Development and validation of a predictive model combining patient-reported outcome measures, spirometry and exhaled nitric oxide fraction for asthma diagnosis

Louis, Gilles; Schleich, Florence; Guillaume, Michèle; Kirkove, Delphine; Zahrei, Halehsadat Nekoee; Donneau, Anne‐Françoise; Henket, Monique; Paulus, Virginie; Guissard, Françoise; Louis, Renaud; Pétré, Benoît

doi:10.1183/23120541.00451-2022

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…Other studies have also evaluated the role of FeNO in predicting asthma diagnosis in combination with other measurements. A recent study by Louis and colleagues demonstrated that a combination of wheezing scale, spirometry and FeNO improves the diagnostic accuracy with area under curve of 0.76 (95% confidence interval 0.66–0.84) [ 21 ]. In another study of 58 adults with persistent allergic rhinitis followed over 1 year showed that basal FeNO levels over 28 parts per billion (ppb) predicted development of asthma [ 22 ].…”

Section: Feno As a Diagnostic Biomarker For Asthmamentioning

confidence: 99%

Update on the Role of FeNO in Asthma Management

et al. 2023

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Asthma is a heterogenous disorder characterized by presence of different phenotypes and endotypes. Up to 10% of the individuals suffer from severe asthma and are at increased risk of morbidity and mortality. Fractional exhaled nitric oxide (FeNO) is a cost-effective, point of care biomarker that is used to detect type 2 airway inflammation. Guidelines have proposed to measure FeNO as an adjunct to diagnostic evaluation in individuals with suspected asthma and to monitor airway inflammation. FeNO has lower sensitivity, suggesting that it may not be a good biomarker to rule out asthma. FeNO may also be used to predict response to inhaled corticosteroids, predict adherence and deciding on biologic therapy. Higher levels of FeNO have been associated with lower lung function and increased risk for future asthma exacerbations and its predictive value increases when combined with other standard measurements of asthma assessment.

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Section: Feno As a Diagnostic Biomarker For Asthmamentioning

confidence: 99%

Update on the Role of FeNO in Asthma Management

et al. 2023

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Association of Cough Severity with Asthma Control and Quality of Life in Patients with Severe Asthma

Lee,

Lee

et al. 2024

Lung

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Deriving and validating an asthma diagnosis prediction model for children and young people in primary care

Daines¹,

Bonnett²,

Tibble³

et al. 2023

Wellcome Open Res

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Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged <25 years (49% female, 8% asthma), model performance as indicated by the C-statistic and calibration slope was 0.86, 95% confidence interval (CI) 0.85–0.87 and 1.00, 95% CI 0.95–1.05 respectively. In the external validation dataset, which included 2,670 participants aged <25 years (50% female, 10% asthma), the C-statistic was 0.85, 95% CI 0.83–0.88, and calibration slope 1.22, 95% CI 1.09–1.35. Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.

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Development and validation of a predictive model combining patient-reported outcome measures, spirometry and exhaled nitric oxide fraction for asthma diagnosis

Cited by 5 publications

References 40 publications

Update on the Role of FeNO in Asthma Management

Update on the Role of FeNO in Asthma Management

Association of Cough Severity with Asthma Control and Quality of Life in Patients with Severe Asthma

Deriving and validating an asthma diagnosis prediction model for children and young people in primary care

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