac intensive care subcommittee since its inception. Several potential quality metrics were discussed in subcommittee meetings: rate of acute kidney injury, rate of cardiac arrest, rate of surgical site infection, and standardized mortality ratio using the Pediatric Index of Mortality-2 [8]. However, all of these metrics failed to win approval based on either relevancy (e.g. metric too dependent on surgical care factors), the perceived inability to accurately measure the outcome (e.g. metric currently not well-defined in a cardiac intensive care population), or both. A general metric related to the frequency of catheter-associated blood stream infections was considered, recognizing that this is not specific to pediatric cardiac intensive care practice. Definition of pediatric cardiac critical care-specific quality metrics, establishment of national benchmarks, and advancement of those proposed metrics through organizations like the ACC and the National Quality Forum for widespread adoption remain high priorities in the field. 2.5. Measures of illness severity and risk adjustment Adequate risk adjustment methods will be necessary in order to correctly interpret the to-be-developed patient outcomes and performance on quality metrics for pediatric cardiac critical care programs. Risk stratification systems like the Risk Adjustment for Congenital
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Heart Surgery-1 (RACHS-1) method [9–12], the Aristotle methodology [11–13], and the STS-EACTS Mortality Score and Categories have been shown to predict mortality after congenital and pediatric heart surgery, but they do not account for physiologic differences after admission to the ICU. For example, RACHS-1, Aristotle, and the STSEACTS Complexity Categories can predict that a patient undergoing a Norwood (Stage 1) operation is more likely to die or have morbidity than a patient undergoing repair of tetralogy of Fallot. However, a good system is not available to predict who is more likely to die or have morbidity when comparing two patients undergoing a Norwood (Stage 1) operation or two patients undergoing repair of tetralogy of Fallot. Measuring performance in the ICU should take this physiologic variability and severity of illness into account; this is another example of attempting to separate and analyze the impact of pediatric cardiac critical care on eventual patient outcome. Furthermore, the procedure-based adjustment methods do not apply to non-surgical patients with critical cardiovascular disease. Thus, other tools are needed to provide adequate risk adjustment in the pediatric cardiac ICU. The previously mentioned PIM-2 is a physiology-based risk prediction scale developed from and subsequently validated in a general mix of ICU patients, including cardiac surgery patients, from Australia, New Zealand, and the United Kingdom [8]. Assessment of performance of PIM-2 in the original validation study demonstrated reasonable discrimination and calibration in patients with critical cardiac disease. However, previous literature demonstrates that heavy shifts in the patient mix of a cohort, as may be seen in specialized ICUs, may decrease the accuracy of a risk model's predictive ability [14–17]. In a recent study by Czaja and colleagues, the performance of PIM-2 was assessed in a cohort composed of only cardiac surgical patients [14]. The authors demonstrated the relatively poor predictive ability of PIM-2 in this patient population, particularly with regards to expected mortality in the highest-risk surgical patients. Similarly poor performance in pediatric cardiac surgical patients has been reported for the Pediatric Risk of Mortality (PRISM) [18]. Currently, both scores are applied shortly after admission for patients treated in the ICU pre-operatively, and they are not re-tabulated after surgery; as such important pieces of data are missing. While these scales may perform better in cardiac medical patients, the imperfections of the current risk-adjustment systems used for general pediatric critical care units highlight the need to develop a robust method that is validated primarily in cardiac critical care patients specifically for application within our population. Only then will the pediatric cardiac critical care community have the appropriate methods for measuring outcomes and quality.
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The Pediatric Cardiac Critical Care Consortium (PC4) now includes nine participating institutions, with data collection beginning in the 4th quarter of 2011. PC4 is committed to partnering with professional organizations across geographic and subspecialty boundaries, to integrate with existing databases, and to harmonize the common efforts to define quality for patients with critical cardiovascular disease. The stated goals of the consortium are to 1) measure outcomes and describe the variability in outcomes between institutions, 2) determine the specific structures and processes that differentiate high-performing centers, and 3) to disseminate those findings throughout the pediatric cardiac critical care community. To accomplish these goals, it will be necessary to develop a risk-adjustment method suitable for this highlyspecialized clinical environment, and this will be a focus of early research within the consortium. Finally, after establishing appropriate, risk-adjusted outcome benchmarks, PC4 will, in partnership with other major professional organizations, be able to propose robust quality metrics to be tracked by institutions, payors, and governing bodies. 3.2. Potential quality metric: failure-to-rescue Failure-to-rescue (FTR) rate is defined as the rate of in-hospital mortality after developing a complication. This metric has been used to explain a mechanism for variation between hospitals in mortality after adult inpatient surgery [19–21]; low-and high-performing hospitals in terms of overall adjusted mortality rates have similar rates of complications after surgical procedures, but low-performing hospitals have significantly higher FTR rates. In a landmark study, Ghaferi and colleagues showed that among Medicare patients undergoing inpatient general or vascular surgical procedures, those treated at highmortality hospitals were almost twice as likely to die after a complication as those treated at low-mortality hospitals despite equivalent rates of major complications [20]. These authors and others have identified the crucial role of the ICU in a hospital's response to complications, suggesting that timely recognition of a complication and rapid, expert treatment are likely characteristics of high-performing hospitals with lower FTR rates. FTR may be a highly-valuable metric for assessing the quality of care given in pediatric cardiac critical care units as well. In fact, the STS Congenital Heart Surgery Database Task Force is involved with preliminary analyses that examine FTR in the domain of pediatric cardiac surgery [22]. PC 4, in partnership with VPS and the STS, is ideally positioned to define, collect and analyze data related to specific ICU complications, and thus to define FTR among pediatric cardiac surgical centers and critical care units. 3.3. Integrating databases
3. Steps toward solutions 3.1. The pediatric critical care consortium (PC 4) Recognizing the challenges described above, new approaches are necessary to improve the measurement of outcomes and quality in the pediatric cardiac critical care setting. In 2009, investigators from C.S. Mott Children's Hospital, Children's Hospital Boston, University of California-San Francisco, Lucille Packard Children's Hospital, Children's Hospital of Wisconsin, and Seattle Children's Hospital formed a consortium of pediatric cardiac intensive care units dedicated to standardized data collection and performance of multi-institutional research to track outcomes and quality metrics. Initially funded by the NIH and the Clinical and Translational Science Awards program, the consortium created, in partnership with VPS, LLC, a pediatric cardiac critical care registry module to complement data currently entered into the existing VPS critical care and STS congenital databases. This module focuses on data collection for patients admitted to intensive care units after surgery, with variables collected in the pre- and post-operative periods covering the domains of critical care practices, physiologic/laboratory data, and outcomes.
The clinical care of patients with pediatric and congenital cardiac disease requires true multi-disciplinary collaboration, and this holds true for the process of measuring outcomes and quality. Platforms must be created to allow linkage of existing and future multiinstitutional subspecialty databases to promote seamless sharing of longitudinal data across temporal, geographical, and subspecialty boundaries [23–25]. To perform meaningful multi-institutional outcomes analyses and quality improvement, any database must incorporate the following seven essential elements: (1) Use of a common language and nomenclature. (2) Use of a database with an established uniform core dataset for collection of information. (3) Incorporation of a mechanism to evaluate the complexity of cases. (4) Implementation of a mechanism to assure and verify the completeness and accuracy of the data collected. (5) Collaboration between medical and surgical subspecialties. (6) Standardization of protocols for life-long longitudinal followup.
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(7) Implementation of strategies to use the data from quality improvement initiatives. Description of outcomes requires input from surgeons, cardiologists, anesthesiologists, intensivists, perfusionists, neurologists, educators, primary care physicians, nurses, respiratory therapists, and physical therapists, among others. Outcomes should inform current practice, but with therapeutic decision-making shifting toward maximization of long-term benefits there is an increased onus to monitor outcomes much later in life. Measurement has shifted beyond recording 30-day or hospital mortality, and must encompass longer-term follow-up, including cardiac and non-cardiac morbidities, and importantly, functional status and health-related quality of life. Methodologies must be implemented in databases to facilitate uniform, protocol-driven, and meaningful long-term follow-up [26,27]. The relatively small numbers of patients with critical cardiovascular disease requires multi-institutional, multi-disciplinary cooperation to accomplish these goals. By creating a platform that links extant databases from pediatric cardiology, pediatric cardiac surgery, pediatric cardiac anesthesia, and pediatric critical care, opportunities will be plentiful to improve patient care, research, and education related to patients with congenital and pediatric cardiac disease.
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