Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU

Prognosticating the outcome of intensive care in older patients-a narrative review

Prognosis determines major decisions regarding treatment for critically ill patients. Statistical models have been developed to predict the probability of survival and other outcomes of intensive care. Although they were trained on the characteristics of large patient cohorts, they often do not represent very old patients (age ≥ 80 years) appropriately. Moreover, the heterogeneity within this particular group impairs the utility of statistical predictions for informing decision-making in very old individuals. In addition to these methodological problems, the diversity of cultural attitudes, available resources as well as variations of legal and professional norms limit the generalisability of prediction models, especially in patients with complex multi-morbidity and pre-existing functional impairments. Thus, current approaches to prognosticating outcomes in very old patients are imperfect and can generate substantial uncertainty about optimal trajectories of critical care in the individual. This article presents the state of the art and new approaches to predicting outcomes of intensive care for these patients. Special emphasis has been given to the integration of predictions into the decision-making for individual patients. This requires quantification of prognostic uncertainty and a careful alignment of decisions with the preferences of patients, who might prioritise functional outcomes over survival. Since the performance of outcome predictions for the individual patient may improve over time, time-limited trials in intensive care may be an appropriate way to increase the confidence in decisions about life-sustaining treatment.

Frailty assessment in critically ill older adults: a narrative review

Frailty, a condition that was first defined 20 years ago, is now assessed via multiple different tools. The Frailty Phenotype was initially used to identify a population of "pre-frail" and "frail" older adults, so as to prevent falls, loss of mobility, and hospitalizations. A different definition of frailty, via the Clinical Frailty Scale, is now actively used in critical care situations to evaluate over 65 year-old patients, whether it be for Intensive Care Unit (ICU) admissions, limitation of life-sustaining treatments or prognostication. Confusion remains when mentioning "frailty" in older adults, as to which tools are used, and what the impact or the bias of using these tools might be. In addition, it is essential to clarify which tools are appropriate in medical emergencies. In this review, we clarify various concepts and differences between frailty, functional autonomy and comorbidities; then focus on the current use of frailty scales in critically ill older adults. Finally, we discuss the benefits and risks of using standardized scales to describe patients, and suggest ways to maintain a complex, three-dimensional, patient evaluation, despite time constraints. Frailty in the ICU is common, involving around 40% of patients over 75. The most commonly used scale is the Clinical Frailty Scale (CFS), a rapid substitute for Comprehensive Geriatric Assessment (CGA). Significant associations exist between the CFS-scale and both short and long-term mortality, as well as long-term outcomes, such as loss of functional ability and being discharged home. The CFS became a mainstream tool newly used for triage during the Covid-19 pandemic, in response to the pressure on healthcare systems. It was found to be significantly associated with in-hospital mortality. The improper use of scales may lead to hastened decision-making, especially when there are strains on healthcare resources or time-constraints. Being aware of theses biases is essential to facilitate older adults' access to equitable decision-making regarding critical care. The aim is to help counteract assessments which may be abridged by time and organisational constraints.

The association of premorbid conditions with 6-month mortality in acutely admitted ICU patients over 80 years

BACKGROUND

Premorbid conditions influence the outcome of acutely ill adult patients aged 80 years and over who are admitted to the ICU. The aim of this study was to determine the influence of such premorbid conditions on 6 month survival.

METHODS

Prospective cohort study in 242 ICUs from 22 countries including patients 80 years or above, admitted over a 6 months period to an ICU between May 2018 and May 2019. Only emergency (acute) ICU admissions in adult patients ≥ 80 years of age were eligible. Patients who were admitted after planned/elective surgery were excluded. We measured the Clinical Frailty Scale (CFS), the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), disability with the Katz activities of daily living (ADL) score, comorbidities and a Polypharmacy Score (CPS).

RESULTS

Overall, the VIP2 study included 3920 patients. During ICU stay 1191 patients died (30.9%), and another 436 patients (11.1%) died after ICU discharge but within the first 30 days of admission, and an additional 895 patients died hereafter but within the first 6 months after admission (22.8%). The 6 months mortality was 64%. The median CFS was 4 (IQR 3-6). Frailty (CFS ≥ 5) was present in 26.6%. Cognitive decline (IQCODE above 3.5) was found in 30.2%. The median IQCODE was 3.19. A Katz ADL of 4 or less was present in 27.7%. Patients who surviving > 6 months were slightly younger (median age survivors 84 with IQR 81-86) than patients dying within the first 6 months (median age 84, IQR 82-87, p = 0.013), were less frequently frail (CFS > 5 in 19% versus 34%, p < 0.01) and were less dependent based on their Katz activities of daily living measurement (median Katz score 6, IQR 5-6 versus 6 points, IQR 3-6, p < 0.01).

CONCLUSIONS

We found that Clinical Frailty Scale, age, and SOFA at admission were independent prognostic factors for 6 month mortality after ICU admission in patients age 80 and above. Adding other geriatric syndromes and scores did not improve the model. This information can be used in shared-decision making.

CLINICALTRIALS

gov: NCT03370692.

A scoping review of older patients' health-related quality of life, recovery and well-being after intensive care

AIMS

In the present study, we aimed to determine how Health-Related Quality of Life (HRQoL), recovery (function and capacity in daily life) and well-being are followed up and characterised in persons ≥65 years of age who were being cared for in an intensive care unit (ICU).

DESIGN

A scoping review.

METHODS

CINAHL, MEDLINE (Ovid) and PsycINFO databases were searched in October 2021. 20 studies met the inclusion criteria. The scoping review followed the principles outlined by Arksey and O'Malley, and the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) checklist and Joanna Briggs Institute (JBI) framework were used.

RESULTS

Results are presented under five subheadings: Study characteristics, Type of studies, Methods for follow-up, health-related quality of life, and Recovery. Time seems to be an important factor regarding HRQoL among older patients being cared for in an ICU, with most elderly survivors perceiving their HRQoL as acceptable after 1 year. Nevertheless, several studies showed patients' willingness to be readmitted to the ICU if necessary, indicating that life is worth fighting for.

PATIENT OR PUBLIC CONTRIBUTION

Due to the design of the study, this study involves no patient or public contribution.

A Simple Nomogram for Predicting Hospital Mortality of Patients Over 80 Years in ICU: An International Multicenter Retrospective Study

OBJECTIVES

This study aimed to develop and validate an easy-to-use intensive care unit (ICU) illness scoring system to evaluate the in-hospital mortality for very old patients (VOPs, over 80 years old).

METHODS

We performed a multicenter retrospective study based on the electronic ICU (eICU) Collaborative Research Database (eICU-CRD), Medical Information Mart for Intensive Care Database (MIMIC-III CareVue and MIMIC-IV), and the Amsterdam University Medical Centers Database (AmsterdamUMCdb). Least Absolute Shrinkage and Selection Operator regression was applied to variables selection. The logistic regression algorithm was used to develop the risk score and a nomogram was further generated to explain the score.

RESULTS

We analyzed 23 704 VOPs, including 3 726 deaths (10 183 [13.5% mortality] from eICU-CRD [development set], 12 703 [17.2%] from the MIMIC, and 818 [20.8%] from the AmsterdamUMC [external validation sets]). Thirty-four variables were extracted on the first day of ICU admission, and 10 variables were finally chosen including Glasgow Coma Scale, shock index, respiratory rate, partial pressure of carbon dioxide, lactate, mechanical ventilation (yes vs no), oxygen saturation, Charlson Comorbidity Index, blood urea nitrogen, and urine output. The nomogram was developed based on the 10 variables (area under the receiver operating characteristic curve: training of 0.792, testing of 0.788, MIMIC of 0.764, and AmsterdamUMC of 0.808 [external validating]), which consistently outperformed the Sequential Organ Failure Assessment, acute physiology score III, and simplified acute physiology score II.

CONCLUSIONS

We developed and externally validated a nomogram for predicting mortality in VOPs based on 10 commonly measured variables on the first day of ICU admission. It could be a useful tool for clinicians to identify potentially high risks of VOPs.

Whole transcriptome analysis to explore the impaired immunological features in critically ill elderly patients with sepsis

BACKGROUND

Sepsis is a frequent complication in critically ill patients, is highly heterogeneous and is associated with high morbidity and mortality rates, especially in the elderly population. Utilizing RNA sequencing (RNA-Seq) to analyze biological pathways is widely used in clinical and molecular genetic studies, but studies in elderly patients with sepsis are still lacking. Hence, we investigated the mortality-relevant biological features and transcriptomic features in elderly patients who were admitted to the intensive care unit (ICU) for sepsis.

METHODS

We enrolled 37 elderly patients with sepsis from the ICU at Taichung Veterans General Hospital. On day-1 and day-8, clinical and laboratory data, as well as blood samples, were collected for RNA-Seq analysis. We identified the dynamic transcriptome and enriched pathways of differentially expressed genes between day-8 and day-1 through DVID enrichment analysis and Gene Set Enrichment Analysis. Then, the diversity of the T cell repertoire was analyzed with MiXCR.

RESULTS

Overall, 37 patients had sepsis, and responders and non-responders were grouped through principal component analysis. Significantly higher SOFA scores at day-7, longer ventilator days, ICU lengths of stay and hospital mortality were found in the non-responder group, than in the responder group. On day-8 in elderly ICU patients with sepsis, genes related to innate immunity and inflammation, such as ZDHCC19, ALOX15, FCER1A, HDC, PRSS33, and PCSK9, were upregulated. The differentially expressed genes (DEGs) were enriched in the regulation of transcription, adaptive immune response, immunoglobulin production, negative regulation of transcription, and immune response. Moreover, there was a higher diversity of T-cell receptors on day-8 in the responder group, than on day-1, indicating that they had better regulated recovery from sepsis compared with the non-response patients.

CONCLUSION

Sepsis mortality and incidence were both high in elderly individuals. We identified mortality-relevant biological features and transcriptomic features with functional pathway and MiXCR analyses based on RNA-Seq data; and found that the responder group had upregulated innate immunity and increased T cell diversity; compared with the non-responder group. RNA-Seq may be able to offer additional complementary information for the accurate and early prediction of treatment outcome.

Impact on 6-month outcomes of hospital trajectory in critically ill older patients: analysis of the ICE-CUB2 clinical trial

Background

Little is known about the impact of hospital trajectory on survival and functional decline of older critically ill patients. We evaluate 6-month outcomes after admission to: intensive care units (ICU), intermediate care units (IMCU) or acute medical wards (AMW).

Methods

Data from the randomised prospective multicentre clinical trial ICE-CUB2 was secondarily analysed. Inclusion criteria were: presenting at emergency departments in critical condition; age ≥ 75 years; activity of daily living (ADL) ≥ 4; preserved nutritional status; and no active cancer. A Cox model was fitted to compare survival according to admission destination adjusting for patient characteristics. Sensitivity analysis using multiple imputation for missing data and propensity score matching were performed.

Results

Among 3036 patients, 1675 (55%) were women; median age was 85 [81–99] years; simplified acute physiology score (SAPS-3) 62 [55–69]; 1448 (47%) were hospitalised in an ICU, 504 in IMCU (17%), and 1084 (36%) in AMW. Six-month mortality was 629 (44%), 155 (31%) and 489 (45%) after admission in an ICU, IMCU and AMW (p < 0.001), respectively. In multivariate analysis, AMW admission was associated with worse 6-month survival (HR 1.31, 95% CI 1.04–1.63) in comparison with IMCU admission, after adjusting for age, gender, comorbidities, ADL, SAPS-3 and diagnosis. Survival was not significantly different between patients admitted in an ICU and an IMCU (HR 1.17, 95% CI 0.95–1.46). Sensitivity analysis using multiple imputation for missing data and propensity score matching found similar results. Hospital destination was not significantly associated with the composite criterion loss of 1-point ADL or mortality. Physical and mental components of the 12-Item Short-Form Health Survey were significantly lower in the acute medical ward group (34.3 [27.5–41.7], p = 0.037 and 44.3 [38.6–48.6], p = 0.028, respectively) than in the ICU group (34.7 [28.4–45.3] and 45.5 [40.0–50.0], respectively) and IMCU group (35.7 [29.7–43.8] and 44.5 [39.7–48.4], respectively).

Conclusions

Admission in an AMW was associated with worse 6-month survival in older critically ill patients in comparison with IMCU admission, with no difference of survival between ICU and IMCU admission. There were no clinically relevant differences in quality of life in each group. These results should be confirmed in specific studies and raise the question of dedicated geriatric IMCUs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-022-01042-4.

State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review

Background

Modern clinical care in intensive care units is full of rich data, and machine learning has great potential to support clinical decision-making. The development of intelligent machine learning–based clinical decision support systems is facing great opportunities and challenges. Clinical decision support systems may directly help clinicians accurately diagnose, predict outcomes, identify risk events, or decide treatments at the point of care.

Objective

We aimed to review the research and application of machine learning–enabled clinical decision support studies in intensive care units to help clinicians, researchers, developers, and policy makers better understand the advantages and limitations of machine learning–supported diagnosis, outcome prediction, risk event identification, and intensive care unit point-of-care recommendations.

Methods

We searched papers published in the PubMed database between January 1980 and October 2020. We defined selection criteria to identify papers that focused on machine learning–enabled clinical decision support studies in intensive care units and reviewed the following aspects: research topics, study cohorts, machine learning models, analysis variables, and evaluation metrics.

Results

A total of 643 papers were collected, and using our selection criteria, 97 studies were found. Studies were categorized into 4 topics—monitoring, detection, and diagnosis (13/97, 13.4%), early identification of clinical events (32/97, 33.0%), outcome prediction and prognosis assessment (46/97, 47.6%), and treatment decision (6/97, 6.2%). Of the 97 papers, 82 (84.5%) studies used data from adult patients, 9 (9.3%) studies used data from pediatric patients, and 6 (6.2%) studies used data from neonates. We found that 65 (67.0%) studies used data from a single center, and 32 (33.0%) studies used a multicenter data set; 88 (90.7%) studies used supervised learning, 3 (3.1%) studies used unsupervised learning, and 6 (6.2%) studies used reinforcement learning. Clinical variable categories, starting with the most frequently used, were demographic (n=74), laboratory values (n=59), vital signs (n=55), scores (n=48), ventilation parameters (n=43), comorbidities (n=27), medications (n=18), outcome (n=14), fluid balance (n=13), nonmedicine therapy (n=10), symptoms (n=7), and medical history (n=4). The most frequently adopted evaluation metrics for clinical data modeling studies included area under the receiver operating characteristic curve (n=61), sensitivity (n=51), specificity (n=41), accuracy (n=29), and positive predictive value (n=23).

Conclusions

Early identification of clinical and outcome prediction and prognosis assessment contributed to approximately 80% of studies included in this review. Using new algorithms to solve intensive care unit clinical problems by developing reinforcement learning, active learning, and time-series analysis methods for clinical decision support will be greater development prospects in the future.

COVID-19 pandemic in ICU. Limited resources for many patients: approaches and criteria for triaging

The COVID-19 pandemic has shattered the illusion that healthcare resource shortages that require rationing are problems restricted to low- and middle-income countries. During the pandemic surges, many high-income countries have been confronted with unprecedented demands for healthcare systems that dramatically exceeded available resources. Hospitals capacities were overwhelmed, and physicians working in intensive care units (ICUs) were often forced to deny admissions to patients in desperate need of intensive care. To support these difficult decisions, many scientific societies and governmental bodies have developed guidelines on the triage of patients in need of mechanical ventilation and other life-support treatments. The ethical approaches underlying these guidelines were grounded on egalitarian or utilitarian principles. Thus far, however, consensus on the approaches used, and, above all, on the solutions adopted have been limited, giving rise to a clash of opinions that has further complicated health professionals' ability to respond optimally to their patients' needs. As the COVID-19 crisis moves toward a phase of what some have called "pandemic normalcy," the need to debate the merits and demerits of the individual decisions made in the allocation of ICU resources seems less pressing. Instead, the aims of the authors are: 1) to critically review the approaches and criteria used for triaging patients to be admitted in ICU; 2) to clarify how macro- and micro-allocation choices, in their interdependance, can condition decision-making processes regarding the care of individual patients; 3) to reflect on the need for decision-makers and professionals working in ICUs to maintain a proper degree of "honesty" towards citizens and patients regarding the causes of the resource shortages and the decision-making processes, which, in different ways routinely and in crisis times, involve the need to make "tragic choices" at both levels.

Frailty and survival in elderly intensive care patients in Norway

BACKGROUND

Today, 10%-15% of Norwegian intensive care patients are ≥80 years. This proportion will increase significantly over the next 20 years, but it is unlikely that resources for intensive care increase correspondingly. Thus, it is important to establish which patients among elderly people will benefit from intensive care. The main objective of the study was to investigate the relationships between geriatric scoring tools and 30-day mortality.

METHODS

The study included 451 Norwegian patients ≥80 years who were included in two prospective European observation studies (VIP (very old intensive care patient)1 of VIP2). Both studies included clinical frailty scale (CFS) while VIP2 also obtained the geriatric scores, comorbidity and polypharmacy score (CPS), Short Form of Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), and Katz Activity of Daily Living score (Katz ADL).

RESULTS

Survival after 30 days was 59.9%. Risk factors for 30-day mortality were increasing Sequential Organ Failure Assessment (SOFA) score (odds ratio (OR) 1.30; confidence interval (CI) 95% 1.22-1.39) and (CFS) > 3 (CFS 4: OR 1.96 (CI 95% 1.01-3.81); CFS 5-9: OR 1.81 (CI) 95% 1.12-2.93)). Data from VIP2 showed that CFS was the only independent predictor of 30-day mortality when these scores were tested in multivariate analyses separately together with age, SOFA, and gender (OR 1.21 (95% CI 1.03-1.41)).

CONCLUSIONS

Elderly intensive care patients had a 30-day survival rate of 59.9%. Factors strongly associated with 30-day mortality were increasing SOFA score and increasing frailty (CFS). Other geriatric scores had no significant association with survival in multivariate analyses.

Physicians' decreased tendency to choose palliative care for patients with advanced dementia between 1999 and 2015

Background

Physicians’ decision-making for seriously ill patients with advanced dementia is of high importance, especially as the prevalence of dementia is rising rapidly, and includes many challenging ethical, medical and juridical aspects. We assessed the change in this decision-making over 16 years (from 1999 to 2015) and several background factors influencing physicians’ decision.

Methods

A postal survey including a hypothetical patient-scenario representing a patient with an advanced dementia and a life-threatening gastrointestinal bleeding was sent to 1182 and 1258 Finnish physicians in 1999 and 2015, respectively. The target groups were general practitioners (GPs), surgeons, internists and oncologists. The respondents were asked to choose between several life-prolonging and palliative care approaches. The influence of physicians’ background factors and attitudes on their decision were assessed.

Results

The response rate was 56%. A palliative care approach was chosen by 57 and 50% of the physicians in 1999 and 2015, respectively (p = 0.01). This change was statistically significant among GPs (50 vs 40%, p = 0.018) and oncologists (77 vs 56%, p = 0.011). GPs chose a palliative care approach less often than other responders in both years (50 vs. 63% in 1999 and 40 vs. 56% in 2015, p < 0.001). In logistic regression analysis, responding in 2015 and being a GP remained explanatory factors for a lower tendency to choose palliative care. The impact of family’s benefit on the decision-making decreased, whereas the influence of the patient’s benefit and ethical values as well as the patient’s or physician’s legal protection increased from 1999 to 2015.

Conclusions

Physicians chose a palliative care approach for a patient with advanced dementia and life-threatening bleeding less often in 2015 than in 1999. Specialty, attitudes and other background factors influenced significantly physician decision-making. Education on the identification and palliative care of the patients with late-stage dementia are needed to make these decisions more consistent.

ICU-Mortality in Old and Very Old Patients Suffering From Sepsis and Septic Shock

Purpose: Old (>64 years) and very old (>79 years) intensive care patients with sepsis have a high mortality. In the very old, the value of critical care has been questioned. We aimed to compare the mortality, rates of organ support, and the length of stay in old vs. very old patients with sepsis and septic shock in intensive care.

Methods: This analysis included 9,385 patients, from the multi-center eICU Collaborative Research Database, with sepsis; 6184 were old (aged 65–79 years), and 3,201 were very old patients (aged 80 years and older). A multi-level logistic regression analysis was used to fit three sequential regression models for the binary primary outcome of ICU mortality. A sensitivity analysis in septic shock patients (n = 1054) was also conducted.

Results: In the very old patients, the median length of stay was shorter (50 ± 67 vs. 56 ± 72 h; p < 0.001), and the rate of a prolonged ICU stay was lower (>168 h; 9 vs. 12%; p < 0.001) than the old patients. The mortality from sepsis was higher in very old patients (13 vs. 11%; p = 0.005), and after multi-variable adjustment being very old was associated with higher odds for ICU mortality (aOR 1.32, 95% CI 1.09–1.59; p = 0.004). In patients with septic shock, mortality was also higher in the very old patients (38 vs. 36%; aOR 1.50, 95% CI 1.10–2.06; p = 0.01).

Conclusion: Very old ICU-patients suffer from a slightly higher ICU mortality compared with old ICU-patients. However, despite the statistically significant differences in mortality, the clinical relevance of such minor differences seems to be negligible.

Feature engineering combined with 1-D convolutional neural network for improved mortality prediction

Objectives

The appropriate care for patients admitted in Intensive care units (ICUs) is becoming increasingly prominent, thus recognizing the use of machine learning models. The real-time prediction of mortality of patients admitted in ICU has the potential for providing the physician with the interpretable results. With the growing crisis including soaring cost, unsafe care, misdirected care, fragmented care, chronic diseases and evolution of epidemic diseases in the domain of healthcare demands the application of automated and real-time data processing for assuring the improved quality of life. The intensive care units (ICUs) are responsible for generating a wealth of useful data in the form of Electronic Health Record (EHR). This data allows for the development of a prediction tool with perfect knowledge backing.

Method

We aimed to build the mortality prediction model on 2012 Physionet Challenge mortality prediction database of 4,000 patients admitted in ICU. The challenges in the dataset, such as high dimensionality, imbalanced distribution and missing values, were tackled with analytical methods and tools via feature engineering and new variable construction. The objective of the research is to utilize the relations among the clinical variables and construct new variables which would establish the effectiveness of 1-Dimensional Convolutional Neural Network (1-D CNN) with constructed features.

Results

Its performance with the traditional machine learning algorithms like XGBoost classifier, Light Gradient Boosting Machine (LGBM) classifier, Support Vector Machine (SVM), Decision Tree (DT), K-Neighbours Classifier (K-NN), and Random Forest Classifier (RF) and recurrent models like Long Short-Term Memory (LSTM) and LSTM-attention is compared for Area Under Curve (AUC). The investigation reveals the best AUC of 0.848 using 1-D CNN model.

Conclusion

The relationship between the various features were recognized. Also, constructed new features using existing ones. Multiple models were tested and compared on different metrics.

Frailty in Critical Care Medicine: A Review

Traditional approaches to clinical risk assessment utilize age as a marker of increased vulnerability to stress. Relatively recent advancements in the study of aging have led to the concept of the frailty syndrome, which represents a multidimensional state of depleted physiologic and psychosocial reserve and clinical vulnerability that is related to but variably present with advancing age. The frailty syndrome is now a well-established clinical entity that serves as both a guide for clinical intervention and a predictor of poor outcomes in the primary and acute care settings. The biological aspects of the syndrome broadly represent a network of interrelated perturbations involving the age-related accumulation of molecular, cellular, and tissue damage that leads to multisystem dysregulation, functional decline, and disproportionately poor response to physiologic stress. Given the complexity of the underlying biologic processes, several well-validated approaches to define frailty clinically have been developed, each with distinct and reasonable considerations. Stemming from this background, the past several years have seen a number of observational studies conducted in intensive care units that have established that the determination of frailty is both feasible and prognostically useful in the critical care setting. Specifically, frailty as determined by several different frailty measurement tools appears associated with mortality, increased health care utilization, and disability, and has the potential to improve risk stratification of intensive care patients. While substantial variability in the implementation of frailty measurement likely limits the generalizability of specific findings, the overall prognostic trends may offer some assistance in guiding management decisions with patients and their families. Although no trials have assessed interventions to improve the outcomes of critically ill older people living with frailty, the particular vulnerability of this population offers a promising target for intervention in the future.

The good, the bad and the ugly: pandemic priority decisions and triage

In this analysis we discuss the change in criteria for triage of patients during three different phases of a pandemic like COVID-19, seen from the critical care point of view. Availability of critical care beds has become a hot topic, and in many countries, we have seen a huge increase in the provision of temporary intensive care bed capacity. However, there is a limit where the hospitals may run out of resources to provide critical care, which is heavily dependent on trained staff, just-in-time supply chains for clinical consumables and drugs and advanced equipment. In the first (good) phase, we can still do clinical prioritisation and decision-making as usual, based on the need for intensive care and prognostication: what are the odds for a good result with regard to survival and quality of life. In the next (bad phase), the resources are mostly available, but the system is stressed by many patients arriving over a short time period and auxiliary beds in different places in the hospital being used. We may have to abandon admittance of patients with doubtful prognosis. In the last (ugly) phase, usual medical triage and priority setting may not be sufficient to decrease inflow and there may not be enough intensive care unit beds available. In this phase different criteria must be applied using a utilitarian approach for triage. We argue that this is an important transition where society, and not physicians, must provide guidance to support triage that is no longer based on medical priorities alone.