How Accurate are Flu Tests?

This time of year, every patient presenting to a hospital or clinic with a cough, cold, sniffle or temperature above 98.6 degrees thinks they have the flu.  The media hype over influenza this year hasn’t helped the matter.  In reality, most of these patients are likely suffering from some other type of virus.  A flu swab may help dissuade patient’s beliefs they have influenza, but are these tests really telling the truth?

Typically, when I swab a patient’s nostrils to test for influenza I am almost certain of my diagnosis.  The temperature of 102, dry cough and severe body-aches are telltale signs, combined with the influenza epidemic in the community I am expecting a positive result from the flu swab.  But, then the test comes back negative and I recant my original suspicions diagnosing the patient with an ‘influenza-like’ virus.  But am I really wrong?

According to the CDC, flu-swabs aren’t all that accurate.  In fact, the sensitivity (percentage of positives that are correctly identified) of most rapid flu tests is just 50-70%.  Specificity (number of negative results that is correctly identified) is much better at 90-95%.  This means that half of individuals tested who do in fact have influenza may test negative.  Specimens collected closer to onset of the illness, ideally within 4 to 5 days, are most accurate.

It looks like rapid influenza testing is not that useful.  A positive result does confirm with a high degree of certainty that the patient does in fact have the flu but a negative result doesn’t mean much. I recommend testing for the flu only if it will change your treatment.  Once anti-viral therapies are no longer recommended, about 48 hours after onset of illness, determining if your patient truly has influenza versus another viral ailment isn’t necessary.  Given the inaccuracy of influenza testing and the limited treatment options, the role of flu tests is limited.

Curious about influenza activity in your area?  Check out the CDC Influenza Map updated weekly to show rate of the illness in your state.

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