The Tertiary Survey For Trauma: Residents vs APPs

This is the final installment of my series on the tertiary survey for trauma.  For years, this exam was performed by trauma surgeons or residents. However, over the years advanced practice providers (APPs) such as physician assistants and nurse practitioners have become more common in trauma. It is now commonplace for these providers to participate on the trauma service, perform procedures, and document examinations such as the tertiary survey.

But until now, no one has compared the accuracy of this exam when performed by a physician vs an APP. One would assume that the results should be the same, but as we’ve seen time and time again, common sense doesn’t always pan out. A group at the Royal Brisbane and Women’s Hospital in Queensland, Australia tried to answer this question using a retrospective review of their experience.

This busy trauma center admits about 2,250 patients per year, and began to employ clinical nurse consultants on the trauma service nearly ten years ago. Since there was no formal trauma curriculum for these nurses, they were required to complete the Trauma Nursing Core Curriculum (TNCC) or an equivalent prior to hire. The nurses were supervised by one of the trauma / emergency physicians.

For this study, 165 patients who underwent a tertiary survey by both an emergency medicine resident and a trauma nurse over a three year period were reviewed. The surveys were typically performed within 24 hours of admission to a ward bed or 24 hours before transfer from ICU to the ward. Typically, the resident and nurse tertiary surveys were performed within 30 minutes of each other to avoid any effects from injury progression.

All missed injuries were graded for severity by an attending physician using the Clavien-Dindo system. Here’s what it looks like:

And here are the factoids:

  • A total of 3,065 patients had a tertiary survey performed during the study period, but only 165 had it performed by both a resident and an APP
  • Based on their surveys, additional investigations were ordered in 35 patients, 14 by the trauma nurse, 11 by the resident, and 10 by both
  • Eight of 14 studies ordered by the nurse identified a missed injury, two of 11 studies ordered by the resident did, and two were identified in the studies ordered by both
  • Of the 12 identified missed injuries, the Clavien-Dindo (C-D) score was 0 in one, I in ten patients, and III (required surgery) in one
  • The nurses identified a higher number of missed injuries (10 of 24) than the residents (4 of 21) without significantly increasing the number of tests ordered

The authors concluded that performance of the nurses was similar to that of the house officers.

Bottom line: Maybe the authors were trying to be gentle on their residents. But it looks to me like the trauma nurses did a much better job of finding occult injuries. I wish the authors had broken down the C-D scores to see which group identified the score III patient.

To be fair, this study has some significant limitations. Out of more than 3,000 eligible patients, only 165 had a dual tertiary survey. So the sample may not be representative. But the results were impressive enough that I would speculate the results of a larger group may be similar.

So I think it is safe to assume that APPs (specifically nurse practitioners, but this can probably be generalized to physician assistants as well) can do a tertiary survey just as well as a resident. And possibly better!

Reference: Trauma tertiary survey: trauma service medical officers and trauma nurses detect similar rates of missed injuries. J Trauma Nursing 28(3):166-172, 2021.

The Tertiary Survey For Trauma: Does It Work?

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.

The Tertiary Survey for Trauma: The Basics

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?

Best Of EAST #17: Artificial Intelligence vs TRISS

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!


Best Of EAST #16: More On TXA

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!