# EAST 2018 #4: Machine Prediction Of Instability In ICU Patients

In trauma care, as in all of medical care, we try to predict the future. What injuries does my patient have? What will happen if I treat this fracture that way? Is she going to live? How much disability can we expect given this degree of head injury?

Trauma professionals are constantly tapping into their own experience and that of others to predict the future and try to shape it in the best way for their patients. And now more than ever, with the combination of mathematical algorithms and powerful machine learning systems, we’ve been able to move past simple correlations and linear regressions to try to peer into that future.

A group at Washington University in St. Louis previously developed a real time risk score that claims to predict the need for cardiovascular support in ICU patients.  It is called the hemodynamic instability indicator (HII). The exact details of this score are not included in the abstract, and I have not found it published yet so I have no idea how it was derived. The presenters prospectively applied this system to 126 stable patients who were admitted to the ICU and were expected to stay at least 24 hours and survive at least 48 hours. They wanted to determine how well HII predicted an episode of hemodynamic instability.

Here are the factoids:

• The majority were male (64%) acute care surgery patients (55%) with a median age of 60
• Only 60 of the 126 patients had sufficient data to calculate HII in the pre-intervention period of unstable patients (!)
• HII predicted the need for pressors/inotropes with a sensitivity of 0.56 and specificity of 0.76. The authors claim that this was statistically significant (p < 0.01) (???)
• The system got better as the time to intervention for instability grew closer

Bottom line: Machine learning and prediction systems can be tricky tools. They are very good at identifying patterns without anything more than a good training dataset. However, they are only as good as that dataset. It is crucial that the system be trained with and tested against other large sets of data with a variety of patients. Otherwise, you will create a great system for predicting events in 60 year old male acute care surgery patients, and no one else.

Here are some questions for the authors to consider before their presentation:

• Be prepared to describe in detail how you derived the original HII system. How big was the dataset, and what did it look like in terms of demographics?
• What statistics did you use to conclude you had a statistical p value of < 0.01? Your sensitivity and specificity numbers do not look that good. What about negative and positive predictive values?
• You mentioned that the system made better predictions as the episode of instability grew closer. Predicting an adverse event 24 hours in advance vs 5 minutes in advance is very different. How near did the event have to be for good prediction? Did this factor into your significance calculation above?
• Why not use a receiver operating characteristic curve to show your data? It is a much better analysis tool.
• Big picture questions: Why do you expect that you can generalize the results of your HII system to new and disparate datasets? Have you tried it on major trauma patients?

Reference: EAST 2018 Podium paper #5.

# EAST 2018 #3: Platelet Transfusion In Patients On Anti-Platelet Agents?

When patients with significant brain injuries present while taking drugs that interfere with clotting, we seem to have this burning desire to neutralize those drugs, right? Warfarin? Give PCC. Aspirin or clopidogrel? Well, not quite so easy. You can’t neutralize them, but can’t you just transfuse some working platelets?

That is the current practice among many clinicians, although there isn’t really much data to support it. A group at Iowa Methodist Hospital in Des Moines looked at using a commercial platelet reactivity test (PRT) to determine if platelets should be given in patients with moderate to severe TBI who were known or suspected to be taking an anti-platelet drug.

This was a retrospective study of 167 patients with a head Abbreviated Injury Scale score of 2 or higher. Patients had to have received at least 2 head CT scans in order to judge progression of any bleeds.

Here are the factoids:

• Nearly a third of patients (29%) were non-therapeutic on their anti-platelet medication, meaning that platelet function as judged by PRT was not abnormal
• No platelet transfusions were given to 92% of patients with non-therapeutic meds, and only 2 of these patients (4%) had clinical progression of their bleed
• Overall, using a selective platelet transfusion policy decreased platelet transfusions and their attendant costs by about half

Bottom line: So this is one of those “how we do it” studies. This means that the authors have been doing it this way for a while, and wanted to examine the results. It is not a comparison to their historical control, but it’s likely that their current usage is much lower than it used to be. Regardless, the results are impressive, and would seem to indicate that we are throwing a lot of platelets away based on a rumor that our patient is taking an anti-platelet medication.

Here are some questions for the authors to consider before their presentation:

• How did you define “clinically significant bleed” in the two patients that had them? Did they eventually get some platelets? Did it help?
• Have you looked at patients that did receive platelets for an abnormal PRT to see if their platelet function improves?
• Big picture question: What evidence is there that PRT results are meaningful? How do we know that abnormal PRT is associated with bleeding in head injured patients, or that normal PRT is not associated with it? In other words, is it a valid test?

Reference: EAST 2018 Podium abstract #4.

# EAST 2018 #2: Blood Product Age And Mortality

Ever since the start of the modern transfusion age (which was really only about 75 years ago), we’ve been trying to extend the life of banked blood products. Currently, we get about 6 weeks of useful life from packed red blood cells, and varying amounts from other frozen or non-frozen products.

What happens at day 42 for red cells? Or day 5 for platelets or thawed plasma? It’s not like a switch gets flipped and it suddenly goes bad. Each of these products slowly degrades over time, and the myriad components that make them up (proteins, clotting factors, etc) do so at varying rates. It has been recognized for years that some of these products “don’t work so well” when they age, and this has been termed the “storage lesion” of blood.

The next EAST paper I’ll review looks for associations between use of older blood products which probably have a storage lesion, and mortality in trauma patients. It re-analyzed the prospectively collected data on the 680 patients enrolled in the PROPPR trial, which was originally designed to examine the mortality difference between patients with specific FFP:platelet:PRBC ratios given during massive transfusion. In this re-analysis, the authors looked at the mortality after 6 hrs, 24 hrs, and 30 days in patients undergoing massive transfusion, and examined the impact of using “older” blood products. “Old” was defined using the median age of the product; RBCs were old after 20/42 days, plasma after 2/5 days, and platelets after 4/5 days.

Here are the factoids:

• Plasma age decreased with increasing transfusion. There was no similar change in average platelet or RBC age, though.
• Patients receiving older RBC and younger plasma had higher mortality
• Receiving older PRBC was associated with mortality at 6 and 24 hrs, but not 30 days

Bottom line: First, this is an association study, not a causation one. Don’t read anything more into it than you see. And what do you think when you see random mortality numbers like this? For me, either mortality is too crude of a variable to use, or the association is just too weak. If you look at the data table for the study, the confidence intervals of the computed “hazard ratios” barely clear the 1.0 line. To me, this looks like an interesting mathematical exercise, but I can’t tease any clinical significance out of it at all. And I don’t think that re-analyzing this dataset will provide any further clarity.

Here are some questions for the authors to consider before their presentation:

• Did you try to calculate the statistical power of your dataset? As mentioned above, the associations look weak at best.
• Did you look at other potential factors like injury severity score or massive transfusion volumes? These would seem to have a much more significant impact on the three survival cohorts?
• Big picture questions: Where can you go from here? What kind of study could you do to see if this is a real effect vs just a statistical anomaly?

Reference: EAST 2018 Podium paper #3.

# EAST 2018 #1: Plasma Over-Resuscitation And Mortality In Pediatric TBI

The first EAST abstract I will discuss is the very first to be presented at the annual meeting. This is a prospective, observational studied that was carried out at the University of Pittsburgh. It looked at the association between repeated rapid thromboelastography (rTEG) results in pediatric patients and their death and disability after plasma administration. They specifically looked at the degree of fibrinolysis 30 minutes after maximum clot amplitude and tried to correlate this to mortality.

For those of you who need a refresher on TEG, the funny sunfish shape above shows the clot amplitude as it increases from nothing at the end of R, hits its maximum at TMA, then begins to lyse. The percent that has lysed at 30 mins (LY30%) gives an indication if the clot is dissolving too quickly (LY30% > 3%) or too slowly (LY30% < 0.8%).

The authors selected pediatric patients with TBI and performed an initial rTEG, then one every day afterward. They looked at correlations with transfusion of blood, plasma, and platelets.

Here are the factoids:

• A total of 101 patients under age 18 were studied, with a median age of 8, median ISS of 25, and 47% with severe TBI (head AIS > 3)
• Overall mortality was 16%, with 45% having discharge disability
• On initial analysis, it appeared that transfusion of any product impeded fibrinolysis, but when controlling for the head injury, only plasma infusion correlated with this
• Increasing plasma infusion was associated with increasing shutdown of fibrinolysis
• The combination of severe TBI and plasma transfusion showed sustained fibrinolysis shutdown, and was associated with 75% mortality and 100% disability in the remaining survivors
• The authors conclude that transfusing plasma in pediatric patients with severe TBI may lead to poor outcomes, and that TEG should be used for guidance rather than INR values.

Bottom line: There is a lot that is not explained well in this abstract. It looks like an attempt at justification for using TEG in place of chasing INR in pediatric TBI patients. This may be a legitimate thing, but I can’t really come to any conclusions based on what has been printed in this abstract so far.

Here are some questions for the authors to consider before their presentation:

• There seem to be a lot of typos, especially with < and > signs in the methods.
• Disability is a vague term. What was it exactly? Was it related to TBI or the other injuries as well?
• These children also appear to have had other injuries, otherwise why would they need what looks like massive transfusion activation? Why did they need so much blood? Could that be the reason for their fibrinolysis changes and poor outcomes?
• I can see the value of the initial rTEG, and maybe one the next day. But why daily? What did you learn from the extra days of measurements? Would a pre- and post-resuscitation pair have been sufficient?
• Plasma is the focus of this abstract, but it does not describe how much plasma was given, or whether there was any departure from the usual acceptable ratios of PRBC to plasma administration.
• Big picture questions: Most importantly, why would you think that poor outcomes, which are the focus of this paper, are related to plasma administration? Why haven’t we noticed this correlation before? And how does daily TEG testing help you identify and/or avoid this? What questions raised here are you going to pursue?

Reference: EAST 2018 Podium paper #1.

# The EAST Annual Meeting Is Coming!

The EAST Annual Scientific Assembly is just around the corner. The meeting takes place January 9-13 at Disney World in Orlando. As in previous years, I am going to select some of the more interesting (to me) podium abstracts and analyze them, one per day until the meeting. I will pick them apart, provide some clinical perspective, and most importantly, provide a bullet list of questions the presenter may hear at the podium. Hint, hint.

On Christmas day, I’ll publish the list of abstracts that I’ll be reviewing. Then daily, until the meeting is over, I’ll tease one apart for you. Stay tuned!