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Prediction of survival time after terminal extubation: the balance between critical care unit utilization and hospice medicine in the COVID-19 pandemic era

European Journal of Medical Research volume 28, Article number: 21 (2023) Cite this article

Abstract

Background

We established 1-h and 1-day survival models after terminal extubation to optimize ventilator use and achieve a balance between critical care for COVID-19 and hospice medicine.

Methods

Data were obtained from patients with end-of-life status at terminal extubation from 2015 to 2020. The associations between APACHE II scores and parameters with survival time were analyzed. Parameters with a p-value ≤ 0.2 in univariate analysis were included in multivariate models. Cox proportional hazards regression analysis was used for the multivariate analysis of survival time at 1 h and 1 day.

Results

Of the 140 enrolled patients, 76 (54.3%) died within 1 h and 35 (25%) survived beyond 24 h. No spontaneous breathing trial (SBT) within the past 24 h, minute ventilation (MV) ≥ 12 L/min, and APACHE II score ≥ 25 were associated with shorter survival in the 1 h regression model. Lower MV, SpO2 ≥ 96% and SBT were related to longer survival in the 1-day model. Hospice medications did not influence survival time.

Conclusion

An APACHE II score of ≥ 25 at 1 h and SpO2 ≥ 96% at 1 day were strong predictors of disposition of patients to intensivists. These factors can help to objectively tailor pathways for post-extubation transition and rapidly allocate intensive care unit resources without sacrificing the quality of palliative care in the era of COVID-19.

Trial registration They study was retrospectively registered. IRB No.: 202101929B0.

Introduction

“Hospice Palliative Care Regulations” were established in Taiwan in 2000, and further amendments on January 10, 2011 stated that terminally ill patients can be extubated. Family meetings are conducted to rule out possible alternative treatment options, determine the irreversibility of the patient’s clinical condition, and reach consensus on the indications for palliative extubation. In cases of withdrawal of life-sustaining treatment (WLST) for unconscious intubated terminal patients who are unable to express their wishes, the appointed medical agent can sign termination consent. Ventilator support can be discontinued to avoid costly and nonbeneficial treatments after a multidisciplinary meeting approved by the hospital’s medical ethics committee. Taiwan pioneers legislation to protect natural death, and promote “advance care planning” and “shared decision-making” [1].

The “Patient Right to Autonomy Act”, the first patient-centered bill in Asia that fully respects a patient’s autonomy, was implemented in 2019. The Act clearly states that everyone has the right to know, choose and make personal medical decisions. For those who make advance directive decisions, are too ill to make decisions, or fall into a coma, their free will is protected and enforced by law. However, the nature of hospice medicine faces challenges from COVID-19. After the first confirmed case of COVID-19 in Taiwan in January 2020, both personal protective equipment and critical care resources have been impacted by the pandemic. The outbreak has severely impacted the daily practice of public health and palliative care globally, and consequently a balance should be struck between intensive care unit (ICU) utilization and hospice medication [2].

Previous articles have focused on heterogeneous factors and prediction models for WLST in different populations, especially with regard to 1-h death for ischemic time of organ donation after cardiac death (DCD). However, a more generalizable tool is needed to evaluate the potential for donation or end-of-life care across ICUs and identify appropriate time points [3]. The Acute Physiology and Chronic Health Assessment (APACHE) II score system is used to assess the severity of critical illness and risk of mortality, and it has been used extensively in the ICU for more than 30 years [4, 5]. It has been validated as a predictor of survival time and mortality in many studies of neurocritical patients, those with terminal diseases, and clinical purposes [6,7,8,9,10,11].

During the era of the COVID-19 pandemic, the availability of ICU beds is an important issue. The optimal usage of ventilators is of particular importance for COVID-19 critical care. The more known about survival time after terminal extubation can assist in the more efficient use of ICU resources. Current survival models for WLST involve multiple variables and are complex [12]. Therefore, the aim of this study was to develop a simpler model using APACHE II score, which already incorporates many 1-h mortality factors, as ICU staff time is also an important asset in the COVID-19 era. Furthermore, predictors for long-term survival (> 24 h after WLST) are still lacking. Therefore, we developed a 1-day survival model after terminal extubation for critically ill ICU patients.

Methods

Study population and setting

The data for this study were obtained from interdisciplinary palliative care team at Chang Gung Memorial Hospital in Keelung and Lovers’ Lake Branch, before and after palliative extubation from 2015 to 2020. All participants were terminally ill, defined as having end-of-life status and no chance of returning to a meaningful life based on the judgment of at least two specialist physicians. Eight patients and/or their families who refused to forgo life-sustaining therapies or withdraw mechanical ventilation were excluded. The terminal extubation process, consistent with the Hospice Palliative Care Act (Natural Death Act) Amendment, was initiated by the families or intensivists after consensus with the family and other medical staff. This retrospective 6-year study was approved by the Institutional Review Board of Chang Gung Medical Foundation Institution, and the requirement for participants’ informed consent was waived (IRB file No. 202101929B0).

Variables and measures

Different clinical and demographic characteristics were summarized according to extubation status. All possible physiologic and respiratory parameters associated with survival time were calculated. Continuous data are expressed as mean ± standard deviation (SD) or median and interquartile ranges (IQR), and categorical variables are expressed as proportions. Vital signs and oxygen saturation were recorded at the discontinuation of mechanical ventilation. The APACHE II score was reassessed according to the latest laboratory data and physiological variables at extubation. Any unknown or out-of-date variables were scored as 0 points when calculating the APACHE II score.

Statistical analysis

Continuous variables were analyzed using the Mann–Whitney U test, with comparisons of medians for variables with skewed distribution. Categorical variables were compared using Pearson’s Chi-square test or Fisher’s exact test at survival times of 1 h and 24 h. We conducted Cox proportional hazards regression analysis to compute the hazard ratios (HRs) and 95% confidence intervals (CIs) to identify the independent predictors associated with death within 1 h and survival beyond 1 day. Two multiple linear regression models with backward stepwise elimination were conducted on all factors with a p-value ≤ 0.2 in univariate analysis. A two-sided p-value < 0.05 was considered to indicate statistical significance. Data analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY).

Results

From January 2015 to December 2020, 18,738 patients who were admitted to an ICU were screened, of whom 144 met the criteria for palliative extubation. Four patients were excluded due to a young age (< 18 years old) or incomplete data (Fig. 1A). The remaining 140 patients who died after palliative extubation after a mean 17.8 days on ventilation (range 1–65 days) were included in the study. Of these patients, 112 (80.0%) had do not resuscitate (DNR) orders prior to family meetings.

Fig. 1
figure 1

a Flow diagram of patient inclusion, exclusion and distribution. b Kaplan–Meier estimate of survival after palliative extubation

Full size image

Baseline demographics

The mean age of the study population was 67 years (range 19–94 years), and there were more males (62.1%) than females. Eighty-nine (63.6%) patients were treated with surgical services. Cerebrovascular accident was the main comorbidity (55.0%), followed by congestive heart failure (54.3%), renal failure (45.7%), chronic respiratory disease (42.1%), diabetes mellitus (40.7%), advanced malignancy (37.9%), and chronic liver disease (30.7%). The mean APACHE II score assessed at ICU admission was 25.2 (range 6–42), which increased to 31.3 (range 14–66) at extubation. The clinical characteristics are listed in Table 1.

Table 1 Biosociodemographic characteristics in our participants (n = 140)
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Time to death

The Kaplan–Meier curve for time to death in the 140 extubation patients is shown in Fig, 1B. The time to death after the extubation ranged from 0.02 to 401.72 h (median 0.79 h). Seventy-six patients (54.3%) died within 1 h, and 35 patients (25%) survived beyond 24 h. After extubation, most patients died in the ICU (72.1%), while others died in the ward, hospice and home according to individual circumstances. The mean ICU stay was 18.6 days (range 1–65 days). Of the eight patients (5.7%) whose family members were willing to donate organs, two (1.4%) eventually completed organ transplantation (Additional file 1).

Univariate analysis

The results of univariate analysis of continuous variables are listed in Table 2. The significant factors (p-value < 0.05) associated with death at 1 h of extubation were total Glasgow Coma Scale (GCS) score, diastolic blood pressure (DBP), mean arterial pressure (MAP), pulse rate, respiratory rate, fraction of inspired oxygen (FiO2) from the ventilator, positive end-expiratory pressure (PEEP), static pressure, minute ventilation (MV), and APACHE II score at extubation. The significant factors associated with death at 1 day were GCS score, systolic blood pressure (SBP), DBP, MAP, peripheral arterial oxygen saturation (SpO2) from pulse oximetry, FiO2 from the ventilator, PEEP, static pressure, and MV.

Table 2 Univariate analysis of continuous variables at 1 h and 1 day after extubation
Full size table

The results of univariate analysis of categorical variables are listed in Table 3. The significant factors associated with death at 1 h of extubation were comorbid cerebrovascular disease, chronic respiratory disease, advanced malignancy, SpO2 ≥ 96%, MV ≥ 12 L/min, spontaneous breathing trial (SBT) within the past 24 h, use of inotropic agents in the past 12 h, and APACHE II score at extubation ≥ 25 and ≥ 30. The significant factors associated with death at 1 day were SpO2 ≥ 96%, MV ≥ 12 L/min, SBT within the past 24 h, and the use of inotropic agents in the past 12 h.

Table 3 Univariate analysis of categorical variables at 1 h and 1 day after extubation
Full size table

Multivariate analysis

The results of Cox proportional hazards regression analysis for survival at 1 h and 1 day are shown in Table 4. In the 1-h model, intubation duration, SpO2 from pulse oximetry, total dose of opioids within 24 h, total dose of BZDs within 24 h, education level, comorbid cerebrovascular accident had p-values ≤ 0.2 in the univariate analysis and were entered into the multivariate analysis. Items that overlapped including GCS, DBP, MAP, pulse rate, respiratory rate, FiO2 from the ventilator in APACHE II score were excluded. To increase clinical relevance, we used MV 12 L/min in regression multivariate analysis according to a previous study [13]. In the 1-day model, intubation duration, APACHE II score at extubation, and DNR orders signed before family meeting had p-values ≤ 0.2 in the univariate analysis and were included in the multivariate analysis. Items correlated with APACHE II score were removed. To enhance application, SpO2 < 96% was used in the analysis.

Table 4 Multivariate Cox regression analysis of factors associated with patient death within 1 h and survival beyond 1 day
Full size table

The final Cox regression model for death within 1 h showed that no SBT within the past 24 h, MV ≥ 12 L/min, and APACHE II score ≥ 25 were associated with higher mortality. Meanwhile, the model for survival beyond 1 day indicated that lower MV, SpO2 ≥ 96%, and SBT within the past 24 h were associated with longer survival.

Discussion

To the best of our knowledge, this is the first study to use APACHE II score to predict the time to death after terminal extubation. In the 140 terminal patients enrolled in this study, a reassessed APACHE II score 25 at terminal extubation was a practical and helpful tool to assess survival, which may be of particular use in the battle against COVID-19. We also found that no SBT within the past 24 h and MV ≥ 12 L/min were significantly associated with 1-h mortality. As these patients survived for longer than 1 h, APACHE II score was not suitable to predict survival longer than 24 h. SpO2 ≥ 96%, MV, and SBT within the past 24 h could be used as an indication of when to transfer patients from an ICU to hospice unit.

Reassessed APACHE II score at terminal extubation

Previous studies have reported that various factors are associated with time to death within 1 h after WLST, including age, FiO2, body temperature, MAP, blood pH, heart rate, respiratory rate, serum sodium, potassium, creatinine, white blood cell count, GCS and severe organ system insufficiency, which are also the main variables used to calculate the APACHE II score [3, 6, 12,13,14,15,16,17,18]. The APACHE II scoring system is a simple and widely used reproducible ICU prognostic model, and our data showed that it could be used to predict survival time after compassionate extubation. Recalculation of the “Acute Physiology Score” part based on the updated status of the patient after extubation is relatively convenient for multidisciplinary teams. Although the reassessed APACHE II score did not reach significance in the 1-day model, an APACHE II score ≥ 25 closely predicted 1-h mortality (Fig. 2C). As an APACHE II score of 25 represents an approximately 50% mortality rate in clinical practice, a cutoff value of 25 has been well validated in predicting mortality in ventilator-associated pneumonia, emergency surgical patients, and patients with severe sepsis, carbon monoxide poisoning, and hematological cancer [7, 19,20,21].

Fig. 2
figure 2

Survival curves by a spontaneous breathing trial within the past 24 h, b minute ventilation 12 L/min, c APACHE II score 25 at extubation, and d pulse oximetry: SpO2 96%

Full size image

Our results are also consistent with discharge APACHE II score being superior to admission APACHE II score in predicting post-ICU mortality [22]. Given that more patient parameters and surgical status are taken into account in the APACHE II score, it can serve as a new prognostic tool for hospice care.

Respiratory-related factors

Respiratory variables are consistently associated with time to death [13, 15]. With regard to the items excluded from the APACHE II scoring system, MV was of greater significance in multivariate analysis compared with static pressure and PEEP. MV has also been shown to play an important role in predicting noninvasive ventilation failure in patients with early mild acute respiratory distress syndrome (ARDS) induced by pneumonia, successful extubation, and mortality caused by ARDS in patients with COVID-19 [23,24,25].

SBT, an indicator for liberation from ventilation in different populations, is performed using T-piece ventilation and pressure support ventilation lasting between 0.5 and 2 h [26, 27]. Patients eligible for SBT are screened according to low FiO2 (< 0.5) and PEEP (< 5–8 cmH2O) requirements, stable hemodynamics, and the ability to initiate spontaneous breathing, all of which are also favorable predictors for longer survival after WLST [3, 18, 28]. In addition, the components of the SBT can be used to measure the burden of post-extubation symptoms and guide the anticipatory dose of medication after terminal extubation [29]. It is therefore reasonable that the subjects with lower MV and attempting SPT within 24 h had less dependency on mechanical ventilation and a longer survival (Fig. 2A, B).

Peripheral arterial oxygen saturation

Pulse oximetry is used to measure SpO2, and pulse oximeters are standard equipment in ICUs [30]. It is considered to be the “fifth vital sign” to monitor systemic oxygen delivery in a noninvasive and continuous fashion, especially in critically ill patients supported by extracorporeal membrane oxygenation and mechanical ventilation [31,32,33]. A lower SpO2, compared with SpO2 ≥ 96%, has been associated with an increased risk of all-cause mortality in the general adult population [34]. In a systematic review conducted in 2018, oxygen therapy was associated with increased mortality in acutely ill adults with SpO2 > 96% [35]. The authors suggested that critically ill patients with SpO2 ≥ 96% have a lower oxygen demand and better compensation. Our study supports these findings, as SpO2 was the only factor significantly associated with 1-day survival (Fig. 2D). Moreover, the target of SpO2 should be 92% to 96% in adults with COVID-19 who need supplemental oxygen according to treatment guidelines as home pulse oximetry has become increasingly popular during the COVID-19 era [36].

Disposition after palliative extubation in the COVID-19 pandemic era

Our results showed that combining the APACHE II score with other respiratory parameters was effective in predicting 1-h mortality. Therefore, our model could be used to identify which terminal patients with irreversible illness should remain in the ICU without being transferred, regardless of comorbidities. Our 1-h model could help physicians to quickly detect suitable candidates for DCD. The 1-day model based on SpO2 could be used to identify patients with a likelihood of longer survival, as a significant minority are discharged alive after palliative extubation [5, 37]. Transition to a general care ward, hospice department ward or home where comfort-oriented care can be provided is suitable for patients who are predicted to survive for more than 1 day according to the consensus of family meetings [12] (Fig. 1B).

ICU facilities are important for patients with moderate and severe COVID-19 infection. When COVID-19 peaks occur, hospitals may run out of beds and other medical supplies. In this situation, available ICU beds are recruited by the government to avoid collapse of the health system. Hospital capacity is consequently reduced, and other medical practices including hospice care are also likely to be over-utilized. Under the “coexisting with the virus” and “zero severe cases” policy of the Ministry of Health and Welfare in Taiwan, subjects who are expected to survive for 1 h to 1 day can be transferred to palliative home care directly depending on religious needs and individual differences. Some studies have also suggested that ICU specialist opinion was closely associated with the time of death [16]. When staff are overwhelmed by the number of COVID patients, a simple global guide is needed to avoid overloading healthcare systems and the guilt that decision-making can create.

Furthermore, DNR orders, consideration of organ donation, and hospice medications for pain/symptom relief did not affect the time to natural death in the multivariate analysis. Adequate medications and supplies should be considered based on probable survival time to reduce distress and family anxiety during and after transfer from the ICU. Although not a perfect substitute, well-designed apps and online counseling are practical in home hospice practice [38].

Limitations and strengths

As this was a retrospective study, some data were not re-examined at the time of compassionate extubation to reduce possible patient discomfort. In addition, we enrolled terminally ill Asian patients from one institute, and the sample size was relatively small. Further external validation studies are needed. Nonetheless, reassessed APACHE II score compensated for the missing lab data, and we provide a convenient tool with the potential for global use to avoid the interference of repeated testing during natural death. The 1-day model offers evidence for dealing with contradictions between COVID-19 treatment and hospice care. Data on survival time after WSLT in Asia are extremely limited due to legal and religious restrictions, so our results add to the knowledge of this group of patients. Further artificial intelligence analysis of different studies could be used to prospectively validate models applied to other ICUs and decision-making after extubation [12, 38].

Conclusions

In conclusion, the accurate estimation of time to death can optimize the use of hospital resources. The 1-h and 1-day models showed that a reassessed APACHE II score of ≥ 25 and SpO2 ≥ 96%, respectively, were practical predictors of mortality in the terminal patients in this study. These clinical factors may help to objectively tailor pathways for post-extubation transition and rapidly allocate ICU resources without sacrificing the quality of palliative care in the era of COVID-19. (Additional file1: Fig S1)

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

APACHE:

Acute Physiology and Chronic Health Evaluation

ARDS:

Acute respiratory distress syndrome

BZD:

Benzodiazepines

CI:

Confidence interval

CNS:

Central nervous system

CVA:

Cerebrovascular accident

DBP:

Diastolic blood pressure

DCD:

Donation after cardiac death

DNR:

Do no resuscitation

ECMO:

Extracorporeal membrane oxygenation

FiO2:

Fraction of inspired oxygen

GCS:

Glasgow Coma Scale

HR:

Hazard ratio

ICU:

Intensive care unit

IQR:

Interquartile range

MAP:

Mean arterial pressure

OS:

Overall survival

PEEP:

Positive end-expiratory pressure

RC:

Regression coefficient

SBP:

Systolic blood pressure

SBT:

Spontaneous breathing trial

SD:

Standard deviation

SpO2:

Peripheral arterial oxygen saturation

WLST:

Withdrawal of life-sustaining therapy

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Acknowledgements

The authors would like to thank the clinicians, the patients, and all investigators who contributed to this study. We also thank Cancer Center of Chang Gung Memorial Hospital, Keelung branches, for their excellent support.

Funding

All authors declare that: 1. no support, financial or otherwise, has been received from any organization that may have an interest in the submitted work; and 2. there are no other relationships or activities that could appear to have influenced the submitted work.

Author information

Authors and Affiliations

  1. Departments of Neurosurgery, Chang Gung Memorial Hospital, Keelung and Linkou & Chang Gung University, Taoyuan, Taiwan

    Yun-Cong Zheng & Pin-Yuan Chen

  2. Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan

    Yun-Cong Zheng

  3. Division of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital, No. 222, Maijin Rd., Anle Dist., Keelung, 204, Taiwan

    Yen-Min Huang, Ning-Yi Shih & Chien-Hong Lai

  4. Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan

    Yen-Min Huang

  5. School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan

    Hsiao-Yean Chiu

  6. Research Center of Sleep Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan

    Hsiao-Yean Chiu

  7. Department of Nursing, Taipei Medical University Hospital, Taipei, Taiwan

    Hsiao-Yean Chiu

  8. Division of Pulmonary, Critical Care and Sleep Medicine, Chang Gung Memorial Hospital, Keelung, 20401, Taiwan

    Huang-Pin Wu & Chien-Ming Chu

  9. College of Medicine, Chang Gung University, Taoyuan, 33302, Taiwan

    Huang-Pin Wu

  10. Division of Cardiology Section, Internal Medicine, Chang Gung Memorial Hospital, Keelung & Chang Gung University, Taoyuan, Taiwan

    Wei-Siang Chen & Yu-Cheng Kao

  11. Department of Social Services, Chang Gung Memorial Hospital, Keelung, Taiwan

    Ching-Fang Lai

Authors
  1. Yun-Cong Zheng
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  2. Yen-Min Huang
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  3. Pin-Yuan Chen
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  4. Hsiao-Yean Chiu
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  5. Huang-Pin Wu
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  6. Chien-Ming Chu
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  7. Wei-Siang Chen
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  8. Yu-Cheng Kao
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  9. Ching-Fang Lai
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  10. Ning-Yi Shih
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  11. Chien-Hong Lai
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Contributions

YCZ, YMH, PYC, HYC, HPW, CMC, WSC, YCK, CFL, NYS, CHL contributed to the data collection and analysis. YCZ, PYC, HYC contributed to the conception and design of the study. YCZ, YMH contributed to drafting the text and preparing the figures. We have prepared the manuscript according to the standard publishing guidelines. In the event that the article is accepted for publication, it will be reformatted following your journal-specific guidelines. I agree to pay for full color reproduction with this manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Chien-Hong Lai.

Ethics declarations

Ethics approval and consent to participate

The need for consent to participate was waived by above committee. The IRB is organized and operates in accordance with Good Clinical Practice and the applicable laws and regulations. Thus, our study was followed by the BMC guidelines of retrospective ethics approval. (IRB file No. 202101929B0, see the uploaded related file).

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Fig S1.

Trends in ICUs utilization and length of stay.

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Zheng, YC., Huang, YM., Chen, PY. et al. Prediction of survival time after terminal extubation: the balance between critical care unit utilization and hospice medicine in the COVID-19 pandemic era. Eur J Med Res 28, 21 (2023). https://doi.org/10.1186/s40001-022-00972-w

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Keywords

European Journal of Medical Research

ISSN: 2047-783X

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