Evidence map›Paper›PMID 25560457›Full record

ArticleCritical care (London, England)2015

The relationship between serum potassium, potassium variability and in-hospital mortality in critically ill patients and a before-after analysis on the impact of computer-assisted potassium control.

Lara Hessels, Miriam Hoekstra, Lisa J Mijzen, Mathijs Vogelzang, Wim Dieperink, Annemieke Oude Lansink, Maarten W Nijsten

Abstract read
In one paragraph

Article in Critical care (London, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Lara HesselsDepartment of Critical Care, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, 9700 RB, the Netherlands. lara.hessels@gmail.com.
Miriam HoekstraDepartment of Critical Care, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, 9700 RB, the Netherlands. miriamhoekstra@gmail.com.
Lisa J MijzenDepartment of Critical Care, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, 9700 RB, the Netherlands. lisamijzen@gmail.com.
Mathijs VogelzangDepartment of Critical Care, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, 9700 RB, the Netherlands. mathijs@gmail.com.
Wim DieperinkDepartment of Critical Care, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, 9700 RB, the Netherlands. w.dieperink@umcg.nl.
Annemieke Oude LansinkDepartment of Critical Care, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, 9700 RB, the Netherlands. a.oudelansink@umcg.nl.
Maarten W NijstenDepartment of Critical Care, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, 9700 RB, the Netherlands. m.w.n.nijsten@umcg.nl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe relationship between potassium regulation and outcome is not known. Our first aim in the present study was to determine the relationship between potassium level and variability in (ICU) stay and outcome. The second aim was to evaluate the impact of a computer-assisted potassium regulation protocol.

methodsWe performed a retrospective before-after study including all patients >15 years of age admitted for more than 24 hours to the ICU of our university teaching hospital between 2002 and 2011. Potassium control was fully integrated with computerized glucose control (glucose and potassium regulation program for intensive care patients (GRIP-II)). The potassium metrics that we determined included mean potassium, potassium variability (defined as the standard deviation of all potassium levels) and percentage of ICU time below and above the reference range (3.5 through 5.0 mmol/L). These metrics were determined for the first ICU day (early phase) and the subsequent ICU days (late phase; that is, day 2 to day 7). We also compared potassium metrics and in-hospital mortality before and after GRIP-II was implemented in 2006.

resultsOf all 22,347 ICU admissions, 10,451 (47%) patients were included. A total of 206,987 potassium measurements were performed in these patients. Glucose was regulated by GRIP-II in 4,664 (45%) patients. The overall in-hospital mortality was 22%. There was a U-shaped relationship between the potassium level and in-hospital mortality (P <0.001). Moreover, potassium variability was independently associated with outcome. After implementation of GRIP-II, in the late phase the time below 3.5 mmol/L decreased from 9.2% to 3.9% and the time above 5.0 mmol/L decreased from 6.1% to 5.2%, and potassium variability decreased from 0.31 to 0.26 mmol/L (all P <0.001). The overall decrease in in-hospital mortality from 23.3% before introduction of GRIP-II to 19.9% afterward (P <0.001) was not related to a specific potassium subgroup.

conclusionsHypokalemia, hyperkalemia and potassium variability were independently associated with increased mortality. Computerized potassium control clearly resulted in improved potassium metrics.

Indexed as

Point-of-Care SystemsTherapy, Computer-AssistedAdultAgedBlood GlucoseCritical IllnessFemaleHospital MortalityHumansHyperkalemiaHypokalemiaIntensive Care UnitsMaleMiddle AgedPotassiumRetrospective StudiesBlood GlucosePotassium

Identifiers

PMID25560457
PMCPMC4312438

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.