Abstract
Substantial over-diagnosis and under-diagnosis of asthma in adults and children has recently been reported. As asthma is mostly diagnosed in non-specialist settings, a clinical prediction model (CPM) to aid the diagnosis of asthma in primary care may help improve diagnostic accuracy. We aim to systematically identify, describe, compare, and synthesise existing CPMs designed to support the diagnosis of asthma in children and adults presenting with symptoms suggestive of the disease, in primary care settings or equivalent populations. We will systematically search Medline, Embase and CINAHL from 1 January 1990 to present. Any CPM derived for use in a primary care population will be included. Equivalent populations in countries without a developed primary care service will also be included. The probability of asthma diagnosis will be the primary outcome. We will include CPMs designed for use in clinical practice to aid the diagnostic decision making of a healthcare professional during the assessment of an individual with symptoms suggestive of asthma. We will include derivation studies, and external model validation studies. Two reviewers will independently screen titles/abstracts and full texts for eligibility and extract data from included papers. The CHARMS checklist (or PROBAST if available) will be used to assess risk of bias within each study. Results will be summarised by narrative synthesis with meta-analyses completed if possible. This systematic review will provide comprehensive information about existing CPMs for the diagnosis of asthma in primary care and will inform the development of a future diagnostic model.
Highlights
Asthma is a chronic respiratory disease affecting an estimated 334 million people worldwide.[1]
By conducting this systematic review of clinical prediction model (CPM) for the diagnosis of asthma in primary care, we will critically appraise each included model, understand may use the broader outcome of obstructive airways disease. These studies will be included if data pertaining to asthma diagnosis can be extracted
CPMs derived for the diagnosis of occupational asthma will be included if the intended use is to aid diagnostic decision making in patients with undifferentiated symptoms
Summary
Asthma is a chronic respiratory disease affecting an estimated 334 million people worldwide.[1]. The current diagnostic strategies recommended in asthma guidelines are based on expert consensus and in some instances informed by health economic modelling, yet the accuracy with which the advocated clinical assessment pathways result in a correct diagnosis have not been formally evaluated.[7,8,15]. A decision tree used to differentiate obstructive respiratory conditions has been derived and validated in the Netherlands.[19] To build on these foundations, inform future development and progress towards wider implementation of a CPM for asthma diagnosis in primary care, a systematic review is needed
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