ArticleHormones (Athens, Greece)2023
Machine learning-based prediction of risk factors for abnormal glycemic control in diabetic cancer patients receiving nutrition support: a case-control study.
Article in Hormones (Athens, Greece), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
- Using music to soothe our patients, measuring hair cortisol, and machine learning in diabetes: Prof. Batrinos would have been very happy!Hormones (Athens, Greece) · 2023Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo date, risk factors affecting abnormal glycemic control have not been investigated. This study aimed to analyze risk factors for hypoglycemia or hyperglycemia in diabetic cancer patients receiving nutritional support by using machine learning methods.
methodsThis retrospective two-center study was performed using medical records. Odds ratios and adjusted odds ratios were estimated from univariate and multivariate analyses, respectively. Machine learning algorithms, including five-fold cross-validated multivariate logistic regression, elastic net, and random forest, were developed to predict risk factors for hypoglycemia and hyperglycemia.
resultsData from 127 patients were analyzed. The use of sulfonylurea (SU) and blood urea nitrogen (BUN) level > 20 mg/dL increased hypoglycemia by 6.3-fold (95% CI 1.30-30.47) and 5.0-fold (95% CI 1.06-23.46), respectively. In contrast, patients who received an actual energy intake/total energy expenditure (TEE) ≥ 120% and used dipeptidyl peptidase-4 (DPP-4) inhibitors had a higher risk of hyperglycemia by 19.3- (95% CI 1.46-254.78) and 3.3-fold (95% CI 1.23-8.61), respectively. An initial blood glucose level ≥ 182.5 mg/dL also increased the risk of hyperglycemia by 15.3-fold. AUROC values for all machine learning methods indicated acceptable and excellent performance for hypoglycemia and hyperglycemia.
conclusionThe use of SU and BUN level > 20 mg/dL increased the risk of hypoglycemia, whereas an initial blood glucose level ≥ 182.5 mg/dL, a supplied actual energy intake/ TEE ≥ 120%, and the use of DPP-4 inhibitors increased the risk of hyperglycemia.
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