Predictors of Hyperkalemia and Hypokalemia in Individuals with Diabetes: a Classification and Regression Tree Analysis.
Predictors of Hyperkalemia and Hypokalemia in Individuals with Diabetes: a Classification and Regression Tree Analysis.
复制标题
糖尿病患者高钾血症和低钾血症的预测因子：分类和回归树分析。
DOI：
10.1007/s11606-020-05799-x
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发表时间：
2020
影响因子：
5.7
通讯作者：
Steiner,JohnF
中科院分区：
文献类型：
--
作者：
Schroeder,EmilyB;Adams,JohnL;Chonchol,Michel;Nichols,GregoryA;O'Connor,PatrickJ;Powers,JDavid;Schmittdiel,JulieA;Shetterly,SusanM;Steiner,JohnF
背景高钾血症和低钾血症均可导致心律失常并与死亡率增加相关。常规临床实践中缺乏关于糖尿病患者钾的预测因素的信息。目的确定成人糖尿病患者高钾血症和低钾血症的预测因素。设计回顾性队列研究，采用分类和回归树（CART）分析。参与者 321,856 名糖尿病患者从 2012 年至 2013 年登记在四个大型综合医疗保健系统中。 测量我们使用 2012 年或 2013 年收集的单一血清钾结果。高钾血症定义为血清钾≥ 5.5 mEq/L，低钾血症定义为< 3.5 mEq/L。预测因素包括人口因素、实验室测量、合并症、药物使用和医疗保健利用。 主要结果 共有 2556 起低钾血症事件 (0.8%) 和 1517 起高钾血症事件 (0.5%)。在单变量分析中，我们确定了一致的预测因子（与高钾血症和低钾血症的可能性增加相关）、不一致的预测因子以及仅高钾血症或低钾血症的预测因子。在 CART 模型中，高钾血症“树”有 5 个节点，ac 统计量为 0.76。节点由先前的钾结果和 eGFR 定义，5 个末端“叶”的高钾血症概率为 0.2% 至 7.2%。低钾血症树有 4 个节点，ac 统计量为 0.76。低钾血症树包括由先前钾结果定义的节点，并且 4 个顶叶的低钾血症概率为 0.3% 至 17.6%。近期血钾在 4.0 至 5.0 mEq/L、eGFR ≥45 mL/min/1.73m2 且上一年无低钾血症的个体中，低钾血症或高钾血症的发生率< 1%。 结论 对于近期血清钾在 4.0 至 5.0 mEq/L、eGFR 之间的个体，常规血清钾检测的检出率可能较低 ≥45 mL/min/1.73m2，近期无低钾血症史。我们没有研究近期临床状况或药物变化对急性钾变化的影响。
BackgroundBoth hyperkalemia and hypokalemia can lead to cardiac arrhythmias and are associated with increased mortality. Information on the predictors of potassium in individuals with diabetes in routine clinical practice is lacking.ObjectiveTo identify predictors of hyperkalemia and hypokalemia in adults with diabetes.DesignRetrospective cohort study, with classification and regression tree (CART) analysis.Participants321,856 individuals with diabetes enrolled in four large integrated health care systems from 2012 to 2013.Main MeasuresWe used a single serum potassium result collected in 2012 or 2013. Hyperkalemia was defined as a serum potassium ≥ 5.5 mEq/L and hypokalemia as < 3.5 mEq/L. Predictors included demographic factors, laboratory measurements, comorbidities, medication use, and health care utilization.Key ResultsThere were 2556 hypokalemia events (0.8%) and 1517 hyperkalemia events (0.5%). In univariate analyses, we identified concordant predictors (associated with increased probability of both hyperkalemia and hypokalemia), discordant predictors, and predictors of only hyperkalemia or hypokalemia. In CART models, the hyperkalemia “tree” had 5 nodes and ac-statistic of 0.76. The nodes were defined by prior potassium results and eGFRs, and the 5 terminal “leaves” had hyperkalemia probabilities of 0.2 to 7.2%. The hypokalemia tree had 4 nodes and ac-statistic of 0.76. The hypokalemia tree included nodes defined by prior potassium results, and the 4 terminal leaves had hypokalemia probabilities of 0.3 to 17.6%. Individuals with a recent potassium between 4.0 and 5.0 mEq/L, eGFR ≥ 45 mL/min/1.73m2, and no hypokalemia in the previous year had a < 1% rate of either hypokalemia or hyperkalemia.ConclusionsThe yield of routine serum potassium testing may be low in individuals with a recent serum potassium between 4.0 and 5.0 mEq/L, eGFR ≥ 45 mL/min/1.73m2, and no recent history of hypokalemia. We did not examine the effect of recent changes in clinical condition or medications on acute potassium changes.