Erratum
After publication of the original article [1], it was noticed some of the data presented
in the body of the article was incorrect. This erratum contains the correct version
of this article.
Abstract
Background: The knowledge of the frequency and associated mortality of shock in the
emergency department (ED) is limited. The aim of this study was to describe the incidence,
all-cause mortality and factors associated with death among patients suffering shock
in the ED.
Methods: Population-based cohort study at an University Hospital ED in Denmark from
January 1, 2000, to December 31, 2011. All patients aged ≥18 years living in the hospital
catchment area with a first time ED presentation with shock (n = 1553) defined as
hypotension (systolic blood pressure (SBP) ≤100 mmHg)) and ≥1 organ failures. Outcomes
were annual incidence per 100,000 person-years at risk (pyar), all-cause mortality
at 0–7, and 8–90 days and risk factors associated with death.
Results: We identified 1553 of 438,191 (0.4%) ED patients with shock at arrival. Incidence
of shock increased from 53.6–74.8 cases per 100,000 pyar. The 7-day, and 90-day mortality
was 23.3% (95% CI: 21.2–25.4) and 41.1% (95% CI: 38.6–43.5), respectively. Independent
predictors of 7-day mortality were: age (adjusted HR 1.03 (95% CI: 1.03–1.04), and
number of organ failures (≥3 organ failures; adjusted HR 3.30 95% CI: 2.33–4.66).
Age, comorbidity level and number of organ failure were associated with 90-day mortality.
Conclusion: Shock is a frequent and critical finding in the ED, carrying a 7- and,
90- day mortality of 23.3% and 41.1%, respectively. Age and number of organ failures
are independent prognostic factors for death within 7 days, whereas age, comorbidity
and organ failures are of significance within 8–90 days.
Keywords: Shock, Epidemiology, Incidence, Mortality
Introduction
Shock is a life-threatening condition of circulatory failure that requires prompt
recognition, diagnosis, and resuscitation [1]. It is a substantial cause of morbidity
and mortality and is associated with high healthcare costs [1, 2].
Although the majority of critically ill patients are identified and initially resuscitated
in the Emergency Department (ED) setting, the knowledge of outcomes and the epidemiological
characteristics of shock has traditionally been limited to the post ED-period [3].
As these studies are based on different populations sampled several hours after the
initial ED identification and resuscitation, the estimates are of limited value for
understanding the early characteristics at presentation in the ED.
While trends in frequency and mortality of undifferentiated ED shock are largely unexplored,
the few studies available report in-hospital mortality of up to 24% in US ED settings
[4, 5]. Despite the substantial mortality reported, there is limited information on
the epidemiological characteristics of shock from a population-based perspective.
Clarifying the epidemiology of shock at presentation in the ED, in a population-based
context, are critical steps to uncover the full burden of shock in the pre-intensive
care unit (ICU) period.
The aim of the present study is to examine the epidemiological characteristics of
shock in an ED setting in Denmark. Our primary objective was to examine the 7- and
90- day all cause mortality of patients arriving to the ED in Odense University Hospital
during the period 2000–2011. Secondary, factors associated with death were explored,
as well as trends in annual incidence and mortality.
Material and methods
Study design and setting
We conducted a population-based cohort study in patients treated at the ED at Odense
University Hospital, Denmark, between 1st January 2000 - 31th December 2011. This
ED serves a mixed rural-urban population of 225,000 person (age ≥ 18) and provides
24-h acute medical care with 37,000 annual adult visits. Odense University Hospital
is a 1000-bed university teaching hospital that serves as the only primary hospital
for the local community as well as a Level 1 trauma center with all specialties represented
(see Table 1). At Odense University Hospital patients are usually assessed in the
ED and hereafter allocated and admitted to one of the specialties presented in Table
1 or referred to primary care after primary ED evaluation. In the prehospital setting,
the basic response to a request of prehospital assistance is an ambulance staffed
by two emergency medical technicians (EMTs) [6]. The competences are restricted to
initial treatment of patients with myocardial infarction (nitroglycerine, thrombolytic
agents, opioids), fluid administration and defibrillation, as well as inhalational
therapy, rectal administration of benzodiazepines, intramuscular administration of
naloxone and adrenaline [6]. From 2006 and onwards a physician-staffed mobile emergency
care unit (MECU) manned with a physician specialist in anesthesiology and an EMT were
added to the prehospital emergency medical system [6]. This unit serves as a second
tier providing prehospital advanced medical treatment exceeding the competences of
the EMTs (High-velocity car crash, absence of breathing, drowning etc.) (see Table
2) [6].
Table 1
In-hospital characteristics of shock (2000–2011)
Overall Mortality, n (%)
Specialty/Department
n (%)
Duration of admission in days (mean)
7-days
90-days
Emergency Department
570 (36.7)
0.3
119 (20.9)
193 (33.9)
General Internal Medicine
160 (10.3)
8.7
50 (31.3)
77 (48.1)
Cardiology
143 (9.2)
5.5
40 (28.0)
64 (44.8)
Gastroenterology
128 (8.2)
8.5
26 (20.3)
43 (33.6)
Geriatriology
111 (7.2)
10.4
23 (20.7)
63 (56.8)
General Surgery
95 (7.2)
9.2
22 (23.2)
45 (47.4)
Orthopedic Surgery
53 (3.4)
14.2
9 (17.0)
19 (35.9)
Pulmonology
52 (3.4)
10.2
14 (26.9)
25 (48.1)
Endocrinology
46 (3.0)
7.1
10 (21.7)
21 (45.7)
Infectious Diseases
48 (3.1)
9.8
12 (25.0)
22 (45.8)
Heart, Pulmonary and vascular Surgery
39 (2.5)
8.5
16 (41.0)
21 (53.9)
Neurology
40 (2.6)
18.8
9 (22.5)
13 (32.5)
Nephrology
16 (1.0)
3.5
7 (43.8)
10 (62.5)
Hematology
9 (0.6)
14.6
1 (11.1)
3 (33.3)
Rheumatology
9 (0.6)
14.4
0 (0.0)
4 (44.4)
Oncology
8 (0.5)
6.8
2 (25.0)
4 (50.0)
Urology
8 (0.5)
9.1
1 (12.5)
4 (50.0)
Otorhinolaryngology
6 (0.4)
5.7
0 (0.0)
0 (0.0)
Neurosurgery
4 (0.3)
15.0
1 (25.0)
2 (50.0)
Plastic Surgery
5 (0.3)
17.2
0 (0.0)
2 (40.0)
Hospice
3 (0.2)
28.0
0 (0.0)
3 (100.0)
Total
1553 (100.0)
6.0
362 (23.3)
638 (41.1)
Table 2
Prehospital and in-hospital characteristics of shock (2007–2011)
MECU, n (%)*
Overall Mortality, n (%)
Specialty/Department
ED contacts, n (%)
Prehospital contacts, n (%)
Intravenous fluid therapy
Intravenous vasopressor therapy
Mechanical ventilation
Cardiac arrest
ICU admission, n (%)
7-days
90-days
Emergency Department
193 (26.1)
35 (18.1)
14 (7.3)
9 (4.7)
10 (5.2)
4 (2.1)
9 (4.7)
44 (22.7)
65 (33.7)
General Internal Medicine
36 (4.9)
2 (5.6)
1 (2.8)
0 (0.0)
1 (2.8)
1 (2.8)
14 (38.9)
10 (27.8)
14 (38.9)
Cardiology
73 (9.9)
17 (23.3)
11 (15.1)
11 (15.1)
11 (15.1)
10 (12.3)
19 (26.0)
22 (30.1)
35 (47.9)
Gastroenterology
67 (9.1)
9 (13.4)
7 (10.5)
2 (3.0)
3 (4.5)
0 (0.0)
15 (22.4)
13 (19.4)
25 (37.3)
Geriatriology
75 (10.1)
16 (21.3)
5 (6.7)
2 (2.7)
5 (6.7)
2 (2.7)
3 (4.0)
15 (20.0)
42 (56.0)
General Surgery
53 (7.2)
5 (9.4)
3 (5.7)
1 (1.9)
1 (1.9)
1 (1.9)
18 (34.0)
14 (26.4)
27 (50.0)
Orthopedic Surgery
34 (4.6)
4 (11.8)
3 (8.8)
0 (0.0)
0 (0.0)
0 (0.0)
8 (23.5)
6 (17.1)
13 (38.2)
Pulmonology
52 (7.0)
12 (23.1)
11 (21.2)
5 (9.6)
6 (11.5)
2 (3.9)
15 (28.9)
14 (26.9)
25 (48.1)
Endocrinology
27 (3.6)
1 (3.7)
1 (3.7)
0 (0.0)
0 (0.0)
0 (0.0)
3 (11.1)
2 (7.4)
8 (29.6)
Infectious Diseases
48 (6.5)
18 (37.5)
9 (18.8)
5 (10.4)
6 (12.5)
1 (2.1)
26 (54.2)
12 (25.0)
22 (45.8)
Heart, Pulmonary and vascular Surgery
20 (2.7)
4 (20.0)
3 (15.0)
2 (10.0)
2 (10.0)
0 (0.0)
9 (45.0)
8 (40.0)
9 (45.0)
Neurology
17 (2.3)
8 (47.1)
4 (23.5)
3 (17.7)
2 (11.8)
0 (0.0)
7 (41.2)
1 (5.9)
2 (11.8)
Nephrology
12 (1.6)
1 (8.3)
1 (8.3)
0 (0.0)
0 (0.0)
0 (0.0)
4 (33.3)
6 (50.0)
8 (66.7)
Hematology
4 (0.5)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
1 (25.0)
0 (0.0)
0 (0.0)
Rheumatology
9 (1.2)
1 (11.1)
1 (11.1)
0 (0.0)
1 (11.1)
0 (0.0)
2 (22.2)
0 (0.0)
4 (44.4)
Oncology
6 (0.8)
2 (33.3)
1 (16.7)
0 (0.0)
0 (0.0)
0 (0.0)
1 (16.7)
2 (33.3)
4 (66.7)
Urology
2 (0.3)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
1 (50.0)
Otorhinolaryngology
5 (0.7)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
1 (20.0)
0 (0.0)
0 (0.0)
Neurosurgery
4 (0.5)
3 (80.0)
4 (75.0)
1 (25.0)
2 (50.0)
1 (25.0)
4 (100.0)
1 (25.0)
2 (50.0)
Plastic Surgery
2 (0.3)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
1 (50.0)
Hospice
1 (0.1)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
0 (0.0)
1 (100.0)
Total
740 (100.0)
138 (18.6)
78 (10.5)
41 (5.5)
52 (6.8)
21 (2.8)
159 (21.5)
180 (24.3)
308 (41.6)
ED Emergency Department, ICU Intensive Care Unit, MECU Physician-staffed mobile emergency
care units
*Data on MECU transportation and ICU admission available from 2007 to 2011
In 2009 the Adaptive process triage (ADAPT) was implemented in the ED at Odense University
Hospital and is the most commonly used triage system in Denmark [7]. Prior to 2009,
the severity and urgency of a patient’s condition were evaluated by an experienced
nurse who measured vital values in accordance with a clinical judgment followed by
blood test analysis based at a doctors prescription. Patients suffering minor complaints
(e.g. sprained ankle, insect bite without systemic reactions etc.) had usually not
their vital values measured or blood test analysis performed.
Participants
Eligible patients were all patients aged ≥18 years presenting to the ED with a systolic
blood pressure (SBP) ≤ 100 mmHg registered within 3 h upon arrival during the study
period. We chose to use a higher threshold (100 mmHg) of hypotension than the traditional
90 mmHg. This decision was based on increasing evidence advocating for a redefinition
of arterial hypotension [8–11], which we also have underlined in a recent study within
our research unit [12]. The primary date of contact defined the index date. If a patient
had multiple ED visits with hypotension over the study period, only the first was
included in the cohort. Patients residing outside the hospitals catchment area at
the time of contact and patients without a Danish personal identification number were
excluded. Patients who had visited the ED between 1 of January 1998 and 1 of January
2000 with hypotension were excluded to minimize left sided censoring. The background
population, from which patients were retrieved, was all adult (≥18 years) Danish citizens
living in the hospitals catchment area. Patients were followed from index date until
the date of death, emigration, December 31, 2011, or completion of 90 days follow-up,
whichever came first.
Data sources and processing
Database
Since 1996 all patients records from the ED are registered electronically and available
as patients record notes from the primary contact. The record notes are available
in structured text-format, in which vital parameters are consistently stated, including
measured SBP and heart rate (HR) and time of admission. By electronic screening it
was possible to identify and retrieve information on all patients with the unique
registered value of SBP and HR. The principle of free-text search has been validated
in the context of extracting numerical data, including blood pressure recordings [13].
The data extraction process used has previously been validated in 500 random ED notes
to have a sensitivity of 95.8% (95% CI [91.2, 98.5]) and a specificity of 100% (95%
CI [99.0, 100]) for retrieving correct SBP [12, 14].
Population-based registers
In Denmark every Danish citizen has free individual access to tax-supported health
care provided by The Danish National Health Service. At birth the Danish Civil Registration
system (CRS) assigns a unique 10-digit civil personal registry number (PRN-number)
to each Danish citizen and to residents upon immigration since 1968. This unique PRN-number
enables accurate linkage of the Danish national registers [15]. True population-based
studies are hereby possible as all patient contacts are registered and linked between
all Danish registries using the patients unique PRN-number.
The Danish National Patient Registry
Since 1995 the Danish National Patient Registry has been covering all in-patient and
out-patient clinic contacts at hospitals in Denmark assembling data regarding dates
of admission and discharge, admitting departments, and all primary and secondary discharge
diagnoses (ICD-10 code system) from hospitals [16]. Since 1994 every patient admission,
discharge and procedures performed has been registered according to the ICD-10 code
system [17]. At discharge every patient is assigned one primary diagnosis and up to
20 secondary diagnoses. Data on municipality of residence, migration-, vital status,
and date of birth were retrieved from The Danish Civil Registration System [15].
Outcome measures, exposure and possible confounders
We defined shock as the presence SBP ≤ 100 mmHg [12] and ≥1 organ failures.
The following organ failures were included: Cardiovascular, Renal, Coagulation and
Hepatic. Biochemical variables (creatine, bilirubin, platelets and INR (international
normalized ratio)) registered 180 days before and 1 day after the index date was used
to identify renal, hepatic and coagulative failure (see Appendix1 for details). We
used the Shock Index (SI) as a measure of cardiovascular failure. SI is calculated
as the ratio of heart rate to SBP and included as a categorical variable (<0.7, 0.7–1,
≥1) [18]. We defined cardiovascular failure as SI ≥1. SBP was measured with an automated
oscillometric device or manual cuff and sphygmomanometer. Heart rate was measured
with ECG, palpation or pulse oximetry. The primary outcome was all-cause mortality
within 7-days of the index date. Secondary outcomes were 90-day mortality as well
as factors associated with death and annual IRs during the study period. The primary
exposure variables were the first recorded SBP ≤ 100 mmHg at presentation, registered
within 3 h upon arrival and the presence of ≥1 organ failures. As the laboratorial
analysis of biochemical variables could exceed 3 h (due to busy hours, crowding etc.)
we computed organ failures based on variables registered within 24 h after arrival
to the ED.
We also included information on the additional covariates; gender, age, SBP level
(90 > SBP ≤ 100 mmHg, 80 > SBP ≤ 90 mmHg, SBP ≤ 80 mmHg) and Charlson comorbidity
index. The latter was used as a proxy for comorbid illness [19]. We used discharge
diagnoses from the previous 10 years in order to generate the Charlson comorbidity
index (CCI; 0, 1–2, >2) for each enrolled patient upon the index contact date [19].
Statistical analysis
We presented continuous and categorical data as medians (interquartile range (IQR))
and numbers (%), respectively.
Incidence rates
The crude annual IRs were calculated as the number of IRs per 100,000 person-years
at risk (pyar) (age ≥ 18 years) with the corresponding 95% confidence intervals (95%
CI) assuming a Poisson distribution. The annual IRs were adjusted using direct standardization
to the sex- and age distribution of the municipalities of the EDs catchment area midyear
population in the year 2000. The population was defined as contributing to one pyar
per resident per year in the analyses http://www.statistikbanken.dk/FOLK1, http://www.statistikbanken.dk/BEF6,
http://www.statistikbanken.dk/BEF607. The incidence rates were estimated and analyzed
using a Poisson regression model. Sex, age group, calendar time in years, and interaction
between age group and sex were used in the adjusted model. Calender time was entered
in the model as a continuous variable. Age was divided into four predefined age intervals:
18–39, 40–64, 65–84 and ≥85 years. The Poisson model was assessed using the Hosmere
Lemeshow goodness-of-fit test.
All-cause mortality analysis
All-cause mortality was presented in a Kaplan-Meier plot and comparison between survival
curves was tested using log-rank test. All-cause mortality proportions were reported
at 7-, and 90-days after the index date. Risk factors for all-cause mortality were
evaluated by Cox regression and presented as unadjusted and adjusted hazard ratios
(HRs) with 95% confidence intervals (CIs) for time periods 0 to 7-days and 8 to 90-days.
The models were adjusted for the following predefined variables: sex, age, Charlson
comorbidity index, and number of organ failures (1, 2 and ≥3).
Interaction between covariates where examined on all covariates and none were included.
We included age as a continuous variable after testing the assumptions of linearity
using a restricted cubic spline with 5 knots. Furthermore, the proportional hazards
assumption was checked by visual inspection of log–log plots of survival using the
scaled Schoenfeld residuals. We finally tested the model using Cox-Snell residuals
and found the model fitting the data well. Cuzick’s test was used for trends in annual
mortality.
Statistical analyses were performed using Stata version 13.1 (Stata Corporation LP
®, Texas, USA).
Ethics committee approval
The study was approved by the Danish Data Protection Agency (J.nr 2008–58-0035) and
the Danish Health and Medicines Authority (j.nr. 3–3013-205/1). In accordance with
Danish law, observational studies performed in Denmark do not need approval from the
Medical Ethics Committee.
The study was reported according to the STROBE statement [20].
Results
Participants
Of 438,191 ED contacts a total 1553 (0.4%) patients presented with shock and were
included in the analysis. Reasons for exclusions are presented in Fig. 1 and baseline
characteristics in Table 3.
Fig. 1
Flow chart of patients recruited to the study
Table 3
Baseline characteristics at time of arrival to the EDa
Variable
Total (%)
N (%)
1553 (100)
Age in years, Median (IQR)
70 (56–81)
Sex (%)
Male
830 (53.4)
Female
723 (46.6)
Age in age groups, yr. (%)
18–39
147 (9.5)
40–64
468 (30.1)
65–84
691 (44.5)
85+
247 (15.9)
Charlson Comorbidity Index (%)
0
477 (30.7)
1
589 (37.9)
> 2
487 (31.4)
Vital variables
Systolic blood pressure, Median (IQR)
88 (80–94)
Diastolic blood pressure, Median (IQR)
52 (44–62)
Heart rate, Median (IQR)
101 (88–115)
Shock Index (SI), n (%)
SI, Median (IQR)
1.2 (1.0–1.4)
SI ≤ 0.7
68 (4.5)
0.7 > SI ≤1
204 (13.4)
SI >1.0
1245 (82.1)
Number of organ failures, n (%)
1
1160 (74.7)
2
311 (20.0)
3+
82 (5.3)
Site of organ failure (%)
Cardiovascular
1245 (80.2)
Renal
333 (21.4)
Coagulation
387 (24.9)
Hepatic
72 (4.6)
aValues expressed as total number (fraction) and medians [25 percentile-75 percentile]
as appropriate
The median SBP on presentation was 88 mmHg (IQR, 80–94 mmHg) with a median SI 1.2
(IQR, 1.0–1.4). The most frequent organ failure was cardiovascular present in 80.2%
(1245) of the patients). One organ failure was present in 74.7% (1160), 21.4% (333)
had 2 and 4.6% (72) had ≥3 failures (Table 3). The proportion of admittance to non-surgical
specialties was 49.1% (765), whereas 36.7% (570) patients were evaluated exclusively
in the ED (Table 1). In the period 2007–2011, 740 patients were assessed in the ED
of which 18.6% (138) had a prehospital contact to a physician (MECU), and 21.5% (159)
were admitted to the ICU (Table 2).
Incidence of shock
The yearly crude IR are shown in Fig. 2 together with the standardized IR. The mean
annual IR of shock was 59.6 cases per 100,000 pyar (95% CI: 56.7–62.3). The IR increased
from 53.6–74.8 cases per 100,000 pyar, during the period 2000–2011, with an average
adjusted annual increase of 2.7% (95% CI: 1.2–4.3). The average annual increase using
standardized estimates was 2.6% (95% CI: 1.0–4.6). The estimated incidence rates stratified
by sex and age group with incidence rate ratios are shown in Fig. 3. Men aged 85+
had a forty-nine-time higher IR than men aged 18–39 years.
Fig. 2
Annual incidence rate during 2000–2011. The crude annual incidence rates of shock
from 2000 to 2011 and the standardized incidence rate to the population of the EDs
cathment area in 2000 (using direct standardization on sex and ten-year age bands).
Bars indicate the 95% confidence interval based on a Poisson distribution
Fig. 3
Estimated incidence rates stratified by sex and age group from 2000 to 2011. Incidence
rates estimated on the basis of a Poisson model adjusting for sex, age group, interaction
between sex and age group, and calendar years. The table is showing the corresponding
estimated incidence rate ratios with 95% confidence intervals (95% CI)
Mortality among patients with shock
Among patients presenting with shock 362/1553 died within 7 days (23.3% (95% CI: 21.2–25.4))
and a total 638/1553 died within 90 days 41.1% (95% CI: 38.6–43.5)) (Table 1). Trend
analysis of the annual 7-, and 90-day mortality proportions did not show any significant
change during the entire observation period (7-day mortality: P
trend
= 0.513 and 90-day mortality: P
trend
= 0.674). Kaplan-Meier curves are shown in Fig. 4 with the overall estimated probability
of 90-day survival stratified into age (Fig. 4a), Charlson comorbidity index (Fig.
4b), organ failures (Fig. 4c) and systolic blood pressure (Fig. 4d).
Fig. 4
Kaplan-Meier curves illustrating overall 90-day survival according to age (a), Charlson
comorbidity index (b), organ failures (c) and systolic blood pressure levels (d).
Below the curves are listed the number at risk at corresponding intervals in survival
time
Prognostic factors of death among patients with shock
In the multivariate analysis patients with organ failures of 2 (HR = 2.09 (95% CI,
1.66–2.63)) and ≥3 (HR = 3.30 (95% CI, 2.33–4.66)) had a higher rate as compared to
the reference within 0–7 days. Concordantly, patients with 2 failures (HR = 1.90 (95%
CI, 1.45–2.50)) failures had a higher rate as compared to the reference within 8–90 days.
Age depicted an increased risk of death within 7 days, whereas comorbidity was not
a significant predictor. Within 8–90 days, predictors; age, and Charlson comorbidity
index >2 were associated with increased risk of death (Table 4).
Table 4
Prognostic factors of death in patients presenting with shock at presentation to the
ED – Cox regression
0–7 days
8–90 days
N, total (%)
N, died (%)
Crude HR (95% CI)
p Value
Adjusted HR (95% CI)*
p Value
N, died (%)
Crude HR (95% CI)
p Value
Adjusted HR (95% CI)*
p Value
Gender
Female (reference)
723 (46.6%)
162 (22.4)
1
1
129 (17.8)
1
1
Male
830 (53.4%)
200 (24.1)
1.10 (0.90–1.35)
0.391
1.08 (0.87–1.33)
0.496
147 (17.7)
1.01 (0.80–1.28)
0.928
1.00 (0.79–1.27)
0.994
Age (continous)
1.03 (1.03–1.04)
<0.001
1.03 (1.03–1.04)
<0.001
1.04 (1.04–1.05)
<0.001
1.04 (1.03–1.05)
<0.001
Comorbidity level
0 (reference)
477 (30.7%)
92 (19.3)
1
1
50 (13.0)
1
1
1 to 2
589 (37.9%)
125 (21.2)
1.11 (0.85–1.46)
0.436
0.87 (0.66–1.14)
0.304
102 (22.3)
1.79 (1.28–2.51)
0.001
1.31 (0.93–1.84)
0.121
> 2
487 (31.4%)
145 (29.8)
1.61 (1.24–2.09)
<0.001
1.19 (0.91–1.55)
0.204
124 (36.9)
3.26 (2.34–4.53)
<0.001
2.23 (1.60–3.11)
<0.001
Number of organ failures
1 (reference)
1160 (74.7%)
210 (18.1)
1
1
194 (16.7)
1
1
2
311 (20.0%)
113 (36.3)
2.20 (1.75–2.76)
<0.001
2.09 (1.66–2.63)
<0.001
70 (22.5)
1.90 (1.45–2.50)
<0.001
1.90 (1.45–2.50)
<0.001
3+
82 (5.3%)
39 (47.6)
3.14 (2.23–4.42)
<0.001
3.30 (2.33–4.66)
<0.001
20 (24.4)
1.44 (0.81–2.58)
0.218
1.50 (0.83–2.68)
0.180
*Cox proportional hazard model adjusted for sex, age as a continuous variable, Charlson
comorbidity level (0, 1–2, >2) and number of organ failures. Patients who died during
the first 7 days after admission were excluded from the analyses of 8- to 90-day mortality
Discussion
In the present study, we have described a well-defined cohort of patients suffering
shock upon arrival to the ED. The results reveal that shock is frequently encountered
in the ED and is associated with a substantial mortality.
We found the prevalence of hypotensive shock to be 0.4% (1553/438,191), corresponding
to a mean annual incidence of 59.6/100,000 pyar (95% CI: 56.7–62.3). The overall IR
of registered shock increased during 2009–2011 compared to the previous years. This
increase could be attributed to the introduction of the ADAPT algorithm in our ED
in 2009 by which the identification of critically ill patients became more standardized,
as compared to the years before. We found shock to be most common among the elderly
with a higher incidence among men. The gender specific difference in the IR could
be due to the fact that men in general have more comorbidity than women. Whether increased
awareness across etiologies during this period (surviving sepsis campaign and percutaneous
coronary intervention of myocardial infarction) is of importance remains to be explored.
However, the present finding suggests shock to be as frequent as an ED presentation
of ST-elevation myocardial infarction [21]. As opposed to myocardial infarction, research
investigating characteristics of ED shock have been limited [22].
This cohort further demonstrates shock as a critical finding carrying a 7-, and 90-day
mortality of 23.3% and 41.1%, respectively. Although it is well accepted, that shock
associates poor prognosis, the mortality reported here exceeds previous reported estimates
of shock in the ICU and ED setting [4, 5, 23, 24]. Comparing mortality outcomes depends
largely on setting of research and the underlying etiology. Prior studies typically
evaluate outcomes in patients with a single etiology of shock, whereby extrapolation
to an open general ED is somewhat arbitrary. Although prognosis have improved across
etiologies of shock, mortality continuous to be critically high [1]. Studies investigating
non-traumatic shock report inhospital mortality of 16%–25% [4, 5, 23] in the ED, whereas
mortality estimates in the ICU setting is 38% [24]. For patients with septic- or cardiogenic
shock mortality is 32% [25] and 34% [26], respectively. Traumatic shock carries a
somewhat lower mortality of 16% [27]. The estimates from our study should be interpreted
in the context of the undifferentiated population from which they are derived, as
opposed to the selected patient populations in the ICU’s or specialized units with
well-defined etiologies.
In the current study, severity of shock (based on the number of organ failures) and
age appears to be the most important determinants of clinical outcome within the first
week after presentation. Conditional upon surviving the first week, the underlying
comorbid burden is an important factor for death within 8–90 days as well as the number
of organ failures and age. These findings are in line with previous studies investigating
critical illness and outcomes, suggesting multiple organ dysfunction and multiple
comorbidities to depict poor outcomes [28].
Despite technical improvement in diagnostics and advances in treatment, during the
past decades, shock is still a critical finding in the acute medical care and ED setting.
Steps to improve outcome have been implemented in which acute medical personal identify
life-threatening conditions, mobilize critical resources, and initiate relevant therapy.
Within specific groups of critically ill populations, goal directed team approaches
have been successful (trauma, cardiac arrest, and sepsis). Patient suffering undifferentiated
shock may benefit from a similar approach [29]. However, reducing time to recognition
is a critical aspect of caring for patients suffering shock. Clinical recognition
of shock is traditionally based on vital sign abnormalities. Measurement of SBP and
heart rate is a commonly used clinical practice to assess the circulatory state of
acutely ill patients. The presence of hypotension often signifies overt shock and
even a transient presentation of hypotension should alert the clinician to warrant
careful attention and evaluation for the presence of shock. Future studies should
refine the diagnostic process of recognizing shock in the ED. Moreover, exploring
baseline etiological characteristics of undifferentiated shock at presentation in
the ED are needed.
Study strengths and limitations
In this study, we analyzed a large cohort of acutely ill, undifferentiated patients
arriving to the ED. We had no loss to follow-up do to the unique personal registration
numbers in Denmark. The Danish public healthcare system, with a complete, independently
and prospectively recorded medical history, made it possible to identify all included
patients in the population-based registries. We were hereby able to compute robust
estimates on incidence, all-course mortality and predictive factors for death.
The blood pressure measurements were registered prospectively and as a routine documentation
and triage in the ED population. In order to avoid possible overestimation of the
IR, we excluded patient with residency outside the catchment area and a previously
reported admission with SBP ≤ 100 mmHg in the years 1998–99. To minimize bias from
repeated measurements we used the first contact with shock, within the study period.
There are limitations and possible bias that must be kept in mind when interpreting
our findings. This was a single-center, retrospective study from a University Hospital
ED serving a well-defined catchment area and is the primary and only hospital in this
area of Denmark. The results may, however, not necessarily be generalized to other
hospitals. Although our ED is the only on serving this part of Denmark, we are not
able to adjust for patients living in our catchment area, who have had contact to
other hospitals. However, in order minimize this proportion (n = 516, Fig. 1) we excluded
patients living in municipalities outside of our ED catchment.
An important limitation is the proportion of patients who were not included as a SBP
was not measured upon arrival (n = 273,774). These patients suffered minor complaints
and the triaging nurses did not measure SBP based on a clinical judgment. These circumstances
also apply for the proportion of patients, who did not have blood test performed upon
arrival (n = 689). However, the retrospective data at hand are a reflection of the
everyday procedures in our ED and not necessarily collected for research purposes.
We defined hypotension as SBP ≤ 100 mmHg, based on increasing evidence supporting
a higher threshold, as opposed to the traditional 90 mmHg [11, 12]. We used the first
recorded SBP value registered and did not have the possibility to examine individual
dynamic trends by serial measurements. Although a more detailed definition of hypotension
taking into account a patient’s baseline blood pressure as well as repeated measurements
in the ED would be ideal, it was not feasible in this study. However, a single measurement
approach is a common clinical applied triage method in emergency medicine settings.
Another important limitation is the number of organ failures defining our cohort.
Metabolic failure was not included, as arterial punctures were not systematically
collected. Moreover, respiratory frequencies and Glasgow Coma Scale were not consistently
registered, whereby organ failures related to the respiratory system, and failure
of the central nervous system were not included. We used a Shock Index ≥1 to define
cardiovascular failure, as this index has been shown to prognosticate outcome across
several etiologies of shock and critical illnesses [18, 30–37]. Ideally, cardiac output
measurements would have been desirable but not feasible based on the present design.
As not all variables for assessing organ failure were available, the incidence rate
and the mortality outcomes should be interpreted bearing this in mind.
Furthermore, we acknowledge the presence of a physician in the prehospital setting
(MECU) (from 2006 and onwards) could induce referral bias, as certain “high-risk”
patients are prone to be transported directly from the pre-hospital setting to the
operational theater or ICU, and thereby by-pass the ED. Moreover, in the period 2000–2008
(prior to the implementation of the ADAPT algorithm) blood pressure and blood test
were taken only if the acute care ED personal deemed it appropriate whereby our outcomes
could be susceptible to selection bias.
Lastly, a significant proportion of patients were evaluated in the ED and either discharged,
died or admitted to the ICU (Table 2). The later could be susceptible to information
bias, as the registration of ICU admission directly from the ED was not consistently
documented during the period of observation. Although limited, we had missing values
on covariates; ICD-codes (2 patients) and HR (36 patients).
Conclusion
Shock is present in 0.4% of ED encounters, with a mean annual IR of 59.6/100,000 pyar
(95% CI: 56.7–62.3) carrying a substantial 7-day, and 90-day all-cause mortality.
Age and increasing number of organ failures are important prognostic factors associated
with increased risk 7 days after ED presentation, whereas age, comorbidity and number
of organ failures are prognostic factors at 8 to 90 days.