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      Combining quick sequential organ failure assessment score with heart rate variability may improve predictive ability for mortality in septic patients at the emergency department

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          Abstract

          Background

          Although the quick Sequential Organ Failure Assessment (qSOFA) score was recently introduced to identify patients with suspected infection/sepsis, it has limitations as a predictive tool for adverse outcomes. We hypothesized that combining qSOFA score with heart rate variability (HRV) variables improves predictive ability for mortality in septic patients at the emergency department (ED).

          Methods

          This was a retrospective study using the electronic medical record of a tertiary care hospital in Singapore between September 2014 and February 2017. All patients aged 21 years or older who were suspected with infection/sepsis in the ED and received electrocardiography monitoring with ZOLL X Series Monitor (ZOLL Medical Corporation, Chelmsford, MA) were included. We fitted a logistic regression model to predict the 30-day mortality using one of the HRV variables selected from one of each three domains those previously reported as strong association with mortality (i.e. standard deviation of NN [SDNN], ratio of low frequency to high frequency power [LF/HF], detrended fluctuation analysis α-2 [DFA α-2]) in addition to the qSOFA score. The predictive accuracy was assessed with other scoring systems (i.e. qSOFA alone, National Early Warning Score, and Modified Early Warning Score) using the area under the receiver operating characteristic curve.

          Results

          A total of 343 septic patients were included. Non-survivors were significantly older (survivors vs. non-survivors, 65.7 vs. 72.9, p <0.01) and had higher qSOFA (0.8 vs. 1.4, p <0.01) as compared to survivors. There were significant differences in HRV variables between survivors and non-survivors including SDNN (23.7s vs. 31.8s, p = 0.02), LF/HF (2.8 vs. 1.5, p = 0.02), DFA α-2 (1.0 vs. 0.7, P < 0.01). Our prediction model using DFA-α-2 had the highest c-statistic of 0.76 (95% CI, 0.70 to 0.82), followed by qSOFA of 0.68 (95% CI, 0.62 to 0.75), National Early Warning Score at 0.67 (95% CI, 0.61 to 0.74), and Modified Early Warning Score at 0.59 (95% CI, 0.53 to 0.67).

          Conclusions

          Adding DFA-α-2 to the qSOFA score may improve the accuracy of predicting in-hospital mortality in septic patients who present to the ED. Further multicenter prospective studies are required to confirm our results.

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          Most cited references31

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          Time to Treatment and Mortality during Mandated Emergency Care for Sepsis.

          Background In 2013, New York began requiring hospitals to follow protocols for the early identification and treatment of sepsis. However, there is controversy about whether more rapid treatment of sepsis improves outcomes in patients. Methods We studied data from patients with sepsis and septic shock that were reported to the New York State Department of Health from April 1, 2014, to June 30, 2016. Patients had a sepsis protocol initiated within 6 hours after arrival in the emergency department and had all items in a 3-hour bundle of care for patients with sepsis (i.e., blood cultures, broad-spectrum antibiotic agents, and lactate measurement) completed within 12 hours. Multilevel models were used to assess the associations between the time until completion of the 3-hour bundle and risk-adjusted mortality. We also examined the times to the administration of antibiotics and to the completion of an initial bolus of intravenous fluid. Results Among 49,331 patients at 149 hospitals, 40,696 (82.5%) had the 3-hour bundle completed within 3 hours. The median time to completion of the 3-hour bundle was 1.30 hours (interquartile range, 0.65 to 2.35), the median time to the administration of antibiotics was 0.95 hours (interquartile range, 0.35 to 1.95), and the median time to completion of the fluid bolus was 2.56 hours (interquartile range, 1.33 to 4.20). Among patients who had the 3-hour bundle completed within 12 hours, a longer time to the completion of the bundle was associated with higher risk-adjusted in-hospital mortality (odds ratio, 1.04 per hour; 95% confidence interval [CI], 1.02 to 1.05; P<0.001), as was a longer time to the administration of antibiotics (odds ratio, 1.04 per hour; 95% CI, 1.03 to 1.06; P<0.001) but not a longer time to the completion of a bolus of intravenous fluids (odds ratio, 1.01 per hour; 95% CI, 0.99 to 1.02; P=0.21). Conclusions More rapid completion of a 3-hour bundle of sepsis care and rapid administration of antibiotics, but not rapid completion of an initial bolus of intravenous fluids, were associated with lower risk-adjusted in-hospital mortality. (Funded by the National Institutes of Health and others.).
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            Software for advanced HRV analysis.

            A computer program for advanced heart rate variability (HRV) analysis is presented. The program calculates all the commonly used time- and frequency-domain measures of HRV as well as the nonlinear Poincaré plot. In frequency-domain analysis parametric and nonparametric spectrum estimates are calculated. The program generates an informative printable report sheet which can be exported to various file formats including the portable document format (PDF). Results can also be saved as an ASCII file from which they can be imported to a spreadsheet program such as the Microsoft Excel. Together with a modern heart rate monitor capable of recording RR intervals this freely distributed program forms a complete low-cost HRV measuring and analysis system.
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              Heart-Rate Variability—More than Heart Beats?

              Heart-rate variability (HRV) is frequently introduced as mirroring imbalances within the autonomous nerve system. Many investigations are based on the paradigm that increased sympathetic tone is associated with decreased parasympathetic tone and vice versa. But HRV is probably more than an indicator for probable disturbances in the autonomous system. Some perturbations trigger not reciprocal, but parallel changes of vagal and sympathetic nerve activity. HRV has also been considered as a surrogate parameter of the complex interaction between brain and cardiovascular system. Systems biology is an inter-disciplinary field of study focusing on complex interactions within biological systems like the cardiovascular system, with the help of computational models and time series analysis, beyond others. Time series are considered surrogates of the particular system, reflecting robustness or fragility. Increased variability is usually seen as associated with a good health condition, whereas lowered variability might signify pathological changes. This might explain why lower HRV parameters were related to decreased life expectancy in several studies. Newer integrating theories have been proposed. According to them, HRV reflects as much the state of the heart as the state of the brain. The polyvagal theory suggests that the physiological state dictates the range of behavior and psychological experience. Stressful events perpetuate the rhythms of autonomic states, and subsequently, behaviors. Reduced variability will according to this theory not only be a surrogate but represent a fundamental homeostasis mechanism in a pathological state. The neurovisceral integration model proposes that cardiac vagal tone, described in HRV beyond others as HF-index, can mirror the functional balance of the neural networks implicated in emotion–cognition interactions. Both recent models represent a more holistic approach to understanding the significance of HRV.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – original draft
                Role: Data curationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Visualization
                Role: Data curationRole: InvestigationRole: MethodologyRole: Visualization
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                18 March 2019
                2019
                : 14
                : 3
                : e0213445
                Affiliations
                [1 ] Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
                [2 ] Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
                [3 ] Department of Emergency and Critical Care Medicine, Nippon Medical School Tama Nagayama Hospital, Tokyo, Japan
                [4 ] Health Services Research Centre, Singapore Health Services, Singapore, Singapore
                [5 ] Duke-NUS Medical School, Singapore, Singapore
                [6 ] Department of Emergency Medicine, Singapore General Hospital, Singapore, Singapore
                Hospital Universitari Bellvitge, SPAIN
                Author notes

                Competing Interests: NL and MO have a patent filing that is not directly related to this study (System and method of determining a risk score for triage, Application Number: US 13/791,764). MO has a similar patent filing unrelated to this study (Method of predicting acute cardiopulmonary events and survivability of a patient, Application Number: US 13/047,348). MO also has a licensing agreement with ZOLL Medical Corporation for the above patented technology. There are no further patents, products in development or marketed products to declare. All the other authors do not have either commercial or personal associations or any sources of support that might pose a conflict of interest in the subject matter or materials discussed in this manuscript. Our conflict of interest does not alter our adherence to PLOS ONE policies on sharing data and materials.

                Author information
                http://orcid.org/0000-0001-6953-1932
                Article
                PONE-D-18-25807
                10.1371/journal.pone.0213445
                6422271
                30883595
                9d1e9e8c-848c-425f-9433-126913bd79e3
                © 2019 Prabhakar et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 3 September 2018
                : 21 February 2019
                Page count
                Figures: 1, Tables: 3, Pages: 11
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Forecasting
                Medicine and Health Sciences
                Critical Care and Emergency Medicine
                Medicine and Health Sciences
                Cardiology
                Heart Rate
                Medicine and Health Sciences
                Diagnostic Medicine
                Signs and Symptoms
                Sepsis
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Signs and Symptoms
                Sepsis
                Research and Analysis Methods
                Bioassays and Physiological Analysis
                Electrophysiological Techniques
                Cardiac Electrophysiology
                Electrocardiography
                Research and Analysis Methods
                Database and Informatics Methods
                Health Informatics
                Electronic Medical Records
                Research and Analysis Methods
                Computational Techniques
                Fluctuation Analysis
                Biology and Life Sciences
                Physiology
                Respiratory Physiology
                Medicine and Health Sciences
                Physiology
                Respiratory Physiology
                Custom metadata
                The authors cannot share their data publicly because of the following institutional policy: SingHealth Cluster Research Data Management Policy - SingHealth Access Restricted Cluster for External Collaborations; Document Number: SHS-RSH-HSRC-CWP-201; Date Released: 26/04/2018. Data are available upon request, and requests for data can be submitted to Singhealth Office of Research ( office.research@ 123456singhealth.com.sg ) or to the corresponding author.

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