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Published on: January 29, 2011
Causes of Shortness of Breath in the Acute Patient: A National Study
Zachariah E Hale1, Astha Singhal2, Renee Y Hsia3
1School of Medicine, University of California at San Francisco, San Francisco, CA.
Background:
Dyspnea is a common and ambiguous complaint that results in 3.4 million emergency department (ED) visits annually. While learners may encounter lists of differential diagnoses to consider while in training, often these lists are not empirically based. We sought to establish an evidence-based differential diagnosis for dyspnea and to determine whether normal vital signs can rule out a life-threatening diagnosis.
Methods:
We analyzed data from the National Hospital Ambulatory Medical Care Survey from 2005 to 2014 for ED visits with a chief complaint of dyspnea and tallied the principal discharge diagnosis. We included 10,170 sampled ED visits by adults with a chief complaint of dyspnea, representing nearly 42 million visits nationally. We then calculated the most common principal discharge diagnoses by age group and the frequency of abnormal respiratory vital signs in cases with life-threatening diagnoses.
Results:
The most represented age group was 45 to 64 years (31.6%). Most visits were discharged directly from the ED (57.5%), while 8.1% required admission to an intensive care unit (ICU). The most common diagnosis in patients aged 18 to 44 was acute asthma exacerbation (14.8%). Obstructive chronic bronchitis was the most common specified diagnosis in both patients ages 45 to 64 (11.1%) and patients ages 65 to 79 (12.4%), while congestive heart failure was the most common for patients ages 80 and over (15.9%). Respiratory vital signs were frequently normal in the 44.6% of visits that resulted in a potentially life-threatening diagnosis but corresponded to increased ICU utilization when abnormal.
Conclusions:
For patients with dyspnea, the high utilization of ICUs highlights the importance of an accurate differential. The frequencies established here can be used as pretest probabilities in Bayesian analysis to improve the accuracy of differential diagnosis lists.
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