Trauma Care

[Dustdevil]

I doubt it. No proficient medic in his right mind will stay on scene longer than the time needed to correct immediate life threatening issues.

I wish there were more "proficient" medics out there then. I still see and hear of this happening today, twenty years after we realized it was a bad idea. It seems to be a mixture of medics who simply don't have the sense to know when to stay and when to go, as well as those who simply get carried away with the moment, intoxicated by the smell of blood, and totally lose track of time as they play around. Either way, it's unacceptable and still happens. :?

[Ace844]

http://pdm.medicine.wisc.edu/20-4%20PDFs/Isenberg.pdf

http://www.cochrane-injuries.lshtm.ac.uk/P...INALReport2.pdf

http://www.hta.nhsweb.nhs.uk/execsumm/SUMM217.HTM

http://pdm.medicine.wisc.edu/19-4%20PDFs/Mock.pdf

http://www.medscape.com/viewarticle/448918

http://www.nhtsa.dot.gov/people/injury/ems...ppendices-a.htm

http://bmj.bmjjournals.com/cgi/content/full/324/7346/1135

http://www.who.int/violence_injury_prevent...l/en/index.html

www.aetmis.gouv.qc.ca/site/download. php?3f84607e6bb5c2dbe12e736439956b41

http://pdm.medicine.wisc.edu/birnbaum4.htm

http://www.rcsi.ie/documents/SEVERLYINJUREDGUIDELINES.pdf

www.bryanbledsoe.com/pdf/handouts/ PowerPoint/The%20Science%20Behind%20Trauma%20Care.ppt

[EMSLuke134]

Bad ALS= DEAD PATIENT

Good ALS= More skill and knowledge present. A good provider will know not to sit onscene.

Bad BLS=DEAD PATIENT

Good BLS= good chance for pt

CONCLUSION

BAD AND STUPID EMS PROVIDERS KILL PATIENTS.

Of course, a good emt is better than a bad medic. but a good medic does more than a good emt. This whole trauma study is pointless. good care provides best outcomes. bad care kills. ON ANY LEVEL, BLS OR ALS.

[Ridryder 911]

Bad ALS= DEAD PATIENT

Good ALS= DEAD PATIENT

Bad BLS= NO CHANCE IN HELL !

Good BLS= DEAD PATIENT

CONCLUSION

PATIENTS DIE... short and simple, there is very "few" saves out there. This is part of the problems of EMT's today, unrealistic expectations... you may get 1 or 2 saves per 50 or hundred. If you don't count them dying shortly after... really resuscitations is about 1 in 500 maybe.

Even 20/20 had a repeat documentary on how the public's expectations of cardiac arrest and saves is way overrated.. so is our professions as well.

Now I will admit that this job is important, even more so we do it as the best as we can. It is our JOB to attempt to resuscitate and save a life... but the chances are against you. Even though we did not create that rule, we just have to deal with it. Stabilizing for transport (within common sense) and administering BLS or ALS most of the time is not enough, people will die.. no matter what we do.

The reason I am being so blunt, I have had the fortunate event to assist in teaching a Basic class this week...Wow the expectations of some is incredible ! Now, I am not pessimistic, I am a realist.. the numbers are there, facts don't lie... There are very few we save ... I don't care how good or how much ALS, med.'s . electricity, or the old "diesel medicine".. you loos a heck of a lot more than we save... thank goodness we don't get paid based on our + results...

No trying to bust chops... just a little reality check....

R/r 911

[stcommodore]

I know a resident namded Derek Isenberg....

[chbare]

Ridryder 911, thank you for the dose of reality. We as human beings cannot expect to alter the course of universal inevitability. People will die regardless of what we do to them. I go to great lengths to educate family members and friends about the reality of the patients condition. I cannot tell you how many people expect somebody to live just because we got the heart to start beating and I cannot tell you how many people actually believe that we can give them an actual number (%) on the patients chances of making it. I really believe we can be better health care providers by educating family/friends on the patients condition and help them accept the situation. Of course this should be accomplished with the greatest care and concern for both the patient and family. A little off topic but a good story. We got report from EMS that they were transporting a hospice DNR patient with end stage cancer. Everybody in the er groaned about getting a patient with this complaint. The patient and her family arrived and I took over the care of this patient. The er doc and I went in the room with the patient and the family. We talked to everybody and helped get them all on the same page. The family and patient knew that death was near, but wanted something to help with the pain. The patient was in severe pain and the family/patient wanted pain relief. I positioned the patient in the most comfortable position possible and the doctor assured everybody that we would do whatever it would take to alleviate the patients pain. I ended up giving allot of morphine that night. By the morning the patient was lying in bed sleeping soundly. Everybody told me this was the first time in a long while that the patient was not in severe pain. The patients breathing became shallow and the patient started to look pale and everybody knew the patients time was near. All of the family was with the patient and the patient ended up dying a peaceful death without pain. The family dealt with the patients death very well and everybody was happy that the patient did not die in pain. Even though the patient died I did not see this as a bad outcome. We helped somebody leave this world with dignity and comfort and helped a family through a stressful time.

Take care,

chbare.

[Jeep_911]

ALS vs BLS.....There is a lot a paramedic can do for a trauma patient. Advanced skills that is out f my scope...so are we talking strickly intubation and airway management here or in the diffs in scope?

[AZCEP]

Instead of ALS vs. BLS, maybe we should be looking at urban vs. rural.

ALS two blocks from a trauma center is not going to make a difference. ALS 20-30 minutes from a trauma center can. Is it more cost effective to have the highest trained providers further from the tax base that can support them? Probably not, but that is where they are most needed.

[Ace844]

(Early Predictors of Transfusion and Mortality After Injury: A Review of the Data-Based Literature

[Article)

Eastridge, Brian J. MD; Malone, Debra MD; Holcomb, John B. MD, FACS

From the Department of Surgery, Division of Burn, Trauma, and Critical Care, (B.J.E.), University of Texas Southwestern Medical Center, Dallas, Texas; the Department of Surgery, University of Maryland School of Medicine (D.M.), R. Adams Cowley Shock Trauma Center, Baltimore, Maryland; and the U.S. Army Institute of Surgical Research (J.B.H.), Fort Sam Houston, Texas.

Submitted for publication November 18, 2005.

Accepted for publication November 28, 2005.

Address for reprints: Brian J. Eastridge, MD, Brooke Army Medical Center, 3851 Roger Brooke Drive, Ft. Sam Houston, TX 78243-6315; email: Brian.Eastridge@amedd.army.mil.]

Trauma is the leading cause of mortality in the first four decades of life and even though later superseded by heart disease and malignancy, remains a significant cause of death and disability among all age groups. In all settings, hemorrhage is one of the most substantial determinants of poor outcomes and death.1,2 Though blood transfusion has the obvious benefit of volume restoration and improved oxygen carrying capacity in the injured patient, there are risks and consequences to the use of blood including transfusion reaction, transmission of blood-borne pathogens, and impact on limited supply. In the past several years, evidence has emerged that allogeneic red blood cell transfusion may have significant deleterious immunologic impact upon the injured host.3–6 For all of these reasons, there has been a trend to restrict transfusion in non-urgent clinical settings, and, in general, replacement of blood loss is reserved for urgent conditions in which patients exhibit signs and symptoms of class III or class IV hemorrhage, that is, ongoing or imminently life-threatening. Through this same period, knowledge of the cellular mechanisms of shock and the physiologic response to resuscitation has increased greatly and could be anticipated to provide data-based guidance for changes in practice. The purpose of this paper is to review published data sets on early indicators of mortality after trauma and for the need for transfusion and then, based on these data, to evaluate the risk/benefit ratio of contemporary transfusion strategies in these injured patients.

METHODS/SEARCH STRATEGY

To develop this review, a systematic search of available scientific evidence was conducted utilizing OVID/MEDLINE (1966–present). Selected search terms included combinations of the flowing key words and terms: “trauma”, “injury”, “blood”, “transfusion”, “hemorrhage”, “mortality”, “outcome”, “base deficit”, “coagulopathy”, “hypothermia”, “physiology”, “blood pressure” and “SIRS”. Accessory searches were also conducted using the Cochrane Database of Systematic Reviews 2005 (http://www.cochrane.org ), the National Guideline Clearinghouse (http://www.guidelines.gov/index.asp ), and the Agency for Healthcare Research and Quality (AHRQ) (http://www.ahrq.gov ). In addition, reference lists and bibliographies were analyzed for additional relevant articles.

Prediction of Blood Transfusion After Injury

The National Blood Data Resources Center report of 2002 states that approximately 12,000,000 units of packed red blood cells (pRBC) are transfused each year in the United States and that ten to fifteen percent of the national blood supply is directed toward the treatment of injured patients.7 Como and his colleagues reviewed one year’s admissions to the Shock Trauma unit at the University of Maryland Medical Center. Of the 5645 patients admitted, eight percent were transfused. Four hundred seventy nine (479) received 5,219 units of pRBC. Overall mortality in the transfused group was 27%. The 3% of the total population who received more than ten units of blood had a mortality of 39%.8 It is unclear from these data to what degree transfusion itself contributed independently to this increase in mortality. Similar findings have been demonstrated by Dunne et al.9

Since substantial numbers and proportions of injured patients receive red blood cell (RBC) transfusion, it is important to know who actually needs this intervention. Evaluation of the literature suggests several important predictors of the need for RBC transfusion. These include specific parameters of pre-hospital and presentation physiology, assembled as trauma scoring systems, and additional physiologic parameters during resuscitation, including measures of oxygen debt, injury severity, coagulopathy, and hypothermia.

Scoring Systems

Numerous physiologic scoring systems have been developed over the two decades for the initial evaluation of the trauma patient. These include pre-hospital index, trauma score, and revised trauma score. All of the scoring systems currently in use include blood pressure, respiratory rate, and Glasgow Coma Score. In a small review of 250 patients, published in 1983, West and colleagues compared age, sex, injury severity, mechanism of injury and trauma score. Their results suggested that trauma score was the best predictor of blood requirement among those predictors evaluated. Of the patients with trauma scores <14, 90% did not require transfusion, whereas, 70% of patients with trauma scores >=14 did required transfusion.10 Similarly, Jones demonstrated, in a study population of 217 patients, that a pre-hospital index score >3 was associated with a 77% chance of requiring red cell therapy and, among those with a pre-hospital index score <=3, only 14% required transfusion.11 In a series published in 2002 by Starr and colleagues, the revised trauma score showed a significant correlation with the requirement for blood transfusion after traumatic injury and pelvic fracture.12 Given the complexity of some of the pre-hospital trauma scoring systems, Franklin and colleagues evaluated pre-hospital hypotension as a surrogate for pre-hospital scoring and found that nearly 50% of patients with survivable pre-hospital hypotension required specific therapy for hemorrhage.13

Injury severity scoring has been demonstrated to correlate with transfusion volume. In Como’s study, survivors had an associated trend toward increased transfusion with increasing ISS. Patients requiring 1–10 units of pRBC had a mean ISS of 17, patients requiring 11–20 units had a mean ISS of 28, and those requiring >20 units had a mean ISS of approximately 33.8 In Malone’s cohort of 15,534 trauma patients, a mean ISS 22 was associated with transfusion, whereas a mean ISS of 8 was not. In as yet unpublished data from combat casualty care in the current conflict in Iraq, Eastridge, Wade and Holcomb show similar results, that is, that there is a positive correlation between increasing injury severity score and increasing likelihood of transfusion.

Combinations of physiologic scoring data and injury severity have also been used to attempt to predict the need for transfusion. Baker and colleagues identified 4 risk factors for transfusion after injury: blood pressure <90 mm Hg, heart rate >120, GCS <9, and high risk injury (trauma to the central chest, abdominal injury with diffuse tenderness, survival of a vehicular crash in which another occupant died, vehicular ejection, or penetrating torso injury). Patients with all 4 risk factors had a 100% transfusion rate; 3/4 factors, 68%; 2/4 factors, 42%; 1/4 factors, 12%, and 0/4 factors, 2%. In this series, blood pressure <90 mm Hg demonstrated the highest relative risk for transfusion.18

Oxygen Debt

Perhaps the most convincing data on early predictors for blood transfusion is in the compendium of scientific literature on oxygen debt. Davis and colleagues evaluated 192 trauma patients with shock—defined by base deficit after injury—and correlated these findings with blood transfusion data. Base deficits 2–5 were associated with blood transfusion requirements of 213 mL, 311 mL and 401 mL at 1, 2 and 24 hours respectively. Base deficits 6–14 were associated with blood requirements of 583 mL, 1,201 mL, and 1,538 mL at corresponding time points, and base deficits >15 were associated with 1,082 mL, 2,097 mL, and 2,476 mL, respectively.14 In a later study, Davis, Parks et al., substantiated the dose-dependent response between admission base deficit and volume of transfusion, demonstrating that in this second group of patients, those with a base deficit 3–5 required 1.4 units of pRBC, base deficit 6–9 required 3.8 units of pRBC, and >10 required 8.3 units of pRBC at 24 hours post admission. Interestingly, these oxygen debt data also correlated in a stepwise fashion with diminished trauma score and revised trauma score.15 Serum lactate has also been used as a marker of tissue oxygen debt, with similar results.16,17

Coagulopathy and Hypothermia

Coagulopathy and hypothermia have also been used to predict the need for transfusion, however, they are not mutually exclusive, and their interactions are complex and further complicated in any given individual by the nature and degree of injury. Acute traumatic hemorrhage, resuscitation, and transfusion are all independently associated with abnormalities of clotting factors, acid-base homeostasis, and thermoregulation. Coagulation in the injured patient is disrupted by consumption and dilution of coagulation factors, hypothermia, acidosis, excessive fibrinolytic activity, and tissue thromboplastin released in response to brain and other tissue injury. Thrombo-elastography is useful in the evaluation of platelet dysfunction and fibrinolysis and appears also to have some utility in predicting the necessity for blood transfusion in injured patients.19,20

Jurkovich was one of the first investigators to evaluate the effects of hypothermia after injury, demonstrating substantially decreased survival with progressively worsening hypothermia.21 Numerous investigators have carried this work forward, correlating hypothermia with blood loss and the necessity for red cell transfusion.22–25 Luna and colleagues have shown that blood transfusion requirements are directly proportional to injury severity and inversely proportional to core temperature. The degree to which transfusion itself may be causally related to decreasing temperature was not clear from this study but must be considered. Ferrara and colleagues, in a study of operative trauma patients at the Detroit Receiving Hospital, compared blood loss between those patients with intra-operative core temperatures between 33–35 degrees and those with core temperature >35 degrees centigrade (°C). Intra-operative blood loss averaged 540 mL in those maintaining core temperature >35°C and 1820 mL, in the profoundly hypothermic group (33–35°C), a greater than three-fold difference. Unfortunately, blood transfusion requirements in the study groups is not reported.24 Whatever their limits as observational data sets, these results imply close correlation between hypothermia and coagulopathy in trauma and the likelihood of the need for transfusion.

Prediction of Mortality After Injury

A number of variables predicting the likelihood of mortality after injury have been reported in the medical literature. In this review, we concentrate on early predictive indices and, specifically, on those related to blood transfusion. Not surprisingly, many of the same factors that predict RBC transfusion also predict patient mortality. These include pre-hospital and presentation physiology, measures of oxygen debt, injury severity, coagulopathy and hypothermia, and evidence of systemic inflammatory response syndrome on admission.

Scoring Systems

A large prospective cohort study of over 20,000 patients by MacLeod and colleagues in Florida demonstrated a number of factors associated with mortality in injured patients. The most significantly predictive included partial thromboplastin time (PTT), abnormal computerized tomography (CT) of the head, low initial hemoglobin, a base deficit, and hypotension in the emergency department.26 Trauma scoring systems for use in the field or upon presentation to the emergency department have only a moderate predictive capacity to determine mortality after injury.27–30 Kuhls and colleagues studied a multivariable model strategy in which physiologic trauma score (admission SIRS score—see below—Glasgow Coma score, and age) provided superior predictive potential when compared with anatomic scoring systems such as injury severity score.28 In a related study, Bochicchio demonstrated that SIRS score (temperature >38°C or <36°C, heart rate >120 beats per minute, respiratory rate >20 per minute, leucocytosis >12,000 or <4,000 cells per mL) was an independent predictor of mortality after injury. The relative risk of mortality increased ten-fold from a value of SIRS = 1 (3.5%) to SIRS = 4 (37.3%).31

Base Deficit

Numerous investigations have demonstrated an association between elevated base deficit and increase in patient mortality after injury.14–15,32–36 However, specifically, the failure to resuscitate from oxygen debt within the first 24 hours is correlated with the worst prognosis. Kincaid, Chang and their colleagues showed that patients whose base deficits could not be improved by resuscitation to >4 by 24 hours into care, had a 50% mortality. Those whose base deficit could be normalized within 24 hours had only a 9% risk of mortality.33 In their study of major trauma patients with occult hypo-perfusion demonstrated by lactic acidosis during the first 24 hours of ICU care, Blow and colleagues showed that of the 44/58 patients whose lactic acidosis could be corrected within the first 24 hours, none died. Among the remaining 14 patients, those with persistent lactic acidosis, 43% died.34

Anatomic injury severity demonstrates a modest statistical relationship with adverse survival outcomes after injury.37–38 Age shows strong positive correlation with mortality after injury, out of proportion to that which can be explained by simple anatomic scoring algorithms.39–40

Hypothemia

In several of the series cited above, increasing hypothermia is correlated with increasing mortality. Temperature of <34°C is associated with 40–60% patient mortality,22–24 and this association becomes stronger with decreasing core temperature.21 Patients with significant hypothermia in these studies were also prone to coagulopathy. In the study by MacCleod et al., abnormal protime (PT) and partial thromboplastin time (PTT) were independent risk factors for mortality after trauma.25

Transfusion as a Predictor of Mortality

A substantial medical literature links increasing volume of blood transfusion after injury with increased mortality.3,41–60 Due the heterogeneity of the patient populations involved and the pathophysiology of severe trauma, elucidating the true relationship between blood transfusion and mortality in the severely injured patient has been difficult. However, concern has arisen recently that allogeneic blood therapy has a more than just statistically independent association with mortality in trauma patients.3,43,48 Robinson evaluated blood transfusion in a sample population of blunt hepatic and splenic injuries. Mortality in this population was 14.2%. Utilizing multiple logistic regression analysis and controlling for indices of shock and injury severity, blood transfusion was a strong predictor of mortality, with an odds ratio of 4.75. This result was even more substantial in patients managed non-operatively: transfused patients were 8.45 times more likely to die from their injury than those who were not transfused.43 Red blood cell transfusion and patient age were both found to be independently associated with increased patient mortality from all causes of injury, and these two markers also acted synergistically, to predict mortality from all causes of injury.48 Malone and colleagues, as noted above, evaluated a cohort of 15,534 trauma patients of whom 1,703 required blood transfusion. In this cohort, transfused patients were older, had higher injury severity scores, lower GCS scores, more significant tissue oxygen debt, and were more likely to have a penetrating mechanism of injury. However, statistical analysis identified blood transfusion as the strongest independent predictor of death in these patients.3

CONCLUSIONS

Despite the limitation that many of the analyses in this review were done on class II and class III data, that is, data from retrospective, observational studies, it is apparent that the need for blood transfusion in trauma patients can be predicted by variables measured early in the patient’s resuscitative course. These include: prehospital and presentation physiology, measures of oxygen debt, injury severity, coagulopathy, and hypothermia. Shock indices and measures of oxygen debt appear to be strong positive predictors of transfusion volume requirements and transfusion volume requirements, in turn, appear to be strong predictors of mortality in this patient group.

Future areas for research in this area will be studies aimed at further quantifying these variables to more accurately predict transfusion volume needs. Additional important areas of research include optimal timing of blood transfusion, so-called “transfusion triggers”, and the effects and outcomes of massive transfusions.

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51. Holcomb JB. Methods for improved hemorrhage control. Crit Care. 2004;8:S59–S60. [Context Link]

52. Blackmore CC, Jurkovich GJ, Linnau KF, Cummings P, Hoffer EK, Rivara FP. Assessment of volume of hemorrhage and outcome from pelvic fracture. Arch Surg. 2003;138:504–509. Ovid Full Text Bibliographic Links [Context Link]

53. Phillips GR 3rd, Kauder DR, Schwab CW. Massive blood loss in trauma patients. The benefits and dangers of transfusion therapy. Postgrad Med. 1994;95:61–62, 67–72. Bibliographic Links [Context Link]

54. Shafi S, Kauder KA. Fluid resuscitation and blood replacement in patients with polytrauma. Clin Orth Rel Res. 2004;422:37–42. Ovid Full Text Bibliographic Links [Context Link]

55. Wudel JH, Morris JA Jr., Yates K, Wilson A, Bass SM. Massive transfusion: outcome in blunt trauma patients. J Trauma. 1991;31:1–7. Bibliographic Links [Context Link]

56. Sawyer PR, Harrison CR. Massive transfusion in adults. Diagnoses, survival and blood bank support. Vox Sang. 1990;58:199–203. Bibliographic Links [Context Link]

57. Gilham MJ, Parr MJ. Resuscitation for major trauma. Curr Opin Anaesthesiol. 2002;15:167–172. Ovid Full Text [Context Link]

58. Vaslef SN, Knudsen NW, Neligan PJ, Sebastian MW. Massive transfusion exceeding 50 units of blood products in trauma patients. J Trauma. 2002;53:291–296. Ovid Full Text Bibliographic Links [Context Link]

59. Velmahos GC, Chan L, Chan M, et al. Is there a limit to massive blood transfusion after severe trauma? Arch Surg. 1998;133:947–952. Ovid Full Text Bibliographic Links [Context Link]

60. Hebert PC, Fergusson DA, Stather D, et al. for the Canadian Critical Care Trials Group. Revisiting transfusion practices in critically ill patients. Crit Care Med. 2005;33:7–12. Ovid Full Text Bibliographic Links [Context Link]

DISCUSSION

Dr. Donat Spahn: I think you have convinced me that the more severely you are injured, the more likely you are to receive a blood transfusion, the more likely you go into multi-organ failure and the more likely you are going to die. But what is the contribution of each of these different factors? I think we have no answer. Is it interrelated, or can we dissect the effects of each different factor? Is it really transfusion that leads to the complication and death, or is it simply the fact that those who received the transfusion are more severely injured in the first place? For a more controlled situation of surgery or intensive care unit, there’s only one study that has ever approached this, the TRICC trial. A subgroup analysis published earlier this year, only 200 patients, found no difference. In terms of outcome, there were very slight trends of less infection and less multi-organ failure in the lesser transfused group, but nothing real. On the other hand, the study wasn’t powered nor designed to have the power to answer these questions. So my question is can we avoid a prospective randomized study in this patient group, at least in the civilian arena, to answer this question? If we conduct a study, I propose that we have strict guidelines, not only for red blood cells, but for all the blood components as well. And, of course, the problem is what type of blood product and what type of red blood cells do we use?

Dr. Stephen Cohn: Over the last decade a number of folks in the room here have been trying to design clinical trials that address transfusion avoidance. How do we design a trial that will allow us to discern a reduction in transfusion requirements? This kind of work aims at trying to identify the at-risk group for transfusion requirements to define our study group. In some ways, we’re in need of technology. Imagine that your soldiers wore monitors that could measure base deficit real time continuously and transmit it to an AWAC, or to Dr. Holcomb at the ISR who could say, this guy needs to go to the Combat Support Hospital not to the Battalion Aid Station because he’s going to bleed to death. We’re not quite there yet. But even if this information would be very valuable in terms of trial design, I’m not sure as clinicians how much it would change our management because in most situations we’re already in the mode of transfusing someone even before we get base deficits back.

Dr. Lloyd Ketchum: As we try to design rational clinical trials of massive transfusion, that we’re talking about a very small subset of patients. Can we come up with a way to predict in the trauma bay with very readily available data which person is going to require massive transfusion and which person is not?

Dr. Brian Eastridge: This will not guide anybody’s specific therapy. But, as was mentioned by the discussants, this was more of a baseline to figure out who are those specifically at risk for massive transfusion.

Dr. Debra Malone: Is it blood transfusion or is it shock, injury severity, base deficit or everything else that goes into it? To the best of our ability, statistically, we really think we have determined that blood transfusion in and of itself, after controlling for indices of shock and patient demographics, is an independent predictor of worse outcome.

The big picture here is how do we develop a prospective clinical trial? That’s going to be tough, because I think we’re talking about two different things. We’re talking about systemic inflammation and what goes bad because of systemic inflammation. There is this other entity though, massive transfusion. Until we actually have a better way to replace blood volume, I don’t know that it’s going to be possible to develop a prospective randomized clinical trial for blood transfusion.

Dr. John Owings: Is blood transfusion fundamentally bad? The answer is yes and no. We’re looking at some fairly esoteric immunologic consequences of transfusion, including leukocyte survival in trauma patients, which is unique in the trauma population compared to other populations. If you give it when you don’t need it, then that’s obviously bad, because transfusion has very, very poor consequences. If you don’t give enough that’s a problem. So ideally why we’re all here is to try to figure out a way to minimize the need for transfusion so that we hit right squarely in the center zone, which is only transfuse absolutely the minimum amount. The point the presenter made is that, when we swing too far in either direction, the cure then becomes worse than the disease.

Dr. Morris Blajchman: But the allogeneic RBCs that we use to replace bleeding is not the best product, the blood that we use to replace stored blood is acidotic and hyperkalemic due to storage. In addition, stored RBCs have storage defects, which render the RBCs hypofunctional. Mortality occurs primarily when the hemoglobin is below six grams per dL. So we know that it is unnecessary to get patients back to 10 or 12 g/dL or even 10 g/dL, which often happens in patients because they are over-transfused. My main message is that we don’t have to replace the hemoglobin to physiologically “normal” levels. Just because we’re all walking around with hemoglobin levels of 15 g/dL doesn’t mean that that’s the best level to achieve when transfusing trauma patients.

Dr. Spahn: I completely agree. And, you know, the question of what type of blood we’re using in such a trial is a key question. Jean-Louis Vincent in the ABC study found there is an add-on negative effect of being transfused in ICU patients. He repeated the same study, exactly the same thing, two years later with leukoreduced blood and they didn’t find this anymore. This has not been published yet.

Dr. Jeff Lawson: Our expectation is trauma is chaos. So in that context I think there’s some hope. But the analysis is not the conventional analysis that we’re all used to. I think the solution will come from the statistical analyses that have been derived from our gene chip array-based information. It’s a completely different statistical paradigm and it takes a big sample number.

Dr. John Holcomb: Many studies being designed, or in process right now, are focusing on the critical small segment of trauma patients who need a lot done to them and who might survive. The challenge is to be able to determine the group that will get a fair amount of blood very soon upon arrival in the ED and have the potential of survival. We need to be able to randomize them and not have excessive noise. I think we need to spend more time with these kinds of data. We need to know which parameters we can easily measure in the first ten minutes in the ED, randomize and apply whatever hemostatic intervention we are studying. The next challenge will be using the same construct and moving to the prehospital arena.

[Ace844]

(Reduced Heart Rate Variability: An Indicator of Cardiac Uncoupling and Diminished Physiologic Reserve in 1 @ 425 Trauma Patients

[Original Articles)

Morris, John A. Jr MD; Norris, Patrick R. MS; Ozdas, Asli PhD; Waitman, Lemuel R. PhD; Harrell, Frank E. Jr PhD; Williams, Anna E. BA; Cao, Hanqing PhD; Jenkins, Judith M. MS, RN

From the Department of Surgical Sciences (J.A.M., P.R.N., A.E.W., H.C., J.M.J.), Department of Biomedical Engineering (P.R.N.), Department of Biomedical Informatics (L.R.W.), and Department of Biostatistics (F.E.H.), Vanderbilt University Medical Center, Nashville, Tennessee.

Submitted for publication September 23, 2005.

Accepted for publication March 7, 2006.

Presented at the 64th Annual Meeting of the American Association for the Surgery of Trauma, September 22–24, 2005, Atlanta, Georgia.

Address for reprints: John A. Morris Jr, MD, 2100 Pierce Ave., 243 MCS, Nashville, TN 37212; email: john.morris@vanderbilt.edu.]

Abstract

Background: Measurements of a patient’s physiologic reserve (age, injury severity, admission lactic acidosis, transfusion requirements, and coagulopathy) reflect robustness of response to surgical insult. We have previously shown that cardiac uncoupling (reduced heart rate variability, HRV) in the first 24 hours after injury correlates with mortality and autonomic nervous system failure. We hypothesized: Deteriorating physiologic reserve correlates with reduced HRV and cardiac uncoupling.

Methods: There were 1,425 trauma ICU patients that satisfied the inclusion criteria. Differences in mortality across categorical measurements of the domains of physiologic reserve were assessed using the [chi]2 test. The relationship of cardiac uncoupling and physiologic reserve was examined using multivariate logistic regression models for various levels of cardiac uncoupling (>0 through 28% reduced HRV in the first 24 hours).

Results: Of these, 797 (55.9%) patients exhibited cardiac uncoupling. Deteriorating measures of physiologic reserve reflected increased risk of death. Measures of acidosis (admission lactate, time to lactate normalization, and lactate deterioration over the first 24 hours), coagulopathy, age, and injury severity contributed significantly to the risk of cardiac uncoupling (area under receiver operator curve, ROC = 0.73). The association between deteriorating reserve and cardiac uncoupling increases with the threshold for uncoupling (ROC = 0.78).

Conclusions: Reduced heart rate variability is a new biomarker reflecting the loss of command and control of the heart (cardiac uncoupling). Risk of cardiac uncoupling increases significantly as a patient’s phyiologic reserve deteriorates and physiologic exhaustion approaches. Cardiac uncoupling provides a noninvasive, overall measure of a patient’s clinical trajectory over the first 24 hours of ICU stay.

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We have previously shown in trauma patients that cardiac uncoupling [reduction in heart rate variability, HRV, measured in the first 24 hours of Intensive Care Unit (ICU) stay] is a predictor of mortality.1,2 We believe cardiac uncoupling reflects a deterioration of multiple communication pathways which link systems, organs, cells, proteins, and genes.3–5 The best documented communication pathways include the autonomic nervous system and other neuroendocrine mechanisms.6,7

The dual concepts of physiologic reserve 8–10 and physiologic exhaustion 11,12 form the foundation for damage control techniques 13,14 in surgery. Physiologic reserve defines a patient’s ability to tolerate injury. It is a function of pre-injury host factors, including genetic and environmental factors unique to the individual patient.15,16 Physiologic exhaustion, characterized by the triad of acidosis,17,18 coagulopathy,19,20 and hypothermia,21–24 defines the point in time where the patient cannot tolerate further surgical insult, and damage control must be implemented, the patient resuscitated and reserve restored. We hypothesized that deteriorating physiologic reserve triggers cardiac uncoupling and foreshadows the onset of physiologic exhaustion.

METHODS

Setting

Vanderbilt University Medical Center (VUMC) is the only level one trauma center serving an 80,000 square-mile catchment area. Approximately 3,500 trauma patients are admitted annually, 1,900 of which are admitted to a 31-bed dedicated trauma unit. Fourteen of the 31 beds are ICU beds equipped with continuous physiologic monitoring capability, SIMON (Signal Interpretation and Monitoring), and accommodate 700 to 800 admissions per year.

Data Sources

VUMC’s clinical information infrastructure provided the linked patient physiologic, demographic, outcome, and laboratory data required for this study. Key components of the infrastructure relevant to this analysis include:

1. SIMON.1,25An ongoing collaborative effort between the Vanderbilt University Medical Center (VUMC) Division of Trauma and Vanderbilt University School of Engineering. Since December 2000, physiologic data from bedside medical devices have been continuously captured and stored from trauma ICU beds. Physiologic parameters captured include heart rate (HR), invasive and noninvasive blood pressures, intracranial and cerebral perfusion pressures, arterial and venous oxygen saturations, core temperature, pulmonary and central venous pressures, cardiac index, and end diastolic volume index. As of August 2005, data has been collected on 3760 patients for their entire length of ICU stay in a SIMON monitored bed. This represents more than 310,000 total hours of continuous monitoring and over 3 billion data points.

2. TRACS.The VUMC Division of Trauma has maintained a trauma registry since 1986 and has participated in the Trauma Registry of the American College of Surgeons (TRACS) since 1996. All patients admitted to VUMC with trauma or burns are entered into this database, which includes all patients with SIMON data. Data are maintained locally and shared quarterly with the national repository. Currently, more than 300 parameters are captured via retrospective chart review, including patient demographics, injuries, diseases, operative procedures, hospital disposition, complications, costs, resource utilization, and length of stay at various levels of care.

3. VUMC Diagnostic Laboratory.The VUMC Diagnostic Laboratory processed all laboratory results from the study population, with all results managed electronically and integrated into our institution’s electronic medical record (EMR) system.

4. De-Identified Repository.Both SIMON and TRACS meet regulatory requirements for data repository status, and are approved as such by the Vanderbilt University Institutional Review Board. SIMON data are prospectively captured during the course of clinical care, while TRACS data are captured retrospectively. Research data requests are processed in accordance with Institution and HIPAA regulations, and are de-identified for analysis.

Inclusion Criteria

This IRB-approved study includes all 1,425 trauma admissions to VUMC who:

1. Were admitted in the 41-month period, December 15, 2000 through May 15, 2004, recorded in TRACS as initial hospital admissions, and not in-hospital transfers or re-admissions.

2. Had sufficient SIMON data to compute measures of heart rate variability (>=12 hours within the first 24 hours).

3. Had an initial serum lactate result ±4 hours of ICU arrival.

Figure 1 illustrates how the study population was derived from the 11,121 trauma admissions during this time period. Additional analysis defined three subgroups based on available data:

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[Email Jumpstart To Image] Fig. 1. Study population selection.

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1. Those patients who also had at least one INR result available (n = 1412, 99.1%).

2. Those patients who had sufficient lactate data to determine time until lactate normalization (n = 1112, 78.0%).

3. Those patients who met criteria of both 1 and 2 (n = 1101, 77.3%).

Measurements

Demographics and Outcome

Patient age in years, gender, ethnicity, and hospital disposition were obtained from TRACS. Age categories were defined as: less than 20, 20 to <30, 30 to <40, 40 to <50, 50 to <60, 60 to <70, 70 to <80, and >=80 years.

Physiologic Reserve

Patient age and Injury Severity Score (ISS), obtained from TRACS, were used to assess preinjury physiologic reserve and magnitude of injury. ISS categories were defined as: 1 to 8, 9 to 16, 25 to 41, and >greater than 41.

Measurements of Physiologic Reserve

Measures for physiologic reserve used in this study include acidosis, coagulopathy, and hemorrhage severity as reflected by transfusion requirements. Acidosis was assessed using four measures of serum lactate values (mEq/L) obtained during the course of routine clinical care:

1. Initial lactate value (LACi), categorized as follows: less than or equal to 2.5, >2.5 to 3.5, >3.5 to 4.5, >4.5 to 7.5, >7.5 to 10.0, and greater than 10 mEq/L.

2. Highest lactate value in the first 24 hours after admission, categorized as above.

3. Time to lactate normalization (LACt), defined as the time in hours between an initial abnormal lactate result and the first normal laboratory result (less than or equal to 2.5 mEq/L), and categorized as follows: 0 (first lactate normal), 0 to <12, 12 to <24, 24 to <36, 36 to <48, 48 to <60, 60 to <72, and 72 or more hours. Patients who died without a recorded normal lactate value were assigned to the last category (>=72 hours), provided that they survived more than 72 hours. Those patients who expired before 72 hours without a normal lactate value were excluded from the study.

4. Deterioration in lactate over the first 24 hours (LAC[DELTA]24), defined as the difference between the admission lactate and the highest abnormal (>2.5 mEq/L) lactate value obtained in the first 24 hours after admission, and categorized as follows: 0 (no deterioration or no abnormal lactate value within the first 24 hours), 0 to <1, 1 to <2, 2 to <3, 3 to <4, and 4 or higher mEq/L increase in lactate over the first 24 hours.

Coagulopathy on admission was measured using the International Normalized Ratio 26 and categorized as follows: less than 1.5, 1.5 to 3.0, and greater than 3.0. The highest INR recorded within the first 24 hours (INRmax24) and for the entire stay were also examined and categorized as above. Transfusion requirement was measured in units of packed red blood cells (uPRBC) received during the hospital stay categorized as follows: 0, 1 to 4, 5 to 9, and 10 or more uPRBC.

Cardiac Uncoupling

Our measurement of interest, cardiac uncoupling (percent low heart rate variability), reflects the percent of time in the first 24 hours that a patient’s short-term heart rate variability (HRV) falls within a critically low range and is computed as follows: Each patient’s heart rate data from the first 24 hours is split into 5-minute intervals. The SD of integer heart rate for each interval is computed, providing a measure of short-term HRV reflecting duration (5 minutes) and intensity (SD) of variability. The percentage of these intervals which fall within a critically low range (0.3–0.6 bpm)27 provide our measure of cardiac uncoupling.

The 5-minute time interval follows established practices for data collection of HRV, and our analysis resembles time-series techniques for assessing HRV (i.e. SDANN).6 Our data, however, differs from that used in traditional HRV analysis because precise instantaneous heart rate is not acquired at every beat. SIMON samples heart rate from a standard monitor (Phillips Viridia) at an average rate of once every 1 to 4 seconds. Thus, a typical 5-minute interval will contain between 75 and 300 heart rate data samples for a single patient. The SD of these points is our basic parameter of short-term HRV, and the units of this measure are beats per minute (bpm).

Analysis

Differences in mortality and cardiac uncoupling across categorical measurements of cardiac uncoupling and the domains of physiologic reserve were assessed using the [chi]2 test. Univariate logistic regression models were constructed to assess the independent relationships between cardiac uncoupling and each measure of physiologic reserve, as well as gender, ethnicity, and head injury score. Finally, multivariate logistic regression models were constructed for various levels of cardiac uncoupling (>0 through 28% reduced HRV in the first 24 hours) to characterize the simultaneous contribution of multiple physiologic reserve measurements to risk of cardiac uncoupling. Multiple models were used to verify the consistency of these relationships at various degrees of cardiac uncoupling, i.e. critically low heart rate variability greater than 0, 2, 4, 8, 12, 16, 20, 24, and 28% of the time in the first 24 hours.

RESULTS

Magnitude of the Data Set

This is a population based study comprised of a study group containing 1,425 patients whose demographics are outlined in Table 1. This population is typical for admission to the Trauma ICU in our catchment area. All patients had SIMON data recorded. There were 185 million heart rate measurements representing 123,000 continuous hours of monitoring. Of these, 628 patients (44.1%) showed no evidence of cardiac uncoupling (i.e. no time spent with HRV in critically low range) and 797 (55.9%) showed some degree of uncoupling in the first 24 hours.

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[Email Jumpstart To Image] Table 1 Study Population Characteristics

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Laboratory data, which included test name, date, and time of sample collection, were merged within the clinical information management system. We analyzed the initial lactate values (n = 1425) in the study group and a total of 4164 lactate values over their hospital stay (range, 0.2–24.0; median, 2.2; mean, 2.7 mEq/L). There were 8811 INRs measured (range, 0.7–16.2; median, 1.2; mean, 1.3) over the course of the entire hospital stay. These 1,425 patients had a total of 7,995 units of PRBCs transfused (range, 0–70; median, 3; mean, 5.6).

Measurements of Physiologic Reserve

Our previous work has demonstrated HRV to be associated with age.1,27 Figure 2 demonstrates mortality and cardiac uncoupling (reduced HRV) in the study group, stratified by age. The breakpoint at age 40 is typical of this population and has been reported by us 16 and others.28 Figure 3 demonstrates mortality and cardiac uncoupling in the study group, stratified by ISS. The 12% mortality in the ISS 25 to 41 group appears low, but may reflect selection bias as all patients in the study group survived to >=12 hours post-ICU admission.

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[Email Jumpstart To Image] Fig. 2. Mortality and cardiac uncoupling stratified by age. Numbers along x axis show numbers of cases in each category. *Denotes p < 0.05 with respect to first category. +Denotes p < 0.05 with respect to previous category. See text for detailed descriptions of category boundaries.

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[Email Jumpstart To Image] Fig. 3. Mortality and cardiac uncoupling stratified by ISS. Numbers along x axis show numbers of cases in each category. *Denotes p < 0.05 with respect to first category. +Denotes p < 0.05 with respect to previous category.

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Our surrogates for deteriorating physiologic reserve include: acidosis (lactate), coagulopathy (INR), and degree of hemorrhage (uPRBC). We examined measurements of acidosis including serum lactate upon admission, worst lactate within the first 24 hours, deteriorating lactate in the first 24 hours, and time to lactate normalization (Fig. 4). There were 57% of patients who presented with an abnormal lactate (>=2.5 mEq/L). Mortality and cardiac uncoupling generally increase as any measurement of acidosis worsens.

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[Email Jumpstart To Image] Fig. 4. Mortality and cardiac uncoupling stratified by measurements of acidosis: (A) Admission lactate value; ( highest lactate value obtained in the first 24 hours of ICU stay; © deterioration in lactate in first 24 hours; and (D). Time until lactate normalization. Numbers along x-axes show numbers of cases in each category. *Denotes p < 0.05 with respect to first category. +Denotes p < 0.05 with respect to previous category. See text for detailed descriptions of category boundaries.

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Figure 4C also demonstrates the subgroup of patients (n = 233) whose lactate deteriorated during the first 24 hours. Mortality increased to 15.5% (p = 0.017) when compared with those patients whose lactate did not deteriorate. While clinically expected, this is the first time this measurement has been quantified to our knowledge.

There was sufficient data to determine time to lactate normalization in 1,112 patients. Figure 4D shows the relationship between time to lactate normalization and mortality and cardiac uncoupling. Patients who failed to normalize their lactate in 48 hours had a dramatic increase in mortality and cardiac uncoupling.

In addition to measurements of acidosis, we examined measurements of coagulopathy (INR) and hemorrhage severity (uPRBC over hospital stay). Figure 5 shows the expected positive relationship between mortality, cardiac uncoupling, and these measures of physiologic reserve.

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[Email Jumpstart To Image] Fig. 5. Mortality and cardiac uncoupling stratified by measurements of coagulopathy and transfusion requirement: (A) Admission INR; ( highest INR obtained within the first 24 hours of ICU stay; © highest INR obtained in the entire stay; and (D). uPRBC transfused during entire stay. Numbers along x axis show numbers of cases in each category. *Denotes p < 0.05 with respect to first category. +Denotes p < 0.05 with respect to previous category.

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Cardiac Uncoupling

Our current measures of cardiac uncoupling reflect the duration that a patient’s heart rate shows low variability during the first 24 hours. Figure 6 demonstrates the relationship between magnitude of cardiac uncoupling and mortality. Mortality nearly doubles with even a small degree of cardiac uncoupling, demonstrated by an increase from 4.3% to 7.4% between first two categories. Further, as the magnitude of uncoupling increases, mortality increases.

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[Email Jumpstart To Image] Fig. 6. Mortality stratified by cardiac uncoupling. Numbers along x axis show numbers of cases in each category. *Denotes p < 0.05 with respect to first category. See text for detailed descriptions of category boundaries.

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This relationship led us to postulate that cardiac uncoupling reflected diminishing physiologic reserve. We found that measures of deteriorating physiologic reserve (lactic acidosis, age, injury severity, and coagulopathy) were associated with any degree of cardiac uncoupling in the first 24 hours. This relationship becomes more robust as the magnitude of uncoupling increases (ROC 0.73–0.78, Fig. 7), and remains fairly consistent regardless of the percent time threshold used to define patients as being uncoupled.

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[Email Jumpstart To Image] Fig. 7. Area under ROC Curve for nine multivariate regression models of cardiac uncoupling.

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Table 2 summarizes the contribution of measurements to risk of cardiac uncoupling, in the nine multivariate models shown in Figure 7. Age, ISS, and lactate deterioration in the first 24 hours (LAC[DELTA]24) were significant contributors to risk in all models, with admission lactate (LACi) and time to lactate normalization (LACt) significant in most models. Our measurement reflecting coagulopathy, the highest INR obtained in the first 24 hours (INRmax24), was a significant contributor to risk of uncoupling in only one model.

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[Email Jumpstart To Image] Table 2 Summary of Inputs to Multivariate Regression Models

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In separate univariate analyses, gender and ethnicity were not associated with risk of cardiac uncoupling. We also examined the association between severe head injury (abbreviated injury scale >=4 for head and neck region) and reduced HRV, previously studied in similar populations.29,30 While severe head injury was associated with uncoupling in the first 24 hours [(odds ratio (OR) 1.64–1.95, p < 0.01], the association was eclipsed by measures of physiologic reserve in multivariate analyses.

DISCUSSION

Overview

The dual concepts of physiologic reserve and physiologic exhaustion are the foundation for damage control techniques in surgery. Conceptually, physiologic reserve defines the patient’s resistance to death following injury. Figure 8 depicts this relationship for an individual patient.

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[Email Jumpstart To Image] Fig. 8. Physiologic exhaustion.

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Before injury, physiologic reserve, (the vertical axis) is defined by the interaction of genetic and environmental factors. These include factors such as age,31–34 gender,16 and pre-existing diseases.35 For example, an octogenarian has less initial reserve than a 20 year old marathon runner.

At the moment of injury, the patient’s reserve is consumed at a rate defined by the magnitude of the injury, i.e. patients with massive injury consume reserve faster than patients with moderate injury. As time progresses, (horizontal axis) reserve deteriorates and eventually the patient enters a premorbid state: physiologic exhaustion. In this state, the patient is unable to tolerate further insult; all procedures must be terminated, and all efforts focused on resuscitation 36,37 or the patient will die. Death can occur early from exsanguination or hypoxemia, or in a delayed fashion as the result of a hyper-inflammatory response, infection or multiple organ failure.

Many authors have contributed to the recognition of physiologic exhaustion, damage control, and the definition of the end points of resuscitation.38–45 Typically, deteriorating physiologic reserve is measured by increasing acidosis,46–48 coagulopathy,19,49,50 and hypothermia.51–54

As we digested our initial concepts on cardiac uncoupling (Fig. 8), it appeared that uncoupling would fit nicely within the framework of the physiologic reserve/exhaustion paradigm. We postulated that disruptions in command and control pathways between systems, organs, and cells would increase as reserve deteriorated and physiologic exhaustion approached. Cardiac uncoupling, i.e. the heart acting autonomously from central regulation, is the first example of this disruption we have illustrated in our dataset.

Using dense physiologic data capture and our evolving clinical information management system, we have explored the relationship between physiologic reserve and cardiac uncoupling. Specifically, we have shown that measures of lactic acidosis, including categories of admission lactate (OR = 1.17–1.40), time to lactate normalization (OR = 1.12–1.26), and lactate deterioration (OR = 1.22–1.37) over the first 24 hours, all contribute significantly to the risk of cardiac uncoupling and ultimately death.

The contribution of coagulopathy is less clear. While the presence of coagulopathy on admission more than triples the mortality rate, our clinical information management system cannot yet distinguish between preinjury drug-induced coagulopathy (anti-coagulant or anti-platelet drugs) and consumptive coagulopathy resulting from hemorrhage. This may explain why the presence of coagulopathy was significant in only one of our models. Additionally, it is clear that transfusion requirements and coagulopathy are measuring similar entities.20,55,56 Because INR can be measured early in the postinjury course, we elected to include it as opposed to transfusion requirement in the multivariate regression models.

However, the significance of admission coagulopathy is reinforced when we look at the transfusion data. In a separate univariate analysis at various levels of cardiac uncoupling, the units of red blood cells administered reflected increased risk of cardiac uncoupling (OR = 1.15–1.21, ROC = 0.62–0.71).

Strengths and Limitations

Strengths

The strengths of this large population-based study are clear: The patient population is homogeneous and the data set is robust and unique. Only trauma patients with sufficient acuity to warrant ICU admission are included in the study population. All patients are managed by a small number of faculty practicing under a single set of evidence based protocols.

Our evolving clinical information management system allows linkage of 300 fields of outcome and demographic data, tens of thousands of laboratory values, and billions of physiologic data points. This results in a uniquely powerful physiologic portrait of our patient population.

The data we present provides an update on previous work by Scalea 57 and others 58–62 on time to lactate normalization, and demonstrates improved survival resulting from the aggressive implementation of damage control techniques in our institution. Additionally, we provide data on a new physiologic tool: cardiac uncoupling. It is possible that this noninvasive tool could be utilized in the prehospital environment to predict patient deterioration and impending physiologic exhaustion. In this capacity, cardiac uncoupling might serve to triage patients following civilian mass casualty or play a similar role in battlefield casualty management.27,63

Limitations

While standard measures of physiologic reserve appear to be associated with cardiac uncoupling, the correlation is not robust for several potential reasons. First, our clinical information management system does not yet contain reliable data on genetics, pre-existing disease, or preinjury medications. We have previously shown the importance of pre-existing conditions in the assessment of physiologic reserve.35

The lack of data on preinjury medications can affect the analysis of uncoupling in two ways: (1) Our early work with uncoupling clearly shows that certain medications have a significant positive or negative effect on heart rate variability, and (2) the presence of preinjury anti-coagulants or anti-platelet aggregates may confuse the diagnoses of physiologic exhaustion by confusing “iatrogenic” coagulopathy for consumptive coagulopathy. However, this confusion likely attenuates the relationship between cardiac uncoupling, physiologic exhaustion, and death and therefore strengthens our hypothesis.

Hypothermia 22 is a common phenomenon in the trauma patient and a clinically relevant harbinger of deteriorating physiologic reserve.64 The relationship between temperature and cardiac uncoupling is unknown. We are currently exploring that relationship in a subset of this population in whom core temperatures from continuous pulmonary artery catheterization data are available. Our initial work suggests a strong relationship between hypothermia and cardiac uncoupling.

Finally, disruption of communication pathways between systems, organs, and cells appears to be signaled by uncoupling of numerous organ systems from physiologic command and control. Cardiac uncoupling is the prototype, illuminated by merging dense physiologic data capture and the clinical information management system. Other factors, alone or in combination with cardiac uncoupling, may demonstrate a more robust relationship between dense physiologic data and deteriorating physiologic reserve. The relationship between cardiac uncoupling and conventional predictors of mortality and morbidity described in this work is an early step toward routine clinical application of heart rate variability. Bringing such measurements to the bedside requires not only identifying relationships between cardiac uncoupling and predictive covariates, but also establishing the independence of heart rate variability from these covariates in predicting morbidity and mortality.

CONCLUSIONS

We conclude that reduced heart rate variability is a new biomarker reflecting the loss of command and control of the heart (cardiac uncoupling). Risk of cardiac uncoupling increases significantly as a patient’s phyiologic reserve deteriorates and physiologic exhaustion approaches. Cardiac uncoupling provides a noninvasive, overall measure of a patient’s clinical trajectory over the first 24 hours of ICU stay.

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DISCUSSION

Dr. Robert J. Winchell (Portland, Maine): As another with a long-standing interest in the analysis of heart rate variability, I too was quite interested to read the manuscript and hear the presentation.

A sound body of evidence does exist, including the previous work of these authors and others, which shows a clear association between altered heart rate variability and patient outcome. The measurement of heart rate variability offers us a dynamic look at autonomic nervous system function, and as such, it has great promise, both in prediction of outcome and in a moment-to-moment view of the patient’s physiological status. To date that promise is largely unfulfilled due to complexities in the calculation of heart rate variability parameters as well as the number of confounding variables that get in the way of the analysis.

In the current study, Dr. Morris and his colleagues have analyzed a truly mountainous data set, and they have proven their initial hypothesis that heart rate variability modifications are associated with other parameters of physiological exhaustion that have come out of their prior damage control work. In this sense, the study is another brick in the wall; suggesting that heart rate variability is a good thing to look at, and that the promise is there to be fulfilled.

However, in this study the authors have not been able to connect the dots and validate the utility of heart rate variability as a new vital sign, which is the ultimate goal. I would suggest that the analysis we would like to see is not whether current measures of physiological exhaustion predict alterations in heart rate variability, but in fact, whether we can use heart rate variability either to replace standard measurements, or better still to augment them and tell us something new. That is where the real value of heart rate variability analysis lies.

My questions: Have you performed an analysis using cardiac uncoupling as an independent variable, seeking to predict physiological exhaustion as measured by your other parameters?

Have you investigated the time sequence of changes in heart rate variability, as you have with lactate levels and with INR?

Finally, in your experience, does cardiac uncoupling tell you something that the eyeball test doesn’t, in terms of prognosis or moment-to-moment status?

Dr. John A. Morris (Nashville, Tennessee): Well, Robert, I love your analogy about a brick in the wall. I mean, this is a big wall. This is one little brick, but I guess the way the Chinese built the great wall was one brick at a time, and that’s kind of the way we’re going here.

Your questions about have we done the reverse. We haven’t because we haven’t figured out how to do it. It’s very difficult to quantify this concept of reserve. We really weren’t quantifying reserve, we were quantifying the progression towards exhaustion, so we don’t really have a number that we can say the reserve is this, and then it’s a little bit less than that and a little bit less than that.

Consequently, we did the analysis the other way as you correctly pointed out. The temporal data is really difficult statistically, and it’s not an accident that we have confined our analysis to date to the first 24 hours. We really don’t have good ways yet of characterizing this data on a minute-to-minute basis in an individual patient.

I think we’re going to be able to develop those tools, we’re working hard to develop those tools, but we don’t have them yet. Then the question I like the best is the eyeball question. Are we getting information that we don’t have via our other parameters, and we are starting to be able to answer that question. I won’t say unequivocally, yes, but there are more and more things that we’re seeing where heart rate variability is telling us stuff that we’re not getting from other more invasive mechanisms, such as continuous cardiac index and so on and so forth.

Dr. Stephen Dirusso (Bronx, New York): Dr. Morris, I just want to tell you, as an electrical engineer by trade, this whole work just totally fascinates me. I’m a little jealous and, I tell you, a little po’d that you’re doing this work now and not when I was one of your residents back then but not enough to make me want to come back and be a resident.

Actually, at our institution, we’re looking at some more sophisticated tools for analysis of some of this physiologic data. For instance, in head injury we’re looking at finite time frequency analysis of signals, of EEG signals to actually see if we can’t look for patterns of say, ischemia in the brain.

Heart rate variability just seems to be a real tip of the iceberg. It’s a very unsophisticated tool, and I was just wondering I think you had mentioned it just a little bit in your previous answer to one of the questions, looking at say, some more sophisticated tools for analysis of these signals.

Dr. John A. Morris: Well, we are looking at more sophisticated tools, and we’re looking at different signals. Intercranial pressure, we are just salivating over, because the relationship between intercranial pressure, as some of the earlier work this morning indicated, there seems to be a cascade effect between a whole lot of variables in intercranial pressure.

There are real technical issues about tackling intercranial pressure that makes it in some respects more difficult then just looking at just heart rate, but it’s the next thing on our list, and we have started to prepare those data sets as we speak.

Dr. Kevin J. Farrell (Atlanta, Georgia): I think that this work and type of work is exciting and very pioneering. I offer two comments. I wonder if you can get more clinical information by correlating this data with clinical information.

You say, you only do the first 24 hours, but can you extend it out and correlate it with a number—there are a number of systems out there to evaluate most system organ failure on a scoring system where it is not a phenomena, and so the correlation of a multiple organ failure index, where this may be helpful.

It may lead to new knowledge, and it may also see how valuable this thing is. Another is, in terms of physiologic exhaustion, just an observation. It seems that when you’re taking care of a patient with multi-system organ failure and you’re literally taking them out to the last. It seems the last few days, their consumption drops way down sometimes, and presumptively they’re exhausted from a physiologic point of view.

Dr. John A. Morris: We see the same thing. You’ve anticipated one of our observations, and that is these patients go through a stage very late.

We’ve only looked at the front end, but we have the data that looks at the back end of their stay, and there is something that we call autonomic exhaustion, where these patients have very low heart rate variability for a very long period of time, and many of those patients don’t rebound.

In terms of multiple organ failure, we will continue to look at the long-term ramifications, and as we get deeper and deeper into the laboratory data, I think that we’re going to be able to quantify exactly the things that you’re talking about.

Dr. Karen Butler (Cincinnati, Ohio): I wonder, in your cohort, did you exclude those patients who were either on beta-blocker therapy before their injury or required beta blockade for cardio protection when they were admitted to the hospital?

Also, those patients, who had primary autonomic dysfunction as an injury, so those patients with spinal chord injury, were they excluded? Secondly, do you think that this is going to predict just ICU mortality or overall hospital mortality?

Dr. John A. Morris: One, the beta blockade question is common. We have not yet analyzed for beta blockade, so patients on beta blockade are in this data. This is just overview data. Same thing with spinal cord injury, we think that’s going to be very interesting to look at, but it’s down on the list.

I’m sorry, Karen, your last question, which I really loved: No, it’s overall hospital mortality. In this, when we look at heart rate variability, its predictive window for mortality is about four to five days, so we’re not showing low heart rate variability in death the next day. We’re showing death four to five days down the road.