Evidence map›Paper›PMID 42789606›Full record

ArticlePLOS digital health2026

Comparative predictive modeling of pediatric spirometry reference equations in Jordanian children: Complex versus simple models.

Walid Al-Qerem, Anan Jarab, Judith Eberhardt, Khalda Smairan, Yousef Mimi, Maher Khdour

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Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Walid Al-QeremDepartment of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, Jordan.
Anan JarabDepartment of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan.
Judith EberhardtDepartment of Psychology, School of Social Sciences, Humanities and Law, Teesside University, Middlesbrough, United Kingdom.ORCID https://orcid.org/0000-0003-0745-178X
Khalda SmairanDepartment of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, Jordan.
Yousef MimiDepartment of Health Sciences, Faculty of Graduated Studies, Arab American University, Jenin, Palestine.
Maher KhdourDepartment of Pharmacy, Faculty of Pharmacy, Al-Quds University, Jerusalem, Palestine.ORCID https://orcid.org/0000-0003-0193-7922

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spirometric interpretation relies on reference equations, yet equations developed in one population or age range may not transport. Reference equations use distributional models to account for nonlinear growth, their calibration may differ across populations, it remains uncertain whether machine-learning algorithms improve prediction beyond simpler transformed regression models. This two-phase cross-sectional study compared sex-specific predictive models. Phase 1 used the same 1,576-child derivation dataset used to develop the original Jordanian GAMLSS equation (Al-Qerem equation), allowing comparison with predictive modeling strategies. Phase 2 evaluated equations in a validation sample of 1,007 healthy children aged 6-18 years. Candidate models were evaluated on the scale after back-transformation of log-outcome predictions. Models included GBM for FEV1 in both sexes, GLM for FVC in both sexes, GLM for FEV1/FVC in girls, and GBM for FEV1/FVC in boys.FEV1 and FVC were predicted more accurately than FEV1/FVC, whose explained variance from age and height remained low across model classes and established equations. In external validation, the study model had the lowest mean squared error for female FEV1 and FVC, but did not consistently outperform GLI 2012, GLI 2022, or Al-Qerem equations in boys or for FEV1/FVC. Age-stratified analyses showed elevated FEV1 and FVC below-LLN rates in boys younger than 10 years across the study, Al-Qerem, GLI 2012, and GLI 2022 equations, whereas FEV1/FVC below-LLN proportions were generally closer to nominal values. These findings support comparative predictive modeling as a useful development framework, but do not show superiority of complex machine-learning methods or readiness for clinical deployment without further calibration and external validation. Carefully chosen transformations and calibration were more important than model complexity, and the best models differed by outcome and sex.

Identifiers

PMID42789606
PMCPMC13614654

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