SynthesisNPJ primary care respiratory medicine2019
Systematic review of clinical prediction models to support the diagnosis of asthma in primary care.
Synthesis in NPJ primary care respiratory medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Exploring the role of cold air in airway hyperresponsiveness and asthma diagnostic testing: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Prediction Models for Maternal and Offspring Short- and Long-Term Outcomes Following Gestational Diabetes: A Systematic Review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025Pooled it
- Clinical predictive models in equine medicine: A systematic review.Equine veterinary journal · 2023Pooled it
- Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic review.BMC medical informatics and decision making · 2021Pooled it
- Periodontitis Prediction Model Using Linked Electronic Health and Dental Records.JDR clinical and translational research · 2026Article
- Asthma and severe acute respiratory infections: a stratified analysis of mortality patterns in Brazil.BMC public health · 2025Article
- Development and validation of a nomogram for predicting cough variant asthma diagnosis.BMC pulmonary medicine · 2025Article
- Advances and Challenges Associated with Low-Cost Pulse Oximeters in Home Care Programs: A Review.Sensors (Basel, Switzerland) · 2024Review
- Article
- Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA).BMJ (Clinical research ed.) · 2023Article
- Deriving and validating an asthma diagnosis prediction model for children and young people in primary care.Wellcome open research · 2023Article
- Implementing asthma management guidelines in public primary care clinics in Malaysia.NPJ primary care respiratory medicine · 2021Article
- Artificial Intelligence Approach to the Monitoring of Respiratory Sounds in Asthmatic Patients.Frontiers in physiology · 2021Article
- Defining high probability when making a diagnosis of asthma in primary care: mixed-methods consensus workshop.BMJ open · 2020Review
- Protocol for the derivation and validation of a clinical prediction model to support the diagnosis of asthma in children and young people in primary care.Wellcome open research · 2020Article
- Resolution of inflammation: from basic concepts to clinical application.Seminars in immunopathology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.