Unplanned Post-Anesthesia Care Unit to ICU Transfer Following Cerebral Surgery: A Retrospective Study

Abstract

Background: Unplanned transfer to intensive care unit (ICU) lead to reduced trust of patients and their families in medical staff and challenge medical staff to allocate scarce ICU resources. This study aimed to explore the incidence and risk factors of unplanned transfer to ICU during emergence from general anesthesia after cerebral surgery, and to provide guidelines for preventing unplanned transfer from post-anesthesia care unit (PACU) to ICU following cerebral surgery. Methods: This was a retrospective case-control study and included patients with unplanned transfer from PACU to ICU following cerebral surgery between January 2016 and December 2020. The control group comprised patients matched (2:1) for age (±5 years), sex, and operation date (±48 hours) as those in the case group. Stata14.0 was used for statistical analysis, and p < .05 indicated statistical significance. Results: A total of 11,807 patients following cerebral surgery operations were cared in PACU during the study period. Of the 11,807 operations, 81 unscheduled ICU transfer occurred (0.686%). Finally, 76 patients were included in the case group, and 152 in the control group. The following factors were identified as independent risk factors for unplanned ICU admission after neurosurgery: low mean blood oxygen (OR = 1.57, 95%CI: 1.20–2.04), low mean albumin (OR = 1.14, 95%CI: 1.03–1.25), slow mean heart rate (OR = 1.04, 95%CI: 1.00–1.08), blood transfusion (OR = 2.78, 95%CI: 1.02–7.58), emergency surgery (OR = 3.08, 95%CI: 1.07–8.87), lung disease (OR = 2.64, 95%CI: 1.06–6.60), and high mean blood glucose (OR = 1.71, 95%CI: 1.21–2.41). Conclusion: We identified independent risk factors for unplanned transfer from PACU to ICU after cerebral surgery based on electronic medical records. Early identification of patients who may undergo unplanned ICU transfer after cerebral surgery is important to provide guidance for accurately implementing a patient’s level of care.

Keywords

post-anesthesia care unit risk factors unplanned ICU transfer cerebral surgery

Introduction

The post-anesthesia care unit (PACU) is where surgical patients under general anesthesia receive monitoring and care immediately after surgery and is an intermediate transitional unit to provide care for patients (Truong et al., 2004; Waddle et al., 1998). Postoperative patients were admitted to the PACU and closely cared for by the anesthetic nurse and anesthesiologist until they recovered from the anesthesia effects, and then safely transferred to the ward by the anesthetic nurse (Lee et al., 2014).

Unplanned transfer of patients from the PACU to the ICU after surgery is one of the common critical events in the PACU (Bothner et al., 1999; Bruins et al., 2017). It is well-known that there is a shortage of ICU beds worldwide (Bing-Hua, 2014; Chomton et al., 2021; Coppadoro et al., 2021). Patients without plans to transfer to the ICU before surgery, and their transport time is often delayed compared with those who have reserved ICU beds (Shank et al., 2020). Limited ICU resources and delays in transfers are associated with increased hospitalization costs and morbidity, which can negatively affect their treatment and prognosis (Hourmant et al., 2021).

The risk of complications in cerebral surgery is high, and patients are prone to unpredictable changes after surgery (Franko et al., 2018; Zhou et al., 2018). In previous clinical practice, patients were usually admitted to the ICU for 12–24 hours of observation and treatment after cerebral surgery; most of them were transferred to the general ward the next day (de Almeida et al., 2018; Ehlers et al., 2020; Ziai et al., 2003). With the recent advancement in medicine and surgery, many studies have shown that patients do not need to be routinely transferred to the ICU after cerebral surgery; patients should be strictly evaluated and then judged whether they need to be transferred to the ICU for treatment and care, even for emergency cerebral surgery (Bonow et al., 2019; Henker et al., 2017; Laan et al., 2020; Lonjaret et al., 2017; Taylor et al., 1995; Volovici et al., 2019). Identifying postoperative patients requiring ICU admission is a challenging but necessary daily task. In the clinical setting, some patients undergoing cerebral surgery require unplanned ICU transfer despite thorough evaluation (Bui et al., 2011; Laan et al., 2020). Therefore, it is vital to improve the positive rate of identification, especially for those patients assessed as transferred to the ward but transferred to the ICU after surgery.

The identification of risk factors is critical to improving preoperative assessment. Therefore, this study aimed to examine preoperative, surgical, and PACU data for potential risk factors for unplanned ICU transfer, which has implications for improving the management and postoperative resource allocation of those vulnerable patients.

Materials and Methods

Design

This was an observational, retrospective, case-control study in which each case was matched with 2 controls. Unplanned transfer to the ICU was defined as no plan to transfer the patient to the ICU before starting anesthesia (i.e., the decision to transfer to ICU was made during or after surgery).

Setting

The study was conducted at the Affiliated Hospital of Jining Medical University, a Shandong Provincial Regional Medical Center with 82 clinical departments, 9 intensive care units, and 3028 beds. We retrospectively reviewed data of patients admitted to PACU after cerebral surgery. Those scheduled to be transferred to the ICU were excluded. Because for patients with scheduled postoperative intensive care admission, the general practice is to transfer them directly from the operating room to the ICU. The study followed the guidelines of the Declaration of Helsinki. The Ethics committee/IRB of Affiliated hospital of Jining Medical University gave ethical approval for this work (approval number: 2021-07-C009). A written informed consent was obtained from all patients upon transfer to the Affiliated Hospital of Jining Medical University.

Cases

Electronic records were used to selected patients ≥18 years of age who had undergone cerebral surgery from January 1, 2016, to December 31, 2020, and who had an unplanned transfer to ICU after cerebral surgery. The researchers reviewed the completeness and accuracy of each case.

Controls

For each patient with an unplanned transfer to ICU after cerebral surgery, 2 controls of the same type of surgery were randomly selected from the electronic medical records. Patients in the control group were matched (2:1) for sex, age (±5 years), and operation date (±48 hours). Researchers reviewed the data from patients in the control group to ensure the integrity of the included patients’ medical records.

Data Collection

All data extraction and data input were completed by members of the research team who have been trained in scientific research. The following demographic and current diseases data were collected: age, sex, smoking history, drinking history, and the presence of lung disease, hypertension, diabetes, hyperlipidemia, and/or cardiovascular disease. Patients who had quit smoking and drinking were still categorized into the smoking and drinking groups, respectively. Data on laboratory indicators, such as platelet volume, hemoglobin, total protein, albumin, uric acid, and blood glucose levels, were also collected. Laboratory indicators were measured on an empty stomach the day before the operation.

Surgical and anesthetic data, including the type of surgery, operation duration, blood loss volume, fluid replacement volume, urine volume, blood transfusion volume, heart rate, percentage of oxygen saturation (SpO₂), removal of tracheal intubation, reintubation, and PACU duration, were collected. Volume of blood loss, of fluid replacement, and of urine were obtained over the entire operation period; heart rate and blood oxygen were obtained immediately after leaving the operating room.

Statistical Analysis

Statistical analysis was performed using Stata version 14.0 (StataCorp, College Station, TX, USA). Categorical data are described in terms of frequencies and percentages. The normality of continuous data was evaluated using the Kolmogorov–Smirnoff test. Normally distributed data are described in terms of means ± standard deviation, and non-normally distributed data in terms of medians and interquartile ranges. The T-test, Mann-Whitney test and the Chi-squared test were used to compare the characteristics between the case and the control groups. Logistic regression analysis was used to identify independent risk factors for unplanned ICU transfer of cerebral surgery patients. Statistical significance was set atp < .05.

Results

Patient Characteristics

From January 2016 to December 2020, 11,807 patients received recovery care after cerebral surgery in the PACU, of which 81 patients had unplanned ICU transfer (incidence of unplanned ICU transfer, 0.686%). Among unplanned ICU transfer patients, 3 patients under the age of 18 years and 2 patients who lacked relevant data were excluded. Finally, 228 patients were enrolled in this study: 76 in the case group and 152 in the control group (Figure 1).

Figure 1.

Study flow diagram.

We compared the preoperative, intraoperative, and PACU characteristics of cases receiving routine postoperative care, and those with unplanned ICU transfer. There were a number of significant differences between the 2 patient populations (Table 1).

Table 1.

Patient Characteristics.

Variables	Cases (n = 76)	Control (n = 152)	p-value
Demographic and current diseases
Age, y (mean (S.D.))	57.05 (11.63)	56.37 (11.45)	.673
Male (n, %)^a	50 (65.79)	100 (65.79)	1.000
Smoking (n, %)^a	60 (78.95)	96 (63.16)	.016
Drinking (n, %)^a	60 (78.95)	92 (60.53)	.005
Lung disease (n, %)^a	30 (36.84)	21 (13.18)	<.001
Hypertension (n, %)^a	36 (47.37)	58 (38.16)	.183
Diabetes (n, %)^a	8 (10.53)	12 (7.89)	.508
Hyperlipidemia (n, %)^a	2 (2.63)	2 (1.32)	.476
Cardiovascular disease (n, %)^a	16 (21.05)	12 (7.89)	.004
Laboratory examination
Hemoglobin, g/L (median (IQR))	134.5 (123–145)	136 (131–145)	.172
Total protein, g/L (median (IQR))	64 (59–69)	67 (64–72)	<.001
Albumin, g/L (median (IQR))	40 (36–43)	43 (40–46)	<.001
Blood glucose, mmol/L (median (IQR))	6.55 (4.60–7.40)	5.13 (4.60–5.40)	.002
Uric acid, umol/L (median (IQR))	220 (158–272)	266 (218–334)	<.001
Surgical and anesthesia
Type of surgery (n, %)^a			<.001
Emergency surgery	23 (30.26)	14 (9.21)
Elective surgery	53 (69.74)	138 (90.79)
Operation duration, min (median (IQR))	218 (120–270)	163 (122–247)	.0439
Fluid replacement volume, mL (median (IQR))	2,200(1,100–2700)	2100 (1600–2700)	.693
Urine volume, mL (mean (S.D.))	713 (705)	719 (461)	.933
Blood loss volume, mL (median (IQR))	100 (20–200)	50 (30–100)	.013
Blood transfusion (n, %)^a	24 (31.58)	15 (9.87)	<.001
Heart rate, times/min (median (IQR))	78 (70–85)	73 (62–80)	<.001
SpO₂, % (median (IQR))	98 (97–100)	99 (99–100)	<.001
Removal of tracheal intubation (n, %)^a	46 (60.53)	152 (100)	<.001
Re-intubation (n, %)^a	6 (7.89)	0	<.001
PACU duration, min (median (IQR))	77 (55–109)	38 (30–55)	<.001
ICU days, min (median (IQR))	10.84 (3–13)	0	<.001
Death (n, %)^a	10 (13.16)	0	<.001

Note. ICU = intensive care unit; SD = Standard deviation; IQR = interquartile range; SpO₂ = blood oxygen saturation; PACU = Post-anesthesia care unit.

Data reported as mean (S.D.) comparison with the t-test.

Data reported as median (IQR) comparison with the Mann–Whitney test.

^aComparison with the chi-square test.

Compared with routine postoperative care, patients who with unplanned ICU transfer were more likely to have a history of smoking and alcohol use; a higher combined rate of pulmonary and cardiovascular disease; lower levels of total protein, albumin, and uric acid; and higher blood glucose levels. They were more likely to undergo emergency surgery, had longer procedures, lost more blood, and receive blood transfusions. They had higher mean heart rates, lower oxygen saturation levels, the possibility of removal of endotracheal tube was lower with higher risk of reintubation, and longer stay in PACU.

Risk Factors for Unplanned ICU Transfer

In univariate analysis, matching factors were removed, such as age, gender, and consequential factors, including ICU days and number of deaths. We used a simple logistic regression model to analyze the risk factors that may affect postoperative unplanned ICU transfer of patients with cerebral surgery. Univariate analysis showed that there were significant differences in 13 variables including smoking, drinking, lung disease, cardiovascular disease, total protein, and albumin between the 2 groups (p < .05) (Table 2).

Table 2.

Univariate Analysis of Unplanned ICU Transfer.

Variables	Odds Ratio (95% CI)	p-value
Smoking	2.18 (1.15–4.16)	.017
Drinking	2.45 (1.29–4.64)	.006
Lung disease	3.64 (1.89–7.00)	<.001
Hypertension	1.46 (0.83–2.54)	.184
Diabetes	1.37 (0.54–3.51)	.509
Hyperlipidemia	2.03 (0.28–14.68)	.484
Cardiovascular disease	3.11 (1.39–6.97)	.006
Hemoglobin	1.01 (0.99–1.03)	.228
Total protein	1.06 (1.02–1.11)	.002
Albumin	1.12 (1.06–1.19)	<.001
Blood glucose	1.70 (1.32–2.19)	<.001
Uric acid	1.01 (1.00–1.01)	<.001
Emergency surgery	4.28 (2.05–8.93)	<.001
Operation duration	1.00 (0.99–1.00)	.099
Fluid replacement volume	1.00 (0.99–1.00)	.974
Urine volume	1.00 (0.99–1.00)	.933
Blood loss volume	1.00 (1.00–1.00)	.004
Blood transfusion	4.22 (2.05–8.66)	<.001
Heart rate	1.04 (1.01–1.07)	<.001
SpO₂	1.55 (1.26–1.90)	<.001

Note. ICU = Intensive Care Unit; CI = confidence interval; SpO₂ = Blood oxygen saturation.

All variables at p < .1 in univariate analysis were further evaluated by multivariate logistic regression to assess independent risk factors for unplanned ICU transfer. Of the initial 20 variables, 7 remained in the final multivariate model. For example, lung disease: Odds Ratio [OR] = 2.64, 95% Confidence Interval [CI] = 1.06 - 6.60; albumin: Odds Ratio [OR] = 1.14, 95% CI = 1.03 - 1.25; emergency surgery: Odds Ratio [OR] = 3.08, 95% Confidence Interval [CI] = 1.07 - 8.87; blood transfusion: Odds Ratio [OR] = 2.78, 95% Confidence Interval [CI] = 1.02 - 7.58 (Figure 2) (Table 3).

Figure 2.

Forest plot for multivariate analysis of unplanned ICU transfers.

Table 3.

Multivariate Analysis of Unplanned ICU Transfer.

Variables	Odds Ratio (95% CI)	Z	p-value
Lung disease	2.64 (1.06–6.60)	2.08	.037
Albumin	1.14 (1.03–1.25)	2.65	.008
Blood glucose	1.71 (1.21–2.41)	3.08	.002
Emergency surgery	3.08 (1.07–8.87)	2.09	.037
Blood transfusion	2.78 (1.02–7.58)	1.99	.046
Heart rate	1.04 (1.00–1.08)	2.01	.045
SpO₂	1.57 (1.20–2.04)	3.32	.001

Note. ICU = intensive care unit; CI = confidence interval; SpO₂ = Blood oxygen saturation.

Discussion

This retrospective study aimed to explore the incidence and influencing factors of unplanned ICU transfer from PACU in patients undergoing cerebral surgery. Our study found that the independent risk factors for unplanned transfer from PACU to ICU in patients after brain surgery were the presence of lung disease, low albumin value, high blood sugar value, emergency surgery type, blood transfusion during operation, high heart rate value, and low blood oxygen saturation value. To the best of our knowledge, this is the first study to investigate the risk factors for unplanned ICU transfer from the PACU in patients undergoing cerebral surgery. This present study serves a reference for our next step in developing an electronic medical record system-based, large data-based intraoperative prediction model for unplanned ICU transfer of surgical patients.

We investigated 11,807 patients admitted to the PACU after cerebral surgery and found that 81 had unplanned ICU transfers, with an incidence rate of 0.686%. A study by Hines et al. involved 18,437 patients in the PACU recorded a total of 186 unplanned ICU admissions (1.01%) (Hines et al., 1992), and a study by Kluger et al. investigated 49,532 patients who were admitted to the PACU for observation and found that 184 had unplanned ICU transfers (0.4%) (Kluger & Bullock, 2002). Overall, the incidence of unplanned ICU transfer in PACU patients ranged from 0.4 to 1.01%. This is similar to the previously reported incidence of unplanned postoperative ICU transfer in adults (0.1–0.9%) (Da et al., 2013; Meziane et al., 2017; Petersen et al., 2017).

Our study showed that 76 patients with unplanned ICU transfer from the PACU had an average ICU length of stay of 10.84 days and an in-hospital mortality rate of 13.16%. A study by Mou et al. found that 40 patients who were unplanned transferred to the ICU by postoperative intermediate care units had an average ICU length of stay of 2.5 days and an in-hospital mortality rate of 12.5% (Fujii et al., 2016). Wanderer et al. reported that the average length of ICU stay was 5 days, and the in-hospital mortality rate was 5.6% in 4847 patients who were unplanned transferred to the ICU from the PACU (Wanderer et al., 2013). In contrast, the mean ICU length of stay for patients who were unplanned to be transferred to the ICU in our hospital was slightly higher than that in the other 2 studies, and the in-hospital mortality rate was similar to that in a certain study. This may be related to the different types of procedures that patients experience.

Some patients developed unplanned ICU transfers due to severe cerebral edema, bleeding, and malignant arrhythmias, and others developed unexplained cognitive dysfunction such as delirium, intense agitation, or somnolence. Six of the patients who developed unplanned ICU transfers required emergency intubation (7.89%), corresponding to 0.05% of all patients during the study period. Rose et al. found that of 21,457 patients under general anesthesia, 0.1% (n = 22) required emergency reintubation in the PACU (Rose, 1996). Bruins et al. reported that the emergency intubation rate in the PACU of their hospital was also 0.05% (25 out of 49,532 procedures) (Bruins et al., 2017). Peskett et al. found that patients required reintubation was 13 (0.08%) (11 out of 266 procedures) postoperatively (Peskett, 1999). In summary, the reintubation rate in PACU centers is approximately between 0.05 and 0.08.

Clinical and Research Implications

Unplanned postoperative ICU transfer is worse than planned postoperative ICU transfer for both patients and medical staff. Spontaneous ICU transfer after surgery challenges medical staff to adjust the allocation of ICU resources and psychologically burdens patients and their families. Furthermore, unplanned transfer to ICU usually lead to longer hospital stay, higher medical bills, and worse outcomes (Gold et al., 2015; Liu et al., 2012). Spontaneous ICU transfer also decrease the trust of patients and their families in medical staff (Jennerich et al., 2020).

Exploring the risk factors of unplanned transfer to ICU is of great significance to improve the awareness of both medical staff and patients and help them take preventive measures for potential risk factors. In the future, we hope to preoperatively stratify patients based on these factors to predict which patients will need a higher level of postoperative care. By preventing or predicting the need for unplanned transfer to ICU after surgery, we can better allocate ICU resources, reduce the morbidity and mortality of vulnerable patients, and ultimately improve the overall safety of cerebral procedures.

Strengths and Limitations

The study had limitations. First, this study was retrospective in nature; hence, all data collected were from cases and medical records. Second, the complete outcome of the patients was unclear, and further longitudinal studies may be needed. Third, was a single-centered study, and that the results may have been affected by regional characteristics, hospital policies, and types of surgical procedures provided by the hospital. Finally, this study aimed to explore risk factors; thus, the results might have been affected by the included variables.

Conclusion

The study identified independent risk factors for unplanned transfer from PACU to ICU after cerebral surgery by screening demographic data, laboratory tests, surgical and anesthesia-related information of patients undergoing cerebral surgery. Combining the results of the study with clinical practice in PACU can identify the possibility of unplanned ICU transfer from cerebral surgery patients who do not have an ICU appointment, providing guidance for accurately identifying the patient’s level of care.

Footnotes

Author Contributions

Qinqin,Cao contributed to conception and design contributed to acquisition, analysis, and interpretation drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy. Chengjuan, Fan contributed to conception contributed to acquisition and analysis drafted manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy. Wei, Li contributed to acquisition drafted manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy. Shuling, Bai contributed to analysis critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy. Hemin, Dong contributed to acquisition critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy. Haihong, Meng contributed to design contributed to analysis and interpretation drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was supported by the Jining Key Research and Development Program (Social People’s Livelihood) project (study code: 2020 YXNS003) hosted by the head nurse Haihong Meng and the “Miao Pu” program of the Affiliated Hospital of Jining Medical University (study code: MP-MS-2022-005) hosted by Hemin Dong.

ORCID iD

Haihong Meng

References

Bing-Hua

Y. U.

(2014). Delayed admission to intensive care unit for critically surgical patients is associated with increased mortality [Journal Article]. American Journal of Surgery, 208(2), 268–274. http://doi.org/10.1016/j.amjsurg.2013.08.044

Bonow

R. H.

Quistberg

Rivara

F. P.

Vavilala

M. S.

(2019). Intensive care unit admission patterns for mild traumatic brain injury in the USA [Journal Article; Research Support, Non-U.S. Gov't]. Neurocritical Care, 30(1), 157–170. http://doi.org/10.1007/s12028-018-0590-0

Bothner

Georgieff

Schwilk

(1999). The impact of minor perioperative anesthesia-related incidents, events, and complications on postanesthesia care unit utilization [Journal Article; Research Support, Non-U.S. Gov't]. ANESTHESIA AND ANALGESIA, 89(2), 506–513. http://doi.org/10.1097/00000539-199908000-00049

Bruins

S. D.

Leong

P. M.

S. Y.

(2017). Retrospective review of critical incidents in the post-anaesthesia care unit at a major tertiary hospital [Journal Article]. Singapore Medical Journal, 58(8), 497–501. http://doi.org/10.11622/smedj.2016126

Bui

J. Q.

Mendis

R. L.

van Gelder

J. M.

Sheridan

M. M.

Wright

K. M.

Jaeger

(2011). Is postoperative intensive care unit admission a prerequisite for elective craniotomy? [Journal article]. Journal of Neurosurgery, 115(6), 1236–1241. http://doi.org/10.3171/2011.8.JNS11105

Chomton

Marsac

Deho

Maroni

Geslain

Frannais-Haverland

Nicolas-Robin

Levy

Assaker

Atger

Avril

Baylet-Fernandes

Benzouid

Bernard

Bernheim

Berthaud

Beyler

Bidaud

Bidault

(2021). Transforming a paediatric ICU to an adult ICU for severe Covid-19: Lessons learned [Journal article]. European Journal of Pediatrics, 180(7), 2319–2323. http://doi.org/10.1007/s00431-021-03992-6

Coppadoro

Benini

Fruscio

Verga

Mazzola

Bellelli

Carbone

Mulinacci

Soria

Noe

Beck

Di Sciacca

Ippolito

Citerio

Valsecchi

M. G.

Biondi

Pesci

Bonfanti

Gaudesi

Bellani

Foti

(2021). Helmet CPAP to treat hypoxic pneumonia outside the ICU: An observational study during the COVID-19 outbreak [Journal article; observational study; research support, non-U.S. Gov't]. Critical Care, 25(1), 80. http://doi.org/10.1186/s13054-021-03502-y

S. P.

de Aguiar

V. E.

Fonseca

M. C.

(2013). Risk factors and outcomes of unplanned PICU postoperative admissions: A nested case-control study [Journal article]. Pediatric Critical Care Medicine: a Journal of the Society of Critical Care, 14(4), 420–428. http://doi.org/10.1097/PCC.0b013e3182720fab

de Almeida

C. C.

Boone

M. D.

Laviv

Kasper

B. S.

Chen

C. C.

Kasper

E. M.

(2018). The utility of routine intensive care admission for patients undergoing intracranial neurosurgical procedures: A systematic review [Journal article; systematic review]. Neurocritical Care, 28(1), 35–42. http://doi.org/10.1007/s12028-017-0433-4

10.

Ehlers

L. D.

Pistone

Haller

S. J.

Will

R. J.

Surdell

(2020). Perioperative risk factors associated with ICU intervention following select neurosurgical procedures [Journal Article]. Clinical Neurology and Neurosurgery, 192, 105716. http://doi.org/10.1016/j.clineuro.2020.105716

11.

Franko

L. R.

Hollon

Linzey

Roark

Rajajee

Sheehan

Teig

Hervey-Jumper

Heth

Orringer

Williamson

C. A.

(2018). Clinical factors associated with ICU-Specific care following supratentoral brain tumor resection and validation of a risk prediction score [Journal Article; Observational Study]. Critical Care Medicine, 46(8), 1302–1308. http://doi.org/10.1097/CCM.0000000000003207

12.

Fujii

Uchino

Takinami

(2016). Life-threatening complications after postoperative intermediate care unit discharge: A retrospective, observational study [Journal article; observational study]. European Journal of Anaesthesiology, 33(1), 22–27. http://doi.org/10.1097/EJA.0000000000000321

13.

Gold

C. A.

Mayer

S. A.

Lennihan

Claassen

Willey

J. Z.

(2015). Unplanned transfers from hospital wards to the neurological intensive care unit [Journal Article]. Neurocritical Care, 23(2), 159–165. http://doi.org/10.1007/s12028-015-0123-z

14.

Henker

Schmelter

Piek

(2017). [Complications and monitoring standards after elective craniotomy in Germany] [Journal Article]. Anaesthesist, 66(6), 412–421. http://doi.org/10.1007/s00101-017-0291-7

15.

Hines

Barash

P. G.

Watrous

O'Connor

(1992). Complications occurring in the postanesthesia care unit: A survey [Journal article]. Anesthesia and Analgesia, 74(4), 503–509. http://doi.org/10.1213/00000539-199204000-00006

16.

Hourmant

Mailloux

Valade

Lemiale

Azoulay

Darmon

(2021). Impact of early ICU admission on outcome of critically ill and critically ill cancer patients: A systematic review and meta-analysis [Journal article]. Journal of Critical Care, 61(1), 82–88. http://doi.org/10.1016/j.jcrc.2020.10.008

17.

Jennerich

A. L.

Hobler

M. R.

Sharma

R. K.

Engelberg

R. A.

Curtis

J. R.

(2020). Unplanned admission to the ICU: A qualitative study examining family member experiences [Journal article; research support, extramural; research support, non-U.S. Gov't]. Chest, 158(4), 1482–1489. http://doi.org/10.1016/j.chest.2020.05.554

18.

Kluger

M. T.

Bullock

M. F.

(2002). Recovery room incidents: A review of 419 reports from the anaesthetic incident monitoring study (AIMS) [Journal article; research support, non-U.S. Gov't]. Anaesthesia, 57(11), 1060–1066. http://doi.org/10.1046/j.1365-2044.2002.02865.x

19.

Laan

M. T.

Roelofs

Van Huet

Adang

Bartels

(2020). Selective intensive care unit admission after adult supratentorial tumor craniotomy: Complications, length of stay, and costs [journal article; research support, Non-U.S. Gov't]. Neurosurgery, 86(1), E54–E59. http://doi.org/10.1093/neuros/nyz388

20.

Lee

Tran

Mayo

N. E.

Carli

Feldman

L. S.

(2014). What does it really mean to "recover" from an operation? [Journal article research support, non-U.S. Gov't; review]. Surgery, 155(2), 211–216. http://doi.org/10.1016/j.surg.2013.10.002

21.

Liu

Kipnis

Rizk

N. W.

Escobar

G. J.

(2012). Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system [Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S.]. Journal of Hospital Medicine, 7(3), 224–230. http://doi.org/10.1002/jhm.964

22.

Lonjaret

Guyonnet

Berard

Vironneau

Peres

Sacrista

Ferrier

Ramonda

Vuillaume

Roux

F. E.

Fourcade

Geeraerts

(2017). Postoperative complications after craniotomy for brain tumor surgery [Journal Article; Observational Study]. Anaesth Crit Care Pain Med, 36(4), 213–218. http://doi.org/10.1016/j.accpm.2016.06.012

23.

Meziane

J. S.

ElKoundi

Bensghir

Baba

Ahtil

Aboulaala

Balkhi

Haimeur

(2017). Unplanned intensive care unit admission following elective surgical adverse events: Incidence, patient characteristics, preventability, and outcome [Journal Article]. Indian J Crit Care Med, 21(3), 127–130. http://doi.org/10.4103/ijccm.IJCCM_428_16

24.

Peskett

M. J.

(1999). Clinical indicators and other complications in the recovery room or postanaesthetic care unit [Journal Article]. Anaesthesia, 54(12), 1143–1149. http://doi.org/10.1046/j.1365-2044.1999.01077.x

25.

Petersen

T. M.

Ludbrook

G. L.

Flabouris

Seglenieks

Painter

T. W.

(2017). Developing models to predict early postoperative patient deterioration and adverse events [Journal Article]. Anz Journal of Surgery, 87(6), 457–461. http://doi.org/10.1111/ans.13874

26.

Rose

D. K.

(1996). Recovery room problems or problems in the PACU [Journal article; review]. Can J Anaesth, 43(5 Pt 2), R116–R128. http://doi.org/10.1007/BF03011674

27.

Shank

C. D.

Erickson

N. J.

Miller

D. W.

Lindsey

B. F.

Walters

B. C.

(2020). Reserved bed program reduces neurosciences intensive care unit capacity strain: An Implementation Study [Journal Article]. Neurosurgery, 86(1), 132–138. http://doi.org/10.1093/neuros/nyz024

28.

Taylor

W. A.

Thomas

N. W.

Wellings

J. A.

Bell

B. A.

(1995). Timing of postoperative intracranial hematoma development and implications for the best use of neurosurgical intensive care [Journal Article]. Journal of Neurosurgery, 82(1), 48–50. http://doi.org/10.3171/jns.1995.82.1.0048

29.

Truong

Moran

J. L.

Blum

(2004). Post anaesthesia care unit discharge: A clinical scoring system versus traditional time-based criteria [Comparative study; Journal article]. Anaesthesia and Intensive Care, 32(1), 33–42. http://doi.org/10.1177/0310057X0403200106

30.

Volovici

Ercole

Citerio

Stocchetti

Haitsma

I. K.

Huijben

J. A.

Dirven

van der Jagt

Steyerberg

E. W.

Nelson

Cnossen

M. C.

Maas

Polinder

Menon

D. K.

Lingsma

H. F.

(2019). Intensive care admission criteria for traumatic brain injury patients across europe [Journal article; multicenter study; observational study; research support, non-U.S. Gov't]. Journal of Critical Care, 49, 158–161. http://doi.org/10.1016/j.jcrc.2018.11.002

31.

Waddle

J. P.

Evers

A. S.

Piccirillo

J. F.

(1998). Postanesthesia care unit length of stay: Quantifying and assessing dependent factors [Journal article]. Anesthesia and Analgesia, 87(3), 628–633. http://doi.org/10.1097/00000539-199809000-00026

32.

Wanderer

J. P.

Anderson-Dam

Levine

Bittner

E. A.

(2013). Development and validation of an intraoperative predictive model for unplanned postoperative intensive care [Journal Article; Validation Study]. Anesthesiology, 119(3), 516–524. http://doi.org/10.1097/ALN.0b013e31829ce8fd

33.

Zhou

Y. T.

Tong

D. M.

Wang

S. D.

B. W.

Yang

C. X.

(2018). Acute spontaneous intracerebral hemorrhage and traumatic brain injury are the most common causes of critical illness in the ICU and have high early mortality [Journal Article]. BMC Neurology, 18(1), 127. http://doi.org/10.1186/s12883-018-1127-z

34.

Ziai

W. C.

Varelas

P. N.

Zeger

S. L.

Mirski

M. A.

Ulatowski

J. A.

(2003). Neurologic intensive care resource use after brain tumor surgery: An analysis of indications and alternative strategies [Journal article; research support, U.S. Gov't, P.H.S.]. Critical Care Medicine, 31(12), 2782–2787. http://doi.org/10.1097/01.CCM.0000098860.52812.24