Transient ST segment depression during holter monitoring: How to avoid false positive findings

Völler, Heinz; Andresen, Dietrich; Brüggemann, Thomas; Jereczek, Matthias; Becker, Bernd; Schröder, Rolf

doi:10.1016/0002-8703(92)90269-2

Cited by 15 publications

(4 citation statements)

References 24 publications

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“…We used broad criteria for diagnosis of CVD in our HD population and have omitted chest pain because in HD patients it may be either nonspecific or nonsensitive. The criteria used for diagnosis of CVD in the Holter monitoring studies were stringent [21]. Typical ischemic changes associated with LVH were also included because LVH is also a major prognostic predictor in HD patients, even without coronary disease [22].…”

Section: Discussionmentioning

confidence: 99%

The Common Mutations C677T and A1298C in the Human Methylenetetrahydrofolate Reductase Gene Are Associated with Hyperhomocysteinemia and Cardiovascular Disease in Hemodialysis Patients

Haviv

Shpichinetsky

Goldschmidt

et al. 2002

Nephron

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Background: Plasma total homocysteine (tHcy) level might be an important risk factor for the development of cardiovascular disease (CVD) in dialysis patients. While both renal failure and mutations of the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene may result in hyperhomocysteinemia and CVD, the distinct roles of the thermolabile MTHFR mutation at nucleotide C677T and the more recently described mutation at nucleotide A1298C have not been evaluated concurrently in patients on hemodialysis. Methods: A cross-sectional study was performed in 120 maintenance HD patients to determine the prevalence of MTHFR C677T and A1298C mutations and their relative association to hyperhomocysteinemia and CVD. Results: Both mutations, the C677T and the A1298C, were highly prevalent in HD patients with allele frequencies of 0.41 and 0.27, respectively. The prevalence of CVD in HD patients was 55% and its significant risk factors included, in descending order, hyperhomocysteinemia, MTHFR C677T mutation, low serum folate levels, diabetes mellitus, hypertension, and double heterozygote state for both MTHFR mutations (677CT/1298AC). MTHFR A1298C mutation alone and gender were not associated with either hyperhomocysteinemia or increased CVD risk, but the HD patients with homozygotes 1298CC and wild alleles 677CC (677CC/1298CC) have significant increase of tHcy (37.7 ± 12) and high prevalence of CVD. Conclusions: Hyperhomocysteinemia, serum folate levels and both C677T and A1298C MTHFR mutations are associated with CVD in HD patients.

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Section: Discussionmentioning

confidence: 99%

The Common Mutations C677T and A1298C in the Human Methylenetetrahydrofolate Reductase Gene Are Associated with Hyperhomocysteinemia and Cardiovascular Disease in Hemodialysis Patients

Haviv

Shpichinetsky

Goldschmidt

et al. 2002

Nephron

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“…Onset and termination of ST segment change should be gradual and not coincident with abrupt changes in QRS amplitude or RR interval. Postural ST segment shifts based on initial orthostatic testing in the supine, prone, sitting, or standing position were excluded from analysis 12,13 . On the 24‐hour ST segment trend curve, transitional augmentation of ST elevation ≥1.5 mm over 20 minutes was determined.…”

Section: Methodsmentioning

confidence: 99%

Vagal Activity Modulates Spontaneous Augmentation of ST Elevation in the Daily Life of Patients with Brugada Syndrome

Mizumaki

Fujiki

Tsuneda

et al. 2004

Cardiovasc electrophysiol

174

115

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“…Another technique, based on the analysis of the ECG waveform, has shown promise since abnormalities in the repolarization of ischaemic myocardial regions are visible in the ST segment of the ECG [2,3]. Although changes in ST elevation/depression can be quantified they can also occur because of a wide variety of other causes, including changes in heart rate, conduction pattern, hyperventilation, electrolyte abnormalities, response to medication, response to temperature changes, position of the subject, and noise in the ECG [4,5].Despite these uncertainties, ECG measurements can be highly sensitive, easy to do, and lend themselves to ambulatory (e.g. 24 hour) assessments [6][7][8] and computer-based automated analyses [9][10][11].…”

mentioning

confidence: 99%

An algorithm to distinguish ischaemic and non-ischaemic ST changes in the Holter ECG

Langley

Bowers

Wild

et al. 2003

Computers in Cardiology, 2003

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Changes in the ECG IntroductionTherapeutic intervention to reduce transient myocardial ischaemic episodes could significantly improve the quality of life in affected subjects by reducing morbidity and mortality. The current methods of diagnosing these ischaemic events include cardiovascular imaging of the coronary arteries [1]. However, such specialised and resource intensive techniques are, arguably, unsuitable for studying ischaemic events brought on by activities of daily living in any one individual. Another technique, based on the analysis of the ECG waveform, has shown promise since abnormalities in the repolarization of ischaemic myocardial regions are visible in the ST segment of the ECG [2,3]. Although changes in ST elevation/depression can be quantified they can also occur because of a wide variety of other causes, including changes in heart rate, conduction pattern, hyperventilation, electrolyte abnormalities, response to medication, response to temperature changes, position of the subject, and noise in the ECG [4,5].Despite these uncertainties, ECG measurements can be highly sensitive, easy to do, and lend themselves to ambulatory (e.g. 24 hour) assessments [6][7][8] and computer-based automated analyses [9][10][11]. If it were possible to accurately distinguish between ischaemic and non-ischaemic ST changes in ambulatory ECG recordings made during subjects' normal activities, the benefits could be immediate and substantial to the patient.The aims of this study were therefore a) to produce a novel algorithm t o distinguish ischaemic and nonischaemic ST changes in the ECG waveform and b) to determine the accuracy of the algorithm using expertly annotated ambulatory ECG data sets as a reference. Methods Basic algorithmData for the changes in ST ( D ST) provided by PhysioNet were used. An example of DST is shown in figure 1. Event start times (T s ), for which the expertly classified ST changes (ischaemic or non-ischaemic) were known, were also provided and used in the development of the algorithm. DST represents the difference in ST between the current ST level and the baseline level. Principal components of ST provided by PhysioNet were also used in the optimization of the algorithm. The algorithm was based on the premise that ischaemic ST changes are large relative to non-ischaemic changes and that they are maintained for a period of time. The algorithm classified events as ischaemic if at the start of the event DST was greater than a threshold DST (V thres ), and before the end of the event DST maintained a minimum level (V min ) for a period of time (T min ).

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Transient ST segment depression during holter monitoring: How to avoid false positive findings

Cited by 15 publications

References 24 publications

The Common Mutations C677T and A1298C in the Human Methylenetetrahydrofolate Reductase Gene Are Associated with Hyperhomocysteinemia and Cardiovascular Disease in Hemodialysis Patients

The Common Mutations C677T and A1298C in the Human Methylenetetrahydrofolate Reductase Gene Are Associated with Hyperhomocysteinemia and Cardiovascular Disease in Hemodialysis Patients

Vagal Activity Modulates Spontaneous Augmentation of ST Elevation in the Daily Life of Patients with Brugada Syndrome

An algorithm to distinguish ischaemic and non-ischaemic ST changes in the Holter ECG

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