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an important factor. Sepsis is frequently accompanied by systolic and diastolic ventricular dysfunction (8-10). The cause is once again uncertain, but seems likely to be multifactorial (11), and can occur in previously healthy hearts and even in children with septic shock (12). However, since the prevalence of diastolic dysfunction may be as high as 27% in the general population (13), it is possible that preexisting heart failure could be a factor. Similarly, patients with chronic lung disease may have an increased prevalence of right ventricular dysfunction. A better understanding of the underlying causes of cTn elevation in septic and other critically ill patients is clearly important as we strive to optimize treatment (2). There is some debate whether plasma cTn is an independent prognostic marker in sepsis or simply a surrogate for overall severity of illness (4, 6). cTn abnormalities are also common in the postoperative setting (14), but some clinicians have argued that routine testing may simply increase the risk of harm from inappropriate treatment for myocardial infarction (15). However, the work by Landesberg et al suggests that there may be value in investigating the cause of cTn abnormalities in sepsis using echocardiography to identify and assess potential targets for therapy.

REFERENCES 1. Thygesen K, Alpert JS, Jaffe AS, et al; Joint ESC/ACCF/AHAAA/HF Task Force for the Universal Definition of Myocardial Infarction: Third universai definition of myocardial Infarction. Circulation 2012; 126:2020-2035 2. Hamilton MA, Toner A, Cecconi M: Troponin in critically ill patients. Minerva Anestesiol 201 2; 78:1039-1045 3. Fahie-Wilson MN, Carmiohael DJ, Delaney MP, et al: Cardiac troponin T circulates in the free, intact form in patients with kidney failure. Clin C/iem 2006; 52:414-420

4. Mehta S, Granton J, Gordon AC, et al; for the Vasopressin and Septic Shock Trial (VASST) Investigators: Cardiac ischemia in patients with septic shock randomized to vasopressin or norepinephrine. Crit Care 2013; 17:R117 5. Altmann DR, Körte W, Maeder MT, et al: Elevated cardiac troponin I in sepsis and septic shock: No evidence for thrombus associated myocardial necrosis. PLoS One 2010; 5:e9017 6. Favory R, Neviere R: Bench-to-bedside review: Significance and interpretation of elevated troponin in septic patients. Crit Care 2006; 10:224 7. Landesberg G, Jaffe AS, Gilon D, et al: Troponin Elevation in Severe Sepsis and Septic Shock: The Role of Left Ventricular Diastolic Dysfunction and Right Ventricular Dilatation. Crit Care Med 2014; 42:790-800 8. Landesberg G, Gilon D, Meroz Y, et al: Diastolic dysfunction and mortality in severe sepsis and septic shock. Eur Heart J 2012; 33:895-903 9. Mourad M, Chow-Chine L, Faucher M, et al: Early diastolic dysfunction is associated with intensive care unit mortality in cancer patients presenting with septic shock. Br J Anaesth 2014; 112:102-109 10. Price LC, Wort SJ, Finney SJ, et al: Pulmonary vascular and right ventricular dysfunction in adult critical care: Current and emerging options for management: A systematic literature review. Crit Care 2010; 14:R169 H.Merx MW, Weber C: Sepsis and the heart. Circulation 2007; 116:793-802 12. Raj S, Killinger JS, Gonzalez JA, et al: Myocardial dysfunction in pédiatrie septic shock. J Pediatr 2014; 164:72-77 13. Kuznetsova T, Herbots L, López B, et al: Prevalence of left ventricular diastolic dysfunction in a general population. Circ Heart Fail 2009; 2:105-112 14. Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) Study Investigators, Devereaux PJ, Chan MTV, Alonso-Coello P, et al: Association between postoperative troponin levels and 30-day mortality among patients undergoing noncardiac surgery. JAMA 2012; 307:2295-2304 15. Beckman JA: Postoperative troponin screening : A cardiac Cassandra? Circulation 2013; 1 27:2253-2256

Early Detection of Deteriorating Patients: Leveraging Clinical Informatics to Improve Outcome* Adel Bassily-Marcus, MD, FCCM, FCCP, FACP Critical Care Consult and Rapid Response Team; Critical Care Informatics; Department of Surgery; and Department of Information Technology Icahn School of Medicine at Mount Sinai New York, NY

*See also p. 801. Key Words: clinical decision support; critical care informatics; early deterioration; Modified Early Warning System (MEWS); rapid response team The author has disolosed that he does not have any potential conflicts of interest. Copyright © 2013 by the Society of Critical Care Medicine and Lippinoott Williams & Wilkins DOI:10.1097/CCM.0000000000000093

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ver the past 25 years, we have witnessed an abundance of studies demonstrating that significant physiologic deterioration occurs in patients prior to cardiac or respiratory arrest ( 1 ). Rapid response systems (RRS) have therefore been introduced to intervene in the care of unexpected deteriorating patients in order to reduce the prevalence of cardiac arrests, unplanned ICU admissions, and deaths (2). The concept of ICU without walls, the core ingredient of the RRS, essentially applies critical care expertise and skills outside the ICU, at the patient's bedside as soon as deterioration is detected. Despite the fact that preventing, recognizing, and treating deteriorating patients early is good common sense, more than a decade after the introduction of such medical emergency teams (3), studies have failed to demonstrate consistent benefit (4). For an RRS to be effective, it must have a reliable afferent limb whereby early detection and recognition of decompensating April 2014« Volume 42 • Number 4

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patients occurs. This must be in tandem with an efferent limb that provides timely effective clinical assessment and management by a dedicated rapid response team (RRT), also known as medical emergency team or Critical Care Outreach Team. RRT have shown to be effective when a clinician with critical care skills leads it (5). The third and fourth limbs of the RRS provide administrative and data analysis to optimize efficacy of the mature RRS (2). Afferent limb failure, that is failure to activate RRT, represents the single most significant contributor of failure to rescue, and thereby has a major impact on ICU admission and hospital mortality. It remains the most important and challenging step in failure to rescue situations. It has been reported that the RRT was called in only 30% of cases when criteria for activating the RRT were fulfilled, thus highlighting the challenge of afferent limb activation (4). To improve early recognition of unexpected deterioration, objective scores have been proposed, most commonly using a scoring system based on change in vital signs known as "the early warning score" (EWS) (6), which is an aggregate weighted scoring system. Subsequent modifications led to multiple variations of the score: Modified Early Warning System (MEWS) (7), National Early Warning Score (8), VitalPAC EWS (ViEWS) (9), ViEWS -I- lactate (ViEWS-L) (10), and HOTEL score (Hypotension, Oxygen saturation. Temperature, ECG abnormality. Loss of independence) ( 11 ). In a landmark multicenter study by Bellomo et al ( 12 ), where MEWS was automatically calculated from a monitor, an abnormal calculated value triggered an alert that recommended RRT activation; however, this process still required chnician activation of the RRT and thereby hmited the automated functionality. Patients who received RRT treatments demonstrated higher survival to hospital discharge and lower hospital stay. Following The Health Information Technology for Economic and Clinical Health Act of 2009, there was a boom in U.S. hospital adoption of the electronic healthcare record (EHR). A great enthusiasm surged to use electronically recorded data elements to improve outcome using EHR support functionality named clinical decision support system (CDSS). Examples of CDSS applications in critical care include severity of illness screening and scoring, diseasespecific risk assessments, sepsis screening tools, best practice alerts for medication selection or dosing adjustments, and improvement in safety through compliance with a variety of preventive measures. Although CDSS has the potential to improve the quality of care, it has not been universally effective (13). This may be due to limits of knowledge synthesis, capture, transformation, localization, and maintenance of the data. In this issue of Critical Care Medicine, Huh et al (14) attempted to overcome afferent limb RRS failure through modifying human computer interaction, using EHR as a screening tool linked to automated RRT activation. After predefined criteria were met, automatic activation of RRT through EHR screening (named electronic medical record Critical Care Medicine

[EMR]-triggered activation) was completed and compared to the outcome of traditional clinician activation of RRT (calltriggered activation). An evaluation of RRT activation using both methods (double triggered activation) was also performed. The efferent limb was well staffed with critical care nurses, residents, fellows, and led by an intensivist who provided critical care interventions outside the ICU walls. This is a distinctly different model from other studies (5) where a call had to be made to activate RRT. The current study investigates an automated process for RRT activation, in addition to the traditional methods. In this retrospective, observational study, the investigators found that 51% of activation of RRT came from EMRtriggering at year 1 and 38% at year 2. Approximately one third of these patients eventually were admitted to the ICU for further management. This demonstrates the sensitivity of the EMR screening process in detecting additional true critically ill patients who were missed by the conventional calling mechanism. Unlike MEWS scores that have no laboratory contribution, 23.4% of the EMR activation of RRT was based on laboratory criteria in the current study. This is an important finding as it demonstrates that laboratory factors can be a central part of a successful screening process. One could theorize that MEWS score insensitivity in detecting sepsis (15) could be improved by incorporating laboratory data. As pointed out by the authors, studies are needed to further evaluate this observation and its contribution to the sensitivity of the screening tool. In this study, there was a significant decrease in mortality among surgical patients for whom RRT was activated by EMR screening. The question as to why this improvement in clinical outcome was seen only among surgical patients is not clear from the data provided, and this result warrants outside validation; however, the improvement in mortality demonstrated in this study is significant and demonstrates feasibility of a successful EHR-based RRT activation algorithm. For total numbers, however, the majority of patients who met screening criteria and managed by RRT were actually medical patients. This group of patients had a lower prevalence of ICU admission, high MEWS score, and higher mortality. EMR-triggered activation of the RRT for these patients did not improve their outcome. This observation raises more questions. Were medical patients recognized later hence their worse outcome? Were there subpopulations of the medical patients for which the EHR-based activation would be successful? Are there other factors that contribute to medical patients that need to be taken into account by redesign of the screening process or modification of the scoring system with weighting factors? This study has several limitations. It was not clear what constitutes a high MEWS score that was predictive of poor outcome. Also the frequency of MEWS scoring was not specified. Automated screening tools (as well as calling criteria) are considered to be highly sensitive with limited specificity; however. www.ccmjournal.org

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the sensitivity and the specificity of the EMR-triggered activation were not stated in this report and lead one to question how many EMR-activated RRT were not needed. In addition, the RRT was fully staffed on a 24-hour basis and likely performed a secondary triage of the automated calls (at the RRT level) to determine which were sufficient to warrant bedside assessment, thereby limit the need for unnecessary RRT intervention. Optimized sensitivity of RRT activation will need to be accomplished with further investigation and refinement of automated systems. Notably, screening and calling criteria were different for the EMR-triggered and call-triggered groups. For example, among the call-triggered cases, about 7% of RRT calls were in response to code blue situations for which a worse outcome would be expected. In addition, the call-triggered activation group included cases in which a clinical judgment or "gut feeling" by the bedside clinicians prompted call-triggered RRT activation. These highly valuable aspects of the traditional afferent limb can never be automated; however, they can lead to an apparent difference in outcome for the traditional mechanism simply due to the severity of clinical findings that warranted call-triggered RRT activation. Despite these limitations, this study is a valuable contribution for the following several reasons. With increasing demands for IGU and monitored beds, this study evaluated CDSS in screening for deteriorating patients by capturing data elements in EHR with a linked automated RRT activation. The process was successful in improving outcome of surgical patients and equally important in its successful implementation of automated RRT activation. This process requires additional evaluation to establish appropriate sensitivity and specificity thresholds for such an automated process, but it has the potential to improve patient outcome, ICU bed utilization, and provide an additional layer of safety for patients in concert with traditional RRT activation mechanisms. In the interim, caution should be exercised when applying such automated systems as they may overwhelm an existing RRT with lack of specificity. This can result in alert fatigue, distracting the team from true decompensations, particularly if not staffed properly

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to manage the large number of events populated by automated trigger.

REFERENCES 1. Franklin C, Mathew J: Developing strategies to prevent inhospital cardiac arrest: Analyzing responses of physicians and nurses in the hours before the event. Crit Care Med 1994; 22:244-247 2. Devita MA, Bellomo R, Hillman K, et al: Findings of the first consensus conference on medical emergency teams. Crit Care Med 2006; 34:2463-2478 3. Bellomo R, Goldsmith D, Uchino S, et al: A prospective beforeand-after trial of a medical emergency team. Mad J Aust 2003; 179:283-287 4. Chrysochoou G, Gunn SR: Demonstrating the benefit of medical emergency teams (MET) proves more difficult than anticipated. Crit Care 2006; 10:306 5. McNeill G, Bryden D: Do either early warning systems or emergency response teams improve hospital patient survival? A systematic review. Resuscitation 2013; 84:1652-1667 6. Morgan R, Williams F Wright M: An early warning scoring system for detecting developing critical illness. Clin Intensive Care 1997; 8:100 7 Subbe CP, Kruger M, Rutherford P, et al: Validation of a modified Early Warning Score in medical admissions. QJM2001; 94:521-526 8. McGinley A, Pearse RM: A national early warning score for acutely ill patients. BMJ 2012; 345:e5310 9. Prytherch DR, Smith GB, Schmidt PE, et al: ViEWS-Towards a national early warning score for detecting adult inpatient deterioration. Resuscitation 201 0; 81:932-937 10. JoS, Lee JB, Jin YH, etal: Modified early warning score with rapid lactate level in critically ill medical patients: the ViEWS-L score. Emerg Med J 2013; 30:123-129 11. Kellett J, Deane B, Gleeson M: Derivation and validation of a score based on Hypotension, Oxygen saturation, low Temperature, ECG changes and Loss of independence (HOTEL) that predicts early mortality between 15 min and 24 h after admission to an acute medical unit. Resuscitation 2008; 78:52-58 12. Bellomo R, Ackerman M, Bailey M, et al; Vital Signs to Identify, Target, and Assess Level of Care Study (VITAL Care Study) Investigators: A controlled trial of electronic automated advisory vital signs monitoring in general hospital wards. Crit Care Med 201 2; 40:2349-2361 13. Shiftman RN, Wright A: Evidence-based clinical decision support. Yearb Med inform 2013; 8:1 20-1 27 14. Huh JW, Lim C-M, Koh Y, et al: Activation of a Medical Emergency Team Using an Electronic Medical Recording-Based Screening System. Crit Care Med 2014; 42:801-808 15. Daniels R, Nutbeam T, McNamara G, et al: The sepsis six and the severe sepsis resuscitation bundle: A prospective observational cohort study. Emerg Med J 2011 ; 28:507-512

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