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Campanaro CK, Nethery DE, Guo F, Kaffashi F, Loparo KA, Jacono FJ, Dick TE, Hsieh YH. Dynamics of ventilatory pattern variability and Cardioventilatory Coupling during systemic inflammation in rats. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1038531. [PMID: 37583625 PMCID: PMC10423997 DOI: 10.3389/fnetp.2023.1038531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 06/20/2023] [Indexed: 08/17/2023]
Abstract
Introduction: Biometrics of common physiologic signals can reflect health status. We have developed analytics to measure the predictability of ventilatory pattern variability (VPV, Nonlinear Complexity Index (NLCI) that quantifies the predictability of a continuous waveform associated with inhalation and exhalation) and the cardioventilatory coupling (CVC, the tendency of the last heartbeat in expiration to occur at preferred latency before the next inspiration). We hypothesized that measures of VPV and CVC are sensitive to the development of endotoxemia, which evoke neuroinflammation. Methods: We implanted Sprague Dawley male rats with BP transducers to monitor arterial blood pressure (BP) and recorded ventilatory waveforms and BP simultaneously using whole-body plethysmography in conjunction with BP transducer receivers. After baseline (BSLN) recordings, we injected lipopolysaccharide (LPS, n = 8) or phosphate buffered saline (PBS, n =3) intraperitoneally on 3 consecutive days. We recorded for 4-6 h after the injection, chose 3 epochs from each hour and analyzed VPV and CVC as well as heart rate variability (HRV). Results: First, the responses to sepsis varied across rats, but within rats the repeated measures of NLCI, CVC, as well as respiratory frequency (fR), HR, BP and HRV had a low coefficient of variation, (<0.2) at each time point. Second, HR, fR, and NLCI increased from BSLN on Days 1-3; whereas CVC decreased on Days 2 and 3. In contrast, changes in BP and the relative low-(LF) and high-frequency (HF) of HRV were not significant. The coefficient of variation decreased from BSLN to Day 3, except for CVC. Interestingly, NLCI increased before fR in LPS-treated rats. Finally, we histologically confirmed lung injury, systemic inflammation via ELISA and the presence of the proinflammatory cytokine, IL-1β, with immunohistochemistry in the ponto-medullary respiratory nuclei. Discussion: Our findings support that NLCI reflects changes in the rat's health induced by systemic injection of LPS and reflected in increases in HR and fR. CVC decreased over the course to the experiment. We conclude that NLCI reflected the increase in predictability of the ventilatory waveform and (together with our previous work) may reflect action of inflammatory cytokines on the network generating respiration.
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Affiliation(s)
- Cara K. Campanaro
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - David E. Nethery
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Fei Guo
- Institute for Smart, Secure and Connected Systems (ISSACS), Case Western Reserve University, Cleveland, OH, United States
| | - Farhad Kaffashi
- Institute for Smart, Secure and Connected Systems (ISSACS), Case Western Reserve University, Cleveland, OH, United States
| | - Kenneth A. Loparo
- Institute for Smart, Secure and Connected Systems (ISSACS), Case Western Reserve University, Cleveland, OH, United States
| | - Frank J. Jacono
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland, OH, United States
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States
| | - Thomas E. Dick
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland, OH, United States
- Department of Neurosciences, Case Western Reserve University, Cleveland, OH, United States
| | - Yee-Hsee Hsieh
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Case Western Reserve University, Cleveland, OH, United States
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Liu N, Chee ML, Foo MZQ, Pong JZ, Guo D, Koh ZX, Ho AFW, Niu C, Chong SL, Ong MEH. Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department. PLoS One 2021; 16:e0249868. [PMID: 34460853 PMCID: PMC8405012 DOI: 10.1371/journal.pone.0249868] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 07/06/2021] [Indexed: 11/18/2022] Open
Abstract
Sepsis is a potentially life-threatening condition that requires prompt recognition and treatment. Recently, heart rate variability (HRV), a measure of the cardiac autonomic regulation derived from short electrocardiogram tracings, has been found to correlate with sepsis mortality. This paper presents using novel heart rate n-variability (HRnV) measures for sepsis mortality risk prediction and comparing against current mortality prediction scores. This study was a retrospective cohort study on patients presenting to the emergency department of a tertiary hospital in Singapore between September 2014 to April 2017. Patients were included if they were above 21 years old and were suspected of having sepsis by their attending physician. The primary outcome was 30-day in-hospital mortality. Stepwise multivariable logistic regression model was built to predict the outcome, and the results based on 10-fold cross-validation were presented using receiver operating curve analysis. The final predictive model comprised 21 variables, including four vital signs, two HRV parameters, and 15 HRnV parameters. The area under the curve of the model was 0.77 (95% confidence interval 0.70–0.84), outperforming several established clinical scores. The HRnV measures may have the potential to allow for a rapid, objective, and accurate means of patient risk stratification for sepsis severity and mortality. Our exploration of the use of wealthy inherent information obtained from novel HRnV measures could also create a new perspective for data scientists to develop innovative approaches for ECG analysis and risk monitoring.
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Affiliation(s)
- Nan Liu
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Health Services Research Centre, Singapore Health Services, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
- * E-mail:
| | - Marcel Lucas Chee
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Mabel Zhi Qi Foo
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
| | - Jeremy Zhenwen Pong
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Dagang Guo
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- SingHealth Duke-NUS Emergency Medicine Academic Clinical Programme, Singapore, Singapore
| | - Zhi Xiong Koh
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
| | - Andrew Fu Wah Ho
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
| | - Chenglin Niu
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Shu-Ling Chong
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Department of Children’s Emergency, KK Women’s and Children’s Hospital, Singapore, Singapore
| | - Marcus Eng Hock Ong
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
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Pong JZ, Fook-Chong S, Koh ZX, Samsudin MI, Tagami T, Chiew CJ, Wong TH, Ho AFW, Ong MEH, Liu N. Combining Heart Rate Variability with Disease Severity Score Variables for Mortality Risk Stratification in Septic Patients Presenting at the Emergency Department. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16101725. [PMID: 31100830 PMCID: PMC6571945 DOI: 10.3390/ijerph16101725] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 05/13/2019] [Accepted: 05/14/2019] [Indexed: 12/29/2022]
Abstract
The emergency department (ED) serves as the first point of hospital contact for many septic patients, where risk-stratification would be invaluable. We devised a combination model incorporating demographic, clinical, and heart rate variability (HRV) parameters, alongside individual variables of the Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation II (APACHE II), and Mortality in Emergency Department Sepsis (MEDS) scores for mortality risk-stratification. ED patients fulfilling systemic inflammatory response syndrome criteria were recruited. National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), quick SOFA (qSOFA), SOFA, APACHE II, and MEDS scores were calculated. For the prediction of 30-day in-hospital mortality, combination model performed with an area under the receiver operating characteristic curve of 0.91 (95% confidence interval (CI): 0.88–0.95), outperforming NEWS (0.70, 95% CI: 0.63–0.77), MEWS (0.61, 95% CI 0.53–0.69), qSOFA (0.70, 95% CI 0.63–0.77), SOFA (0.74, 95% CI: 0.67–0.80), APACHE II (0.76, 95% CI: 0.69–0.82), and MEDS scores (0.86, 95% CI: 0.81–0.90). The combination model had an optimal sensitivity and specificity of 91.4% (95% CI: 81.6–96.5%) and 77.9% (95% CI: 72.6–82.4%), respectively. A combination model incorporating clinical, HRV, and disease severity score variables showed superior predictive ability for the mortality risk-stratification of septic patients presenting at the ED.
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Affiliation(s)
- Jeremy Zhenwen Pong
- Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore.
| | - Stephanie Fook-Chong
- Health Services Research Unit, Singapore General Hospital, Singapore 169608, Singapore.
| | - Zhi Xiong Koh
- Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore.
| | | | - Takashi Tagami
- Department of Emergency and Critical Care Medicine, Nippon Medical School Tama Nagayama Hospital, Tokyo 206-8512, Japan.
| | - Calvin J Chiew
- Preventive Medicine Residency Program, National University Health System, Singapore 119228, Singapore.
| | - Ting Hway Wong
- Department of General Surgery, Singapore General Hospital, Singapore 169608, Singapore.
| | - Andrew Fu Wah Ho
- Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore.
| | - Marcus Eng Hock Ong
- Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore.
- Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore.
| | - Nan Liu
- Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore.
- Health Services Research Centre, Singapore Health Services, Singapore 169856, Singapore.
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Samsudin MI, Liu N, Prabhakar SM, Chong SL, Kit Lye W, Koh ZX, Guo D, Rajesh R, Ho AFW, Ong MEH. A novel heart rate variability based risk prediction model for septic patients presenting to the emergency department. Medicine (Baltimore) 2018; 97:e10866. [PMID: 29879021 PMCID: PMC5999455 DOI: 10.1097/md.0000000000010866] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
A quick, objective, non-invasive means of identifying high-risk septic patients in the emergency department (ED) can improve hospital outcomes through early, appropriate management. Heart rate variability (HRV) analysis has been correlated with mortality in critically ill patients. We aimed to develop a Singapore ED sepsis (SEDS) predictive model to assess the risk of 30-day in-hospital mortality in septic patients presenting to the ED. We used demographics, vital signs, and HRV parameters in model building and compared it with the modified early warning score (MEWS), national early warning score (NEWS), and quick sequential organ failure assessment (qSOFA) score.Adult patients clinically suspected to have sepsis in the ED and who met the systemic inflammatory response syndrome (SIRS) criteria were included. Routine triage electrocardiogram segments were used to obtain HRV variables. The primary endpoint was 30-day in-hospital mortality. Multivariate logistic regression was used to derive the SEDS model. MEWS, NEWS, and qSOFA (initial and worst measurements) scores were computed. Receiver operating characteristic (ROC) analysis was used to evaluate their predictive performances.Of the 214 patients included in this study, 40 (18.7%) met the primary endpoint. The SEDS model comprises of 5 components (age, respiratory rate, systolic blood pressure, mean RR interval, and detrended fluctuation analysis α2) and performed with an area under the ROC curve (AUC) of 0.78 (95% confidence interval [CI]: 0.72-0.86), compared with 0.65 (95% CI: 0.56-0.74), 0.70 (95% CI: 0.61-0.79), 0.70 (95% CI: 0.62-0.79), 0.56 (95% CI: 0.46-0.66) by qSOFA (initial), qSOFA (worst), NEWS, and MEWS, respectively.HRV analysis is a useful component in mortality risk prediction for septic patients presenting to the ED.
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Affiliation(s)
| | - Nan Liu
- Duke-NUS Medical School, National University of Singapore
- Health Services Research Centre, Singapore Health Services
| | | | - Shu-Ling Chong
- Department of Emergency Medicine, KK Women's and Children's Hospital
| | - Weng Kit Lye
- Duke-NUS Medical School, National University of Singapore
| | - Zhi Xiong Koh
- Department of Emergency Medicine, Singapore General Hospital
| | - Dagang Guo
- Department of Emergency Medicine, Singapore General Hospital
| | - R. Rajesh
- Yong Loo Lin School of Medicine, National University of Singapore
| | | | - Marcus Eng Hock Ong
- Duke-NUS Medical School, National University of Singapore
- Department of Emergency Medicine, Singapore General Hospital
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