ArticlePrimary care respiratory journal : journal of the General Practice Airways Group2013
A scoring algorithm for predicting the presence of adult asthma: a prospective derivation study.
Article in Primary care respiratory journal : journal of the General Practice Airways Group, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 4 syntheses or guidelines pooled it, 28 citations in OpenAlex.
- Methodological and applicability pitfalls of clinical prediction models for asthma diagnosis: a systematic review and critical appraisal of evidence.BMC medical research methodology · 2025Pooled it
- Fractional Exhaled Nitric Oxide Testing for the Diagnosis and Management of Asthma: a Health Technology Assessment.Ontario health technology assessment series · 2024Pooled it
- The diagnostic accuracy of lung auscultation in adult patients with acute pulmonary pathologies: a meta-analysis.Scientific reports · 2020Pooled it
- Systematic review of clinical prediction models to support the diagnosis of asthma in primary care.NPJ primary care respiratory medicine · 2019Pooled it
- Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset.Scientific reports · 2025Article
- A survey of severe asthma in Canada: results from the CASCADE practice reflective program.Allergy, asthma, and clinical immunology : official journal of the Canadian Society of Allergy and Clinical Immunology · 2024Article
- Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost.Diagnostics (Basel, Switzerland) · 2023Article
- Development and validation of a predictive model combining patient-reported outcome measures, spirometry and exhaled nitric oxide fraction for asthma diagnosis.ERJ open research · 2023Article
- Excellent asthma and COPD article.Canadian family physician Medecin de famille canadien · 2022Article
- A History of Recurrent Episodes of Prolonged Cough as a Predictive Value for Determining Cough Variant Asthma in a Primary Care Setting.Yonago acta medica · 2021Article
- A dynamic nomogram for predicting the risk of asthma: Development and validation in a database study.Journal of clinical laboratory analysis · 2021Article
- Scoring system to facilitate diagnosis of Gaucher disease.Internal medicine journal · 2020Article
- Respiratory tract infection-related healthcare utilisation in children with Down's syndrome.Infection · 2020Article
- Clinical prediction models to support the diagnosis of asthma in primary care: a systematic review protocol.NPJ primary care respiratory medicine · 2018Article
- GEMA 5.3. Spanish Guideline on the Management of Asthma.Open respiratory archivesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo predict the presence of asthma in adult patients with respiratory symptoms, we developed a scoring algorithm using clinical parameters.
methodsWe prospectively analysed 566 adult outpatients who visited Kinki University Hospital for the first time with complaints of nonspecific respiratory symptoms. Asthma was comprehensively diagnosed by specialists using symptoms, signs, and objective tools including bronchodilator reversibility and/or the assessment of bronchial hyperresponsiveness (BHR). Multiple logistic regression analysis was performed to categorise patients and determine the accuracy of diagnosing asthma.
resultsA scoring algorithm using the symptom-sign score was developed, based on diurnal variation of symptoms (1 point), recurrent episodes (2 points), medical history of allergic diseases (1 point), and wheeze sound (2 points). A score of >3 had 35% sensitivity and 97% specificity for discriminating between patients with and without asthma and assigned a high probability of having asthma (accuracy 90%). A score of 1 or 2 points assigned intermediate probability (accuracy 68%). After providing additional data of forced expiratory volume in 1 second/forced vital capacity (FEV(1)/FVC) ratio <0.7, the post-test probability of having asthma was increased to 93%. A score of 0 points assigned low probability (accuracy 31%). After providing additional data of positive reversibility, the post-test probability of having asthma was increased to 88%.
conclusionsThis pragmatic diagnostic algorithm is useful for predicting the presence of adult asthma and for determining the appropriate time for consultation with a pulmonologist.
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.