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

Luke Daines; LJ Bonnett; Holly Tibble; Andrew Boyd; R Thomas; David Price; Stephen Turner; Steff C Lewis; Aziz Sheikh; Hilary Pinnock

doi:10.12688/wellcomeopenres.19078.2

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

Luke Daines ^* (Corresponding Author), LJ Bonnett , Holly Tibble, Andrew Boyd, R Thomas, David Price, Stephen Turner, Steff C Lewis, Aziz Sheikh, Hilary Pinnock

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

2 Downloads (Pure)

Abstract

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.

Original language	English
Article number	195
Number of pages	20
Journal	Wellcome open research
Volume	8
Issue number	195
Early online date	7 Sept 2023
DOIs	https://doi.org/10.12688/wellcomeopenres.19078.2
Publication status	Published - 7 Sept 2023

Bibliographical note

Grant information: This work was supported by Wellcome [086118, https://doi.org/10.35802/086118], the UK Medical Research Council
and the University of Bristol who provided core support for ALSPAC. LD was supported by a clinical academic fellowship from the Chief
Scientist Office, Edinburgh (CAF/17/01). The ALSPAC data linkage programme and authors AB and RT were supported by the Medical
Research Council (MR/L012081) and the Wellcome Trust [086118]. A comprehensive list of grants funding is available on the ALSPAC
website (www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). Neither funder(s) nor sponsor (University of
Edinburgh) contributed to the manuscript.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability Statement

Underlying data

ALSPAC. ALSPAC data access is through a system of managed open access. The steps below highlight how to apply for access to the data referred to in this article and all other ALSPAC data. The datasets presented in this article are linked to ALSPAC project number B2830, please quote this project number during your application. The ALSPAC variable codes highlighted in the dataset descriptions can be used to specify required variables.

1. Please read the ALSPAC access policy (https://www.bristol.ac.uk/media-library/sites/alspac/documents/researchers/data-access/ALSPAC_Access_Policy.pdf) which describes the process of accessing the data and samples in detail, and outlines the costs associated with doing so.

2. You may also find it useful to browse our fully searchable research proposals database (https://proposals.epi.bristol.ac.uk/), which lists all research projects that have been approved since April 2011.

3. Please submit your research proposal for consideration by the ALSPAC Executive Committee. You will receive a response within 10 working days to advise you whether your proposal has been approved. If you have any questions about accessing ALSPAC data, please email alspac-data@bristol.ac.uk. The study website also contains details of all the data that is available through a fully searchable data dictionary: http://www.bristol.ac.uk/alspac/researchers/data-access/data-dictionary/

Extended data

Open Science Framework: Extended data for ‘Clinical prediction model for the diagnosis of asthma in children and young people in primary care’, https://osf.io/kfz3n/15

This project contains the following extended data:

AsthmaSpecific_ReadcodeList.txt (Asthma-specific read codes.)

LungFunctionAndReversibility_ReadCodeList.txt (Lung function/reversibility testing read codes.)

Keywords

Asthma
Diagnosis
Primary Care
Children and Young People
Prediction Model
ALSPAC
Electronic Health Records

Access to Document

10.12688/wellcomeopenres.19078.2Licence: CC BY

Daines_etal_WOR_Deriving_And_Validating_VoR
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/
Final published version, 1.74 MBLicence: CC BY

Cite this

@article{8cbd07ad890a45be838aa0e4315a8faa,

title = "Deriving and validating an asthma diagnosis prediction model for children and young people in primary care",

abstract = "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.",

keywords = "Asthma, Diagnosis, Primary Care, Children and Young People, Prediction Model, ALSPAC, Electronic Health Records",

author = "Luke Daines and LJ Bonnett and Holly Tibble and Andrew Boyd and R Thomas and David Price and Stephen Turner and Lewis, {Steff C} and Aziz Sheikh and Hilary Pinnock",

note = "Grant information: This work was supported by Wellcome [086118, https://doi.org/10.35802/086118], the UK Medical Research Council and the University of Bristol who provided core support for ALSPAC. LD was supported by a clinical academic fellowship from the Chief Scientist Office, Edinburgh (CAF/17/01). The ALSPAC data linkage programme and authors AB and RT were supported by the Medical Research Council (MR/L012081) and the Wellcome Trust [086118]. A comprehensive list of grants funding is available on the ALSPAC website (www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). Neither funder(s) nor sponsor (University of Edinburgh) contributed to the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.",

year = "2023",

month = sep,

day = "7",

doi = "10.12688/wellcomeopenres.19078.2",

language = "English",

volume = "8",

journal = "Wellcome open research",

issn = "2398-502X",

publisher = "F1000 Research Ltd.",

number = "195",

}

TY - JOUR

T1 - Deriving and validating an asthma diagnosis prediction model for children and young people in primary care

AU - Daines , Luke

AU - Bonnett , LJ

AU - Tibble, Holly

AU - Boyd, Andrew

AU - Thomas, R

AU - Price, David

AU - Turner, Stephen

AU - Lewis, Steff C

AU - Sheikh, Aziz

AU - Pinnock, Hilary

N1 - Grant information: This work was supported by Wellcome [086118, https://doi.org/10.35802/086118], the UK Medical Research Council and the University of Bristol who provided core support for ALSPAC. LD was supported by a clinical academic fellowship from the Chief Scientist Office, Edinburgh (CAF/17/01). The ALSPAC data linkage programme and authors AB and RT were supported by the Medical Research Council (MR/L012081) and the Wellcome Trust [086118]. A comprehensive list of grants funding is available on the ALSPAC website (www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). Neither funder(s) nor sponsor (University of Edinburgh) contributed to the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

PY - 2023/9/7

Y1 - 2023/9/7

N2 - 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.

AB - 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.

KW - Asthma

KW - Diagnosis

KW - Primary Care

KW - Children and Young People

KW - Prediction Model

KW - ALSPAC

KW - Electronic Health Records

U2 - 10.12688/wellcomeopenres.19078.2

DO - 10.12688/wellcomeopenres.19078.2

M3 - Article

C2 - 37928213

SN - 2398-502X

VL - 8

JO - Wellcome open research

JF - Wellcome open research

IS - 195

M1 - 195

ER -

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

Abstract

Bibliographical note

Data Availability Statement

Keywords

Access to Document

Fingerprint

Cite this