4.2 Article

Pediatric blood pressures during anesthesia assessed using normalization and principal component analysis techniques

Journal

JOURNAL OF CLINICAL MONITORING AND COMPUTING
Volume 33, Issue 4, Pages 589-595

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s10877-018-0199-z

Keywords

Pediatrics; Blood pressure; General anesthesia; Monitoring; Normalization; Principal component analysis

Categories

Funding

  1. National Institutes of Health [R01-GM121457]

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Expected values for blood pressure are known for both unanesthetized and anesthetized children. The statistics of changes in blood pressure during anesthesia, which may have important diagnostic significance, have not been reported. The purpose of this study was to report the variation in changes in blood pressure in four pediatric age groups, undergoing both cardiac and non-cardiac surgery. An analysis of the changes in blood pressure using normalization and principal component analysis techniques was performed using an existing electronic dataset of intra-arterial pediatric blood pressure values during anesthesia. Cardiac and noncardiac cases were analyzed separately. For 1361 non-cardiac cases, the average systolic blood pressure increased from 55.2 (17.6)mmHg in the first month of life to 85.4 (17.7)mmHg at 5-6years. For 912 cardiac cases, the average systolic blood pressure increased from 55.7 (16.7) to 71.8 (24.8)mmHg in these cohorts. For non-cardiac cases in the first month, the mean (SD) for changein blood pressure over a 30s period was 0.00 (8.8), for 5-6year olds 0.0 (7.4); for cardiac cases, 0.1 (9.2) to -0.1 (9.2). Variations in systolic blood pressure over a 5-min period were wider: in non-cardiac from 0.1 (12.2) mmHg (first month) to 0.4 (11.5) mmHg (5-6year old) and from 0.2 (12.5) to 0.4 (14.2)mmHg in cardiac cases. Absolute blood pressures and changes in blood pressure during anesthesia in pediatric cardiac and non-cardiac surgical cases have been analyzed from a population database. Using these values, the quantitative methods of normalization and principal component analysis allow the identification of statistically significant changes.

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