Clinical Course and Outcomes of Brain Tumor Patients Admitted to Medical Intensive Care Unit: A Descriptive Analysis

Article information

J Neurointensive Care. 2024;7(2):56-62
Publication date (electronic) : 2024 October 31
doi : https://doi.org/10.32587/jnic.2024.00794
1Consultant, Critical Care Medicine, Soni Hospital, Jaipur, India
2Department of Neurosurgery, SMS Medical College, Jaipur, India
3Department of Preventive and Social Medicine, SN Medical College, Jodhpur, India
Corresponding author: Anisha Beniwal MD, Consultant, Critical Care Medicine, Soni Hospital, Kanota Bagh, 38, Jawahar Lal Nehru Marg, Rambagh, Jaipur, Rajasthan 302004, India Tel: +91-9461837436 Email: dranishadara2889@gmail.com
Received 2024 June 4; Revised 2024 August 9; Accepted 2024 September 9.

Abstract

Background

There is a shortage of data on brain tumor patients admitted in to intensive care unit (ICU) from developing countries. We aimed to assess the clinical course and 30-day mortality with factors affecting the mortality of brain tumor patients who were admitted to medical ICU.

Methods

This study was a single-centre retrospective observational cohort study and was conducted in a medical ICU of a tertiary care center in India. We included 42 patients admitted in to the medical oncology ICU over 3 years. Data regarding demographics, baseline characteristics, clinical and laboratory data, need for organ support, and 30-day mortality were collected. Factors associated with increased mortality in these patients were determined.

Results

Overall 30-day mortality was 30.95%. The most common indication for ICU admission was altered sensorium (57.1%) followed by sepsis (23.8%). Age [odds ratio, OR: 0.843 (95% confidence interval, CI: 0.721–0.986)], and need for invasive mechanical ventilator (IMV) support [OR: 484.62 (95% CI: 2.707–8676.02)] or vasopressor support [OR: 523.83 (95% CI: 2.12– 3,023.13)] were directly associated with 30-day mortality. Severity indices such as Sequential Organ Failure Assessment (SOFA) score, SAPS II (Simplified Acute Physiology Score II), and Acute physiology and chronic health evaluation II (APACHE II), APACHE III and APACHE IV scores were higher in non-survivors than survivors.

Conclusion

Advancing age and need for IMV or vasopressor support may be associated with worse prognosis in brain tumor patients admitted in to ICU. A scoring system could be used along with clinical judgement to triage brain tumor patients for ICU admission.

INTRODUCTION

A considerable improvement in outcomes is seen in critically ill cancer patients because of recent advances in the medical and surgical treatment of cancer patients1,2). This improvement in prognosis leads to an increased number of patients living with cancer causing an increasing number of patients requiring hospitalizations and intensive care unit (ICU) admission3,4). Admission of critically ill patients to an ICU requires vast professional, technological, and costly resources. However, at present, available ICU beds are scarce, especially in developing countries, it is important to predict cancer patients who may benefit from ICU treatment5,6).

Brain tumor patients may require ICU admission due to raised intracranial pressure, developing brain edema, epilepsy or complications because of tumor therapy7). A lot of studies are available on the clinical course of patients with solid tumors and hematological malignancies admitted to an ICU, but there is a shortage of data on primary brain tumor patients8). Most of the studies in critically ill brain tumor patients are done in developed countries, and we didn’t find any study about clinical characteristics, outcomes, and factors associated with increased risk of mortality in primarily brain tumor patients admitted in to ICU from developing countries.

Therefore, we aimed to assess the clinical course and 30-day mortality with factors affecting the mortality of brain tumor patients who were admitted to the medical ICU. As far as we know, this is the first study to evaluate primarily the brain tumor population admitted in to medical ICU in India.

METHODS

This study was a single-centre retrospective observational cohort study and was conducted in a medical ICU of a tertiary care center in India. A consent waiver was collected from the institutional ethics committee. The data were collected and analyzed from the records of adult brain tumor patients who were admitted in to the medical oncology ICU from January 2018 to December 2021.

We evaluated all adult patients (>18 years) with a definite diagnosis of brain tumor at ICU admission. If the patient was admitted multiple times to the ICU during his/her hospital stay, only the first admission was included in the study. Patients who had ICU stay of less than 24 h, post-operative neurosurgery patients, those in complete cancer remission for more than 5years, and those admitted from or discharged to another ICU, were excluded from the study. Data regarding demographics, baseline characteristics, clinical and laboratory data including admission of patients from the emergency room (ER) or ward, tumor characteristics, reasons for ICU admission, need for vasopressor, invasive mechanical ventilation (IMV) support and renal replacement therapy, and 30-day mortality were collected in a pre-designed proforma.

Sequential Organ Failure Assessment (SOFA) score, SAPS II (Simplified Acute Physiology Score II), and Acute physiology and chronic health evaluation II (APACHE II), APACHE III, and APACHE IV scores were calculated from the data obtained on the day of ICU admission. The data, required to calculate various scores, was collected.

Statistical analysis

The collected data were transformed into variables, coded, and then entered in a Microsoft Excel sheet. Data were analyzed and statistically evaluated by using the IBM SPSS-PC-25 version. Quantitative variables were expressed in mean ± SD and analysed by using a way ANOVA test. Qualitative variables were expressed as frequency and percentage and analyzed by using chi-square test or Fisher’s exact test, as appropriate. Multivariable logistic regression analysis was done to identify factors associated with 30-day mortality. The odds ratio (OR) along with its 95% confidence intervals (CIs) were calculated. The P value less than 0.05 was considered statistically significant.

RESULTS

Data were analyzed from 42 patients who fulfilled the inclusion criteria. In our study, 22 patients (52.4%) were admitted from ER and 20 patients (47.6%) were shifted from ward. There was no statistically significant difference among survivors and nonsurvivors according to the type of admission (p = 0.644) (Fig. 1). The two most common indications for ICU admission were altered mental status (57.1%) and sepsis (23.8%) as given in Fig. 2. There was a statistically significant difference in reasons for ICU admission between survivors and nonsurvivors (P value = 0.004). Other causes of ICU admission were respiratory distress, gastrointestinal bleeding, cardiac arrest survivor, acute kidney injury.

Fig. 1.

Distribution of study subjects according to type of admission.

Fig. 2.

Distribution of study subjects according to reason for intensive care unit admission.

As shown in table 1, mean age (SD) of study group was 58.52 (13.06) years and out of them, 50% were females. Comorbidities such as hypertension and diabetes mellitus were present in 20 patients (47.6%). Metastasis from brain to other parts of the central nervous system, such as spinal cord was seen in 5 patients (11.9%). The mean time for ICU admission after diagnosis of malignancy was 26.62 ± 31.48 months and 36 (85.7%) patients were having a history of tumor resection. Mean GCS, serum sodium, serum calcium, arterial lactate, and procalcitonin were 11.62 (3.52), 132.4 (6.81), 8.32 (0.88), 2.53 (3.22) and 4.88 (16.85) respectively. Severity indices were calculated upon hospital admission and found that mean APACHE II, III, IV, SAPS II, and SOFA scores were 18.1 ± 7.1, 23.35 ± 23.86, 58.88 ± 26.87, 33 ± 14.33, and 6.1 ± 2.97 respectively. The mean duration of ICU stay was 7.5 days and hospital stay was 14.71 days. Overall, the 30-day mortality rate was 30.95%. Out of a total of 42 patients, 31% of patients required IMV support and 21.4% of patients received vasopressor support. No patient required renal replacement therapy.

Baseline parameters and outcome variables

Evidence of infection was found in (36 patients) 85% of patients. out of them most common infection was found to be urinary tract infection (80%), followed by bloodstream infection in 13 patients (30.9%) and respiratory tract infection in 8 patients (19%). 14.28% of patients had fungal infections (mainly candida infection). The most common organism was found to be gram-negative bacteria in the urinary and respiratory tract. Blood stream infection was mostly caused by gram-positive bacteria. 38 % of patients with urinary tract infections had mix gram-positive bacteria, gram-negative bacteria, and fungal infections.

Univariate analysis showed that factors associated with higher 30-day mortality were age, higher lactate, procalcitonin, and need for IMV support or vasopressor support and higher APACHE II, III, IV, SAPS II, SOFA SCORE during ICU stay as seen in table 2. Out of them only 3 factors, Age and need for IMV support or vasopressor support, were found to be directly associated with 30-day mortality in multivariate analysis (Table 3).

Comparison of baseline parameters and outcome variables between survivors and non-survivors in brain tumor patients admitted in ICU using univariate analysis

Multivariate analysis for 30-day mortality in brain tumor patients admitted to intensive care unit

DISCUSSION

This is the first descriptive analysis of clinical course and outcomes in brain tumor patients admitted to a medical ICU in India and compared various scoring systems to guide decision-making. Our 30-day mortality rate was 30.95%. It corresponded to the ICU mortality rate in cancer patients and hospital mortality rate in patients with non-oncological diseases as described by previous studies9,10). It is lower than studies done on primary brain tumors by Maxen et al. (73%), Bernhard et al. (60%)7,11). It is higher than studies on PBT done by Kang et al. (20%), and 2 French studies (22–23%)10,12,13). This might be because the patients with primary brain tumors in previously published cohorts were less severely ill than patients in our cohort as they received less vasopressor support and had lower apache scores and lower hospital stay, in contrast to our study.

A most common reason for ICU admission in brain tumor patients was altered mental status similar to Kang et al. Altered mental status can be because of developing brain edema, seizures, encephalopathy, or complications derived from the tumor therapy. New-onset seizures can be the first clinical symptom of a brain tumor14). Sepsis was second most common cause of ICU admission in our study cohort. Sepsis is a major cause of ICU admission that also affects the outcome of the cancer patient. Cancer patients are particularly at risk for severe sepsis than the general population because of immunosuppression caused by the malignancy itself or its treatment, organ involvement by malignant cells, infections from medical devices, such as catheters, nutritional deficiencies, or adverse effects of cancer therapy15).

The mean age of our study cohort was higher in non-survivor group in our study cohort. This may be because of higher comorbid conditions and higher metastasis rates in elderly patients. Previous studies also showed that comorbidities metastasis and increased age were associated with cancer death16,17).

Mean lactate and procalcitonin were higher in the non-survivor in our study cohort. Lactate levels can be estimated via point-of-care testing at the bedside. High lactate level is a guide to poor tissue perfusion and organ dysfunction in critically ill patients. However, it is a non-specific biomarker and may be raised in other clinical conditions also. Previous studies showed that high lactate is associated with increased mortality in patients with sepsis and it should be used as a biomarker for risk stratification in suspected sepsis18-21).

Serum procalcitonin is a biomarker for the early diagnosis of sepsis. It helps monitor the antimicrobial treatment regimen and can be used as a tool for antimicrobial stewardship. However, its level can also be increased in non-infectious processes such as tumorous diseases like medullary thyroid cancer, lung cancer with neuroendocrine component, or liver metastases. Previous brain tumor studies didn’t compare lactate and procalcitonin levels in cancer patients22-24).

ICU admission is usually expensive, so prediction of patients who may benefit from ICU treatment is paramount; especially in developing countries. Intensivists use their clinical judgment or various scoring systems to triage cancer patients for ICU admission. However, clinical judgment is often inaccurate to triage cancer patients for ICU admission. Information derivable from mortality-predicting tools may help guide physician in evidence-based decision-making along with their clinical judgement. So we calculated and compared various severity scores and found that severity scores were significantly higher in non-survivors.

The requirement of invasive mechanical ventilator support and vasopressor support was significantly higher in non-survivors and directly correlated to mortality similar to previous studies25-27). Infectious complications are common in a cancer patient because of their immunocompromised state and neutropenia. Gram-negative bacteria are a common cause of infection and sepsis in patients with cancer similar to our studies28-30).

Our study has several limitations. Because of the retrospective design, there can be data collection bias or patient selection bias. However, our data were extracted from a prospectively managed database, which warrants a certain degree of reliability and it is not feasible to conduct prospective studies in this group of patients because of the rarity of this disease. Our study population was small due to strict inclusion criteria. It is a single-centre study. The collective experience of intensivists from multicenters and specialized ICUs in dedicated brain tumor centers at various places can contribute to favorable outcomes. Because of the retrospective design and the single-center scope which may limit generalizability.

However, our study has some major advantages. We evaluated a homogeneous group of patients with brain tumors admitted in to the medical ICU. We also evaluated the effect of lactate and procalcitonin on survival. We also compared multiple different scoring systems between survivors and nonsurvivors to predict mortality in brain tumor patients.

CONCLUSION

To conclude, advancing age and the need for IMV or vasopressor support may be associated with worse prognosis in brain tumor patients admitted to ICU. A scoring system could be used along with clinical judgement to triage brain tumor patients for ICU admission. A larger retrospective or prospective study is needed to validate and explore potential predictors of survival.

Notes

Ethics statement

This retrospective, observational study was approved by our institutional ethics committee and institutional review board. A consent waiver was collected from the institutional ethical committee.

Author contributions

Conceptualization, Project administration, Visualization: AB, HB. Data curation: AB. Formal analysis, Methodology: AB, MV. Writing - original draft: AB. Writing - review & editing: All authors.

Conflict of interest

There is no conflict of interest to disclose.

Funding

None.

Data availability

None.

Acknowledgments

None.

References

1. Darmon M, Bourmaud A, Georges Q, Soares M, Jeon K, Oeyen S, et al. Changes in critically ill cancer patients‘ shortterm outcome over the last decades: results of systematic review with meta-analysis on individual data. Intensive Care Med 2019;45:977–87.
2. Jemal A, Ward EM, Johnson CJ, Cronin KA, Ma J, Ryerson B, et al. Annual report to the nation on the status of cancer, 1975-2014, featuring survival. J Natl Cancer Inst 2017;109:djx030.
3. Azoulay E, Soares M, Darmon M, Benoit D, Pastores S, Afessa B. Intensive care of the cancer patient: recent achievements and remaining challenges. Ann Intensive Care 2011;1:1–3.
4. Puxty K, McLoone P, Quasim T, Sloan B, Kinsella J, Morrison DS. Risk of critical illness among patients with solid cancers: a population-based observational study. JAMA oncol 2015;1:1078–1085.
5. Schellongowski P, Benesch M, Lang T, Traunmüller F, Zauner C, Laczika K, et al. Comparison of three severity scores for critically ill cancer patients. Intensive Care Med 2004;30:430–436.
6. Ediboğlu Ö, Kirakli SC, MOÇİN ÖY, Güngör G, Anar C, Cimen P, et al. Predictors of mortality in cancer patients who need intensive care unit support: a two center cohort study. Turk J Med Sci 2018;48:744–749.
7. Neumann B, Onken J, König N, Stetefeld H, Luger S, Luger AL, et al. Outcome of glioblastoma patients after intensive care unit admission with invasive mechanical ventilation: A multicenter analysis. J Neurooncol 2023;164:249–256.
8. IJzerman-Korevaar M, Snijders TJ, de Graeff A, Teunissen SC, de Vos FY. Prevalence of symptoms in glioma patients throughout the disease trajectory: a systematic review. J Neurooncol 2018;140:485–496.
9. Aygencel G, Turkoglu M, Sucak GT, Benekli M. Prognostic factors in critically ill cancer patients admitted to the intensive care unit. J Crit Care 2014;29:618–626.
10. Kang JH, Swisher CB, Buckley ED, Herndon JE, Lipp ES, Kirkpatrick JP, et al. Primary brain tumor patients admitted to a US intensive care unit: a descriptive analysis. CNS oncol 2021;10:CNS77.
11. Decavèle M, Rivals I, Marois C, Cantier M, Weiss N, Lemasle L, et al. Etiology and prognosis of acute respiratory failure in patients with primary malignant brain tumors admitted to the intensive care unit. J Neurooncol 2019;142:139–148.
12. Tabouret E, Boucard C, Devillier R, Barrie M, Boussen S, Autran D, et al. Neuro-oncological patients admitted in intensive-care unit: predictive factors and functional outcome. J Neurooncol 2016;127:111–117.
13. Decavèle M, Weiss N, Rivals I, Prodanovic H, Idbaih A, Mayaux J, et al. Prognosis of patients with primary malignant brain tumors admitted to the intensive care unit: a two-decade experience. J Neurol 2017;264:2303–2312.
14. Vecht CJ, Kerkhof M, Duran-Pena A. Seizure prognosis in brain tumors: new insights and evidence-based management. The oncologist 2014;19:751–759.
15. Wang YG, Zhou JC, Wu KS. High 28-day mortality in critically ill patients with sepsis and concomitant active cancer. J Int Med Res 2018;46:5030–5039.
16. Berger NA, Savvides P, Koroukian SM, Kahana EF, Deimling GT, Rose JH, et al. Cancer in the elderly. Trans Am Clin Climatol Assoc 2006;117:147–155.
17. Shih CY, Hung MC, Lu HM, Chen L, Huang SJ, Wang JD. Incidence, life expectancy and prognostic factors in cancer patients under prolonged mechanical ventilation: a nationwide analysis of 5,138 cases during 1998-2007. Crit Care 2013;17:R144.
18. Borthwick HA, Brunt LK, Mitchem KL, Chaloner C. Does lactate measurement performed on admission predict clinical outcome on the intensive care unit? a concise systematic review. Ann Clin Biochem 2012;49:391–394.
19. Liu G, Lv H, An Y, Wei X, Yi H, Yi H. Early actate levelsfor prediction of mortality in patients with sepsis or septic shock: a meta-analysis. Int J Clin Exp Med 2017;10:37–47.
20. Juneja D, Singh O, Dang R. Admission hyperlactatemia: causes, incidence, and impact on outcome of patients admitted in a general medical intensive care unit. J Crit Care 2011;26:316–320.
21. López R, Pérez-Araos R, Baus F, Moscoso C, Salazar Á, Graf J, et al. Outcomes of sepsis and septic shock in cancer patients: focus on lactate. Front Med (Lausanne) 2021;8:603275.
22. Durnaś B, Wątek M, Wollny T, Niemirowicz K, Marzec M, Bucki R, et al. Utility of blood procalcitonin concentration in the management of cancer patients with infections. Onco Targets Ther 2016;9:469–475.
23. Patout M, Salaün M, Brunel V, Bota S, Cauliez B, Thiberville L. Diagnostic and prognostic value of serum procalcitonin concentrations in primary lung cancers. Clin Biochem 2014;47:263–267.
24. Vijayan AL, Vanimaya N, Ravindran S, Saikant R, Lakshmi S, Kartik R. Procalcitonin: a promising diagnostic marker for sepsis and antibiotic therapy. J Intensive Care 2017;5:1–7.
25. Gudiol C, Albasanz-Puig A, Cuervo G, Carratalà J. Understanding and managing sepsis in patients with cancer in the era of antimicrobial resistance. Front Med (Lausanne) 2021;8:636547.
26. Roques S, Parrot A, Lavole A, Ancel PY, Gounant V, Djibre M, et al. Six-month prognosis of patients with lung cancer admitted to the intensive care unit. Intensive Care Med 2009;35:2044–2050.
27. Decavèle M, Dreyfus A, Gatulle N, Weiss N, Houillier C, Demeret S, et al. Clinical features and outcome of patients with primary central nervous system lymphoma admitted to the intensive care unit: a French national expert center experience. J Neurol 2021;268:2141–2150.
28. Williams MD, Braun LA, Cooper LM, Johnston J, Weiss RV, Qualy RL, et al. Hospitalized cancer patients with severe sepsis: analysis of incidence, mortality, and associated costs of care. Crit Care 2004;8:1–8.
29. Zain OM, Elsayed MY, Abdelkhalig SM, Abdelaziz M, Ibrahim SY, Bashir T, et al. Bloodstream infection in cancer patients; susceptibility profiles of the isolated pathogens, at Khartoum Oncology Hospital, Sudan. Afr Health Sci 2022;4:70–76.
30. Sime WT, Biazin H, Zeleke TA, Desalegn Z. Urinary tract infection in cancer patients and antimicrobial susceptibility of isolates in Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia. PLoS One 2020;15e0243474.

Article information Continued

Fig. 1.

Distribution of study subjects according to type of admission.

Fig. 2.

Distribution of study subjects according to reason for intensive care unit admission.

Table 1.

Baseline parameters and outcome variables

Parameters Total (n=42)
Demographics
 Age (Mean ± SD) 58.52 ± 13.06
 Sex (Male) 21 (50%)
 DM 12 (28.6%)
 Hypertension 8(19%)
Tumor characteristics
 Metastasis 5 (11.9%)
 Mean time since duration of malignancy (months) (SD) 26.62 (31.48)
 History of tumor Surgery 36 (85.7 %)
Histopathology of brain tumor
 Glioblastoma multiforme (GBM) 30 (83.33%)
 Medulloblastoma 2 (5.56%)
 Oligodandroglioma 4 (11.11%)
Baseline variables
 Mean GCS (SD) 11.62 (3.52)
 Mean serum sodium (SD) 132.4 (6.81)
 Mean serum calcium (SD) 8.32 (0.88)
 Mean Lactate (SD) 2.53 (3.22)
 Mean Procalcitonin (SD) 4.88 (16.85)
Scores
 Apache II score 18.1 ± 7.1
 Apache III score 23.35 ± 23.86
 Apache IV score 58.88 ± 26.87
 SAPS2 score 33 ± 14.33
 SOFA score 6.1 ± 2.97
Outcome variables
 Mean duration of ICU stay (SD) 7.5 (6)
 Mean duration of hospital stay (SD) 14.71 (11.76)
 IMV support 13 (31%)
 Vasopressor support 9 (21.4%)

DM: Diabetes mellitus; APACHE: Acute physiology and chronic health evaluation; SAPS: Simplified Acute Physiology Score; SOFA: Sequential organ failure assessment; IMV: Invasive mechanical ventilator; SD: Standard deviation.

Table 2.

Comparison of baseline parameters and outcome variables between survivors and non-survivors in brain tumor patients admitted in ICU using univariate analysis

Parameters Total (n=42) Survivors (n= 29) Non-survivors (n= 13) P value
Demographics
 Age (Mean ± SD) 58.52 ± 13.06 48.31 ± 17.6 55.36 ± 15.16 0.042
 Sex (Male) 21 (50%) 15 (51.7%) 6(46.2%) 1.000
 DM 16 (38.0%) 8 (27.5%) 8 (61.5%) 0.079
 Hypertension 13(30.9%) 6 (20.6%) 7 (53.8%) 0.073
Tumor characterstics
 Metastasis 10 (23.8%) 5 (17.2%) 5 (38.4%) 0.623
 Mean time since duration of malignancy (months) (SD) 26.62 (31.48) 23.24 (28.37) 34.15 (37.66) 0.213
 History of tumor Surgery 36 (85.7 %) 25 (86.2%) 11 (84.6%) 0.733
Baseline variables
 Mean GCS (SD) 11.62 (3.52) 12.03 (3.16) 10.69 (4.21) 0.258
 Mean serum sodium (SD) 132.4 (6.81) 131.31 (7.93) 134.92 (1.38) 0.113
 Mean serum calcium (SD) 8.32 (0.88) 8.32 (0.79) 8.3 (1.1) 0.960
 Lactate Mean (SD) 2.53 (3.22) 1.45 (0.93) 4.94 (4.92) 0.001*
 Mean Procalcitonin (SD) 4.88 (16.85) 1.28 (2.28) 12.92 (29.27) 0.037*
Scores
 Apache IIscore 18.1 ± 7.1 15.07 ± 3.24 24.85 ± 8.74 <0.001*
 Apache III score 23.35 ± 23.86 47.69 ± 15.45 83.85 ± 30.53 <0.001*
 Apache IV score 58.88 ± 26.87 47.41 ± 15.6 80.31 ± 27.64 <0.001*
 SAPS2 score 33 ± 14.33 28.52±10.28 43±17.27 0.002*
 SOFA score 6.1 ± 2.97 5.34±2.97 7.77±2.28 0.013*
Outcomes
 Mean duration of ICU stay (SD) 7.5 (6) 7.38 (5.81) 7.77 (6.64) 0.848
 Mean duration of hospital stay (SD) 14.71 (11.76) 15.97 (12.97) 11.92 (8.24) 0.309
 IMV support 13 (31%) 4 (13.8%) 9 (69.2%) 0.001*
 Renal support 0 0 0 0
 Vasopressor support 9 (21.4%) 2 (6.9%) 7 (53.8%) 0.003*

DM: Diabetes mellitus; APACHE: Acute physiology and chronic health evaluation; SOFA: Sequential organ failure assessment; IMV: Invasive mechanical ventilator; SD: Standard deviation.

*

Statistically significant.

Table 3.

Multivariate analysis for 30-day mortality in brain tumor patients admitted to intensive care unit

Parameters P value Odds ratio 95% CI
Age 0.033 0.843 0.721–0.986
Lactate level 0.786 1.306 0.190–8.60
Procalcitonin level 0.934 0.934 0.63 –1.513
IMV support 0.019 484.62 2.707–8676.02
Vasopressor support 0.026 523.83 2.12–13023.13

IMV: Invasive mechanical ventilator.