Analysis of risk factors for serum digoxin concentration exceeding the warning threshold and construction of prediction model

QIU Sujun; CAI Yimei; LIU Jinyong; WANG Hongshan

doi:10.6039/j.issn.1001-0408.2026.06.16

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Analysis of risk factors for serum digoxin concentration exceeding the warning threshold and construction of prediction model

更新时间：2026-03-26

- Analysis of risk factors for serum digoxin concentration exceeding the warning threshold and construction of prediction model
- ZHONGGUO YAOFANG Vol. 37, Issue 6, Pages: 788-793(2026)
- 作者机构：
  
  1.广州市第一人民医院南沙医院药学科，广州　511457
  2.广州市第一人民医院南沙医院门诊部，广州　511457
  3.广州市第一人民医院南沙医院感染管理科，广州　511457
- 作者简介：
- 基金信息：
- DOI：10.6039/j.issn.1001-0408.2026.06.16
  CLC： R969.3
- Received：14 November 2025，
  
  Revised：2026-03-13，
  
  Accepted：13 March 2026，
  
  Published：30 March 2026
- 稿件说明：
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邱素君,蔡艺美,刘金泳,等.血清地高辛浓度超警戒值的危险因素分析及风险预测模型构建 [J].中国药房,2026,37(06):788-793.

QIU Sujun,CAI Yimei,LIU Jinyong,et al.Analysis of risk factors for serum digoxin concentration exceeding the warning threshold and construction of prediction model[J].ZHONGGUO YAOFANG,2026,37(06):788-793.
邱素君,蔡艺美,刘金泳,等.血清地高辛浓度超警戒值的危险因素分析及风险预测模型构建 [J].中国药房,2026,37(06):788-793. DOI： 10.6039/j.issn.1001-0408.2026.06.16.

QIU Sujun,CAI Yimei,LIU Jinyong,et al.Analysis of risk factors for serum digoxin concentration exceeding the warning threshold and construction of prediction model[J].ZHONGGUO YAOFANG,2026,37(06):788-793. DOI： 10.6039/j.issn.1001-0408.2026.06.16.

摘要

目的

分析血清地高辛浓度（SDC）超警戒值发生的危险因素，并构建风险预测模型。

方法

回顾性收集2020年9月至2025年3月于广州市第一人民医院及广州市第一人民医院南沙医院规律口服地高辛并完成治疗药物监测的住院患者的临床资料。以SDC＞2.0 ng/mL的患者作为超警戒值组，SDC≤2.0 ng/mL的患者作为非超警戒值组，在单因素分析的基础上采用多因素Logistic回归分析筛选SDC超警戒值发生的独立危险因素，据此建立风险预测模型并绘制列线图。通过受试者操作特征（ROC）曲线评价模型的区分度；绘制校准曲线评价模型的校准度；采用Hosmer-Lemeshow检验评价模型的拟合优度；采用决策曲线分析（DCA）评估模型的临床应用价值。

结果

共纳入254例患者，其中49例（19.29%）患者的SDC超警戒值。单因素分析和多因素Logistic回归分析显示，单位体重日剂量增加、年龄增长、合并冠心病、血肌酐水平升高、合并使用胺碘酮、合并使用去乙酰毛花苷为SDC超警戒值发生的独立危险因素（

＜0.05）。模型ROC曲线下面积为0.869（95%置信区间为0.81

8～0.920），敏感度为0.796，特异性为0.842；Hosmer-Lemeshow检验的

值为0.570，校准曲线与理想曲线贴合紧密，平均绝对误差为0.012；当风险阈值概率为6%～82%时，使用模型的临床净获益较高。

结论

单位体重日剂量增加、年龄增长、合并冠心病、血肌酐水平升高、合并使用胺碘酮、合并使用去乙酰毛花苷是SDC超警戒值发生的独立危险因素；基于上述因素建立的风险预测模型可用于预测SDC超警戒值的发生风险。

Abstract

OBJECTIVE

To analyze the risk factors associated with serum digoxin concentration （SDC） exceeding the warning threshold and to construct a risk prediction model.

METHODS

Clinical data were retrospectively collected from hospitalized patients who received regular oral digoxin and completed therapeutic drug monitoring at Guangzhou First People’s Hospital and Nansha Branch of Guangzhou First People’s Hospital between September 2020 and March 2025. Patients with SDC＞2.0 ng/mL were classified as exceeding the warning threshold group， while those with SDC≤2.0 ng/mL were classified as the non-exceeding the warning threshold group. Based on univariate factor analysis， multivariate Logistic regression analysis was used to identify independent risk factors for SDC exceeding the warning threshold. A prediction model was developed and a nomogram was plotted accordingly. The discriminative ability of the model was evaluated by receiver operating characteristic （ROC） curve analysis， and the calibration curve were plotted to assess the calibration of the model. The Hosmer-Lemeshow test was employed to evaluate the goodness of fit of the model， and clinical utility was evaluated by decision curve analysis （DCA）.

RESULTS

A total of 254 patients were included， among whom 49 patients （19.29%） had SDC exceeding the warning threshold. Univariate factor analysis and multivariate Logistic regression analysis showed that increased daily dose per kilogram of body weight， advanced age， concomitant coronary heart disease， elevated serum creatinine levels， concomitant use of amiodarone， and concomitant use of deslanoside wer

e independent risk factors for SDC exceeding the warning threshold （

＜0.05）. The area under the ROC curve of the model was 0.869 （95% confidence interval： 0.818-0.920）， with a sensitivity of 0.796 and a specificity of 0.842. The Hosmer-Lemeshow test showed good calibration （

＝0.570）. The calibration curve was closely aligned with the ideal curve， with a mean absolute error of 0.012. The model provided a higher net benefit across a threshold probability range of 6% to 82%.

CONCLUSIONS

The increased daily dose per kilogram of body weight， advanced age， concomitant coronary heart disease， elevated serum creatinine levels， concomitant use of amiodarone， and concomitant use of deslanoside are independent risk factors for SDC exceeding the warning threshold. The nomogram prediction model developed based on the aforementioned factors can be used to predict the risk of SDC exceeding the warning threshold.

关键词

Keywords

references

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