Open Access

Heart rate variability in critical care medicine: a systematic review

  • Shamir N. Karmali1,
  • Alberto Sciusco1,
  • Shaun M. May1 and
  • Gareth L. Ackland1Email author
Intensive Care Medicine Experimental20175:33

https://doi.org/10.1186/s40635-017-0146-1

Received: 29 March 2017

Accepted: 3 July 2017

Published: 12 July 2017

Abstract

Background

Heart rate variability (HRV) has been used to assess cardiac autonomic activity in critically ill patients, driven by translational and biomarker research agendas. Several clinical and technical factors can interfere with the measurement and/or interpretation of HRV. We systematically evaluated how HRV parameters are acquired/processed in critical care medicine.

Methods

PubMed, MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials (1996–2016) were searched for cohort or case–control clinical studies of adult (>18 years) critically ill patients using heart variability analysis. Duplicate independent review and data abstraction. Study quality was assessed using two independent approaches: Newcastle–Ottowa scale and Downs and Black instrument. Conduct of studies was assessed in three categories: (1) study design and objectives, (2) procedures for measurement, processing and reporting of HRV, and (3) reporting of relevant confounding factors.

Results

Our search identified 31/271 eligible studies that enrolled 2090 critically ill patients. A minority of studies (15; 48%) reported both frequency and time domain HRV data, with non-normally distributed, wide ranges of values that were indistinguishable from other (non-critically ill) disease states. Significant heterogeneity in HRV measurement protocols was observed between studies; lack of adjustment for various confounders known to affect cardiac autonomic regulation was common. Comparator groups were often omitted (n = 12; 39%). This precluded meaningful meta-analysis.

Conclusions

Marked differences in methodology prevent meaningful comparisons of HRV parameters between studies. A standardised set of consensus criteria relevant to critical care medicine are required to exploit advances in translational autonomic physiology.

Keywords

AutonomicHeart rate variabilityHumanSystematic review

Background

Autonomic changes are evident from the onset of acute pathology requiring critical care. Cardiac autonomic function can be derived by analysing variability between heart beats to yield time domain and frequency domain (power spectral density) measures that reflect autonomic modulation of cardiac frequency [1, 2]. Heart rate variability (HRV) appears to contribute diagnostic and prognostic value in various cardiometabolic conditions associated with subclinical autonomic dysfunction that predispose to critical illness including hypertension, coronary artery disease, heart failure and diabetes [37]. Similarly, HRV has been proposed to serve as a potential diagnostic and prognostic tool in critically ill patients [8].

However, HRV measures in critically ill patients are fraught with potential problems. [9] Although population norms for HRV parameters have been reported in healthy populations [10], the impact of multiple physiological, procedural and technical factors in critically ill patients has not undergone systematic scrutiny in critical care medicine [11]. Moreover, the validity of HRV as a tool to interrogate autonomic function is increasingly under physiological scrutiny [12, 13], since a strong correlation between HRV and morbidity/mortality appears to be largely attributable to incident heart rate. In addition, recording technique, clinical context and adjustment for incident heart rate are key factors to consider when interpreting the translational relevance of HRV in critically ill patients.

Here, we sought to systematically evaluate the methodology and design of HRV studies in critical care medicine. We focused on whether recommended standards for measurement and reporting have been employed [14, 15], with the aim of identifying areas to refine in future HRV experimental design in critical care medicine.

Methods

Identification of studies

A literature review was performed based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews [16]. The summary of the search strategy employed is shown in Additional file 1.

We searched the electronic databases PubMed, EMBASE, MEDLINE and the Cochrane Central Register of Controlled Clinical Trials for articles investigating HRV measurement in intensive care patients. Inclusion criteria were full-text studies written in English involving adult patients, published after 1996 (following published guidelines) and reporting traditional time and frequency domain parameters [15]. Studies which reported newer analysis techniques of HRV (e.g. entropy analysis) were excluded, as we focussed on those reporting measures in line with recent European guidance [17]. The following Medical Subject Headings (MESH) were used to identify pertinent articles: “Heart rate variability OR HRV AND Sepsis”, “Heart rate variability OR HRV AND multiple organ dysfunction OR MODS”, “Heart rate variability OR HRV AND critical illness”, “Heart rate variability OR HRV AND intensive care OR ICU”. The last search took place on 9 November 2016. We screened articles by title search and abstract review. Relevant articles were analysed for eligibility, and further articles were identified from reference lists. Articles were excluded based on the following criteria: experimental studies, incorrect target population (adult; >18 years old), medical field other than intensive care, not original research, topic not within scope or traditional HRV parameters not reported.

Data extraction

Data was extracted by two independent reviewers (S.K and A.S) and recorded into a standardised excel sheet recording: author, year of publication, study design, number of subjects, mean patient age, proportion of male subjects, risk stratification score, comparator groups, study aim and outcome, study design, protocol for measurement, processing, analysis and reporting of HRV parameters, adjustment and reporting of confounding factors and quality assessment. We identified the following clinical confounding factors: age, gender, average heart rate, average respiratory rate, co-morbidities, drugs, sedative drugs, vasoactive drugs, enteral nutrition and mechanical ventilation. Full details of the impact on HRV of these parameters are provided in Additional file 1. For reporting and analysis purposes, we selected the most commonly used time and frequency domain HRV parameters [15].

Risk of bias and study quality assessment

The quality of studies was assessed by two assessors independently (SK, SM) using two established tools (Newcastle–Ottowa scale, Downs and Black Instrument). The Downs and Black instrument is recommended by the Cochrane Collaboration for use in non-randomised and observational studies (Additional file 1) [18, 19]. Inter-observer reliability evaluating quality within five domains: reporting, external validity, bias, confounding and power. Five questions were omitted because they are designed for interventional trials. The version which we employed in this study therefore has a maximum score of 22. Differences between reviewers were resolved by panel consensus opinion following further review of the article(s) in question by the senior author.

Results

Study selection

We identified 238 studies which underwent screening by title search and abstract review. From these, 31 articles involving 2090 patients (including controls) met the inclusion criteria for assessing the role of HRV in critically ill patients [2053]. Two articles analysed the same cohort of patients [34, 37].

Study characteristics

Demographic and clinical data, including comparator groups are summarised in Table 1. All articles reported cohort or case–control studies. The average age of patients was 60 ± 7 years. The majority of studies (22/31; 71%) explored the association between HRV measures, morbidity and mortality. Key clinical findings from these studies are summarised in Table 2. Due to significant differences in trial design, methodology, confounding, non-standardised comparator groups and inconsistent reporting of summary data, a meta-analysis could not be performed. However, there was consistency between studies in their findings that LF/HF ratio was inversely associated with increased disease severity or mortality. For illustrative purposes, the individual effect sizes across six studies reporting mean and standard deviation data looking at disease severity and mortality using the most commonly reported HRV parameter (LF/HF ratio) are shown (Fig. 1).
Table 1

Demographics and study design of studies

Reference number. author

Year

Study design

Study populations (± comparator group)

Patients (n)

Age (mean ± SD or mean [range])

Male (%)

20. Annane

1999

Case–control

Sepsis (healthy controls)

26

Septic shock 52 ± 14

Sepsis 54 ± 17

Control 43 ± 11

65

21. Korach

2001

Cohort

Sepsis

41

50 [20–90]

44

22. Barnaby

2002

Cohort

Sepsis

15

59 [39–85]

23. Pontet

2003

Case–control

Sepsis + MODS

(Sepsis − MODS)

22

MODS 59.5 ± 17.8

Non-MODS 60 ± 10.4

64

24.Shen

2003

Cohort

Weaning

24

Successful wean 76 ± 12.9

Unsuccessful wean 69.8 ± 17.8

42

25. Schmidt

2005

Cohort

MODS

(literature values)

85

60.4 ± 14

62

26. Papaioannou

2006

Cohort

MODS

53

63.02 ± 14.68

58

44. Bourgault

2006

Cohort

Mixed aetiology

18

60 [33–82]

72

45. Chen

2007

Cohort

Sepsis

81

67 [30–84]

41

50. Passariello

2007

Case–control

Ischaemic sudden death

40

Sudden death 66 ± 8

Pathology matched controls 68 ± 8

 

46. Chen

2008

Cohort

Sepsis

132

67 [27–86]

47

47. Aboab

2008

Case–control

Sepsis ± adrenal insufficiency

(healthy controls)

81

Septic shock and adrenal failure 55 ± 16

Septic shock 58 ± 19

Healthy controls (not provided)

36

27. Nogueira

2008

Cohort

Sepsis

31

Survivors 44.9 ± 5.9

Non-survivors 55.6 ± 4.63

74

28. Papaioannou

2009

Cohort

Sepsis

(Sepsis SOFA <10)

45

57.8

51. Tiainen

2009

Cohort

Out of hospital cardiac arrest

70

Hypothermia 60 (23–75)

Normothermia 59 (18–75)

86

29. Schmidt

2010

Case–controla

MODS

178

61.1 ± 13.2

67

30. Kasaoka

2010

Cohort

SIRS

10

53 ± 15

70

31. Chen

2012

Case–control

Sepsis and out of hospital cardiac arrest

(Non-severe sepsis and healthy controls)

210

Out of hospital cardiac arrest 68 ± 10

Severe sepsis and mechanical ventilation 66 ± 8

Severe sepsis 68 ± 7

Sepsis 67 ± 6

Healthy 66 ± 6

55

32.Gomez Duque

2012

Cohort

Sepsis

(literature values)

100

55 [18–88]

42

33. Brown

2013

Cohort

Sepsis

48

57 [40–63]

46

34. Green

2013

Cohort

MODS

33

56.5 ± 15.9

61

35.Wieske

2013

Cohort

ICU acquired weakness

83

ICU acquired weakness 60 ± 13

No ICU acquired weakness 59 ± 16

60

36. Wieske

2013

Cohort

Mixed aetiology

(healthy controls)

32

Patients 54 ± 15

Healthy control 36 ± 2

70

37. Bradley

2013

Cohort

Mixed aetiology

33

56.5 ± 15.9

61

38. Huang

2014

Cohort

Mixed aetiology

101

Successful 65 ± 18

Unsuccessful 71 ± 16

65

39. Zhang

2014

Cohort

SIRS/MODS

(non-MODS)

41

47 [34–59]

54

40. Schmidt

2014

Case–controla

CCF and MODS

(literature values)

130

CCF 63 ± 10.1

MODS 62.8 ± 10.2

63

52. Tang

2014

Case–control

Stroke

227

AF stroke 74 ± 12

Non-AF stroke 62 ± 15

Age/sex-matched controls 61 ± 10

40

41. Zaal

2015

Case–control

ICU delirium

(no delirium)

25

ICU delirium 67 ± 12

No ICU delirium 57 ± 16

72

42. Hammash

2015

Cohort

Weaning

35

53.3 ± 14.6

66

53. Nagaraj

2016

Case seriesa

Not specified

40

56.3 ± 16.8

62.5

Reference for each paper is shown before first author (first column)

CCF congestive cardiac failure, MODS multiple organ dysfunction syndrome, SIRS systemic inflammatory response syndrome, SOFA sequential organ failure assessment

aRetrospective analysis

Table 2

Study objectives and key findings

Author

Year

Study objectives

Key findings

Annane

1999

Compare HRV between sepsis, septic shock and healthy volunteers

TP, LF, LFnu, LF/HF lower in septic shock vs sepsis

Korach

2001

Effects of sepsis, age, sedation, catecholamines and illness severity on sympathovagal balance (LF/HF)

LF/HF ratio <1.5 was associated with sepsis and mortality

Barnaby

2002

Assess if HRV can predict sepsis severity

Negative correlation between LFnu, LF/HF and SOFA score

Pontet

2003

Assess if HRV can predict MODS in sepsis

Low LF and RMSSD associated with MODS

Shen

2003

Assess changes in cardiac autonomic activity during weaning from mechanical ventilation

HF, LF and TP decreased in unsuccessful group during spontaneous breathing trial

Schmidt

2005

Effects of MODS, age, sedation, catecholamines, mechanical ventilation on HRV

Assess if HRV can predict mortality in MODS

Time and frequency domain reduced in MODS

HRV indices affected by mechanical ventilation but not age, sedation or catecholamines

LnVLF associated with 28-day survival.

Papaioannou

2006

Assess if HRV associated with disease severity and mortality

LF/HF ratio negatively correlated with SOFA score

Bourgault

2006

Effects of endotracheal suction on HRV

No significant differences found in HRV indices between closed or open suctioning

Chen

2007

Assess if HRV can predict sepsis severity

Septic shock associated lower LF, LFnu, LF/HF, and higher RMSSD, HF, HFnu

Passariello

2007

Assess if HRV can predict ischaemic sudden cardiac death

SDNN decreases shortly before ischaemic sudden death

Chen

2008

Assess if HRV can predict 28-day mortality

Low SDNN, TP, VLF, LF and LF/HF associated with increased 28-day mortality

Aboab

2008

Assess effect of steroids on HRV in patients with sepsis

LF, LFnu, LF/HF lower in septic shock. Corticosteroids helped increase LFnu values in adrenal insufficiency group.

Nogueira

2008

Assess relationship between HRV, markers of myocardial damage and free fatty acids in sepsis

Low LF, HF and LF/HF associated with mortality

Papaioannou

2009

Assess relationship between HRV and biomarkers of inflammation (CRP, IL-6, IL-10) in patients with sepsis

There is a negative correlation between LFnu, LF/HF and CRP, IL-6, IL-10, SOFA score

Tiainen

2009

Assess if HRV changes (and has prognostic ability) with therapeutic cooling of resuscitated cardiac arrest patients

Higher SDNN, SDANN, TP, LF, HF in the first 48 h of cooling. SDNN >100 ms predicts better neurological outcome

Schmidt

2010

To assess if ACE-I therapy affects short (28-day) and long (365-day) mortality in patients with MODS

ACE-I associated with preserved VLF, LF, HF, TP and survival (28-day and 365-day)

Kasaoka

2010

To trial a real-time HRV measurement and analysis system

LF, HF and LF/HF higher in patients spontaneously breathing compared to mechanical ventilation

Chen

2012

To compare HRV between post-resuscitation cardiac arrest patients and patients with severe sepsis

No significant differences in HRV indices between OOHCA and Severe Sepsis patients

Low LF, LFnu, LF/HF associated with mortality

Gomez Duque

2012

To investigate the incidence of cardiovascular adverse events in patients with sepsis

Deceased patients demonstrated lower SDNN than survivors

Brown

2013

Assess if HRV can predict vasopressor dependence at 24 h in sepsis

Traditional HRV indices not associated with vasopressor requirement after controlling for HR

Green

2013

Association of HRV and illness severity in MODS

Low LFnu and LF/HF associated with increased MODS

Wieske

2013

Relationship between autonomic dysfunction (HRV) and ICU-acquired weakness

Artefacts, mechanical ventilation, sedation, catecholamines and heart rate all associated with TP

% artefacts were associated with TP and LF/HF

No association between HRV and ICU-acquired weakness

Wieske

2013

Compare different autonomic function tests in critically unwell patients (CFT, SWT and HRV)

Only HRV tests associated with SOFA score

Bradley

2013

Impact of sedation and sedation interruptions on HRV

SDNN, RMSSD and HF all increased during sedation interruption (more pronounced in less unwell patients)

Huang

2014

Assess if HRV associated with weaning success or failure

Reduction in TP during SBT associated with failure

Tang

2014

Assess if HRV predicts outcome in ICU stroke patients

Traditional HRV indices were unable to predict outcome

Zhang

2014

Asses if HRV can predict infected pancreatic necrosis or MODS in patients with severe acute pancreatitis

Low LFnu, LF/HF and high HFnu associated with increased MODS and mortality

Schmidt

2014

Assess relationship between HRV and illness severity in CCF and MODS

MODS patients demonstrated lower HRV indices in all parameters compared to CCF patients.

Zaal

2015

To assess if HRV is abnormal in patients with ICU delirium.

No association between HRV and delirium found

Hammash

2015

Assess relationship between HRV and incidence of dysrhythmias during weaning

LF was higher during spontaneous breathing than during controlled mechanical ventilation.

Nagaraj

2016

Assess if sedation levels can be classified by HRV algorithms

Algorithms using composite measures of HRV may discriminate between levels of sedation in ICU patients

ACE-I angiotensin-converting enzyme inhibitor, CCF congestive cardiac failure, CFT cold face test, CRP C-reactive protein, HF high frequency, HFnu high frequency normalised unit, HRV heart rate variability, IL-6 interleukin 6, IL-10 interleukin 10, LF low frequency, LFnu low frequency normalised unit, MODS multiple organ dysfunction, RMSSD root mean square of successive differences, SOFA sequential organ failure assessment, SBT spontaneous breathing trial, SWT skin wrinkle test, TP total power, VLF very low frequency, LnVLF natural logarithm of very low frequency

Fig. 1

Forest plot of individual effect sizes (Cohen’s d) across six studies investigating the relationship between LF/HF ratio and disease severity and mortality

Quality of studies

No studies reported Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Two studies analysed data retrospectively. A minority of studies (n = 5; 16%) used individualised HRV data—i.e. patients serving as their own control, prior to an intervention. More than one third of studies (n = 13; 42%) did not describe any comparator group. The remainder of studies used non-age matched healthy volunteers, non-critically ill patients with established cardiovascular disease or HRV values derived from the literature. External validity (as adjudged by the Down and Black assessment tool) was poor, with the majority of studies achieving a score of 1.

Risk of bias assessment

We found recurring potential sources of bias in study design, with 19 (61%) studies failing to report whether HRV data analysers were masked to the patient condition/outcome (Additional file 1). Only one study performed a power calculation [41].

Data acquisition and preparation

Details on short-term recordings, including source of heart rate periods [54, 55], duration of recordings, epochs used for analysis and patient position [56] were variable or not reported. Fourteen (45%) studies did not describe the sampling frequency of recordings; four (13%) studies used sampling rates below the recommended 250 Hz [15].

ECG recording in the critically ill population is frequently contaminated by electrical and physiological artefacts. Thus, detailing methods to detect artefact (manual or automated) and its management (segment selection, deletion or interpolation) is important for data interpretation [57]. Fourteen (45%) studies reported automated and/or manual editing of the raw ECG to remove artefact by replacing the missing data with cubic spline or linear interpolation methods. In keeping with guidelines, the majority of studies used interpolation methods as opposed to deletion of abnormal beats to avoid a loss of information [15].

HRV analysis

Measurement protocols, processing and reporting of HRV data are summarised in Table 3.
Table 3

Procedures for measurement, processing and reporting of HRV

Author

Year

Recording protocol (duration/position/time)

Monitor

Sampling frequency (Hz)

Management of artefact

Data presented

Annane

1999

5 min/–/–

PRV

500

Interpolation

TP, LF, HF, LF/HF, Lfnu, Hfnu

Korach

2001

30 min supine/0800–1200

ECG

5

Interpolation

Lfnu, Hfnu, LF/HF

Barnaby

2002

5 min/–/–

ECG

Interpolation

TP, LF, HF, Lfnu, Hfnu, LF/HF

Pontet

2003

10 min/supine/2100–2300

ECG

>500

Interpolation

SDNN, RMSSD, LF, HF, Lfnu, Hfnu, LF/HF

Shen

2003

90 min/semi recumbent/1000–1400

ECG

Interpolation

TP, LnLF, LnHF, Lfnu, Hfnu, LF/HF

Schmidt

2005

24 hours

ECG

256

Interpolation

SDNN, SDANN, RMSSD, pNN50, VLF, LF, HF, LF/HF

Papaioannou

2006

10 min/supine/morning

ECG

250

Segment selection

LF/HF

Bourgault

2006

20 min/–/day and night

ECG

1000

LF, HF, LF/HF, TP

Chen

2007

10 min/supine/day and night

ECG

Interpolation

RMSSD, TP, LF, HF, Lfnu, Hfnu, LF/HF

Passariello

2007

24 h

ECG

SDNN, SDANN, pNN50, RMSSD

Chen

2008

10 min/supine/day and night

ECG

Interpolation

SDNN, RMSSD, TP, LF, HF, Lfnu, Hfnu, LF/HF

Aboab

2008

5 min/supine/–

PRV

Interpolation

TP, Lfnu, Hfnu, LF/HF

Nogueira

2008

30 min/supine/morning

ECG

LF, HF, LF/HF

Papaioannou

2009

10 min/–/–

ECG

250

Segment selection

SDNN, Lfnu, Hfnu, LF/HF

Tiainen

2009

24 h

ECG

SDNN, SDANN, TP, LF, HF,

Schmidt

2010

24 h

ECG

256

Interpolation

LnTP, LnVLF, LnHF, LnLF, LF/HF

Kasaoka

2010

5 min/supine/–

ECG

LnLF, LnHF, LF/HF

Chen

2012

10 min/supine/day and night

ECG

Interpolation

SDNN, TP, VLF, LF, HF, Hfnu, Lfnu, LF/HF

Gomez Duque

2012

24 h

ECG

SDNN, PNN50

Brown

2013

6 h/–/–

ECG

500

Deletion

SDNN, pNN50, Lfnu, Hfnu, LF/HF

Green

2013

24 h

ECG

125

Deletion

SDNN, RMSSD, Lfnu, Hfnu, LF/HF

Wieske

2013

5 min/–/–

ECG

250

Interpolation

HR, TP, LF/HF

Wieske

2013

5 min/supine/–

ECG

250

Deletion

LF, HF, Lfnu, Hfnu, LF/HF

Bradley

2013

24 h

ECG

125

Deletion

SDNN, RMSSD, LF, HF, LF/HF

Huang

2014

5 min/semi-recumbent/0800–1200

ECG

LnTP, LnVLF, Hfnu, Lfnu, LF/HF

Tang

2014

60 min/–/–

ECH

512

SDNN, RMSSD, LF, HF, LF/HF

Zhang

2014

5 min/–/0900–1100

ECG

Deletion

SDNN, RMSSD, TP, VLF, LF, HF, Lfnu, Hfnu, LF/HF

Schmidt

2014

24 h

ECG

256

Interpolation

SDNN, SDANN, SDNNi, RMSSD, pNN50, VLF, LF, HF, LnLF, LnHF, LF/HF

Zaal

2015

15 min/supine, 0800–1700

ECG

500

Segment selection

LnLF, LnHF, Hfnu, LF/HF

Hammash

2015

24 h

ECG

Interpolation

VLF, HF, LF

Nagaraj

2016

24 h (5 min epochs)

ECG

240

Thresholding

SDNN, RMSSD, VLF, LF, HF, LF/HF, LFnu, HFnu

ECG electrocardiogram, HF high frequency, HFnu high frequency normalised unit, LF low frequency, LFnu low frequency normalised unit, Ln natural logarithm, pNN50 percentage of normal–normal intervals >50 ms, PRV pulse rate variability, RMSSD root mean square of successive differences, SDANN standard deviation of average normal–normal intervals, SDNN standard deviation of normal–normal intervals, TP total power

A minority of studies (14; 45%) reported both frequency and time domain data (Table 3). A minority of studies (9; 29%) reported frequency data in normalised units together with absolute values, in keeping with established recommendations. Summary values for commonly reported HRV parameters revealed a wide range of non-normally distributed data for each (Additional file 1: Table S3). Reporting and/or adjustment for heart rate and respiratory rate, which dramatically alter both high and low frequency spectral components [58] was inconsistent between studies. A small majority of studies (17; 55%) reported average heart rate, whilst a minority (6; 19%) adjusted for, or reported, respiratory rate during data acquisition.

Pharmacologic and clinical interventions

Studies varied in their exclusion criteria and reporting of potential confounding factors including age, gender, body mass index [59], common comorbidities [6063], drug therapy [6468] and/or ICU interventions (Tables 4 and 5). Exclusion criteria used and comorbidities/drugs are summarised in Additional file 1. A minority of studies (12; 39%) excluded patients with chronic comorbidities that are commonly associated with autonomic dysfunction. Reporting of drugs that directly affect autonomic function was highly variable across studies. A majority of studies (25; 81%) did not detail drug therapy. Around 22% studies did not report the use of mechanical ventilation, and more than 25% failed to report whether sedation and/or vasoactive drugs were used at the time of HRV recordings.
Table 4

Reporting of potential clinical confounders

Author

Year

Comorbidities

Drugs

Mechanical ventilation (% patients)

Sedation (% patients)

Catecholamines (% patients)

Feeding

HR/RR reported

Annane

1999

Excluded

100%

0%

0%

HR/RR

Korach

2001

41.5%.

19.5%

12.20%

Barnaby

2002

0%

0%

HR/RR

Pontet

2003

Excluded

Excluded

38.5%

17.90%

HR

Shen

2003

+

+

100%

0%

0%

HR/RR

Schmidt

2005

71%

61%

62%

Papaioannou

2006

+

+

Bourgault

2006

Excluded

Excluded

100%

33%

0%

HR

Chen

2007

Excluded/+

0%

HR/RR

Passriello

2007

+

+

HR

Chen

2008

+

0%

HR

Aboab

2008

Excluded

100%

80.9%

100%

HR

Nogueira

2008

Excluded

100%

100%

RR

Papaioannou

2009

Excluded

100%

100%

Tiainen

2009

+

100%

100%

87%

HR

Schmidt 0

2010

+

88%

89%

74%

Kasaoka 1

2010

100%

100%

Chen

2012

+

OHCA 100%, SS + MV 100%, SS 0%, S 0%

OHCA 81, SS + MV 63%, SS 59%, S 0%

OHCA 100%, SS + MV 9%, SS 18.8%, S 0%

HR

Gomez Duque

2012

Excluded/+

72%

Brown

2013

63%

HR

Green

2013

90.90%

+

78.80%

HR

Wieske

2013

Excluded/+

+

+

+

+

HR

Wieske

2013

Excluded/+

100%

Bradley

2013

+

+

+

HR

Huang

2014

Excluded/+

Excluded/+

100%

RR

Zhang

2014

12%

Schmidt

2014

+

89.2%

72.3%

72.3%

HR

Tang

2014

+

+

HR

Zaal

2015

Excluded

Excluded

60%

20%

0%

Hammash

2015

Excluded/+

100%

Nagaraj

2016

100%

100%

HR

Excluded refers to specific comorbidities or drugs were part of exclusion criteria of study

HR heart rate, RR respiratory rate, + reported but proportion of patients not provided, not reported

Table 5

Reporting of potential confounders

Author

Year

Comorbidities

Drugs

Mechanical ventilation (% patients)

Sedation (% patients)

Catecholamines (% patients)

Feeding

HR/RR reported

Annane [17]

1999

Excluded

100%

0%

0%

HR/RR

Korach [18]

2001

41.5%.

19.5%

12.20%

Barnaby [19]

2002

0%

0%

HR/RR

Pontet [20]

2003

Excluded

Excluded

38.5%

17.90%

HR

Shen [21]

2003

+

+

100%

0%

0%

HR/RR

Schmidt [22]

2005

71%

61%

62%

Papaioannou [23]

2006

+

+

Bourgault [24]

2006

Excluded

Excluded

100%

33%

0%

HR

Chen [25]

2007

Excluded/+

0%

HR/RR

Passriello

2007

+

+

HR

Chen [26]

2008

+

0%

HR

Aboab [27]

2008

Excluded

100%

80.9%

100%

HR

Nogueira [28]

2008

Excluded

100%

100%

RR

Papaioannou [29]

2009

Excluded

100%

100%

Tiainen

2009

+

100%

100%

87%

HR

Schmidt [30]

2010

+

88%

89%

74%

Kasaoka [31]

2010

100%

100%

Chen [32]

2012

+

OHCA 100%, SS + MV 100%, SS 0%, S 0%

OHCA 81, SS + MV 63%, SS 59%, S 0%

OHCA 100%, SS + MV 9%, SS 18.8%, S 0%

HR

Gomez Duque [33]

2012

Excluded/+

72%

Brown [34]

2013

63%

HR

Green [35]

2013

90.90%

+

78.80%

HR

Wieske [36]

2013

Excluded/+

+

+

+

+

HR

Wieske [37]

2013

Excluded/+

100%

Bradley [38]

2013

+

+

+

HR

Huang [39]

2014

Excluded/+

Excluded/+

100%

RR

Zhang [40]

2014

12%

Schmidt [41]

2014

+

89.2%

72.3%

72.3%

HR

Tang

2014

+

+

HR

Zaal [42]

2015

Excluded

Excluded

60%

20%

0%

Hammash [43]

2015

Excluded/+

100%

Nagaraj

2016

100%

100%

HR

Excluded refers to specific comorbidities or drugs were part of exclusion criteria of study

HR heart rate, RR respiratory rate, + reported but proportion of patients not provided, – not reported

Discussion

This review is the first to systematically explore how HRV analyses are undertaken and/or reported in critically ill patients. Despite a wealth of laboratory and translational data suggesting that HRV may offer diagnostic and prognostic utility, significant heterogeneity in methodology between HRV articles precluded comparisons across studies and meta-analysis. Our review identifies several areas that require greater scrutiny in future, highlighting the need to develop consensus guidelines that are relevant and tailor-made for the challenges faced by researchers in critical care medicine.

Well-recognised technical, physiologic and clinical factors impact on the measurement, and interpretation of HRV [69, 70]. We found highly variable practice in three key technical areas. Low sampling rates (<250 Hz) impair the precise detection of the R wave fiducial point in the ECG waveform, which consequently affects the power spectrum [15]. This is particularly relevant for studies that derived R–R intervals from arterial pressure waveform analysis [20, 47], since non-neural respiratory influences (e.g. changes in ventricular mechanics) differentially affect mechanical pulse waves and electrical R waves [55]. Manual inspection of the raw ECG to identify artefact is preferred to automated methods to avoid the introduction of false frequency components into the power spectrum [57]. The variable (or unstated) masking of HRV analysers to clinical data also introduces potential significant bias.

From a physiologic perspective, reporting and/or adjustment for heart rate and respiratory rate was inconsistent between studies, with heart rate frequently not reported. Across species with highly variable heart rates, HRV appears to be largely attributable to incident heart rate. If heart rate is not taken into account, erroneous conclusions regarding HRV are likely since differences may merely reflect lower heart rate [12]. This is particularly of relevance to hemodynamically unstable critically ill patients, in whom heart rate may rapidly change. Similarly, increases in respiratory frequency and tidal volume affect both high and low frequency spectral components [58]. Hence, standardised criteria for ventilatory and heart rate reporting are required for the interpretation of HRV data between studies (and hence, potentially, meta-analysis).

From a clinical perspective, HRV parameters are influenced strongly by age, gender, functional capacity and chronic comorbidities. Whilst all studies estimated severity of illness, the most frequently employed—Acute Physiology and Chronic Health Evaluation II (APACHE-II)—are limited in capturing information about chronic comorbid disease that are over-represented in the critical care medicine population. For example, diabetes mellitus, a common condition associated with cardiac autonomic neuropathy, is not captured by this type of assessment [60]. Typically, chronic conditions at the severe end of the disease spectrum are included (e.g. APACHE-II score only includes severe heart failure (≥NYHA class 3). However, HRV parameters have been found to be abnormal in early cases of chronic disease, including preserved ejection fraction, coronary artery disease, chronic kidney disease and hypertension [6063]. Although some studies have considered these factors, serial measures or dynamic autonomic challenges offer a potentially more insightful and individualised approach to assessing HRV. Novel HRV parameters that can be captured within the first few minutes of critical illness, such as deceleration capacity of heart rate [71], may mitigate the need for refining the use of more traditional time and frequency domain measures. For mechanistic studies investigating whether changes in autonomic parameters correlate with, or precede, pathologic events, targeting clinical scenarios where multiple, complementary baseline autonomic measures [72, 73] can be made before critical illness develops may be optimal [74]. Studies where basal autonomic function can be captured, including elective surgery [7376] and oncologic sepsis [48, 49], may provide particularly powerful mechanistic insights since autonomic changes can be individualised and referenced to pre-insult normal, or pre-existing, dysfunction. Several studies have highlighted that HRV values in critical care medicine are similar to those found in common cardiovascular pathologic conditions [74, 75, 77]; this highlights the need for individualised patient data in order to rule out that autonomic dysfunction is not a precursor of critical illness, rather than merely a biomarker.

Commonly used anti-arrhythmic drugs, anti-hypertensive drugs, statins, metformin and inhaled bronchodilators have all been associated with changes in HRV parameters [6063]. However, the lack of reporting on medications that critically ill patients received reduces the mechanistic insight afforded by this approach, particularly given the strong correlation between HRV and morbidity/mortality appears to be largely attributable to incident heart rate. Similarly, the majority of studies in this review failed to consistently report on the use of common critical care interventions. This may explain why conflicting conclusions over how variety of features of critical illness may affect HRV. Continuous enteral or parenteral nutrition are both associated with a reduction in time domain HRV measures indicative of parasympathetic cardiac modulation [67]. However, we did not find any studies that reported on the feeding or fasting status of patients. Although a significant limitation of our study was the lack of primary source data, in any event, we could not identify a single common HRV parameter measured in all studies that enabled comparison. A further limitation is that we did not consider newer nonlinear and multiscale approaches, since very few studies incorporating these analyses have been undertaken. These approaches are also likely to be affected by the same factors that influence traditional HRV parameters [78]. Thus, in a clinical setting, further work is required to establish whether these newer approaches reduce the impact of several confounding factors we have identified in this review.

Conclusions

Heart rate and derived heart rate variability offers a non-invasive, inexpensive tool that may add mechanistic insights to our understanding of critical illness and also assist clinical care. However, the current interpretation of generalizable and clinically relevant values to aid clinical decisions/research is hampered by a non-standardised methodologic approach and lack of adjustment for important confounding factors. For critical care medicine to exploit recent advances in translational autonomic physiology, further high-quality prospective HRV studies underpinned by the development of consensus reporting standards relevant for critical care medicine are needed.

Abbreviations

HRV: 

Heart rate variability

APACHE-II: 

Acute Physiology and Chronic Health Evaluation II

NYHA: 

New York Heart Association

ECG: 

Electrocardiogram

MODS: 

Multiple organ dysfunction syndrome

Declarations

Acknowledgements

n/a

Funding

GLA is supported by a British Journal of Anaesthesia and Royal College of Anaesthetists Basic Science fellowship, British Oxygen Company grant from the Royal College of Anaesthetists and British Heart Foundation programme grant (RG/14/4/30736). Funding bodies played no role in the design of the study and collection, analysis and interpretation of data or in writing the manuscript should be declared.

Availability of data and materials

Not applicable.

Authors’ contributions

GLA devised hypothesis/study plan. SK and AS sourced the primary material. SMM independently verified quality of studies. GLA and SK wrote the first draft of the manuscript. All authors contributed to the final revised draft. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

GLA is a member of the Associate editorial board of Intensive Care Medicine Experimental. GLA has received consultancy fees from Glaxo Smith Kline for unrelated purposes. The other authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Translational Medicine & Therapeutics, William Harvey Research Institute, Queen Mary University of London

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