The Story of the Silent Killer

Kalehoff, Jonathan P; Oparil, Suzanne

doi:10.1007/s11906-020-01077-7

Cited by 39 publications

(11 citation statements)

References 106 publications

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“…Among the most common diseases of old age is hypertension [ 1 ]. Although it does not present any symptoms on its own, it is also known as a ‘silent killer’ due to its ability to lead to a variety of complications, such as stroke, renal failure, cerebral infarction, cerebral hemorrhage, and heart disease [ 2 , 3 , 4 , 5 ]. It is important to note, however, that despite these risks, hypertension has the characteristic that, unlike other chronic degenerative diseases, the risk of complications can be reduced through pharmacotherapy in daily life [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Study on Types of Medication Adherence in Hypertension among Older Patients and Influencing Factors

et al. 2022

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Background: Hypertension has the characteristic that the risk of complications can be reduced through appropriate medication in daily life. Hence, it is important to consider practical measures to increase medication adherence, particularly among older patients. Methods: This study used the Korea Health Panel 2020 data (Version 2.0.1), jointly conducted by Korea Institute for Health and Social Affairs and National Health Insurance. A total of 2300 patients with high blood pressure over 65 years of age were selected. In order to identify types of medication adherence in older hypertensive patients, and examine factors that influence the types, the Latent Profile Analysis (LPA) and logistic regression were performed. Results: The types of adherence groups were classified into two groups: an ‘adherence group’ (87.1%) and a ‘non-adherence group’ (12.9%). Furthermore, age, living alone, and depressive symptoms were identified as determinants of medication adherence type among older hypertensive patients. Conclusion: The significant impact of sociodemographic status (age, living alone, and depressive symptoms) on medication adherence among older hypertensive patients indicates the need to establish more specific empirical interventions based on each type’s characteristics. It is expected that this study will provide an in-depth understanding of factors associated with medication adherence among older patients with hypertension, which can support interventions tailored to the specific needs of those who are non-adherent.

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Section: Introductionmentioning

confidence: 99%

A Study on Types of Medication Adherence in Hypertension among Older Patients and Influencing Factors

et al. 2022

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“…Hypertension, also known as high blood pressure (BP), is a major global public health issue and a “silent killer”, according to the World Health Organization ( 1 ). Elevated BP disproportionately affects populations in low- and middle-income countries where health systems are underdeveloped.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Blood Pressure Using Only a Smartphone and Machine Learning Techniques: A Systematic Review

Haugg

Elgendi²,

Menon³

2022

Front. Cardiovasc. Med.

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Regular monitoring of blood pressure (BP) allows for early detection of hypertension and symptoms related to cardiovascular disease. Measuring BP with a cuff requires equipment that is not always readily available and it may be impractical for some patients. Smartphones are an integral part of the lives of most people; thus, detecting and monitoring hypertension with a smartphone is likely to increase the ability to monitor BP due to the convenience of use for many patients. Smartphones lend themselves to assessing cardiovascular health because their built-in sensors and cameras provide a means of detecting arterial pulsations. To this end, several image processing and machine learning (ML) techniques for predicting BP using a smartphone have been developed. Several ML models that utilize smartphones are discussed in this literature review. Of the 53 papers identified, seven publications were evaluated. The performance of the ML models was assessed based on their accuracy for classification, the mean error measure, and the standard deviation of error for regression. It was found that artificial neural networks and support vector machines were often used. Because a variety of influencing factors determines the performance of an ML model, no clear preference could be determined. The number of input features ranged from five to 233, with the most commonly used being demographic data and the features extracted from photoplethysmogram signals. Each study had a different number of participants, ranging from 17 to 5,992. Comparisons of the cuff-based measures were mostly used to validate the results. Some of these ML models are already used to detect hypertension and BP but, to satisfy possible regulatory demands, improved reliability is needed under a wider range of conditions, including controlled and uncontrolled environments. A discussion of the advantages of various ML techniques and the selected features is offered at the end of this systematic review.

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“…One of the most basic means of detecting irregularities is pulse assessment, which was first used in ancient China and Egypt in centuries B.C. [6][7][8]. In the modern era, the electrocardiogram (ECG) provides a detailed assessment of cardiac electrical activity, reflecting depolarization and repolarization of the atria and ventricles [9].…”

Section: Introductionmentioning

confidence: 99%

Detection and categorization of severe cardiac disorders based solely on pulse interval measurements

Shinomoto

Tsubo

Marunaka

2022

Preprint

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Cardiac disorders are common conditions associated with a high mortality rate. Due to their potential for causing serious symptoms, it is desirable to constantly monitor cardiac status using an accessible device such as a smartwatch. While electrocardiograms (ECGs) can make the detailed diagnosis of cardiac disorders, the examination is typically performed only once a year for each individual during health checkups, and it requires expert medical practitioners to make comprehensive judgments. Here we describe a newly developed automated system for alerting individuals about cardiac disorders solely based on pulse interval measurements. For this purpose, we examined two metrics of heart rate variability (HRV) and analyzed 1-day ECG recordings of more than 1,000 subjects in total. We found that a newly introduced metric of local variation was more efficient than conventional HRV metrics for detecting premature contraction, and furthermore, that a suitable combination of the old and new metrics resulted in much superior detectability particularly for atrial fibrillation, which requires more attention. Even with a 1-minute recording of pulse intervals, our new detection system had a diagnostic performance even better than that of the conventional analysis method applied to a 1-day recording.

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The Story of the Silent Killer

Cited by 39 publications

References 106 publications

A Study on Types of Medication Adherence in Hypertension among Older Patients and Influencing Factors

A Study on Types of Medication Adherence in Hypertension among Older Patients and Influencing Factors

Assessment of Blood Pressure Using Only a Smartphone and Machine Learning Techniques: A Systematic Review

Detection and categorization of severe cardiac disorders based solely on pulse interval measurements

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