Heart rate variability as predictor of mortality in sepsis: A systematic review

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Abstract

Background

Autonomic dysregulation is one of the recognized pathophysiological mechanisms in sepsis, generating the hypothesis that heart rate variability (HRV) can be used to predict mortality in sepsis.

Methods

This was a systematic review of studies evaluating HRV as a predictor of death in patients with sepsis. The search was performed by independent researchers in PubMed, LILACS and Cochrane, including papers in English, Portuguese or Spanish, indexed until August 20 ^th, 2017 with at least 10 patients. Study quality was assessed by Newcastle-Ottawa Scale. To analyze the results, we divided the articles between those who measured HRV for short-term recordings (≤ 1 hour), and those who did long-term recordings (≥ 24 hours).

Results

Nine studies were included with a total of 536 patients. All of them were observational studies. Studies quality varied from 4 to 7 stars in Newcastle-Ottawa Scale. The mortality rate in the studies ranged from 8 to 61%. Seven studies performed HRV analysis in short-term recordings. With the exception of one study that did not explain which group had the lowest results, all other studies showed reduction of several HRV parameters in the non-survivors in relation to the surviving septic patients. SDNN (Standard deviation of the Normal to Normal interval), TP (Total Power), VLF (Very Low Frequency Power), LF (Low Frequency Power), LF/HF (Low Frequency Power / High Frequency Power), nLF (Normalized Low Frequency Power), α1/α2 (short-term and long-term fractal scaling coefficients from DFA) and r-MSSD (Square root of the squared mean of the difference of successive NN-intervals) of the non-survivor group were reduced in relation to the survivors in at least one study. Two studies found that SDNN is associated with mortality in sepsis, even after adjusting for possible confounding factors. Three studies performed HRV analysis using long-term recordings. Only one of these studies found difference between surviving and non-surviving groups, and even so, in only one HRV parameter: LogHF.

Conclusions

Several HRV parameters are reduced in nonsurviving septic patients in short-term recording. Two studies have found that SDNN is associated with mortality in sepsis, even after adjusting for possible confounding factors.

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Most cited references 26

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SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission

Rui P Moreno, Philipp G H Metnitz, Eduardo Almeida … (2005)

Objective To develop a model to assess severity of illness and predict vital status at hospital discharge based on ICU admission data. Design Prospective multicentre, multinational cohort study. Patients and setting A total of 16,784 patients consecutively admitted to 303 intensive care units from 14 October to 15 December 2002. Measurements and results ICU admission data (recorded within ±1 h) were used, describing: prior chronic conditions and diseases; circumstances related to and physiologic derangement at ICU admission. Selection of variables for inclusion into the model used different complementary strategies. For cross-validation, the model-building procedure was run five times, using randomly selected four fifths of the sample as a development- and the remaining fifth as validation-set. Logistic regression methods were then used to reduce complexity of the model. Final estimates of regression coefficients were determined by use of multilevel logistic regression. Variables selection and weighting were further checked by bootstraping (at patient level and at ICU level). Twenty variables were selected for the final model, which exhibited good discrimination (aROC curve 0.848), without major differences across patient typologies. Calibration was also satisfactory (Hosmer-Lemeshow goodness-of-fit test Ĥ=10.56, p=0.39, Ĉ=14.29, p=0.16). Customised equations for major areas of the world were computed and demonstrate a good overall goodness-of-fit. Conclusions The SAPS 3 admission score is able to predict vital status at hospital discharge with use of data recorded at ICU admission. Furthermore, SAPS 3 conceptually dissociates evaluation of the individual patient from evaluation of the ICU and thus allows them to be assessed at their respective reference levels. Electronic Supplementary Material Electronic supplementary material is included in the online fulltext version of this article and accessible for authorised users: http://dx.doi.org/10.1007/s00134-005-2763-5

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Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome.

J C Marshall, D J Cook, N V Christou … (1995)

To develop an objective scale to measure the severity of the multiple organ dysfunction syndrome as an outcome in critical illness. Systematic literature review; prospective cohort study. Surgical intensive care unit (ICU) of a tertiary-level teaching hospital. All patients (n = 692) admitted for > 24 hrs between May 1988 and March 1990. None. Computerized database review of MEDLINE identified clinical studies of multiple organ failure that were published between 1969 and 1993. Variables from these studies were evaluated for construct and content validity to identify optimal descriptors of organ dysfunction. Clinical and laboratory data were collected daily to evaluate the performance of these variables individually and in aggregate as an organ dysfunction score. Seven systems defined the multiple organ dysfunction syndrome in more than half of the 30 published reports reviewed. Descriptors meeting criteria for construct and content validity could be identified for five of these seven systems: a) the respiratory system (Po2/FIO2 ratio); b) the renal system (serum creatinine concentration); c) the hepatic system (serum bilirubin concentration); d) the hematologic system (platelet count); and e) the central nervous system (Glasgow Coma Scale). In the absence of an adequate descriptor of cardiovascular dysfunction, we developed a new variable, the pressure-adjusted heart rate, which is calculated as the product of the heart rate and the ratio of central venous pressure to mean arterial pressure. These candidate descriptors of organ dysfunction were then evaluated for criterion validity (ICU mortality rate) using the clinical database. From the first half of the database (the development set), intervals for the most abnormal value of each variable were constructed on a scale from 0 to 4 so that a value of 0 represented essentially normal function and was associated with an ICU mortality rate of or = 50%. These intervals were then tested on the second half of the data set (the validation set). Maximal scores for each variable were summed to yield a Multiple Organ Dysfunction Score (maximum of 24). This score correlated in a graded fashion with the ICU mortality rate, both when applied on the first day of ICU admission as a prognostic indicator and when calculated over the ICU stay as an outcome measure. For the latter, ICU mortality was approximately 25% at 9 to 12 points, 50% at 13 to 16 points, 75% at 17 to 20 points, and 100% at levels of > 20 points. The score showed excellent discrimination, as reflected in areas under the receiver operating characteristic curve of 0.936 in the development set and 0.928 in the validation set. The incremental increase in scores over the course of the ICU stay (calculated as the difference between maximal scores and those scores obtained on the first day [i.e., the delta Multiple Organ Dysfunction Score]) also demonstrated a strong correlation with the ICU mortality rate. In a logistic regression model, this incremental increase in scores accounted for more of the explanatory power than admission severity indices. This multiple organ dysfunction score, constructed using simple physiologic measures of dysfunction in six organ systems, mirrors organ dysfunction as the intensivist sees it and correlates strongly with the ultimate risk of ICU mortality and hospital mortality. The variable, delta Multiple Organ Dysfunction Score, reflects organ dysfunction developing during the ICU stay, which therefore is potentially amenable to therapeutic manipulation. (ABSTRACT TRUNCATED)

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SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 1: Objectives, methods and cohort description

Philipp G H Metnitz, Rui P Moreno, Francisco Eduardo Almeida … (2005)

Objective Risk adjustment systems now in use were developed more than a decade ago and lack prognostic performance. Objective of the SAPS 3 study was to collect data about risk factors and outcomes in a heterogeneous cohort of intensive care unit (ICU) patients, in order to develop a new, improved model for risk adjustment. Design Prospective multicentre, multinational cohort study. Patients and setting A total of 19,577 patients consecutively admitted to 307 ICUs from 14 October to 15 December 2002. Measurements and results Data were collected at ICU admission, on days 1, 2 and 3, and the last day of the ICU stay. Data included sociodemographics, chronic conditions, diagnostic information, physiological derangement at ICU admission, number and severity of organ dysfunctions, length of ICU and hospital stay, and vital status at ICU and hospital discharge. Data reliability was tested with use of kappa statistics and intraclass-correlation coefficients, which were >0.85 for the majority of variables. Completeness of the data was also satisfactory, with 1 [0–3] SAPS II parameter missing per patient. Prognostic performance of the SAPS II was poor, with significant differences between observed and expected mortality rates for the overall cohort and four (of seven) defined regions, and poor calibration for most tested subgroups. Conclusions The SAPS 3 study was able to provide a high-quality multinational database, reflecting heterogeneity of current ICU case-mix and typology. The poor performance of SAPS II in this cohort underscores the need for development of a new risk adjustment system for critically ill patients. Electronic Supplementary Material Electronic supplementary material is included in the online fulltext version of this article and accessible for authorised users: http://dx.doi.org/10.1007/s00134-005-2762-6

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Author and article information

Contributors

Fábio M. de Castilho:

ORCID: http://orcid.org/0000-0002-0489-3737

Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: Writing – original draftRole: Writing – review & editing

Antonio Luiz P. Ribeiro: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: Writing – review & editing

Vandack Nobre: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Writing – review & editing

Guilherme Barros: Role: Data curationRole: InvestigationRole: Project administrationRole: Resources

Marcos R. de Sousa: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing

Andrea Cortegiani: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 11 September 2018

Publication date Collection: 2018

Volume: 13

Issue: 9

Electronic Location Identifier: e0203487

Affiliations

[1 ] Hospital das Clínicas and School of Medicine, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil

[2 ] Núcleo Interdisciplinar de Investigação em Medicina Intensiva (NIIMI), UFMG, Belo Horizonte, Brazil

University of Palermo, ITALY

Author notes

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: fabiomorcastilho@ 123456gmail.com

Author information

Fábio M. de Castilho http://orcid.org/0000-0002-0489-3737

Article

Publisher ID: PONE-D-18-13395

DOI: 10.1371/journal.pone.0203487

PMC ID: 6133362

PubMed ID: 30204803

SO-VID: e6e3bfe7-3842-4d56-962d-1bf7dd640979

License:

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

Date received : 4 May 2018

Date accepted : 21 August 2018

Page count

Figures: 1, Tables: 5, Pages: 13

Funding

Funded by: Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG - Foundation for the Support of Research in the state of Minas Gerais)

Award ID: APQ04713-10

This study received financial support from the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG http://www.fapemig.br, Foundation for the Support of Research in the state of Minas Gerais - APQ04713-10). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Data Availability All files are available from the Open Science framework database ( https://osf.io/czq6k/).

Heart rate variability as predictor of mortality in sepsis: A systematic review

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Most cited references 26

SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission

Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome.

SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 1: Objectives, methods and cohort description

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