Background

A predictive model that uses the rhythmicity of core body temperature (CBT) could be an easily accessible clinical tool to ultimately improve outcomes among critically ill patients.

Objectives

To assess the relation between the 24-hour CBT profile (CBT-24) before intensive care unit (ICU) discharge and clinical events in the step-down unit within 7 days of ICU discharge.

Methods

This retrospective cohort study in a tertiary ICU at a single center included adult patients requiring acute invasive ventilation for more than 48 hours and assessed major clinical adverse events (MCAEs) and rapid response system activations (RRSAs) within 7 days of ICU discharge (MCAE-7 and RRSA-7, respectively).

Results

The 291 enrolled patients had a median mechanical ventilation duration of 139 hours (IQR, 50-862 hours) and at admission had a median Acute Physiology and Chronic Health Evaluation II score of 22 (IQR, 7-42). At least 1 MCAE or RRSA occurred in 64% and 22% of patients, respectively. Independent predictors of an MCAE-7 were absence of CBT-24 rhythmicity (odds ratio, 1.78 [95% CI, 1.07-2.98]; P = .03), Sequential Organ Failure Assessment score at ICU discharge (1.10 [1.00-1.21]; P = .05), male sex (1.72 [1.04-2.86]; P = .04), age (1.02 [1.00-1.04]; P = .02), and Charlson Comorbidity Index (0.87 [0.76-0.99]; P = .03). Age (1.03 [1.01-1.05]; P = .006), sepsis at ICU admission (2.02 [1.13-3.63]; P = .02), and Charlson Comorbidity Index (1.18 [1.02-1.36]; P = .02) were independent predictors of an RRSA-7.

Conclusions

Use of CBT-24 rhythmicity can assist in stratifying a patient’s risk of subsequent deterioration during general care within 7 days of ICU discharge.

Organ system physiology and behaviors—for example, body temperature, brain wave activity, cardiac and respiratory function, blood coagulation, immune function, and drug metabolism—often follow rhythmic circadian patterns.1,2  Disturbances in the timing of biological processes, or circadian rhythm disruption (CRD), are common in critically ill patients,3  and various factors contribute, such as ambient light and noise, acute and chronic disease, sleep disruption, and the timing of therapy regimens.38  Core body temperature (CBT) is a reliable and stable indicator of circadian timing in health,9  and it is a routine clinical measure for determining whole-body homeostasis or characterizing hypothermia or fever.10 

Patients commonly deteriorate clinically after discharge from an intensive care unit (ICU)11 ; this deterioration results in ICU readmissions,1214  higher mortality,12,15  prolonged hospital stay,16  and higher overall health care costs.17  The presence of CRD may mark the persistence or development of a disease process, putting the patient at risk for clinical deterioration or a new clinical event. The proportion of patients who have CRD at ICU discharge and the relation between CRD and adverse effects during general care after an ICU stay are not known. The Temperature Profile and Adverse Outcomes Post ICU Discharge (TAOPID) study aimed to assess CBT profile during the 24 hours of ICU admission before discharge (CBT-24) as a predictor for rapid response system activation (RRSA) or a major clinical adverse event (MCAE) within 7 days after ICU discharge.

We assembled a retrospective cohort study that included sequential adult patients who were discharged to a step-down unit after an acute admission to an ICU between 2015 and 2016. All included patients required mechanical ventilation for longer than 48 hours. We excluded from the cohort patients who were febrile at the time of ICU discharge or who were discharged with clear limitations on escalating care in the event of clinical deterioration; this group excluded patients who were discharged to palliative care. Patients recovering from acute brain injury or neurological injury were also excluded because of a significant risk of structural injury to their circadian pacemaker. Patients with a burn injury were excluded because they often have abnormal thermoregulation. Patients who met the inclusion criteria were sourced from the Royal Brisbane and Women’s Hospital MetaVision database (iMDsoft). This data set is well established and has been validated; it is used to collect real-time local data for inclusion in a national performance survey of critically ill patients through the Australia and New Zealand Intensive Care Centre for Outcome and Resource Evaluation. For patients with multiple ICU admissions within the study period, we included data from only the first ICU admission. The study received institutional ethical approval by the Research Ethics Committee of the Royal Brisbane and Women’s Hospital (LNR/2018/QRBW/48281).

The principal study outcomes were the number of MCAEs assessed within 7 days of ICU discharge (MCAE-7) and the number of RRSAs assessed within 7 days of ICU discharge (RRSA-7), based on the concept of 7 days as a practical follow-up period after ICU discharge. We collected RRSA data from the Rapid Response System database of the Safety and Quality Unit of the Royal Brisbane and Women’s Hospital. In addition to the time when the RRSA occurred, this database also records call criteria (cardiac arrest, threatened airway, breathing, circulation, neurology, worry) and the call outcome (death, palliative care, transfer to coronary care unit, transfer to ICU, or remained in general care area).

We assessed clinical adverse events as a secondary outcome and defined them a priori; relevant data were collected retrospectively from electronic clinical charts in the integrated electronic medical record (Cerner) (see Supplemental Table 1).18  An MCAE was defined as a combined end point of new-onset nosocomial pneumonia, confusion, or delirium; a deep venous thrombosis or pulmonary embolus; a myocardial infarction or new-onset arrhythmia; and a catheter-related bloodstream infection or a urinary tract infection. Conditions identified during chart review, such as delirium, were defined per clinicians’ records; although such definitions potentially increase the generalizability of the findings, they may limit the findings’ validity.

Supplemental Table 1

Onset of clinical adverse events in general care areas after discharge from the intensive care unit25 

Onset of clinical adverse events in general care areas after discharge from the intensive care unit25
Onset of clinical adverse events in general care areas after discharge from the intensive care unit25

The principal independent variable was the final complete CBT-24, which represents temperature during the 24 hours before ICU discharge and was based on clinical temperatures recorded with an indwelling urinary catheter thermistor. Esophageal, tympanic, and axillary temperatures (in order of preference) were included only when bladder temperatures were not available. We assessed temperature profiles using the R statistical program and R package CircaCompare19 ; we compared patients’ CBT-24 measurements with those from simulated sex-matched controls, which we obtained from publicly available data.20  We analyzed CBT profiles for the presence of circadian rhythmicity (P < .05); if rhythmicity was present we assessed the amplitude, the mesor or the midpoint of the oscillation, and the phase (P < .05). Patients with a body temperature higher than 38.5 °C were considered to be febrile.

Additional clinical data to describe the cohort included the Charlson Comorbidity Index,21  principal diagnosis at ICU admission (surgical, elective, medical, trauma, obstetric), hospital mortality, hospital and ICU lengths of stay, inotrope use, dialysis therapy, ileus, sepsis requiring antibiotic therapy at admission, and Acute Physiology and Chronic Health Evaluation (APACHE) II22  and Sequential Organ Failure Assessment (SOFA)23  scores both at ICU admission and upon discharge. We used APACHE II and SOFA scores at ICU discharge in an attempt to quantify the severity of illness at that time point, consistent with previous studies.24,25  Medications administered within the 24 hours before ICU discharge were characterized by drug class; sedation on the day of discharge was summarized as a composite of major tranquilizers, narcotics, sedatives including α2-agonists, and benzodiazepines.

Statistical Methods

We entered data into a purpose-built Microsoft Access research database (Microsoft Corp) and then analyzed the data using Stata 15 statistical software (StataCorp) to identify univariate associations and perform logistic regression modeling of the outcome of interest. After data linkage, the data used for the analysis were deidentified.

Sample size calculations were limited because no published data were avilable for the rate of circadian disruption or the frequency of RRSAs or MCAEs among patients at ICU discharge. We conservatively estimated the rate of RRSA or MCAE after ICU discharge to be 14%.12  Based on results of the z-test for a single proportion, 58 patients would be required if the absence of circadian temperature rhythmicity increased the RRSA rate to 25%. We aimed to enroll 300 patients in this initial pilot study, and considering the rule of thumb of 10 individuals with either an MCAE or an RRSA for each explanatory variable, the regression modeling should be able to support approximately 5 explanatory variables in the final model.26  For descriptive statistics, we calculated absolute and relative frequencies for categorical parameters and characterized continuous parameters using median and interquartile range (IQR), though we provide an actual range when we consider it to be clinically relevant. We used the t, χ2, Fisher exact, and Wilcoxon rank sum tests for inferential statistics, as appropriate. Results were considered to be statistically significant when P was .05 or less. We constructed forward, stepwise logistic models for RRSA-7 and MCAE-7. We considered variables for inclusion in the model when associations had a P of .25 or less in the univariate analysis or were clinically relevant. Goodness of fit was assessed with the Hosmer-Lemeshow statistic, whereas calibration was assessed on the basis of the area under the receiver operating characteristic curve. Sensitivity/specificity plots were derived for the probability of RRSA-7 and MCAE-7 at the median values of the covariants in the respective regression models.

The rhythmicity of core body temperature profiles was assessed in addition to severity of illness at time of ICU discharge.

We enrolled 291 patients in the study. Table 1 summarizes their demographic information, including age, ICU and hospital lengths of stay, case mix, comorbid conditions, and APACHE II and SOFA scores at admission and discharge. All patients received some form of active central nervous system analgesia or sedation within the 24 hours before ICU discharge. Of the 48 patients prescribed a sedative agent, only 8 (17%) received dexmedetomidine, whereas 30 (63%) received clonidine. Details about medication administration during the 24 hours before ICU discharge are summarized in Supplemental Table 2, and details about the Charlson comorbidities are summarized in Supplemental Table 3.

Table 1

Demographic characteristics of 291 patients in the study

Demographic characteristics of 291 patients in the study
Demographic characteristics of 291 patients in the study
Supplemental Table 2

Drug classes prescribed per patient within 24 hours of discharge from the intensive care unit

Drug classes prescribed per patient within 24 hours of discharge from the intensive care unit
Drug classes prescribed per patient within 24 hours of discharge from the intensive care unit
Supplemental Table 3

Summary of Charlson comorbidities

Summary of Charlson comorbidities
Summary of Charlson comorbidities

Data to analyze temperature profile were available from 289 patients. A median of 22 temperature measurements (IQR 11-24) were available for each of these patients. A minimum of 6 measures were required for cosinor analysis. In most patients, temperature had been measured at a range of sites; a breakdown of temperature measurement sources is detailed in Supplemental Table 4. Among these 289 patients, at ICU discharge, 191 (66%) had a temperature profile that was considered rhythmic by cosinor analysis (P < .05). The median mesor or central temperature of the oscillation was 37.4 °C (IQR 37.1-37.7), with a median amplitude of 0.28 °C (IQR 0.20-0.40) and median peak time of 4:25 PM (IQR 1:20 PM -7:13 PM ). Temperature peaked between 9 PM and 6 AM in 43 patients (23%). Representative temperature profiles are detailed in the Figure.

Supplemental Table 4

Temperature sources and frequency of use among 289 patients during their final 24 hours in the intensive care unit

Temperature sources and frequency of use among 289 patients during their final 24 hours in the intensive care unit
Temperature sources and frequency of use among 289 patients during their final 24 hours in the intensive care unit
Figure

Representative graphs comparing circadian profiles of intensive care patients’ core body temperature with a normal temperature profile. A, Patient showing no significant difference in phase, amplitude, or mesor (midpoint of oscillation) from healthy controls. Rhythmicity difference, P < .001; mesor difference, P = .99; amplitude difference, P = .45; phase difference, P = .69. B, Patient showing a significant phase delay but no amplitude or mesor difference from healthy controls. Rhythmicity, P < .001; mesor difference, P = .08; amplitude difference, P = .18; phase difference, 9.13 (P < .001). C, Patient showing no rhythmic temperature profile. Rhythmicity, P = .37.

Figure

Representative graphs comparing circadian profiles of intensive care patients’ core body temperature with a normal temperature profile. A, Patient showing no significant difference in phase, amplitude, or mesor (midpoint of oscillation) from healthy controls. Rhythmicity difference, P < .001; mesor difference, P = .99; amplitude difference, P = .45; phase difference, P = .69. B, Patient showing a significant phase delay but no amplitude or mesor difference from healthy controls. Rhythmicity, P < .001; mesor difference, P = .08; amplitude difference, P = .18; phase difference, 9.13 (P < .001). C, Patient showing no rhythmic temperature profile. Rhythmicity, P = .37.

Close modal

After ICU discharge, 100 RRSAs occurred; 64 patients (22%) had at least 1 RRSA. Median APACHE II and SOFA scores, along with additional RRSA details, are shown in Table 2. A breathing disturbance was the most common reason for an RRSA (n = 73 [73.0%]), although some patients were delirious or required cardiopulmonary resuscitation. With regard to RRSA outcomes, in most cases patients either remained in the general care area or were readmitted to the ICU; 3 (3.0%) patients died. Only 10 patients received palliative care, 3 at the time of the RRSA and 7 after it. Most RRSAs occurred during the first 7 days following ICU discharge.

Table 2

Summary of characteristics of rapid response system activations among 291 study patients

Summary of characteristics of rapid response system activations among 291 study patients
Summary of characteristics of rapid response system activations among 291 study patients

A total of 192 patients (66%) experienced at least 1 clinical event during their stay in the general care area, with a median of 2 events (IQR, 1-16 events) per patient. The frequencies of each type of clinical event experienced during the stay in the step-down unit are summarized in Table 3. The median time after ICU discharge until the onset of a clinical event was 6 days (IQR, 0-103 days). Among all clinical events, 255 (43%) were MCAEs, the most common of which were delirium, pneumonia, thromboembolic disease, and urinary tract infection. More than 60% of patients experienced at least 1 MCAE. All clinical events that occurred after ICU discharge are summarized in Supplemental Table 5.

Table 3

Summary of clinical event characteristics for 291 patients in the study

Summary of clinical event characteristics for 291 patients in the study
Summary of clinical event characteristics for 291 patients in the study
Supplemental Table 5

Clinical events occurring in general care area after intensive care unit discharge

Clinical events occurring in general care area after intensive care unit discharge
Clinical events occurring in general care area after intensive care unit discharge

An RSSA-7 was associated with age, a diagnosis of sepsis at ICU admission, APACHE II and SOFA scores at ICU discharge, the APACHE III score at ICU admission, and the Charlson Comorbidity Index (Table 4). There was no association of RSSA-7 with CBT-24 rhythmicity at ICU discharge. Only age, sepsis at ICU admission, and Charlson Comorbidity Index were independent predictors of an RRSA-7. The predicted probability for sepsis at ICU admission for an RRSA-7 at the means of the covariates was 0.27 (95% CI, 0.18-0.35). Supplemental Figure 1 summarizes both sensitivity and specificity compared with probability cutoffs.

Table 4

Univariate and multivariate logistic regression models for rapid response system activation within 7 days of ICU dischargea

Univariate and multivariate logistic regression models for rapid response system activation within 7 days of ICU dischargea
Univariate and multivariate logistic regression models for rapid response system activation within 7 days of ICU dischargea
Supplemental Figure 1

Sensitivity and specificity plotted against the probability cutoffs for rapid response system activations within 7 days of discharge from the intensive care unit.

Supplemental Figure 1

Sensitivity and specificity plotted against the probability cutoffs for rapid response system activations within 7 days of discharge from the intensive care unit.

Close modal

For MCAE-7, independent predictors were the absence of a rhythmic CBT-24, SOFA score at ICU discharge, Charlson comorbidity score, age, and male sex (Table 5). The predicted probability for the absence of a rhythmic temperature profile for an MCAE-7 at the means of the covariates was 0.60 (95% CI, 0.50-0.70). Supplemental Figure 2 summarizes sensitivity and specificity compared with probability cutoffs. Of the 147 MCAE-7 events, only 41 (28%) resulted in an RRSA-7.

Table 5

Univariate and multivariate logistic regression models for major clinical adverse events within 7 days of ICU dischargea

Univariate and multivariate logistic regression models for major clinical adverse events within 7 days of ICU dischargea
Univariate and multivariate logistic regression models for major clinical adverse events within 7 days of ICU dischargea
Supplemental Figure 2

Sensitivity and specificity plotted against the probability cutoffs for medical clinical adverse events within 7 days of discharge from the intensive care unit.

Supplemental Figure 2

Sensitivity and specificity plotted against the probability cutoffs for medical clinical adverse events within 7 days of discharge from the intensive care unit.

Close modal

Results of the TAOPID study showed that the absence of CBT-24 rhythmicity was an independent predictor of MCAE-7, as were illness severity at discharge, comorbidities, age, and sex. Such relationships were less robust beyond 7 days after ICU discharge, and the lack of CBT rhythmicity had limited effect in predicting an RRSA-7.

Rhythmicity of physiological processes is a key element of homeostasis and is optimally coordinated with geophysical time.27  Many rhythmic processes are commonly disrupted in patients in an ICU1,3,4,2834 ; the degree of disruption is associated with increasing illness severity.31  Recommendations are available for care routines and environments to facilitate circadian rhythms.35,36  The effectiveness of therapies that attempt to normalize these timings and their benefit to critically ill patients have not been studied sufficiently. Our study demonstrates that, at least for temperature measurement, not all patients had a normal rhythmicity by the time of ICU discharge, which has implications for predicting MCAEs after ICU discharge.

Circadian rhythm dysfunction suggests an underlying abnormality or illness process.37  Assessment of CRD can, however, be complex and expensive because of the need for repeated measurements of surrogate metabolic markers.37  Body temperature is readily measurable in the ICU, but apart from defining fever, patterns of temperature profiles have not been well defined in critically ill populations.38 

Rapid response systems are generally reactive to alerts or parameters from various approaches to monitor vital signs,3942  though an RRSA does not always occur when a patient clinically deteriorates.43  Many MCAEs may result in severe patient morbidity despite an RRSA not being called for all MCAEs. The performance of predictors for patient deterioration following ICU discharge are varied. Objective measures such as the level of C-reactive protein are better predictors than ICU admission type (medical, surgical, trauma), the timing of ICU discharge, SOFA score at ICU discharge, white cell count, or fibrinogen concentration.44  Outreach systems for use after ICU discharge principally aim to preempt deterioration before abnormal physiological signs occur. Phenotypic profiling has utility in reducing ICU readmission rates within 48 hours but has not effected an overall change in patient outcome.45  Existing scoring systems such as the National Early Warning Score have been predictive of clinical deterioration after an ICU stay,46  and in particular for respiratory failure.12,47  Our study did show consistent associations between SOFA score at discharge, MCAE-7, and RRSA-7. In the TAOPID study, we uniquely assessed CBT to objectively and simply predict patients’ progress after ICU discharge. We found utility for it, however, only within the early post-ICU period, similar to findings from previous predictive studies of clinical deterioration.12  This finding is relevant because it also defines a potential optimal time period for outreach services to patients who have been recently discharged from the ICU.

The relation between MCAE and the patient’s sex may reflect a complex relationship between clinical outcomes, age, and sex48 ; women aged younger than 50 years have a lower adjusted mortality rate than men, who receive more intense treatment and have higher ICU readmission rates.4951  Hormonal differences may also influence responses to sepsis and hypoxia.52,53  Illness specificity may be important, as adjusted mortality rates differ between the sexes for illnesses such as chronic obstructive pulmonary disease and ischemic heart disease.48  Men may have higher morbidity than women after trauma.48  Such effects may be reflected in the influence of male sex on the regression model, increasing the likelihood of an adverse clinical event.

Pragmatically, we used all temperature assessment modalities that were available within the last 24 hours of the ICU stay, as this process represents clinical reality. Measurements of CBT obtained with bladder catheter thermistors are not always used in the last days of an ICU stay, when the intensity and invasiveness of clinical monitoring may be reduced. Most patients, however, did have enough clinical measurements available to allow us to determine CBT rhythmicity. The temperature profile may be truncated with the use of all clinically available routes of temperature measurement. As such, in this study we focused assessment on the dichotomized presence of temperature rhythmicity as this is more likely than amplitude or phase to be preserved across measurement methods. Using regularly available data, we were able to predict patient deterioration. The limited comparison data available are from 24-hour temperature profiles of healthy patients with public domain data stratified by sex only. Despite a lack of age matching, normal temperature profiles are still rhythmic across all age groups. We expected that temperature profiles from similarly aged healthy people, used as controls for comparison, would not be arrhythmic or phase reversed, as is the case with temperature profiles in many ICU patients. The TAOPID study was an initial exploration of CBT-24 as a simple, readily available, and objective measure of the cyclic character of temperature profile at ICU discharge and its potential clinical utility. When CBT is collected as part of an electronic clinical information system, its inclusion in scoring systems that are used for predicting clinical deterioration after ICU discharge could provide an additional assessment when stratifying the need for post-ICU follow-up.

The TAOPID study supports the use of readily available CBT-24 measurements along with illness severity at ICU discharge to assist in stratifying the risk of subsequent deterioration of patients during care in a step-down unit.

1
Chan
MC
,
Spieth
PM
,
Quinn
K
,
Parotto
M
,
Zhang
H
,
Slutsky
AS
.
Circadian rhythms: from basic mechanisms to the intensive care unit
.
Crit Care Med
.
2012
;
40
:
246
253
.
2
Anafi
RC
,
Francey
LJ
,
Hogenesch
JB
,
Kim
J
.
CYCLOPS reveals human transcriptional rhythms in health and disease
.
Proc Natl Acad Sci U S A
.
2017
;
114
:
5312
5317
.
3
Frisk
U
,
Olsson
J
,
Nylen
P
,
Hahn
RG
.
Low melatonin excretion during mechanical ventilation in the intensive care unit
.
Clin Sci (Lond)
.
2004
;
107
:
47
53
.
4
Mundigler
G
,
Delle-Karth
G
,
Koreny
M
, et al
.
Impaired circadian rhythm of melatonin secretion in sedated critically ill patients with severe sepsis
.
Crit Care Med
.
2002
;
30
:
536
540
.
5
Fanfulla
F
,
Ceriana
P
,
D’Artavilla Lupo
N
,
Trentin
R
,
Frigerio
F
,
Nava
S
.
Sleep disturbances in patients admitted to a step-down unit after ICU discharge: the role of mechanical ventilation
.
Sleep
.
2011
;
34
:
355
362
.
6
Gabor
JY
,
Cooper
AB
,
Crombach
SA
, et al
.
Contribution of the intensive care unit environment to sleep disruption in mechanically ventilated patients and healthy subjects
.
Am J Respir Crit Care Med
.
2003
;
167
:
708
715
.
7
Trompeo
AC
,
Vidi
Y
,
Locane
MD
, et al
.
Sleep disturbances in the critically ill patients: role of delirium and sedative agents
.
Minerva Anestesiol
.
2011
;
77
:
604
612
.
8
Tan
DX
,
Manchester
LC
,
Terron
MP
,
Flores
LJ
,
Reiter
RJ
.
One molecule, many derivatives: a never-ending interaction of melatonin with reactive oxygen and nitrogen species?
J Pineal Res
.
2007
;
42
:
28
42
.
9
Brown
EN
,
Czeisler
CA
.
The statistical analysis of circadian phase and amplitude in constant-routine core-temperature data
.
J Biol Rhythms
.
1992
;
7
:
177
202
.
10
Saini
C
,
Morf
J
,
Stratmann
M
,
Gos
P
,
Schibler
U
.
Simulated body temperature rhythms reveal the phase-shifting behavior and plasticity of mammalian circadian oscillators
.
Genes Dev
.
2012
;
26
:
567
580
.
11
Tirkkonen
J
,
Tamminen
T
,
Skrifvars
MB
.
Outcome of adult patients attended by rapid response teams: a systematic review of the literature
.
Resuscitation
.
2017
;
112
:
43
52
.
12
Uppanisakorn
S
,
Bhurayanontachai
R
,
Boonyarat
J
,
Kaewpradit
J
.
National Early Warning Score (NEWS) at ICU discharge can predict early clinical deterioration after ICU transfer
.
J Crit Care
.
2018
;
43
:
225
229
.
13
Kramer
AA
,
Higgins
TL
,
Zimmerman
JE
.
Intensive care unit readmissions in U.S. hospitals: patient characteristics, risk factors, and outcomes
.
Crit Care Med
.
2012
;
40
:
3
10
.
14
Ponzoni
CR
,
Corrêa
TD
,
Filho
RR
, et al
.
Readmission to the intensive care unit: incidence, risk factors, resource use, and outcomes. A retrospective cohort study
.
Ann Am Thorac Soc
.
2017
;
14
:
1312
1319
.
15
Ng
YH
,
Pilcher
DV
,
Bailey
M
,
Bain
CA
,
MacManus
C
,
Bucknall
TK
.
Predicting medical emergency team calls, cardiac arrest calls and re-admission after intensive care discharge: creation of a tool to identify at-risk patients
.
Anaesth Intensive Care
.
2018
;
46
:
88
96
.
16
Wong
EG
,
Parker
AM
,
Leung
DG
,
Brigham
EP
,
Arbaje
AI
.
Association of severity of illness and intensive care unit readmission: a systematic review
.
Heart Lung
.
2016
;
45
:
3e.2
9.e2
. doi:
17
Thompson
K
,
Taylor
C
,
Forde
K
,
Hammond
N
.
The evolution of Australian intensive care and its related costs: a narrative review
.
Aust Crit Care
.
2018
;
31
:
325
330
.
18
Australian Commission for Healthcare Standards (ACHS)
.
Australasian Clinical Indicator Report: 2010–2017
. 9th ed.
ACHS Performance and Outcomes Service
;
2018
.
19
Parsons
R
,
Parsons
R
,
Garner
N
,
Oster
H
,
Rawashdeh
O
.
CircaCompare: a method to estimate and statistically support differences in mesor, amplitude and phase, between circadian rhythms
.
Bioinformatics
.
2020
;
36
(
4
):
1208
1212
.
20
Baker
FC
,
Waner
JI
,
Vieira
EF
,
Taylor
SR
,
Driver
HS
,
Mitchell
D
.
Sleep and 24 hour body temperatures: a comparison in young men, naturally cycling women and women taking hormonal contraceptives
.
J Physiol
.
2001
;
530
(
pt 3
):
565
574
.
21
Charlson
ME
,
Pompei
P
,
Ales
KL
,
MacKenzie
CR
.
A new method of classifying prognostic comorbidity in longitudinal studies: development and validation
.
J Chronic Dis
.
1987
;
40
:
373
383
.
22
Knaus
WA
,
Draper
EA
,
Wagner
DP
,
Zimmerman
JE
.
APACHE II: a severity of disease classification system
.
Crit Care Med
.
1985
;
13
:
818
829
.
23
Vincent
JL
,
Moreno
R
,
Takala
J
, et al
.
The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure
.
Intensive Care Med
.
1996
;
22
:
707
710
.
24
Chen
YC
,
Lin
MC
,
Lin
YC
,
Chang
HW
,
Huang
CC
,
Tsai
YH
.
ICU discharge APACHE II scores help to predict post-ICU death
.
Chang Gung Med J
.
2007
;
30
:
142
150
.
25
Ferreira
FL
,
Bota
DP
,
Bross
A
,
Melot
C
,
Vincent
JL
.
Serial evaluation of the SOFA score to predict outcome in critically ill patients
.
JAMA
.
2001
;
286
:
1754
1758
.
26
Dawson
B
,
Trapp
RG
.
Basic & Clinical Biostatistics
. 4th ed.
Lange Medical Books/McGraw-Hill
;
2004
.
27
Hall
JC
,
Rosbash
M
.
Oscillating molecules and how they move circadian clocks across evolutionary boundaries
.
Proc Natl Acad Sci U S A
.
1993
;
90
:
5382
5383
.
28
Mundigler
G
,
Delle-Karth
G
,
Koreny
M
, et al
.
Impaired circadian rhythm of melatonin secretion in sedated critically ill patients with severe sepsis
.
Crit Care Med
.
2002
;
30
:
536
540
.
29
Olofsson
K
,
Alling
C
,
Lundberg
D
,
Malmros
C
.
Abolished circadian rhythm of melatonin secretion in sedated and artificially ventilated intensive care patients
.
Acta Anaesthesiol Scand
.
2004
;
48
:
679
684
.
30
Freedman
NS
,
Gazendam
J
,
Levan
L
,
Pack
AI
,
Schwab
RJ
.
Abnormal sleep/wake cycles and the effect of environmental noise on sleep disruption in the intensive care unit
.
Am J Respir Crit Care Med
.
2001
;
163
:
451
457
.
31
Gazendam
JAC
,
Van Dongen
HPA
,
Grant
DA
,
Freedman
NS
,
Zwaveling
JH
,
Schwab
RJ
.
Altered circadian rhythmicity in patients in the ICU
.
Chest
.
2013
;
144
:
483
489
.
32
Shilo
L
,
Dagan
Y
,
Smorjik
Y
, et al
.
Patients in the intensive care unit suffer from severe lack of sleep associated with loss of normal melatonin secretion pattern
.
Am J Med Sci
.
1999
;
317
:
278
281
.
33
Paul
T
,
Lemmer
B
.
Disturbance of circadian rhythms in analgosedated intensive care unit patients with and without craniocerebral injury
.
Chronobiol Int
.
2007
;
24
:
45
61
.
34
Gehlbach
BK
,
Chapotot
F
,
Leproult
R
, et al
.
Temporal disorganization of circadian rhythmicity and sleep-wake regulation in mechanically ventilated patients receiving continuous intravenous sedation
.
Sleep
.
2012
;
35
:
1105
1114
.
35
McKenna
H
,
van der Horst
GTJ
,
Reiss
I
,
Martin
D
.
Clinical chronobiology: a timely consideration in critical care medicine
.
Crit Care
.
2018
;
22
:
124
.
36
McKenna
HT
,
Reiss
IK
,
Martin
DS
.
The significance of circadian rhythms and dysrhythmias in critical illness
.
J Intensive Care Soc
.
2017
;
18
:
121
129
.
37
Van Dycke
KC
,
Pennings
JL
,
van Oostrom
CT
, et al
.
Biomarkers for circadian rhythm disruption independent of time of day
.
PLoS One
.
2015
;
10
:
e0127075
.
38
Drewry
AM
,
Fuller
BM
,
Bailey
TC
,
Hotchkiss
RS
.
Body temperature patterns as a predictor of hospital-acquired sepsis in afebrile adult intensive care unit patients: a case-control study
.
Crit Care
.
2013
;
17
:
R200
.
39
McGaughey
J
,
O’Halloran
P
,
Porter
S
,
Blackwood
B
.
Early warning systems and rapid response to the deteriorating patient in hospital: a systematic realist review
.
J Adv Nurs
.
2017
;
73
:
2877
2891
.
40
Zografakis-Sfakianakis
M
,
De Bree
E
,
Linardakis
M
, et al
.
The value of the Modified Early Warning Score for unplanned intensive care unit admissions of patients treated in hospital general wards
.
Int J Nurs Pract
.
2018
;
24
:
e12632
. doi:
41
Churpek
MM
,
Snyder
A
,
Han
X
, et al
.
Quick sepsis-related organ failure assessment, systemic inflammatory response syndrome, and early warning scores for detecting clinical deterioration in infected patients outside the intensive care unit
.
Am J Resp Crit Care
.
2017
;
195
:
906
911
.
42
Ghosh
E
,
Eshelman
L
,
Yang
L
,
Carlson
E
,
Lord
B
.
Early deterioration indicator: data-driven approach to detecting deterioration in general ward
.
Resuscitation
.
2018
;
122
:
99
105
.
43
Kastrup
M
,
Powollik
R
,
Balzer
F
, et al
.
Predictive ability of the stability and workload index for transfer score to predict unplanned readmissions after ICU discharge
.
Crit Care Med
.
2013
;
41
:
1608
1615
.
44
Ho
KM
,
Lee
KY
,
Dobb
GJ
,
Webb
SAR
.
C-reactive protein concentration as a predictor of in-hospital mortality after ICU discharge: a prospective cohort study
.
Intensive Care Med
.
2008
;
34
:
481
487
.
45
Fabes
J
,
Seligman
W
,
Barrett
C
,
McKechnie
S
,
Griffiths
J
.
Does the implementation of a novel intensive care discharge risk score and nurse-led inpatient review tool improve outcome? A prospective cohort study in two intensive care units in the UK
.
BMJ Open
.
2017
;
26
:
e018322
.
46
Klepstad
PK
,
Nordseth
T
,
Sikora
N
,
Klepstad
P
.
Use of National Early Warning Score for observation for increased risk for clinical deterioration during post-ICU care at a surgical ward
.
Ther Clin Risk Manag
.
2019
;
15
:
315
322
.
47
Chen
Y-C
,
Yu
W-K
,
Ko
H-K
, et al
.
Post-intensive care unit respiratory failure in older patients liberated from intensive care unit and ventilator: the predictive value of the National Early Warning Score on intensive care unit discharge
.
Geriatr Gerontol Int
.
2019
;
19
:
317
322
.
48
Mahmood
K
,
Eldeirawi
K
,
Wahidi
MM
.
Association of gender with outcomes in critically ill patients
.
Crit Care
.
2012
;
16
:
R92
.
49
Valentin
A
,
Jordan
B
,
Lang
T
,
Hiesmayr
M
,
Metnitz
PG
.
Gender-related differences in intensive care: a multiple-center cohort study of therapeutic interventions and outcome in critically ill patients
.
Crit Care Med
.
2003
;
31
:
1901
1907
.
50
Fowler
RA
,
Sabur
N
,
Li
P
, et al
.
Sex-and age-based differences in the delivery and outcomes of critical care
.
CMAJ
.
2007
;
177
:
1513
1519
.
51
Romo
H
,
Amaral
AC
,
Vincent
JL
.
Effect of patient sex on intensive care unit survival
.
Arch Intern Med
.
2004
;
164
:
61
65
.
52
Choudhry
MA
,
Bland
KI
,
Chaudry
IH
.
Gender and susceptibility to sepsis following trauma
.
Endocr Metab Immune Disord Drug Targets
.
2006
;
6
:
127
135
.
53
Sperry
JL
,
Minei
JP
.
Gender dimorphism following injury: making the connection from bench to bedside
.
J Leukoc Biol
.
2008
;
83
:
499
506
.

Footnotes

FINANCIAL DISCLOSURES

None reported.

 

To purchase electronic or print reprints, contact American Association of Critical-Care Nurses, 27071 Aliso Creek Road, Aliso Viejo, CA 92656. Phone, (800) 899-1712 or (949) 362-2050 (ext 532); fax, (949) 362-2049; email, reprints@aacn.org.