All posts by TheTraumaPro

Fatigue III: Impact On Nurses

Although 8-hour shifts are the most common work arrangement around the country in all most occupations, they are a bit less common in nursing. Nurses have work and sleep patterns equivalent to prehospital providers. And critical care nurses probably have the most variable and punishing work patterns.

One may think that just increasing to a 12-hour shift is not that big of a deal. The nursing school at the University of Auckland performed their own survey of ICU nurses in two separate hospitals in New Zealand. They administered the Occupational Fatigue Exhaustion/Recovery Scale and evaluated differences in relation to a number of demographic variables.

Here are the factoids:

  • There were a total of 67 participants in the two hospitals and all worked 12-hour shifts.
  • Nurses at one hospital (A) worked mostly day or mostly night shifts and tended to be younger. Shifts were more mixed at the other (B).
  • About half of the nurses reported low to moderate fatigue acutely, and two thirds re-ported this level between shifts as well.
  • Factors that correlated with increased fatigue were younger age, fewer children, less years of experience, and less exercise.
  • Higher fatigue levels were reported at hospital A, which had the younger, less experienced nurses.

Bottom line: This is another survey study, but it does illustrate some common issues. Some factors could be changed by rearranging the shift structure to all day or all night shifts. Exercise was associated with less stress and could be encouraged. But the nature and pace of work in the ICU remains constant and is difficult to control for. Some strategies for positive change are listed on the next page of the newsletter.

In my next post, I’ll review the impact of sleep problems on trauma surgeons and residents.

Reference: Exploring the impact of 12-hour shifts on nurse fatigue in intensive care. Applied Nurs Res 50:151191, Dec 2019.

Fatigue II: Sleep Quality and Fatigue in Prehospital Providers

EMS providers across the country are assigned to a variety of schedules, ranging from shift work to continuous 24 hour service. Overnight duty, rotating schedules, early awakening and sleep interruptions are common. Unfortunately, there are not many studies on the effects of fatigue on EMS. I did manage to find an interesting study from last year that I’d like to share.

A group of about 3,000 providers attending a national conference were surveyed using 2 test instruments (Pittsburgh Sleep Quality Index (PSQI) and Chalder Fatigue Questionnaire (CFQ)). The PSQI measures subjective sleep quality, sleep duration, disturbances, use of sleeping meds and daytime dysfunction. The CFQ measures both physical and mental fatigue.

Only 119 surveys were completed, despite the fact that a $5 gift card was offered (not enough?). The most common certification was EMT-Basic (63%) and most had worked less than 10 years. Most were full-time, with most working 4-15 shifts per month. The following demographics were of interest:

  • Self-reported good health – 70%
  • Nonsmokers – 85%
  • Moderate alcohol or less – 62%
  • Overweight or obese – 85%

A total of 45% reported experiencing severe physical and mental fatigue at work, and this increased with years of experience. The sleep quality score confirmed this fact. Also of interest was the incidental finding of a high proportion of overweight or obese individuals. Sleep deprivation is known to increase weight, and increased weight is known to increase sleep problems, creating a vicious cycle.

Bottom line: This is a small convenience study, but it was enough to show that there is a problem with fatigue and sleep quality in EMS providers. Federal law mandates rest periods for pilots, truck drivers and tanker ship personnel. The accrediting body for resident physicians has guidelines in place that limit their time in the hospital. Prehospital providers perform a service that is just as vital, so it may be time to start looking at a more reasonable set of scheduling and work guidelines to protect them and their precious cargo.

In my next post, we’ll cover the impact of sleep loss on nurses.

Reference: Sleep quality and fatigue among prehospital providers. Prehos Emerg Care 14(2):187-193, April 6, 2010.

Fatigue: Sleep Deprivation Changes The Way We Make Risky Decisions

I’m expanding my series dealing with the issues surrounding lack of sleep. As you all know, trauma professionals are expected to perform even if they have not had adequate sleep. This can occur with certain shift schedules, long periods of work, or due to call schedules and duration of call. What do we really know about the effects of sleep deprivation on us?

For the next few weeks I’ll be writing about the effects of fatigue on trauma professionals, including prehospital providers, residents, surgeons, and nurses. And I’ll finish up with some new research on the effects on our patients.

In this post, we’ll talk about decision making. Neuroscientists at Duke looked at how we approach risky decisions when we are sleep deprived. A total of 29 adults (average age 22) were studied. They were not allowed to use tobacco, alcohol and most medications prior to sleep deprivation, which lasted for 24 hours. They were given a risky decision making task (a controlled form of gambling), and two other tests while in a functional MRI unit to watch areas of brain activation.

The researchers found that, when well rested, the subjects had a bias toward avoiding loss in the gambling test. After a single night of sleep deprivation, this shifted to pursuing gain. The MRI also showed an increased activity in the reward anticipation parts of the brain. Overall decreased vigilance was noted, but this did not correlate with the shift away from risk avoidance.

Bottom line: Sleep deprivation appears to create an optimism bias. Fatigued individuals act like positive outcomes are more likely and negative consequences are less likely. One of the most common and important things that trauma professionals do is to make decisions that may affect patient outcome (e.g. choose a destination hospital, intubate, order and interpret a test, move to the operating room, choose a specific operative procedure). We all have a set of thresholds that help us come to the “right” decision based on many variables. It appears that a single night of sleep deprivation has the potential to skew those thresholds, potentially in directions that may not benefit the patient.

In the next post, I’ll turn my attention to the impact of sleep loss on prehospital providers.

Reference: Sleep deprivation biases the neural mechanisms underlying economic preferences. J Neuroscience 31(10):3712-3718, March 9, 2011.

Closing Velocity And Injury Severity

Trauma professionals, both prehospital and in trauma centers, make a big deal about “closing velocity” when describing motor vehicle crashes.  How important is this?

So let me give you a little quiz to illustrate the concept:

Two cars, of the same make and model, are both traveling on a two lane highway at 60 mph in opposite directions. Car A crosses the midline and strikes Car B head-on. This is the same as:

  1. Car A striking a wall at 120 mph
  2. Car B striking a wall at 60 mph
  3. Car A striking a wall at 30 mph

2010-saab-9-5-head-on-crash-test_100313384_m1

The closing velocity is calculated by adding the head-on components of both vehicles. Since the cars struck each other exactly head-on, this would be 60+60 = 120 mph. If the impact is angled there is a little trigonometry involved, which I will avoid in this example. And if there is a large difference in mass between the vehicles, there are some other calculation nuances as well.

So a closing velocity of 120 mph means that the injuries are worse than what you would expect from a car traveling at 60 mph, right?

Wrong!

In this example, since the masses are the same, each vehicle would come to a stop on impact because the masses are equal. This is equivalent to each vehicle striking a solid wall and decelerating from 60 mph to zero immediately. Hence, answer #2 is correct. If you remember your physics, momentum must be conserved, so both of these cars can’t have struck each other at the equivalent of 120 mph. The injuries sustained by any passengers will be those expected in a 60 mph crash.

If you change the scenario a little so that a car and a freight train are traveling toward each other at 60 mph each, the closing velocity is still 120 mph. However, due the the fact that the car’s mass is negligible compared to the train, it will strike the train, decelerate to 0, then accelerate to -60 mph in mere moments. The train will not slow down a bit. For occupants of the car, this would be equivalent to striking an immovable wall at 120 mph. The injuries will probably be immediately fatal for all.

Bottom line: Closing velocity has little relationship to the injuries sustained for most passenger vehicle crashes. The sum of the decelerations of the two vehicles will always equal the closing velocity. Those injuries will be consistent with the change in speed of the vehicle the occupants were riding, and not the sum of the velocities of the vehicles. 

What Is The Zumkeller Index in TBI?

I learned something new today: the Zumkeller index. Exciting! Most trauma professionals who take care of serious head trauma have already recognized the importance of quantifying extra-axial hematoma thickness (HT) and midline shift (MLS) of the brain. Here’s a picture to illustrate the concept:

Source: Trauma Surgery Acute Care Open

Zumkeller and colleagues first described the use of the mathematical difference between these two values in prognosticating outcomes in severe TBI in 1996.

Zumkeller Index (ZI) = Midline shift (MDI) – Hematoma thickness (HT)

Intuitively, we’ve been using this all along. At some point, we recognized that if the degree of midline shift exceeds the hematoma thickness, it’s a bad sign. The easiest way to explain this is that there is injury to the brain that is causing swelling so the shift is greater than the size of the hematoma. 

The authors of the current paper from Brazil decided to quantify the prognostic value of the ZI by doing a post-hoc analysis of a previously completed prospective study.  They limited their study to adult patients with an acute traumatic subdural hematoma confirmed by CT scan. It used data from the 4-year period from 2012-2015.

They compared demographics and outcomes in three cohorts of ZI:

  • Zero or negative ZI, meaning that the midline shift was less than the size of the hematoma
  • ZI from 0.1 mm to 3.0 mm
  • ZI > 3.0 mm

And here are the factoids:’

  • A total of 114 patients were studied, and the mechanism of injury was about 50:50 from motor vehicle crashes vs falls
  • About two thirds were classified as severe and the others were mild to moderate, based on GCS
  • Median initial GCS decreased from 6 in the low ZI group to 3 in the highest ZI group, implying that injuries were worse in the highest ZI group
  • Mortality (14-day) was 91% in the highest ZI group and only in the low 30% range in the others
  • Regression analysis showed that patients with ZI > 3 had an 8x chance of dying within 14 days compared to the others

Source: Trauma Surgery Acute Care Open

Bottom line: This study confirms and quantifies something that many of us have been unconsciously using all along. Of course there are some possible confounding factors that were not quantified in this study. Patients with the more severe injuries tended to also have subarachnoid hemorrhage and/or intra-ventricular blood. Both are predictors of worse prognosis. But this is a nice study that quantifies our subjective impressions.

The Zumkeller Index is an easily applied tool using the measuring tool of your PACS application. It can be used to determine how aggressively to treat your patient, and may help the neurosurgeons decide who should receive a decompressive craniectomy and how soon.

Reference: Mismatch between midline shift and hematoma thickness as a prognostic factor of mortality in patients sustaining acute subdural hematomaTrauma Surgery & Acute Care Open 2021;6:e000707. doi: 10.1136/tsaco-2021-000707