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

Abstract

<ns4:p><ns4:bold>Background: </ns4:bold>Accurately diagnosing asthma can be challenging. Uncertainty about the best combination of clinical features and investigations for asthma diagnosis is reflected in conflicting recommendations from international guidelines. One solution could be a clinical prediction model to support health professionals estimate the probability of an asthma diagnosis. However, systematic review evidence identifies that existing models for asthma diagnosis are at high risk of bias and unsuitable for clinical use. Being mindful of previous limitations, this protocol describes plans to derive and validate a prediction model for use by healthcare professionals to aid diagnostic decision making during assessment of a child or young person with symptoms suggestive of asthma in primary care.</ns4:p><ns4:p> <ns4:bold>Methods:</ns4:bold> A prediction model will be derived using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and linked primary care electronic health records (EHR). Data will be included from study participants up to 25 years of age where permissions exist to use their linked EHR. Participants will be identified as having asthma if they received at least three prescriptions for an inhaled corticosteroid within a one-year period and have an asthma code in their EHR. To deal with missing data we will consider conducting a complete case analysis. However, if the exclusion of cases with missing data substantially reduces the total sample size, multiple imputation will be used. A multivariable logistic regression model will be fitted with backward stepwise selection of candidate predictors. Apparent model performance will be assessed before internal validation using bootstrapping techniques. The model will be adjusted for optimism before external validation in a dataset created from the Optimum Patient Care Research Database.</ns4:p><ns4:p> <ns4:bold>Discussion: </ns4:bold>This protocol describes a robust strategy for the derivation and validation of a prediction model to support the diagnosis of asthma in children and young people in primary care.</ns4:p>

Derivation and Validation of a Risk Score to Predict Mortality of Early Neonates at Neonatal Intensive Care Unit: The END in NICU Score

“… 51–55 Additionally, no prior estimates were available to calculate the sample size for the derivation study. Hence, as the rule of thumb of at least 10 events per candidate variable for logistic regression prediction models was used to estimate the sample size 55–58 in line with by Hosmer and Lemeshow recommendation. 59 Since there are 23 candidate prognostic determinants considered, by taking 10 events per predictor parameter, the estimated number of outcome events for the derivation study becomes 230.…”

“…Missing data pattern was assessed and we assumed data were missing at random, and we, therefore, implemented a multiple imputations by creating up to 10 imputed datasets via chained equations 65 was considered. 55 However, since maternal TT vaccination status and number of ANC attended had more than 30% of missing values, we excluded from imputation and further consideration.…”

ERS International Congress 2021: highlights from the Respiratory Clinical Care and Physiology Assembly

“…Finally, as the International Primary Care Respiratory Group (IPCRG) global e-Delphi highlighted that the top research needs were in the areas of diagnosis and management of asthma, COPD and chronic cough [ 47 ], we should expect next year's congress to bring more studies aiming to develop prediction models and ultimately artificial intelligence programmes or electronic calculators such as the one presented by D aines et al [ 48 , 49 ], which could assist front-line physicians in decision making processes from diagnosis to management of diseases.…”

Patient views on asthma diagnosis and how a clinical decision support system could help: A qualitative study

“…Having derived and validated a clinical prediction model for asthma diagnosis, 15 , 16 we plan to implement the model in primary care as a CDSS. Being aware that a previous systematic review found that CDSS for asthma were infrequently utilized, 17 we wanted to understand patient views on asthma diagnosis and how a CDSS could help to maximize the potential value of a future CDSS for patients.…”

“…In a Norwegian study, a web-based CDSS designed to aid the diagnosis and classification of chronic obstructive pulmonary disease (COPD) in primary care was found to reduce misdiagnosis and increase the number of patients receiving smoking cessation advice but did not improve the prescription of pharmacological treatment. 14 Having derived and validated a clinical prediction model for asthma diagnosis, 15,16 we plan to implement the model in primary care as a CDSS. Being aware that a previous systematic review found that CDSS for asthma were infrequently utilized, 17 we wanted to understand patient views on asthma diagnosis and how a CDSS could help to maximize the potential value of a future CDSS for patients.…”