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已发表论文
老年急性脑出血患者低钾血症并发症的风险因素及预测模型
Authors Jing S, Zhang L , Xu L
Received 1 July 2025
Accepted for publication 6 November 2025
Published 21 November 2025 Volume 2025:20 Pages 2153—2162
DOI https://doi.org/10.2147/CIA.S550753
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 5
Editor who approved publication: Dr Maddalena Illario
Shanquan Jing,1,* Lizhuang Zhang,2,* Lifeng Xu1
1Department of Neurosurgery, The First Hospital of Hebei Medical University, Shijiazhuang, People’s Republic of China; 2Department of Rehabilitation Medicine, the First Hospital of Hebei Medical University, Shijiazhuang, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Lifeng Xu, Department of Neurosurgery, The First Hospital of Hebei Medical University, No. 89 Donggang Road, Shijiazhuang, Hebei, 050031, People’s Republic of China, Tel +86-311-87155042, Email skyofx@163.com
Objective: This study aimed to identify the key risk factors for hypokalemia in older adults with acute cerebral hemorrhage (ACH) and to develop a clinically practical risk predictive model based on logistic regression.
Methods: A total of 209 older adult ACH patients (age 60– 82 years) treated at The First Hospital of Hebei Medical University from July 2022 to July 2024 were included in this retrospective cohort study. Patients were divided into two groups: hypokalemic (serum potassium < 3.5 mmol/L, n = 56) and normokalemic (serum potassium 3.5– 5.5 mmol/L, n = 153). Clinical outcomes were compared, and logistic regression was used to identify risk factors for hypokalemia. A risk prediction model was constructed and presented as a nomogram. The diagnostic value of the model was assessed using receiver operating characteristic (ROC) curves.
Results: Hypokalemia was associated with significantly higher in-hospital mortality, poorer functional outcomes, longer hospital stays, and more frequent neurological deterioration (all P < 0.05). Univariate and multivariate logistic regression identified female gender (OR=2.713), higher NIHSS scores at admission (OR=2.375), GFR ≤ 60 mL/min/1.73 m2 (OR=2.316), and furosemide use > 20 mg/d (OR=2.351) as independent risk factors for hypokalemia. ROC analysis showed an area under the curve (AUC) for the multivariable predictive model of 0.859, which was superior to individual predictors.
Conclusion: Female gender, higher neurological deficit severity (NIHSS score), impaired renal function (GFR ≤ 60 mL/min/1.73 m2), and use of furosemide > 20 mg/d are significant independent risk factors for hypokalemia in older adult ACH patients. Given its association with adverse outcomes, early prediction is crucial. The predictive model and corresponding nomogram provide a practical tool for identifying high-risk patients, facilitating timely intervention.
Keywords: cerebral hemorrhage, hypokalemia, risk factors, clinical outcomes, risk prediction model, older adults
1Department of Neurosurgery, The First Hospital of Hebei Medical University, Shijiazhuang, People’s Republic of China; 2Department of Rehabilitation Medicine, the First Hospital of Hebei Medical University, Shijiazhuang, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Lifeng Xu, Department of Neurosurgery, The First Hospital of Hebei Medical University, No. 89 Donggang Road, Shijiazhuang, Hebei, 050031, People’s Republic of China, Tel +86-311-87155042, Email skyofx@163.com
Objective: This study aimed to identify the key risk factors for hypokalemia in older adults with acute cerebral hemorrhage (ACH) and to develop a clinically practical risk predictive model based on logistic regression.
Methods: A total of 209 older adult ACH patients (age 60– 82 years) treated at The First Hospital of Hebei Medical University from July 2022 to July 2024 were included in this retrospective cohort study. Patients were divided into two groups: hypokalemic (serum potassium < 3.5 mmol/L, n = 56) and normokalemic (serum potassium 3.5– 5.5 mmol/L, n = 153). Clinical outcomes were compared, and logistic regression was used to identify risk factors for hypokalemia. A risk prediction model was constructed and presented as a nomogram. The diagnostic value of the model was assessed using receiver operating characteristic (ROC) curves.
Results: Hypokalemia was associated with significantly higher in-hospital mortality, poorer functional outcomes, longer hospital stays, and more frequent neurological deterioration (all P < 0.05). Univariate and multivariate logistic regression identified female gender (OR=2.713), higher NIHSS scores at admission (OR=2.375), GFR ≤ 60 mL/min/1.73 m2 (OR=2.316), and furosemide use > 20 mg/d (OR=2.351) as independent risk factors for hypokalemia. ROC analysis showed an area under the curve (AUC) for the multivariable predictive model of 0.859, which was superior to individual predictors.
Conclusion: Female gender, higher neurological deficit severity (NIHSS score), impaired renal function (GFR ≤ 60 mL/min/1.73 m2), and use of furosemide > 20 mg/d are significant independent risk factors for hypokalemia in older adult ACH patients. Given its association with adverse outcomes, early prediction is crucial. The predictive model and corresponding nomogram provide a practical tool for identifying high-risk patients, facilitating timely intervention.
Keywords: cerebral hemorrhage, hypokalemia, risk factors, clinical outcomes, risk prediction model, older adults