INTRODUCTION
The provision of sedation is almost universal in mechanically ventilated patients, and is a modifiable variable related to clinical outcomes during critical illness. Evidence demonstrates that efforts to decrease sedation in the intensive care unit (ICU) improve outcome [1, 2]. However, the majority of data come from randomized controlled trials (RCT) which enrolled patients at 48-96 hours after intubation, or from observational data from an entire ICU stay [3-6]. Recently, prospective, observational data showed that deep sedation during the first 48 hours of mechanical ventilation was associated with worse short- and long-term outcomes [7, 8]. A systematic review and meta-analysis also showed harm associated with early deep sedation in the ICU [9]. Despite this, up to 70% of ventilated patients arrive to the ICU deeply sedated, suggesting the pre-ICU environment could play a role in the genesis of deep sedation [8].
The initial management of mechanical ventilation and sedation occurs in the ED for approximately 250,000 patients annually in the U.S. [10]. Despite this, the potential impact of ED-based sedation on clinical outcome has received little attention. In a prior investigation, ED sedation practices were discordant with guideline recommendations, including a high incidence of deep sedation and benzodiazepine use [11-13]. Deep sedation in the ED was associated with increased mortality, longer ventilation duration, and longer lengths of stay [11]. However, this was a single-center, retrospective study; it is therefore unknown if the results are generalizable. As a result, a knowledge gap persists regarding ED sedation practices and potential impact on outcome.
Given the outcome data associated with early sedation in the ICU, and the initial ED-based data that exists, the Emergency Department SEDation (ED-SED) Study was conducted to: 1) further characterize ED sedation practices across multiple centers; and 2) test the hypothesis that deep sedation in the ED is associated with worse clinical outcomes.
MATERIALS AND METHODS
Study Design
This was a multi-center (n= 15), prospective cohort study, and reported in accordance with the Strengthening Reporting of Observational Studies in Epidemiology (STROBE) Statement. (Supplemental Table 1, Supplemental Digital Content 1). The original design called for each of eighteen sites to enroll for a 30-day period. Protocol initiation varied between institutions resulting in an enrollment period between June 1 and August 31, 2018. When three centers could not participate, enrollment was extended beyond one month in three sites to achieve the desired sample size and mirror accrual which would have occurred had the three original centers participated.
The study was conducted with waiver of consent. Approval from the Human Research Protection Office was obtained at each center prior to data collection. A detailed description of the study has been published [14].
Participants
All consecutive mechanically ventilated adult ED patients were screened. Inclusion criterion: receipt of mechanical ventilation via an endotracheal tube in the ED. Exclusion criteria: 1) death or discontinuation of mechanical ventilation within 24 hours; 2) transfer to another hospital; 3) neurological injury (i.e. acute cerebrovascular accident, traumatic brain injury, status epilepticus, sudden cardiac arrest); and 4) chronic/home ventilation.
Assessments and Outcome Measures
Baseline data included demographics, co-morbidities, vital signs, and laboratory variables. ED processes of care included length of stay, transfusion, antibiotic administration, central venous catheter placement, and vasopressor infusion.
Sedation-related data in the ED included neuromuscular blockers and induction agents for intubation. Subsequent medications related to ED analgesia and sedation included opiates, benzodiazepines, propofol, ketamine, dexmedetomidine, etomidate, haloperidol, quetiapine, and neuromuscular blockers.
Sedation depth in the ED was recorded. Given the pragmatic intent of the study and equivalence between scales, sedation depth was monitored according to standard operating procedures at each site [15]. This included the Richmond Agitation-Sedation Scale (RASS; deep sedation defined as score of −3 to −5), or the Riker Sedation-Agitation Scale (SAS; deep sedation defined as score of 2 or 1) [15]. When more than one sedation depth per patient was documented, the median value was used. In patients for whom no ED sedation depth was documented, the first ICU sedation depth was used as a surrogate, congruent with prior approach [11]. We anticipated that some EDs may not routinely monitor sedation depth for mechanically ventilated patients, as ED-based sedation has not received clinical or research focus. In that situation, a documented GCS was used as a surrogate for sedation depth (≤ 9 defined as deep sedation) [16].
Agents administered for analgesia and sedation during the first 48 hours of ICU admission were collected. Patients were followed until hospital day 28 or death. The primary outcome was ventilator-free days. Secondary outcomes included acute brain dysfunction during the first 48 hours after admission, mortality, ICU-, and hospital-free days. Acute brain dysfunction is a composite of delirium and coma [17]. Delirium was assessed with the Confusion Assessment Method for the ICU (CAM-ICU) per institutional protocols. Coma was defined as being unresponsive or responsive only to physical stimulus (i.e. RASS −4 or −5) with every measurement of sedation depth [17, 18].
Statistical Analysis
Patient characteristics were assessed with descriptive statistics and frequency distributions. Categorical characteristics were compared using chi-square test or Fisher’s exact test. Continuous characteristics were compared using independent samples t-test or Mann-Whitney U test.
The primary analysis examined ventilator-free days as a function of ED sedation depth. A multivariable linear regression model was constructed to adjust for potentially confounding variables using backward elimination. A priori baseline characteristics with known prognostic significance for mortality in ED mechanically ventilated patients were purposefully selected for model inclusion (age, indication for mechanical ventilation, tidal volume, illness severity). Other clinically relevant and biologically plausible variables significant in univariate analysis at a p < 0.10 level were also included in the model. Collinearity was assessed and the model used variables that were independent of other variables. All tests were two-tailed, and a p value < 0.05 was considered statistically significant.
From prior work regarding early deep sedation in the ICU and ED, we assumed a difference in mean ventilator-free days of 2.5 between groups. For 80% power and α of 0.05, we estimated a sample size of 324 patients (162 per group) would be required [8, 9, 11, 14].
RESULTS
Study Population
A total of 15 centers participated, and details regarding each are in Supplemental Table 2, Supplemental Digital Content 2. One thousand ninety-four patients were assessed for inclusion and 324 comprised the final population (Figure 1). Baseline characteristics are in Table 1.
Figure 1. Flow diagram of patients in the study.
ED: emergency department
Table 1.
Characteristics of mechanically ventilated emergency department patients
| ED Sedation Depth Status | ||||
|---|---|---|---|---|
| Baseline characteristics | All subjects (n= 324) |
Deep sedation (n= 171) |
Light sedation (n= 153) |
P value |
| Age (yr) | 56.1 (18.2) | 56.2 (17.7) | 56.2 (19.4) | 0.99 |
| Male, n (%) | 197 (60.8) | 106 (62.0) | 91 (59.5) | 0.64 |
| Race, n (%) | ||||
| White | 188 (58.0) | 92 (53.8) | 96 (62.7) | 0.08 |
| African-American | 93 (28.7) | 54 (31.6) | 39 (25.5) | 0.23 |
| Hispanic | 22 (6.8) | 13 (7.6) | 9 (5.9) | 0.54 |
| Asian | 5 (1.5) | 3 (1.8) | 2 (1.3) | 0.74 |
| Native American | 5 (1.5) | 1 (0.3) | 4 (1.2) | 0.14 |
| Other | 11 (3.4) | 8 (4.7) | 3 (2.0) | 0.08 |
| Comorbidities, n (%) | ||||
| Diabetes mellitus | 80 (24.7) | 51 (29.8) | 29 (19.0) | 0.02 |
| Cirrhosis | 18 (5.6) | 11 (6.4) | 7 (4.6) | 0.47 |
| CHF | 52 (16.0) | 22 (12.9) | 30 (19.6) | 0.10 |
| COPD | 77 (23.8) | 32 (18.7) | 45 (29.4) | 0.02 |
| Malignancy | 42 (13.0) | 19 (11.1) | 23 (15.0) | 0.29 |
| Psychiatric* | 86 (26.5) | 41 (24.0) | 45 (29.4) | 0.27 |
| MAP | 96.0 (79.0 – 112.0) | 95.7 (78.3 – 112.3) | 96.7 (80.0 – 111.3) | 0.76 |
| Lactate (mmol/L), n= 283 | 2.6 (1.4 – 4.6) | 2.8 (1.5 – 4.6) | 2.5 (1.4 – 4.6) | 0.24 |
| Creatinine (mg/dl), n= 316 | 1.1 (0.8 – 1.6) | 1.2 (0.9 – 1.7) | 1.1 (0.8 – 1.4) | 0.04 |
| Platelet (10^9/L), n= 321 | 234 (105.9) | 229 (102.1) | 241 (110.0) | 0.34 |
| SOFA** | 4.2 (3.3) | 4.5 (3.4) | 3.8 (3.1) | 0.07 |
| Reason for mechanical ventilation, n (%) | ||||
| Sepsis | 55 (17.0) | 27 (15.8) | 28 (18.3) | 0.55 |
| Trauma | 65 (20.1) | 36 (21.1) | 29 (19.0) | 0.64 |
| COPD | 31 (9.6) | 12 (7.0) | 19 (12.4) | 0.10 |
| Drug overdose | 31 (9.6) | 20 (11.7) | 11 (7.2) | 0.17 |
| CHF/pulmonary edema | 16 (4.9) | 5 (1.5) | 11 (3.4) | 0.08 |
| Asthma | 6 (1.9) | 2 (0.6) | 4 (1.2) | 0.34 |
| Other | 120 (37.0) | 69 (40.4) | 51 (33.3) | 0.19 |
| Tidal volume (mL/kg PBW) | 6.9 (6.2 – 7.8) | 6.9 (6.2 – 7.9) | 6.8 (6.1 – 7.8) | 0.81 |
| PEEP (cm H20) | 5.0 (5.0 – 8.0) | 5.0 (5.0 – 8.0) | 5.0 (5.0 – 8.0) | 0.50 |
| Process of Care Variables | ||||
| ED length of stay (hours) | 4.8 (2.8 – 7.4) | 4.3 (2.9 – 7.7) | 5.1 (2.8 – 7.2) | 0.34 |
| Blood product transfusion, n (%) | 41 (12.7) | 20 (11.7) | 21 (13.7) | 0.58 |
| Central venous catheter, n (%) | 65 (20.1) | 40 (23.4) | 25 (16.3) | 0.11 |
| Antibiotics for infection, n (%) | 152 (46.9) | 73 (42.7) | 79 (51.6) | 0.09 |
| Vasopressor infusion, n (%) | 88 (27.2) | 53 (31.0) | 35 (22.9) | 0.10 |
CHF: congestive heart failure; ESRD: end-stage renal disease; COPD: chronic obstructive pulmonary disease; BMI: body mass index; MAP: mean arterial pressure; SOFA: sequential organ failure assessment score; PEEP: positive end-expiratory pressure; ED: emergency department Continuous variables are reported as mean (standard deviation) and median (interquartile range).
schizophrenia, bipolar disorder, major depression, anxiety
modified score, which excludes Glasgow Coma Scale
Medications Administered
Medications used for intubation were recorded separately from post-intubation sedation (Supplemental Table 3, Supplemental Digital Content 3).
Sedation-related variables are in Table 2. The most commonly used agents were fentanyl (64.5%), propofol (65.7%), and midazolam (23.8%). Variability existed in dosing and frequency of use at each site (e.g. midazolam use ranged from 0-64.3%). Ninety-two patients (28.4%) were given no analgesia, 69 (21.3%) received no sedation, and 35 (10.8%) received neither sedation nor analgesia in the ED. Two patients receiving no analgesia or sedation were given long-acting neuromuscular blockade after intubation (RASS of 1 and −4, respectively). Self-extubation occurred in two patients (0.62%).
Table 2.
Sedation variables in the emergency department
| ED Sedation Depth Status | ||||
|---|---|---|---|---|
| Drug | All Subjects n = 324 |
Deep sedation (n= 171) |
Light sedation (n= 153) |
p |
| Fentanyl | ||||
| n (%) | 209 (64.5) | 105 (61.4) | 104 (68.0) | 0.22 |
| Cumulative dose (mcg) | 200 (100 – 325.0) | 200 (100 – 350.0) | 188 (100 – 300.0) | 0.64 |
| Weight-based dose (mcg/kg) | 2.2 (1.1 – 4.6) | 2.3 (1.2 - 4.7) | 2.2 (1.1 - 4.5) | 0.73 |
| Dose (mcg)/hour ED ventilation time | 67.6 (39.0 – 113.5) | 71.2 (39.8 – 112.1) | 65.2 (33.9 – 119.4) | 0.52 |
| Propofol | ||||
| n (%) | 213 (65.7) | 108 (63.2) | 105 (68.6) | 0.30 |
| Cumulative dose (mg) | 315.0 (151.2 – 659.2) | 334.5 (163.6 – 744.7) | 252.0 (111.6 – 598.8) | 0.04 |
| Weight-based dose (mg/kg) | 3.6 (1.8 – 8.1) | 4.2 (2.4 – 8.4) | 3.1 (1.2 – 6.7) | 0.02 |
| Dose (mg)/hour ED ventilation time | 101.6 (55.2 – 195.6) | 117.0 (67.8 – 215.0) | 91.4 (41.3 – 151.6) | 0.03 |
| Midazolam | ||||
| n (%) | 77 (23.8) | 38 (22.2) | 39 (25.5) | 0.49 |
| Cumulative dose (mg) | 5.0 (2.0 – 7.0) | 5.0 (2.0 – 8.0) | 4.0 (2.0 – 6.0) | 0.80 |
| Weight-based dose (mg/kg) | 0.05 (0.03 – 0.09) | 0.06 (0.02 – 0.11) | 0.05 (0.03 – 0.08) | 0.88 |
| Dose (mg)/hour ED ventilation time | 1.3 (0.69 – 2.75) | 1.2 (0.66 – 2.8) | 1.4 (0.70 – 2.7) | 0.99 |
| Ketamine* | ||||
| n (%) | 15 (4.6) | 8 (4.7) | 7 (4.6) | 0.97 |
| Cumulative dose (mg) | 100 (50.0 – 100) | 75 (40.0 – 175) | 100 (85.0 – 100) | 0.54 |
| Weight-based dose (mg/kg) | 1.1 (0.69 – 1.4) | 0.70 (0.57 – 1.7) | 1.2 (1.0 – 1.4) | 0.40 |
| Lorazepam | ||||
| n (%) | 35 (10.8) | 14 (8.2) | 21 (13.7) | 0.11 |
| Cumulative dose (mg) | 3.0 (2.0 – 6.0) | 2.0 (2.0 – 4.0) | 4.0 (1.0 – 9.5) | 0.49 |
| Weight-based dose (mg/kg) | 0.03 (0.02 – 0.08) | 0.03 (0.03 – 0.05) | 0.04 (0.02 – 0.12) | 0.63 |
| Etomidate* | ||||
| n (%) | 5 (1.5) | 2 (1.2) | 3 (2.0) | 0.56 |
| Cumulative dose (mg) | 24.0 (20.0 – 36.0) | 20.0 (20.0 – NA) | 30.0 (24.0 – 30.0) | 0.56 |
| Weight-based dose (mg/kg) | 0.28 (0.17 – 0.36) | 0.17 (0.17 – NA) | 0.34 (0.28 – 0.34) | 0.20 |
| Morphine | ||||
| n (%) | 7 (2.2) | 1 (0.6) | 6 (3.9) | 0.04 |
| Cumulative dose (mg) | 8.0 (4.0 – 8.0) | 8.0 (NA) | 6.0 (3.5 – 9.0) | 1.0 |
| Weight-based dose (mg/kg) | 0.08 (0.05 – 0.12) | 0.12 (NA) | 0.07 (0.04 – 0.13) | 0.57 |
| Hydromorphone | ||||
| n (%) | 21 (6.5) | 9 (5.3) | 12 (7.8) | 0.35 |
| Cumulative dose (mg) | 2.0 (1.0 – 10.5) | 2.0 (1.5 – 8.5) | 3.0 (1.0 – 10.8) | 0.86 |
| Weight-based dose (mg/kg) | 0.03 (0.02 – 0.15) | 0.03 (0.02 – 0.18) | 0.05 (0.02 – 0.16) | 0.81 |
| Diazepam | ||||
| n (%) | 1 (0.3) | 0 (0.0) | 1 (0.7) | 0.29 |
| Cumulative dose (mg) | 30 (NA) | NA | 30 (NA) | NA |
| Weight-based dose (mg/kg) | 0.30 (NA) | NA | 0.30 (NA) | NA |
| Haloperidol | ||||
| n (%) | 6 (1.9) | 4 (2.3) | 2 (1.3) | 0.49 |
| Cumulative dose (mg) | 5.0 (4.0 – 7.8) | 5.0 (2.0 – 8.8) | 6.0 (5.0 - NA) | 0.80 |
| Weight-based dose (mg/kg) | 0.06 (0.05 – 0.12) | 0.06 (0.03 – 0.10) | 0.09 (0.06 - NA) | 0.53 |
| No analgesia in ED, n (%) | 92 (28.4) | 55 (32.2) | 37 (24.2) | 0.11 |
| No sedation in ED, n (%) | 69 (21.3) | 39 (22.8) | 30 (19.6) | 0.48 |
| No analgesia or sedation in ED, n (%) | 35 (10.8) | 20 (11.7) | 15 (9.8) | 0.58 |
| Neuromuscular blocker, n (%) | 29 (9.0) | 17 (9.9) | 12 (7.8) | 0.51 |
| Sedation tool used | ||||
| RASS, n (%) | 253 (78.1) | 138 (80.7) | 115 (75.2) | 0.23 |
| ED RASS Level | −3 (−4 to −1) | −4 (−5 to −3) | −1 (−2 to 1) | <0.001 |
| SAS, n (%) | 50 (15.4) | 19 (11.1) | 31 (20.3) | 0.03 |
| ED SAS Level | 3 (2 - 3) | 1 (1 - 2) | 3 (3 - 4) | <0.001 |
| GCS, n (%) | 21 (6.5) | 14 (8.2) | 7 (4.6) | 0.19 |
| ED GCS Level | 7 (4 - 13) | 6 (3 - 7) | 14 (11 - 15) | <0.001 |
ED= emergency department, RASS= Richmond Agitation-Sedation Scale, SAS= Riker Sedation-Agitation Scale, GCS= Glasgow Coma Scale
These are doses separate from those given for intubation.
Sedation variables in the ICU during the first 48 hours of admission are presented in Supplemental Table 4, Supplemental Digital Content 4.
Depth of Sedation
The incidence of deep sedation in the ED was 52.8% (n= 171), and there were significant differences (p < 0.001) in sedation levels between the two groups [deep sedation, RASS −4 (−5 to −3), SAS 1 (1-2); light sedation, RASS −1 (−2 to 1), SAS 3 (3-4)] (Table 2). Deeply sedated patients received higher cumulative doses of fentanyl, propofol, and midazolam, with statistically significant differences existing for propofol.
In the deep sedation group, 92 (75%) and 54 (69%) patients were deeply sedated on ICU day 1 and 2 respectively. In contrast, in the light sedation group, 31 (20.3%) and 24 (16.9%) patients were deeply sedated on ICU day 1 and 2 respectively. Overall, patients exposed to deep sedation in the ED had higher incidence of deep sedation on ICU day one (53.8% ED-deep sedation vs. 20.3% ED-light sedation, p <0.001) and day 2 (33.3% ED-deep sedation vs. 16.9% ED-light sedation, p= 0.001) (Supplemental Table 4, Supplemental Digital Content 4). The median RASS during the first 24 hours in the ICU was −3 (−4 to −2) in deeply sedated ED patients compared to −1 (−2 to −1) in those lightly sedated in the ED (p <0.001). When compared to light sedation, deep sedation in the ED persisted such that significant differences in sedation depth existed for almost every hour during the first ICU day (Figure 2). The median RASS during the 2nd 24 hours in the ICU was −2 (−4 to 0) in deeply sedated ED patients compared to −1 (−2 to 0) in those lightly sedated (p= 0.02).
Figure 2. Hourly differences in RASS during the first 24 hours in the ICU. When compared to light sedation, deep sedation in the ED persisted such that statistically significant differences in sedation depth existed for almost every hour during the first ICU day.
Clinical Outcomes
Clinical outcomes according to ED sedation depth are in Table 3. There was an unadjusted mean difference in ventilator-free days of 1.9 (95% CI −0.40 to 4.13, p= 0.11) between groups. After adjusting for confounders, multivariable linear regression analysis demonstrated illness severity (SOFA score) was associated with fewer ventilator-free days (Supplemental Table 5, Supplemental Digital Content 5).
Table 3.
Unadjusted analysis of clinical outcomes according to emergency department sedation depth.
| Outcome | Deep sedation (n= 171) |
Light sedation (n= 153) |
Unadjusted OR or Between-Group Difference (95% CI) |
p |
|---|---|---|---|---|
| Ventilator-free days | 18.1 (10.8) | 20.0 (9.8) | 1.9 (−0.40 to 4.13) | 0.107 |
| ICU-free days | 16.3 (10.5) | 17.9 (9.4) | 1.6 (−0.54 to 3.83) | 0.139 |
| Hospital-free days | 11.8 (9.6) | 14.1 (8.9) | 2.3 (0.26 to 4.32) | 0.027 |
| Mortality, n (%) | 36 (21.1) | 26 (17.0) | 1.30 (0.74 - 2.28) | 0.354 |
| Acute brain dysfunction, n (%) | 117 (68.4) | 85 (55.6) | 1.73 (1.10 - 2.73) | 0.017 |
| Delirium, n (%) | 106 (62.0) | 84 (54.9) | 1.34 (0.86 - 2.09) | 0.196 |
| Coma, n (%) | 16 (9.4) | 3 (2.0) | 5.12 (1.47 - 18.08) | 0.005 |
ICU=intensive care unit; OR: odds ratio; CI: confidence interval
Ventilator-, ICU-, and hospital-free days are indexed to study day 28. Mortality refers to all cause in-hospital mortality, censured at day 28. Acute brain dysfunction is a composite outcome comprised of delirium and coma, and was assessed over the first 48 hours in the ICU. Delirium was assessed with the Confusion Assessment Method for the ICU (CAM-ICU), and coma was defined as being unresponsive or responsive to only physical stimulus (i.e. RASS −4 or −5) with every measurement of sedation depth.
Similar results according to ED sedation depth existed for ICU-free days (unadjusted mean difference 1.6; 95% CI −0.54 to 3.83, p= 0.14) and hospital-free days (unadjusted mean difference 2.3; 95% CI 0.26 to 4.32, p= 0.03). Mortality was 21.1% in the deep sedation group and 17.0% in the light sedation group (between-group difference 4.1%; OR 1.30; 0.74 - 2.28, p= 0.35).
The incidence of acute brain dysfunction was 68.4% in the deep sedation group and 55.6% in the light sedation group (between-group difference 12.8%; OR 1.73; 1.10 - 2.73, p= 0.02). Given this, a post hoc logistic regression model was conducted to examine the association between ED deep sedation and acute brain dysfunction. The effect estimate [adjusted odds ratio (95% CI)] of the association between ED deep sedation and acute brain dysfunction during the first 48 hours in the ICU was 2.15 (1.18 – 3.92, p= 0.01), Supplemental Table 6, Supplemental Digital Content 6.
DISCUSSION
Key Findings
Prior work demonstrated a high incidence of deep sedation in the ED, which was negatively associated with outcomes [11]. Given the lack of ED-based sedation data, we conducted a multi-center, prospective cohort study to further characterize ED sedation practices and assess relationships between ED sedation depth and outcomes across multiple centers. We found that deep sedation was delivered to over half of mechanically ventilated patients, with significant carryover of sedation depth into the early phase of ICU care. In addition, our descriptive data related to delivery of sedation in the ED suggests areas in need for quality improvement.
Comparison With Previous Investigations
The ED-SED Study contributes novel data and addresses some weaknesses related to prior early sedation research. It is only the second investigation into sedation practices in the ED and the only ED-based sedation study to date that is prospective and multi-center [9, 11]. It also highlights the influence that ED sedation depth may hold over early sedation depth in the ICU, and its potential impact on outcome.
The majority of sedation research has ignored the most proximal time period of mechanical ventilation, allowing for pre-trial sedation depth and sedative delivery to go unchecked [19]. Deep sedation during the first 48 hours of mechanical ventilation and its impact on outcome was recently demonstrated in a systematic review and meta-analysis which included two small randomized trials and seven cohort studies [9]. The incidence of early deep sedation was 34.7% (range 19.6% to 80.6%), and was associated with higher mortality, ventilator duration, and lengths of stay. Distinct from that analysis, patients in the current study were followed prospectively from the time of intubation, allowing both an assessment of the impact of ED sedation depth on outcome and subsequent care. The ED was the origin of deep sedation in > 70% of the patients deeply sedated during the first two ICU days. In contrast, <20% of patients with light sedation in the ED were subsequently deeply sedated in the ICU. In addition to a higher incidence of deep sedation during the first two ICU days among patients deeply sedated in the ED, there was persistent separation in hourly ICU sedation depth between groups. These data suggest that carryover of sedation into the ICU is significant, and ED-based sedation could play a vital role in preventing iatrogenic coma and should receive increased attention clinically and in future research.
With respect to clinical outcomes, previous data have demonstrated negative consequences associated with deep sedation in the early ICU period and a single-center ED-based study [7-9, 11]. The only statistically significant association between ED sedation depth and outcome was related to acute brain dysfunction. There was no difference between groups with respect to other clinical outcomes. However, clinically important effect sizes existed between groups, and are congruent with prior research examining light vs. deep sedation [4, 8, 16, 17, 20, 21]. These effect estimates are imprecise and should be interpreted with caution at this time.
Mechanically ventilated ED patients were sedated primarily with fentanyl, propofol, and midazolam, consistent with prior single-center data and that from the ICU [8, 11, 15, 22]. A protocol-driven approach to delivery of analgesia and sedation in the ICU is common and associated with a reduction in medication requirements, ventilator duration, and lengths of stay [12]. In the current study, a higher propofol dose was observed in the deep sedation group and only 6/15 sites employed sedation protocols in the ED. There was wide practice variability with respect to medication use (i.e. midazolam in > 60% of patients in one site) and delivered doses across study sites. Further, no analgesia was given to 28.4% of patients, and 10.8% received no sedation or analgesia. Our descriptive data suggests areas for quality improvement related to sedation for mechanically ventilated ED patients, including protocolized assessments of pain and sedation depth, as well as sedation delivery, in order to reduce the unnecessary practice variability which seems to exist in the post-intubation sedation in the ED.
Taken as a whole, our data suggest that sedation practices in the ED: 1) influence sedation depth in the ICU; 2) have considerable practice variability (e.g. lack of goal-directed sedation or monitoring of sedation depth); and 3) may influence clinical outcome. Given the volume of patients receiving mechanical ventilation annually in the U.S., even a small improvement in care could have great impact.
Limitations
The current study addresses some weaknesses related to prior ED-based sedation research, as it is prospective and multi-center. However, multiple limitations persist. The design allows us to only comment on associations and not causal effect. In calculating the sample size of 324 patients, we estimated a difference of 2.5 ventilator-free days between the two groups. After examining the impact of deep sedation in the ED across multiple centers for the first time, we saw an effect size difference of 1.9 ventilator-free days between the two groups, which did not achieve statistical significance. Therefore, our effect estimates were imprecise, yet the effect sizes were clinically meaningful and suggest this is an area in need of further work. Sedation depth was recorded with multiple sedation scales in the ED, and not at all for 24 patients. This required us to use GCS in these patients, which is an inconsistent surrogate for validated sedation scales. While this may have introduced heterogeneity, it reflects real-world practice, and provides valuable information to tell the story regarding ED sedation. We did not assess the entire safety profile of light sedation in the ED, and only tracked self-extubation. Based on these preliminary results, it seems that light sedation can be safely achieved in the ED, but future studies should assess for potential spikes in adverse events such as awareness, distress, device removal, etc. It is possible that ICU-based guidelines should not be applied to the ED, given the different models of practice between the two locations (e.g. staffing, nurse-to-patient ratios, etc). Therefore, future studies should assess impact of ED-based goal-directed sedation on potential positive and negative outcomes, as well impact on staff. Finally, deep sedation may reflect illness severity, as there were observed differences between the two groups with respect to SOFA scores and vasopressor use.
CONCLUSIONS
Deep sedation in the ED is common in mechanically ventilated patients, carries over into the ICU, and may be associated with worse outcomes. Sedation practices in the ED and associated clinical outcomes are in need of further investigation.
Supplementary Material
Supplemental Digital Content 1, Supplemental Table 1. STROBE Checklist
Supplemental Digital Content 2, Supplemental Table 2. Enrolling sites for the ED-SED Study.
Supplemental Digital Content 3, Supplemental Table 3. Medications used for endotracheal intubation
Supplemental Digital Content 4, Supplemental Table 4. Sedation variables in the intensive care unit during the first 48 hours of admission
Supplemental Digital Content 5, Supplemental Table 5. Multivariable linear regression analysis with ventilator-free days as the dependent variable
Supplemental Digital Content 6, Supplemental Table 6. Multivariable logistic regression analysis with acute brain dysfunction as the dependent variable
ACKNOWLEDGMENTS
We acknowledge and thank the following people for their assistance in the conduct of the study: Cooper University Hospital: Lisa Shea, BA; University of Iowa: Alexander J. Tomesch, MD; University of Arizona: Beth Campbell, PhD, Jose Camarena, Alexia Demitsas; Cleveland Clinic: Sharon Mace, MD; University of Pennsylvania: Vincent Collins; University of Washington/Harborview Medical Center: Sarah Dean; University of Cincinnati: Jacqueline Davis, CCRP; University of Utah: Chloe Skidmore, MS; Henry Ford Health System: Gina Hurst, MD, Jacqueline Pflaum-Carlson, MD, Hannah Gruse; Michigan Medicine: Christopher Fung, MD, Ivan Co, MD; Christiana Care Health System: Michael Murphey, MD, Steven Martinez, MD;
Conflicts of Interest and Sources of Funding:
RDP was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1 TR002345. NJJ receives funding from the NIH (U01HL123008-02) and Medic One Foundation. CLH receives funding from the NIH (U01HL123008-02). SL was supported by NIH/NHLBI T32 HL007287-39. JET was supported by a career development award (K23HL141596) from the National Heart, Lung, And Blood Institute (NHLBI) of the National Institutes of Health (NIH), and, in part, by the University of Utah Study Design and Biostatistics Center, with funding in part from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 5UL1TR001067-02 (formerly 8UL1TR000105 and UL1RR025764). BWR receives funding from the NIH/NHLBI (K23HL126979). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. MHK received funding from the Barnes-Jewish Hospital Foundation.
Copyright form disclosure: Dr. Roberts’ institution received funding from National Heart, Lung, and Blood Institute (NHLBI) K23HL126979. Drs. Roberts, Pappal, Lokhandwala, and Tonna received support for article research from the National Institutes of Health (NIH). Dr. Knight received funding from BD and Genentech (speaker bureau for both). Dr. Pappal’s institution received funding from National Center for Advancing Translational Sciences. Dr. Johnson’s institution received funding from NHLBI and Medic One Foundation. Dr. Hough’s institution received funding from the NIH. Dr. Tonna’s institution received funding from NIH K23HL141596 and NIH 5UL1TR001067-02, and he received funding from NIH/NSF and Philips Healthcare. Dr. Carpenter disclosed he is a Member of American College of Emergency Physicians Clinical Policy Committee, a Chair of Schwartz-Reisman Emergency Medicine Research Institute International Advisory Board, and a Speaker for Best Evidence in Emergency Medicine (CME product) and for Emergency Medical Abstracts (CME product). Dr. Avidan received funding from UptoDate. The remaining authors have disclosed that they do not have any potential conflicts of interest.
Footnotes
This work was performed at: Washington University School of Medicine in St. Louis, University of Iowa, Cooper University Hospital, University of New Mexico, The Cleveland Clinic, MedStar Washington Hospital Center, Christiana Care Health System, University of Cincinnati, Henry Ford Health System, University of Arizona/Banner University Medical Center-Tucson, Lahey Hospital & Medical Center, University of Washington Harborview Medical Center, University of Utah Health, University of Pennsylvania, Michigan Medicine.
Reprints will not be ordered.
All authors have no relevant financial disclosures or conflicts of interest.
Contributor Information
Brian M. Fuller, Departments of Anesthesiology and Emergency Medicine, Division of Critical Care, Washington University School of Medicine in St. Louis, St. Louis, MO 63110.
Brian W. Roberts, Department of Emergency Medicine, Cooper University Hospital, Cooper Medical School of Rowan University, One Cooper Plaza, K152, Camden, NJ 08103.
Nicholas M. Mohr, Departments of Emergency Medicine and Anesthesiology, Division of Critical Care, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, 200 Hawkins Drive, 1008 RCP, Iowa City, IA 52242.
William A. Knight, IV, Departments of Emergency Medicine, Neurology and Neurosurgery, Division of Critical Care, University of Cincinnati Medical Center, 231 Albert Sabin Way, PO Box 670769, Cincinnati, OH 45267.
Opeolu Adeoye, Departments of Emergency Medicine and Neurosurgery, Division of Critical Care, University of Cincinnati Medical Center, 231 Albert Sabin Way, PO Box 670769, Cincinnati, OH 45267.
Ryan D. Pappal, Washington University School of Medicine in St. Louis, 660 S Euclid Ave, St. Louis, MO 63110.
Stacy Marshall, Department of Emergency Medicine, Cooper University Hospital, Cooper Medical School of Rowan University, One Cooper Plaza, K152, Camden, NJ 08103.
Robert Alunday, Department of Emergency Medicine, Division of Critical Care, University of New Mexico, 2211 Lomas Blvd NE, Albuquerque, NM 87106.
Matthew Dettmer, Emergency Services and Respiratory Institutes, Cleveland Clinic Foundation, 9500 Euclid Ave, E19, Cleveland, OH 44195.
Munish Goyal, Department of Emergency Medicine, MedStar Washington Hospital Center, 110 Irving Street, NW #NA1177, Washington, DC 20010.
Colin Gibson, Georgetown University School of Medicine, 3900 Reservoir Road, NW, Washington, DC 20007.
Brian J. Levine, Department of Emergency Medicine, Christiana Care Health System, 4755 Ogletown Stanton Road, Newark, DE 19718.
Jayna M. Gardner-Gray, Departments of Emergency Medicine and Medicine, Division of Pulmonary and Critical Care Medicine, Henry Ford Health System, Detroit, MI 48202.
Jarrod Mosier, Departments of Emergency Medicine and Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep, University of Arizona College of Medicine, 1501 N. Campbell Ave, Tucson, AZ 85724.
James Dargin, Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, 41 Mall Road, Burlington, MA 01805.
Fraser Mackay, Emergency Medicine and Pulmonary and Critical Care Medicine, Lahey Hospital & Medical Center, 41 Mall Road, Burlington, MA 01805.
Nicholas J. Johnson, Departments of Emergency Medicine and Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington/Harborview Medical Center, 325 9th Avenue, Seattle, WA 98104.
Sharukh Lokhandwala, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington/Harborview Medical Center, 325 9th Avenue, Seattle, WA 98104.
Catherine L. Hough, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington/Harborview Medical Center, 325 9th Avenue, Seattle, WA 98104.
Joseph E. Tonna, Department of Surgery, Division of Cardiothoracic Surgery, Division of Emergency Medicine, University of Utah Health, 30 N. 1900 E., 3C127, Salt Lake City, UT 84132.
Rachel Tsolinas, University of Utah School of Medicine, 30 N. 1900 E, Salt Lake City, UT 84132.
Frederick Lin, Department of Emergency Medicine, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104.
Zaffer A. Qasim, Department of Emergency Medicine, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104.
Carrie E. Harvey, Department of Emergency Medicine, University of Michigan Medical School, 1500 E Medical Center Drive, Ann Arbor, MI 48109.
Benjamin Bassin, Department of Emergency Medicine, University of Michigan Medical School, 1500 E Medical Center Drive, Ann Arbor, MI 48109.
Robert J. Stephens, Washington University in St. Louis School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110
Yan Yan, Division of Public Health Sciences, Department of Surgery, Division of Biostatistics, Washington University School of Medicine, 418E, 2nd floor, 600 South Taylor Ave., St. Louis, MO 63110.
Christopher R. Carpenter, Department of Emergency Medicine, Washington University in St. Louis School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110.
Marin H. Kollef, Department of Medicine, Division of Pulmonary and Critical Care Medicine, Washington University School of Medicine in St. Louis.
Michael S. Avidan, Department of Anesthesiology, Washington University School of Medicine in St. Louis.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Digital Content 1, Supplemental Table 1. STROBE Checklist
Supplemental Digital Content 2, Supplemental Table 2. Enrolling sites for the ED-SED Study.
Supplemental Digital Content 3, Supplemental Table 3. Medications used for endotracheal intubation
Supplemental Digital Content 4, Supplemental Table 4. Sedation variables in the intensive care unit during the first 48 hours of admission
Supplemental Digital Content 5, Supplemental Table 5. Multivariable linear regression analysis with ventilator-free days as the dependent variable
Supplemental Digital Content 6, Supplemental Table 6. Multivariable logistic regression analysis with acute brain dysfunction as the dependent variable