Persistent Inflammation and Recovery after Intensive Care: A Systematic Review

Authors

David M Griffith MD, Matthew E Vale MBBS, Christine Campbell PhD2, Steff Lewis PhD2,

Timothy S Walsh MD1

Affiliation

1Department of Critical Care, Centre for Inflammation Research, Queen’s Medical

Research Institute, University of Edinburgh, UK

2Centre for Population Health Sciences, Medical School, University of Edinburgh, Teviot

Place, EH8 9AG

Corresponding author and institution where work performed

David M Griffith

Department of Critical Care, Room W2.03, Centre for Inflammation Research, Queen’s

Medical Research Institute, University of Edinburgh, 47 Little France Crescent,

Edinburgh, EH16 4TJ, UK. Tel: +44 131 242 6661, Fax: 0141 242 6578

Email: david.m.griffith@ed.ac.uk

Abstract

Purpose

Physical weakness is common after critical illness however it is not clear how best to treat

it. Inflammation characterizes critical illness, is associated with loss of muscle mass

during critical illness and potentially modifies post-ICU recovery. We sought to identify

published reports on the prevalence of systemic inflammation after critical illness and its

association with physical recovery.

Methods

Systematic review of the literature. Sources: MEDLINE, EMBASE, CINAHL, CPCISSH,

and CPCIC. January 1982-December 2011.

Results

From 7433 references, 207 full text articles were reviewed, 57 were eligible and 22 were

included. Inflammation was present in most patients at ICU discharge according to CRP

concentration (range 70-100%), pro-calcitonin (range 89-100%), TNFα (100%), and SIRS

criteria (range 92-95%). Fewer patients had elevated MPO concentrations (range 0-

56%). At hospital discharge, 9/10 COPD patients (90%) had elevated CRP. No studies

tested the association between inflammation and physical recovery.

Conclusions

Inflammation is present in most patients at ICU discharge, but little is known or has been

investigated about persistent inflammation after this time point. No studies have explored

the relationship between persistent inflammation and physical recovery. Further research

is proposed.

Keywords

Intensive Care, Critical Care, Critical illness, Rehabilitation, Inflammation, Recovery,

Quality of Life

Introduction

Annually, around 10,000 patients are admitted to Scottish Intensive Care Units (ICUs)

with a critical illness; numbers are increasing and the aging general population means

that numbers of elderly patients are predicted to increase substantially over the next 20

years. Improvements in ICU treatment mean that about 75% of patients survive to

hospital discharge [1], but many have persisting physical disability that reduces quality of

life, and places high care burden on families and health services. Whilst persistent ICU

acquired disability is now recognized, it is not clear how best to prevent or treat it [2].

The most prevalent symptoms for the ICU survivor are fatigue and muscle

weakness [3][4]. Muscle biopsy studies reveal skeletal muscle abnormalities in virtually

all patients recovering from critical illness [5]. These include axonal neuropathy,

denervation, fibre atrophy, non-specific neuropathy and necrotising myopathy. Recovery

of muscle function after critical illness is often incomplete [2].

Critical illness is characterised by global activation of the immune system causing

a coordinated sequence of events known as the systemic inflammatory response

syndrome (SIRS). Inflammatory cytokines have an established role in regulating muscle

mass. TNFα, IL-1, IL-6, and endotoxin infusions result in muscle wasting syndromes [6,

7] due to increased protein catabolism [8-12], inhibition of protein synthesis [13], inhibition

of muscle cell differentiation [14] and reduced amino acid uptake [15]. Chronic diseases

such as cancer, COPD, heart failure and end stage renal disease, as well as normal

aging are associated with loss of muscle mass and function. Numerous studies have

observed associations between markers of inflammation and muscle function in these

groups [16-25].

Inflammation in critical illness has been extensively studied in the acute phase of

the illness, but it is unclear how many patients have evidence of ongoing inflammation in

the recovery phase. In addition, it is unclear how inflammation and muscle dysfunction

are inter-related in the rehabilitation stage of critical illness. The aim of this systematic

review is to collate the available data describing the prevalence of persistent, systemic

inflammation after critical illness and to establish whether inflammation is linked to

markers of physical dysfunction in these patients. We aimed to seek data on persistent

inflammation at 3 time points: at the point of ICU discharge, between ICU discharge and

hospital discharge, and at any time point after hospital discharge.

Methods

This systematic review has been reported according to the relevant sections of the

MOOSE guidelines for Meta-Analyses and Systematic Reviews of Observational Studies

[26].

Search Strategy

Electronic databases EMBASE, MEDLINE, and CINAHL were systematically

searched using the OVID user interface. In addition, grey literature sources were

searched for conference citations (CPCISSH and CPCIC) using the Web of Science

interface. An example search strategy for the MEDLINE database is given in Table 1.

We searched for studies published between January 1982 and December 2011 of human

intensive care unit patients who had a clinical or biochemical marker of systemic

inflammation measured.

Study characteristics

Inclusion and exclusion criteria are summarized in table 2. Studies carried out in

medical, surgical, or mixed intensive care units were considered. Studies including

children, neonates, neurosurgical, or post-operative cardiothoracic patients were not

considered.

A study was deemed to include a measure of systemic inflammation if it recorded

all of the systemic inflammatory response syndrome (SIRS) criteria (i.e. white cell count,

respiratory rate, body temperature, heart rate), C-reactive protein (CRP), or any

established pro-inflammatory mediator (e.g. IL-1, IL-6, or TNF-alpha).

For a study to be considered, the marker of systemic inflammation had to be

measured at one of 3 pre-specified time points: within 24 hours of ICU discharge,

between ICU discharge and hospital discharge, and after hospital discharge.

If a study reported a measurement of systemic inflammation whilst the patient was

in ICU, it was included if sampling continued until ICU discharge. If there was no

reference to ICU discharge, the study was only considered if the last sample taken was at

a time point >14 days after ICU admission. This considers that there was reasonable

probability that the majority of patients being sampled at this time point would have been

discharged from ICU. In such studies, the authors were contacted for further information.

No language restrictions were placed on the search. Where an English abstract

was available, the study remained in the review provided there was sufficient information

in the abstract. Where no English abstract was available, foreign language publications

were excluded.

Selection of studies

De-duplication was carried out automatically using the OVID user interface (Ovid

Technologies, New York), then manually using Endnote X4 software (Thompson Reuters,

New York). Following this, the title list was searched to remove clearly irrelevant studies

(e.g. studies of paediatric, neonatal, cardiothoracic, or neurosurgical patients, review

articles, editorials, case reports and commentaries). The abstracts of the remaining

studies were screened independently by 2 authors, and those not meeting the inclusion

criteria were excluded. Disagreements about eligibility were resolved by discussion

between the 2 screening authors. An inclusive approach was adopted. Where it was not

clear from the abstract whether a study should be included, it remained in the review list.

Full text versions of the remaining articles were obtained whenever possible using

the resources of the NHS, University of Edinburgh, and the British Library. Where an

article could not be retrieved in full text, and there was insufficient information in the

abstract to determine eligibility, it was excluded from the review (4 articles).

The full text articles were reviewed independently by 2 authors against inclusion

and exclusion criteria. This resulted in a final short list for further evaluation and data

extraction.

Data extraction

Each short-listed article was reviewed by 1 author looking specifically for an

estimate of prevalence of systemic inflammation. Where a prevalence estimate was not

provided in the text, attempts were made to contact authors for raw data to allow

calculation of prevalence estimates. Acknowledging that raw data may not be available in

older studies, authors were asked if they could provide summary measures (central

tendency and sample variability). Authors were contacted by email and traditional mail on

2 occasions, 1 month apart, thus allowing 2 months in total to respond after the initial

contact.

Data was extracted using a standard form. Parameters included were: author,

publication title, publication journal, publication year, number of patients at start of study,

number of ICU survivors, number of patients in whom inflammatory marker was available,

inflammatory mediator including units of measurement, time point, prevalence estimate

and/or summary estimate. Articles were also screened for any statistic that related

persistent inflammation and physical recovery after critical illness.

Data synthesis

For each circulating biomarker, the upper limit of normal was defined as the 97.5 th

centile (or suitable alternative) from a previously published study of healthy volunteers.

Prevalence estimates were calculated as the proportion of included patients exceeding

this limit. In addition summary estimates (a measure of central tendency and

distribution) were quoted.

Meta-analysis

Meta-analysis was not considered to be methodologically appropriate due to the

considerable heterogeneity of the study populations under study and high risk of selection

bias. For example, some studies focused on single diseases, certain ICU complications,

or specific settings.

Risk of Bias Assessment

A bespoke ‘risk of bias’ instrument was developed by the authors to allow

assessment of bias in prevalence estimates across a variety of study designs. This

instrument was a modification of the instrument produced by Hoy & Colleagues [27]

taking into account the major sources of bias affecting prevalence estimates, and the

criteria identified previously by consensus [28]. External validity was assessed according

to 4 criteria (target population, sampling frame, selection method, and risk of non-

response bias). Internal validity was assessed according to 3 criteria (case definition,

measurement instrument, and data collection method). For each of the 7 criteria, studies

were assessed as high risk, low risk or not reported (NR). Within each domain (internal

or external validity), an overall assessment of risk of bias was given according to the

following rules: 0 criteria at high risk – low risk; 1 criterion at high risk – moderate risk; 2

or more at high risk – high. In the case of missing information, risk of bias was deemed to

be ‘unclear’.

Results

Included Studies

Following electronic database searching and de-duplication, 7433 unique

references were retrieved. In total, 3327 abstracts were scrutinised and from these, 207

articles fulfilling or potentially fulfilling eligibility criteria were retrieved for full text review.

57 papers appeared to fulfill eligibility criteria for the review. A flow diagram detailing

exclusions at various stages of the review are detailed in Figure 1. Details of the included

studies can be found in table 3.

Data Completeness

Of the 57 papers considered to be eligible after full text review, none had

prevalence estimates for systemic inflammation and only 3 studies had summary

estimates. Therefore the authors of all these studies were contacted to provide further

data. The authors for 34 (65%) of the articles responded [29-60]. Seven of these did not

measure inflammation at an appropriate time point [34, 49, 51-53, 60, 61] and were

excluded. Two studies [55, 59] used the same data as other included studies [42, 48]

and were excluded. Raw data to allow calculation of prevalence estimates was provided

for 13 studies (23%) [29-31, 35, 39, 41, 42, 45-47, 56, 62, 63]. These studies were

included in the analysis. In the 12 studies where prevalence data was not provided,

summary estimates of biomarker concentrations were available for 5 studies and these

were also included in the analysis [32, 33, 38, 40, 43, 64-66]. The remaining 7 studies

were excluded. None of the studies measured physical function after ICU discharge.

One investigator had measured health-related quality of life but was unable to provide

data to allow calculation of association with inflammatory markers [33]. Finally, 1 author

volunteered data from another published study [67]. This study was missed from the

initial search because inflammation was not the main focus of the paper. The summary

data from this study was included.

Study Design

Of 22 included papers, 19 (86% were observational, 3 (14%) were interventional.

Of the observational studies, 3 (16%) were case control studies, 1 (5%) was cross-

sectional, and 15 (79%) were cohort studies.

Biochemical measures of inflammation

C-reactive protein (CRP) was measured in 20 (91%) of studies. Pro-calcitonin

(PCT) was measured in 3 (14%) studies. IL-6 was measured in 3 (14%) studies. TNF α

was measured in 1 (5%) study. SIRS criteria were measured in 1 (5%) study.

Myeloperoxidase (MPO) was measured in 1 study (5%). The cut-off values derived from

healthy populations are given in the Electronic Supplement (eTable 1).

Validity

A summary of the risk of bias assessment is provided in the final columns of table

3. The detailed scoring can be found in the electronic data supplement (eTable2). In

external validity terms, 13 papers were at high risk, 5 papers were at moderate risk, and 4

papers lacked enough information to make an assessment. In internal validity terms, 14

papers were at low risk. The remaining 8 papers lacked enough information to make an

assessment.

CRP concentration at ICU discharge

Of the 22 included studies, 18 (82%) measured CRP at the point of ICU discharge.

CRP concentration was elevated (>10mg/L) in the majority of patients ranging from 70%

in a large study of mixed medical and surgical ICU patients [62] to 100% in patients with

severe sepsis [47] and a cohort of patients who subsequently were readmitted to ICU

[39].

The CRP concentration varied according to the population studied. The mean of

the median concentrations of CRP at ICU discharge in the mixed medical / surgical

cohorts was 60mg/L. Lower mean CRP concentration was observed in trauma ICU

patients (23mg/L), patients with VAP (46mg/L), prolonged length of stay (45mg/L), and

medical ICU patients (36mg/L). Higher mean CRP concentrations were noted in sepsis

survivors (107mg/L) and surgical ICU patients (99mg/L). Unsurprisingly, the patients

selected as cases for the observational studies of ICU readmission [39, 65] and

unexpected death after ICU discharge [45] had high concentrations of CRP in their blood

at ICU discharge (131 and 218mg/L respectively).

IL-6 concentration at ICU discharge

Three studies (14%) measured IL-6 at ICU discharge [41, 42, 56]. These included

one study of mixed medical and surgical ICU survivors [56], one study of ICU patients

with a length of stay longer than 6 days [41], and one study of ICU patients with sepsis

[42]. The percentage of patients in each of these samples with IL-6 concentration above

3.5 pg/mL was 99%, 63% and 100% respectively. Median (IQR) IL-6 concentration at

ICU discharge in these samples were 80 (42-183) pg/mL, 76 (2-100) pg/mL, and 20 (15-

39) pg/mL. There is therefore evidence of significant elevations in IL-6 concentration in

the 3 studies at ICU discharge.

Pro-calcitonin concentration at ICU discharge

Three studies (14%) measured pro-calcitonin at ICU discharge [32, 41, 47]. Only 2

of the authors of these studies provided data to allow a prevalence calculation [41, 47].

All the patients in the Iapichino study of ICU patients with a stay of greater than 6 days

had a PCT concentration greater than 0.05ng/mL [42]. All subgroups of sepsis survivors

in Martensson’s study had elevated PCT concentrations according to this definition. In

the subgroup of patients that had SIRS, 89% of patients had elevated PCT.

TNF α concentration at ICU discharge

One study measured TNF-α at ICU discharge [42]. In this study of septic ICU

patients the median (IQR) TNF α concentration was 20 (15-39) pg/mL. The percentage

of patients with a TNF α concentration above 4.5 pg/mL was 100%.

Myeloperoxidase (MPO) concentration at ICU discharge

MPO was measured at ICU discharge in 1 study of patients with SIRS and sepsis

[47]. MPO was elevated at ICU discharge in 0%, 11%, 56% and 0% of ICU survivors with

SIRS, severe sepsis without AKI, septic shock without AKI, and septic shock with AKI

respectively. Median MPO concentrations for each sample are presented in Table 4.

Notably, the concentrations measured are much lower than the expected reference

ranges given in the control cohort from which the cut off was derived (951ng/mL).

SIRS criteria at ICU discharge

One study measured SIRS criteria at ICU discharge [46]. Ninety-five percent of

ICU survivors who were subsequently readmitted to ICU during the same hospital

admission met criteria for SIRS. A comparable percentage (92%) of patients who were

not subsequently re-admitted to ICU had SIRS.

Inflammation after ICU discharge

Three of the included studies measured inflammation after ICU discharge [29, 33,

46]. Makris & Colleagues measured SIRS criteria 72 hours after ICU discharge in a case

control study comparing 244 patients who were subsequently re-admitted to ICU (cases),

and 244 controls that were not readmitted (controls) [46]. In these cases, 69% of the

patients fulfilled criteria for SIRS, whilst in the controls only 42% of the patients fulfilled

criteria for SIRS.

Akbas & Colleagues measured CRP concentration in 10 ICU survivors at hospital

discharge and found that 9 of them (90%) had a CRP concentration of greater than

10mg/L [29]. The median (IQR) of CRP concentration was 23 (16-93) in this sample.

Bateman & Colleagues studied 24 ICU survivors that had evidence of anaemia at

ICU discharge (Haemoglobin concentration <100g/dL) [33]. CRP and IL-6 were

measured at weeks 1, 3, 6, 9, 13, and 26 after ICU discharge. Following a steep decline

in both biomarkers, CRP concentration fell below 10mg/L at 13 weeks. IL-6 remained

elevated even after 26 weeks.

Association between inflammation and physical recovery

None of the studies had measured physical recovery outcomes as well as markers

of inflammation after ICU discharge. There is therefore no known work that links

inflammation after ICU discharge with physical recovery after ICU discharge. One study

[33] had measured health related quality of life after ICU discharge but was unable to

provide data to test the association with systemic inflammation.

Discussion

We aimed to assess the plausibility of the hypothesis that post-ICU exposure to

inflammation negatively influences physical recovery in survivors of critical illness. We

sought firstly to identify evidence that the exposure was indeed present in the post-ICU

period, and secondly to assess whether the presence of the exposure was causally

related to physical outcome.

The existing body of studies of inflammatory biomarkers in ICU patients has

enrolled patients still resident within intensive care. In the majority of these, the data has

been collected for a time-limited period and does not necessarily include data relating to

post-ICU discharge. In those studies that do include ICU discharge, inflammation is

almost universally evident. These data suggest that early in the post-ICU period, patients

have an ongoing exposure to inflammation, and this is supported by one study that found

a high proportion of patients fulfilling SIRS criteria in the early post-ICU period [46].

From this literature, it is not clear how long the inflammatory exposure lasts.

Beyond the early post-ICU period, only small, highly selective cohorts have been studied,

and whilst these suggest ongoing inflammation lasting for as much as 6 months in some

patients, this data cannot be reliably extrapolated to a general ICU population or other

patient subsets.

Thus, the available data do not appear to address the key question of inflammatory

mediation of functional recovery. Indeed, no investigators have yet reported correlations

between inflammatory biomarkers and physical recovery outcomes.

Against this backdrop, it is considered that this review significantly adds to the

existing literature in that a broad range of databases covering conventional and grey

literature sources were searched. The robust and comprehensive nature of this

investigation thus encourages the belief that all important and relevant studies in this field

have been diligently examined. Moreover, to further extend the validity of this review,

reports of observational study designs, as well as interventional clinical studies have been

included. Indeed, relevant variables were measured even in cases where the latter had

not been intended as part of the primary aims of the original study.

In terms of usefulness, none of the previous studies reviewed held in their reports

sufficient data to carry out the proposed analyses. To address this, considerable efforts

were made to contact authors for clarification of summary data and, where necessary to

make requests for raw data. While a 65% response rate was considered satisfactory, only

half of those that responded were able to provide sufficient data. However, despite the

relatively low yield from this exercise, it is considered likely that the review reports a high

proportion of ‘accessible’ data.

This inclusive design of the review did present some challenges for the

assessment of bias. Many tools have been developed for assessing risk of bias but most

focus on the single study designs [68]. We developed a review-specific tool to assess

bias in prevalence estimates that was not specific to a single study design and allowed its

broader application.

In terms of interpretation of the work, it is worth noting that the focus was firmly

fixed on the post-ICU period. Due to the extensive previous work on inflammatory

markers measured whilst patients are critically unwell (and the established link with ICU

acquired weakness), we did not include papers that did follow up patients up to or beyond

ICU discharge.

In summary, this rigorous systematic review is intended as an important first step

in exploring the hypothesis that exposure to post-ICU systemic inflammation is a

causative factor in post-ICU disability. It provides initial evidence that the exposure of

interest is present in the early post-ICU period but highlights gaps in our knowledge with

respect to the key processes involved as the patient transitions into the community.

Although previous studies suggest biological plausibility that inflammation might be

important in physical recovery, we found no studies that explored this link. It is thus

concluded that future studies are required to characterize the inflammatory profile in the

post-ICU period and to explore its relationship with physical recovery. This may allow us

to better identify patients likely to experience a poor recovery trajectory in order to

specifically target physical interventions, and potentially to identify processes that might

be amenable to pharmacological intervention. It is hoped that these together will improve

functional recovery for ICU survivors in the future.

Acknowledgements

We wish to acknowledge the help of Sheila Fisken, Senior Librarian at the University of

Edinburgh Library for her assistance with design of the search strategy and retrieval of full

text articles for review. In addition we wish to acknowledge the contribution of the

following authors who provided additional information for the review: T Akbas, N al Subai,

L Azevedo, C Balci, A Bateman, Y Cho, P Damas, R de Pablo, W Grander, G Van den

Berghe, K Ho, G Iapichino, J Jensen, I Kauss, E Litton, N Makris, J Martensson, D

Memis, S Oda, P Povoa, J Reny, Tsangaris I, Tsuruta R, Umbrello M, Watanabe E,

Weimann A, Yousef A, Yucel T, Zugel N.

Figure legends

Figure 1 – Article selection flow diagram.

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