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Complications

The Tertiary Survey For Trauma: Does It Work?

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Here’s the second part in my series on the tertiary survey for trauma. In my last post I discussed the basics, and in the next and final one I’ll review who can do it.

Delayed diagnoses / missed injuries are with us to stay. The typical trauma activation is a fast-paced process, with lots of things going on at once. Trauma professionals are very good about doing a thorough exam and selecting pertinent diagnostic tests to seek out the obvious and not so obvious injuries.

But we will always miss a few. The incidence varies from 1% to about 40%, depending on who your read. Most of the time, they are subtle and have little clinical impact. But some are not so subtle, and some of the rare ones can be life-threatening.

The trauma tertiary survey has been around for at least 30 years, and is executed a little differently everywhere you go. But the concept is the same. Do another exam and check all the diagnostic tests after 24 to 48 hours to make sure you are not missing the obvious.

Does it actually work? There have been a few studies over the years that have tried to find the answer. A paper was published that used meta-analysis to figure this out. The authors defined two types of missed injury:

  • Type I – an injury that was missed during the initial evaluation but was detected by the tertiary survey.
  • Type II – an injury missed by both the initial exam and the tertiary survey

Here are the factoids:

  • Only 10 observational studies were identified, and only 3 were suitable for meta-analysis
  • The average Type I missed injury rate was 4.3%. The number tended to be lower in large studies and higher in small studies.
  • Only 1 study looked at the Type II missed injury rate – 1.5%
  • Three studies looked at the change in missed injury rates before and after implementation of a tertiary survey process. Type I increased from 3% to 7%, and Type II decreased from 2.4% to 1.5%, both highly significant.
  • 10% to 30% of missed injuries were significant enough to require operative management

Bottom line: In the complex dance of a trauma activation, injuries will be missed. The good news is that the tertiary survey does work at picking up many, but not all, of the “occult” injuries. And with proper attention to your patient, nearly all will be found by the time of discharge. Develop your process, adopt a form, and crush missed injuries!

Reference: The effect of tertiary surveys on missed injuries in trauma: a systematic review. Scand J Trauma Resusc Emerg Med 20:77, 2012.

Complications

The Tertiary Survey for Trauma: The Basics

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After a recent request, I’m re-posting a three part series on the trauma tertiary survey. Today, I’ll cover the basics. In the next two posts I’ll dig into how well it works and who can do it.

Major trauma victims are evaluated by a team to rapidly identify life and limb threatening injuries. This is accomplished during the primary and secondary surveys done in the ED. The ATLS course states that it is more important for the team to identify that the patient has a problem (e.g. significant abdominal pain) than the exact diagnosis (spleen laceration). However, once the patient is ready for admission to the trauma center, it is desirable to know all the diagnoses.

This is harder than it sounds. Physical examination tends to direct diagnostic testing, and some patients may not be feeling pain, or be awake enough to complain of it. Injuries that are painful enough may distract the patient’s attention away from other significant injuries. Overall, somewhere between 7-13% of patients have injuries that are missed during the initial evaluation.

A well-designed tertiary survey helps identify these occult injuries before they are truly “missed.” This survey consists of a structured and comprehensive re-examination that takes place within 48-72 hours, and includes a review of every diagnostic study performed. Ideally, it should be carried out by two people: one familiar with the patient, and the other not. It is desirable that the examiners have some experience with trauma (sorry, medical students).

Why 48-72 hours? Why not just do it when the patient leaves the ED, or when they arrive on the floor? Many occult injuries take time to show themselves. Swelling or bruising takes many hours to become obvious. And the patient may have distracting injuries and just won’t notice a sore finger or wrist that early.

And you can’t wait too long either! Otherwise the issue becomes a clearly delayed injury. A best practice is to require the tertiary survey be done within a specific window (24-48 hours, 48-72 hours, whatever works for your trauma team. Any injuries found in that time interval are not delayed diagnoses, since this process is designed to identify those pesky injuries. Any found after the time interval expires must go through a formal PI review at the primary and/or secondary levels.

The patients at highest risk for a missed injury are those with severe injuries (ISS>15) and/or impaired mental status (GCS<15). These patients are more likely to be unable to participate in their exam, so a few injuries may still go undetected despite a good exam.

I recommend that any patient who triggers a trauma team activation should receive a tertiary survey. Those who have an ISS>15 should also undergo the survey. Good documentation is essential, so an easy to use form should be used. Click here to get a copy of our original paper form. We have changed over to an electronic record, and have created a dot phrase template, which you can download here.

In my next post: Does the tertiary survey actually work?

Complications

Best Of EAST #17: Artificial Intelligence vs TRISS

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The TRISS score is the great grand-daddy of probability of survival prediction in trauma, first introduced in 1981. It is a somewhat complicated equation that takes the injury severity score (ISS), revised trauma score (RTS), and age and cranks out a probability between 0 and 100%. Over the years, this system has been well validated, and its shortcomings have been elucidated as well.

Many authors have attempted to develop a system that is better than TRISS. Years ago, there was the New-TRISS. And back in the day (early 1990s) I even developed a neural network to replace TRISS. In general, all of these systems may improve accuracy by a few percent. But it has never been enough to prompt us to ditch the original system.

The group at the University of California at Los Angeles developed a machine learning algorithm using ICD-10 anatomic codes and a number of physiologic variables to try to improve upon the original TRISS score. They analyzed three years of NTDB data and attempted to predict in-hospital survival.

Here are the factoids:

  • The authors used over 1.4 million records to develop their model
  • Overall, 97% of patients survived, and survivors tended to be younger, have higher blood pressure, and have sustained a blunt mechanism (no surprises here at all)
  • The ROC C-statistic for the false positive rate was better with the machine learning model (0.940 vs 0.908), as was the calibration statistic (0.997 vs 0.814)

Here is the ROC curve for machine (blue) vs TRISS (yellow):

The authors conclude that the machine learning model performs better than TRISS and that it may improve stratification of injury.

Bottom line: This study is one of many attempting to improve upon good old TRISS probability of survival. Why have there been so many attempts, and none that have appeared to “stick?” Here are my thoughts:

  • They are complicated. Sure, the original TRISS equation is slightly complicated, but it’s nothing close to a machine AI algorithm.
  • The inner workings are opaque. It’s not very easy to “open the box” and see which variables are actually driving the survival calculations.
  • The results are only as good as the training data. There is a real skew toward survival here (97%), so the algorithm will more likely be right in guessing that the patients will survive.
  • The improvements in these systems are generally incremental. In this case the ROC value increases from .908 to .940. Both of these values are very good.

In general, any time a new and better algorithm is introduced that shows much promise, someone wants to patent it so they can monetize the work.  Obviously, I don’t know anything about the plans for this algorithm. Somehow I doubt that many centers would be willing to abandon TRISS for an incremental improvement that may not be clinically significant at any price.

Here are my questions for the authors and presenter:

  • Please detail how you selected the variables to enter into the machine learning algorithm. Were they chosen by biased humans who had some idea they might be important, or did the AI comb the data and try to find the best correlations?
  • Be sure to explain the ROC and calibration statistics well. Most of the audience will be unfamiliar.
  • Are you using your model in your own performance improvement program now? If so, how is it helping you? If not, why?

Fascinating paper! Let’s here more about it!

Reference: ICD-10-BASED MACHINE LEARNING MODELS OUTPERFORM THE TRAUMA AND INJURY SEVERITY SCORE (TRISS) IN SURVIVAL PREDICTION, EAST 35th ASA, oral abstract #38.

Prehospital, Resuscitation

Best Of EAST #16: More On TXA

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Here’s another abstract dealing with TXA. But this one deals with the classic CRASH-2 use for patients with major bleeding. The original patient showed that TXA improves survival if given within 3 hours of injury. More and more prehospital units (particularly aeromedical services) have been administering TXA enroute to the trauma center to ensure that this drug is given as early as possible.

Many of these same services carry packed cells (or in rare cases, whole blood) so that proper resuscitation can be started while enroute as well. A multicenter group led by the University of Pittsburgh evaluated the utility of giving both TXA and blood during prehospital transport.

Their study summarizes some of the results of the Study of Tranexamic Acid During Air and Ground Medical Prehospital Transport Trial (STAAMP Trial). This study ran from 2015 to 2019 and randomized patients to receive either TXA or placebo during air or ground transport to a trauma center. It included blunt or penetrating patients at risk for hemorrhage within 2 hours of injury who were either hypotensive or tachycardic. Outcome measures included 30-day mortality, 24-hour mortality, and a host of complications.

This abstract outlines a secondary analysis that retrospectively reviewed the impact of using prehospital packed red cells (pRBC) in addition to the TXA/placebo during transport. 

Here are the factoids:

  • There were 763 patients in total, broken down as follows
    • TXA only – 350
    • pRBC only – 35
    • TXA + pRBC – 22
    • Neither – 356
  • Patients who received blood with or without TXA were more severely injured with ISS 22 vs 10-12 in the non-pRBC groups
  • Mortality was higher in the pRBC (23%) and TXA+pRBC groups (29%)
  • TXA alone did not decrease mortality
  • TXA + pRBC resulted in a 46% reduction in 30-day mortality but not at 24 hours
  • packed cells alone decreased 24-hour mortality by 47%

The authors concluded basically what was stated in the results: short term mortality was decreased by pRBC alone, and 30-day mortality with TXA + pRBC. They recommended further work to elucidate the mechanisms involved.

Bottom line: This abstract may also suffer from the “low numbers” syndrome I’ve written about so many times before. The conclusions are based on two small groups that make up only 7% of the entire study group. And these are the two groups with more than double the ISS of the rest of the patients. The authors used some sophisticated statistics to test their hypotheses, and they will need to explain how and why they are appropriate for this analysis. Nevertheless, the mortalities in the blood groups number only in the single digits, so I worry about these statistics.

Here are my questions for the authors and presenter:

  • How do you reconcile the significantly higher ISS in the two (very small) groups who got blood? How might this skew your conclusions regarding mortality? Couldn’t the TXA just be superfluous?
  • How confident are you with the statistical analysis? Could the results be a sampling error given that red cells were given to only 7% of the overall study group?
  • I am having a difficult time understanding the conclusion that mortality was reduced in the blood groups. Specifically, it is stated that 24-hour mortality is reduced by 47% in the blood-only group.  But the mortality is 14% (5 patients)! Reduced 47% from what? I don’t see any other numbers to compare with in the table. Confusing!

Obviously, there must be more information that was not listed in the abstract. Can’t wait to see it!

Reference: PREHOSPITAL SYNERGY: TRANEXAMIC ACID AND BLOOD TRANSFUSION IN PATIENTS AT RISK FOR HEMORRHAGE. EAST 35th ASA, oral abstract #39.

 

 

Reference: PREHOSPITAL SYNERGY: TRANEXAMIC ACID AND BLOOD TRANSFUSION IN PATIENTS AT RISK FOR HEMORRHAGE. EAST 35th ASA, oral abstract #39.

Head, Resuscitation

Best Of EAST #15: Prehospital TXA

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The world is divided into trauma centers that are TXA believers and those that are TXA nonbelievers. It all depends on how one interprets the CRASH-2 data and subsequent studies. Then came CRASH-3 with TXA use for patients with TBI. This large study found improved survival in patients with mild to moderate head injury when given “early.”

The group at Oregon Health Science University tried to better define this concept of “early.” They examined early vs later administration of TXA in patients with moderate to severe TBI. Note that this degree of head injury is a bit different than CRASH-3 (mild to moderate in CRASH-3 and moderate to severe in this one). This was a multicenter trial that included patients with GCS < 12 and who were hypotensive with SBP < 90. Patients received either a 1g bolus followed by a 1g infusion over 8 hours, or a 2 g bolus only. The authors subdivided these patients into early administration (<45 minutes after injury) or late (45 minutes to 2 hours after injury).

Here are the factoids:

  • There were 354 patients in the early administration group and 259 in the late group
  • All outcomes, including 1 month and 6 month mortality and the extended Glasgow outcome scale were not significantly different between early and late groups (exact numbers were not given)
  • There was no difference in secondary complications between the groups (again, exact numbers or complication types were not given)

The authors concluded that there was no difference in outcomes in early vs later administration of TXA in these head injured patients. They suggest that patients can be given TXA anytime within two hours without loss of benefit.

Bottom line: Essentially, this ends up as a noninferiority study. The biggest question with this type of study is, do you have enough subjects to detect a significant difference? Taken to an extreme, let’s say you have 5 patients who receive a drug who are compared to 5 who did not for some mystery condition. Three who did not get the drug die (60% mortality), but only two who get it do (40% mortality). In relative terms, there was 33% decrease in mortality with the drug. But in absolute terms, it was one patient. Would anyone see this as a significant result with such small numbers?

But now multiply by a thousand, and 300 die without the drug and only 200 die who were given it. The relative difference is the same, but the absolute difference is beginning to look large and significant.

So the smaller study won’t meet the test of significance but the larger one will. The key question in the TXA study here is, do they have enough patients enrolled to show there is no real difference between the groups? I love doing back of the napkin power analyses, and I admit I certainly don’t have all the numbers and probabilities needed for a precise calculation. But the groups sizes in this study (354 vs 259) seem a bit small to achieve significance unless there are large disparities in outcomes. 

I certainly recognize that it’s just not possible to put all the relevant information for a research project into a four paragraph abstract. One would need to be able to submit 12 slide PowerPoint decks. So I’m sure more info will be available as I take in the presentation next Friday.

Here are my questions for the authors and presenter:

  • The study is nicely designed as a randomized, double-blind trial, but how did you blind one vs two doses? Did everyone get an infusion of something, TXA vs saline?
  • Why did you select 45 minutes as the cutoff for early vs late administration? Was this arbitrary or is it based some data?
  • Show us the power analysis that demonstrates the total number of patients in the study is sufficient to show us true non-significance in your results.
  • And I’m sure you will show the actual survival and complications numbers (and type) in the presentation, since they were not available in the abstract.

Reference: THE EFFECTS OF TIMING OF PREHOSPITAL TRANEXAMIC ACID ON OUTCOMES AFTER TRAUMATIC BRAIN INJURY. EAST 35th ASA, oral abstract #40.