ArticleBMC medical informatics and decision making2025
Hypercubes to identify geomarkers of rapid cystic fibrosis lung disease progression.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundPrior research has shown that place-based environmental exposures and community characteristics, known as geomarkers, are associated with accelerated lung function decline and increased mortality in individuals with cystic fibrosis (CF). Although geomarkers have been linked to pulmonary outcomes in other respiratory diseases, it is unknown which have the greatest predictive power for rapid lung function decline in CF.
methodsWe adapted an existing statistical procedure, which arranges candidate variables in a k-dimensional hypercube, where the hypercube forms a set of variables for a multi-stage selection process involving complex longitudinal data. We embedded the hypercube within a dynamic prediction model of rapid lung function decline, in order to accommodate complexity in lung function trajectories. This practical approach simultaneously selects a handful of genuinely predictive markers among candidates and accounts for complex correlations in longitudinal marker data. Our method is applied to actual geomarker and lung-function outcomes data from the existing Cystic Fibrosis Patient Registry and Cincinnati Cystic Fibrosis Center datasets.
resultsWe applied a 4 × 4 × 4 3-D hypercube to the national and local datasets and selected a subset of geomarkers using p-values from testing coefficients of the association between each geomarker and lung function decline in the dynamic prediction model. Based on the national data analyses, some road density-related geomarkers were selected, including some air pollution-related and greenspace-related variables. Simulations showed the proposed method's variable selection efficacy and robust performance in identifying true predictors, particularly under weak correlation (ρ≤0.6), although performance dipped with stronger correlations (ρ=0.9).
conclusionsThe proposed method is a useful approach for selecting a small set of truly relevant demographic, clinical, and place-based predictors of rapid lung function decline while accounting for the complex correlations inherent in longitudinal lung-function data. We found that selection results differed according to spatial resolution of the geomarkers. Our findings have potential to improve care decisions for people with CF.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.