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 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. Machine learning (ML)-based prediction models have been increasingly developed to predict asthma exacerbations in recent years. This systematic review and meta-analysis aimed to identify the prediction performance of ML-based prediction models for asthma exacerbations and address the uncertainty of whether modern ML methods could become an alternative option to predict asthma exacerbations. […] The overall pooled area under the curve of the receiver operating characteristics (AUROC) of 11 studies (23 prediction models) was 0.80 (95% CI 0.770.83). […] This study identified that ML was the potential tool to achieve great performance in predicting asthma exacerbations.

• #3 A prognosis prediction chromatin regulator signature for patients with severe asthma | Allergy, Asthma & Clinical Immunology | Full Text

  https://aacijournal.biomedcentral.com/articles/10.1186/s13223-023-00796-1

  Severe asthma imposes a physical and economic burden on both patients and society. […] The nomogram constructed using the four CRs, SMARCC1, SETD2, KMT2B, and CHD8, may be a useful tool for predicting the prognosis of patients with severe asthma. […] To further investigate the relationship between CRs and prognosis in patients with severe asthma, we constructed a prognostic prediction model using the four identified key CRs: SMARCC1, CHD8, SETD2, and KMT2B. The model showed a good predictive performance for prognosis. […] The results showed a negative correlation between SMARCC1 and T helper cells and a negative correlation between SETD2 and APC co-inhibition, para-inflammation, treg, and type I IFN responses. […] These four genes, SMARCC1, SETD2, KMT2B, and CHD8, were used to construct a nomogram model for predicting the prognosis of patients with severe asthma.

• #4 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.

• #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

  Different parameters have been used in asthma prediction models, the most important of which are age, gender, wheeze frequency, the presence of eczema, rhinitis, non-cold-related wheeze, peripheral blood eosinophilia, skin testing, specific IgE, biomarkers, gene expression, exercise-related cough/wheeze, and aeroallergen-related cough/wheeze. […] The first asthma prediction model was the original asthma predictive index (API), which originated from the TCRS in 2000, and two sets of prediction guidelines were established: a loose API and a strict API. […] The API has been suggested in childhood asthma guidelines, and it is one of the few tools that have been validated in new populations and in different cohorts. […] 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. […] Current knowledge of epigenetics, the use of biomarkers, and different types of algorithms in the prediction of asthma in children provide an opportunity to improve the accuracy of these diagnostic tools.

• #7 Acute severe asthma in emergency department: clinical characteristics, risk factors, and predictors for poor outcome | The Egyptian Journal of Bronchology | Full Text

  https://ejb.springeropen.com/articles/10.1186/s43168-022-00160-8

  Severe asthma exacerbations can be predicted by old age, previous history of mechanical ventilation, obstructive sleep apnea, overuse of SABA, uncontrolled asthma, moderate to severe depression, eosinophilia, SO2 90%, and low peak expiratory flow rates. […] The most important predictors for severe exacerbations were SO2 90% at baseline (OR = 4.56; 95% CI = 3.457.56; P0.001), PEFR after 1 h (OR= 3.34; 95%CI = 1.904.90; P0.001), and uncontrolled asthma (OR= 3.33; 95%CI = 2.505.05; P0.001). […] Based on the current study, the predictors of hospitalization were old age (OR= 1.11; 95%CI= 1.092.11; P0.001), uncontrolled asthma (OR= 2.34; 95%CI= 2.014.40; P0.001), PEFR after 1 h (OR= 4.44; 95%CI= 3.247.68; P0.001), and SO2 90% at baseline (OR= 5.67; 95%CI= 3.988.50; P0.001). […] The most accurate independent predictor for severe asthma exacerbation is the PEFR value after 1 h of treatment.

• #8 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.

• #9 Seasonal variation of pediatric asthma exacerbations and its association with asthma phenotypes | Pediatric Research

  https://www.nature.com/articles/s41390-025-04073-2

  HBE/LBN phenotype had a higher risk of asthma exacerbations among hospitalized pediatric asthma patients in the winter and autumn, while LBE/LBN phenotype had a lower risk in the winter, spring, and summer. […] Blood eosinophils and neutrophils have been indicated to have a potential influence on pediatric asthma development and severity. […] HBE/LBN phenotype was associated with increased asthma exacerbations among hospitalized pediatric asthma patients during winter and autumn. […] Eosinophil and neutrophil predominance exhibited a higher influence on pediatric asthma exacerbations. […] The novelty of this study is that we investigated the associations of blood inflammatory phenotypes with acute exacerbations and hospital admissions in pediatric asthma patients across various seasons.

• #10 Seasonal variation of pediatric asthma exacerbations and its association with asthma phenotypes | Pediatric Research

  https://www.nature.com/articles/s41390-025-04073-2

  We observed that the winter and autumn seasons increased the risk of acute exacerbations among hospitalized pediatric asthma patients. […] These results suggest that pediatric asthma patients with HBE/LBN phenotypes may be particularly susceptible to seasonal variations and meteorological exposures. […] Our study further identified that asthma blood inflammatory phenotypes were associated with an increase in asthma exacerbations of hospitalized pediatric asthma patients during winter and autumn. […] We further observed the protective effect of low blood eosinophils and neutrophils in pediatric asthma exacerbations. […] The findings suggest that seasonal variations may influence pediatric asthma exacerbations and treatment responsiveness across different asthma blood inflammatory phenotypes.

• #11 Seasonal variation of pediatric asthma exacerbations and its association with asthma phenotypes | Pediatric Research

  https://www.nature.com/articles/s41390-025-04073-2

  In conclusion, our study highlights that the HBE/LBN phenotype was associated with increased asthma exacerbations among hospitalized pediatric asthma patients during the winter and autumn seasons. […] These findings underscore the potential of asthma blood inflammatory phenotypes in predicting asthma control and management in clinical practice.

• #12 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

  The overall pooled AUROC (0.8, 95% CI 0.760.83) and DOR (7.02, 95% CI 5.209.47) indicated that ML-based prediction models for asthma exacerbation could achieve good discrimination. […] 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.

• #13 An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques

  https://www.mdpi.com/2673-4060/6/1/15

  Asthma remains a prevalent chronic condition, impacting millions globally and presenting significant clinical and economic challenges. This study develops a predictive model for asthma outcomes, leveraging automated machine learning (AutoML) and explainable AI (XAI) to balance high predictive accuracy with interpretability. […] Managing asthma effectively is challenging due to the variability in patient responses and the condition’s sensitivity to both clinical and environmental factors. Consequently, there is an increasing need for advanced tools that can predict asthma exacerbations and guide personalized treatment interventions. Such predictive tools could significantly improve patient outcomes by enabling early intervention and customized management strategies tailored to individual risk profiles.

• #14 An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques

  https://www.mdpi.com/2673-4060/6/1/15

  The model demonstrated high predictive accuracy, achieving an accuracy rate of 98.99% and an AUC-ROC of 0.9996 on the validation dataset. These metrics confirm the model’s ability to effectively differentiate between patients with varying levels of asthma exacerbation risk. […] The model’s accuracy and balanced performance across classes address critical clinical needs, particularly in minimizing false negatives (missed exacerbations), which could lead to adverse outcomes. […] Explainable AI techniques, such as SHAP and LIME, significantly enhanced the transparency of the model’s predictions. […] A key innovation in this study is the incorporation of counterfactual analysis, which enables clinicians to simulate hypothetical scenarios by modifying specific variables (e.g., adjusting Budesonide dosage).

• #15 An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques

  https://www.mdpi.com/2673-4060/6/1/15

  Validation on the CAMP dataset demonstrated robust performance, with the model achieving an overall accuracy of 93% and an AUC-ROC of 0.97. […] The development of a Streamlit-based application underscores the model’s readiness for clinical integration. The app facilitates real-time predictions, interpretability insights, and counterfactual analysis, making it a practical tool for clinicians. […] By allowing clinicians to simulate and assess the impact of potential interventions, the model ensures that decisions are made through a comprehensive understanding of each patient’s unique condition.

• #16 A Prognostic Model to Improve Asthma Prediction Outcomes Using Machine Learning

  https://openbioinformaticsjournal.com/VOLUME/17/ELOCATOR/e18750362306414/FULLTEXT/

  The utility of predictive models for the prognosis of asthma disease that rely on clinical history and findings has been on the constant rise owing to the attempts to achieve better disease outcomes through improved clinical processes. […] With the prognostic model, the primary focus is on the search for a combination of features that are as robust as possible in predicting the disease outcome. […] The model was able to predict the disease outcome with a precision of 86.1% and recall of 84.7%, accounting for an F1 measure of 84.5%. […] It is evident from the experimental results that using a combination of medical history data and significant clinical findings results in a better prognostic model. […] Clinical decisions concerning obstructive lung diseases such as COPD have a high chance of leading to results that can be misinterpreted with wrong inferences drawn that may have long-term implications, including the targeted therapy that can be mistakenly beset. […] Hence, we provide data-centric approaches that harness machine learning techniques to facilitate the disease prediction process, which can augment the inferences through clinical findings.

• #17 Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model | BMJ Open

  https://bmjopen.bmj.com/content/9/7/e028375

  Asthma is a long-term condition with rapid onset worsening of symptoms (attacks) which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificity to avoid unnecessary prescribing of preventative medications that carry an associated risk of adverse events. […] We aim to create a personalised risk assessment tool to assist primary care clinicians in predicting asthma attacks over a period of 1, 4, 12, 26, and 52 weeks, employing machine-learning methodologies such as nave Bayes classifiers, random forests, and support vector machines, as well as ensemble algorithms. The model will build on previous research to improve the sensitivity of our event prediction, without unduly compromising the specificity. This is crucial in order to reduce prescribing steroid, diminish the long-term effects of high steroid use over a lifetime, which have adverse effects, and reduce patient anxiety when risk of an asthma attack is low.

• #18 Primary Care Asthma Attack Prediction Models for Adults | JAA

  https://www.dovepress.com/primary-care-asthma-attack-prediction-models-for-adults-a-systematic-r-peer-reviewed-fulltext-article-JAA

  Prognostic models hold great potential for predicting asthma exacerbations, providing opportunities for early intervention, and are a popular area of current research. […] This systematic review aimed to identify novel predictive models of asthma attacks in adults and compare differences in construction related to populations, outcome definitions, prediction time horizons, algorithms, validation, and performance estimation. […] The outcome of the prediction model was the onset of an asthma attack, and studies that only reported other asthma-related events (such as post-asthma attack hospital discharge) or statuses (such as uncontrolled asthma or asthma severity) were excluded. […] The predictive performance is heavily influenced by the study design, including the population, the outcome definition, the algorithm, and the model validation procedures. Identifying the most clinically meaningful model characteristics is necessary to enable a best model to be identified and highlight routes for future development. This will boost likelihood of successful translation, adoption, and implementation at scale of clinical prediction models, and to bring benefits to patients.

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