ArticleJournal of translational medicine2023
A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010-2022) multicenter retrospective case-control study.
Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Study on the construction of a predictive model for delayed encephalopathy after acute carbon monoxide poisoning.BMC neurology · 2026Article
- Machine learning-based prediction of prolonged length of stay in older patients with type 2 diabetes mellitus and cardiovascular disease.Frontiers in cardiovascular medicine · 2026Article
- Influencing factors of hospitalization costs for patients with subsequent osteoporotic hip fractures: multilevel model analysis by fracture episode.Frontiers in endocrinology · 2026Article
- Prediction models for encephalitis and in-hospital death associated with severe fever with thrombocytopenia syndrome.BMC infectious diseases · 2025Article
- Association between neutrophil percentage to albumin ratio and short term prognosis of acute on chronic liver failure treated with artificial liver support system.Scientific reports · 2025Article
- Development and validation of a risk prediction model for 30-day readmission in elderly type 2 diabetes patients complicated with heart failure: a multicenter, retrospective study.Frontiers in endocrinology · 2025Article
- In-patient expenditure between 2011 and 2021 for patients with type 2 diabetes mellitus: a hospital-based multicenter retrospective study in southwest China.Frontiers in public health · 2025Article
- Derivation and Validation of Prediction Models for Prolonged Length of Stay and 30-Day Readmission in Elderly Patients With Type 2 Diabetes Mellitus: A Multicenter Study.Journal of diabetes research · 2025Article
- Association between neutrophil-percentage-to-albumin ratio and diabetic kidney disease in type 2 diabetes mellitus patients: a cross-sectional study from NHANES 2009-2018.Frontiers in endocrinology · 2025Article
- Development and validation of a predictive model for prolonged length of stay in elderly type 2 diabetes mellitus patients combined with cerebral infarction.Frontiers in neurology · 2024Article
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16 authors.
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Abstract
backgroundLength of stay (LOS) is an important metric for evaluating the management of inpatients. This study aimed to explore the factors impacting the LOS of inpatients with type-2 diabetes mellitus (T2DM) and develop a predictive model for the early identification of inpatients with prolonged LOS.
methodsA 13-year multicenter retrospective study was conducted on 83,776 patients with T2DM to develop and validate a clinical predictive tool for prolonged LOS. Least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis were adopted to build the risk model for prolonged LOS, and a nomogram was taken to visualize the model. Furthermore, receiver operating characteristic curves, calibration curves, and decision curve analysis and clinical impact curves were used to respectively validate the discrimination, calibration, and clinical applicability of the model.
resultsThe result showed that age, cerebral infarction, antihypertensive drug use, antiplatelet and anticoagulant use, past surgical history, past medical history, smoking, drinking, and neutrophil percentage-to-albumin ratio were closely related to the prolonged LOS. Area under the curve values of the nomogram in the training, internal validation, external validation set 1, and external validation set 2 were 0.803 (95% CI [confidence interval] 0.799-0.808), 0.794 (95% CI 0.788-0.800), 0.754 (95% CI 0.739-0.770), and 0.743 (95% CI 0.722-0.763), respectively. The calibration curves indicated that the nomogram had a strong calibration. Besides, decision curve analysis, and clinical impact curves exhibited that the nomogram had favorable clinical practical value. Besides, an online interface ( https://cytjt007.shinyapps.io/prolonged_los/ ) was developed to provide convenient access for users.
conclusionIn sum, the proposed model could predict the possible prolonged LOS of inpatients with T2DM and help the clinicians to improve efficiency in bed management.
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