4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Serum Potassium and Mortality Risk in Hemodialysis Patients: A Cohort Study

      research-article
      1 , 2 , , 1 , 1 , 1 , 3 , 2 , 2 , 4 , Netherlands Cooperative Study on the Adequacy of Dialysis-2 (NECOSAD) Study Group
      Kidney Medicine
      Elsevier
      hemodialysis, hyperkalemia, hypokalemia, mortality, potassium

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Rationale & Objective

          Both hypo- and hyperkalemia can cause fatal cardiac arrhythmias. Although predialysis serum potassium level is a known modifiable risk factor for death in patients receiving hemodialysis, especially for hypokalemia, this risk may be underestimated. Therefore, we investigated the relationship between predialysis serum potassium level and death in incident hemodialysis patients and whether there is an optimum level.

          Study Design

          Prospective multicenter cohort study.

          Setting & Participants

          1,117 incident hemodialysis patients (aged >18 years) from the Netherlands Cooperative Study on the Adequacy of Dialysis-2 study were included and followed from their first hemodialysis treatment until death, transplantation, switch to peritoneal dialysis, or a maximum of 10 years.

          Exposure

          Predialysis serum potassium levels were obtained every 6 months and divided into 6 categories: ≤4.0 mmol/L, >4.0 mmol/L to ≤4.5 mmol/L, >4.5 mmol/L to ≤5.0 mmol/L, >5.0 mmol/L to ≤5.5 mmol/L (reference), >5.5 mmol/L to ≤6.0 mmol/L, and >6.0 mmol/L.

          Outcomes

          6-month all-cause mortality.

          Analytical Approach

          Cox proportional hazards and restricted cubic spline analyses with time-dependent predialysis serum potassium levels were used to calculate the adjusted HRs for death.

          Results

          At baseline, the mean age of the patients was 63 years (standard deviation, 14 years), 58% were men, 26% smoked, 24% had diabetes, 32% had cardiovascular disease, the mean serum potassium level was 5.0 mmol/L (standard deviation, 0.8 mmol/L), 7% had a low subjective global assessment score, and the median residual kidney function was 3.5 mL/min/1.73 m 2 (IQR, 1.4-4.8 mL/min/1.73 m 2). During the 10-year follow-up, 555 (50%) deaths were observed. Multivariable adjusted HRs for death according to the 6 potassium categories were as follows: 1.42 (95% CI, 1.01-1.99), 1.09 (95% CI, 0.82-1.45), 1.21 (95% CI, 0.94-1.56), 1 (reference), 0.95 (95% CI, 0.71-1.28), and 1.32 (95% CI, 0.97-1.81).

          Limitations

          Shorter intervals between potassium measurements would have allowed for more precise mortality risk estimations.

          Conclusions

          We found a U-shaped relationship between serum potassium level and death in incident hemodialysis patients. A low predialysis serum potassium level was associated with a 1.4-fold stronger risk of death than the optimal level of approximately 5.1 mmol/L. These results may imply the cautious use of potassium-lowering therapy and a potassium-restricted diet in patients receiving hemodialysis.

          Graphical abstract

          Related collections

          Most cited references25

          • Record: found
          • Abstract: found
          • Article: not found

          Using the outcome for imputation of missing predictor values was preferred.

          Epidemiologic studies commonly estimate associations between predictors (risk factors) and outcome. Most software automatically exclude subjects with missing values. This commonly causes bias because missing values seldom occur completely at random (MCAR) but rather selectively based on other (observed) variables, missing at random (MAR). Multiple imputation (MI) of missing predictor values using all observed information including outcome is advocated to deal with selective missing values. This seems a self-fulfilling prophecy. We tested this hypothesis using data from a study on diagnosis of pulmonary embolism. We selected five predictors of pulmonary embolism without missing values. Their regression coefficients and standard errors (SEs) estimated from the original sample were considered as "true" values. We assigned missing values to these predictors--both MCAR and MAR--and repeated this 1,000 times using simulations. Per simulation we multiple imputed the missing values without and with the outcome, and compared the regression coefficients and SEs to the truth. Regression coefficients based on MI including outcome were close to the truth. MI without outcome yielded very biased--underestimated--coefficients. SEs and coverage of the 90% confidence intervals were not different between MI with and without outcome. Results were the same for MCAR and MAR. For all types of missing values, imputation of missing predictor values using the outcome is preferred over imputation without outcome and is no self-fulfilling prophecy.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Regression modelling strategies for improved prognostic prediction.

            Regression models such as the Cox proportional hazards model have had increasing use in modelling and estimating the prognosis of patients with a variety of diseases. Many applications involve a large number of variables to be modelled using a relatively small patient sample. Problems of overfitting and of identifying important covariates are exacerbated in analysing prognosis because the accuracy of a model is more a function of the number of events than of the sample size. We used a general index of predictive discrimination to measure the ability of a model developed on training samples of varying sizes to predict survival in an independent test sample of patients suspected of having coronary artery disease. We compared three methods of model fitting: (1) standard 'step-up' variable selection, (2) incomplete principal components regression, and (3) Cox model regression after developing clinical indices from variable clusters. We found regression using principal components to offer superior predictions in the test sample, whereas regression using indices offers easily interpretable models nearly as good as the principal components models. Standard variable selection has a number of deficiencies.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Handling time varying confounding in observational research

                Bookmark

                Author and article information

                Contributors
                Journal
                Kidney Med
                Kidney Med
                Kidney Medicine
                Elsevier
                2590-0595
                22 October 2021
                January 2022
                22 October 2021
                : 4
                : 1
                : 100379
                Affiliations
                [1 ]Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
                [2 ]Department of Nephrology, Leiden University Medical Center, Leiden, The Netherlands
                [3 ]Department of Internal Medicine, Division of Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, The Netherlands
                [4 ]Department of Nephrology, Jeroen Bosch Hospital, Den Bosch, The Netherlands
                Author notes
                [] Address for Correspondence: Esther N.M. de Rooij, MD, Department of Nephrology, Leiden University Medical Center, Building 1, C7-Q, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands. e.n.m.rooij@ 123456lumc.nl
                Article
                S2590-0595(21)00222-3 100379
                10.1016/j.xkme.2021.08.013
                8767120
                35072043
                d4a6f1bf-fcb6-4cb2-859d-7dd78a60fe64
                © 2021 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                Categories
                Original Research

                hemodialysis, hyperkalemia, hypokalemia, mortality, potassium

                Comments

                Comment on this article