GPs are one of the key players in the early diagnosis of chronic diseases, such as asthma in pre-school children, by detecting symptoms of illness as early as possible. Patient health data is collected on an ongoing basis in GPs' electronic medical records, but remains little exploited despite its potential. Helping GPs to identify asthma in pre-school children, based on the information in their electronic medical records, could help them to diagnose the condition early and thereby reduce the morbidity and mortality associated with it. An algorithm developed and evaluated in a primary care data warehouse should help GPs to identify children with a diagnosis of asthma at an early stage.
Asthma is the most common chronic disease affecting children. It is defined by repeated episodes of heterogeneous respiratory symptoms, such as wheezing, breathlessness, chest tightness and cough, which vary in time and intensity, as well as variable expiratory flow limitation. Asthma in pre-school children corresponds to asthma in children under the age of 6. Diagnosis in children is particularly complex, due to the difficulty of performing respiratory tests such as spirometry, and the fact that symptoms often diminish with age. Diagnosis is based on a number of factors, including response to treatment and the absence of a differential diagnosis. Although asthma in pre-school children is frequent and sometimes serious, it is under-diagnosed and not optimally treated. GPs are among the key players in the early diagnosis of chronic diseases, by detecting symptoms of illness as early as possible. Patient health data is collected on an ongoing basis in GPs' electronic medical records, but remains little exploited despite its potential. Helping GPs to identify asthma in pre-school children, based on the information in their electronic medical records, could help them to diagnose the condition at an early stage, thereby reducing the morbidity and mortality associated with it. An algorithm, developed and evaluated in a primary care data warehouse, should help GPs to identify children with a diagnosis of asthma at an early stage.
Study Type
OBSERVATIONAL
Enrollment
300
150 medical files of children identified by the algorithm as having asthma will be randomly selected for expert appraisal.
150 medical files of children not identified by the algorithm as having asthma will be randomly selected for expert appraisal.
Maison de Santé Amstrong
Le Grand-Quevilly, France
Maison de Santé des Carmes
Rouen, France
Maison de Santé de la Plaine
Val-de-Reuil, France
Evaluating the sensitivity of an algorithm for the early identification of extracurricular children with asthma
Evaluate the algorithm's predictions against expert opinion to estimate the Sensitivity of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives)
Time frame: At enrollment visit
Assessing the specificity of an algorithm for the early identification of pre-school children with asthma
Evaluate the algorithm's predictions against expert opinion to estimate the Specificity of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives)
Time frame: At enrollment visit
Assessing the positive predictive value of an algorithm for the early identification of pre-school children with asthma
Evaluate the algorithm's predictions against expert opinion to estimate the positive predictive value of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives)
Time frame: At enrollment visit
Assessing the negative predictive value of an algorithm for the early identification of pre-school children with asthma
Evaluate the algorithm's predictions against expert opinion to estimate the negative predictive value of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives)
Time frame: At enrollment visit
Reliability of an algorithm for the early identification of children of pre-school age (2 years)
Evaluate the algorithm's predictions against expert opinion to estimate the negative predictive value, the positive value, the specificity and the sensibilité of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives) of children of pre-school age (2 years) with asthma
Time frame: At enrollment visit
Reliability of an algorithm for the early identification of children of pre-school age (4 years)
Evaluate the algorithm's predictions against expert opinion to estimate the negative predictive value, the positive value, the specificity and the sensibilité of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives) of children of pre-school age (4 years) with asthma
Time frame: At enrollment visit
Reliability of an algorithm for the early identification of children of pre-school age (5 years and 11 months)
Evaluate the algorithm's predictions against expert opinion to estimate the negative predictive value, the positive value, the specificity and the sensibilité of the algorithm with a minimum accuracy of +/- 0.10 at an α risk of 5%, based on the following variables (number of true positives, number of false positives, number of true negatives and number of false negatives) of children of pre-school age (5 years and 11 months) with asthma
Time frame: At enrollment visit
Population with asthma identified by the algorithm
Describe the population identified by the algorithm and the experts, and compare it with patients already identified as having asthma (in their history) by their GP.
Time frame: At enrollment visit
Number of asthma patients newly detected thanks to the algorithm
Estimate the number of asthma patients newly detected thanks to the algorithm who were not initially detected.
Time frame: At enrollment visit
Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs
Number of patients already identified by their GP
Time frame: At enrollment visit
Estimate of the percentage of asthma patients identified using this algorithm who were not initially identified by their GPs
Number of patients newly identified by the algorithm and the expert group
Time frame: At enrollment visit
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