Decision-making in ICU - A systematic review of factors considered important by ICU clinician decision makers with regard to ICU triage decisions

A multivariable analysis to predict variations in hospital mortality using systems-based factors of healthcare delivery to inform improvements to healthcare design within the English NHS

Over the last decade, the strain on the English National Health Service (NHS) has increased. This has been especially felt by acute hospital trusts where the volume of admissions has steadily increased. Patient outcomes, including inpatient mortality, vary between trusts. The extent to which these differences are explained by systems-based factors, and whether they are avoidable, is unclear. Few studies have investigated these relationships. A systems-based methodology recognises the complexity of influences on healthcare outcomes. Rather than clinical interventions alone, the resources supporting a patient's treatment journey have near-equal importance. This paper first identifies suitable metrics of resource and demand within healthcare delivery from routinely collected, publicly available, hospital-level data. Then it proceeds to use univariate and multivariable linear regression to associate such systems-based factors with standardised mortality. Three sequential cross-sectional analyses were performed, spanning the last decade. The results of the univariate regression analyses show clear relationships between five out of the six selected predictor variables and standardised mortality. When these five predicators are included within a multivariable regression analysis, they reliably explain approximately 36% of the variation in standardised mortality between hospital trusts. Three factors are consistently statistically significant: the number of doctors per hospital bed, bed occupancy, and the percentage of patients who are placed in a bed within four hours after a decision to admit them. Of these, the number of doctors per bed had the strongest effect. Linear regression assumption testing and a robustness analysis indicate the observations have internal validity. However, our empirical strategy cannot determine causality and our findings should not be interpreted as established causal relationships. This study provides hypothesis-generating evidence of significant relationships between systems-based factors of healthcare delivery and standardised mortality. These have relevance to clinicians and policymakers alike. While identifying causal relationships between the predictors is left to the future, it establishes an important paradigm for further research.

Limited English Proficiency and Outcomes in the Intensive Care Unit: An Integrated Review

INTRODUCTION

Language barriers place patients at risk of substandard care. Hospitalized patients with limited English proficiency (LEP) face unique challenges, especially in the intensive care unit (ICU). The purpose of this review is to critique and synthesize quantitative evidence on LEP and ICU outcomes.

METHODOLOGY

Quantitative studies published in English between 1999 and 2022 were queried using intentional terminology.

RESULTS

Searches yielded 138 results, with 12 meeting inclusion criteria. The analysis resulted in the extrapolation of five themes pertinent to outcomes of ICU patients or families with LEP: (a) knowledge deficit relating to conditions and care; (b) lack of language-appropriate care; (c) alienation from care process; (d) decreased confidence and ownership of care; and (e) relationship to clinical quality indicators.

DISCUSSION

Outcomes associated with LEP were largely negative and revealed unmet needs for ICU patients with LEP. More research is needed to improve linguistically and culturally congruent care in the ICU.

Physicians' perceptions of intensive care patients' 1-year prognoses compared to realistic prognoses

BACKGROUND

It is unknown whether physicians treating critically ill patients have realistic perceptions of their patients' prognoses.

METHODS

We sent a survey by email to Finnish anesthesiologists to investigate their ability to estimate the probability of 1-year survival of intensive care unit (ICU) patients based on data available at the beginning of intensive care. We presented 12 fictional but real-life-based patient cases and asked the respondent to estimate the probability of 1-year survival in each case by choosing one of the alternatives 5%, 10%-90% in 10% intervals and 95%. We compared the physicians' estimates to registry data-based realistic prognoses of comparable patients treated in the ICU. Based on the difference between the estimate and the realistic prognosis, we categorized the estimates into three groups: (1) difference less than 10 percentage points, (2) difference between 10 and 20 percentage points, and (3) difference over 20 percentage points.

RESULTS

We received 210 responses (totally 2520 estimates). Of the respondents, 43 (20.5%) were specialists working mainly in the ICU, 81 (38.6%) were specialists working occasionally in the ICU, 47 (22.4%) were specialists not working in the ICU, and 39 (18.6%) were doctors in training. The difference between the estimate and the realistic prognosis was less than 10 percentage points for 1083 (43.0%) estimates, between 10 and 20 percentage points for 645 (25.6%) estimates, and over 20 percentage points for 792 (31.4%) estimates, out of which 612 (24.3% of all estimates) underestimated and 180 (7.1%) overestimated the likelihood of survival. The median error (the median of the differences between the estimate and the realistic prognosis) for all estimates was -8.8 [interquartile range (IQR), -20.0 to -0.2], which means that the most typical response underestimated the likelihood of survival by 9 percentage points. Based on the 12 estimates, we calculated the median error for each respondent. The median (IQR) of these median errors was -8.6 (-12.6 to -5.0) for specialists working mainly in the ICU, -8.1 (-13.0 to -5.2) for specialists working occasionally in the ICU, -9.7 (-17.7 to -6.3) for specialists not working in the ICU, and -9.1 (-14.5 to -5.1) for doctors in training (p = .29).

CONCLUSION

Finnish anesthesiologists commonly misestimate the long-term prognoses of ICU patients, more often underestimating than overestimating the likelihood of 1-year survival. More education about critically ill patients' prognoses and better prediction tools are needed.

Factors influencing decision-making processes for intensive care therapy goals: A systematic integrative review

BACKGROUND

Delivering intensive care therapies concordant with patients' values and preferences is considered gold standard care. To achieve this, healthcare professionals must better understand decision-making processes and factors influencing them.

AIM

The aim of this study was to explore factors influencing decision-making processes about implementing and limiting intensive care therapies.

DESIGN

Systematic integrative review, synthesising quantitative, qualitative, and mixed-methods studies.

METHODS

Five databases were searched (Medline, The Cochrane central register of controlled trials, Embase, PsycINFO, and CINAHL plus) for peer-reviewed, primary research published in English from 2010 to Oct 2022. Quantitative, qualitative, or mixed-methods studies focussing on intensive care decision-making were included for appraisal. Full-text review and quality screening included the Critical Appraisal Skills Program tool for qualitative and mixed methods and the Medical Education Research Quality Instrument for quantitative studies. Papers were reviewed by two authors independently, and a third author resolved disagreements. The primary author developed a thematic coding framework and performed coding and pattern identification using NVivo, with regular group discussions.

RESULTS

Of the 83 studies, 44 were qualitative, 32 quantitative, and seven mixed-methods studies. Seven key themes were identified: what the decision is about; who is making the decision; characteristics of the decision-maker; factors influencing medical prognostication; clinician-patient/surrogate communication; factors affecting decisional concordance; and how interactions affect decisional concordance. Substantial thematic overlaps existed. The most reported decision was whether to withhold therapies, and the most common decision-maker was the clinician. Whether a treatment recommendation was concordant was influenced by multiple factors including institutional cultures and clinician continuity.

CONCLUSION

Decision-making relating to intensive care unit therapy goals is complicated. The current review identifies that breadth of decision-makers, and the complexity of intersecting factors has not previously been incorporated into interventions or considered within a single review. Its findings provide a basis for future research and training to improve decisional concordance between clinicians and patients/surrogates with regards to intensive care unit therapies.

Continuous monitoring of physiological data using the patient vital status fusion score in septic critical care patients

Accurate and standardized methods for assessing the vital status of patients are crucial for patient care and scientific research. This study introduces the Patient Vital Status (PVS), which quantifies and contextualizes a patient's physical status based on continuous variables such as vital signs and deviations from age-dependent normative values. The vital signs, heart rate, oxygen saturation, respiratory rate, mean arterial blood pressure, and temperature were selected as input to the PVS pipeline. The method was applied to 70 pediatric patients in the intensive care unit (ICU), and its efficacy was evaluated by matching high values with septic events at different time points in patient care. Septic events included systemic inflammatory response syndrome (SIRS) and suspected or proven sepsis. The comparison of maximum PVS values between the presence and absence of a septic event showed significant differences (SIRS/No SIRS: p < 0.0001, η2 = 0.54; Suspected Sepsis/No Suspected Sepsis: p = 0.00047, η2 = 0.43; Proven Sepsis/No Proven Sepsis: p = 0.0055, η2 = 0.34). A further comparison between the most severe PVS in septic patients with the PVS at ICU discharge showed even higher effect sizes (SIRS: p < 0.0001, η2 = 0.8; Suspected Sepsis: p < 0.0001, η2 = 0.8; Proven Sepsis: p = 0.002, η2 = 0.84). The PVS is emerging as a data-driven tool with the potential to assess a patient's vital status in the ICU objectively. Despite real-world data challenges and potential annotation biases, it shows promise for monitoring disease progression and treatment responses. Its adaptability to different disease markers and reliance on age-dependent reference values further broaden its application possibilities. Real-time implementation of PVS in personalized patient monitoring may be a promising way to improve critical care. However, PVS requires further research and external validation to realize its true potential.

Data Representation Structure to Support Clinical Decision-Making in the Pediatric Intensive Care Unit: Interview Study and Preliminary Decision Support Interface Design

BACKGROUND

Clinical decision-making is a complex cognitive process that relies on the interpretation of a large variety of data from different sources and involves the use of knowledge bases and scientific recommendations. The representation of clinical data plays a key role in the speed and efficiency of its interpretation. In addition, the increasing use of clinical decision support systems (CDSSs) provides assistance to clinicians in their practice, allowing them to improve patient outcomes. In the pediatric intensive care unit (PICU), clinicians must process high volumes of data and deal with ever-growing workloads. As they use multiple systems daily to assess patients' status and to adjust the health care plan, including electronic health records (EHR), clinical systems (eg, laboratory, imaging and pharmacy), and connected devices (eg, bedside monitors, mechanical ventilators, intravenous pumps, and syringes), clinicians rely mostly on their judgment and ability to trace relevant data for decision-making. In these circumstances, the lack of optimal data structure and adapted visual representation hinder clinician's cognitive processes and clinical decision-making skills.

OBJECTIVE

In this study, we designed a prototype to optimize the representation of clinical data collected from existing sources (eg, EHR, clinical systems, and devices) via a structure that supports the integration of a home-developed CDSS in the PICU. This study was based on analyzing end user needs and their clinical workflow.

METHODS

First, we observed clinical activities in a PICU to secure a better understanding of the workflow in terms of staff tasks and their use of EHR on a typical work shift. Second, we conducted interviews with 11 clinicians from different staff categories (eg, intensivists, fellows, nurses, and nurse practitioners) to compile their needs for decision support. Third, we structured the data to design a prototype that illustrates the proposed representation. We used a brain injury care scenario to validate the relevance of integrated data and the utility of main functionalities in a clinical context. Fourth, we held design meetings with 5 clinicians to present, revise, and adapt the prototype to meet their needs.

RESULTS

We created a structure with 3 levels of abstraction-unit level, patient level, and system level-to optimize clinical data representation and display for efficient patient assessment and to provide a flexible platform to host the internally developed CDSS. Subsequently, we designed a preliminary prototype based on this structure.

CONCLUSIONS

The data representation structure allows prioritizing patients via criticality indicators, assessing their conditions using a personalized dashboard, and monitoring their courses based on the evolution of clinical values. Further research is required to define and model the concepts of criticality, problem recognition, and evolution. Furthermore, feasibility tests will be conducted to ensure user satisfaction.

Identifying the need for infection-related consultations in intensive care patients using machine learning models

Infection-related consultations on intensive care units (ICU) have a positive impact on quality of care and clinical outcome. However, timing of these consultations is essential and to date they are typically event-triggered and reactive. Here, we investigate a proactive approach to identify patients in need for infection-related consultations by machine learning models using routine electronic health records. Data was retrieved from a mixed ICU at a large academic tertiary care hospital including 9684 admissions. Infection-related consultations were predicted using logistic regression, random forest, gradient boosting machines, and long short-term memory neural networks (LSTM). Overall, 7.8% of admitted patients received an infection-related consultation. Time-sensitive modelling approaches performed better than static approaches. Using LSTM resulted in the prediction of infection-related consultations in the next clinical shift (up to eight hours in advance) with an area under the receiver operating curve (AUROC) of 0.921 and an area under the precision recall curve (AUPRC) of 0.541. The successful prediction of infection-related consultations for ICU patients was done without the use of classical triggers, such as (interim) microbiology reports. Predicting this key event can potentially streamline ICU and consultant workflows and improve care as well as outcome for critically ill patients with (suspected) infections.

Impact of age on mortality and transfer to long-term care in patients in an intensive care unit

BACKGROUND

In the global trend of population aging, age is one of the significant factors to be considered in critically ill patients. However, the impact of age on clinical outcomes and long-term prognosis in this population varies across different studies.

METHODS

We conducted a retrospective cohort analysis for patients admitted to the medical intensive care unit (ICU) (30 beds) between January 2017 and December 2020 at the tertiary referral hospital in Korea. Patients were classified into three groups according to age: <65 years, old age (65-79 years), and very old age (≥ 80 years). Subsequently, enrolled patients were analyzed for acute mortality and long-term prognosis.

RESULTS

Among the 1584 patients, the median age was 67.0 (57.0-76.0) years, and 65.2% were male. Median ICU length of stay (LOS) (8, 9, and 10 days in < 65, 65-79, and ≥ 80 years, respectively; p = 0.006) and the proportion of patients who were transferred to long-term care hospital at the time of discharge (12.9% vs. 28.3% vs. 39.4%, respectively; p < 0.001) increased with age. Multivariable logistic analysis showed no significant difference in the 28-day mortality in the old age (adjusted odds ratio [aOR] 0.88; 95% confidence interval [CI] 0.65-1.17) and very old age (aOR 1.05; 95% CI 0.71-1.55) groups compared to that in patients with age < 65 years. However, the relevance of the proportion of ICU LOS ≥ 7 days and transfers to other hospitals after discharge increased with age.

CONCLUSIONS

Age did not affect acute mortality in critical illness patients. However, surviving older age groups required more long-term care facilities compared to patients younger than 65 years after acute management. These results indicate that in an aging society, the importance of not only acute management but also long-term care facilities may increase for critical illness patients.

Triage of Critically Ill Patients: Characteristics and Outcomes of Patients Refused as Too Well for Intensive Care

BACKGROUND

The appropriate selection of patients for the intensive care unit (ICU) is a concern in acute care settings. However, the description of patients deemed too well for the ICU has been rarely reported.

METHODS

We conducted a single-centre retrospective observational study of all patients either deemed "too well" for or admitted to the ICU during one year. Refused patients were screened for unexpected events within 7 days, defined as either ICU admission without another indication, or death without treatment limitations. Patients' characteristics and organisational factors were analysed according to refusal status, outcome and delay in ICU admission.

RESULTS

Among 2219 enrolled patients, the refusal rate was 10.4%. Refusal was associated with diagnostic groups, treatment limitations, patients' location on a ward, night time and ICU occupancy. Unexpected events occurred in 16 (6.9%) refused patients. A worse outcome was associated with time spent in hospital before refusal, patients' location on a ward, SOFA score and physician's expertise. Delayed ICU admissions were associated with ICU and hospital length of stay.

CONCLUSIONS

ICU triage selected safely most patients who would have probably not benefited from the ICU. We identified individual and organisational factors associated with ICU refusal, subsequent ICU admission or death.

Inter-hospital Transfer Decision-making During the COVID-19 Pandemic: a Qualitative Study

BACKGROUND

Inter-hospital patient transfers to hospitals with greater resource availability and expertise may improve clinical outcomes. However, there is little guidance regarding how patient transfer requests should be prioritized when hospital resources become scarce.

OBJECTIVE

To understand the experiences of healthcare workers involved in the process of accepting inter-hospital patient transfers during a pandemic surge and determine factors impacting inter-hospital patient transfer decision-making.

DESIGN

We conducted a qualitative study consisting of semi-structured interviews between October 2021 and February 2022.

PARTICIPANTS

Eligible participants were physicians, nurses, and non-clinician administrators involved in the process of accepting inter-hospital patient transfers. Participants were recruited using maximum variation sampling.

APPROACH

Semi-structured interviews were conducted with healthcare workers across Michigan.

KEY RESULTS

Twenty-one participants from 15 hospitals were interviewed (45.5% of eligible hospitals). About half (52.4%) of participants were physicians, 38.1% were nurses, and 9.5% were non-clinician administrators. Three domains of themes impacting patient transfer decision-making emerged: decision-maker, patient, and environmental factors. Decision-makers described a lack of guidance for transfer decision-making. Patient factors included severity of illness, predicted chance of survival, need for specialized care, and patient preferences for medical care. Decision-making occurred within the context of environmental factors including scarce resources at accepting and requesting hospitals, organizational changes to transfer processes, and alternatives to patient transfer including use of virtual care. Participants described substantial moral distress related to transfer triaging.

CONCLUSIONS

A lack of guidance in transfer processes may result in considerable variation in the patients who are accepted for inter-hospital transfer and in substantial moral distress among decision-makers involved in the transfer process. Our findings identify potential organizational changes to improve the inter-hospital transfer process and alleviate the moral distress experienced by decision-makers.

Causes and Outcomes of Intensive Care Admission Refusals: A Retrospective Audit from a Rural Teaching Hospital in Eastern Cape, South Africa

(1) Background: Patients who deserve intensive care unit (ICU) admission may be denied due to a lack of resources, complicating ICU triage decisions for intensive care unit (ICU) clinicians. Among the resources that may be unavailable are trained personnel and monitored beds. In South Africa, the distribution of healthcare resources is reflected in the availability of ICU beds, with more ICU beds available in more affluent areas. Data on ICU refusal rates, reasons for refusal, patient characteristics, and outcomes are scarce in resource-constrained rural settings. Hence, this study sheds light on the ICU refusal rates, reasons for refusal, characteristics, and outcomes of refused patients at NMAH. (2) Methods: This was a three-month retrospective cross-sectional record review of refused and admitted patients from January to March 2022. COVID-19 patients and those younger than 13 years old were excluded. Refusal rates, reasons for refusal, characteristics, and outcomes of refused patients were analysed quantitatively using SPSS VS 20 software. Reasons for refusal were categorised as "too well", "too sick", and "suitable for admission but no resources". (3) Results: A total of 135 patients were discussed for ICU admission at NMAH during the study period; 73 (54.07%) were refused admission, and 62 (45.92%) were admitted. Being considered too sick to benefit from ICU was the most common reason for refusal (53.23%). Too well and no resources contributed 27.42% and 19.35%, respectively. Patients with poor functional status, comorbidities, medical diagnoses, and those referred from the ward or accident and emergency unit rather than the operating room were more likely to be refused ICU admission. Refused patients had a seven-day mortality rate of 47%. (4) Conclusions and recommendations: The study found an unmet need for critical care services at our institution, as well as a need for tools to help clinicians make objective triage decisions for critically ill patients. Therefore, the study suggests a need to improve the quality of services provided outside of the ICU, particularly for patients who were refused ICU admission, to improve their outcomes.

Factors that influence intensive care admission decisions for older people: A systematic review

BACKGROUND

The population worldwide is rapidly ageing, and demand for intensive care is increasing. People aged 85 years and above, known as the oldest old, are particularly vulnerable to critical illness owing to the physiological effects of ageing. Evidence surrounding admission of the oldest old to the intensive care is limited.

OBJECTIVE

The objective of this study was to systematically and comprehensively review and synthesise the published research investigating factors that influence decisions to admit the oldest old to the intensive care unit.

METHOD

This was a systematic review and narrative synthesis. Following a comprehensive search of CINAHL, Embase, and Medline databases, peer-reviewed primary research articles examining factors associated with admission or refusal to admit the oldest old to intensive care were selected. Data were extracted into tables and narratively synthesised.

RESULTS

Six studies met the inclusion criteria. Three studies identified factors associated with admission such as greater premorbid self-sufficiency, patient preferences, alignment between patient and physicians' goals of treatment, age less than 85 years, and absence of cancer, or previous intensive care admission. Factors associated with refusal to admit were identified in all six studies and included limited or no bed availability, level of ICU physician experience, patients being deemed too ill or too well to benefit, and older age.

CONCLUSIONS

Published research investigating decision-making about admission or refusal to admit the oldest old to the intensive care unit is scant. The ageing population and increasing demand for intensive care unit resources has amplified the need for greater understanding of factors that influence decisions to admit or refuse admission of the oldest old to the intensive care unit. Such knowledge may inform guidelines regarding complex practice decisions about admission of the oldest old to an intensive care unit. Such guidelines would ensure the specialty needs of this population are considered and would reduce admission decisions that might disadvantage older people.

Investigating the impact of brief training in decision-making on treatment escalation to intensive care using objective structured clinical examination-style scenarios

Background

The decision to admit patients to the intensive care unit (ICU) is complex. Structuring the decision-making process may be beneficial to patients and decision-makers alike. The aim of this study was to investigate the feasibility and impact of a brief training intervention on ICU treatment escalation decisions using the Warwick model- a structured decision-making framework for treatment escalation decisions.

Methods

Treatment escalation decisions were assessed using Objective Structured Clinical Examination-style scenarios. Participants were ICU and anaesthetic registrars with experience of making ICU admission decisions. Participants completed one scenario, followed by training with the decision-making framework and subsequently a second scenario. Decision-making data was collected using checklists, note entries and post-scenario questionnaires.

Results

Twelve participants were enrolled. Brief decision-making training was successfully delivered during the normal ICU working day. Following training participants demonstrated greater evidence of balancing the burdens and benefits of treatment escalation. On visual analogue scales of 0-10, participants felt better trained to make treatment escalation decisions (4.9 vs 6.8, p = 0.017) and felt their decision-making was more structured (4.7 vs 8.1, p = 0.017).Overall, participants provided positive feedback and reported feeling more prepared for the task of making treatment escalation decisions.

Conclusion

Our findings suggest that a brief training intervention is a feasible way to improve the decision-making process by improving decision-making structure, reasoning and documentation. Training was implemented successfully, acceptable to participants and participants were able to apply their learning. Further studies of regional and national cohorts are needed to determine if training benefit is sustained and generalisable.

Contribution of information about acute and geriatric characteristics to decisions about life-sustaining treatment for old patients in intensive care

BACKGROUND

Life-sustaining treatment (LST) in the intensive care unit (ICU) is withheld or withdrawn when there is no reasonable expectation of beneficial outcome. This is especially relevant in old patients where further functional decline might be detrimental for the self-perceived quality of life. However, there still is substantial uncertainty involved in decisions about LST. We used the framework of information theory to assess that uncertainty by measuring information processed during decision-making.

METHODS

Datasets from two multicentre studies (VIP1, VIP2) with a total of 7488 ICU patients aged 80 years or older were analysed concerning the contribution of information about the acute illness, age, gender, frailty and other geriatric characteristics to decisions about LST. The role of these characteristics in the decision-making process was quantified by the entropy of likelihood distributions and the Kullback-Leibler divergence with regard to withholding or withdrawing decisions.

RESULTS

Decisions to withhold or withdraw LST were made in 2186 and 1110 patients, respectively. Both in VIP1 and VIP2, information about the acute illness had the lowest entropy and largest Kullback-Leibler divergence with respect to decisions about withdrawing LST. Age, gender and geriatric characteristics contributed to that decision only to a smaller degree.

CONCLUSIONS

Information about the severity of the acute illness and, thereby, short-term prognosis dominated decisions about LST in old ICU patients. The smaller contribution of geriatric features suggests persistent uncertainty about the importance of functional outcome. There still remains a gap to fully explain decision-making about LST and further research involving contextual information is required.

TRIAL REGISTRATION

VIP1 study: NCT03134807 (1 May 2017), VIP2 study: NCT03370692 (12 December 2017).

Risk factors associated with unplanned ICU admissions following paediatric surgery: A systematic review

Background

Unplanned admissions to the intensive care unit (ICU) have important implications in the general management of patients. Research in this area has been conducted in the adult and non-surgical population. To date, there is no systematic review addressing risk factors in the paediatric surgical population.

Objectives

To synthesise the information from studies that explore the risk factors associated with unplanned ICU admissions following surgery in children through a systematic review process.

Methods

We conducted a systematic review of published literature (PROSPERO registration CRD42020163766), adhering to the Preferred Reporting of Observational Studies and Meta-Analysis (PRISMA) statement. The Population, Exposure, Comparator, Outcome (PECO) strategy used was based on: population – paediatric population, exposure – risk factors, comparator – other, and outcome – unplanned ICU admission. Data that reported on unplanned ICU admissions following paediatric surgery were extracted and analysed. Quality of the studies was assessed using the Newcastle-Ottawa Scale.

Results

Seven studies were included in the data synthesis. Four studies were of good quality with the Newcastle-Ottawa Scale score ≥7 points. The pooled prevalence (95% confidence interval) estimate of unplanned ICU stay was 2.69% (0.05 - 8.6%) and ranged between 0.06% and 8.3%. Significant risk factors included abnormal sleep studies and the presence of comorbidities in adenotonsillectomy surgery. In the general surgical population, younger age, comorbidities and general anaesthesia were significant. Abdominal surgery and ear, nose and throat (ENT) surgery resulted in a higher risk of unplanned ICU admission. Owing to the heterogeneity of the data, a meta-analysis with risk prediction could not be performed.

Conclusion

Significant patient, surgical and anaesthetic risk factors associated with unplanned ICU admission in children following surgery are described in this systematic review. A combination of these factors may direct planning toward anticipation of the need for a higher level of postoperative care. Further work to develop a predictive score for unplanned ICU stay is desirable.

Contributions of the study

Unplanned admissions to the intensive care unit (ICU) have been acknowledged as an overall marker of safety.^[1] Awareness of this concept has encouraged research to determine the incidence and risk factors of these occurrences. This research has been interrogated in a systematic review process with beneficial conclusions drawn; however, these studies included adults and non-surgical patients.^[2–4] To date, we have not been able to find a systematic review addressing the risk factors associated with unplanned ICU admissions in paediatric surgical patients.

CRITCON-Pandemic levels: A stepwise ethical approach to clinician responsibility

CRITCON-Pandemic levels with an associated operational responsibility matrix were recently published by the Intensive Care Society as a modification to Winter Flu CRITCON levels, to better account for differences between a winter flu surge in critical care activity and the capacity challenges of the COVID-19 pandemic. In this paper, we propose an expansion and explanation of the operational matrix to suggest a stepwise ethical approach to clinician responsibility. We propose and outline the main ethical risks created at each level and discuss how those risks can be mitigated through a balanced application of the predominant ethical principle which in turn provides practical guidance to clinician responsibility. We thus seek to specify the ethical and legal principles that should be used in applying the operational matrix, and what the practical effects could be.

Intensive Care Unit Utilization Following Major Surgery and the Nurse Work Environment

BACKGROUND

Across hospitals, there is wide variation in ICU utilization after surgery. However, it is unknown whether and to what extent the nurse work environment is associated with a patient's odds of admission to an intensive care unit.

PURPOSE

To estimate the relationship between hospitals' nurse work environment and a patient's likelihood of ICU admission and mortality following surgery.

METHODS

A cross-sectional study of 269 764 adult surgical patients in 453 hospitals was conducted. Logistic regression models were used to estimate the effects of the work environment on the odds of patients' admission to the intensive care unit and mortality.

RESULTS

Patients in hospitals with good work environments had 16% lower odds of intensive care unit admission and 15% lower odds of mortality or intensive care unit admission than patients in hospitals with mixed or poor environments.

CONCLUSIONS

Patients in hospitals with better nurse work environments were less likely to be admitted to an intensive care unit and less likely to die. Hospitals with better nurse work environments may be better equipped to provide postoperative patient care on lower acuity units.

End-of-Life Decisions in Intensive Care Units in Croatia-Pre COVID-19 Perspectives and Experiences From Nurses and Physicians

Healthcare professionals working in intensive care units (ICUs) are often involved in end-of-life decision-making. No research has been done so far about these processes taking place in Croatian ICUs. The aim of this study was to investigate the perceptions, experiences, and challenges healthcare professionals face when dealing with end-of-life decisions in ICUs in Croatia. A qualitative study was performed using professionally homogenous focus groups of ICU nurses and physicians (45 in total) of diverse professional and clinical backgrounds at three research sites (Zagreb, Rijeka, Split). In total, six institutions at the tertiary level of healthcare were included. The constant comparative analysis method was used in the analysis of the data. Differences were found between the perceptions and experiences of nurses and physicians in relation to end-of-life decisions. Nurses' perceptions were more focused on the context and features of immediate care, while physicians' perceptions also included the wider sociocultural context. However, the critical issues these specific professional groups face when dealing with end-of-life decisions seem to overlap. A high variability of practices, both between individual practitioners and between different organizational units, was omnipresent. The lack of adequate legal, professional, and clinical guidelines was commonly expressed as one of the most critical source of difficulties.

Demand for Intensive Care beds and patient classification according to the priority criterion

OBJECTIVE

to assess the demand for Intensive Care Unit beds as well as the classification of the patients for admission, according to the priority system.

METHOD

a retrospective and cross-sectional study, developed from January2014 to December2018 in two Intensive Care Units for adults of a university hospital. The sample consisted of the requests for vacancies according to the priority system(scale from 1 to 4, where 1 is the highest priority and 4 is no priority), registered in the institution's electronic system.

RESULTS

a total of 8,483 vacancies were requested, of which 4,389(51.7%) were from unitB. The highest percentage in unitA was of Priority2 patients(32.6%); and Priority1 was prevalent in unitB(45.4%). The median lead time between request and admission to unitA presented a lower value for priority1 patients(2h57) and a higher value for priority4 patients(11h24); in unitB, priority4 patients presented shorter time(5h54) and priority3 had longer time(11h54). 40.5% of the requests made to unitA and 48.5% of those made to unitB were fulfilled, with 50.7% and 48.5% of these patients being discharged from the units, respectively.

CONCLUSION

it is concluded that the demand for intensive care beds was greater than their availability. Most of the patients assisted were priorities1 and2, although a considerable percentage of those classified as priorities3 and4 is observed.

Physicians' Views and Agreement about Patient- and Context-Related Factors Influencing ICU Admission Decisions: A Prospective Study

Background: Single patient- and context-related factors have been associated with admission decisions to intensive care. How physicians weigh various factors and integrate them into the decision-making process is not well known. Objectives: First, to determine which patient- and context-related factors influence admission decisions according to physicians, and their agreement about these determinants; and second, to examine whether there are differences for patients with and without advanced disease. Method: This study was conducted in one tertiary hospital. Consecutive ICU consultations for medical inpatients were prospectively included. Involved physicians, i.e., internists and intensivists, rated the importance of 13 factors for each decision on a Likert scale (1 = negligible to 5 = predominant). We cross-tabulated these factors by presence or absence of advanced disease and examined the degree of agreement between internists and intensivists using the kappa statistic. Results: Of 201 evaluated patients, 105 (52.2%) had an advanced disease, and 140 (69.7%) were admitted to intensive care. The mean number of important factors per decision was 3.5 (SD 2.4) for intensivists and 4.4 (SD 2.1) for internists. Patient’s comorbidities, quality of life, preferences, and code status were most often mentioned. Inter-rater agreement was low for the whole population and after stratifying for patients with and without advanced disease. Kappa values ranged from 0.02 to 0.34 for all the patients, from −0.05 to 0.42 for patients with advanced disease, and from −0.08 to 0.32 for patients without advanced disease. The best agreement was found for family preferences. Conclusion: Poor agreement between physicians about patient- and context-related determinants of ICU admission suggests a lack of explicitness during the decision-making process. The potential consequences are increased variability and inequity regarding which patients are admitted. Timely advance care planning involving families could help physicians make the decision most concordant with patient preferences.

Detecting Deteriorating Patients in the Hospital: Development and Validation of a Novel Scoring System

Rationale: Late recognition of patient deterioration in hospital is associated with worse outcomes, including higher mortality. Despite the widespread introduction of early warning score (EWS) systems and electronic health records, deterioration still goes unrecognized. Objectives: To develop and externally validate a Hospital- wide Alerting via Electronic Noticeboard (HAVEN) system to identify hospitalized patients at risk of reversible deterioration. Methods: This was a retrospective cohort study of patients 16 years of age or above admitted to four UK hospitals. The primary outcome was cardiac arrest or unplanned admission to the ICU. We used patient data (vital signs, laboratory tests, comorbidities, and frailty) from one hospital to train a machine-learning model (gradient boosting trees). We internally and externally validated the model and compared its performance with existing scoring systems (including the National EWS, laboratory-based acute physiology score, and electronic cardiac arrest risk triage score). Measurements and Main Results: We developed the HAVEN model using 230,415 patient admissions to a single hospital. We validated HAVEN on 266,295 admissions to four hospitals. HAVEN showed substantially higher discrimination (c-statistic, 0.901 [95% confidence interval, 0.898-0.903]) for the primary outcome within 24 hours of each measurement than other published scoring systems (which range from 0.700 [0.696-0.704] to 0.863 [0.860-0.865]). With a precision of 10%, HAVEN was able to identify 42% of cardiac arrests or unplanned ICU admissions with a lead time of up to 48 hours in advance, compared with 22% by the next best system. Conclusions: The HAVEN machine-learning algorithm for early identification of in-hospital deterioration significantly outperforms other published scores such as the National EWS.

When do caregivers ignore the veil of ignorance? An empirical study on medical triage decision-making

In principle, all patients deserve to receive optimal medical treatment equally. However, in situations in which there is scarcity of time or resources, medical treatment must be prioritized based on a triage. The conventional guidelines of medical triage mandate that treatment should be provided based solely on medical necessity regardless of any non-medical value-oriented considerations ("worst-first"). This study empirically examined the influence of value-oriented considerations on medical triage decision-making. Participants were asked to prioritize medical treatment relating to four case scenarios of an emergency situation resulting from a car collision. The cases differ by situational characteristics pertaining to the at-fault driver, which were related to culpability attribution.In three case scenarios most participants gave priority to the most severely injured individual, unless the less severely injured individual was their brother. Nevertheless, in the aftermath of a vehicle-ramming terror attack most participants prioritized the less severely injured individual ("victim-first").Our findings indicate that when caregivers are presented with concrete highly conflictual triage situations their choices may be based on value-oriented considerations related to contextual characteristics of the emergency situation. Philosophical and practical ramifications of our findings are discussed.

Development of a practically usable prediction model for quality of life of ICU survivors: A sub-analysis of the MONITOR-IC prospective cohort study

PURPOSE

As the goal of ICU treatment is survival in good health, we aimed to develop a prediction model for ICU survivors' change in quality of life (QoL) one year after ICU admission.

MATERIALS & METHODS

This is a sub-study of the prospective cohort MONITOR-IC study. Adults admitted ≥12 h to the ICU of a university hospital between July 2016-January 2019 were included. Moribund patients were excluded. Change in QoL one year after ICU admission was quantified using the EuroQol five-dimensional (EQ-5D-5L) questionnaire, and Short-Form 36 (SF-36). Multivariable linear regression analysis and best subsets regression analysis (SRA) were used. Models were internally validated by bootstrapping.

RESULTS

The PREdicting PAtients' long-term outcome for Recovery (PREPARE) model was developed (n = 1308 ICU survivors). The EQ-5D-models had better predictive performance than the SF-36-models. Explained variance (adjusted R²) of the best model (33 predictors) was 58.0%. SRA reduced the number of predictors to 5 (adjusted R² = 55.3%, SE = 0.3), including QoL, diagnosis of a Cardiovascular Incident and frailty before admission, sex, and ICU-admission following planned surgery.

CONCLUSIONS

Though more long-term data are needed to ascertain model accuracy, in future, the PREPARE model may be used to better inform and prepare patients and their families for ICU recovery.

Physicians' perspective on potentially non-beneficial treatment when assessing patients with advanced disease for ICU admission: a qualitative study

OBJECTIVE

The use of intensive care at the end of life can be high, leading to inappropriate healthcare utilisation, and prolonged suffering for patients and families. The objective of the study was to determine which factors influence physicians' admission decisions in situations of potentially non-beneficial intensive care.

DESIGN

This is a secondary analysis of a qualitative study exploring the triage process. In-depth interviews were analysed using an inductive approach to thematic content analysis.

SETTING

Data were collected in a Swiss tertiary care centre between March and June 2013.

PARTICIPANTS

12 intensive care unit (ICU) physicians and 12 internists routinely involved in ICU admission decisions.

RESULTS

Physicians struggled to understand the request for intensive care for patients with advanced disease and full code status. Physicians considered patients' long-term vital and functional prognosis, but they also resorted to shortcuts, that is, a priori consensus about reasons for admitting a patient. Family pressure and unexpected critical events were determinants of admission to the ICU. Patient preferences, ICU physician's expertise and collaborative decision making facilitated refusal. Physicians were willing to admit a patient with advanced disease for a limited amount of time to fulfil a personal need.

CONCLUSIONS

In situations of potentially non-beneficial intensive care, the influence of shortcuts or context-related factors suggests that practice variations and inappropriate admission decisions are likely to occur. Institutional guidelines and timely goals of care discussions with patients with advanced disease and their families could contribute to ensuring appropriate levels of care.

Use of intensive care unit priority model in directing intensive care unit admission in Sudan: A prospective cross-sectional study

Background:

The shortage of specialized intensive care beds is one of the principal factors that limit intensive care unit (ICU) admissions. This study explores the utilization of priority criteria in directing ICU admission and predicting outcomes.

Methods:

This was a prospective cross-sectional study conducted in two ICUs in Sudan from April to December 2018. Patients were assessed for ICU admission and were ranked by priority into Groups 1, 2, 3, and 4 (1 highest priority and 4 lowest priority), and these groups were compared using independent t-test, Chi-square, and ANOVA.

Results:

A total of 180 ICU admitted patients were enrolled, 53% were male. The prioritization categories showed that 86 (47.8%), 50 (27.8%), 13 (7.2%), and 31 (17.2%) were categorized as priority 1, 2, 3, and 4, respectively. Patients in priority groups 3 and 4had significantly higher ICU mortality rates compared to those in groups 1 and 2 (P < 0.001), were likely to be older (P < 0.001), had significantly more comorbidities (P = 0.001), were more likely to be dependent (P < 0.001), and had longer ICU length of stay (P = 0.028).

Conclusion:

Patients classified as priority 3 and 4 were predominantly older and had many comorbidities. They were likely to be dependent, stay longer in ICU, and exhibit mortality.

Direct and indirect neurological, cognitive, and behavioral effects of COVID-19 on the healthy elderly, mild-cognitive-impairment, and Alzheimer's disease populations

Introduction

Healthy elderly, mild cognitive impairment and Alzheimer’s disease populations have been among the most affected in the early stages of the COVID-19 pandemic due to the direct effects of the virus, and numerous indirect effects now emerge and will have to be carefully assessed over time.

Methods

This article reviews the main articles that have been published so far about the direct and indirect effects of the COVID-19 pandemic on these particularly fragile populations.

Results

The pandemic associated to COVID-19 has shifted most of the health resources to the emergency area and has consequently left the three main medical areas dealing with the elderly population (oncology, time-dependent diseases and degenerative disease) temporarily “uncovered”. In the phase following the emergency, it will be crucial to guarantee to each area the economic and organizational resources to quickly return to the level of support of the prepandemic state.

Conclusions

The emergency phase represented a significant occasion of discussion on the possibilities of telemedicine which will inevitably become increasingly important, but all the limits of its use in the elderly population have to be considered. In the post-lockdown recovery phase, alongside the classic medical evaluation, the psychological evaluation must become even more important for doctors caring about people with cognitive decline as well as with their caregivers.

Predictors of Delayed Recognition of Critical Illness in Emergency Department Patients and Its Effect on Morbidity and Mortality

PURPOSE

Timely recognition of critical illness is associated with improved outcomes, but is dependent on accurate triage, which is affected by system factors such as workload and staffing. We sought to first study the effect of delayed recognition on patient outcomes after controlling for system factors and then to identify potential predictors of delayed recognition.

METHODS

We conducted a retrospective cohort study of Emergency Department (ED) patients admitted to the Intensive Care Unit (ICU) directly from the ED or within 48 hours of ED departure. Cohort characteristics were obtained through electronic and standardized chart abstraction. Operational metrics to estimate ED workload and volume using census data were matched to patients' ED stays. Delayed recognition of critical illness was defined as an absence of an ICU consult in the ED or declination of ICU admission by the ICU team. We employed entropy-balanced multivariate models to examine the association between delayed recognition and development of persistent organ dysfunction and/or death by hospitalization day 28 (POD+D), and multivariable regression modeling to identify factors associated with delayed recognition.

RESULTS

Increased POD+D was seen for those with delayed recognition (OR 1.82, 95% CI 1.13-2.92). When the delayed recognition was by the ICU team, the patient was 2.61 times more likely to experience POD+D compared to those for whom an ICU consult was requested and were accepted for admission. Lower initial severity of illness score (OR 0.26, 95% CI 0.12-0.53) was predictive of delayed recognition. The odds for delayed recognition decreased when ED workload is higher (OR 0.45, 95% CI 0.23-0.89) compared to times with lower ED workload.

CONCLUSIONS

Increased POD+D is associated with delayed recognition. Patient and system factors such as severity of illness and ED workload influence the odds of delayed recognition of critical illness and need further exploration.

Triage and Ongoing Care for Critically Ill Patients in the Emergency Department: Results from a National Survey of Emergency Physicians

INTRODUCTION

We conducted a cross-sectional study at the Icahn School of Medicine at Mount Sinai to elicit emergency physician (EP) perceptions regarding intensive care unit (ICU) triage decisions and ongoing management for boarding of ICU patients in the emergency department (ED). We assessed factors influencing the disposition decision for critically ill patients in the ED to characterize EPs' perceptions about ongoing critical care delivery in the ED while awaiting ICU admission.

METHODS

Through content expert review and pilot testing, we iteratively developed a 25-item written survey targeted to EPs, eliciting current ICU triage structure, opinions on factors influencing ICU admission decisions, and views on caring for critically ill patients "boarding" in the ED for >4-6 hours.

RESULTS

We approached 732 EPs at a large, national emergency medicine conference, achieving 93.6% response and completion rate, with 54% academic and 46% community participants. One-fifth reported having formal ICU admission criteria, although only 36.6% reported adherence. Common factors influencing EPs' ICU triage decisions were illness severity (91.1%), ICU interventions needed (87.6%), and diagnosis (68.2%), while ICU bed availability (13.5%) and presence of other critically ill patients in ED (10.2%) were less or not important. While 72.1% reported frequently caring for ICU boarders, respondents identified high patient volume (61.3%) and inadequate support staffing (48.6%) as the most common challenges in caring for boarding ICU patients.

CONCLUSION

Patient factors (eg, diagnosis, illness severity) were seen as more important than system factors (eg, bed availability) in triaging ED patients to the ICU. Boarding ICU patients is a common challenge for more than two-thirds of EPs, exacerbated by ED volume and staffing constraints.

Time-limited trials: A qualitative study exploring the role of time in decision-making on the Intensive Care Unit

Background

Withholding and withdrawing treatment are deemed ethically equivalent by most Bioethicists, but intensivists often find withdrawing more difficult in practice. This can lead to futile treatment being prolonged. Time-limited trials have been proposed as a way of promoting timely treatment withdrawal whilst giving the patient the greatest chance of recovery. Despite being in UK guidelines, time-limited trials have been infrequently implemented on Intensive Care Units. We will explore the role of time in Intensive Care Unit decision-making and provide a UK perspective on debates surrounding time-limited trials.

Methods

This qualitative study recruited 18 participants (nine doctors, nine nurses) from two Intensive Care Units in North West England for in-depth, one-to-one semi-structured interviews. A thematic analysis was performed of the data.

Results

Our findings show time is utilised by Intensive Care Unit staff in a variety of ways including managing uncertainty when making decisions about a patient’s prognosis or the reversibility of a disease, constructing relationships with patients’ relatives, communicating difficult messages to patients’ relatives, justifying resource allocation decisions to colleagues, and demonstrating compassion towards patients and their families.

Conclusions

Time shifts the balance towards greater certainty in Intensive Care Unit decision-making, by demonstrating futility, and can ease the difficult transition for staff and families from active treatment to palliation. However, this requires clear and open communication, both within the Intensive Care Unit team and with the family, being prioritised when time is used in decision-making.

Changes in End-of-Life Practices in European Intensive Care Units From 1999 to 2016

IMPORTANCE

End-of-life decisions occur daily in intensive care units (ICUs) around the world, and these practices could change over time.

OBJECTIVE

To determine the changes in end-of-life practices in European ICUs after 16 years.

DESIGN, SETTING, AND PARTICIPANTS

Ethicus-2 was a prospective observational study of 22 European ICUs previously included in the Ethicus-1 study (1999-2000). During a self-selected continuous 6-month period at each ICU, consecutive patients who died or had any limitation of life-sustaining therapy from September 2015 until October 2016 were included. Patients were followed up until death or until 2 months after the first treatment limitation decision.

EXPOSURES

Comparison between the 1999-2000 cohort vs 2015-2016 cohort.

MAIN OUTCOMES AND MEASURES

End-of-life outcomes were classified into 5 mutually exclusive categories (withholding of life-prolonging therapy, withdrawing of life-prolonging therapy, active shortening of the dying process, failed cardiopulmonary resuscitation [CPR], brain death). The primary outcome was whether patients received any treatment limitations (withholding or withdrawing of life-prolonging therapy or shortening of the dying process). Outcomes were determined by senior intensivists.

RESULTS

Of 13 625 patients admitted to participating ICUs during the 2015-2016 study period, 1785 (13.1%) died or had limitations of life-prolonging therapies and were included in the study. Compared with the patients included in the 1999-2000 cohort (n = 2807), the patients in 2015-2016 cohort were significantly older (median age, 70 years [interquartile range {IQR}, 59-79] vs 67 years [IQR, 54-75]; P < .001) and the proportion of female patients was similar (39.6% vs 38.7%; P = .58). Significantly more treatment limitations occurred in the 2015-2016 cohort compared with the 1999-2000 cohort (1601 [89.7%] vs 1918 [68.3%]; difference, 21.4% [95% CI, 19.2% to 23.6%]; P < .001), with more withholding of life-prolonging therapy (892 [50.0%] vs 1143 [40.7%]; difference, 9.3% [95% CI, 6.4% to 12.3%]; P < .001), more withdrawing of life-prolonging therapy (692 [38.8%] vs 695 [24.8%]; difference, 14.0% [95% CI, 11.2% to 16.8%]; P < .001), less failed CPR (110 [6.2%] vs 628 [22.4%]; difference, -16.2% [95% CI, -18.1% to -14.3%]; P < .001), less brain death (74 [4.1%] vs 261 [9.3%]; difference, -5.2% [95% CI, -6.6% to -3.8%]; P < .001) and less active shortening of the dying process (17 [1.0%] vs 80 [2.9%]; difference, -1.9% [95% CI, -2.7% to -1.1%]; P < .001).

CONCLUSIONS AND RELEVANCE

Among patients who had treatment limitations or died in 22 European ICUs in 2015-2016, compared with data reported from the same ICUs in 1999-2000, limitations in life-prolonging therapies occurred significantly more frequently and death without limitations in life-prolonging therapies occurred significantly less frequently. These findings suggest a shift in end-of-life practices in European ICUs, but the study is limited in that it excluded patients who survived ICU hospitalization without treatment limitations.