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International Emergency Nursing
journal homepage:www.elsevier.com/locate/aaen
Characteristics, management and outcome of critically ill general medical patients in the Emergency Department: An observational study
Stine Engebretsen
a,b,⁎, Stig Tore Bogstrand
c,d, Dag Jacobsen
e,b, Rune Rimstad
faEmergency Department, Division of Emergencies and Critical Care, Oslo University Hospital, Postboks 4950 Nydalen, 0424 Oslo, Norway
bInstitute of Clinical Medicine, University of Oslo, Postboks 1171 Blindern, 0318 Oslo, Norway
cDepartment of Forensic Sciences, Oslo University Hospital, Postboks 4950 Nydalen, 0424 Oslo, Norway
dInstitute of Health and Society, University of Oslo, Postboks 1130 Blindern, 0318 Oslo, Norway
eDepartment of Acute Medicine, Division of Medicine, Oslo University Hospital, Postboks 4950 Nydalen, 0424 Oslo, Norway
fMedicine, Health, Patient Safety and Integration, Oslo University Hospital, Postboks 4950 Nydalen, 0424 Oslo, Norway
A R T I C L E I N F O Keywords:
Emergency Service, Hospital Critical Care
Internal medicine Patient Care Management Hospital Rapid Response Team
A B S T R A C T
Background:Critically ill general medical patients are an increasing group in the Emergency Department (ED).
This register-based cohort study aimed to examine these patients’ characteristics, ED management and outcome, and investigate factors associated with ICU admission.
Methods:The study comprised all adult medical triage 1 patients treated by a specialized multidisciplinary team in 2015 and 2016. Univariate and multivariate analysis were used.
Results:1294 patients were included. Mean age was 59 years, 56% (n = 725) were male, mean National Early Warning Score 2 (NEWS2) was 7, intensive care unit (ICU) admission was 56.8% (n = 735) and mortality rate was 16.8% (n = 217). Median ED length of stay (LOS) was 1.6 h, 1.2 h if admitted to ICU. The most frequent discharge diagnosis was acute poisoning (24.0%, n = 308). Younger age, male gender, arriving at nighttime weekdays, higher NEWS2 at arrival, critical care interventions or medications in the ED was associated with ICU admission.
Conclusion:More than half of the patients were admitted to ICU, and the mortality rate was 16.8%. A large proportion was diagnosed with acute poisoning. Younger age, higher NEWS and critical care in ED were asso- ciated with ICU admission. The short ED LOS suggests that management by a multidisciplinary team is bene- ficial.
1. Background
Critically ill general medical patients typically have impaired re- spiration, circulation or reduced level of consciousness, and require early intervention and treatment to be stabilized[1]. Despite being an important and increasing part of Emergency Department (ED) work [2,3], these patients’ characteristics, ED management and outcome are scarcely described in existing literature.
Patients in need of intensive care unit (ICU) admittance strain ED resources and might receive suboptimal care in the ED [1]. Delayed treatment and increased ED length of stay (LOS) may increase mortality and cause sentinel events [1,4,5]. ED and ICU collaborative models exist in some countries to meet the patients’ special needs and optimize initial management. One model is the EDICU, where ICU providers assist in critical care in the ED while awaiting ICU admission. Others use a Medical Emergency Team; an ICU-based team specialized in care
for critically ill patients elsewhere in the hospital. Another model is direct provider-provider communication, with a more direct and easy communication-line between ED and ICU providers. The effectiveness of these models is unclear[1].
For patients with trauma, myocardial infarction (MI) or stroke, specialized multidisciplinary teams are commonly used as a measure to optimize initial management[6,7], showing positive effect on time to treatment, mortality and morbidity [8–11]. Only a few studies have investigated the use of multidisciplinary teams for critically ill general medical patients, finding that few EDs have access to such teams[12], but that a team approach ensures early diagnostic measures, early treatment and short ED LOS[13,14].
To improve initial care of critically ill general medical patients, Oslo University Hospital Ullevål (OUH-U) implemented a multidisciplinary team response to these patients in the ED in 2013. Before the effec- tiveness of different types of management can be explored, it is
https://doi.org/10.1016/j.ienj.2020.100939
Received 6 May 2020; Received in revised form 23 September 2020; Accepted 1 October 2020
⁎Corresponding author.
E-mail addresses:[email protected](S. Engebretsen),[email protected](S.T. Bogstrand),[email protected](D. Jacobsen),[email protected](R. Rimstad).
1755-599X/ © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
T
important to first gain more knowledge about the patient group. The aim of this study was therefore to analyze characteristics of patients treated by this team, and in particular the differences between patients being admitted to ICU and those not. The objectives were to 1) in- vestigate the patients’ status before the critical illness and at ED arrival, the ED management and the outcome, and 2) investigate which para- meters were associated with ICU admission.
2. Methods
2.1. Study design and setting
Patients presenting to EDs in Norway are referred by telephone by primary care physicians or ambulance personnel prior to arrival. Self- referral is rare. Type of complaint decide which subspecialist (internal medicine, neurology, orthopedic etc) should review the patients in the ED. No Emergency Physician specialty existed in Norway at the time of the study. Patients with no need for specialist review in the hospital are managed by their general practitioner or in Emergency Care Centers in the primary health service. In our ED patients receive primary nursing care and are managed by on-call subspecialists. Critically ill and injured patients are managed by multidisciplinary teams.
This register-based cohort study of critically ill general medical patients included patients from 2015 and 2016 at OUH-U, a tertiary referral hospital for selected medical services and local hospital for 265 000 inhabitants (2018). It has 213 medical beds, including a 10 bed Medical Intensive Care Unit (MICU) and a 13 bed Coronary Intensive Care Unit (CICU). In 2015 the ED treated 28 053 patients, and 25 115 (90%) were admitted. Of these, 12,392 (49%) were adult medical pa- tients. Mean ED LOS for all patients was 3.4 h.
Critically ill patients with medical complaints, hereafter called triage 1 patients, are managed in resuscitation rooms by a multi- disciplinary team. The team leader is an experienced internal medicine registrar with easy access to MICU and CCU beds. The team is on call 24/7 and can be activated before arrival or during the ED stay. Team- criteria and -members are presented inadditional file 1. Patients with stroke considered for thrombolysis, with MI considered for percuta- neous coronary intervention or with cardiac arrest have other path- ways, but some might be treated by the team. All other medical patients are triaged using the Manchester Triage System.
2.2. Participants and data sources
All adult triage 1 patients from 2015 and 2016 were included. Data were extracted anonymously from the hospital’s quality register for patients treated by the team. Only patients holding a Norwegian social security number were included in the register. In the study period 44 patients without this number were not included.
2.3. Variables
Presenting complaint was text-data, and categories were made by grouping the most frequent complaints. If category was uncertain, au- thors SE and RR discussed until agreement. Categories for primary di- agnosis at discharge were made to be as equal to the categories for presenting complaint as possible. Patients with a diagnosis of infection, irrespective of site, were categorized as infection. Weekend was defined as between 15:00 Friday and 07:00 Monday. Blood tests outside of re- ference range consisted of lactate, creatinine, platelets, white blood cells and bilirubin. Critical care intervention included intubation, other airway interventions, non-invasive ventilation, arterial line, central venous line, pacing, cardioversion, cardiopulmonary resuscitation, pleural catheter and administration of blood products. Critical care medication included sedatives, anesthetic agents, antiarrhythmics and vasopressors[15]. ICU was defined as MICU, CICU or any other ICU in the hospital. The variables ED LOS > 3.3 h and hospital LOS > 167 h
were made using the 75 percentile of LOS from historic hospital data.
In multivariate analysis the dependent variable was ICU admittance.
The model was built using clinical rational and included factors con- sidered likely to contribute to ICU admission. Patient status factors included in the analysis were illness severity, gender, age, comorbidity and living in a care home or institution. National Early Warning Score 2 (NEWS2) [16] and blood tests outside of reference on arrival was chosen as expressing illness severity. NEWS2 was calculated retro- spectively and included in analysis if three or more of the part scores were present. For comorbidity Charlson Comorbidity Index (CCI)[17], past history of substance abuse and psychiatric history were used. All were retrieved from the known past medical history at admittance. ED management factors included were critical care interventions and cri- tical care medication, as well as limitations of medical treatment (LOMT) decided in the ED. In addition we included the factors seen by doctor before arrival and arriving weekday 20–08, due to univariate analysis showing significant difference between ICU and non-ICU pa- tients.
2.4. Statistical methods
IBM SPSS®version 25.0 for Windows (Armonk, NY, USA) was used for analysis. Continuous variables are presented as mean with con- fidence interval (CI) if normally distributed and median with inter- quartile range (IQR) if not, and categorical variables as number (n) and percentage. Numbers of missing items from the register are reported.
Group-comparison was two-sided, using T-test or Mann-Whitney rank sum for continuous variables and Chi-square test for categorical.
Multivariate logistic regression was performed to investigate para- meters associated with ICU admission, with advice from statistician.
Data are presented as unadjusted odds ratio (OR) with CI and p-values.
Factors included in the final model based on forward wald regression are presented with adjusted OR, CI and p-values. In all analysis p-va- lues < 0.05 were considered statistically significant.
2.5. Ethical considerations
The study was approved by the Data Protection Officer at OUH (2016/10319). Consent was waived, as all data were routinely collected data from medical records retrieved anonymously from the register.
3. Results 3.1. Patient status
Triage 1 patients had a mean age of 59 years, 56.0% (n = 725) were male and 18.1% (n = 231) lived in a care home or institution (Table 1).
Comorbidity according to CCI was low; 44.0% (n = 533) had no co- morbidities. A total of 21.5% (n = 278) had a history of substance abuse, and 14.2% (n = 184) had a psychiatric history. Altered con- sciousness or neurological problems were the most frequent presenting complaint (26.5%, n = 342), followed by respiratory problems (22.3%, n = 288) and suspected acute poisoning (22.1%, n = 285). Mean NEWS2 on arrival was 7, 55.9% (n = 709) had NEWS2 over 6 and 81.1% (n = 1050) had blood tests outside of reference range.
ICU-patients were younger than non-ICU patients (56 vs 62 years), and had a higher male to female ratio (60.4%, n = 444 vs 50.3%, n = 281). Fewer were living in care home or institution (15.1%, n = 109 vs 22.1%, n = 122). More also had a history of substance abuse (25.3%, n = 186 vs 16.5%, n = 92), and a larger group had acute poisoning as presenting complaint (24.8%, n = 182 vs 18.6%, n = 103), being the most frequent complaint for this group. ICU-pa- tients had more often been seen by a doctor before arrival (31.0%, n = 228 vs 25.6%, n = 143), and more often arrived at nighttime in weekdays (26.4%, n = 194 vs 19.1%, n = 107). Mean NEWS2 on ar- rival was higher (7.4 vs 6.7), and more had NEWS2 over 6 (60.7%,
n = 436 vs 49.5%, n = 273) and blood tests outside of reference range (83.1%, n = 611 vs 78.5%, n = 439).
3.2. ED management
The most frequently performed investigations were electro- cardiogram (ECG) (82.8%, n = 1072), arterial blood gas (81.1%, n = 1049) and x-ray (67.5%, n = 874) (table 2). A critical care in- tervention was performed in 52.2% (n = 676) of the patients, and 9.1%
(n = 118) were intubated. Critical care medication was given to 33.1%
(n = 428) of the patients, 8.0% (n = 103) received vasopressors. A limitation of medical treatment (LOMT) decision was set for 10.6%
(n = 137) of the patients.
Patients admitted to ICU had more x-rays (72.2%, n = 531 vs 61.4%, n = 343) and arterial blood gases (83.8%, n = 616 vs 77.5%, n = 433) performed than non ICU-patients. They also received more critical care interventions (67.2%, n = 494 vs 32.6%, n = 182) and medications (43.3%, n = 318 vs 19.7%, n = 110), including intubation (14.6%, n = 107 vs 2.0%, n = 11) and vasopressors (12.5%, n = 92 vs 2.0%, n = 11). Fewer had a LOMT-decision made in the ED (6.7%, n = 49 vs 15.7%, n = 88).
3.3. Outcome
Triage 1 patients had a median ED LOS of 1.6 h, and 14.7%
(n = 190) had a stay over 3.3 h (table 3). Altogether 56.8% (n = 735) were admitted to ICU. Median hospital LOS was 66 h, and 27.4% stayed longer than 167 h. Mortality at 30 days or before hospital discharge was 16.8% (n = 217). The most common primary diagnosis at discharge was acute poisoning (24.0%, n = 308), followed by a cardiac/circu- latory diagnosis (21.2%, n = 273) and infection (19.1%, n = 246).
ICU patients had shorter median ED LOS than non-ICU patients (1.2 vs 2.0 h), and fewer stayed > 3.3 h (5.9%, n = 43 vs 26.3%, n = 147).
They also had longer median hospital LOS (85 vs 42 h), and more of them had hospital stay over 167 h (32.1%, n = 236 vs 21.3%, n = 119). ICU patients had lower mortality rate than non-ICU patients (12.5%, n = 92 vs 22.4%, n = 125). More were diagnosed with acute poisoning (26.2%, n = 192 vs 21.0%, n = 116), which was the most common discharge diagnoses also for ICU patients.
3.4. Parameters associated with ICU admission
Table 4presents the unadjusted and adjusted odds ratios for factors associated with ICU admission. In unadjusted analysis patient and management factors associated with increased odds of ICU admission were history of substance abuse (OR 1.720, CI 1.302–2.272), seen by doctor prehospital (OR 1.308, CI 1.023–1.673), arriving weekday 20–08 (OR 1.515, CI 1.160–1.978), increasing NEWS2 at arrival (OR 1.070, CI 1.034–1.108), blood tests outside of reference range (OR 1.347, CI 1.019–1.781), critical care intervention (OR 4.246, CI 3.358–5.368) and critical care medication (OR 3.113, CI 2.413–4.015).
Factors associated with decreased odds of ICU admission were in- creasing age (OR 0.988, CI 0.984–0.993), female gender (OR 0.662, CI 0.531–0.827), and limitations of medical treatment (OR 0.382, CI 0.264–0.553).
In adjusted analysis factors that continued to be associated with increased odds of ICU admission were arriving weekday 20–08 (OR 1.578, CI 1.161–2.146), increasing NEWS2 at arrival (OR 1.062, CI 1.017–1.109), critical care intervention (OR 4.260, CI 3.253–5.579) and critical care medication (OR 2.571, CI 1.972–3.430). Increasing age (OR 0.987, CI 0.981–0.994), female gender (OR 0.656, CI 0.504–0.853) and having limitation of medical treatment (OR 0.397, CI 0.256–0.616) continued to be associated with decreased odds of ICU admission.
Table 1
Descriptive analysis of patient status (n = 1294/735/559 unless otherwise stated).
Whole cohort ICU patients Non-ICU patients
Age, mean (CI) 58.83
(57.6–60.05) 56.3
(54.8–57.9)** 62.1 (60.1–64.1)
Male gender 725 (56.0%) 444 (60.4%)** 281 (50.3%)
Living in care home or institution (n = 722/552)
231 (18.1%) 109 (15.1%)* 122 (22.1%)
CCI, median (IQR)
(n = 683/529) 1 (2) 1 (2)* 1(2)
CCI categorical
0p 533 (44.0%) 321 (47.0%) 212 (40.1%)
1–2p 458 (37.8%) 250 (36.6%) 208 (39.3%)
3–4p 164 (13.5%) 85 (12.4%) 79 (14.9%)
> 4p 57 (4.7%) 27 (4.0%) 30 (5.7%)
History of substance abuse 278 (21.5%) 186 (25.3%)** 92 (16.5%) Psychiatric history 184 (14.2%) 116 (15.8%) 68 (12.2%) Presenting complaint
(n = 734/555)
Cardiac/circulatory 166 (12.9%) 92 (12.5%) 74 (13.3%)
Poisoning 285 (22.1%) 182 (24.8%)* 103 (18.6%)
Respiratory 288 (22.3%) 170 (23.2%) 118 (21.3%)
Consciousness/
neurologic 342 (26.5%) 179 (24.4%)* 163 (29.4%)
Abdominal 52 (4.0%) 36 (4.9%) 16 (2.9%)
Infection 110 (8.5%) 50 (6.8%)* 60 (10.8%)
Others 46 (3.6%) 25 (3.4%) 21 (3.8%)
Seen by doctor before
arrival 371 (28.7%) 228 (31.0%)* 143 (25.6%)
Time of day and week at arrival
Weekday 08–20 477 (36.9%) 258 (35.1%) 219 (39.2%) Weekday 20–08 301 (23.3%) 194 (26.4%)* 107 (19.1%) Weekend 08–20 250 (19.3%) 133 (18.1%) 117 (20.9%) Weekend 20–08 266 (20.6%) 150 (20.4%) 116 (20.8%) NEWS2 at arrival, mean
(CI) (n = 718/551) 7.09
(6.90–7.27) 7.4 (7.2–7.6)** 6.7 (6.4–7.0) NEWS2 > 6 at arrival
(n = 718/551) 709 (55.9%) 436 (60.7%)** 273 (49.5%) Blood tests outside of
reference range at arrival
1050 (81.1%) 611 (83.1%)* 439 (78.5%)
CI: Confidence Interval, CCI: Charlson Comorbidity Index, IQR: Interquartile range, NEWS2: National Early Warning Score 2, ICU: Intensive Care Unit, * p < 0.05, **p < 0.001.
Table 2
Descriptive analysis of ED management (n = 1294/735/559 unless otherwise stated).
Whole
cohort ICU patients Non-ICU patients Investigations
CT 427 (33.0%) 254 (34.6%) 173 (30.9%)
Ultrasound 97 (7.5%) 58 (7.9%) 39 (7.0%)
X-ray 874 (67.5%) 531 (72.2%)** 343 (61.4%)
ECG 1072
(82.8%) 614 (83.5%) 458 (81.9%) Arterial blood gas 1049
(81.1%) 616 (83.8%)* 433 (77.5%) Critical care interventions, any 676 (52.2%) 494 (67.2%)** 182 (32.6%)
Intubation 118 (9.1%) 107 (14.6%)** 11 (2.0%)
Critical care medication, any 428 (33.1%) 318 (43.3%)** 110 (19.7%)
Vasopressors 103 (8.0%) 92 (12.5%)** 11 (2.0%)
Limitation of medical
treatment 137 (10.6%) 49 (6.7%)** 88 (15.7%)
CT: computed tomography, ECG: electrocardiogram, ICU: Intensive Care Unit, * p < 0.05, **p < 0.001.
4. Discussion
Triage 1 patients were relatively young and with low comorbidity, but almost one in five lived in care home or institution. Almost half had a NEWS2 under 7 at arrival. More than half were admitted to ICU. ICU patients had higher NEWS2, were younger and more were men. This group also received more critical care intervention and medication, had fewer LOMT-decisions made in the ED, and had shorter ED LOS. The mortality rate for triage 1 patients was 17%, and lower for ICU than non-ICU patient. Acute poisoning was the most frequent discharge di- agnosis. Factors associated with ICU admission were younger age, male gender, arriving at nighttime on weekdays and higher NEWS2 at ar- rival. Being given critical care interventions or medications in the ED and not having a LOMT decision was also associated with ICU admis- sion.
The young age in the triage 1 group is in line with some studies [2,13,18], but younger than in others[19,20]. The large proportion with acute poisoning could explain the younger age. Such large pro- portions have not been found in international studies of critically ill patients[13,18–20], but in other cohorts of critically ill medical ICU patients in Norway[21,22]. The latter found that 26% of all patients in two medical ICUs in Oslo had acute poisoning, while the former only found this in 122 of 1369 patients outside of Oslo, suggesting that acute poisoning is an urban problem. The large proportion of patients with acute poisoning is probably also due to the many having chronic sub- stance abuse.
Younger age in ICU than non-ICU patients is consistent with other studies[2], and we also found younger age to be associated with ICU admission in multivariate analysis. A large proportion of the triage 1 patients, with an overweight of non-ICU patients, were living in a care home or institution, suggesting that these patients had severe chronic illnesses. It could be suspected that institutionalization would cause under-triage, but our findings do not support this. It may reflect lack of LOMT-decisions from the care homes or institutions. This is supported by the many LOMT-decisions made in the ED. A review of rapid re- sponse teams; teams responding to critically ill patients in wards, found that these teams often initiate implementations of LOMT[23]. This has also been found in two studies investigating multidisciplinary teams for critically ill ED patients[13,20], indicating that any team response to critically ill patients facilitates decisions about LOMT. The younger age in the ICU-group could be explained by fewer living in care homes, fewer LOMT-decisions and more patients with acute poisoning.
We also found that there were more men than women in the triage 1 group as a whole, and in the ICU-group in particular. Male gender was one of the factors associated with ICU-admission. It is known that there is a gender gap in ICU admittance, with more men than women being admitted to ICU[2,24]. It is suggested that this could be due to women and men experiencing different clinical symptoms, and to different practice among male and female physicians, among other things[24].
It is recommended that patients with NEWS2 > 6 should be as- sessed by a clinical team with critical care competencies[16]. Other studies have investigated the use of NEWS in ED settings for specific Table 3
Descriptive analysis of outcome (n = 1294/735/559 unless otherwise stated).
Whole cohort ICU patients Non-ICU patients
Length of stay
ED median (IQR) hours 1.6 (1.5) 1.2 (1.0)** 2.0 (2.1)
ED > 3.3 hours 190 (14.7%) 43 (5.9%)** 147 (26.3%)
Hospital median (IQR) hours 66 (169) 85 (191)** 42 (132)
Hospital > 167 hours 355 (27.4%) 236 (32.1%)** 119 (21.3%)
Mortality at 30 days or hospital discharge 217 (16.8%) 92 (12.5%)** 125 (22.4%)
Primary diagnosis at discharge (n = 734/552)
Cardiac/circulatory 273 (21.2%) 152 (20.7%) 121 (21.9%)
Poisoning 308 (24.0%) 192 (26.2%)* 116 (21%)
Respiratory 109 (8.5%) 66 (9.0%) 43 (7.8%)
Neurologic 102 (7.9%) 67 (9.1%) 35 (6.3%)
Abdominal 92 (7.2%) 51 (6.9%) 41 (7.4%)
Infection 246 (19.1%) 126 (17.2%)* 120 (21.7%)
Other 156 (12.1%) 80 (10.9%) 76 (13.8%)
ED: Emergency Department, IQR: Interquartile range, ICU: Intensive Care Unit, * p < 0.05, **p < 0.001.
Table 4
Multivariate analysis of factors associated with ICU admission. Hosmer and Lemeshow: 0.359.
Variable Crude OR 95% CI p-value Adjusted OR 95% CI p-value
Age 0.988 0.984–0.993 < 0.001** 0.987 0.981–0.994 < 0.001**
Female gender 0.662 0.531–0.827 < 0.001** 0.656 0.504–0.853 0.002*
Living in care home/institution 0.627 0.471–0.834 0.001*
CCI0p Reference
1–2p 1.682 0.973–2.910 0.063
3–4p 1.335 0.769–2.318 0.304
> 4p 1.195 0.654–2.186 0.562
History if substance abuse 1.720 1.302–2.272 < 0.001**
Psychiatric history 1.353 0.981–1.867 0.066
Seen by doctor prehospital 1.308 1.023–1.673 0.032*
Arriving weekday 20–08 1.515 1.160–1.978 0.002* 1.578 1.161–2.146 0.004*
NEWS2 at arrival 1.070 1.034–1.108 < 0.001** 1.062 1.017–1.109 0.006*
Blood tests outside of reference range 1.347 1.019–1.781 0.037*
Critical care intervention 4.246 3.358–5.368 < 0.001** 4.260 3.253–5.579 < 0.001**
Critical care medication 3.113 2.413–4.015 < 0.001** 2.571 1.972–3.430 < 0.001**
Limitations of medical treatment 0.382 0.264–0.553 < 0.001** 0.397 0.256–0.616 < 0.001**
CCI: Charlson Comorbidity Index, NEWS2: National early warning score 2, OR: odds ratio, CI: confidence interval, *p < 0.05, **p < 0.001.
patient groups, finding good outcome prediction [25,26]. We found higher NEWS2 at arrival to be associated with ICU admission, but both among the ICU and the non-ICU patients large groups did not have NEWS2 > 6. This could be due to one of the calling criteria for the team; Glasgow Coma Scale (GCS) < 9, and the large proportion of patients with acute poisoning. These often have low GCS without im- paired respiration or circulation, thus resulting in a lower NEWS2.
Being managed with critical care interventions and medications in the ED was associated with ICU admission. This is not surprising, as critical care management often needs to be continued over time. The intubation rate, however, is lower than international findings [2,4,18,19]. Two of these studies only included patients being admitted to ICU and with a rather long ED LOS[4,18], so higher intubation rate would be expected. The other two included cardiac arrest patients [2,19], which could impact on intubation rate. The large group of pa- tients with acute poisoning could contribute to lower intubation rate.
Acute poisoning often gives low GCS and a threatened airway, but often other airway interventions than intubation is performed initially. We also found lower use of vasopressors than in other studies examining resuscitation room (RR) patients [2,19], and believe that the many patients with acute poisoning could explain also this.
Triage 1 patients had shorter ED LOS than the mean LOS for ED patients, in particular those admitted to ICU. Shorter RR LOS for triage 1 patients have been found internationally [2,19], but this may not reflect ED LOS, which could be longer. The latter study compared ICU and non-ICU patients and found longer ED stay for ICU-patients, which is the opposite of our results. Others have found longer ED LOS[18,27], also after implementing a system to expedite patient review[4]. Our findings suggest that a multidisciplinary team approach, with a team leader with easy access to ICU beds, expedites care in the ED, and in particular for ICU-patients.
About half of the triage 1 patients were admitted to ICU, which is lower[19], similar[20]and higher[13]than international findings.
This discrepancy could reflect difference in ICU bed capacity. We did not investigate this further, but in Norway there is a relatively high such capacity compared to some other countries[2]. The mortality rate is higher in other studies than ours[2,13,19,20], also if cardiac arrest is excluded [19]. The mortality rate was lower for ICU than non-ICU patients, which has also been found in another study [2]. One ex- planation for the lower ICU admission rate and mortality rate of triage 1 patients compared to international findings could be over-triage in our criteria for team. In contrast to trauma teams, where criteria for team are agreed upon internationally and over-triage is warranted[7], no standard criteria for team or RR admission exist for critically ill medical patients[19]. The many patients with acute poisoning could also con- tribute to these findings.
4.1. Implications
The large group of acute poisoned patients implies that preventive measures are needed. This could reduce the amount of triage 1 patients and thereby resource use, both in the ED and ICU. We suggest that all patients with acute poisoning should undergo screening before dis- charge, and be referred to specialist or primary care as needed.
The gender difference for ICU admittance suggests that ED per- sonnel needs to enhance their knowledge about differences in clinical symptoms of critical illness for men and women, and that more research should focus on these differences.
The recommended NEWS2 score for review by a team with critical care competencies is > 6. Half of the triage 1 patients did not have > 6, suggesting that NEWS2 trigger thresholds should be investigated for a broader population of critically ill general medical patients in the ED.
Our and international findings suggests that critically ill medical patients in the ED is a heterogeneous group, identified and managed in various ways. Identification should nevertheless be as uniform as pos- sible, and warrants further research to find a balance between over- and
undertriage and successive resource use. Management by a multi- disciplinary team with easy access to ICU beds seems promising in re- ducing ED LOS, and should also be tested internationally.
5. Limitations
Our study was performed at a single center, and may not be re- presentative of other centers. The large group of acute poisoned pa- tients has not been found in international studies. The health system in Norway, where patients are referred and on-call subspecialists manage the patients in the ED, complicates direct comparison to health systems with self-referral and emergency physicians.
The retrospective and observational nature of the study may not have entirely excluded confounding factors, but the use of multivariate analysis strengthens our findings. The study being register-based, with data from medical records, also limits the findings. All documentation was not complete, and the variables investigated were limited to the ones present in the register. The register however included all triage 1 patients, and waiving of consent ensured that also the most ill patients were included. This resulted in a quite large total number of included patients.
6. Conclusion
Triage 1 patients were a heterogeneous group, with a mean NEWS2 of 7, ICU admittance rate of 56.8% and mortality rate of 16.8%. One in four was diagnosed with acute poisoning, indicating that preventive measures are needed. They had a short ED length of stay, in particular if admitted to ICU, suggesting that management by a multidisciplinary team may be beneficial. Younger age, male gender and higher NEWS2 on arrival, as well as being managed with critical care interventions and medications, were factors associated with ICU admittance. We re- commend increased further research to ensure uniform identification and initial management of this patient group.
Ethical statement
The study was approved by the Data Protection Officer at OUH, reference number 2016/10319. Consent was waived, as all data were retrieved anonymously from a register, using routinely collected data from medical records.
Funding source
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
CRediT authorship contribution statement
Stine Engebretsen:Conceptualization, Methodology, Formal ana- lysis, Writing - original draft.Stig Tore Bogstrand:Conceptualization, Methodology, Supervision. Dag Jacobsen: Conceptualization, Supervision. Rune Rimstad: Conceptualization, Methodology, Supervision, Project administration.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influ- ence the work reported in this paper.
Acknowledgements
The authors wish to thank Inger Larsen for support in preparing and executing the study, all staff involved in treating the patients and
collecting data, and Valeria Vitelli for statistical advice.
Appendix A. Supplementary data
Supplementary data to this article can be found online athttps://
doi.org/10.1016/j.ienj.2020.100939.
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