ArticleBMC geriatrics2025
Machine learning algorithms for risk factor selection with application to 60-day sepsis morbidity risk for a geriatric hip fracture cohort.
Article in BMC geriatrics, 2025. 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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Who cites it
1 citing paper in PubMed.
- Development and validation of a nutrition-integrated nomogram for predicting 28-day mortality in sepsis patients.Frontiers in nutrition · 2025Article
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8 authors.
Funding
Abstract
backgroundSepsis after hip fracture in elderly people is a risk factor for mortality. The purpose of this study was to screen for risk factors for 60-day sepsis morbidity after hip fracture and to establish a predictive model using various machine learning algorithms.
methodsA total of 697 patients who were older than 65 years of age were selected; 590 patients were divided into training and test sets (at a ratio of 7:3), and 107 individuals were used for external validation. Least absolute shrinkage and selection operator (LASSO), the Boruta algorithm, and random survival forest (RSF) were used to screen for risk factors for sepsis incidence. Correlation coefficients and variance inflation factors (VIFs) were calculated to assess multicollinearity. Cox proportional hazards model analysis was used to establish a model. A restricted cubic spline (RCS) was used. The discrimination, calibration, and decision curve analysis (DCA) results were evaluated. The prediction model that was constructed was compared with those of the qSOFA.
resultsThere was no multicollinearity between variables (r < 0.6, variance inflation factor (VIF) < 5). Pneumonia, nutritional status, age, diabetes status, hemoglobin level, and BMI were the final variables. The C-indices of the training and test sets and the external validation set were 0.83 (95% CI 0.78 ~ 0.88), 0.78 (95% CI 0.70 ~ 0.85), and 0.76 (95% CI 0.70 ~ 0.83), respectively. The calibration and DCA results indicated that the model had good predictive value. The RCS revealed a linear relationship between age, BMI, hemoglobin and sepsis incidence (p > 0.05). The discrimination ability and clinical applicability of the model were superior to those of the qSOFA.
conclusionsThis study, which uses various feature selection methods, can effectively construct a 60-day sepsis morbidity prediction model after hip fracture in elderly people and could be superior to the qSOFA.
trial registrationDate of registry: 02/08/2023; Trial number: ChiCTR2300074246.
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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.