Browse the corpus

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 [3–7]. 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.

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.

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.

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].

ory 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 [20–53]. Two articles analysed the same cohort of patients [34, 37].

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 [20–53]. 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

d 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.

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

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.

den 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.

on (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 M

al 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 M ODS 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

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].

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].

m 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

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 Z

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 Z aal 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

ncy, 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.

justment 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 [60–63], drug therapy [64–68] 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

riable 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

uded/+ 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

uded/+ 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

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.

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.

aves 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).

e 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 [60–63]. 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 [73–76] 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.

develops may be optimal [74]. Studies where basal autonomic function can be captured, including elective surgery [73–76] 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.

e 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 [60–63]. 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.

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. Additional file Additional file 1: Clinical confounding factors. (DOCX 34 kb) Abbreviations HRVHeart rate variability APACHE-IIAcute Physiology and Chronic Health Evaluation II NYHANew York Heart Association ECGElectrocardiogram MODSMultiple organ dysfunction syndrome Electronic supplementary material The online version of this article (doi:10.1186/s40635-017-0146-1) contains supplementary material, which is available to authorized users. Acknowledgements n/a

APACHE-IIAcute Physiology and Chronic Health Evaluation II NYHANew York Heart Association ECGElectrocardiogram MODSMultiple organ dysfunction syndrome Electronic supplementary material The online version of this article (doi:10.1186/s40635-017-0146-1) contains supplementary material, which is available to authorized users. 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. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.