Vital-sign circadian rhythms in patients prior to discharge from an ICU: a retrospective observational analysis of routinely recorded physiological data
The original online version of this article was revised: the authors identified an error in Fig. 2. The error was that Figs. 2 and 3 were both displaying the same figure.
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Abkürzungen
ICU
Intensive care unit
EEG
Electroencephalography
SBP
Systolic blood pressure
HR
Heart rate
RR
Respiratory rate
T
Temperature
MIMIC-III
Medical Information Mart for Intensive Care III
eICU-CRD
eICU Collaborative Research Database
PICRAM
Post-Intensive-Care Risk-adjusted Alerting and Monitoring
BIDMC
Beth Israel Deaconess Medical Centre
HIPAA
Health Insurance Portability and Accountability Act
CI
Confidence interval
OASIS
Oxford Acute Severity of Illness Score
Introduction
Patient care in an intensive care unit (ICU) typically involves maintaining homeostasis or ‘normalisation’ of vital signs [1‐3], where the body is unable to provide this for itself. However, the process of controlling and regulating vital signs, combined with sedation, inflammation, environmental light, and noise levels, can disrupt a patient’s natural circadian rhythms [4]. ICU practice in general does not emphasise support of a patient’s circadian rhythms, though there is a growing desire to improve upon this [5]. Chronically disrupted circadian rhythms are associated with metabolic disorders such as obesity and diabetes, cardiovascular disease, and cancer [6‐9]. In an ICU, disruption or loss of a patient’s circadian rhythms is associated with complications such as immune system disruption [10], delirium [11, 12], and mortality [13, 14].
The assessment of circadian behaviour in the ICU typically focuses on the study of sleep, ideally recorded using polysomnography [15, 16]. However, difficulties with instrumentation in the ICU [17], abnormal electroencephalography (EEG, brain activity) patterns [16, 18, 19], and the relative sensitivity of sleep to events such as lighting or environmental noise variations mean that sleep is not necessarily an ideal or easily established marker of patient circadian behaviour. Thus, a recent review commented that ‘Finding the optimum tool to monitor (circadian rhythms in) critically ill patients therefore remains a key to research progress in this area’ [3]. Healthy individuals exhibit circadian rhythms in several vital signs, including systolic blood pressure (SBP), heart rate (HR), respiratory rate (RR), and core body temperature (T) [3]. However, the ‘severe circadian deregulation’ [4] experienced by patients treated in ICUs can result in abnormal vital-sign patterns.
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Several studies have established typical circadian vital-sign behaviour in healthy individuals. Hermida et al. [20] conducted a study using ambulatory monitoring on 278 healthy individuals with mean ± SD age 22.7 ± 3.3 years, synchronising measurements to individual sleep/wake times rather than time of day. They observed an elevated SBP during the day, which reached an approximate plateau (with 3 periodic local maxima) between ≈ 3 and ≈ 13 h after awakening, followed by a sinusoidal dip during sleep. The observed rhythms in HR closely corresponded to those observed in SBP, with an elevated plateau (again with 3 periodic local maxima) between ≈ 2 and ≈ 14 h after awakening, decreasing slightly during the day, and a sinusoidal dip overnight.
Bosco et al. [21] conducted a study in which 6 males (competitive scuba divers with mean ± SEM age 39 ± 3 years) were kept in a constant routine protocol (sustained wakefulness, minimal activity). RR was observed to peak late in the day (≈ 8 pm) with a trough at ≈ 3–7 am, roughly in phase with HR. Spengler et al. [22] conducted a similar study in which 10 healthy males with mean ± SD age 23.7 ± 3.9 years were kept in a relaxed, semi-recumbent position isolated from any indication of time of day for 41 h. As in [20], measurements were synchronised to individual sleep/wake times. While they do not report RR, Spengler et al. report ventilation (V E) in l/min, which was elevated between ≈ 2 h before awakening and ≈ 8 h after awakening, and decreased to an approximate plateau overnight. Core body temperature showed an approximately sinusoidal form, with nadir that lagged behind the nadir in V E by ≈ 6–8 h.
Given previous work has suggested circadian behaviour is severely disrupted in an ICU [3, 4, 15, 23], and assuming that circadian behaviour in the majority of patients returns to normality post-ICU discharge, patients treated in an ICU undergo a ‘circadian recovery’ process as part of their overall recovery. If this ‘circadian recovery’ process was shown to begin prior to ICU discharge in patients who subsequently recovered (i.e. were discharged home), and this circadian state was shown to be generalisable across different ICU populations (i.e. not due to external behaviour such as nursing shift changes), there are several potential clinical applications. These include the monitoring of ICU patient recovery, as well as monitoring the development of complications associated with disrupted circadian rhythms such as delirium. We hypothesise that vital-sign circadian rhythms will be observable in the 24 h prior to discharge from the ICU in patients who subsequently fully recovered, that these circadian rhythms will resemble known behaviour in healthy individuals, and that these circadian rhythms will be generalisable across different populations of ICU patients. We set out to validate these hypotheses across three large, retrospective clinical databases.
Materials and methods
Databases
This study makes use of three clinical databases:
Medical Information Mart for Intensive Care III (MIMIC-III) is a database of critical care information gathered at the Beth Israel Deaconess Medical Centre (BIDMC) in Boston, MA, USA, between 2001 and 2012 [24, 25].
The eICU Collaborative Research Database (eICU-CRD) is a database of critical care information gathered from 208 hospitals across the continental USA between 2014 and 2015 [26].
The Post-Intensive-Care Risk-adjusted Alerting and Monitoring (PICRAM) database (ISRCTN32008295) includes patients admitted to the adult ICU or coronary care unit at the John Radcliffe Hospital, Oxford, UK, between 2008 and 2015, as well as patients admitted to the ICU at the Reading Berkshire Hospital, Reading, UK, between 2009 and 2015.
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Access to the MIMIC-III and eICU-CRD databases for the purpose of this study was granted by the institutional review boards of the BIDMC and Massachusetts Institute of Technology (Cambridge, MA, USA). Access to the PICRAM database was granted by the Critical Care Research Group Data Access Committee of the University of Oxford. Combined, these databases span 211 hospitals across two countries, with different patient demographics, standards of clinical practice, and use of equipment.
Data selection
Data from the MIMIC-III, eICU-CRD, and PICRAM databases were selected according to the following criteria:
1.
The patient must have had at least one cuff SBP reading recorded.
2.
The patient must not have died over the course of the given hospital stay, nor have been discharged to hospice (end of life) care.
3.
In the case of MIMIC-III and eICU-CRD, the patient must have been discharged ‘home’ or to ‘home health care’. In the case of PICRAM, the patient must have been discharged with an expected dependency of ‘Able to live without assistance in daily activities’.
4.
The patient must not have had any Do Not Resuscitate (DNR), Do Not Intubate (DNI), or ‘Comfort Measures Only’ codes, as this indicates a deviation from typical ICU care.
5.
The patient must have been at least 15 years of age.
6.
The patient must have spent at least 24 h in an ICU continuously.
7.
Only measurements taken during the final 24 h before a patient was discharged from an ICU were included. That is, if a patient was discharged at 11 am, measurements from 11 am on the previous day were included.
8.
Measurements outside of the broad physiological bounds (60 mmHg < SBP < 280 mmHg, 30 bpm < HR < 240 bpm, 4 breaths/min < RR < 60 breaths/min, 34∘C < T < 40∘C) were excluded.
9.
Measurements taken while the patient was under the effect of treatments that were likely to significantly affect the vital signs being measured were excluded. This process focused on removing measurements taken while vasopressors, β-blockers, or other blood pressure medication were likely to be active, and is discussed in more detail in Additional file 1. Vital-sign measurements were excluded:
Up to 1 h after a patient was administered dobutamine, dopamine, adrenaline/epinephrine, noradrenaline/norepinephrine, metaraminol, glyceryl trinitrate, dopexamine, nitroprusside, or isoprenaline [27, 28].
Up to 2 h after a patient was administered vasopressin, propofol, magnesium sulphate, ephedrine, or phentolamine [29‐31].
Up to 24 h after a patient was administered milrinone, terlipressin, labetalol, metoprolol, or hydralazine [27, 32, 33].
10.
The patient must have had at least one night-time (12 midnight–5:59 am) and one day-time (10 am–7:59 pm) SBP measurement as in [34]. The majority of patients will have more available SBP measurements than this (see Additional file 2), but this requirement ensures each patient contributes to both ‘day-time’ and ‘night-time’ behaviour.
11.
For MIMIC-III and PICRAM, if the patient had multiple ICU stays within 6 months of each other, all ICU stays within this period were excluded due to it being unlikely the patient was discharged ‘healthy’. An ICU stay is defined as a period during which a patient occupied a bed in the ICU, including short-term removal for surgery, scans, or other interventions. A hospital admission is defined as the time between patient admission to and discharge from the hospital. In the eICU-CRD, no relative dates are recorded for hospital admissions. Instead, any hospital admission containing multiple ICU admissions was discarded entirely.
12.
For each ICU stay, all measurements in each 1-h period were averaged for each vital sign for the final 24 h of that ICU stay. This process avoids weighting data towards ICU stays where patients are more ill, and thus likely to have more regular vital-sign measurements. These mean hourly values were recorded left aligned (e.g. the mean of measurements between 1:00 am and 1:59 am was recorded as occurring at 1:00 am). Vital signs were typically measured at least hourly, with the exception of temperature in MIMIC-III and PICRAM which was typically measured once every 4 h. If there were no measurements of a given vital sign in a given 1-h period in an ICU stay, that ICU stay did not contribute any measurement for that hour to the overall analysis.
Data analysis
Patients were separated into groups by gender and age, as there are established trends in mean SBP and HR associated with gender and age [34]. Observation of similar trends in the selected ICU cohorts would give support to the notion that underlying physiological, rather than treatment or pathology driven, behaviour is being observed in these patients. The age groups used in this paper are a modified set of those specified in ‘Provisional guidelines on standard international age classifications’ for ‘health, health services and nutrition - morbidity and handicaps’ [35]. These age groups are as follows: 15–45 years (combining the recommended 15–25- and 25–45-year groups due to the low number of patients under 45 treated in ICUs), 45–65 years, and 65+ years. Per HIPAA regulations, the ages of individuals greater than 89 were not recorded in MIMIC-III or eICU-CRD. These patients were treated as 91 years of age; thus, all fell within the 65+-year age group.
The median Oxford Acute Severity of Illness Score (OASIS), a severity of illness score used for predicting patient outcomes [36], was determined for each patient subgroup. The OASIS was designed to require a minimal set of physiological parameters. In contrast, common severity of illness scores such as APACHE and SAPS employ a wide variety of physiological measurements that are not necessarily well recorded or easy to recover from large ICU databases. As such, OASIS is more easily and consistently applicable across a range of retrospective clinical databases with different recording standards.
Evaluation of circadian rhythmicity was performed using several approaches, which were performed using the 24-h mean vital-sign profiles established for each patient cohort. The observed profiles were visually compared to circadian vital-sign profiles found in the literature, typically available for non-ICU cohorts. As a quantitative indication of rhythm amplitude or strength, the peak-nadir excursion [37] was calculated, expressed as both a raw value and as a percentage of the 24-h mean for that vital-sign profile. These values were compared to values reported in the literature. To evaluate the consistency of corresponding vital-sign profiles between databases, the cross correlation (R) and accompanying p values (p) were calculated. For the correlation analysis, temperature profiles from eICU-CRD were subsampled at 4-hourly intervals to allow for comparison with MIMIC-III and PICRAM.
To provide an indication of intra-cohort variability, the hourly 95% confidence intervals (CIs) of the vital-sign mean were calculated for each 24-h vital-sign profile [38]. Graphically, if two vital-sign CIs do not overlap for any given hour, their means are significantly different at the p = 0.05 level. Where comparison between databases is desired, a two-sample Student’s t test was used to compare each hourly bin of vital-sign measurements. As before, mean vital-sign levels were deemed significantly different if each of the 24 hourly bins was found to be significantly different at the p = 0.05 level.
Results
Database and cohort demographics
Tables 1 and 2 present demographic data for the entire databases and the selected cohort from each database, respectively. The overall median age of patients in the selected cohort for PICRAM (61.2 years) was greater than that for the corresponding cohort in MIMIC-III (59.6 years) or eICU-CRD (60.0 years). Similarly, the overall median OASIS of patients in the selected cohort PICRAM (33) was greater than that for MIMIC-III (27) or eICU-CRD (26). These results suggest that on average, the selected PICRAM patients were older and more ill, corresponding to the increased LOS observed in the selected PICRAM cohort (Table 2).
Table 1
Overall demographics for each database (no exclusion criteria applied), grouped by gender
MIMIC-III
eICU-CRD
PICRAM
Statistic
Men
Women
Men
Women
Men
Women
No. of patients
26,121
20,399
75,188
64,044
7196
5090
No. of hospital admissions
32,950
26,026
89,391
76,802
7810
5559
No. of ICU stays
34,469
27,063
108,379
92,303
8176
5767
Age (years), median (IQR)
61 (30)
64 (34)
64 (22)
66 (25)
65 (24)
62 (27)
ICU LOS* (days), median (IQR)
2.1 (3.3)
2.1 (3.4)
1.6 (2.1)
1.6 (2.2)
2.0 (3.7)
2.0 (3.1)
OASIS, median (IQR)
29 (12)
30 (13)
27 (14)
29 (14)
33 (17)
33 (18)
ICU mortality (%)
7.4
8.0
8.9
9.1
12.8
11.7
*Length of stay
Table 2
Demographics of the selected cohort of ICU stays from each database, grouped by gender
MIMIC-III
eICU-CRD
PICRAM
Age group
Men
Women
Men
Women
Men
Women
Breakdown of no. (%) of ICU stays in the selected cohort by age group
15–44
1395 (11.8)
1095 (9.2)
3670 (10.4)
3574 (10.2)
412 (12.5)
390 (11.9)
45–64
3054 (25.7)
1869 (15.7)
7991 (22.7)
6089 (17.3)
621 (18.9)
486 (14.8)
65+
2608 (22.0)
1851 (15.6)
7572 (21.5)
6238 (17.8)
844 (25.7)
528 (16.1)
Total
7057 (59.4)
4815 (40.6)
19,233 (54.7)
15,901 (45.2)
1877 (57.2)
1404 (42.8)
Median (IQR) ICU LOS*(days)
15–44
2.1 (2.1)
2.1 (1.8)
1.9 (1.6)
1.8 (1.5)
3.6 (5.2)
3.2 (5.2)
45–64
2.1 (1.8)
2.1 (1.7)
2.0 (1.7)
2.0 (1.6)
3.8 (6.8)
3.3 (4.8)
65+
2.1 (1.7)
2.0 (1.6)
1.9 (1.6)
1.9 (1.5)
3.1 (4.2)
3.7 (4.5)
Overall
2.1 (1.8)
2.1 (1.7)
1.9 (1.6)
1.9 (1.5)
3.4 (5.0)
3.5 (4.7)
Median (IQR) OASIS
15–44
25 (11)
25 (10)
24 (11)
24 (12)
33 (15)
30 (16)
45–64
26 (9)
27 (10)
25 (12)
26 (12)
33 (16)
33 (17)
65+
28 (9)
29 (9)
27 (11)
29 (11)
33 (18)
34 (18)
Overall
27 (10)
27 (10)
26 (12)
27 (12)
33 (17)
33 (16)
*Length of stay
Additionally, median OASIS were identical for the PICRAM cohort selected in this paper and the overall PICRAM database (33), unlike MIMIC-III (27 selected and 29 overall) and eICU-CRD (26 selected and 28 overall). This suggests the employed selection criteria were less discriminatory in PICRAM. This notion is supported by Table 3, which shows the number of patients (#Pat.), hospital admissions (#Hosp.), ICU stays (#ICU), and vital-sign measurements (#SBP, #HR, #RR, #T) that met the cumulative application of the criteria set out previously for each database. In this table, it can be observed that a higher portion of PICRAM ICU stays were retained (23.5%) by the selection process than for MIMIC-III (19.3%) or eICU-CRD (17.5%). Despite this higher retention rate, the selected cohort of PICRAM ICU stays (3283 ICU stays, Table 2) is still significantly smaller than the size of the selected MIMIC-III (11,872 ICU stays) or eICU-CRD (35,143 ICU stays) cohorts.
Table 3
The number of patients, hospital admissions, ICU stays, and vital-sign measurements in each database that matched each selection criterion applied cumulatively
Subset
#Patients
#Hosp.
#ICU
#SBP
#HR
#RR
#T
Breakdown of MIMIC-III measurements
All
46,476
57,786
61,532
2,871,980
7,936,326
6,520,159
1,128,747
1. Cuff SBP
37,671
48,033
51,392
2,871,980
6,168,906
6,433,882
1,114,487
2. Survived
33,483
42,604
45,217
2,455,896
4,991,389
5,158,016
915,228
3. Disch. home
20,376
24,308
25,169
986,392
1,774,032
1,778,552
322,043
4. No DNR/DNI
20,212
24,039
24,888
970,349
1,736,234
1,740,712
318,767
5. Age 15+
20,212
24,039
24,888
970,349
1,736,234
1,740,712
318,767
6. Stay 24 h+
17,017
19,915
20,531
901,239
1,635,417
1,641,151
303,039
7. Last 24 h
16,994
19,883
20,496
320,982
488,386
481,243
87,358
8. Valid meas.
16,994
19,883
20,496
320,560
488,276
480,119
86,769
9. BP medication
16,093
18,760
19,284
281,334
403,196
395,149
69,742
10. Day/night BP
11,886
13,791
14,082
259,502
318,404
311,561
52,165
11. No repeat stays
11,194
11,872
11,872
218,625
266,983
261,084
44,022
12. Hourly avr.
11,194
11,872
11,872
209,595
230,648
225,262
31,628
Breakdown of eICU-CRD measurements
All
139,367
166,355
200,859
22,079,437
146,070,343
128,586,418
13,267,119
1. Cuff SBP
127,486
151,397
176,497
20,666,164
135,849,528
119,354,199
12,444,242
2. Survived
116,598
138,226
160,825
17,370,839
117,960,824
103,466,212
9,673,554
3. Disch. home
78,489
89,120
101,953
8,119,745
57,392,933
49,471,570
3,637,550
4. No DNR/DNI
74,679
84,478
96,452
7,460,215
53,032,738
45,605,571
3,387,661
5. Age 15+
74,603
84,391
96,362
7,458,391
53,013,037
45,587,254
3,387,661
6. Stay 24 h+
52,904
58,460
62,120
6,567,760
47,237,116
40,566,523
3,227,193
7. Last 24 h
52,655
58,161
61,748
1,946,890
16,122,421
13,714,491
579,237
8. Valid meas.
52,654
58,159
61,746
1,944,573
16,119,746
13,626,000
576,511
9. BP medication
42,269
46,350
48,813
1,474,812
11,904,784
10,183,343
461,021
10. Day/night BP
37,615
41,170
43,086
1,435,798
11,212,410
9,645,215
414,644
11. No repeat stays
32,385
35,143
35,143
1,177,751
9,151,423
7,905,285
346,133
12. Hourly avr.
32,385
35,143
35,143
685,626
790,718
691,886
30,080
Breakdown of PICRAM measurements
All
12,290
13,138
13,949
334,120
1,295,070
1,306,271
346,474
1. Cuff SBP
11,351
12,113
12,845
334,120
1,265,862
1,277,367
341,646
2. Survived
10,034
10,736
11,382
291,376
1,033,072
1,042,400
287,620
3. Disch. home
7823
8249
8724
195,253
716,926
717,929
200,002
4. No DNR/DNI
7731
8149
8613
188,800
694,441
695,583
195,635
5. Age 15+
7713
8131
8595
188,452
693,344
694,510
195,063
6. Stay 24 h+
5971
6268
6608
178,371
663,006
664,570
185,809
7. Last 24 h
5970
6267
6607
43,195
100,657
99,859
35,688
8. Valid meas.
5970
6267
6607
43,049
100,521
99,786
35,562
9. BP medication
5877
6156
6488
40,464
88,717
88,232
32,782
10. Day/night BP
3385
3480
3613
33,199
44,298
44,154
18,441
11. No repeat stays
3237
3282
3283
30,090
40,213
40,091
16,768
12. Hourly avr.
3237
3282
3283
29,572
39,628
39,520
16,694
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Circadian vital-sign qualitative analyses
Figure 1 shows the circadian profiles for SBP, HR, RR, and T grouped by gender for MIMIC-III, eICU-CRD, and PICRAM, with ‘night-time’ represented from 12 midnight–5:59 am and ‘day-time’ from 10 am–7:59 pm. By visual inspection, these profiles correspond well to those reported in healthy cohorts [20, 22] described previously and to those reported for non-ICU patients [34, 39]. In MIMIC-III and eICU-CRD, SBP is elevated between ≈ 2 and 14 h after the end of night-time, with three periodic maxima, though these are more pronounced than those observed in [20]. This elevated period is followed by a sinusoidal dip during night-time. HR in MIMIC-III and eICU-CRD is similarly elevated between ≈ 2 and 14 h after the end of night-time, again with three periodic maxima. Elevated SBP and reduced HR for men relative to women (p < 0.05, Fig. 1) also correspond to observations in [20]. In both SBP and HR, the smaller sample size in PICRAM results in more variability and makes features (especially maxima) more difficult to distinguish. However, the overall periods of elevated and reduced SBP and HR in PICRAM appear similar to those observed in MIMIC-III and eICU-CRD. The PICRAM cohort has an elevated mean HR (p < 0.05) relative to MIMIC-III and eICU-CRD and SBP (p < 0.05) relative to MIMIC-III.
Fig. 1
Circadian vital-sign profiles in the 24 h prior to discharge from the ICU for the selected patient cohorts for MIMIC-III, eICU-CRD, and PICRAM, grouped by gender: (a) SBP, (b) HR, (c) RR, and (d) T. The solid line represents the mean profile, and the shaded area the 95% CI of the population mean
×
There is a resemblance between the profiles in RR observed in Fig. 1 and the profiles in RR and V E reported in [21] and [22], respectively. Elevated RR can be observed between ≈ 2 and 14 h after the end of night-time, peaking at 8 pm, with a trough between ≈ 2 and 6 am. T shows the expected sinusoidal behaviour [22, 40], though the low measurement frequency in MIMIC-III and PICRAM makes this harder to discern. T also shows a lag in the nadir of approximately 6–8 h relative to RR (and indeed SBP and HR) as observed relative to V E in [22].
The vital-sign patterns mentioned above largely hold for smaller cohorts grouped by gender and age, as shown in Fig. 2 for men and Fig. 3 for women. Once again, the smaller of these cohorts, such as those from PICRAM or the younger 15–44-year cohorts, show a greater degree of variability which makes features more difficult to distinguish. Figures 2 and 3 also show expected age-related trends [34]. These trends include progressively decreased HR in older age groups (p < 0.05) for MIMIC-III and eICU-CRD and between the 45–64 and 65+ groups in men in PICRAM. Women also show the expected increase in SBP with age (p < 0.05 for MIMIC-III and eICU-CRD); however, this trend is largely absent in men. A consistent increase in magnitude and duration of ascent prior to the morning (8:00 am) SBP peak across men and women as they age can be observed, similar to the trends reported in [34]. RR and T do not show clear variations with age, but both show consistent 24-h patterns across all age groups.
Fig. 2
Circadian vital-sign profiles in the 24 h prior to discharge from the ICU for men in MIMIC-III, eICU-CRD, and PICRAM, grouped by age: (a) SBP, (b) HR, (c) RR, and (d) T. The solid line represents the mean profile, and the shaded area the 95% CI of the population mean for a group
Fig. 3
Circadian vital-sign profiles in the 24 h prior to discharge from the ICU for women in MIMIC-III, eICU-CRD, and PICRAM, grouped by age: (a) SBP, (b) HR, (c) RR, and (d) T. The solid line represents the mean profile, and the shaded area the 95% CI of the population mean for a group
×
×
Circadian vital-sign quantitative analyses
Table 4 shows that the peak-nadir excursions in all three ICU databases were attenuated relative to the peak-nadir excursions reported for non-ICU cohorts in the literature. The peak-nadir excursions for SBP and HR in the ICU cohorts in Table 4 are a factor of 4–5 times smaller than the values reported in [20]. Similarly, the peak-nadir excursions for RR in the ICU cohorts are a factor of 2 smaller than the value reported in [21], and the peak-nadir excursion in temperature is a factor of 2–3 times smaller than the corresponding value in [22].
Table 4
Peak-nadir excursion, expressed as raw value and as percentage of 24-h mean for vital signs. Overall data grouped by gender
MIMIC-III
eICU-CRD
PICRAM
Literature*
Vital sign
Men
Women
Men
Women
Men
Women
Men
Women
SBP, mmHg (%)
4.9 (4.1)
5.5 (4.6)
5.2 (4.2)
5.6 (4.5)
5.7 (4.5)
6.1 (4.9)
25.9 (22.3)
22.5 (21.1)
HR, bpm (%)
5.1 (6.4)
5.5 (6.6)
5.9 (7.2)
6.2 (7.5)
5.5 (6.4)
5.1 (5.7)
21.2 (30.5)
20.1 (25.9)
RR, breaths/min (%)
1.4 (7.4)
1.6 (8.3)
1.8 (9.5)
2.1 (10.5)
1.3 (7.1)
1.4 (7.3)
3.2 (16.8)
–
T, ∘C (%)
0.2 (0.6)
0.2 (0.5)
0.2 (0.6)
0.3 (0.7)
0.2 (0.7)
0.2 (0.6)
0.6 (1.6)
–
SBP and HR values from [20], RR values from [21], and temperature values from [22]
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Table 5 shows that there is a strong correlation in vital-sign trends between all of the three databases. All vital-sign profiles were correlated between databases at the p = 0.05 level, and 20 of 24 correlated at the p = 0.01 level. Of the four exceptions, three were temperature profiles, where the lower p values observed were likely due to the lower frequency of the available measurements (once every 4 h).
Table 5
Pearson’s correlation coefficients (R) and p values (p) for inter-database vital-sign circadian pattern correlation. Data grouped by gender
MIMIC-III and eICU-CRD
MIMIC-III and PICRAM
eICU-CRD and PICRAM
Vital sign
Men
Women
Men
Women
Men
Women
SBP, R (p)
0.95 (0.00)
0.95 (0.00)
0.60 (0.00)
0.81 (0.00)
0.50 (0.01)
0.79 (0.00)
HR, R (p)
0.95 (0.00)
0.98 (0.00)
0.90 (0.00)
0.90 (0.00)
0.96 (0.00)
0.93 (0.00)
RR, R (p)
0.97 (0.00)
0.98 (0.00)
0.88 (0.00)
0.95 (0.00)
0.91 (0.00)
0.97 (0.00)
T, R (p)
0.88 (0.02)
0.84 (0.04)
0.92 (0.01)
0.97 (0.00)
0.98 (0.00)
0.88 (0.02)
Discussion
Presence of circadian rhythms
From Table 5, we can reasonably assert we are observing a generalisable vital-sign circadian ‘rhythm’ (i.e. a recurring vital-sign pattern with 24-h periodicity). This assertion is based on the high cross-correlations between 24-h vital-sign profiles from different databases, which are subject to different demographics and standards of care. That each individual’s contributing vital-sign profile may begin and end at any point within the 24 h adds further credence to the physiological, rather than environmental, origin of the observed rhythmicity.
Further evidence that we are observing vital-sign circadian rhythms is provided in Figs. 1, 2, and 3, where the observed vital-sign profiles show similar patterns across databases and with respect to those reported in the literature for non-ICU cohorts [20, 22, 40, 41]. Additionally, the relative trends between genders and between age groups are consistent with the literature, and across databases [34]. While a previous study [42] noted variations related to time of day in the agreement between nurse-verified and waveform-derived vital-sign measurements in MIMIC-II, these variations were of a ‘clinically insignificant amount’, and only measurement variability, not measurement bias, showed significant variation with time of day. As such, this behaviour is unlikely to contribute significantly to the observed profiles.
Overall, these results suggest observation of an intrinsic, consistent, demographically modified 24-h pattern in vital signs observable in the last 24 h prior to discharge from an ICU in the selected cohort of patients. This behaviour, observable across 50,298 ICU stays drawn from 211 hospitals across the UK and the USA with different patient demographics and standards of care, suggests that there is a typical circadian pattern in vital signs present in patients near recovery and discharge from an ICU.
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Rhythm topography
The peak-nadir excursions in SBP, HR, RR, and T (Table 4) were found to be 2–5 times smaller than those reported in the literature for healthy cohorts [20, 22]. There are several potential causes for this apparent attenuation of circadian amplitude. The suppression may be due to pathology or medication in the selected ICU cohort. The computation of average rhythms does not distinguish between amplitude attenuation caused by a mix of ‘healthy’ and attenuated rhythms and a consistent, cohort-wide attenuation, though the narrow 95% CIs of the mean would lend support to the latter. Alternatively, the observed reduced amplitudes may be demographically driven, as the results in both [20] and [22] are for young, healthy adults, and the results in [21] for competitive scuba divers, as opposed to the more heterogeneous, and generally older, cohort employed in this study. Also of note is that the data in both [20] and [22] are synchronised for waking time, rather than clock time, which may accentuate circadian rhythms. However, one would expect some degree of synchronicity in waking time within a given ICU, and for reduced synchronicity to ‘smear’ or laterally shift patterns rather than significantly decrease their peak amplitude.
A final potential cause of circadian amplitude attenuation is the fact that patients in an ICU are typically recumbent and physically inactive, which can affect circadian rhythm amplitude [43]. This consideration is supported by the fact that rhythm amplitude showed a factor of 4–5 times reduction in HR and SBP compared to [20], where subjects were ambulatory, but only a reduction of 2–3 times in RR and T compared to [21, 22], where patients were recumbent and inactive. Despite the intuitive appeal of these results, caution should be taken as [20‐22] report different sets of vital signs using different protocols and equipment, and [21, 22] contain data from ≤ 10 individuals, making comparison difficult. Overall, it seems likely that amplitudes of circadian variation in vital signs are attenuated by some combination of pathology, treatment, and inactivity, with each vital sign responding differently.
Variability between demographic cohorts
As previously mentioned, Figs. 2 and 3 largely show the expected age-related increase in mean SBP and decrease in mean HR [34, 44]. That these results are consistent across databases, and with results reported for non-ICU cohorts in the literature provides further support to the notion that the behaviour being observed is physiological behaviour, rather than behaviour governed by environmental influences.
However, mean SBP in men does not show age-related variations despite these being well documented in healthy men and present for women in the selected cohort [34]. It is important to note that the ICU population for a given demographic group is not necessarily representative of the general population for that demographic group, and this is elaborated further in Additional file 3. Broadly, young men (between 15 and 44 years) have a relatively high prevalence of admission diagnoses codes for HIV, alcohol abuse, and trauma not seen in younger or older women, or in older men. These variations in cause of ICU admission, and thus patient condition and treatment, may explain this lack of expected trends with age in mean SBP for men.
Variability between databases
As previously mentioned, PICRAM shows both an increased retention rate in the selected cohort (Table 3) and an elevated mean HR and SBP (Fig. 1). It is likely the increased retention rate of PICRAM ICU stays relative to MIMIC-III or eICU-CRD is due in part to the lack of discharge destination coding in the UK, leading to all patients expected to make a full recovery in PICRAM being retained, as opposed to only those discharged home as in MIMIC-III and eICU-CRD. Thus, it is likely that the increase in mean HR and SBP observed in PICRAM is due to the PICRAM cohort being older and more ill, rather than local variables or changes in clinical practice. These observations correspond to data present in the literature that suggest that patients in UK ICUs are on average more ill than those in US ICUs, associated with the lower number of ICU beds per capita available in the UK [45, 46]. It is important to note that the circadian pattern shapes and intra-database trends with gender and age hold across all three databases, regardless of differences in clinical behaviour or shift timings, suggesting these profiles are widely generalisable.
Limitations
This study has several limitations that are worth discussing. All trends reported in this paper are for the average of large numbers of vital-sign measurements across a reasonably diverse cohort of patients. Thus, while the trends observed match trends reported for healthy individuals outside the ICU, and the trends are generally maintained when the data are broken up into subgroups by gender or age, there is little indication as to how consistently these trends can be observed on an individual basis. This is important as any prospective tracking of patient recovery, or of the development of complications such as delirium [5], would require the ability to meaningfully establish an individual’s vital-sign circadian rhythms using routine clinical measurements.
While a large amount of patient data has been gathered across a large number of different hospitals, it also worth noting that data are still only gathered from 2 countries with lifestyles and demographics that are reasonably similar. Thus, further work is required to assess the generalisability of any trends observed to other countries where diet, clinical practice, and cause of ICU admission may vary to a greater extent.
This paper does not compare circadian rhythmicity between patients who ‘recovered’ and those who died. As such, the paper does not provide evidence of the ‘sensitivity’ of observable circadian vital-sign patterns to patient recovery, only that this behaviour can be observed in those who recovered. Research into generalisable circadian vital-sign behaviour in the ICU is relatively new. As such, it is important to establish that generalisable circadian behaviour exists prior to discharge in ICU patients who recovered, thus laying the groundwork for future comparisons between cohorts.
This paper does not demonstrate loss of circadian rhythmicity in the selected cohorts earlier in their ICU stay. Instead, it relies on existing literature that suggests circadian rhythms are severely disrupted in an ICU [3, 4, 15, 23]. Patients early in an ICU stay are likely to have their vital-sign patterns directly disrupted by medication and clinical interventions, making observation of any underlying circadian pattern, whether present or not, significantly more challenging. Finally, this paper seeks to evaluate circadian rhythmicity in the last 24 h prior to discharge from an ICU in the typical ICU patient who recovered. As such, the relatively short stay of ICU patients in the US databases, attributable to broader intake criteria used in US ICUs [45, 46], should be noted.
Conclusion
This paper investigated the presence of, and the relationships between, circadian rhythms in SBP, HR, RR, and T across a subset of patients in the MIMIC-III, eICU-CRD, and PICRAM ICU databases deemed most likely to exhibit circadian behaviour. Circadian patterns in SBP, HR, RR, and T that visually corresponded to those reported in the literature for non-ICU cohorts were observed, and these circadian patterns showed strong correlations between databases (mean R of 0.89). The peak-nadir excursions of the observed circadian patterns were reduced by a factor of 2–5 compared to behaviour reported in the literature for young, healthy individuals. These results support the existence of circadian rhythms in ICU patients who are within 24 h of discharge, and the generalisability of these circadian patterns across different cohorts subject to different standards of clinical practice. The existence of a generalisable circadian state prior to ICU discharge in patients who recovered has potential application in both prospective and retrospective tracking of patient recovery in the ICU, as well as the development of complications such as delirium.
The use of the MIMIC-III database was approved by the Institutional Review Boards of the BIDMC and Massachusetts Institute of Technology (Cambridge, MA, USA). The use of the eICU-CRD database was approved by the Institutional Review Board of the Massachusetts Institute of Technology (Cambridge, MA, USA). The use of the PICRAM database was approved by the Critical Care Research Group Data Access Committee of the University of Oxford (Oxford, UK).
Consent for publication
Not applicable.
Competing interests
PW works part-time for Sensyne Health and has share options in Sensyne Health. LT is a non-executive director of Sensyne Health and holds share options in the company.
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Vital-sign circadian rhythms in patients prior to discharge from an ICU: a retrospective observational analysis of routinely recorded physiological data
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Shaun Davidson Mauricio Villarroel Mirae Harford Eoin Finnegan Joao Jorge Duncan Young Peter Watkinson Lionel Tarassenko
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