Integrating heart rate variability, vital signs, electrocardiogram, and troponin to triage chest pain patients in the ED

2018 ◽  
Vol 36 (2) ◽  
pp. 185-192 ◽  
Author(s):  
Jeffrey Tadashi Sakamoto ◽  
Nan Liu ◽  
Zhi Xiong Koh ◽  
Dagang Guo ◽  
Micah Liam Arthur Heldeweg ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Chest Pain ◽  
Vital Signs ◽  
Pain Patients

scholarly journals 38 Heart rate variability and pre-hospital risk stratification of chest pain patients

2018 ◽  
Author(s):  
Chu En Ting ◽  
Nan Liu ◽  
Zhi Xiong Koh ◽  
Dagang Guo ◽  
Janson Cheng Ji NG ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Chest Pain ◽  
Risk Stratification ◽  
Pain Patients

Heart Rate Variability Analysis Is More Sensitive At Identifying Neonatal Sepsis Than Conventional Vital Signs

2014 ◽  
Vol 186 (2) ◽  
pp. 507
Author(s):  
A.A. Mrazek ◽  
M.T. Shabana ◽  
G.L. Radhakrishnan ◽  
G.C. Kramer ◽  
R.S. Radhakrishnan
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Neonatal Sepsis ◽  
Vital Signs ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis

Patient Outcome Prediction with Heart Rate Variability and Vital Signs

2010 ◽  
Vol 64 (2) ◽  
pp. 265-278 ◽  
Author(s):  
Nan Liu ◽  
Zhiping Lin ◽  
Zhixiong Koh ◽  
Guang-Bin Huang ◽  
Wee Ser ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Outcome Prediction ◽  
Vital Signs

Pain Assessment in Musculoskeletal Pain Patients by Heart Rate Variability

2007 ◽  
Vol 15 (4) ◽  
pp. 67-74 ◽  
Author(s):  
Chiung-Cheng Chuang ◽  
Wen-Yaw Chung ◽  
Chen Shu ◽  
Miao-Wen Chen
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Musculoskeletal Pain ◽  
Pain Assessment ◽  
Pain Patients

Heart Rate Variability Analysis in Patients Who Have Bradycardia Presenting to the Emergency Department with Chest Pain

2018 ◽  
Vol 54 (3) ◽  
pp. 273-280 ◽  
Author(s):  
Jeffrey Tadashi Sakamoto ◽  
Nan Liu ◽  
Zhi Xiong Koh ◽  
Dagang Guo ◽  
Micah Liam Arthur Heldeweg ◽  
...  
Keyword(s):  
Emergency Department ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Chest Pain ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis

Early prediction of serious infections in febrile infants incorporating heart rate variability in an emergency department: a pilot study

2021 ◽  
pp. emermed-2020-210675
Author(s):  
Shu-Ling Chong ◽  
Gene Yong-Kwang Ong ◽  
John Carson Allen ◽  
Jan Hau Lee ◽  
Rupini Piragasam ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Vital Signs ◽  
Model Performance ◽  
Diagnostic Challenge ◽  
Poincaré Plot ◽  
Time Frequency ◽  
Young Infants ◽  
Serious Infections ◽  
Non Linear

BackgroundEarly differentiation of febrile young infants with from those without serious infections (SIs) remains a diagnostic challenge. We sought to (1) compare vital signs and heart rate variability (HRV) parameters between febrile infants with versus without SIs, (2) assess the performance of HRV and vital signs with reference to current triage tools and (3) compare HRV and vital signs to HRV, vital signs and blood biomarkers, when predicting for the presence of SIs.MethodsUsing a prospective observational design, we recruited patients <3 months old presenting to a tertiary paediatric ED in Singapore from December 2018 through November 2019. We obtained patient demographic characteristics, triage assessment (including the Severity Index Score (SIS)), HRV parameters (time, frequency and non-linear domains) and laboratory results. We performed multivariable logistic regression analyses to predict the presence of an SI, using area under the curve (AUC) with the corresponding 95% CI to assess predictive capability.ResultsAmong 203 infants with a mean age of 38.4 days (SD 27.6), 67 infants (33.0%) had an SI. There were significant differences in the time, frequency and non-linear domains of HRV parameters between infants with versus without SIs. In predicting SIs, gender, temperature and the HRV non-linear parameter Poincaré plot SD2 (AUC 0.78, 95% CI 0.71 to 0.84) performed better than SIS alone (AUC 0.61, 95% CI 0.53 to 0.68). Model performance improved with the addition of absolute neutrophil count and C reactive protein (AUC 0.82, 95% CI 0.76 to 0.89).ConclusionAn exploratory prediction model incorporating HRV and biomarkers improved prediction of SIs. Further research is needed to assess if HRV can identify which young febrile infants have an SI at ED triage.Trial registration numberNCT04103151.

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