Yan Guanqiang, Li Jingxiao, Su Yiji, Li Guosheng, Feng Guiyu, Liu Jun, Gao Xiang, Zhou Huafu
Department of Cardio-Thoracic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Front Surg. 2025 Jan 7;11:1433751. doi: 10.3389/fsurg.2024.1433751. eCollection 2024.
This study aimed to explore the risk factors of hypokalemia after radical resection of esophageal cancer (EC) and establish a nomogram risk prediction model to evaluate hypokalemia risk after esophagectomy. Thus, this study provides a reference for the clinical development of intervention measures.
Clinical data of EC patients who underwent radical surgery from January 2020 to November 2022 in the First Affiliated Hospital of Guangxi Medical University were retrospectively collected. The relevant variables were screened using multivariate logistic regression analysis with IBM SPSS 25.0 and R 4.2.0 software, and a nomogram for predicting hypokalemia risk was established. The established nomogram was evaluated by receiver operating characteristic (ROC), calibration, and decision curves. The model was also internally validated by 1000 bootstrap resampling methods.
After radical EC resection, the incidence rate of hypokalemia in 213 patients was 19.2% (41/213). The hemoglobin levels, total serum protein, serum albumin, calcium ion concentration, direct bilirubin, prothrombin time (PT), and activated partial thromboplastin time (APTT) were related ( < 0.05). The multivariate logistic analysis showed that the white blood cell count, serum albumin level, direct bilirubin, and operation time were risk factors for hypokalemia after radical EC resection ( < 0.05). The area under the ROC curve (AUC) was 0.764, demonstrating the good discriminative ability of the established nomogram for hypokalemia prediction. The calibration curve showed a good fit between the predicted and actual observed probabilities. The model maintained a high C-index in the internal validation (C-index = 0.758), supporting that the nomogram can be widely used for hypokalemia prediction.
The prediction model for hypokalemia risk with individualized scores based on the patient's white blood cell count, serum albumin level, direct bilirubin, and operation time can screen out high-risk patients who might develop hypokalemia. It is of certain reference value for clinicians to screen and follow up with patients with emphasis and to formulate preoperative and postoperative intervention strategies.
本研究旨在探讨食管癌根治术后低钾血症的危险因素，并建立列线图风险预测模型以评估食管癌切除术后低钾血症风险。从而为临床制定干预措施提供参考。
回顾性收集2020年1月至2022年11月在广西医科大学第一附属医院接受根治性手术的食管癌患者的临床资料。使用IBM SPSS 25.0和R 4.2.0软件通过多因素逻辑回归分析筛选相关变量，并建立预测低钾血症风险的列线图。通过受试者工作特征（ROC）曲线、校准和决策曲线对建立的列线图进行评估。该模型还通过1000次自抽样重采样方法进行内部验证。
213例患者食管癌根治术后低钾血症发生率为19.2%（41/213）。血红蛋白水平、总血清蛋白、血清白蛋白、钙离子浓度、直接胆红素、凝血酶原时间（PT）和活化部分凝血活酶时间（APTT）与之相关（<0.05）。多因素逻辑分析显示，白细胞计数、血清白蛋白水平、直接胆红素和手术时间是食管癌根治术后低钾血症的危险因素（<0.05）。ROC曲线下面积（AUC）为0.764，表明所建立的列线图对低钾血症预测具有良好的判别能力。校准曲线显示预测概率与实际观察概率之间拟合良好。该模型在内部验证中保持较高的C指数（C指数 = 0.758），支持该列线图可广泛用于低钾血症预测。
基于患者白细胞计数、血清白蛋白水平、直接胆红素和手术时间的个体化评分的低钾血症风险预测模型可筛选出可能发生低钾血症的高危患者。对临床医生重点筛查和随访患者以及制定术前和术后干预策略具有一定的参考价值。
