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REFERENCES

1. Mendelsohn AB, Belle SH, Fischhoff B, et al: How patients feel about prolonged mechanical ventilation 1 yr later. Crit Care Med 2002; 30:1439–1445 2. R Galinkin J, Koh JL; Committee on Drugs, Section on Anesthesiology and Pain Medicine; American Academy of Pediatrics: Recognition and management of iatrogenically induced opioid dependence and withdrawal in children. Pediatrics 2014; 133:152–155 3. Tobias JD: Tolerance, withdrawal, and physical dependency after long-term sedation and analgesia of children in the pediatric intensive care unit. Crit Care Med 2000; 28:2122–2132 4. Hünseler C, Balling G, Röhlig C, et al; for the Clonidine Study Group: Continuous Infusion of Clonidine in Ventilated Newborns and Infants: A Randomized Controlled Trial. Pediatr Crit Care Med 2014; 15:511–522 5. Schnabel A, Poepping DM, Pogatzki-Zahn EM, et al: Efficacy and safety of clonidine as additive for caudal regional anesthesia: A quantitative systematic review of randomized controlled trials. Paediatr Anaesth 2011; 21:1219–1230 6. Tobias JD: Dexmedetomidine: Applications in pediatric critical care and pediatric anesthesiology. Pediatr Crit Care Med 2007; 8:115–131 7. Potts AL, Anderson BJ, Warman GR, et al: Dexmedetomidine pharmacokinetics in pediatric intensive care—A pooled analysis. Paediatr Anaesth 2009; 19:1119–1129 8. Davidson A, Flick RP: Neurodevelopmental implications of the use of sedation and analgesia in neonates. Clin Perinatol 2013; 40:559–573

9. Blaylock M, Engelhardt T, Bissonnette B: Fundamentals of neuronal apoptosis relevant to pediatric anesthesia. Paediatr Anaesth 2010; 20:383–395 10. Sanders RD, Sun P, Patel S, et al: Dexmedetomidine provides cortical neuroprotection: Impact on anaesthetic-induced neuroapoptosis in the rat developing brain. Acta Anaesthesiol Scand 2010; 54:710–716 11. Sanders RD, Xu J, Shu Y, et al: Dexmedetomidine attenuates isoflurane-induced neurocognitive impairment in neonatal rats. Anesthesiology 2009; 110:1077–1085 12. Pontén E, Viberg H, Gordh T, et al: Clonidine abolishes the adverse effects on apoptosis and behaviour after neonatal ketamine exposure in mice. Acta Anaesthesiol Scand 2012; 56:1058–1065 13. Ely EW, Shintani A, Truman B, et al: Delirium as a predictor of mortality in mechanically ventilated patients in the intensive care unit. JAMA 2004; 291:1753–1762 14. Marcantonio ER, Juarez G, Goldman L, et al: The relationship of postoperative delirium with psychoactive medications. JAMA 1994; 272:1518–1522 15. Riker RR, Shehabi Y, Bokesch PM, et al; SEDCOM (Safety and Efficacy of Dexmedetomidine Compared With Midazolam) Study Group: Dexmedetomidine vs midazolam for sedation of critically ill patients: A randomized trial. JAMA 2009; 301:489–499 16. Barr J, Fraser GL, Puntillo K, et al; American College of Critical Care Medicine: Clinical practice guidelines for the management of pain, agitation, and delirium in adult patients in the intensive care unit. Crit Care Med 2013; 41:263–306

Toward Unreasonable Effectiveness of Cardiac ICU Data: Artificial Intelligence in Pediatric Cardiac Intensive Care* The end of theory: the (cardiac ICU) data deluge makes the scientific method obsolete. —Chris Anderson, Editor-in-Chief, Wired Anthony C. Chang, MD, MBA, MPH Division of Pediatric Cardiology; Heart Institute; and Medical Intelligence and Innovation Institute (MI3) Children’s Hospital of Orange County Orange, CA Juliette Hunt, MD Pediatric Cardiac Intensive Care Unit Children’s Hospital of Orange County Orange, CA *See also p. 529. Key Words: artificial intelligence; database; data mining The authors have disclosed that they do not have any potential conflicts of interest. Copyright © 2014 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies DOI: 10.1097/PCC.0000000000000176

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he title of this editorial resonates with the seminal article on “unreasonable effectiveness of data” (as proposed by the Google chief scientist Peter Norvig) that elaborates on how elegant models can make large volumes of data powerful, similar to how physics can be explained by simple mathematical equations (1, 2). However, creating the models for large volumes of data to be effective is not always easy, and it has been quoted by several authorities in healthcare data circles that in clinical medicine (including critical care), we are “dying of thirst for information in an ocean of data.” The potential of a new era in medicine is dawning upon us, with the advent of applications of data analytical methodologies and artificial intelligence techniques in clinical medicine, or “medical intelligence.” In spite of the exponential rise in computing power and storage capability as well as emergence of “big data” in healthcare, there has been a paucity of reports on artificial intelligence methodologies in the ICU setting since an early review of this topic (3). www.pccmjournal.org

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In a “prior editorial,” we expounded on the role of “big data” in the care of cardiac children and summarized conventional database strategies in pediatric critical care (4). In this issue of Pediatric Critical Care Medicine, Gaies et al (5) published the findings of a data project with the Pediatric Cardiac Critical Care Consortium (PC4), a new qualitative improvement collaborative with participation of 11 North American pediatric cardiac intensive care centers. The authors should be lauded for their efforts to use this cardiac ICU database for new information via data warehousing, yet dividends from this type of database could be infinitely more rewarding in sophisticated heuristic efforts in the future. In the pediatric intensive care arena, in addition to PC4, there are additional such efforts reported in the literature: 1) the “virtual PICU system”: a robust clinical database of over 300,000 patients from North America with data elements as well as severity of illness-risk adjustment for comparison with other units and quality improvement projects (6) and 2) Pediatric Intensive Care Audit Network, a database established in the United Kingdom (7). Overall, these databases remain “top-down” in infrastructural approach as well as demanding from a human resource perspective. There remains, therefore, a dire need for a “different database paradigm”: a “bottom-up” strategy that is vastly more comprehensive and less resource-intensive and possesses a real-time application of artificial intelligence methodologies to carry our databases well beyond quality improvement, clinical decision support, and database management with statistical analysis (8). An initial effort to use a web-based, scalable, clinician-designed computer application that uses real-time cardiac ICU data for data-driven decisions is being made (P Laussen, personal communication, January 2014). One adult ICU project, with significant input from computer scientists and data analytics experts, has successfully explored the application of artificial intelligence in the ICU data arena. The Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II (9) project (soon to be MIMIC-III) (L Celi, MIT ICU Data Summit, personal communication, January 2014), a National Institutes of Health–funded collaborative project (Beth Israel Deaconess Medical Center, Phillips Medical Systems, and Massachusetts Institute of Technology), is an adult ICU database that applies automated techniques to aggregate diagnostic and therapeutic data (including highresolution physiological and waveform data) from over 40,000 ICU patients. The MIMIC-II project serves as a free publicaccess resource for education (data analytics, clinical decisionrule development, and electronic tool development) as well as research. A myriad of examples of published research using the MIMIC-II database include an earlier report by Celi et al (10) delineating the use of artificial intelligence via learning a “Bayesian network” from the high-resolution MIMIC-II database so as to result in a personalized, real-time approach to fluid resuscitation in ICU patients. More recently, Cismondi et al (11) from the same group reported the use of predictive “fuzzy modeling” with 11 input variables to identify laboratory tests that do not contribute an information gain. Finally, Cheng et al (12) from outside the Massachusetts Institute of Technology group reported successful use of “association rule mining,” with the MIMIC-II database as a real-time ICU clinical decision support system. 566

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The aforementioned quote from Chris Anderson is indeed controversial in healthcare because the quote focuses more on correlation patterns (which computers are good at) than on causal analyses (which humans may be better at). It behooves us, however, to embrace the exigencies of the current era of “petabyte” information and cloud computing as well as the impending tsunamic flow of individualized genomic data with a clear strategy. First, instead of a persistent penchant for databases and registries (there are now more than a dozen such fragmented efforts in congenital heart disease), we in pediatric cardiac intensive care need an imprimatur from all stakeholders to build a universal and comprehensive “Google-like” medical data cloud (beyond traditional multidimensional databases) for all aspects of care for cardiac children. Massive amounts of data, even with partly missing data and suboptimal data mining (13), can yield valuable dividends more so than with lesser data no matter how perfect (think pointillism art where the more dots the more obvious a painting is) (R Patel, Google, personal communication, July 2013). In addition, we need an influx of data scientists/ analytic specialists to be part of our team in the pediatric cardiac care arena. Concomitantly, physicians and researchers need to embed new skills into their clinical portfolios and to be familiar with salient features and nuanced limitations of concepts such as artificial neural network, natural language processing, predictive modeling, machine learning, and data mining (14). This is akin to an orchestra conductor having an inherent advantage if he plays several musical instruments in the orchestra. Lastly, all of this Herculean effort will yield exponential results if all pediatric cardiac centers from around the world are invited to participate. In the future, we will need to transform the manner we collect, interpret, and use cardiac ICU data in a substantive and ecumenical way. With coupling of clinician intuition and data abundance (in essence we humans “amplifying” the signal in the data noise), we will finally find meaningful answers to our perennial clinical inquiries. We can perhaps reach a human-computer clinical “synergy”: a symmetric but dynamic state in which we clinicians work tacitly with computers to transform one single, all-encompassing biomedical data cloud (including preadmission data, laboratory and imaging data, ICU monitor physiological tracings, and drug infusions in the realm of “Internet of things” [a term describing the future state in which billions of digital machines will be connected to the Internet]) into real-time, crowd-sourced, datadriven application of medical knowledge and intelligence for personalized and contextualized patient care (15). In short, rather than simply allowing the “numbers speak for themselves” and concede to a “digital” empiricism, we can strive to attain a balanced state of clinical-digital convergence (16). If we continue the classical music and clinical data analogy, this is akin to the present orchestral cacophony (as during a preperformance tuning session) being transformed into the wondrous aural alchemy of a Mozart symphony.

REFERENCES

1. Wagner E: The unreasonable effectiveness of physics in the natural sciences. Commun Pure Appl Math 1960; 13:1–14 2. Halevy A, Norvig P, Pereira F: The unreasonable effectiveness of data. IEEE Intell Syst 2009; 24:8–12 July 2014 • Volume 15 • Number 6

Editorials 3. Hanson CW III, Marshall BE: Artificial intelligence applications in the intensive care unit. Crit Care Med 2001; 29:427–435 4. Hunt J, Chang AC: Big data in pediatric cardiac care: Is it time? Pediatr Crit Care Med 2013; 14:548–549 5. Gaies MG, Jeffries HE, Niebler RA, et al: Vasoactive-Inotropic Score Is Associated With Outcome After Infant Cardiac Surgery: An Analysis From the Pediatric Cardiac Critical Care Consortium and Virtual PICU System Registries. Pediatr Crit Care Med 2014; 15:529–537 6. Gupta P, Green JW, Tang X, et al: Comparison of high-frequency oscillatory ventilation and conventional mechanical ventilation in pediatric respiratory failure. JAMA Pediatr 2014; 168:243–249 7. LaRovere JM, Jeffries HE, Sachdeva RC, et al: Databases for assessing the outcomes of the treatment of patients with congenital and paediatric cardiac disease—The perspective of critical care. Cardiol Young 2008; 18(Suppl 2):130–136 8. Schulman J: Managing Your Patients’ Data in the Neonatal and Pediatric ICU. Malden, MA, Blackwell Publishing, 2006 9. Saeed M, Villarroel M, Reisner AT, et al: Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II): A public-access intensive care unit database. Crit Care Med 2011; 39:952–950

10. Celi LA, Hinske LC, Alterovitz G, et al: An artificial intelligence tool to predict fluid requirement in the intensive care unit: A proof-of-concept study. Crit Care 2008; 12:R151 11. Cismondi F, Celi LA, Fialho AS, et al: Reducing unnecessary lab testing in the ICU with artificial intelligence. Int J Med Inform 2013; 82:345–358 12. Cheng FW, Chanani N, Venugopalan J, et al: icuARM—An ICU Clinical Decision Support System Using Association Rule Mining. IEEE J Transl Eng Health Med 2013;1:4400110 13. Cismondi F, Fialho AS, Vieira SM, et al: Missing data in medical databases: Impute, delete or classify? Artif Intell Med 2013; 58:63–72 14. Cios KJ, Moore GW: Uniqueness of medical data mining. Artif Intell Med 2002; 26:1–24 15. Celi L, Stone DJ, Montgomery RA: “Big data” in the intensive care unit: Closing the data loop. Am J Respir Crit Care Med 2013; 187:1157–1160 16. Salmon F: Numbed by numbers: Why quants don’t know everything. Wired 2014; 27–33

How Does One Use Extracorporeal Cardiopulmonary Resuscitation Risk Factor Data?* D. Michael McMullan, MD Division of Pediatric Cardiac Surgery Seattle Children’s Hospital Seattle, WA

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he article by Jolley et al (1) in this issue of Pediatric Critical Care Medicine represents the largest retrospective multicenter analysis of neonates with hypoplastic left heart syndrome (HLHS) who received extracorporeal cardiopulmonary resuscitation (ECPR). The main findings of the study are that 36% of these patients survive to hospital discharge and that lower bodyweight, increased duration of extracorporeal life support (ECLS), and renal failure are associated with increased risk of death. A common criticism of registry-based studies is that the results often appear to be largely intuitive, and this study is no exception. The importance of this study lies in the fact that the findings are based on current broad clinical practice rather than a carefully selected group of patients from a single center. Although the authors characterize the observed 36% survival rate in these patients as “poor,” one must bear in mind the likely outcome in the absence of ECPR. Although ECPR is now generally accepted as a standard perioperative rescue therapy for patients with single-ventricle heart

*See also p. 538. Key Words: congenital heart disease; extracorporeal cardiopulmonary resuscitation; extracorporeal membrane oxygenation; hypoplastic left heart syndrome; Norwood operation The author has disclosed that he does not have any potential conflicts of interest. Copyright © 2014 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies DOI: 10.1097/PCC.0000000000000167

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disease, there has never been a randomized study to evaluate the efficacy of ECPR in neonates. Given that the survival rate for neonates who receive ECPR is approximately 39% (2), whereas the estimated overall survival rate for children who receive 30 minutes of cardiopulmonary resuscitation (CPR) is only about 20% (3) or less, it is doubtful that a prospective randomized trial will ever be performed. Death is almost certain for palliated single-ventricle neonates who fail to recover adequate cardiac output after 30 minutes of CPR. Consequently, the 36% survival rate reported by Jolley et al (1) is likely a realistic estimate of the true survival benefit of ECPR in this patient population. Body weight, renal failure, and duration of support are consistently reported as risk factors for poor outcome in neonates who receive ECLS (1, 2, 4, 5). Odds of survival were found to be significantly lower in patients who required support beyond 96 hours in the study by Jolley et al (1). This is consistent with multiple single-center reports that have identified an inflection point for survival after 4 days of support (2, 5–8). A meta-analysis of pediatric ECPR patients demonstrated that the overall median duration of support is 4.3 days in this cohort (9). By comparison, the average duration of non-ECPR ECLS is greater than 6 days in children (10). This difference likely reflects the fact that arrest-related end-organ damage, including neurologic injury, and inadequate recovery of myocardial function become apparent within a few days of cardiac arrest. Perhaps the most important determinants of ECPR survival are duration and adequacy of CPR before initiation of ECLS. Survival has been reported to decrease by 5% with each elapsed minute of CPR in children (3). Unfortunately, information related to duration of CPR is not reported to the Extracorporeal Life Support Organization registry. The literature contains conflicting information regarding the impact of www.pccmjournal.org

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