Schroeder Emily B, Adams John L, Chonchol Michel, Nichols Gregory A, O'Connor Patrick J, Powers J David, Schmittdiel Julie A, Shetterly Susan M, Steiner John F
Institute for Health Research, Kaiser Permanente Colorado, 2550 S. Parker Road, Suite 200, Aurora, CO, 80014, USA.
Parkview Health, 11109 Parkview Plaza Drive, Fort Wayne, IN, 46845, USA.
J Gen Intern Med. 2020 Aug;35(8):2321-2328. doi: 10.1007/s11606-020-05799-x. Epub 2020 Apr 16.
Both 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.
To identify predictors of hyperkalemia and hypokalemia in adults with diabetes.
Retrospective cohort study, with classification and regression tree (CART) analysis.
321,856 individuals with diabetes enrolled in four large integrated health care systems from 2012 to 2013.
We 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.
There 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 a c-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 a c-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/1.73m, and no hypokalemia in the previous year had a < 1% rate of either hypokalemia or hyperkalemia.
The 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/1.73m, and no recent history of hypokalemia. We did not examine the effect of recent changes in clinical condition or medications on acute potassium changes.
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。在常规临床实践中，缺乏关于糖尿病患者钾水平预测因素的信息。
确定成年糖尿病患者高钾血症和低钾血症的预测因素。
回顾性队列研究，并采用分类回归树（CART）分析。
2012年至2013年在四个大型综合医疗保健系统中登记的321856例糖尿病患者。
我们使用了2012年或2013年采集的单次血清钾结果。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。预测因素包括人口统计学因素、实验室测量值、合并症、药物使用情况和医疗保健利用情况。
有2556例低钾血症事件（0.8%）和1517例高钾血症事件（0.5%）。在单因素分析中，我们确定了一致的预测因素（与高钾血症和低钾血症发生概率增加相关）、不一致的预测因素以及仅与高钾血症或低钾血症相关的预测因素。在CART模型中，高钾血症“树”有5个节点，c统计量为0.76。这些节点由既往钾结果和估算肾小球滤过率（eGFR）定义，5个终末“叶”的高钾血症概率为0.2%至7.2%。低钾血症树有4个节点，c统计量为0.76。低钾血症树包括由既往钾结果定义的节点，4个终末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之间、eGFR≥45 mL/min/1.73m²且前一年无低钾血症的个体，低钾血症或高钾血症的发生率<1%。
对于近期血清钾在4.0至5.0 mEq/L之间、eGFR≥45 mL/min/1.73m²且近期无低钾血症病史的个体，常规血清钾检测的收益可能较低。我们未研究临床状况或药物近期变化对急性钾变化的影响。
