Dataset Information

Derivation and Internal Validation of a Clinical Risk Prediction Tool for Hyperkalemia-Related Emergency Department Encounters Among Hemodialysis Patients.

ABSTRACT:

Background

Approximately 10% of emergency department (ED) visits among dialysis patients are for conditions that could potentially be managed in outpatient settings, such as hyperkalemia.

Objective

Using population-based data, we derived and internally validated a risk score to identify hemodialysis patients at increased risk of hyperkalemia-related ED events.

Design

Retrospective cohort study.

Setting

Ten in-center hemodialysis sites in southern Alberta, Canada.

Patients

All maintenance hemodialysis patients (≥18 years) between March 2009 and March 2017.

Measurements

Predictors of hyperkalemia-related ED events included patient demographics, comorbidities, health-system use, laboratory measurements, and dialysis information. The outcome of interest (hyperkalemia-related ED events) was defined by International Classification of Diseases (10th Revision; ICD-10) codes and/or serum potassium [K⁺] ≥6 mmol/L.

Methods

Bootstrapped logistic regression was used to derive and internally validate a model of important predictors of hyperkalemia-related ED events. A point system was created based on regression coefficients. Model discrimination was assessed by an optimism-adjusted C-statistic and calibration by deciles of risk and calibration slope.

Results

Of the 1533 maintenance hemodialysis patients in our cohort, 331 (21.6%) presented to the ED with 615 hyperkalemia-related ED events. A 9-point scale for risk of a hyperkalemia-related ED event was created with points assigned to 5 strong predictors based on their regression coefficients: ≥1 laboratory measurement of serum K⁺ ≥6 mmol/L in the prior 6 months (3 points); ≥1 Hemoglobin A1C [HbA1C] measurement ≥8% in the prior 12 months (1 point); mean ultrafiltration of ≥10 mL/kg/h over the preceding 2 weeks (2 points); ≥25 hours of cumulative time dialyzing over the preceding 2 weeks (1 point); and dialysis vintage of ≥2 years (2 points). Model discrimination (C-statistic: 0.75) and calibration were good.

Limitations

Measures related to health behaviors, social determinants of health, and residual kidney function were not available for inclusion as potential predictors.

Conclusions

While this tool requires external validation, it may help identify high-risk patients and allow for preventative strategies to avoid unnecessary ED visits and improve patient quality of life.

Trial registration

Not applicable-observational study design.

SUBMITTER: Ronksley PE

PROVIDER: S-EPMC7485157 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Derivation and Internal Validation of a Clinical Risk Prediction Tool for Hyperkalemia-Related Emergency Department Encounters Among Hemodialysis Patients.

Ronksley Paul E PE Wick James P JP Elliott Meghan J MJ Weaver Robert G RG Hemmelgarn Brenda R BR McRae Andrew A James Matthew T MT Harrison Tyrone G TG MacRae Jennifer M JM

Canadian journal of kidney health and disease 20200904

<h4>Background</h4>Approximately 10% of emergency department (ED) visits among dialysis patients are for conditions that could potentially be managed in outpatient settings, such as hyperkalemia.<h4>Objective</h4>Using population-based data, we derived and internally validated a risk score to identify hemodialysis patients at increased risk of hyperkalemia-related ED events.<h4>Design</h4>Retrospective cohort study.<h4>Setting</h4>Ten in-center hemodialysis sites in southern Alberta, Canada.<h4> ...[more]

PMID: 32953128

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Dataset Information

Derivation and Internal Validation of a Clinical Risk Prediction Tool for Hyperkalemia-Related Emergency Department Encounters Among Hemodialysis Patients.

Background

Objective

Design

Setting

Patients

Measurements

Methods

Results

Limitations

Conclusions

Trial registration

Publications

Derivation and Internal Validation of a Clinical Risk Prediction Tool for Hyperkalemia-Related Emergency Department Encounters Among Hemodialysis Patients.

Similar Datasets

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