This has stimulated the search for indices or models that, combining different risk factors known to increase persistence of symptoms in young wheezing children, would allow identifying children more likely to have symptoms during the school years. The purpose of this review is to compare and contrast different prediction tools developed during the last two decades.
At this moment, indices and models are also not useful for identifying children who will or will not respond to asthma therapy during the school years. Much more appropriate for this latter purpose are clinical trials such as the INFANT study 3 in which phenotypes and biomarkers are directly tested for their capacity to predict which children will respond to a certain therapy as compared to other therapies or placebo.
The lain purpose of these indices and models is, as stated to predict outcomes and therefore, to allow clinicians to have meaningful discussions about prognosis with parents and caregivers, and to open the way to prevention studies that will be targeted to children at the highest risk of developing persistent symptoms later in life.
Clinical prediction rules CPR are decision-making clinical tools that use variables of medical history, physical examination, and simple laboratory tests to provide the probability of an outcome, prognosis, or likely response to treatment in an individual patient 8. At this point, it is very important to remark that the clinical usefulness of a diagnostic test is determined by the extent to which it helps to modify the pretest probability of occurrence of a certain diagnosis.
For this purpose, the calculation and application of likelihood ratios LR is a very useful tool, reflecting the magnitude by which the pretest probability increases or decreases and thereby helping the physician rule out, confirm, or continue investigating a diagnosis with new tests 9. Therefore, LR, not the sensitivity or positive predicted value, is the best parameter reflecting the diagnostic accuracy of any diagnosis or prognosis model 10 , It is important to remember that four consecutive steps are needed for prognostic or diagnostic prediction rules to become universal accepted for massive use.
These steps are: development, validation, impact, and implementation At this moment, at least seven predictive models or scores for asthma have been developed. The characteristics and predictors used in these models were shown in Table 1.
In clinical prediction rules, each score level represents a different LR. Calculation is based on a proportion, in which we consider in the numerator, the proportion of patients with the condition with the given score level, and in the denominator, the proportion of patients without the condition with the given score level 9.
For the purpose of simplifying, scores are sometimes dichotomized into positive and negative, i. Regarding prediction of asthma, the positive LR is the probability of a child with active asthma to have been classified as being at risk divided by the probability of a child without active asthma to have been classified as being at risk.
The negative LR is the probability of a child with active asthma to have been classified as not being at risk divided by the probability of a child without active asthma to have been classified as not being at risk. However, beside the number, it is more important how big the change between the pre-test probabilities and the post-test probability is 9. For example, to determine the risk of asthma in preschoolers from countries with high e.
However, since their negative LR is not good, it cannot be used to rule out the probability for the development of asthma. Figure 1. Application of the original positive Asthma predictive index API in hypothetical different scenarios with a low, moderate, or high-risk population of having asthma at school age.
Several important issues need to be addressed to determine the internal validity of the optimal prediction model. Among these seven models, only the original API 13 is relatively generalizable since it was developed in an unselected ethnically diverse birth cohort. The PIAMA 15 is more laborious to determine because the many criteria used have different weights, in addition, its generalizability may be reduced since it includes health beliefs and socioeconomic information that may vary between ethnicities and countries.
Similarly, the mAPI initially proposed by expert opinion was recently also developed in a high-risk of atopy birth cohort Moreover, it is well-known that peripheral blood eosinophilia is a better predictor of remission of asthma, than specific IgE or skin prick test 21 , Therefore, predictive indices that use peripheral blood eosinophilia a cheaper and worldwide common test instead of specific or total IgE or skin prick test, will be more useful for predicting asthma.
Therefore, the incorporation of specific IgE or positive skin test in any predictive model without taking these consideration are questionable.
The latest model developed was the ademAPI This model is maybe the most complete and sophisticated model, but although it reaches the best positive and negative LRs 8. Moreover, the increase of positive LR from 7. After developing any prognosis model the next step is the validation, i. Temporal, geographical, and domain validations can be distinguished. The original API was validated in three different independent large cohorts and one small high-risk cohort.
It is important to note that a different combination of API criteria was used in two validated studies 15 , 24 that made the interpretation difficult. The next step is to determine the impact of the CPR, i.
Only a limited number of CPR worldwide have completed this step, probably because of the big methodological challenge and high costs of such studies for CPRs. At this time, none of the prediction models has moved forward to this stage.
Therefore, there is little evidence to support impact and implementation. Nevertheless, at this moment the original stringent API has been the most common predicted model tested worldwide in different non-randomized studies, the other was the mAPI. For example, Wi et al. This could suggest that application of API to retrospective studies for ascertaining asthma status is suitable. In a case-control study 31 on Brazilian children aged 6—24 months comparing recurrent wheezing vs.
A study 32 on Turkish school children with history of recurrent wheezing, reported that those with negative API in the past, had significantly shortened wheezing duration. A recent cross-sectional study 33 , nested in a US birth cohort, was done to develop and validate a natural language processing NLP algorithm to identify patients that meet the original API criteria.
Asthma status ascertained by manual chart review based on API criteria served as gold standard. NLP-API was able to ascertain asthma status in children mining from electronic health records and has a potential to enhance asthma care and research through population management and large-scale studies when identifying children who meet API criteria.
Also, the original stringent API was compared and correlated with surrogated markers of airway inflammation using two non-invasive tests, i. In a study on Switzerland preschoolers age 3—47 mo , FeNO was significantly higher in those with positive stringent API than positive loose API or recurrent cough without history of wheeze controls Finally, a study on German preschoolers median age 4.
Other inflammatory biomarkers e. A study done in 48 Chilean preschoolers aged 24—71 mo reported no significant differences in serum periostin levels for those with positive API and negative API; and no significant correlation between serum periostin levels and peripheral blood eosinophils Similarly, a recent study done in 98 Turkish preschoolers showed that periostin and angiopoietin serum levels were similar between positive and negative mAPI Finally, a longitudinal Sweden study of preschoolers with recurrent wheeze and healthy controls, showed that YKL levels were elevated during acute wheeze exacerbation in positive and negative API maybe related with current neutrophilic inflammation compared to controls, but not at 3 and 12 month follow-up after the acute exacerbation The API was also compared and correlated with airway inflammation in bronchial biopsies.
A study on endobronchial biopsies obtained from 30 Czech preschoolers median age Since there were some criticism about the value of the original API, some studies were performed changing or adding new biomarkers in order to improve the API performance for predicting asthma at 6 years of age.
For example, on Germany preschoolers median age 4. In other study, interleukinreceptor-like 1 or sST2 a well-replicated asthma-gene and associates with eosinophilia was compared to the API in Dutch wheezing children and 50 healthy controls reporting that serum sST2 levels at 2—3 years could not distinguish which of the preschoolers developed asthma at school age; consequently, serum sST2 did not significantly add to the prediction of asthma diagnosis than the used of API API alone vs.
Finally, the last step is the implementation, i. Although, like formerly exposed, none of the CPR has completed the impact step, the original API and mAPI are the only asthma prediction models that have been implemented worldwide over the years Table 3.
Table 3. Steps for develop a prognostic or diagnostic prediction model For example, the API has been used to explore the airway lung function in young asthmatic children. A case-control study 49 on Chilean recurrent wheezing preschoolers aged 24—72 mo showed no differences in basal lung function and post-bronchodilator response to salbutamol by IOS or spirometry between positive and negative API preschoolers. Asthma Resources Asthma Resources.
Assistance Programs. MI Asthma Surveillance. Alternative Treatments. Guidelines for the Diagnosis and Treatment of Asthma. Control of Environmental Factors. Asthma and Comorbid Conditions.
Asthma Continuing Education Opportunities. Asthma Predictive Index. Occupational Asthma. Types of Work Related Asthma. Myths About Work-Related Asthma. Diagnosing and Preventing Work Related Asthma. AIM Interventions. Clinicians may be more aggressive with trials of asthma medications in patients who are likely to be diagnosed with asthma later in life.
Please fill out required fields. His research focuses on respiratory diseases, such as asthma, bronchitis and tuberculosis, in pediatric patients. To view Dr. This is an unprecedented time. It is the dedication of healthcare workers that will lead us through this crisis.
Thank you for everything you do. Calc Function Calcs that help predict probability of a disease Diagnosis. Subcategory of 'Diagnosis' designed to be very sensitive Rule Out. Disease is diagnosed: prognosticate to guide treatment Prognosis. Numerical inputs and outputs Formula. Med treatment and more Treatment. Suggested protocols Algorithm. Disease Select Specialty Select
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