Abstract
Diagnosing asthma is challenging. Misdiagnosis can lead to untreated symptoms, incorrect treatment and avoidable deaths. The best combination of clinical features and tests to achieve a diagnosis of asthma is unclear. As asthma is usually diagnosed in non-specialist settings, a clinical prediction model to aid the assessment of the probability of asthma in primary care may improve diagnostic accuracy. We aimed to identify and describe existing prediction models to support the diagnosis of asthma in children and adults in primary care. We searched Medline, Embase, CINAHL, TRIP and US National Guidelines Clearinghouse databases from 1 January 1990 to 23 November 17. We included prediction models designed for use in primary care or equivalent settings to aid the diagnostic decision-making of clinicians assessing patients with symptoms suggesting asthma. Two reviewers independently screened titles, abstracts and full texts for eligibility, extracted data and assessed risk of bias. From 13,798 records, 53 full-text articles were reviewed. We included seven modelling studies; all were at high risk of bias. Model performance varied, and the area under the receiving operating characteristic curve ranged from 0.61 to 0.82. Patient-reported wheeze, symptom variability and history of allergy or allergic rhinitis were associated with asthma. In conclusion, clinical prediction models may support the diagnosis of asthma in primary care, but existing models are at high risk of bias and thus unreliable for informing practice. Future studies should adhere to recognised standards, conduct model validation and include a broader range of clinical data to derive a prediction model of value for clinicians.
Highlights
Making an accurate diagnosis of asthma is fundamental to improving asthma care and outcomes
This systematic review identified seven clinical prediction models to support the diagnosis of asthma in primary care
Allergy, allergic rhinitis, symptom variability and exerciseinduced symptoms were associated with asthma and should be considered as predictors in future prediction models
Summary
Making an accurate diagnosis of asthma is fundamental to improving asthma care and outcomes. Asthma is commonly misdiagnosed, with over- and under-diagnosis of asthma in children and adults reported.[1,2,3] Over-diagnosis leads to costly, potentially harmful treatment and unnecessary health care, whilst under-diagnosis risks inadequate treatment and avoidable morbidity and mortality. Asthma is a clinical diagnosis, but individual symptoms, signs and tests have poor sensitivity/specificity for the diagnosis. Uncertainty about the best combination of clinical features and tests for asthma diagnosis is reflected in conflicting recommendations between national[5,6] and international[7] guidelines and highlighted in commentaries seeking to reduce confusion for clinicians.[8,9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
