Evidence map›Paper›PMID 26502065›Full record

ArticlePhysiological measurement2015

Developing new predictive alarms based on ECG metrics for bradyasystolic cardiac arrest.

Quan Ding, Yong Bai, Adelita Tinoco, David Mortara, Duc Do, Noel G Boyle, Michele M Pelter, Xiao Hu

Abstract read
In one paragraph

Article in Physiological measurement, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Quan DingDepartment of Physiological Nursing, School of Nursing, University of California, 2 Koret Way, Nursing, San Francisco, CA 94143, USA.
Yong Bai
Adelita Tinoco
David Mortara
Duc Do
Noel G Boyle
Michele M Pelter
Xiao Hu
University of California, San Francisco · USUCLA Health · USSamueli Institute · USUniversity of California, Los Angeles · US

Funding

Develop&validate SuperAlarm to detect patient deterioration with few false alarmsR01HL128679 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HU, XIAO · 2015 to 2018
$2.4M
NHLBI NIH HHS R01 HL128679NHLBI NIH HHS R01HL128679
6 · The paper itself

Abstract

We investigated 17 metrics derived from four leads of electrocardiographic (ECG) signals from hospital patient monitors to develop new ECG alarms for predicting adult bradyasystolic cardiac arrest events.A retrospective case-control study was designed to analyze 17 ECG metrics from 27 adult bradyasystolic and 304 control patients. The 17 metrics consisted of PR interval (PR), P-wave duration (Pdur), QRS duration (QRSdur), RR interval (RR), QT interval (QT), estimate of serum K  +  using only frontal leads (SerumK2), T-wave complexity (T Complex), ST segment levels for leads I, II, V (ST I, ST II, ST V), and 7 heart rate variability (HRV) metrics. These 7 HRV metrics were standard deviation of normal to normal intervals (SDNN), total power, very low frequency power, low frequency power, high frequency power, normalized low frequency power, and normalized high frequency power. Controls were matched by gender, age (±5 years), admission to the same hospital unit within the same month, and the same major diagnostic category. A research ECG analysis software program developed by co-author D M was used to automatically extract the metrics. The absolute value for each ECG metric, and the duration, terminal value, and slope of the dominant trend for each ECG metric, were derived and tested as the alarm conditions. The maximal true positive rate (TPR) of detecting cardiac arrest at a prescribed maximal false positive rate (FPR) based on the trending conditions was reported. Lead time was also recorded as the time between the first time alarm condition was triggered and the event of cardiac arrest.While conditions based on the absolute values of ECG metrics do not provide discriminative information to predict bradyasystolic cardiac arrest, the trending conditions can be useful. For example, with a max FPR  =  5.0%, some derived alarms conditions are: trend duration of PR  >  2.8 h (TPR  =  48.2%, lead time  =  10.0  ±  6.6 h), trend duration of QRSdur  >  2.7 h (TPR  =  40.7%, lead time  =  8.8  ±  6.2 h), trend duration of RR  >  3.5 h (TPR  =  51.9%, lead time  =  6.4  ±  5.5 h), trend duration of T Complex  >  2.9 h (TPR  =  40.7%, lead time  =  6.8  ±  5.5 h), trend duration of ST I  >  3.0 h (TPR of 51.9%, lead time  =  8.4  ±  8.0 h), trend duration of SDNN  >  3.6 h (TPR of 40.7%, lead time  =  11.0  ±  8.6 h), trend duration of HRV total power  >  3.0 h (TPR of 25.9%, lead time  =  7.5  ±  8.1 h), terminal value of ST I  <  -56 µV (TPR  =  22.2%, lead time  =  12.8  ±  8.3 h), and slope of QR  >  19.4 ms h(-1) (TPR  =  25.9%, lead time  =  6.7  ±  6.9 h). Eleven trend duration alarms, eight terminal value alarms, and ten slope alarms, achieved a positive TPR with zero FPR. Furthermore, these alarms conditions with zero PFR can be combined by the 'OR'logic could further improve the TPR without increasing the FPR.The trend duration, terminal value, and slope of the dominant trend of the ECG metrics considered in this study are able to predict a subset of patients with bradyasystolic cardiac arrests with low or even zero FPR, which can be used for developing new ECG alarms.

Indexed as

Clinical AlarmsElectrocardiographySignal Processing, Computer-AssistedSystoleCase-Control StudiesFemaleHeart ArrestHumansMaleMiddle AgedRetrospective Studies

Identifiers

PMID26502065
PMCPMC4838570
OpenAlexW2247410316

What OpenQuestion holds

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Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.