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Population repeated time-to-event analysis of exacerbations in asthma patients: A novel approach for predicting asthma exacerbations based on biomarkers, spirometry, and diaries/questionnaires.

ABSTRACT: Identification of covariates, including biomarkers, spirometry, and diaries/questionnaires, that predict asthma exacerbations would allow better clinical predictions, shorter phase II trials and inform decisions on phase III design, and/or initiation (go/no-go). The objective of this work was to characterize asthma-exacerbation hazard as a function of baseline and time-varying covariates. A repeated time-to-event (RTTE) model for exacerbations was developed using data from a 52-week phase IIb trial, including 502 patients with asthma randomized to placebo or 70 mg, 210 mg, or 490 mg astegolimab every 4 weeks. Covariate analysis was performed for 20 baseline covariates using the full random effects modeling approach, followed by time-varying covariate analysis of nine covariates using the stepwise covariate model (SCM) building procedure. Following the SCM, an astegolimab treatment effect was explored. Diary-based symptom score (difference in objective function value [dOFV] of -83.7) and rescue medication use (dOFV = -33.5), and forced expiratory volume in 1 s (dOFV = -14.9) were identified as significant time-varying covariates. Of note, time-varying covariates become more useful with more frequent measurements, which should favor the daily diary scores over others. The most influential baseline covariates were exacerbation history and diary-based symptom score (i.e., symptom score was important as both time-varying and baseline covariate). A (nonsignificant) astegolimab treatment effect was included in the final model because the limited data set did not allow concluding the remaining effect size as irrelevant. Without time-varying covariates, the treatment effect was statistically significant (p < 0.01). This work demonstrated the utility of a population RTTE approach to characterize exacerbation hazard in patients with severe asthma.

SUBMITTER: Svensson RJ

PROVIDER: S-EPMC8520748 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Population repeated time-to-event analysis of exacerbations in asthma patients: A novel approach for predicting asthma exacerbations based on biomarkers, spirometry, and diaries/questionnaires.

Svensson Robin J RJ Ribbing Jakob J Kotani Naoki N Dolton Michael M Vadhavkar Shweta S Cheung Dorothy D Staton Tracy T Choy David F DF Putnam Wendy W Jin Jin J Budha Nageshwar N Karlsson Mats O MO Quartino Angelica A Zhu Rui R

CPT: pharmacometrics & systems pharmacology 20210805 10

Identification of covariates, including biomarkers, spirometry, and diaries/questionnaires, that predict asthma exacerbations would allow better clinical predictions, shorter phase II trials and inform decisions on phase III design, and/or initiation (go/no-go). The objective of this work was to characterize asthma-exacerbation hazard as a function of baseline and time-varying covariates. A repeated time-to-event (RTTE) model for exacerbations was developed using data from a 52-week phase IIb tr ...[more]

PMID: 34346168

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Project description:IntroductionAlthough clinical, functional, and biomarker data predict asthma exacerbations, newer approaches providing high accuracy of prognosis are needed for real-world decision-making in asthma. Machine learning (ML) leverages mathematical and statistical methods to detect patterns for future disease events across large datasets from electronic health records (EHR). This study conducted training and fine-tuning of ML algorithms for the real-world prediction of asthma exacerbations in patients with physician-diagnosed asthma.MethodsAdults with ≥ 2 ICD9/10 asthma codes within 1 year and at least 30 days apart were identified from the Optum Panther EHR database between 2016 and 2023. An emergency department (ED), urgent care, or inpatient visit for asthma, while on systemic administration of corticosteroids, was considered an exacerbation. To predict factors associated with exacerbations in a 6-month study period, clinical information from patients was retrieved in the preceding 6-month baseline period. Clinical information included demographics, lab results, diagnoses, medications, immunizations, and allergies. Three models built using Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Transformers algorithms were trained and tested on independent datasets. Predictions were explained using the SHAP (SHapley Additive exPlanations) library.ResultsOf 1,331,934 patients with asthma, 16,279 (1.2%) experienced ≥ 1 exacerbation. XGBoost was the best predictive algorithm (area under the curve [AUC] = 0.964). Factors associated with exacerbations included a prior history of exacerbation, prednisone usage, high-dose albuterol usage, and elevated troponin I. Reduced probability of exacerbations was associated with receiving inhaled albuterol, vitamins, aspirin, statins, furosemide, and influenza vaccination.ConclusionThis ML-based study on asthma in the real world confirmed previously known features associated with increased exacerbation risk for asthma, while uncovering not entirely understood features associated with reduced risk of asthma exacerbations. These findings are hypothesis-generating and should contribute to ongoing discussion of the strengths and limitations of ML and other supervised learning models in patient risk stratification.

Project description:THREE DIISOCYANATES CAN CAUSE OCCUPATIONAL ASTHMA (OA): toluene diisocyanate (TDI), 4,4 diphenylmethane diisocyanate (MDI), and 1,6-hexamethylene diisocyanate (HDI). We analyzed potential biomarkers of isocyanate-induced OA, based on investigated immunologic, genetic, neurogenic, and protein markers, because there is no serological testing method. The prevalence of serum IgG to cytokeratin (CK)18 and CK19 in TDI-OA was significantly higher than in controls, although the prevalence of these antibodies was too low for them to be used as biomarkers. Another candidate biomarker was serum IgG to tissue transglutaminase (tTG), because the prevalence of serum specific IgG to tTG was significantly higher in patients with TDI-OA than in controls. The human leukocyte antigen (HLA) DRB1*1501-DQB1*0602-DPB1*0501 haplotype may be used as a genetic marker for TDI-OA in Koreans via enhanced specific IgE sensitization in exposed subjects. The genetic polymorphisms of catenin alpha 3, alpha-T catenin (CTNNA3) were significantly associated with TDI-OA. Additionally, examining the neurokinin 2 receptor (NK2R) 7853G>A and 11424 G>A polymorphisms, the NK2R 7853GG genotype had higher serum vascular endothelial growth factor (VEGF) levels than the GA or AA genotypes among Korean workers exposed to TDI. To identify new serologic markers using a proteomic approach, differentially expressed proteins between subjects with MDI-OA and asymptomatic exposed controls in a Korean population showed that the optimal serum cutoff levels were 69.8 ng/mL for ferritin and 2.5 µg/mL for transferrin. When these two parameters were combined, the sensitivity was 71.4% and the specificity was 85.7%. The serum cytokine matrix metalloproteinase-9 (MMP-9) level is a useful biomarker for identifying cases of TDI-OA among exposed workers. Despite these possible biomarkers, more effort should be focused on developing early diagnostic biomarkers using a comprehensive approach based on the pathogenic mechanisms of isocyanate-induced OA.

Dataset Information

Population repeated time-to-event analysis of exacerbations in asthma patients: A novel approach for predicting asthma exacerbations based on biomarkers, spirometry, and diaries/questionnaires.

Publications

Population repeated time-to-event analysis of exacerbations in asthma patients: A novel approach for predicting asthma exacerbations based on biomarkers, spirometry, and diaries/questionnaires.

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