Editorials
that a patient will be screened positive for delirium and that sedation-induced delirium has a better outcome than delirium that is unrelated to sedation (9,18,19). Based on the results of this analysis, clinicians should consider the risk for delirium when making corticosteroid prescribing decisions in their critically ill patients. In conjunction with other multifactorial, time-dependent analyses that have explored the association between benzodiazepine use and delirium in the IGU (11,17), this investigation represents an important step forward in our understanding of the cognitive risks associated with commonly administered medications in the IGU.
REFERENCES 1. Zaal IJ, Slooter AJ: Delirium in critically ill patients: Epidemiology, pathophysiology, diagnosis and management. Drugs 2012; 72:1457-1471 2. Wolters AE, Slooter AJ, van der Kooi AW, et al: Cognitive impairment after intensive care unit admission: A systematic review, intensive Care Med 2013; 39:376-386 3. Barr J, Fraser GL, Puntillo K, et al: Clinical praotioe guidelines for the management of pain, agitation and delirium in adult ICU patients. Crit Care Med 2013; 41:263-306 4. Micromedex. Available at: http://www.Micromedex.com. Accessed January 4, 2014 5. Devlin JW, Fraser GL, Riker RR: Drug-induoed coma and delirium. in: Drug-lnduoed Compiioations in the Critically III Patient: A Guide for Recognition and Treatment. First Edition. Papadopoulos J, Cooper B, Kane-Gill S, et al (Eds). Chicago, IL, Society of Criticai Care Medicine, 2011, pp 1 26-136 6. Best O, Gnjidic D, Hilmer SN, et al: Investigating polypharmaoy and drug burden index in hospitalised older people, intern Med J 2013; 43:912-918 7. Lampela P, Lavikainen P, Garcia-Horsman JA, et al: Antioholinergic drug use, serum anticholinergio aotivity, and adverse drug events
among older people: A population-based study. Drugs Aging 2013; 30:321-330 8. Zaal I, Devlin JW, Slooter A: A systematic review of the risk factors for delirium in the intensive care unit. Crit Care Med 2013; 41(Suppl):A558 9. Devlin JW, Fraser GL, Joffe A, et al: The accurate recognition of delirium in the ICU: More new clothes for the emperor, intensive Care Med 2013; 39:2196-2199 10. Shintani AK, Girard TD, Ely EW: Immortal time bias in critical oare. Crit Care Med 2010; 38:1229 11. Pandharipande P, Shintani A, Peterson J, et al: Lorazepam is an independent risk factor for transitioning to delirium in intensive care unit patients. Anesthesiology 2006; 104:21-26 12. Tang BM, Craig JC, Eslick GD, et al: Use of cortioosteroids in acute lung injury and acute respiratory distress syndrome: A systematic review and meta-analysis. Crit Care Med 2009; 37:1594-1603 13. Lamontagne F, Quiroz MH, Adhikari NK, et al: Cortioosteroid use in the intensive oare unit: A survey of intensivists. Can J Anaesth 2013; 60:652-659 14. Sauer AM, Slooter AJC, Veldhuijzen DS, et al: Intraoperative dexamethasone and delirium after oardiac surgery: A randomized olinical trial. Anesth Analgesia 2014; In Press 15. Lin Y, Chen J, Wang Z: MeMeta-analysis of faotors which influence delirium following cardiac surgery. J Card Surg 201 2; 27:481-492 16. Schreiber MP, Colantuoni E, Bienvenu CJ, et ai: Cortioosteroids and Transition to Delirium in Patients With Aoute Lung Injury. Crit Care Med 2014; 42:1480-1486 17. Zaal I, Devlin JW, van der Kooi A, et al: The association between benzodiazepine use and delirium in the ICU: A prospeotive oohort study. Crit Care Med 2013; 41 (Suppi):A851 18. Haenggi M, Blum S, Brechbuehl R, et al: Effect of sedation level on the prevalence of delirium when assessed with CAM-ICU and ICDSC. intensive Care Med 2013; 39:2171 -2179 19. Patel SB,Poston JT, Pohlman A, et al: Rapidly reversible, sedationrelated delirium versus persistent delirium in the ICU. Am J Respir Crit Care Med 2014; 189:658-665
Data-Driven, Evidenced-Based, Computational Modeling Research Is Still Needed in Trauma Research* Fanglong Dong, PhD Robert B. Hines, PhD Elizabeth Ablah, PhD, MPH Trade Collins, MD, MPH Department of Preventive Medicine and Public Health University of Kansas School of Medicine-Wichita Wichita, KS
*See also p. 1487. Key Words: ohemokines; dynamic Bayesian network inference; inflammation; in silico; traumatic spinal oord injury The authors have disclosed that they do not have any potential oonfliots of interest. Copyright © 2014 by the Society of Critioal Care Medicine and Lippinoott Williams & Wilkins DOI: 10.1097/CCIVI.0000000000000269
1566
www.ccmjournal.org
T
he annual incidence of spinal cord injury (SGI) is approximately 40 cases per million population in the United States or 12,000 new cases each year (1,2). These incident cases disproportionately occur among healthy males in their late teens or twenties (1, 3-5). The primary cause of SGIs for individuals 45 years or younger is automobile crash (6). In treating patients involved in automobile crashes, practitioners have relied on professional knowledge and evidence-based guidelines to save patients' lives. Due to ethical considerations surrounding the traumatic nature of SGIs, there is a lack of randomized controlled trials (RGTs) evaluating therapeutic interventions. Most study designs have consisted of retrospective data reviews involving a single cohort. In animal models, RGTs of rehabilitative and ceUular/molecular therapies for SGIs have been promising (3). However, reproducible evidence of safety and efficacy is needed before the initiation of RGTs involving human populations (3). June 2014 • Volume 42 • Number 6
Editorials
There is no fully restorative treatment for SCI. Failure of the respiratory system is the leading cause of death for patients with SCI (7). Several factors have been identified as contributors to respiratory complications after trauma. For example, leukotriene B4 has been identified as a marker of compromised respiration (8). Although individual therapies are unlikely to be a magic bullet, identification of these trauma-associated biomarkers offers the opportunity to conduct RCTs of new therapeutics aimed at these factors. In this issue of Critical Care Medicine, the study by Zaaqoq et al (9) compared the dynamic systemic inflammatory response of traumatic spinal cord injury patients (TSCI) versus non-SCI patients. They reported that interleukin ( IL) -10 is highly elevated in TSCI patients, whereas other inflammatory mediators (e.g., IL-lß) are suppressed. They concluded that circulating inducible protein (IP)-IO may serve a key role in driving systemic IL-10 and morbidity among TSCI patients, whereas chemokine monocyte chemotactic protein-1/chemokine (C-C motif) ligand 2 influences multiple mediators in non-SCI patients. A strength of the study by Zaaqoq et al (9) was the matching process. They identified 569 patients admitted to ICUs, including 85 TSCI and 484 non-SCI patients. They matched 21 TSCI patients to 21 non-SCI patients on age, gender, and injury severity score (ISS). This matching process allowed investigators to control confounding variables, and the study design tried to mimic a RCT with an observational study design. This matching technique ensured that the matched pairs have the same injury severity (ISS) while removing the confounding effects of age and gender. Additionally, the application of the dynamic Bayesian network technique used historical data to predict future patient performance. Estimations based on large sample data can predict the likelihood of an event at an aggregated level. However, individualized estimation is often not accurate, which comphcates interpretation. Last, the in silico (10) method has been used in trauma and sepsis research and may have future promise in discovering more key inflammatory drivers. A weakness of the study is the sample size. With 42 total patients (21 matched pairs), there is limited statistical power
Critical Care Medicine
which precluded the analysis of more inflammatory mediators. Zaaqoq et al (9) also suggested possible interactions among inflammatory mediators, but there was limited ability to detect this interaction again due to small sample size. Further research to evaluate these relationships is called for with larger patient cohorts. IP-10 may be relevant to critical care practitioners' trauma patients. IP-10/C-X-C motif chemokine 10 and IL-10 may serve as biomarkers of adverse outcomes postinjury. Obviously, a single-cohort observational study cannot prove a causal relationship between an exposure and outcome. Therefore, studies with greater statistical power which evaluate the role of IP-10 are needed to determine whether this protein can be a target for clinical intervention among TSCI patients. Additionally, future studies with greater statistical power will enable the examination of other potential inflammatory mediators.
REFERENCES 1. National Spinal Cord Injury Statistical Center: Spinal Cord Injury Facts and Figures at a Glance. 2012. Available at: https://www. nscisc.uab.edu/PublicDocuments/factJigures_docs/Facts%20 2012%20Feb%20Final.pdf. Accessed December 12, 2013 2. Devivo MD: Epidemiology of traumatic spinal cord injury: Trends and future implications. Spinal Cord 201 2; 50:365-372 3. Thuret S, Moon LD, Gage FH: Therapeutic interventions after spinal cord injury. Nat Rev Neurosci 2006; 7:628-643 4. Burt BM, Afifi HY, Wantz GE, et al: Traumatic lumbar hernia: Report of cases and comprehensive review of the literature. J Trauma 2004; 57:1361-1370 5. McTigue DM: Potential therapeutic targets for PPARgamma after spinal cord injury. PPAR Res 2008; 2008:5171 62 6. Chen Y, Tang Y, Vogel LC, et al: Causes of spinal cord injury. Top Spinal Cord Inj Rehabil 2013; 19:1-8 7. van den Berg ME, Castellote JM, de Pedro-Cuesta J, et al: Survival after spinal cord injury: A systematic review. J Neurotrauma 2010; 27:1517-1528 8. Auner B, Geiger EV, Henrich D: Circulating leukotriene B4 identifies respiratory complications after trauma. Mediators Inftamm 2012; 2012:536156 9. Zaaqoq AM, Namas R, Almahmoud K, et al: Inducible Protein-10, a Potential Driver of Neurally Controlled lnterleukin-10 and Morbidity in Human Blunt Trauma. Crit Care Med 2014; 42:1487-1497 10. Vodovotz Y, Billiar TR: In silico modeling: Methods and applications to trauma and sepsis. Crit Care Med 2013; 41:2008-2014
www.ccmjournal.org
1567
Copyright of Critical Care Medicine is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
that a patient will be screened positive for delirium and that sedation-induced delirium has a better outcome than delirium that is unrelated to sedation (9,18,19). Based on the results of this analysis, clinicians should consider the risk for delirium when making corticosteroid prescribing decisions in their critically ill patients. In conjunction with other multifactorial, time-dependent analyses that have explored the association between benzodiazepine use and delirium in the IGU (11,17), this investigation represents an important step forward in our understanding of the cognitive risks associated with commonly administered medications in the IGU.
REFERENCES 1. Zaal IJ, Slooter AJ: Delirium in critically ill patients: Epidemiology, pathophysiology, diagnosis and management. Drugs 2012; 72:1457-1471 2. Wolters AE, Slooter AJ, van der Kooi AW, et al: Cognitive impairment after intensive care unit admission: A systematic review, intensive Care Med 2013; 39:376-386 3. Barr J, Fraser GL, Puntillo K, et al: Clinical praotioe guidelines for the management of pain, agitation and delirium in adult ICU patients. Crit Care Med 2013; 41:263-306 4. Micromedex. Available at: http://www.Micromedex.com. Accessed January 4, 2014 5. Devlin JW, Fraser GL, Riker RR: Drug-induoed coma and delirium. in: Drug-lnduoed Compiioations in the Critically III Patient: A Guide for Recognition and Treatment. First Edition. Papadopoulos J, Cooper B, Kane-Gill S, et al (Eds). Chicago, IL, Society of Criticai Care Medicine, 2011, pp 1 26-136 6. Best O, Gnjidic D, Hilmer SN, et al: Investigating polypharmaoy and drug burden index in hospitalised older people, intern Med J 2013; 43:912-918 7. Lampela P, Lavikainen P, Garcia-Horsman JA, et al: Antioholinergic drug use, serum anticholinergio aotivity, and adverse drug events
among older people: A population-based study. Drugs Aging 2013; 30:321-330 8. Zaal I, Devlin JW, Slooter A: A systematic review of the risk factors for delirium in the intensive care unit. Crit Care Med 2013; 41(Suppl):A558 9. Devlin JW, Fraser GL, Joffe A, et al: The accurate recognition of delirium in the ICU: More new clothes for the emperor, intensive Care Med 2013; 39:2196-2199 10. Shintani AK, Girard TD, Ely EW: Immortal time bias in critical oare. Crit Care Med 2010; 38:1229 11. Pandharipande P, Shintani A, Peterson J, et al: Lorazepam is an independent risk factor for transitioning to delirium in intensive care unit patients. Anesthesiology 2006; 104:21-26 12. Tang BM, Craig JC, Eslick GD, et al: Use of cortioosteroids in acute lung injury and acute respiratory distress syndrome: A systematic review and meta-analysis. Crit Care Med 2009; 37:1594-1603 13. Lamontagne F, Quiroz MH, Adhikari NK, et al: Cortioosteroid use in the intensive oare unit: A survey of intensivists. Can J Anaesth 2013; 60:652-659 14. Sauer AM, Slooter AJC, Veldhuijzen DS, et al: Intraoperative dexamethasone and delirium after oardiac surgery: A randomized olinical trial. Anesth Analgesia 2014; In Press 15. Lin Y, Chen J, Wang Z: MeMeta-analysis of faotors which influence delirium following cardiac surgery. J Card Surg 201 2; 27:481-492 16. Schreiber MP, Colantuoni E, Bienvenu CJ, et ai: Cortioosteroids and Transition to Delirium in Patients With Aoute Lung Injury. Crit Care Med 2014; 42:1480-1486 17. Zaal I, Devlin JW, van der Kooi A, et al: The association between benzodiazepine use and delirium in the ICU: A prospeotive oohort study. Crit Care Med 2013; 41 (Suppi):A851 18. Haenggi M, Blum S, Brechbuehl R, et al: Effect of sedation level on the prevalence of delirium when assessed with CAM-ICU and ICDSC. intensive Care Med 2013; 39:2171 -2179 19. Patel SB,Poston JT, Pohlman A, et al: Rapidly reversible, sedationrelated delirium versus persistent delirium in the ICU. Am J Respir Crit Care Med 2014; 189:658-665
Data-Driven, Evidenced-Based, Computational Modeling Research Is Still Needed in Trauma Research* Fanglong Dong, PhD Robert B. Hines, PhD Elizabeth Ablah, PhD, MPH Trade Collins, MD, MPH Department of Preventive Medicine and Public Health University of Kansas School of Medicine-Wichita Wichita, KS
*See also p. 1487. Key Words: ohemokines; dynamic Bayesian network inference; inflammation; in silico; traumatic spinal oord injury The authors have disclosed that they do not have any potential oonfliots of interest. Copyright © 2014 by the Society of Critioal Care Medicine and Lippinoott Williams & Wilkins DOI: 10.1097/CCIVI.0000000000000269
1566
www.ccmjournal.org
T
he annual incidence of spinal cord injury (SGI) is approximately 40 cases per million population in the United States or 12,000 new cases each year (1,2). These incident cases disproportionately occur among healthy males in their late teens or twenties (1, 3-5). The primary cause of SGIs for individuals 45 years or younger is automobile crash (6). In treating patients involved in automobile crashes, practitioners have relied on professional knowledge and evidence-based guidelines to save patients' lives. Due to ethical considerations surrounding the traumatic nature of SGIs, there is a lack of randomized controlled trials (RGTs) evaluating therapeutic interventions. Most study designs have consisted of retrospective data reviews involving a single cohort. In animal models, RGTs of rehabilitative and ceUular/molecular therapies for SGIs have been promising (3). However, reproducible evidence of safety and efficacy is needed before the initiation of RGTs involving human populations (3). June 2014 • Volume 42 • Number 6
Editorials
There is no fully restorative treatment for SCI. Failure of the respiratory system is the leading cause of death for patients with SCI (7). Several factors have been identified as contributors to respiratory complications after trauma. For example, leukotriene B4 has been identified as a marker of compromised respiration (8). Although individual therapies are unlikely to be a magic bullet, identification of these trauma-associated biomarkers offers the opportunity to conduct RCTs of new therapeutics aimed at these factors. In this issue of Critical Care Medicine, the study by Zaaqoq et al (9) compared the dynamic systemic inflammatory response of traumatic spinal cord injury patients (TSCI) versus non-SCI patients. They reported that interleukin ( IL) -10 is highly elevated in TSCI patients, whereas other inflammatory mediators (e.g., IL-lß) are suppressed. They concluded that circulating inducible protein (IP)-IO may serve a key role in driving systemic IL-10 and morbidity among TSCI patients, whereas chemokine monocyte chemotactic protein-1/chemokine (C-C motif) ligand 2 influences multiple mediators in non-SCI patients. A strength of the study by Zaaqoq et al (9) was the matching process. They identified 569 patients admitted to ICUs, including 85 TSCI and 484 non-SCI patients. They matched 21 TSCI patients to 21 non-SCI patients on age, gender, and injury severity score (ISS). This matching process allowed investigators to control confounding variables, and the study design tried to mimic a RCT with an observational study design. This matching technique ensured that the matched pairs have the same injury severity (ISS) while removing the confounding effects of age and gender. Additionally, the application of the dynamic Bayesian network technique used historical data to predict future patient performance. Estimations based on large sample data can predict the likelihood of an event at an aggregated level. However, individualized estimation is often not accurate, which comphcates interpretation. Last, the in silico (10) method has been used in trauma and sepsis research and may have future promise in discovering more key inflammatory drivers. A weakness of the study is the sample size. With 42 total patients (21 matched pairs), there is limited statistical power
Critical Care Medicine
which precluded the analysis of more inflammatory mediators. Zaaqoq et al (9) also suggested possible interactions among inflammatory mediators, but there was limited ability to detect this interaction again due to small sample size. Further research to evaluate these relationships is called for with larger patient cohorts. IP-10 may be relevant to critical care practitioners' trauma patients. IP-10/C-X-C motif chemokine 10 and IL-10 may serve as biomarkers of adverse outcomes postinjury. Obviously, a single-cohort observational study cannot prove a causal relationship between an exposure and outcome. Therefore, studies with greater statistical power which evaluate the role of IP-10 are needed to determine whether this protein can be a target for clinical intervention among TSCI patients. Additionally, future studies with greater statistical power will enable the examination of other potential inflammatory mediators.
REFERENCES 1. National Spinal Cord Injury Statistical Center: Spinal Cord Injury Facts and Figures at a Glance. 2012. Available at: https://www. nscisc.uab.edu/PublicDocuments/factJigures_docs/Facts%20 2012%20Feb%20Final.pdf. Accessed December 12, 2013 2. Devivo MD: Epidemiology of traumatic spinal cord injury: Trends and future implications. Spinal Cord 201 2; 50:365-372 3. Thuret S, Moon LD, Gage FH: Therapeutic interventions after spinal cord injury. Nat Rev Neurosci 2006; 7:628-643 4. Burt BM, Afifi HY, Wantz GE, et al: Traumatic lumbar hernia: Report of cases and comprehensive review of the literature. J Trauma 2004; 57:1361-1370 5. McTigue DM: Potential therapeutic targets for PPARgamma after spinal cord injury. PPAR Res 2008; 2008:5171 62 6. Chen Y, Tang Y, Vogel LC, et al: Causes of spinal cord injury. Top Spinal Cord Inj Rehabil 2013; 19:1-8 7. van den Berg ME, Castellote JM, de Pedro-Cuesta J, et al: Survival after spinal cord injury: A systematic review. J Neurotrauma 2010; 27:1517-1528 8. Auner B, Geiger EV, Henrich D: Circulating leukotriene B4 identifies respiratory complications after trauma. Mediators Inftamm 2012; 2012:536156 9. Zaaqoq AM, Namas R, Almahmoud K, et al: Inducible Protein-10, a Potential Driver of Neurally Controlled lnterleukin-10 and Morbidity in Human Blunt Trauma. Crit Care Med 2014; 42:1487-1497 10. Vodovotz Y, Billiar TR: In silico modeling: Methods and applications to trauma and sepsis. Crit Care Med 2013; 41:2008-2014
www.ccmjournal.org
1567
Copyright of Critical Care Medicine is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
