ArticleThe Journal of clinical endocrinology and metabolism2022
Machine Learning-Based Prediction of Elevated PTH Levels Among the US General Population.
Article in The Journal of clinical endocrinology and metabolism, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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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
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Pooled it
- Causal inference and machine learning in endocrine epidemiology.Endocrine journal · 2024Review
- A new model for determining risk of male infertility from serum hormone levels, without semen analysis.Scientific reports · 2024Article
- Machine Learning-Based Prediction of Elevated PTH Levels Among the US General Population.The Journal of clinical endocrinology and metabolism · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
contextAlthough elevated parathyroid hormone (PTH) levels are associated with higher mortality risks, the evidence is limited as to when PTH is expected to be elevated and thus should be measured among the general population.
objectiveThis work aimed to build a machine learning-based prediction model of elevated PTH levels based on demographic, lifestyle, and biochemical data among US adults.
methodsThis population-based study included adults aged 20 years or older with a measurement of serum intact PTH from the National Health and Nutrition Examination Survey (NHANES) 2003 to 2006. We used the NHANES 2003 to 2004 cohort (n = 4096) to train 6 machine-learning prediction models (logistic regression with and without splines, lasso regression, random forest, gradient-boosting machines [GBMs], and SuperLearner). Then, we used the NHANES 2005 to 2006 cohort (n = 4112) to evaluate the model performance including area under the receiver operating characteristic curve (AUC).
resultsOf 8208 US adults, 753 (9.2%) showed PTH greater than 74 pg/mL. Across 6 algorithms, the highest AUC was observed among random forest (AUC [95% CI] = 0.79 [0.76-0.81]), GBM (AUC [95% CI] = 0.78 [0.75-0.81]), and SuperLearner (AUC [95% CI] = 0.79 [0.76-0.81]). The AUC improved from 0.69 to 0.77 when we added cubic splines for the estimated glomerular filtration rate (eGFR) in the logistic regression models. Logistic regression models with splines showed the best calibration performance (calibration slope [95% CI] = 0.96 [0.86-1.06]), while other algorithms were less calibrated. Among all covariates included, eGFR was the most important predictor of the random forest model and GBM.
conclusionIn this nationally representative data in the United States, we developed a prediction model that potentially helps us to make accurate and early detection of elevated PTH in general clinical practice. Future studies are warranted to assess whether this prediction tool for elevated PTH would improve adverse health outcomes.
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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.