Dataset Information

A Novel Electrocardiogram Algorithm Utilizing ST-Segment Instability for Detection of Cardiopulmonary Arrest in Single Ventricle Physiology: A Retrospective Study.

ABSTRACT:

Objective

We evaluated ST-segment monitoring to detect clinical decompensation in infants with single ventricle anatomy. We proposed a signal processing algorithm for ST-segment instability and hypothesized that instability is associated with cardiopulmonary arrests.

Design

Retrospective, observational study.

Setting

Tertiary children's hospital 21-bed cardiovascular ICU and 36-bed step-down unit.

Patients

Twenty single ventricle infants who received stage 1 palliation surgery between January 2013 and January 2014. Twenty rapid response events resulting in cardiopulmonary arrests (arrest group) were recorded in 13 subjects, and nine subjects had no interstage cardiopulmonary arrest (control group).

Interventions

None.

Measurements and main results

Arrest data were collected over the 4-hour time window prior to cardiopulmonary arrest. Control data were collected from subjects with no interstage arrest using the 4-hour time window prior to cardiovascular ICU discharge. A paired subgroup analysis was performed comparing subject 4-hour windows prior to arrest (prearrest group) with 4-hour windows prior to discharge (postarrest group). Raw values of ST segments were compared between groups. A 3D ST-segment vector was created using three quasi-orthogonal leads (II, aVL, and V5). Magnitude and instability of this continuous vector were compared between groups. There was no significant difference in mean unprocessed ST-segment values in the arrest and control groups. Utilizing signal processing, there was an increase in the ST-vector magnitude (p = 0.02) and instability (p = 0.008) in the arrest group. In the paired subgroup analysis, there was an increase in the ST-vector magnitude (p = 0.05) and instability (p = 0.05) in the prearrest state compared with the postarrest state prior to discharge.

Conclusions

In single ventricle patients, increased ST instability and magnitude were associated with rapid response events that required intervention for cardiopulmonary arrest, whereas conventional ST-segment monitoring did not differentiate an arrest from control state.

SUBMITTER: Vu EL

PROVIDER: S-EPMC5466171 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Novel Electrocardiogram Algorithm Utilizing ST-Segment Instability for Detection of Cardiopulmonary Arrest in Single Ventricle Physiology: A Retrospective Study.

Vu Eric L EL Rusin Craig G CG Penny Dan J DJ Kibler Kathy K KK Easley Ronald Blaine RB Smith Brendan B Andropoulos Dean D Brady Ken K

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 20170101 1

<h4>Objective</h4>We evaluated ST-segment monitoring to detect clinical decompensation in infants with single ventricle anatomy. We proposed a signal processing algorithm for ST-segment instability and hypothesized that instability is associated with cardiopulmonary arrests.<h4>Design</h4>Retrospective, observational study.<h4>Setting</h4>Tertiary children's hospital 21-bed cardiovascular ICU and 36-bed step-down unit.<h4>Patients</h4>Twenty single ventricle infants who received stage 1 palliati ...[more]

PMID: 27755397

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Project description:Differentiation between ischaemic and non-ischaemic transient ST segment events of long term ambulatory electrocardiograms is a persisting weakness in present ischaemia detection systems. Traditional ST segment level measuring is not a sufficiently precise technique due to the single point of measurement and severe noise which is often present. We developed a robust noise resistant orthogonal-transformation based delineation method, which allows tracing the shape of transient ST segment morphology changes from the entire ST segment in terms of diagnostic and morphologic feature-vector time series, and also allows further analysis. For these purposes, we developed a new Legendre Polynomials based Transformation (LPT) of ST segment. Its basis functions have similar shapes to typical transient changes of ST segment morphology categories during myocardial ischaemia (level, slope and scooping), thus providing direct insight into the types of time domain morphology changes through the LPT feature-vector space. We also generated new Karhunen and Lo ève Transformation (KLT) ST segment basis functions using a robust covariance matrix constructed from the ST segment pattern vectors derived from the Long Term ST Database (LTST DB). As for the delineation of significant transient ischaemic and non-ischaemic ST segment episodes, we present a study on the representation of transient ST segment morphology categories, and an evaluation study on the classification power of the KLT- and LPT-based feature vectors to classify between ischaemic and non-ischaemic ST segment episodes of the LTST DB. Classification accuracy using the KLT and LPT feature vectors was 90% and 82%, respectively, when using the k-Nearest Neighbors (k = 3) classifier and 10-fold cross-validation. New sets of feature-vector time series for both transformations were derived for the records of the LTST DB which is freely available on the PhysioNet website and were contributed to the LTST DB. The KLT and LPT present new possibilities for human-expert diagnostics, and for automated ischaemia detection.

Dataset Information

A Novel Electrocardiogram Algorithm Utilizing ST-Segment Instability for Detection of Cardiopulmonary Arrest in Single Ventricle Physiology: A Retrospective Study.

Objective

Design

Setting

Patients

Interventions

Measurements and main results

Conclusions

Publications

A Novel Electrocardiogram Algorithm Utilizing ST-Segment Instability for Detection of Cardiopulmonary Arrest in Single Ventricle Physiology: A Retrospective Study.

Similar Datasets

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