Stress Hyperglycemia Ratio and Preoperative Acute Heart Failure in Elderly Hip Fracture Patients with Chronic Cerebrovascular Diseases
- 1Geriatric Orthopedics
- 2The Third Hospital of Hebei Medical University
- 3Department of Geriatric Orthopedics
Abstract
Mingming Fu,1 Aoying Min,2 Zhiyong Hou,3– 6 Zhiqian Wang21Department of Electromyography, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, People’s Republic of China; 2Department of Geriatric Orthopedics, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, People’s Republic of China; 3Department of Orthopaedic Surgery, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, People’s Republic of China; 4Engineering Research Center of Orthopedic Minimally Invasive Intelligent Equipment, Ministry of Education, Shijiazhuang, Hebei, People’s Republic of China; 5Hebei Province Biomechanics Key Laboratory, Shijiazhuang, People’s Republic of China; 6NHC Key Laboratory of Intelligent Orthopedic Equipment, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, People’s Republic of ChinaCorrespondence: Zhiqian Wang, Department of Geriatric Orthopedics, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, 050051, People’s Republic of China, Email [email protected]; Zhiyong Hou, Department of Orthopaedic Surgery, Hebei Medical University Third Hospital, 050051, Shijiazhuang, Hebei, People’s Republic of China, Email [email protected]: The prognostic significance of the stress hyperglycemia ratio (SHR), calculated using admission blood glucose (ABG) and glycated hemoglobin (HbA1c), remains unclear in the context of perioperative complications associated with hip fractures. This study evaluates the association and predictive capability of the SHR for preoperative acute heart failure in hip fracture patients with chronic cerebrovascular disease.Methods and Results: A total of 515 patients were analyzed, revealing a 16.8% incidence of preoperative HF. Significant intergroup differences in age (P < 0.001), ASA classification (P < 0.001), coronary heart disease (P=0.025), serum albumin level (P < 0.001), and admission SHR (P=0.033) were identified between patients with and without preoperative heart failure. Logistic regression identified SHR, CHD, ASA, neutrophils, and hypoproteinemia as independent predictors of preoperative HF, leading to the development of a nomogram that demonstrated good predictive performance (AUC 0.72). Calibration curves and decision curve analysis indicated the model’s clinical utility. Machine learning algorithms, especially the XGBoost model, surpassed traditional models by achieving an AUC of 0.787, demonstrating excellent calibration and clinical applicability. SHAP analysis validated the stable contribution of SHR in risk prediction.Conclusions: This study highlights the SHR’s strong predictive ability for preoperative HF in hip fracture patients with chronic cerebrovascular disease. The XGBoost machine learning model could improve risk stratification and clinical decision-making for this at-risk group.Keywords: stress hyperglycemia ratio, hip fracture, heart failure, chronic cerebrovascular diseases, machine learning
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BibTeX
@article{fu2026stress,
title = {{Stress Hyperglycemia Ratio and Preoperative Acute Heart Failure in Elderly Hip Fracture Patients with Chronic Cerebrovascular Diseases}},
author = {Fu M and Min A and Hou Z and Wang Z},
journal = {Clinical Interventions in Aging},
year = {2026},
month = nov,
url = {https://www.dovepress.com/stress-hyperglycemia-ratio-and-preoperative-acute-heart-failure-in-eld-peer-reviewed-fulltext-article-CIA},
note = {CC BY},
}
RIS
TY - JOUR
TI - Stress Hyperglycemia Ratio and Preoperative Acute Heart Failure in Elderly Hip Fracture Patients with Chronic Cerebrovascular Diseases
AU - Fu M
AU - Min A
AU - Hou Z
AU - Wang Z
PY - 2026
DA - 2026/11/05
JO - Clinical Interventions in Aging
UR - https://www.dovepress.com/stress-hyperglycemia-ratio-and-preoperative-acute-heart-failure-in-eld-peer-reviewed-fulltext-article-CIA
AB - Mingming Fu,1 Aoying Min,2 Zhiyong Hou,3– 6 Zhiqian Wang21Department of Electromyography, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, People’s Republic of China; 2Department of Geriatric Orthopedics, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, People’s Republic of China; 3Department of Orthopaedic Surgery, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, People’s Republic of China; 4Engineering Research Center of Orthopedic Minimally Invasive Intelligent Equipment, Ministry of Education, Shijiazhuang, Hebei, People’s Republic of China; 5Hebei Province Biomechanics Key Laboratory, Shijiazhuang, People’s Republic of China; 6NHC Key Laboratory of Intelligent Orthopedic Equipment, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, People’s Republic of ChinaCorrespondence: Zhiqian Wang, Department of Geriatric Orthopedics, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, 050051, People’s Republic of China, Email [email protected]; Zhiyong Hou, Department of Orthopaedic Surgery, Hebei Medical University Third Hospital, 050051, Shijiazhuang, Hebei, People’s Republic of China, Email [email protected]: The prognostic significance of the stress hyperglycemia ratio (SHR), calculated using admission blood glucose (ABG) and glycated hemoglobin (HbA1c), remains unclear in the context of perioperative complications associated with hip fractures. This study evaluates the association and predictive capability of the SHR for preoperative acute heart failure in hip fracture patients with chronic cerebrovascular disease.Methods and Results: A total of 515 patients were analyzed, revealing a 16.8% incidence of preoperative HF. Significant intergroup differences in age (P < 0.001), ASA classification (P < 0.001), coronary heart disease (P=0.025), serum albumin level (P < 0.001), and admission SHR (P=0.033) were identified between patients with and without preoperative heart failure. Logistic regression identified SHR, CHD, ASA, neutrophils, and hypoproteinemia as independent predictors of preoperative HF, leading to the development of a nomogram that demonstrated good predictive performance (AUC 0.72). Calibration curves and decision curve analysis indicated the model’s clinical utility. Machine learning algorithms, especially the XGBoost model, surpassed traditional models by achieving an AUC of 0.787, demonstrating excellent calibration and clinical applicability. SHAP analysis validated the stable contribution of SHR in risk prediction.Conclusions: This study highlights the SHR’s strong predictive ability for preoperative HF in hip fracture patients with chronic cerebrovascular disease. The XGBoost machine learning model could improve risk stratification and clinical decision-making for this at-risk group.Keywords: stress hyperglycemia ratio, hip fracture, heart failure, chronic cerebrovascular diseases, machine learning
SN - 1178-1998
ER -