| Title |
Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score
|
|---|---|
| Published in |
Critical Care, June 2012
|
| DOI | 10.1186/cc11396 |
| Pubmed ID | |
| Authors |
Marcus Eng Hock Ong, Christina Hui Lee Ng, Ken Goh, Nan Liu, Zhi Xiong Koh, Nur Shahidah, Tong Tong Zhang, Stephanie Fook-Chong, Zhiping Lin |
| Abstract |
ABSTRACT: INTRODUCTION: A key aim of triage is to identify those with high risk of cardiac arrest, as they require intensive monitoring, resuscitation facilities, and early intervention. We aim to validate a novel machine learning (ML) score incorporating heart rate variability (HRV) for triage of critically ill patients presenting to the emergency department by comparing the area under the curve, sensitivity and specificity with the modified early warning score (MEWS). METHODS: We conducted a prospective observational study of critically ill patients (Patient Acuity Category Scale 1 and 2) in an emergency department of a tertiary hospital. At presentation, HRV parameters generated from a 5-minute electrocardiogram recording are incorporated with age and vital signs to generate the ML score for each patient. The patients are then followed up for outcomes of cardiac arrest or death. RESULTS: From June 2006 to June 2008 we enrolled 925 patients. The area under the receiver operating characteristic curve (AUROC) for ML scores in predicting cardiac arrest within 72 hours is 0.781, compared with 0.680 for MEWS (difference in AUROC: 0.101, 95% confidence interval: 0.006 to 0.197). As for in-hospital death, the area under the curve for ML score is 0.741, compared with 0.693 for MEWS (difference in AUROC: 0.048, 95% confidence interval: -0.023 to 0.119). A cutoff ML score ≥ 60 predicted cardiac arrest with a sensitivity of 84.1%, specificity of 72.3% and negative predictive value of 98.8%. A cutoff MEWS ≥ 3 predicted cardiac arrest with a sensitivity of 74.4%, specificity of 54.2% and negative predictive value of 97.8%. CONCLUSION: We found ML scores to be more accurate than the MEWS in predicting cardiac arrest within 72 hours. There is potential to develop bedside devices for risk stratification based on cardiac arrest prediction. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 14% |
| Norway | 1 | 14% |
| United Kingdom | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 71% |
| Practitioners (doctors, other healthcare professionals) | 2 | 29% |
Mendeley readers
The data shown below were compiled from readership statistics for 270 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | <1% |
| United Kingdom | 2 | <1% |
| Canada | 2 | <1% |
| Finland | 1 | <1% |
| India | 1 | <1% |
| Czechia | 1 | <1% |
| France | 1 | <1% |
| South Africa | 1 | <1% |
| Singapore | 1 | <1% |
| Other | 2 | <1% |
| Unknown | 256 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 53 | 20% |
| Student > Ph. D. Student | 39 | 14% |
| Student > Master | 33 | 12% |
| Student > Bachelor | 26 | 10% |
| Other | 16 | 6% |
| Other | 56 | 21% |
| Unknown | 47 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 88 | 33% |
| Engineering | 36 | 13% |
| Computer Science | 30 | 11% |
| Nursing and Health Professions | 19 | 7% |
| Agricultural and Biological Sciences | 6 | 2% |
| Other | 30 | 11% |
| Unknown | 61 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 April 2022.
All research outputs
#2,562,383
of 25,374,647 outputs
Outputs from Critical Care
#2,224
of 6,554 outputs
Outputs of similar age
#15,751
of 177,440 outputs
Outputs of similar age from Critical Care
#12
of 126 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,554 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.8. This one has gotten more attention than average, scoring higher than 66% of its peers.
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We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.