Simple Beats Complex: Rethinking Lung Function Reference Equations for Jordanian Children

Spirometry, the most widely used test of lung function in medicine, depends on a deceptively simple question: compared with what? A child’s forced expiratory volume in one second, or FEV1, means little as an isolated number. Clinicians interpret it against reference equations, statistical models that predict what a healthy child of a given age, height, and sex should achieve. If those equations are miscalibrated for the population being tested, the entire interpretation chain collapses, potentially mislabeling healthy children as impaired or missing genuine disease. A new study from Jordan, published in PLOS Digital Health, puts competing approaches to that predictive task under rigorous scrutiny and arrives at a conclusion that challenges a fashionable assumption: more complex machine-learning models do not automatically deliver better predictions.

The research team, led by Walid Al-Qerem and colleagues, framed their work around a persistent problem in respiratory medicine. Reference equations developed in one population or one age range often fail to transport cleanly to another. Growth patterns, body proportions, environmental exposures, and genetic backgrounds all shape how lung function scales with body size, and distributional models that capture nonlinear growth in one group may be systematically biased in another. At the same time, an open methodological question has hovered over the field: do modern machine-learning algorithms, with their capacity to capture intricate interactions, genuinely improve prediction beyond simpler regression models built on well-chosen transformations of the data?

To answer that question, the investigators designed a two-phase cross-sectional study comparing sex-specific predictive models for the three core spirometric outcomes: FEV1, forced vital capacity (FVC), and the ratio between them, FEV1/FVC, which is the standard marker of obstructive lung physiology. The first phase used the same derivation dataset of 1,576 children on which the original Jordanian reference equation, known as the Al-Qerem equation, had been built. That choice was deliberate. By training candidate models on identical data, the researchers could compare predictive modeling strategies on equal footing, isolating the effect of model class from the effect of the underlying sample.

The candidate models spanned a spectrum of complexity. On the simpler end sat generalized linear models, or GLMs, which predict outcomes through straightforward mathematical relationships after appropriate transformations. On the more complex end sat gradient boosting machines, or GBMs, an ensemble machine-learning technique that builds predictions by sequentially combining many weak learners, each correcting the errors of its predecessors. GBMs can, in principle, capture nonlinearities and interactions that a GLM would miss, but they also carry a risk of overfitting, learning quirks of the training sample that do not generalize. The study assigned the best-performing model class separately for each outcome and sex: GBMs for FEV1 in both sexes, GLMs for FVC in both sexes, a GLM for FEV1/FVC in girls, and a GBM for FEV1/FVC in boys.

A crucial technical detail shaped the evaluation. Because several models were trained on log-transformed outcomes, the researchers assessed performance on the original measurement scale, after back-transformation of predictions. This matters more than it might appear. Error metrics computed on a log scale can obscure clinically meaningful biases in absolute volume units, and a model that looks sharp in transformed space may perform unevenly where it counts, at the extremes of the distribution where clinical decisions are made. By evaluating on the natural scale, the team kept the comparison anchored to the quantities clinicians actually use.

The second phase provided the real test. The models, alongside established reference equations including the Global Lung Function Initiative 2012 equations, the updated GLI 2022 equations, and the original Al-Qerem equation, were evaluated in an external validation sample of 1,007 healthy Jordanian children aged 6 to 18 years. External validation, testing on data not used in model development, is the gold standard for judging whether a predictive tool is ready for real-world use, and it is precisely where many promising models stumble.

The results revealed a clear hierarchy of predictability. FEV1 and FVC were predicted substantially more accurately than FEV1/FVC across every model class and every established equation. The explained variance of the ratio, based on age and height alone, remained low regardless of the modeling approach. This finding has physiological logic: the ratio is designed to be relatively independent of body size, so the very variables that powerfully predict absolute lung volumes carry less information about the proportion between them. For clinicians, it underscores that FEV1/FVC reference limits rest on a narrower statistical foundation than the volume measures, a point often underappreciated in routine reporting.

In external validation, the picture grew more nuanced. The study’s own model achieved the lowest mean squared error for female FEV1 and FVC, a genuine win for the comparative modeling framework in girls. But it did not consistently outperform the GLI 2012, GLI 2022, or Al-Qerem equations in boys, nor for FEV1/FVC in either sex. Complexity, in other words, bought no blanket advantage. The best-performing model differed by outcome and by sex, and carefully chosen transformations and calibration steps mattered more than the sophistication of the algorithm itself. A gradient boosting machine trained carelessly could lose to a well-calibrated linear model, and often did.

One of the most clinically consequential findings emerged from age-stratified analyses. When the equations were applied to boys younger than 10 years, all four reference standards, the study model, the Al-Qerem equation, GLI 2012, and GLI 2022, produced elevated rates of FEV1 and FVC values falling below the lower limit of normal, a threshold that typically flags potentially abnormal lung function. In a validation sample of healthy children, such elevations signal systematic miscalibration: the equations are, in effect, setting the bar too high for young boys in this population, risking false-positive classifications. By contrast, below-limit proportions for FEV1/FVC were generally closer to the nominal values expected from the statistical definition of the lower limit of normal, suggesting the ratio equations were better calibrated even where their predictive precision was weaker.

The authors are careful about what their findings do and do not license. Comparative predictive modeling, they conclude, is a useful development framework, a disciplined way to test candidate equations against one another before deployment. But the study does not demonstrate superiority of complex machine-learning methods, and it does not establish readiness for clinical use without further calibration and additional external validation. For pediatric respiratory practice in Jordan and comparable populations, the message is both sobering and constructive: the path to better lung function reference equations runs not through ever-more-elaborate algorithms, but through meticulous attention to transformation, calibration, population fit, and honest out-of-sample testing, with special vigilance for the youngest children in whom existing equations appear least reliable.

Subject of Research: Comparative predictive modeling of pediatric spirometry reference equations in Jordanian children

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

Article References: Al-Qerem, W., Jarab, A., Eberhardt, J., Smairan, K., Mimi, Y., & Khdour, M. (2026). Comparative predictive modeling of pediatric spirometry reference equations in Jordanian children: Complex versus simple models. PLOS Digital Health, 5(9), e0001746. https://doi.org/10.1371/journal.pdig.0001746

Image Credits: AI Generated

DOI: 10.1371/journal.pdig.0001746

Keywords: spirometry, pediatric lung function, reference equations, machine learning, gradient boosting, FEV1, FVC, Jordan, GLI 2012, external validation, GAMLSS, predictive modeling