Developing prediction models for electrolyte abnormalities in patients indicated for antihypertensive therapy: evidence-based treatment and monitoring recommendations

Ariel Wang; Constantinos Koshiaris; Lucinda Archer; Richard D Riley; Kym I E Snell; Richard Stevens; Amitava Banerjee; Juliet A Usher-Smith; Subhashisa Swain; Andrew Clegg; Christopher E Clark; Rupert A Payne; F D Richard Hobbs; Richard J McManus; James P Sheppard; STRATIFY investigators

doi:10.1097/HJH.0000000000004032

Developing prediction models for electrolyte abnormalities in patients indicated for antihypertensive therapy: evidence-based treatment and monitoring recommendations

J Hypertens. 2025 Apr 22. doi: 10.1097/HJH.0000000000004032. Online ahead of print.

Authors

Ariel Wang¹, Constantinos Koshiaris^{1

2}, Lucinda Archer^{3

4}, Richard D Riley^{3

4}, Kym I E Snell^{3

4}, Richard Stevens¹, Amitava Banerjee⁵, Juliet A Usher-Smith⁶, Subhashisa Swain¹, Andrew Clegg⁷, Christopher E Clark⁸, Rupert A Payne⁸, F D Richard Hobbs¹, Richard J McManus¹, James P Sheppard¹; STRATIFY investigators

Affiliations

¹ Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
² Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, Cyprus.
³ National Institute for Health and Care Research (NIHR), Birmingham Biomedical Research Centre.
⁴ Department of Applied Health Sciences, School of Health Sciences, College of Medicine and Health, University of Birmingham, Birmingham.
⁵ Institute of Health Informatics, University College London, London.
⁶ Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge.
⁷ Academic Unit for Ageing and Stroke Research, Bradford Institute for Health Research, University of Leeds, Leeds.
⁸ Exeter Collaboration for Academic Primary Care, University of Exeter Medical School, St Luke's Campus, Exeter, UK.

PMID: 40377096
DOI: 10.1097/HJH.0000000000004032

Abstract

Objectives: Evidence from clinical trials suggests that antihypertensive treatment is associated with an increased risk of common electrolyte abnormalities. We aimed to develop and validate two clinical prediction models to estimate the risk of hyperkalaemia and hyponatraemia, respectively, to facilitate targeted treatment and monitoring strategies for individuals indicated for antihypertensive therapy.

Design and methods: Participants aged at least 40 years, registered to an English primary care practice within the Clinical Practice Research Datalink (CPRD), with a systolic blood pressure reading between 130 and 179 mmHg were included the study. The primary outcomes were first hyperkalaemia or hyponatraemia event recorded in primary or secondary care. Model development used a Fine-Gray approach with death from other causes as competing event. Model performance was assessed using C-statistic, D-statistic, and Observed/Expected (O/E) ratio upon external validation.

Results: The development cohort included 1 773 224 patients (mean age 59 years, median follow-up 6 years). The hyperkalaemia model contained 23 predictors and the hyponatraemia model contained 29 predictors, with all antihypertensive medications associated with the outcomes. Upon external validation in a cohort of 3 805 366 patients, both models calibrated well (O/E ratio: hyperkalaemia 1.16, 95% CI 1.13-1.19; hyponatraemia 1.00, 95% CI 0.98-1.02) and showed good discrimination at 10 years (C-statistic: 0.69, 95% CI 0.69-0.69; 0.80, 95% CI 0.80-0.80, respectively).

Conclusion: Current clinical guidelines recommend monitoring serum electrolytes after initiating antihypertensive treatment. These clinical prediction models predicted individuals' risk of electrolyte abnormalities associated with antihypertensive treatment and could be used to target closer monitoring for individuals at a higher risk, where resources are limited.

Keywords: ACE inhibitors; Angiotensin-converting enzyme inhibitors; CI; CPRD; CVD; Cardiovascular Disease; Confidence Interval; EHRs; Electronic Frailty Index; Electronic health records; Estimate Glomerular Filtration Rate; GP; General Practice; HES; Hospital Episode Statistics; IMD; IQR; ISAC; Independent Scientific Advisory Committee; Index of Multiple Deprivation; Interquartile Range; NICE; National Institute for Health and Care Excellence; O/E; ONS; Observed/Expected; RAAS; SD; SHR; STRATIFY; Standard Deviation; Sub-distribution hazard ratios; TRIPOD; The Clinical Practice Research Datalink; The Office for National Statistics; The Renin-Angiotensin-Aldosterone System; The STRAtifying Treatments In the multimorbid Frail elderlY; Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis; antihypertensive therapy; clinical decision-making; drug-related adverse effects; eFI; eGFR; serum electrolytes monitoring.