Materiały źródłowe

• #1 Early Prediction of Asthma

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10455492/

  The clinical manifestations of asthma in children are highly variable, are associated with different molecular and cellular mechanisms, and are characterized by common symptoms that may diversify in frequency and intensity throughout life. […] It is essential to promptly identify patients at high risk of developing asthma by using different prediction models. […] The aim of this review regarding the early prediction of asthma is to summarize predictive factors for the course of asthma, including lung function, allergic comorbidity, and relevant data from the patients medical history, among other factors. […] The early identification of wheezing in children younger than five years old can provide valuable information to parents and medical professionals and aid in the early stratification and close monitoring of patients at risk of asthma.

• #2 Early Prediction of Asthma

  https://www.mdpi.com/2077-0383/12/16/5404

  The clinical manifestations of asthma in children are highly variable, are associated with different molecular and cellular mechanisms, and are characterized by common symptoms that may diversify in frequency and intensity throughout life. […] It is essential to promptly identify patients at high risk of developing asthma by using different prediction models. […] The early identification of wheezing in children younger than five years old can provide valuable information to parents and medical professionals and aid in the early stratification and close monitoring of patients at risk of asthma. […] Prediction models for childhood asthma are helpful for identifying likely future asthma patients from high-risk groups; children in preschool who develop symptoms could benefit from early diagnosis and intervention.

• #3 Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma

  https://www.mdpi.com/2227-9067/8/5/376

  Asthma in children is a heterogeneous disease manifested by various phenotypes and endotypes. The level of disease control, as well as the effectiveness of anti-inflammatory treatment, is variable and inadequate in a significant portion of patients. By applying machine learning algorithms, we aimed to predict the treatment success in a pediatric asthma cohort and to identify the key variables for understanding the underlying mechanisms. We predicted the treatment outcomes in children with mild to severe asthma (N = 365), according to changes in asthma control, lung function (FEV1 and MEF50) and FENO values after 6 months of controller medication use, using Random Forest and AdaBoost classifiers. The highest prediction power is achieved for control- and, to a lower extent, for FENO-related treatment outcomes, especially in younger children. The most predictive variables for asthma control are related to asthma severity and the total IgE, which were also predictive for FENO-based outcomes. MEF50-related treatment outcomes were better predicted than the FEV1-based response, and one of the best predictive variables for this response was hsCRP, emphasizing the involvement of the distal airways in childhood asthma. Our results suggest that asthma control- and FENO-based outcomes can be more accurately predicted using machine learning than the outcomes according to FEV1 and MEF50. This supports the symptom control-based asthma management approach and its complementary FENO-guided tool in children. T2-high asthma seemed to respond best to the anti-inflammatory treatment. The results of this study in predicting the treatment success will help to enable treatment optimization and to implement the concept of precision medicine in pediatric asthma treatment.

• #4 Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning | Pediatric Research

  https://www.nature.com/articles/s41390-023-02988-2

  Early identification of children at risk of asthma can have significant clinical implications for effective intervention and treatment. […] This study aims to disentangle the relative timing and importance of early markers of asthma. […] Early-life data (1 year) has limited predictive ability for physician-diagnosed asthma at age 5 years (area under the precision-recall curve (AUPRC)0.35). […] The earliest reliable prediction of asthma is achieved at age 3 years, (area under the receiver-operator curve (AUROC)0.90) and (AUPRC0.80). […] Maternal asthma, antibiotic exposure, and lower respiratory tract infections remained highly predictive throughout childhood. […] Wheezing status and atopy are the most important predictors of early childhood asthma from among the factors included in this study.

• #5 Early Prediction of Asthma

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10455492/

  Prediction models for childhood asthma are helpful for identifying likely future asthma patients from high-risk groups; children in preschool who develop symptoms could benefit from early diagnosis and intervention. […] The parameters used in the different asthma prediction models have been generally validated in the populations that have been studied, favoring early diagnosis, intervention, and preventive measures. […] Prediction models for childhood asthma have been proven to be functional in recognizing future asthmatics in high-risk groups of patients through their use in the preschool period, which is a crucial period for immune development and lung growth.

• #6 Early Prediction of Asthma

  https://www.mdpi.com/2077-0383/12/16/5404

  The parameters used in the different asthma prediction models have been generally validated in the populations that have been studied, favoring early diagnosis, intervention, and preventive measures. […] Prediction models for childhood asthma have been proven to be functional in recognizing future asthmatics in high-risk groups of patients through their use in the preschool period, which is a crucial period for immune development and lung growth.

• #7 Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning | Pediatric Research

  https://www.nature.com/articles/s41390-023-02988-2

  Childhood asthma is predictable from non-biological measurements from the age of 3 years, primarily using parental asthma and patient history of wheezing, atopy, antibiotic exposure, and lower respiratory tract infections. […] Machine learning models can predict physician-diagnosed asthma in early childhood (AUROC0.90 and AUPRC0.80) using 3 years of non-biological and non-genetic information, whereas prediction with the same patient information available before 1 year of age is challenging. […] Wheezing, atopy, antibiotic exposure, lower respiratory tract infections, and the child’s mother having asthma were the strongest early markers of 5-year asthma diagnosis, suggesting an opportunity for earlier diagnosis and intervention and focused assessment of patients at risk for asthma, with an evolving risk stratification over time.

• #8 Predicting exacerbation in young children with intermittent asthm

  https://www.openaccessjournals.com/articles/predicting-exacerbation-in-young-children-with-intermittent-asthma-12480.html

  Levels of FEV1, FEV1/VC and PD20FEV1 below cut off levels are highly predictive for a consecutive AE. […] The presence of two or three risk factors was associated with a significantly higher risk of AE. […] Our results demonstrate that levels of FEV1, FEV1/FVC and PD20FEV1 below the cut-off levels are highly predictive for a consecutive AE.

• #9 Applying UK real-world primary care data to predict asthma attacks in 3776 well-characterised children: a retrospective cohort study | npj Primary Care Respiratory Medicine

  https://www.nature.com/articles/s41533-018-0095-5

  Current understanding of risk factors for asthma attacks in children is based on studies of small but well-characterised populations or pharmaco-epidemiology studies of large but poorly characterised populations. […] In this large population, several factors were associated with a future asthma attack, but a past history of attacks was most strongly associated with future attacks. […] Our results indicate that, of all the outcomes collected in this large study, a past asthma attack (and especially two attacks) is likely to be the best method to identify children who might benefit from a stratified intervention aimed at reducing their risk for future asthma attacks. […] Factors other than a past history of attacks which were associated with increased risk for future asthma attacks included blood eosinophilia, reduced PEF, lower respiratory tract infection and younger age, and although these associations were highly significant, they were weakly related to risk for asthma attacks and therefore not likely to be particularly helpful in risk analysis. […] In summary, we find that a past history of asthma attacks is the best predictor of future attacks, and that blood eosinophilia and reduced PEF do not add substantially to predicting attacks.

• #10 Risk factors for asthma-related hospital and intensive care admissions in children, adolescents and adults: a cohort study using primary and secondary care data | BMJ Open Respiratory Research

  https://bmjopenrespres.bmj.com/content/11/1/e001746

  Younger age groups, females and those from ethnic minority and lower socioeconomic backgrounds had an increased risk of asthma-related hospital admissions. […] The key potentially modifiable or treatable risk factors were smoking in adolescents and adults (PAF 6.8%, 95%CI 0.9% to 12.3% and 4.3%, 95%CI 3.0% to 5.7%, respectively), and obesity (PAF 23.3%, 95%CI 20.5% to 26.1%), depression (11.1%, 95% CI 9.1% to 13.1%), gastro-oesophageal reflux disease (2.3%, 95% CI 1.2% to 3.4%), anxiety (2.0%, 95% CI 0.5% to 3.6%) and chronic rhinosinusitis (0.8%, 95% CI 0.3% to 1.3%) in adults. […] Treating age-specific modifiable risk factors should be considered an integral part of asthma management, which could potentially reduce the rate of avoidable hospital admissions. […] Overall asthma medication burden is strongly associated with the risk of asthma-related hospital and intensive care unit admissions and should be used for assessing disease severity and monitoring asthma control and prognosis.

• #11 Risk factors for asthma-related hospital and intensive care admissions in children, adolescents and adults: a cohort study using primary and secondary care data | BMJ Open Respiratory Research

  https://bmjopenrespres.bmj.com/content/11/1/e001746

  The management of modifiable risk factors such as smoking and obesity and the treatment of comorbidities, including atopic diseases, depression, anxiety and GORD should be integrated with the clinical management of asthma. […] Finally, asthma medication burden is an important prognostic measure of asthma severity that could be used for disease monitoring.

• #12 Predictors of the persistence of childhood asthma | Allergologia et Immunopathologia

  https://www.elsevier.es/en-revista-allergologia-et-immunopathologia-105-articulo-predictors-persistence-childhood-asthma-S0301054608725254

  The factors relevant to the prognosis of childhood asthma differ from one population to another. […] To characterize the course of childhood asthma in the catchment area of our hospital, and to identify prognostic factors for this population. […] Satisfactory control of asthma was achieved in 69 % of cases. The factors identified as associated with poor control were allergy to cats and pollen, a large number of crises in the year prior to diagnosis, and younger age at onset. […] In our region, childhood asthma has a relatively favourable prognosis. The subsequent course of the disease appears to be determined in childhood. The persistence of symptoms appears to depend to a significant extent on the degree of atopy. […] The variables shown by univariate analyses to be associated with poor control at 5-year follow-up were severity at diagnosis, poor adherence to treatment, specific allergen immunotherapy, allergy to cats or pollen, younger age at onset of the disease, and the number of crises or hospitalization episodes in the year prior to diagnosis.

• #13 Predictors of the persistence of childhood asthma | Allergologia et Immunopathologia

  https://www.elsevier.es/en-revista-allergologia-et-immunopathologia-105-articulo-predictors-persistence-childhood-asthma-S0301054608725254

  Of these, those shown by multivariate logistic regression analysis to have independent effects were: allergy to cats; allergy to pollen; the number of crises or hospitalization episodes in the year prior to diagnosis; and younger age at onset. […] The poor prognosis of patients with greater severity at diagnosis suggests that good control of the disease at onset may reduce its effects in later life. […] That satisfactory control was achieved for 69 % of the patients included in this study suggests that in our region the prognosis of childhood asthma is fairly favourable.

• #14 Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8151683/

  Asthma in children is a heterogeneous disease manifested by various phenotypes and endotypes. The level of disease control, as well as the effectiveness of anti-inflammatory treatment, is variable and inadequate in a significant portion of patients. […] We predicted the treatment outcomes in children with mild to severe asthma (N = 365), according to changes in asthma control, lung function (FEV1 and MEF50) and FENO values after 6 months of controller medication use, using Random Forest and AdaBoost classifiers. The highest prediction power is achieved for control- and, to a lower extent, for FENO-related treatment outcomes, especially in younger children. […] Our results suggest that asthma control- and FENO-based outcomes can be more accurately predicted using machine learning than the outcomes according to FEV1 and MEF50. This supports the symptom control-based asthma management approach and its complementary FENO-guided tool in children. […] The results of this study in predicting the treatment success will help to enable treatment optimization and to implement the concept of precision medicine in pediatric asthma treatment.

• #15 Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma

  https://www.mdpi.com/2227-9067/8/5/376

  The primary aim of our research was to test the predictive possibilities for treatment success after 6 months of medication use in pediatric asthma patients and reveal the key variables for understanding the mechanisms underlying such responses using machine learning algorithms. Identifying non-responders vs. responders to a treatment with machine learning tools such as those employed in this study is essential in asthma management, as predicting treatment failure is extremely hard (if not impossible) in the current practice. Our study was based on an observational childhood asthma cohort that was ethnically homogeneous, age diverse and reflected real-life clinical situations, with the majority of patients having mild-to-moderate disease. The main outcomes were four different parameters of the response to treatment (after 6 months) assessed by changes in the lung functions and level of control. Each of these targets was evaluated at baseline (t0) and after 6 months (t0 + 6) of treatment use, alongside the other parameters and biomarkers. The predictive possibilities were tested by means of machine learning, i.e., Random Forest and AdaBoost classification algorithms, where the treatment outcomes (as binary variables) were set as values to be predicted based on other clinically relevant data and assessments.

• #16 Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma

  https://www.mdpi.com/2227-9067/8/5/376

  Our results show that asthma control (LOAC) was well-predicted, while the prediction quality of lung function-based treatment outcomes (FEV1 and MEF50) was rather low. These results are in concordance with the GINA control-based management approach, while the lung function may not be the tool of choice to be used in guiding the treatment in children with asthma. The prediction model for the FENO-based treatment response performed better in almost all aspects compared to lung function-related outcomes, which suggests that treatment success guided by changes in FENO might be a complementary tool in childhood asthma management. Our results also suggest that the current guidelines in asthma management and the current expertise in a clinical assessment (assessment of severity and disease control) are satisfactory in most cases but, also, that our models were quite accurate in identifying patients with ineffective and suboptimal treatment success, which is hardly the case in the current clinical practice. Additionally, our findings emphasize that the distal airway impairment in childhood asthma might have greater diagnostic and assessment value over FEV1.

• #17 Machine learning for prediction of asthma exacerbations among asthmatic patients: a systematic review and meta-analysis | BMC Pulmonary Medicine | Full Text

  https://bmcpulmmed.biomedcentral.com/articles/10.1186/s12890-023-02570-w

  Asthma exacerbations reduce the patients quality of life and are also responsible for significant disease burdens and economic costs. […] This study identified that ML was the potential tool to achieve great performance in predicting asthma exacerbations. […] Early identification of asthmatic patients at high risk of asthma exacerbations guides physicians to take closer management and timely intervention. This study showed that ML could achieve great performance in predicting asthma exacerbations. Future studies should focus on improving models’ generalizability and practicability, thus driving the application of these models in clinical practice.

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