Decision-making around admission to intensive care in the UK pre-COVID-19: a multicentre ethnographic study

Examining how goals of care communication are conducted between doctors and patients with severe acute illness in hospital settings: A realist systematic review

BACKGROUND

Patient involvement in goals of care decision-making has shown to enhance satisfaction, affective-cognitive outcomes, allocative efficiency, and reduce unwarranted clinical variation. However, the involvement of patients in goals of care planning within hospitals remains limited, particularly where mismatches in shared understanding between doctors and patients are present.

AIM

To identify and critically examine factors influencing goals of care conversations between doctors and patients during acute hospital illness.

DESIGN

Realist systematic review following the RAMESES standards. A protocol has been published in PROSPERO (CRD42021297410). The review utilised realist synthesis methodology, including a scoping literature search to generate initial theories, theory refinement through stakeholder consultation, and a systematic literature search to support program theory.

DATA SOURCES

Data were collected from Medline, PubMed, Embase, CINAHL, PsychINFO, Scopus databases (1946 to 14 July 2023), citation tracking, and Google Scholar. Open-Grey was utilized to identify relevant grey literature. Studies were selected based on relevance and rigor to support theory development.

RESULTS

Our analysis included 52 papers, supporting seven context-mechanism-output (CMO) hypotheses. Findings suggest that shared doctor-patient understanding relies on doctors being confident, competent, and personable to foster trusting relationships with patients. Low doctor confidence often leads to avoidance of discussions. Moreover, information provided to patients is often inconsistent, biased, procedure-focused, and lacks personalisation. Acute illness, medical jargon, poor health literacy, and high emotional states further hinder patient understanding.

CONCLUSIONS

Goals of care conversations in hospitals are nuanced and often suboptimal. To improve patient experiences and outcome of care interventions should be personalised and tailored to individual needs, emphasizing effective communication and trusting relationships among patients, families, doctors, and healthcare teams. Inclusion of caregivers and acknowledgment at the service level are crucial for achieving desired outcomes. Implications for policy, research, and clinical practice, including further training and skills development for doctors, are discussed.

A novel score for predicting 1-year mortality of intensive care patients

BACKGROUND

We aimed to develop a simple scoring table for predicting probability of death within 1-year after admission to an intensive care unit. We analysed data on emergency admissions from the nationwide Finnish intensive care quality registry.

METHODS

We included first admissions of adult patients with data available on 1-year vital status (dead or alive) and all five variables included in a premorbid functional status score, which is the number of activities the person can manage independently of the following five: get out of bed, move indoors, dress, climb stairs and walk 400 m. We analysed data on patient characteristics and admission-associated factors from 2012 to 2014 to find predictors of 1-year mortality and to develop a score for predicting probability of death. We tested the performance of this score in data from 2015. We assessed the 1-year functional status score of survivors with data available.

RESULTS

Out of 25,261 patients, 20,628 (81.7%) patients were able to perform all five functional activities independently prior to the intensive care unit admission. At 1-year post admission, 19,625 (77.7%) patients were alive. 1-year functional status score was known for 11,011 patients and 8970 (81.5%) patients achieved functional status score 5, managing all five activities independently. The score based on age, sex, preceding functional status, type of intensive care unit admission, severity of acute illness and the most significant diagnoses predicted 1-year mortality with an area under the receiver operating characteristic curve 0.78 (95% CI, 0.76-0.79). The calibration of our prediction model was good, with calibration intercept -0.01 (-0.07 to 0.05) and calibration slope 0.96 (0.90 to 1.02).

CONCLUSION

Our score based on data available at intensive care unit admission predicted 1-year mortality with fairly good discrimination. Most survivors achieved good functional recovery.

Limiting life-sustaining treatment for very old ICU patients: cultural challenges and diverse practices

BACKGROUND

Decisions about life-sustaining therapy (LST) in the intensive care unit (ICU) depend on predictions of survival as well as the expected functional capacity and self-perceived quality of life after discharge, especially in very old patients. However, prognostication for individual patients in this cohort is hampered by substantial uncertainty which can lead to a large variability of opinions and, eventually, decisions about LST. Moreover, decision-making processes are often embedded in a framework of ethical and legal recommendations which may vary between countries resulting in divergent management strategies.

METHODS

Based on a vignette scenario of a multi-morbid 87-year-old patient, this article illustrates the spectrum of opinions about LST among intensivsts with a special interest in very old patients, from ten countries/regions, representing diverse cultures and healthcare systems.

RESULTS

This survey of expert opinions and national recommendations demonstrates shared principles in the management of very old ICU patients. Some guidelines also acknowledge cultural differences between population groups. Although consensus with families should be sought, shared decision-making is not formally required or practised in all countries.

CONCLUSIONS

This article shows similarities and differences in the decision-making for LST in very old ICU patients and recommends strategies to deal with prognostic uncertainty. Conflicts should be anticipated in situations where stakeholders have different cultural beliefs. There is a need for more collaborative research and training in this field.

Doctors' experiences of referring and admitting patients to the intensive care unit: a qualitative study of doctors' practices at two tertiary hospitals in Malawi

OBJECTIVE

To explore doctors' experiences of referring and admitting patients to the intensive care unit (ICU) at two tertiary hospitals in Malawi.

DESIGN

This was a qualitative study that used face-to-face interviews. The interviews were audiotaped and transcribed verbatim into English. The data were analysed manually through conventional content analysis.

SETTING

Two public tertiary hospitals in the central and southern regions of Malawi. Interviews were conducted from January to June 2021.

PARTICIPANTS

Sixteen doctors who were involved in the referral and admission of patients to the ICU.

RESULTS

Four themes were identified namely, lack of clear admission criteria, ICU admission requires a complex chain of consultations, shortage of ICU resources, and lack of an ethical and legal framework for discontinuing treatment of critically ill patients who were too sick to benefit from ICU.

CONCLUSION

Despite the acute disease burden and increased demand for ICU care, the two hospitals lack clear processes for referring and admitting patients to the ICU. Given the limited bed space in ICUs, hospitals in low-income countries, including Malawi, need to improve or develop admission criteria, severity scoring systems, ongoing professional development activities, and legislation for discontinuing intensive care treatments and end-of-life care.

CHALLENGES AT THE FRONT: EXPERIENCES OF PROFESSIONALS IN ADMITTING PATIENTS TO THE INTENSIVE CARE UNIT DURING THE COVID-19 PANDEMIC

ABSTRACT Objective: to know the perspectives, practices and challenges in decision-making for admitting patients into the Intensive Care Unit during the Covid-19 pandemic. Methods: a qualitative study developed in two public hospitals in Maranhão, Brazil, from November/2020 to January/2021. Data collection took place through individual interviews guided by a script. A total of 22 professionals participated in the study: nurses and doctors who worked in the Intensive Care Unit and Bed Regulation in the first wave of the pandemic. Content Analysis was used in the thematic mode, with support from the Qualitative Data Analysis software program for data categorization. The theory of Responsibility for Reasonableness guided the study. Results: two main categories emerged: “The context of the decision-making process - the paradox of celestial discharges” and “Decision-making for admission”. In the scenario of high demand, a lack of beds, and the uncertainties of the “new disease”, deciding who would occupy the bed was arduous and conflicting. Clinical and non-clinical criteria such as severity, chance of survival, distance to be covered and transport conditions were considered. It was found that the ambivalence of feelings attributed to death and care at that moment of the pandemic marked the social and technical environment of intensive care. Conclusions: the complexity of the decision-making process for admission to an intensive care unit was evidenced, demonstrating the importance of analyzing the allocation of critical resources in pandemic scenarios. Knowing the perspectives of professionals and their reflections on the experiences in that period can help in planning the allocation of health resources in future emergency scenarios.

Understanding inequalities in COVID-19 outcomes following hospital admission for people with intellectual disability compared to the general population: a matched cohort study in the UK

OBJECTIVES

This study explores the hospital journey of patients with intellectual disabilities (IDs) compared with the general population after admission for COVID-19 during the first wave of the pandemic (when demand on inpatient resources was high) to identify disparities in treatment and outcomes.

DESIGN

Matched cohort study; an ID cohort of 506 patients were matched based on age, sex and ethnicity with a control group using a 1:3 ratio to compare outcomes from the International Severe Acute Respiratory and emerging Infections Consortium WHO Clinical Characterisation Protocol UK.

SETTING

Admissions for COVID-19 from UK hospitals; data on symptoms, severity, access to interventions, complications, mortality and length of stay were extracted.

INTERVENTIONS

Non-invasive respiratory support, intubation, tracheostomy, ventilation and admission to intensive care units (ICU).

RESULTS

Subjective presenting symptoms such as loss of taste/smell were less frequently reported in ID patients, whereas indicators of more severe disease such as altered consciousness and seizures were more common. Controls had higher rates of cardiovascular risk factors, asthma, rheumatological disorder and smoking. ID patients were admitted with higher respiratory rates (median=22, range=10-48) and were more likely to require oxygen therapy (35.1% vs 28.9%). Despite this, ID patients were 37% (95% CI 13% to 57%) less likely to receive non-invasive respiratory support, 40% (95% CI 7% to 63%) less likely to receive intubation and 50% (95% CI 30% to 66%) less likely to be admitted to the ICU while in hospital. They had a 56% (95% CI 17% to 102%) increased risk of dying from COVID-19 after they were hospitalised and were dying 1.44 times faster (95% CI 1.13 to 1.84) compared with controls.

CONCLUSIONS

There have been significant disparities in healthcare between people with ID and the general population during the COVID-19 pandemic, which may have contributed to excess mortality in this group.

Deep Learning for Identification of Acute Illness and Facial Cues of Illness

Background: The inclusion of facial and bodily cues (clinical gestalt) in machine learning (ML) models improves the assessment of patients' health status, as shown in genetic syndromes and acute coronary syndrome. It is unknown if the inclusion of clinical gestalt improves ML-based classification of acutely ill patients. As in previous research in ML analysis of medical images, simulated or augmented data may be used to assess the usability of clinical gestalt. Objective: To assess whether a deep learning algorithm trained on a dataset of simulated and augmented facial photographs reflecting acutely ill patients can distinguish between healthy and LPS-infused, acutely ill individuals. Methods: Photographs from twenty-six volunteers whose facial features were manipulated to resemble a state of acute illness were used to extract features of illness and generate a synthetic dataset of acutely ill photographs, using a neural transfer convolutional neural network (NT-CNN) for data augmentation. Then, four distinct CNNs were trained on different parts of the facial photographs and concatenated into one final, stacked CNN which classified individuals as healthy or acutely ill. Finally, the stacked CNN was validated in an external dataset of volunteers injected with lipopolysaccharide (LPS). Results: In the external validation set, the four individual feature models distinguished acutely ill patients with sensitivities ranging from 10.5% (95% CI, 1.3-33.1% for the skin model) to 89.4% (66.9-98.7%, for the nose model). Specificity ranged from 42.1% (20.3-66.5%) for the nose model and 94.7% (73.9-99.9%) for skin. The stacked model combining all four facial features achieved an area under the receiver characteristic operating curve (AUROC) of 0.67 (0.62-0.71) and distinguished acutely ill patients with a sensitivity of 100% (82.35-100.00%) and specificity of 42.11% (20.25-66.50%). Conclusion: A deep learning algorithm trained on a synthetic, augmented dataset of facial photographs distinguished between healthy and simulated acutely ill individuals, demonstrating that synthetically generated data can be used to develop algorithms for health conditions in which large datasets are difficult to obtain. These results support the potential of facial feature analysis algorithms to support the diagnosis of acute illness.

UK consultants' experiences of the decision-making process around referral to intensive care: an interview study

OBJECTIVE

The decision whether to initiate intensive care for the critically ill patient involves ethical questions regarding what is good and right for the patient. It is not clear how referring doctors negotiate these issues in practice. The aim of this study was to describe and understand consultants' experiences of the decision-making process around referral to intensive care.

DESIGN

Qualitative interviews were analysed according to a phenomenological hermeneutical method.

SETTING AND PARTICIPANTS

Consultant doctors (n=27) from departments regularly referring patients to intensive care in six UK hospitals.

RESULTS

In the precarious and uncertain situation of critical illness, trust in the decision-making process is needed and can be enhanced through the way in which the process unfolds. When there are no obvious right or wrong answers as to what ought to be done, how the decision is made and how the process unfolds is morally important. Through acknowledging the burdensome doubts in the process, contributing to an emerging, joint understanding of the patient's situation, and responding to mutual moral duties of the doctors involved, trust in the decision-making process can be enhanced and a shared moral responsibility between the stake holding doctors can be assumed.

CONCLUSION

The findings highlight the importance of trust in the decision-making process and how the relationships between the stakeholding doctors are crucial to support their moral responsibility for the patient. Poor interpersonal relationships can damage trust and negatively impact decisions made on behalf of a critically ill patient. For this reason, active attempts must be made to foster good relationships between doctors. This is not only important to create a positive working environment, but a mechanism to improve patient outcomes.