Prevention of hypokalemia induced adverse cardiovascular effects in diabetic ketoacidosis: novel role of pH-adjusted potassium

Usman, Atif; Mustafa, Norlaila; Dujaili, Juman Abdulelah; Makmor‐Bakry, Mohd; Gan, Siew Hua

doi:10.20452/pamw.15634

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Using an iPhone app for diabetes self-management and help from a Certified Diabetes Educator (CDE) using the data generated by the app is one option for intervention. e other is a control group that continues to get the same level of treatment as before [11]. It is called "Glucose Buddy," and it has useful features like letting you manually enter your blood glucose levels, insulin dosages, nutrition, and viewing a personalised health information graph.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Machine Learning Implementation of a Diabetic Patient Monitoring System Using Interactive E‐App

Alazzam

Mansour

Alassery

et al. 2021

Computational Intelligence and Neuroscience

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Lifestyle influences morbidity and mortality rates in the world. Physical activity, a healthy weight, and a healthy diet are key preventative health behaviours that help reduce the risk of developing type 2 diabetes and its complications, such as cardiovascular disease. A healthy lifestyle has been shown to prevent or delay chronic diseases and their complications, but few people follow all recommended self-management behaviours. This work seeks to improve knowledge of factors affecting type 2 diabetes self-management and prevention through lifestyle changes. This paper describes the design, development, and testing of a diabetes self-management mobile app. The app tracked dietary consumption and health data. Bluetooth movement data from a pair of wearable insole devices are used to track carbohydrate intake, blood glucose, medication adherence, and physical activity. Two machine learning models were constructed to recognise sitting and standing. The SVM and decision tree models were 86% accurate for these tasks. The decision tree model is used in a real-time activity classification app. It is exciting to see more and more mobile health self-management apps being used to treat chronic diseases.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Machine Learning Implementation of a Diabetic Patient Monitoring System Using Interactive E‐App

Alazzam

Mansour

Alassery

et al. 2021

Computational Intelligence and Neuroscience

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show abstract

“…For once, this seems surprising considering it is well known that the measured S K upon DKA presentation is because of ECF borrowing potassium from ICF and hence, the actual loss of potassium is curtained. In this regard, we recently found that the pH K is a significant indicator for hypokalemic ECG changes than S K 15 . Therefore, to fill this newly revisited gap regarding hypokalemia‐related burden in DKA guidelines, we aimed to further analyse our data to find if pH K influences hypokalemia and its related parameters?…”

Section: Introductionmentioning

confidence: 99%

“…In this regard, we recently found that the pH K is a significant indicator for hypokalemic ECG changes than S K . 15 Therefore, to fill this newly revisited gap regarding hypokalemia-related burden in DKA guidelines, we aimed to further analyse our data to find if pH K influences hypokalemia and its related parameters? We set objectives to identify all the hypokalemia-related entities where pH K played a role in determining hypokalemia in DKA patients.…”

mentioning

confidence: 99%

Mapping the role of pH‐adjusted potassium in diabetic ketoacidosis: Hypokalemia and the patient outcomes

et al. 2021

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Hypokalemia is a frequently observed complication during the treatment of diabetic ketoacidosis (DKA). Its incidence ranges anywhere between 4% and 60% where it adversely affects patient outcomes and may also add to the total cost of DKA treatment and extended hospitalisation. 1,2 Mainly, insulin is responsible to maintain the intracellular-(ICF) and extracellular-fluid (ECF) balance of potassium. 3 In addition, catecholamines and aldosterone also regulate serum potassium (S K ) level. 3,4 Pathophysiology of DKA causes an abrupt change in the concentration of these hormones and total body water (TBW). However, the resultant effect is observed partially at the time of admission when patients are either hyperkalemic or eukalemic. 5,6 With the commencement of DKA treatment, expansion of contracted hemoconcentration and insulin-mediated ECF to ICF movement of potassium causes a sudden drop in S K . 7,8 Though such

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Prevention of hypokalemia induced adverse cardiovascular effects in diabetic ketoacidosis: novel role of pH-adjusted potassium

Cited by 2 publications

References 7 publications

[Retracted] Machine Learning Implementation of a Diabetic Patient Monitoring System Using Interactive E‐App

[Retracted] Machine Learning Implementation of a Diabetic Patient Monitoring System Using Interactive E‐App

Mapping the role of pH‐adjusted potassium in diabetic ketoacidosis: Hypokalemia and the patient outcomes

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