Document Detail


An algorithm for QT interval monitoring in neonatal intensive care units.
MedLine Citation:
PMID:  17993306     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
QT surveillance of neonatal patients, and especially premature infants, may be important because of the potential for concomitant exposure to QT-prolonging medications and because of the possibility that they may have hereditary QT prolongation (long-QT syndrome), which is implicated in the pathogenesis of approximately 10% of sudden infant death syndrome. In-hospital automated continuous QT interval monitoring for neonatal and pediatric patients may be beneficial but is difficult because of high heart rates; inverted, biphasic, or low-amplitude T waves; noisy signal; and a limited number of electrocardiogram (ECG) leads available. Based on our previous work on an automated adult QT interval monitoring algorithm, we further enhanced and expanded the algorithm for application in the neonatal and pediatric patient population. This article presents results from evaluation of the new algorithm in neonatal patients. Neonatal-monitoring ECGs (n = 66; admission age range, birth to 2 weeks) were collected from the neonatal intensive care unit in 2 major teaching hospitals in the United States. Each digital recording was at least 10 minutes in length with a sampling rate of 500 samples per second. Special handling of high heart rate was implemented, and threshold values were adjusted specifically for neonatal ECG. The ECGs studied were divided into a development/training ECG data set (TRN), with 24 recordings from hospital 1, and a testing data set (TST), with 42 recordings composed of cases from both hospital 1 (n = 16) and hospital 2 (n = 26). Each ECG recording was manually annotated for QT interval in a 15-second period by 2 cardiologists. Mean and standard deviation of the difference (algorithm minus cardiologist), regression slope, and correlation coefficient were used to describe algorithm accuracy. Considering the technical problems due to noisy recordings, a high fraction (approximately 80%) of the ECGs studied were measurable by the algorithm. Mean and standard deviation of the error were both low (TRN = -3 +/- 8 milliseconds; TST = 1 +/- 20 milliseconds); regression slope (TRN = 0.94; TST = 0.83) and correlation coefficients (TRN = 0.96; TST = 0.85) (P < .0001) were fairly high. Performance on the TST was similar to that on the TRN with the exception of 2 cases. These results confirm that automated continuous QT interval monitoring in the neonatal intensive care setting is feasible and accurate and may lead to earlier recognition of the "vulnerable" infant.
Authors:
Eric D Helfenbein; Michael J Ackerman; Pentti M Rautaharju; Sophia H Zhou; Richard E Gregg; James M Lindauer; David Miller; John J Wang; Scott S Kresge; Saeed Babaeizadeh; Dirk Q Feild; Francis P Michaud
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of electrocardiology     Volume:  40     ISSN:  1532-8430     ISO Abbreviation:  J Electrocardiol     Publication Date:    2007 Nov-Dec
Date Detail:
Created Date:  2007-11-12     Completed Date:  2008-01-07     Revised Date:  2009-11-11    
Medline Journal Info:
Nlm Unique ID:  0153605     Medline TA:  J Electrocardiol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  S103-10     Citation Subset:  IM    
Affiliation:
Advanced Algorithm Research Center, Philips Medical Systems, Milpitas, CA, USA. eric.helfenbein@philips.com
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Diagnosis, Computer-Assisted / methods*
Electrocardiography / methods*
Humans
Infant, Newborn
Intensive Care / methods*
Long QT Syndrome / diagnosis*
Reproducibility of Results
Sensitivity and Specificity

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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