Lower Serum Potassium Levels at Admission are Associated with the Risk of Recurrent Stroke in Patients with Acute Ischemic Stroke or Transient Ischemic Attack

Wang, Anxin; Cao, Shuang; Tian, Xue; Zuo, Yingting; Meng, Xia; Pan, Chen; Li, Hao; Zhao, Lu; Wang, Yongjun

doi:10.1159/000520052

Cited by 5 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a study, it has been suggested that MCV could predict AIS short-term mortality ( 44 ). In addition, the relationship between serum potassium levels and stroke risk or stroke recurrence has been shown in the literature ( 45 , 46 ). Our ML algorithm included serum potassium levels, MCV, and hemoglobin in the model after RFE.…”

Section: Discussionmentioning

confidence: 99%

Development of machine learning models for predicting outcome in patients with distal medium vessel occlusions: a retrospective study

Ozkara,

Karabacak,

Kotha

et al. 2023

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background While numerous prognostic factors have been reported for large vessel occlusion (LVO)-acute ischemic stroke (AIS) patients, the same cannot be said for distal medium vessel occlusions (DMVOs). We used machine learning (ML) algorithms to develop a model predicting the short-term outcome of AIS patients with DMVOs using demographic, clinical, and laboratory variables and baseline computed tomography (CT) perfusion (CTP) postprocessing quantitative parameters. Methods In this retrospective cohort study, consecutive patients with AIS admitted to two comprehensive stroke centers between January 1, 2017, and September 1, 2022, were screened. Demographic, clinical, and radiological data were extracted from electronic medical records. The clinical outcome was divided into two categories, with a cut-off defined by the median National Institutes of Health Stroke Scale (NIHSS) shift score. Data preprocessing involved addressing missing values through imputation, scaling with a robust scaler, normalization using min-max normalization, and encoding of categorical variables. The data were split into training and test sets (70:30), and recursive feature elimination (RFE) was employed for feature selection. For ML analyses, XGBoost, LightGBM, CatBoost, multi-layer perceptron, random forest, and logistic regression algorithms were utilized. Performance evaluation involved the receiver operating characteristic (ROC) curve, precision-recall curve (PRC), the area under these curves, accuracy, precision, recall, and Matthews correlation coefficient (MCC). The relative weights of predictor variables were examined using Shapley additive explanations (SHAP). Results Sixty-nine patients were included and divided into two groups: 35 patients with favorable outcomes and 34 patients with unfavorable outcomes. Utilizing ten selected features, the XGBoost algorithm achieved the best performance in predicting unfavorable outcomes, with an area under the ROC curve (AUROC) of 0.894 and an area under the PRC curve (AUPRC) of 0.756. The SHAP analysis ranked the following features in order of importance for the XGBoost model: mismatch volume, time-to-maximum of the tissue residue function (T max ) >6 s, diffusion-weighted imaging (DWI) volume, neutrophil-to-platelet ratio (NPR), mean corpuscular volume (MCV), T max >10 s, hemoglobin, potassium, hypoperfusion index (HI), and T max >8 s. Conclusions Our ML models, trained on baseline quantitative laboratory and CT parameters, accurately predicted the short-term outcome in patients with DMVOs. These findings may aid clinicians in predicting prognosis and may be helpful for future research.

show abstract

Section: Discussionmentioning

confidence: 99%

Development of machine learning models for predicting outcome in patients with distal medium vessel occlusions: a retrospective study

Ozkara,

Karabacak,

Kotha

et al. 2023

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…Hypokalemia also contributed a certain weight to the nomogram. The study by Wang et al ( 35 ) showed that serum potassium level at admission was associated with stroke recurrence in AIS patients, and some scholars have proposed that emergency stroke patients should be screened immediately for electrolyte disturbances. Early detection and correction of the imbalance of serum potassium improve the prognosis of stroke patients ( 36 ).…”

Section: Discussionmentioning

confidence: 99%

Systemic immune inflammatory index is an independent predictor for the requirement of decompressive craniectomy in large artery occlusion acute ischemic stroke patients after mechanical thrombectomy

Zhou

Zhu

et al. 2022

Front. Neurol.

View full text Add to dashboard Cite

Background and purposeFollowing mechanical thrombectomy (MT), patients with large artery occlusive acute ischemic stroke (LAO-AIS) often have cerebral herniation due to its complications, resulting in poor prognosis. Decompressive craniectomy (DC) can markedly improve patient prognosis. This study aimed to verify the predictive value of clinical parameters such as the systemic immune-inflammatory index (SII) for DC in patients with LAO-AIS after MT.MethodsClinical data of a total of 173 patients with LAO-AIS treated with MT between January 2020 and January 2022 were retrospectively analyzed. Patients receiving DC were grouped into an experimental group or a control group (no DC). The patients were randomly divided into the training set (n = 126, 75%) and validation set (n = 43, 25%). Multivariate logistic regression was used to construct a nomogram predictive model.ResultsThe SII value in the experimental group (median: 2851.1×109/L) was significantly higher than that in the control group (median: 1898.6 × 109/L) (P = 0.019). Receiver operating characteristic (ROC) analyses showed that the best cutoff value of the SII was 2505.7 × 109/L with a sensitivity of 55%, a specificity of 75.8%, and an area under the curve (AUC) of 0.649. Multivariate logistic regression indicated that the SII was an independent predictor for performing DC in patients with LAO-AIS after MT (OR = 3.579, 95% CI = 1.360–9.422, P = 0.01). The AUC was 0.728 in the training set and 0.583 in the validation set. The average error of the calibration curve was 0.032 in the training set and 0.023 in the validation set. The average error was relatively small and consistent in the training set and validation set. The C-index of the nomogram was 0.804 suggesting good accuracy.ConclusionsThe SII at admission is an independent predictor for the requirement of DC in patients with LAO-AIS after MT. The SII-based nomogram helps doctors make decisions on whether DC is needed timely and rationally, and thereby may improve the prognosis of these patients.

show abstract

Complications in Intracranial Surgery of Companion Animals

Shores,

Mendoza

2025

Veterinary Clinics of North America: Small Animal Practice

View full text Add to dashboard Cite

Lower Serum Potassium Levels at Admission are Associated with the Risk of Recurrent Stroke in Patients with Acute Ischemic Stroke or Transient Ischemic Attack

Cited by 5 publications

References 31 publications

Development of machine learning models for predicting outcome in patients with distal medium vessel occlusions: a retrospective study

Development of machine learning models for predicting outcome in patients with distal medium vessel occlusions: a retrospective study

Systemic immune inflammatory index is an independent predictor for the requirement of decompressive craniectomy in large artery occlusion acute ischemic stroke patients after mechanical thrombectomy

Complications in Intracranial Surgery of Companion Animals

Contact Info

Product

Resources

About