Luke’s current research seeks to improve the accuracy with which a diagnosis of asthma can be made in clinical practice. He was awarded a Chief Scientist Office Clinical Academic Fellowship (2017) for “deriving and validating a clinical prediction rule for the diagnosis of asthma in primary care”.
His latest study 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, aged under 25 years old. The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records (n=11,972 participants). The prediction model was derived using logistic regression. External validation was conducted using electronic health records from the Optimum Patient Care Research Database (OPCRD; n=2670 participants). Discrimination in the external validation dataset was good (c-statistic 0.85, 95% CI 0.83–0.88) but calibration was poor (calibration slope 1.22, 95% CI 1.09–1.35) which may be because some predictors were infrequently coded in health records.
See further:
@ljdaines on Twitter
Systematic Review of Previous Models
Patient Views of Diagnosis Tools
