What is the treatment and management for patients with hypokalaemia? A critical review of the literature
Notice bibliographique
Résumé
Abstract Hypokalaemia is a common electrolyte disturbance in emergency departments and hospital settings with significant clinical and prognostic implications. About 11% of ED patients present with hypokalaemia (serum potassium [K+] <3.5 mmol/L), while 1.1% have severe cases (serum K+ <2.6 mmol/L). Despite its frequency, the UK lacks unified national guidelines and standardised protocols, resulting in variable treatment practices and increased patient safety risks. This dissertation critically examines current treatment and management strategies for hypokalaemia, emphasizing individualised care, vigilant monitoring, and protocol-driven approaches. Synthesising evidence from studies published between 2014 and 2024, the review analyses existing management practices, highlights gaps in clinical guidelines, and offers insights for evidence-based care. A critical literature review was conducted using databases such as Medline, CINAHL, and Scopus. English-language articles were selected based on criteria including adult populations, primary quantitative and qualitative research, guidelines, reviews, and meta-analyses. A thematic analysis approach was employed to identify patterns in hypokalaemia management, categorising data into three themes: individualised treatment approaches, monitoring and safety, and protocol-driven management. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included studies. Findings indicate that hypokalaemia management could benefit from more consistent protocols, as treatment decisions are influenced by clinical severity, patient-specific factors, and resource availability. Tailored approaches are crucial for vulnerable groups such as patients with heart disease, diabetes, and chronic kidney disease. Rigorous monitoring—including frequent serum K+ checks and electrocardiography (ECG)—is essential to prevent complications like overcorrection and arrhythmias. Magnesium supplementation, particularly in the presence of hypomagnesemia, appears beneficial in optimising K+ replacement. Protocol-driven methods, including automated systems (e.g., the GRIP-II algorithm) and sliding-scale dosing protocols, show promise in improving consistency and reducing clinical errors. However, the absence of national guidelines in the UK fosters inconsistency, with empirical rather than evidence-based practices prevailing. Integrating individualised treatment with thorough monitoring and automated protocols may lead to more consistent outcomes, reduced complications, and better resource allocation in emergency settings. Thus, coordinated efforts among clinicians and policymakers are essential for implementing unified, evidence-based guidelines for optimal hypokalaemia management.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».