Division of Pharmacy Practice, Department of Pharmaceutical Care, School of Pharmaceutical Sciences, University of Phayao, Phayao, Thailand.
Department of Pharmacy, Phrae Hospital, Phrae, Thailand.
J Clin Pharm Ther. 2020 Oct;45(5):997-1005. doi: 10.1111/jcpt.13123. Epub 2020 Feb 3.
Hyponatremia is a common side effect of thiazide diuretics that can lead to increased mortality and hospitalization. A rapid and accurate screening tool is needed for rapid and appropriate management. In this study, we report on the development of a simple clinical screening tool for hyponatremia using thiazide diuretics.
This nested case-control study was performed by collecting data from 1 January 2015 to 30 June 2017. Univariable and multivariable logistic regressions were used to identify potential risk factors. The regression coefficients were converted into item scores by dividing each regression coefficient with the minimum coefficient in the model and rounding to the nearest integer. This value was then summed to the total score. The prediction power of the model was determined by the area under the receiver operating characteristic curve (AuROC).
Six clinical risk factors, namely age ≥65 years, benzodiazepine use, history of a cerebrovascular accident, dose of hydrochlorothiazide ≥25 mg, female sex and statin use, were included in our ABCDF-S score. The model showed good power of prediction (AuROC 81.53%, 95% confidence interval [CI]: 78%-84%) and good calibration (Hosmer-Lemeshow X = 23.20; P = .39). The positive likelihood ratios of hyponatremia in patients with low risk (score ≤ 6) and high risk (score ≥ 8) were 0.26 (95% CI: 0.21-0.32) and 3.89 (95% CI: 3.11-4.86), respectively.
The screening tool with six risk predictors provided a useful prediction index for thiazide-associated hyponatremia. However, further validation of the tool is warranted prior to its utilization in routine clinical practice.
噻嗪类利尿剂引起的低钠血症是一种常见的副作用，可导致死亡率和住院率增加。需要一种快速准确的筛查工具来进行快速和适当的管理。在本研究中，我们报告了一种使用噻嗪类利尿剂筛查低钠血症的简单临床筛查工具的开发。
这项嵌套病例对照研究通过收集 2015 年 1 月 1 日至 2017 年 6 月 30 日的数据进行。采用单变量和多变量逻辑回归来确定潜在的危险因素。将回归系数除以模型中的最小系数，并四舍五入到最接近的整数，将其转换为项目得分。然后将此值加到总分中。通过接收者操作特征曲线下的面积（AuROC）来确定模型的预测能力。
本研究纳入了 6 个临床危险因素，即年龄≥65 岁、使用苯二氮䓬类药物、有脑血管意外史、氢氯噻嗪剂量≥25mg、女性和使用他汀类药物。该模型显示出良好的预测能力（AuROC 81.53%，95%置信区间[CI]：78%-84%）和良好的校准（Hosmer-Lemeshow X=23.20；P=0.39）。低风险（评分≤6）和高风险（评分≥8）患者发生低钠血症的阳性似然比分别为 0.26（95%CI：0.21-0.32）和 3.89（95%CI：3.11-4.86）。
该具有 6 个风险预测因子的筛查工具为噻嗪类相关低钠血症提供了一个有用的预测指标。然而，在将该工具用于常规临床实践之前，需要进一步验证该工具。
