SGEM#23: A Bump Up Ahead (Diagnosis of Appendicitis)

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Date: 10 February 2013

Case Scenario: 28-year-old woman presents to the emergency department at 2am with steadily increasing right lower quadrant (RLQ) pain. She has a past medical history of ovarian cysts. Her vital signs are stable, afebrile and tender over the RLQ. The blood work is unremarkable and specifically her pregnancy test is negative. Ultrasound and CT scan are not available overnight. What is your disposition and management of this patient?

Background: Undifferentiated abdominal pain is a high volume, high risk complaint. It represents approximately 7% of emergency department visits. Acute appendicitis is the second most common cause of malpractice litigation in children 6 – 17 years old. Ten percent of all closed malpractice cases are due to missed diagnoses of appendicitis. It is not practical to image everyone with lower abdominal pain to rule out acute appendicitis in every case.

Lifetime acute appendicitis incidence is 8.6% in males and 6.7% in females
Lifetime appendectomy rates are 12% for males and 23.1% for females.
Negative laparotomy rate is 10-20%.
Appendectomy complications rate is 4-13%

Question: Does a bumpy car ride predict appendicitis?

Reference: Ashdown el al. Pain over speed bumps in diagnosis of acute appendicitis : A diagnostic accuracy study. BMJ Christmas Issue 2012

Population: Adults >16yrs referred to on-call surgery for assessment
  Intervention: Speed bumps
Comparison: Migratory pain, nausea and vomiting, and rebound tenderness
Outcome: Sensitivity/specificity and likely hood ratios for appendicitis

Results: A total of 101 patients were included in this study. Sixty-eight reported driving over speed bumps on the way to the hospital. Four patients were excluded from the 68 (1-no histology available and 3-treated with antibiotics). Fifty four were “speed bump positive” of the 64. The diagnosis of appendicitis was confirmed histologically in 33 or the 34 who reported worsened pain over speed bumps. This gives a sensitivity of 97% (85% to 100%) and a specificity of 30% (15% to 49%). The positive predictive value (PPV) was 61% (47% to 74%), and the negative predictive value (NPV) was 90% (56% to 100%). The positive likelihood ratio (LR) was 1.4 (1.1 to 1.8) and the negative LR was 0.1 (0.0 to 0.7).

Additonal Resources:

Evidence-Based Emergency Care: Diagnostic Testing and Clinical Decision Rules. This is a great referrence book for emergency physicians and fellows training in emergency medicine. Dr. Chris Carpenter and his fellow editors explain various diagnostic tests and clinical decision instruments.
Diagnostic testing: an emergency medicine perspective. Worster et al. CJEM 2002; 4(5). This is an excellent article written by the founder of BEEM, Dr. Andrew Worster. It helps emergency physicians understand the statistics of diagnositic testing.

Authors’ Conclusions: “Presence of pain while travelling over speed bumps was associated with an increased likelihood of acute appendicitis. As a diagnostic variable, it compared favourably with other features commonly used in clinical assessment. Asking about speed bumps may contribute to clinical assessment and could be useful in telephone assessment of patients.”

BEEM Commentary:

Anthony: Can not be generalized to a pediatric population and more pot-holes than speed bumps in Canada.
Jo-Ann: There was referral bias in this study because patients had to be referred to surgery to be included in the study.
Suneel: Likelihood ratios (LR) are a good way to present the results because LR are immune to prevalence of events.
Ken: Relatively small study (n=101) but inexpensive and no delay in lab turn around time.

The Boys of the BEEM Dream Team: Ken, Suneel and Anthony

Jo-Ann Talbot

BEEM Bottom Line: Perhaps we should ask our patients if it was a bumpy ride to the ED and did the bumps hurt?

Keener Contest: Yifan Li from Western University correctly answered last weeks Keener question. Fixed-effect models assume only one true effect size. Thus, all differences in observed effects are due to sampling error. However, Random-effect models assume that your measurements draw from a random sample in a large population. Thus, the true effect varies from study to study and the variance tells us something about the large population. The difference between them is interference. In the Fixed-effect model, you can only make inferences about your study population. In the Random-effect model, you can make inferences on the large population since you have taken random sampling into account.

Be sure to listen to the podcast to hear this weeks Keener Kontest question. Email your answer to TheSGEM@gmail.com. Use “Keener Kontest” in the subject line. First one to email me the correct answer will win a cool skeptical prize:)

Just came back from SkiBEEM 2013. We had a wonderful time and Silver Star Mountain in BC. Lots of people eager to cut the KT window to less than one year. Don’t Panic if you missed SkiBEEM. You can join us for SteeleBEEM 2013 Feb 21st and 22nd in Hamilton, Ontario.