Analysis of risk factors for serum digoxin concentration exceeding the warning threshold and construction of pre-diction model
- VernacularTitle: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
- Author:
Sujun QIU
1
;
Yimei CAI
2
;
Jinyong LIU
3
;
Hongshan WANG
1
Author Information
1. Dept. of Pharmacy，Nansha Branch，Guangzhou First People’s Hospital，Guangzhou 511457，China
2. Dept. of Outpatient，Nansha Branch，Guangzhou First People’s Hospital，Guangzhou 511457，China
3. Dept. of Infection Management，Nansha Branch，Guangzhou First People’s Hospital，Guangzhou 511457，China
- Publication Type:Journal Article
- Keywords:
digoxin;
serum drug concentration;
exceeding the warning threshold;
risk factors;
nomogram;
prediction model
- From:
China Pharmacy
2026;37(6):788-793
- CountryChina
- Language:Chinese
-
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 （ P ＜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 （ P ＝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.
