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Evan Kuhl, MD

October 13, 2016

New data published this summer studies medical marijuana trends in the United States. Specifically, one article published in Health Affairs and written by Ashley and David Bradford of the University of Georgia reviews how medical marijuana availability has impacted other prescriptions filled by Medicare Part D enrollees1. The study, also noted in the BMJ, found there were several categories where prescriptions where reduced2. On average, states with medical marijuana saw a 20% decrease in prescription medications for spasticity, and an 8-13% decrease in medications for anxiety, depression, nausea, psychosis, and sleep disorders. The decrease in prescription drug use saved an estimated $165.2 million in Medicare funds in 2013 alone. If medical marijuana was available nationwide, the authors estimated that Medicare could see a 0.5% reduction of the program’s nation-wide spending.

 

Overall, this decrease in prescription drug utilization saved approximately $165.2 million in Medicare funds in 2013 alone.

 

While the Bradford study shows that marijuana appears to be replacing several other medications, the question is whether marijuana actually as effective as other drugs? The answer is that several randomized clinical trials data support the effectiveness of marijuana , some researchers question the standards used to evaluate how well it really works. For example, research has examined how marijuana helps gastrointestinal conditions, such as Crohn’s disease3.  Yet, most of this research evaluates subjective responses to marijuana, and do not focus on objective disease markers such as intestinal inflammation or C-reactive protein levels.  Therefore, even if patients using marijuana note pleasurable side-effects of the drug as effective treatment, it may not directly impact the disease process itself.  Dartmouth Drs. Koliani-Pace and Siegel co-authored an article calling for increasing the quality control of marijuana research. They wrote: “If marijuana can meet all of the FDA requirements, including efficacy data, evidence of safety, and a meticulous quality control program, then the medicinal use of marijuana could be supported”3. Besides objective outcome data, other major concerns still left unanswered include the untoward respiratory side effects of marijuana smoke, as well as the co-use of marijuana and tobacco, with some research showing that marijuana use can increase tobacco use and dependence4.

Currently, 39 states now allow medical marijuana despire still being listed as a Schedule 1 drug by the Federal government. Although individual state regulations vary, every program requires a physician to prescribe the use of marijuana or marijuana-derived substances. As the policies and regulations regarding marijuana continue to change, it will be essential to continue researching both the efficacy of marijuana treatment and the impact on healthcare utilization.  As providers, it is important to understand the impact these policies on our patients. The research and experience of providers is an important part of creating safe and viable policies to protect and treat our patients.

 

1.         Bradford AC, Bradford WD. Medical Marijuana Laws Reduce Prescription Medication Use In Medicare Part D. Health Aff (Millwood)                     2016;35:1230-6.

2.         Dyer O. US states that allow medical marijuana see drop in prescriptions for other drugs, study finds. BMJ 2016;354:i3942.

3.         Koliani-Pace J, Siegel CA. Is the Hype of Medical Marijuana All Smoke and Mirrors? Am J Gastroenterol 2016;111:161-2.

4.         Wang JB, Ramo DE, Lisha NE, Cataldo JK. Medical marijuana legalization and cigarette and marijuana co-use in adolescents and                       adults. Drug Alcohol Depend 2016;166:32-8.


Evan Kuhl, MD is an Emergency Medicine Resident at The George Washington University Hospital

Ameer Khalek

October 10, 2016

Triage is the process of determining treatment priority based on the severity of a patient’s condition. The term triage is derived from the French word trier which means “to sort.” The concept of triage was first applied to medicine by French military surgeon Baron Dominique-Jean Larrey, a chief surgeon of Napoleon’s Imperial Guard. Larrey recognized the need to categorize the wounded during battle to treat and evacuate those requiring the most urgent medical attention (Iserson et al.). While medical technologies advance and the time-sensitive nature of emergencies stay the same, it is important to continually revise triage systems to fit local needs and resources.

Currently, the most common emergency department (ED) triage system in the United States is the Emergency Severity Index (ESI). The ESI is a five-level tool for experienced ED personnel to rate patient acuity which takes into consideration both clinical and operational decisions such as resource allocation. In a recent study by Khan et al., a new triage system is proposed that better matches the needs of austere environments. In her podcast with Urgent Matters, Dr. Khan described the many reasons why traditional triage systems are not as useful where there are fewer experienced emergency providers and a scarcity of resources, such as in low- and middle-income countries. Low- and middle-income countries can’t afford to have experienced providers conduct triage assessments because they are needed for patient care, and the resource-based triage in the ESI is not as relevant when there are fewer resources available. For example, providers in austere settings may not have the ability to order certain radiography or laboratory tests.

Khan and her team proposed the one-two-triage (OTT) system. OTT is a two-triage system that can be reliably applied after only eight hours of training. Instead of relying on experience in making subjective clinical decisions, Stage 1 is designed to quickly separate out patients categorized as critical (red) and emergent (orange) from a simple assessment of the patient’s airway, breathing, circulation, and disability. Patients are greeted and pulse oximetry is assessed. If the patient does not qualify as critical or emergent, they are sent to registration. Stage 2 separates patients into urgent (yellow) or non-urgent (green) based on their chief complaint and vital signs, taking the person who is conducting triage – who may have little medical training – through a series of protocols related to each chief complaint to determine how emergent a specific condition might be. This allows less experienced providers to assess severity of illness based on medically sound algorithms. The complete OTT process can be found here.

OTT is currently being used as an alternative to ESI in 22 Cambodian hospitals (with 21 additional EDs coming on board soon), a healthcare population where there is a relative paucity of experienced healthcare professionals. As Dr. Khan discussed in her podcast, crowd-sourcing information about triage needs in specific settings can catalyze the process of customizing systems in various environments. For more information, you can reach out to her here. 


Ameer Khalek is a MPH student at the GWU Milken Institute School of Public Health

Will Denq, MD

October 6, 2016

Emergency physicians commonly use a combination of history, physical exam, EKG, labs, and imaging to help diagnose congestive heart failure (CHF) in the emergency department in patients who present with shortness of breath. However, sometimes it can be confusing and what initially looks like heart failure ends up being something else, such as pneumonia (1-3). Misdiagnosis can have real consequences, particularly when diuretics used to improve CHF result in worsened conditions such as a pulmonary embolus or other serious pathologies.

In a recent paper, Basset et al derived and validated a CHF clinical prediction rule called the Brest score (4) which is intended to improve the accuracy of a heart failure diagnosis in ED patients. They looked at 927 undifferentiated dyspneic patients in their ED to create it and externally validated it in 206 patients. The score relies on 11 variables based only on history, physical, and EKG to risk stratify patients into low, intermediate, and high probability groups.

The prevalence of confirmed CHF patients was 6.7% in the low probability group, 58% in the intermediate, and 91.5% in the high. The authors argue that these results are accurate enough when weighing risks and benefits to begin early treatment in the high risk group. Unlike many diagnoses made in the ED, a healthcare provider using this score may not necessarily have to wait for labs or radiology results to initiate management. On the other spectrum, with so few CHF patients in the low risk group, patients who are low risk by the Brest score should prompt the provider to search for other etiologies of dyspnea.

There were several limitations of the paper. The score still needs a prospective impact analysis validation, it was from a single center, and several pathologies may coexist in a single patient. Although the risk-benefit ratio skews towards treatment in the high risk group, the subsequent management of CHF is not benign especially in volume-depleted patients.

The Brest score represents an interesting adjunct in the early diagnosis of CHF in the undifferentiated dyspneic ED patient. With time being an important factor in management and disposition of CHF, the score stands alone in quickly risk stratifying the patient without waiting for labs or radiology results. Other CHF decision rules, such as the Boston criteria (5), Steingart and PRIDE (6,7) decision tools, rely on time-delaying lab results such as BNP or radiology results such as a CXR. The Framingham (8) and Gothenburg (9) scores were epidemiologic studies and utilize certain factors that are not readily obtainable at the bedside.

Along with decision rules, other data at the bedside can be helpful to diagnose CHF, such as lung ultrasound.  A 2015 multi-center prospective study (n=1005) by Pivetta et al demonstrated a significantly higher accuracy than all other standards when using the clinical evaluation and bedside lung ultrasound. Sensitivity was reported at 97% with specificity being 97.4%. Perhaps a completely validated Brest score could improve the diagnosis when used in conjunction with lung ultrasound and move away from using data such as BNP or chest xray. It could result in an accurate, bedside diagnosis that does not rely on labs or imaging. Although not ready for prime time, it could significantly increase the accuracy of CHF diagnosis and accelerate management in the undifferentiated dyspneic ED patient.

Brest score:

Variables

Points (+2)

Age > 65

1

Anamnesis variables

Sudden dyspnea

2

Onset of symptoms at night

1

Orthopnea

1

Risk Factor variables

Prior CHF episode

2

Myocardial infarction

1

Chronic pulmonary disease

-2

Clinical Examination variables

Pulmonary crackles

2

Pitting leg edema

1

EKG Abnormality variables

ST segment abnormalities

1

Atrial fibrillation/flutter

1

Max score

15

 

Low Risk: ≤ 3

Medium Risk: 4 - 8

High Risk: ≥ 9

The Brest score starts automatically with 2 points – perhaps a superstitious move by the authors to avoid an unlucky total of 13 points.

References:

  1. Remes J, Miettinen H, Reunanen A, Pyörälä K. Validity of clinical diagnosis of heart failure in primary health care. Eur Heart J 1991;12:315–21.
  2. Fuat A, Hungin AP, Murphy JJ. Barriers to accurate diagnosis and effective management of heart failure in primary care: qualitative study. BMJ 2003;326:196.
  3. Ansari M, Massi BM. Heart failure: how big is the problem? Who are the patients? What does the future hold? Am Heart J 2003;146:1–4.
  4. Basset A, et al, Development of a clinical prediction score for congestive heart failure diagnosis in the emergency care setting: The Brest score, Am J Emerg Med (2016), http://dx.doi.org/10.1016/j.ajem.2016.08.023
  5. Schocken DD, Arrieta MI, Leaverton PE, Ross EA. Prevalence and mortality rate of congestive heart failure in the United States. J Am Coll Cardiol 1992;20:301–6.
  6. Baggish AL, Sieber U, Lainchbury JG, Cameron R, Anwaruddin S, Chen A, et al. A validated clinical and biochemical score for the diagnosis of acute heart failure. The ProBNP Investigation of Dyspnea in the Emergency Department (PRIDE) Acute Heart Failure Score. Am Heart J 2006;151:48–54.
  7. Steinhart B, Thorpe KE, Bayoumi AM, Moe G, Januzzi Jr JL, Mazer CD. Improving the diagnosis of acute heart failure using a validated prediction model. J Am Coll Cardiol 2009;54:1515–21.
  8. McKee PA, Castel WP, McNamara PM, KannelWB. The natural history of congestive heart failure: the Framingham Study. N Engl J Med 1971;285:1441–6.
  9. Eriksson H, Caidahl K, Larsson B, Ohlson LO,Welin L,Wilhelmsen L, et al. Cardiac and pulmonary causes of dyspnoea–validation of a scoring test for clinical-epidemiological use: the Study of Men Born in 1913. Eur Heart J 1987;8:1007–14.

Will Denq, MD is an Emergency Medicine Resident at The George Washington University Hospital