Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Oct;32(7):1464-1473.
doi: 10.1111/pai.13530. Epub 2021 May 25.

Decision tree-based rules outperform risk scores for childhood asthma prognosis

Arthur H Owora  1   2 , Robert S Tepper  3 , Clare D Ramsey  4 , Allan B Becker  2
Affiliations

Decision tree-based rules outperform risk scores for childhood asthma prognosis

Arthur H Owora et al. Pediatr Allergy Immunol. 2021 Oct.

Abstract

Background: There are no widely accepted prognostic tools for childhood asthma; this is in part due to the multifactorial and time-dependent nature of mechanisms and risk factors that contribute to asthma development. Our study objective was to develop and evaluate the prognostic performance of conditional inference decision tree-based rules using the Pediatric Asthma Risk Score (PARS) predictors as an alternative to the existing logistic regression-based risk score for childhood asthma prediction at 7 years in a high-risk population.

Methods: The Canadian Asthma Primary Prevention Study data were used to develop, compare, and contrast the prognostic performance (area under the curve [AUC], sensitivity, and specificity) of conditional inference tree-based decision rules to the pediatric asthma risk score for the prediction of childhood asthma at 7 years.

Results: Conditional inference decision tree-based rules have higher prognostic performance (AUC: 0.85; 95% CI: 0.81, 0.88; sensitivity = 47%; specificity = 93%) than the pediatric asthma risk score at an optimal cutoff of ≥6 (AUC: 0.71; 95% CI: 0.67, 0.76; sensitivity = 60%; specificity = 74%). Moreover, the pediatric asthma risk score is not linearly related to asthma risk, and at any given pediatric asthma risk score value, different combinations of its pediatric asthma risk score clinical variables differentially predict asthma risk.

Conclusion: Conditional inference tree-based decision rules could be a useful childhood asthma prognostic tool, providing an alternative way to identify unique subgroups of at-risk children, and insights into associations and effect mechanisms that are suggestive of appropriate tailored preventive interventions. However, the feasibility and effectiveness of such decision rules in clinical practice is warranted.

Keywords: asthma prediction; childhood asthma; decision rules; prognosis.

PubMed Disclaimer

References

REFERENCES

    1. Kothalawala DM, Kadalayil L, Weiss VBN, et al. Prediction models for childhood asthma: a systematic review. Pediatr Allergy Immunol. 2020;31(6):616-627.
    1. Owora AH, Zhang Y. Comments on Kothalawala et al. Pediatr Allergy Immunol. 2021;32(2):389-392.
    1. Subbarao P, Mandhane PJ, Sears MR. Asthma: epidemiology, etiology and risk factors. CMAJ. 2009;181(9):E181-E190.
    1. Castro-Rodríguez JA, Holberg CJ, Wright AL, Martinez FD. A clinical index to define risk of asthma in young children with recurrent wheezing. Am J Respir Crit Care Med. 2000;162(4 Pt 1):1403-1406.
    1. Biagini Myers JM, Schauberger E, He H, et al. A pediatric asthma risk score to better predict asthma development in young children. J Allergy Clin Immunol. 2019;143(5):1803-1810.e1802.

Publication types