Risk Factors for 1-Year Mortality and Hospital Utilization Patterns in Critical Care Survivors: A Retrospective, Observational, Population-Based Data Linkage Study

A prolonged intensive care unit stay defines a worse long-term prognosis - Insights from the critically ill mortality by age (Cimba) study

BACKGROUND

Patients with critical illness often survive the intensive care unit (ICU) at a cost of prolonged length of stay (LOS) and slow recovery. This chronic critically ill disease may lead to long-term poor outcomes, especially in older or frail patients.

OBJECTIVES

The main goal of this study was to address the characteristics and outcomes of patients with prolonged ICU LOS. Mainly, short- and long-term admissions were compared to identify risk factors for persistent critical illness and to characterise the impact on ICU, hospital, and long-term mortality.

METHODS

Subanalysis of a retrospective, multicentric, observational study addressing the 2-year outcome of patients admitted to Portuguese ICUs (the Cimba study). Patients were segregated according to an ICU LOS of ≥14 days.

RESULTS

Data from 37 118 patients were analysed, featuring a median ICU LOS of 4 days (percentile: 25-75 2-9), and a mortality of 16.1% in the ICU, 24.0% in the hospital, and 38.7% after 2 years. A total of 5334 patients (14.4%) had an ICU LOS of ≥14 days (corresponding to 48.9% of all ICU patients/days). Patients with prolonged LOS were more often younger (52.8% vs 46.4%, were ≤65 years of age , p < 0.001), although more severe (Simplified Acute Physiology Score II: 49.1 ± 16.9 vs 41.8 ± 19.5, p < 0.001), and had higher ICU and hospital mortality (18.3% vs 15.7%, and 31.2 vs 22.8%, respectively). Prolonged ICU LOS was linked to an increased risk of dying during the 2-year follow-up (adjusted Cox proportional hazard: 1.65, p < 0.001).

CONCLUSION

Prolonged LOS is associated with a long-term impact on patient prognosis. More careful planning of care should incorporate these data.

Dying to be better: Outlining the growing benefits of palliative care training in intensive care medicine

A core part of an intensivist's work involves navigating the challenges of End of Life Care. While rates of survival from critical illness have gradually improved, 15%-20% of our patients die during their hospital admission, and a further 20% die within a year. 80% of our patients lack capacity to express their wishes with regard to treatment escalation planning. The critical care unit can be an excellent place to provide a good death, however the very nature of critical illness provides some obstacles to this. Prognostic uncertainty, time-pressured critical decision making, and lack of meaningful contact with a patient and their loved ones are but a few. In this article, we compare the ethos of critical care and palliative care medicine and explore how training in both of these specialities could be brought closer together and more formalised such that the intensivists of the future are more strongly equipped with the skills to shape a critical care unit to overcome these challenges and provide the best care to these patients, many of whom may be in the final phase of their life.

Development and validation of a risk score to predict unplanned hospital readmissions in ICU survivors: A data linkage study

BACKGROUND

Intensive Care Unit (ICU) follow-up clinics are growing in popularity internationally; however, there is limited evidence as to which patients would benefit most from a referral to this service.

OBJECTIVES

The objective of this study was to develop and validate a model to predict which ICU survivors are most likely to experience an unplanned hospital readmission or death in the year after hospital discharge and derive a risk score capable of identifying high-risk patients who may benefit from referral to follow-up services.

METHODS

A multicentre, retrospective observational cohort study using linked administrative data from eight ICUs was conducted in the state of New South Wales, Australia. A logistic regression model was developed for the composite outcome of death or unplanned readmission in the 12 months after discharge from the index hospitalisation.

RESULTS

12,862 ICU survivors were included in the study, of which 5940 (46.2%) patients experienced unplanned readmission or death. Strong predictors of readmission or death included the presence of a pre-existing mental health disorder (odds ratio [OR]: 1.52, 95% confidence interval [CI]: 1.40-1.65), severity of critical illness (OR: 1.57, 95% CI: 1.39-1.76), and two or more physical comorbidities (OR: 2.39, 95% CI: 2.14-2.68). The prediction model demonstrated reasonable discrimination (area under the receiver operating characteristic curve: 0.68, 95% CI: 0.67-0.69) and overall performance (scaled Brier score: 0.10). The risk score was capable of stratifying patients into three distinct risk groups-high (64.05% readmitted or died), medium (45.77% readmitted or died), and low (29.30% readmitted or died).

CONCLUSIONS

Unplanned readmission or death is common amongst survivors of critical illness. The risk score presented here allows patients to be stratified by risk level, enabling targeted referral to preventative follow-up services.

Anticipating ICU discharge and long-term follow-up

PURPOSE OF REVIEW

This review aims to summarize recent literature findings on long-term outcomes following critical illness and to highlight potential strategies for preventing and managing health deterioration in survivors of critical care.

RECENT FINDINGS

A substantial number of critical care survivors experience new or exacerbated impairments in their physical, cognitive or mental health, commonly named as postintensive care syndrome (PICS). Furthermore, those who survive critical illness often face an elevated risk of adverse outcomes in the months following their hospital stay, including infections, cardiovascular events, rehospitalizations and increased mortality. These findings underscore the need for effective prevention and management of long-term health deterioration in the critical care setting. While robust evidence from well designed randomized clinical trials is limited, potential interventions encompass sedation limitation, early mobilization, delirium prevention and family presence during intensive care unit (ICU) stay, as well as multicomponent transition programs (from ICU to ward, and from hospital to home) and specialized posthospital discharge follow-up.

SUMMARY

In this review, we offer a concise overview of recent insights into the long-term outcomes of critical care survivors and advancements in the prevention and management of health deterioration after critical illness.

The impact of critical illness on patients' physical function and recovery: An explanatory mixed-methods analysis

OBJECTIVES

To determine how the perception of physical function 6-months following critical illness compares to objectively measured function, and to identify key concerns for patients during recovery from critical illness.

RESEARCH METHODOLOGY AND DESIGN

A nested convergent parallel mixed methods study assessed physical function during a home visit 6-months following critical illness, with semi-structured interviews conducted at the same time.

SETTING

Participants were recruited from two hospitals at one healthcare network in Melbourne, Australia from September 2017 to October 2018 with follow-up data completed in April 2019.

MAIN OUTCOME MEASURES

Physical function was assessed through four objective outcomes: the functional independence measure, six-minute walk test, functional reach test, and grip strength. Semi structured interviews focused on participants function, memories of the intensive care and hospital stay, assistance required on discharge, ongoing limitations, and the recovery process.

FINDINGS

Although many participants (12/20, 60%) stated they had recovered from their critical illness, 14 (70%) had function below expected population norms. Decreased function on returning home was commonly reported, although eleven participants were described as independent and safe for discharge from hospital-based staff. The importance of family and social networks to facilitate discharge was highlighted, however participants often described wanting more support and issues accessing services. The effect of critical illness on the financial well-being of the family network was confirmed, with difficulties accessing financial support identified.

CONCLUSION

Survivors of critical illness perceived a better functional state than measured, but many report new limitations 6-months after critical illness. Family and friends play a crucial role in facilitating transition home and providing financial support.

IMPLICATIONS FOR CLINICAL PRACTICE

Implementation of specific discharge liaison personnel to provide education, support and assist the transition from hospital-based care to home, particularly in those without stable social supports, may improve the recovery process for survivors of critical illness.

Three-year mortality, readmission, and medical expenses in critical care survivors: A population-based cohort study

BACKGROUND

Due to the increasing number of critical care survivors, population-based studies on the long-term outcomes after discharge are necessary to inform local decision-making.

OBJECTIVES

This study aimed to investigate mortality and its risk factors, readmissions, and medical expenses of intensive care unit survivors for 3 years after hospital discharge.

METHODS

This retrospective study analysed data from the National Health Insurance Service-National Sample Cohort in Korea. Of the 195,702 patients who survived and were discharged from hospital in 2012, 2693 intensive care unit patients were assigned to the case group for the study, and the remaining 193,009 were assigned to the comparison group. The primary outcome was all-cause mortality for 3 years after discharge. Secondary outcomes were all-cause hospital readmission and medical expenses in 3 years. We analysed risk factors for mortality using the Cox proportional hazard regression. The differences in hospital readmission and medical expenses between the case and comparison groups were analysed by multivariate logistic regression and independent t-tests.

RESULTS

The 1-year, 2-year, and 3-year cumulative mortality rates in the case group were 15.9%, 20.5%, and 24.4%, respectively, and older age, disability, medical admission, and longer hospital stay increased mortality. Almost 40% of intensive care unit survivors were readmitted to hospital within 6 months of discharge, and their odds of being readmitted were significantly higher than those of the comparison group. Medical expenses were also significantly higher in the case group, with the highest paid within 6 months.

CONCLUSIONS

Mortality, hospital readmission, and medical expenses for intensive care unit survivors were the worst within 6 months of discharge. In light of the long-term recovery trajectory of critical illness, it is necessary to investigate what factors may have contributed to the negative outcome during this period. Further research is needed to determine which services primarily contributed to the increase in medical expenses.

Do critical illness survivors with multimorbidity need a different model of care?

There is currently a lack of evidence on the optimal strategy to support patient recovery after critical illness. Previous research has largely focussed on rehabilitation interventions which aimed to address physical, psychological, and cognitive functional sequelae, the majority of which have failed to demonstrate benefit for the selected outcomes in clinical trials. It is increasingly recognised that a person's existing health status, and in particular multimorbidity (usually defined as two or more medical conditions) and frailty, are strongly associated with their long-term outcomes after critical illness. Recent evidence indicates the existence of a distinct subgroup of critical illness survivors with multimorbidity and high healthcare utilisation, whose prior health trajectory is a better predictor of long-term outcomes than the severity of their acute illness. This review examines the complex relationships between multimorbidity and patient outcomes after critical illness, which are likely mediated by a range of factors including the number, severity, and modifiability of a person's medical conditions, as well as related factors including treatment burden, functional status, healthcare delivery, and social support. We explore potential strategies to optimise patient recovery after critical illness in the presence of multimorbidity. A comprehensive and individualized approach is likely necessary including close coordination among healthcare providers, medication reconciliation and management, and addressing the physical, psychological, and social aspects of recovery. Providing patient-centred care that proactively identifies critical illness survivors with multimorbidity and accounts for their unique challenges and needs is likely crucial to facilitate recovery and improve outcomes.

Palliative Care in Survivors of Critical Illness: A Qualitative Study of Post-Intensive Care Unit Program Clinicians

Background: Survivors of critical illness experience high rates of serious health-related suffering. The delivery of palliative care may assist in decreasing this burden for survivors and their families. Objectives: To understand beliefs, attitudes, and experiences of post-intensive care unit (ICU) program clinicians regarding palliative care and explore barriers and facilitators to incorporating palliative care into critical illness survivorship care. Design: Qualitative inquiry using semistructured interviews and framework analysis. Results were mapped using the Consolidated Framework for Implementation Research. Setting/Subjects: We interviewed 29 international members (United States, United Kingdom, Canada) of the Critical and Acute Illness Recovery Organization post-ICU clinic collaborative. Results: All interprofessional clinicians described components of palliative care as essential to post-ICU clinic practice, including symptom management, patient/family support, facilitation of goal-concordant care, expectation management and anticipatory guidance, spiritual support, and discussion of future health care wishes and advance care planning. Facilitators promoting palliative care strategies were clinician level, including first-hand experience, perceived value, and a positive attitude regarding palliative care. Clinician-level barriers were reciprocals and included insufficient palliative care knowledge, lack of self-efficacy, and a perceived need to protect ICU survivors from interventions the clinician felt may adversely affect recovery or change the care trajectory. System-level barriers included time constraints, cost, and lack of specialty palliative care services. Conclusion: Palliative care may be an essential element of post-ICU clinic care. Implementation efforts focused on tailoring strategies to improve post-ICU program clinicians' palliative care knowledge and self-efficacy could be a key to enhanced care delivery for survivors of critical illness.

Health system approaches to providing posthospital care for survivors of sepsis and critical illness

PURPOSE OF REVIEW

In the current review, we highlight developing strategies taken by healthcare systems to improve posthospital outcomes for sepsis and critical illness.

RECENT FINDINGS

Multiple studies conducted in the adult population over the last 18 months have advanced current knowledge on postdischarge care after sepsis and critical illness. Effective interventions are complex and multicomponent, targeting the multilevel challenges that survivors face. Health systems can leverage existing care models such as primary care or invest in specialty programs to deliver postdischarge care. Qualitative and implementation science studies provide insights into important contextual factors for program success. Several studies demonstrate successful application of telehealth to improve reach of postdischarge support. Research is beginning to identify subtypes of survivors that may respond to tailored intervention strategies.

SUMMARY

Several successful critical illness survivor models of care have been implemented and knowledge about effectiveness, cost, and implementation factors of these strategies is growing. Further innovation is needed in intervention development and evaluation to advance the field.

Characteristics, clinical course and outcome of ventilated patients at a non-surgical intensive care unit in Germany: a single-centre, retrospective observational cohort analysis

OBJECTIVES

The objective of this study was to evaluate epidemiological characteristics, clinical course and outcome of mechanically ventilated non-surgical intensive care unit (ICU) patients, with the aim of improving the strategic planning of ICU capacities.

DESIGN

We conducted a retrospective observational cohort analysis. Data from mechanically ventilated intensive care patients were obtained by investigating electronic health records. The association between clinical parameters and ordinal scale data of clinical course was evaluated using Spearman correlation and Mann-Whitney U test. Relations between clinical parameters and in-hospital mortality rates were examined using binary logistic regression analysis.

SETTING

A single-centre study at the non-surgical ICU of the University Hospital of Frankfurt, Germany (tertiary care-level centre).

PARTICIPANTS

All cases of critically ill adult patients in need of mechanical ventilation during the years 2013-2015 were included. In total, 932 cases were analysed.

RESULTS

From a total of 932 cases, 260 patients (27.9%) were transferred from peripheral ward, 224 patients (24.1%) were hospitalised via emergency rescue services, 211 patients (22.7%) were admitted via emergency room and 236 patients (25.3%) via various transfers. In 266 cases (28.5%), respiratory failure was the reason for ICU admission. The length of stay was higher in non-geriatric patients, patients with immunosuppression and haemato-oncological disease or those in need of renal replacement therapy. 431 patients died, which corresponds to an all-cause in-hospital mortality rate of 46.2%. 92 of 172 patients with presence of immunosuppression (53.5%), 111 of 186 patients (59.7%) with pre-existing haemato-oncological disease, 27 of 36 patients (75.0%) under extracorporeal membrane oxygenation (ECMO) therapy, and 182 of 246 patients (74.0%) undergoing renal replacement therapy died. In logistic regression analysis, these subgroups and older age were significantly associated with higher mortality rates.

CONCLUSIONS

Respiratory failure was the main reason for ventilatory support at this non-surgical ICU. Immunosuppression, haemato-oncological diseases, the need for ECMO or renal replacement therapy and older age were associated with higher mortality.

Examining the needs of survivors of critical illness through the lens of palliative care: A qualitative study of survivor experiences

OBJECTIVE

To examine the needs of adult survivors of critical illness through a lens of palliative care.

RESEARCH METHODOLOGY

A qualitative study of adult survivors of critical illness using semi-structured interviews and framework analysis.

SETTING

Participants were recruited from the post-intensive care unit clinic of a mid-Atlantic academic medical center in the United States.

FINDINGS

Seventeen survivors of critical illness aged 34-80 (median, 66) participated in the study. The majority of patients were female (64.7 %, n = 11) with a median length of index ICU stay of 12 days (interquartile range [IQR] 8-19). Interviews were conducted February to March 2021 and occurred a median of 20 months following the index intensive care stay (range, 13-33 months). We identified six key themes which align with palliative care principles: 1) persistent symptom burden; 2) critical illness as a life-altering experience; 3) spiritual changes and significance; 4) interpreting/managing the survivor experience; 5) feelings of loss and burden; and 6) social support needs.

CONCLUSION

Our findings suggest that palliative care components such as symptom management, goals of care discussions, care coordination, and spiritual and social support may assist in the assessment and treatment of survivors of critical illness.

Patterns of Healthcare Resource Utilisation of Critical Care Survivors between 2006 and 2017 in Wales: A Population-Based Study

In this retrospective cohort study, we used the Secure Anonymised Information Linkage (SAIL) Databank to characterise and identify predictors of the one-year post-discharge healthcare resource utilisation (HRU) of adults who were admitted to critical care units in Wales between 1 April 2006 and 31 December 2017. We modelled one-year post-critical-care HRU using negative binomial models and used linear models for the difference from one-year pre-critical-care HRU. We estimated the association between critical illness and post-hospitalisation HRU using multilevel negative binomial models among people hospitalised in 2015. We studied 55,151 patients. Post-critical-care HRU was 11-87% greater than pre-critical-care levels, whereas emergency department (ED) attendances decreased by 30%. Age ≥50 years was generally associated with greater post-critical-care HRU; those over 80 had three times longer hospital readmissions than those younger than 50 (incidence rate ratio (IRR): 2.96, 95% CI: 2.84, 3.09). However, ED attendances were higher in those younger than 50. High comorbidity was associated with 22-62% greater post-critical-care HRU than no or low comorbidity. The most socioeconomically deprived quintile was associated with 24% more ED attendances (IRR: 1.24 [1.16, 1.32]) and 13% longer hospital stays (IRR: 1.13 [1.09, 1.17]) than the least deprived quintile. Critical care survivors had greater 1-year post-discharge HRU than non-critical inpatients, including 68% longer hospital stays (IRR: 1.68 [1.63, 1.74]). Critical care survivors, particularly those with older ages, high comorbidity, and socioeconomic deprivation, used significantly more primary and secondary care resources after discharge compared with their baseline and non-critical inpatients. Interventions are needed to ensure that key subgroups are identified and adequately supported.

The association between hemoglobin A1c and all-cause mortality in the ICU: A cross-section study based on MIMIC-IV 2.0

BACKGROUND

Hyperglycemia has been reported to be associated with the outcomes of patients in the intensive care unit (ICU). However, the relationship between hemoglobin A1c (HbA1c) and long-term or short-term mortality in the ICU is still unknown. This study used the Medical Information Mart for Intensive Care (MIMIC)-IV database to investigate the relationship between HbA1c and long-term or short-term mortality among ICU patients without a diabetes diagnosis.

METHODS

A total of 3,154 critically ill patients without a diabetes diagnosis who had HbA1c measurements were extracted and analyzed from the MIMIC-IV. The primary outcome was 1-year mortality, while the secondary outcomes were 30-day mortality and 90-day mortality after ICU discharge. HbA1c levels were classified into four levels according to three HbA1c values (5.0%, 5.7%, and 6.5%). The Cox regression model was used to investigate the relationship between the highest HbA1c measurement and mortality. Finally, this correlation was validated using the XGBoost machine learning model and Cox regression after propensity score matching (PSM).

RESULTS

The study eventually included 3,154 critically ill patients without diabetes who had HbA1c measurements in the database. HbA1c levels of below 5.0% or above 6.5% were significantly associated with 1-year mortality after adjusting for covariates in Cox regression (HR: 1.37; 95% CI: 1.02-1.84 or HR: 1.62; 95% CI: 1.20-2.18). In addition, HbA1c 6.5% was linked to 30-day mortality (HR: 1.81; 95% CI: 1.21-2.71) and 90-day mortality (HR: 1.62; 95% CI: 1.14-2.29). The restricted cubic spline demonstrated a U-shaped relationship between HbA1c levels and 1-year mortality. The AUCs of the training and testing datasets in the XGBoost model were 0.928 and 0.826, respectively, while the SHAP plot revealed that HbA1c was somewhat important for the 1-year mortality. Higher HbA1c levels in Cox regression were still significantly associated with 1-year mortality after PSM for other factors.

CONCLUSIONS

The 1-year mortality, 30-day mortality, and 90-day mortality rates for critically ill patients after discharge from ICU are significantly associated with HbA1c. HbA1c < 5.0% and ≥6.5% would increase 30-day, 90-day, and 1-year mortality, while levels between 5.0% and 6.5% of HbA1c did not significantly affect these outcomes.

Frailty as a Priority-Setting Criterion for Potentially Lifesaving Treatment-Self-Fulfilling Prophecy, Circularity, and Indirect Discrimination?

Frailty is a state of increased vulnerability to poor resolution of homeostasis after a stressor event. Frailty is most frequently assessed in the old using the Clinical Frailty Scale (CSF) which ranks frailty from 1 to 9. This assessment typically takes less than one minute and is not validated in patients with learning difficulties or those under 65 years old. The National Institute for Health and Care Excellence (NICE) developed guidelines that use "frailty" as one of the priority-setting criteria for how scarce, but potentially lifesaving, health care resources should be allocated during the COVID-19 pandemic. Similar guidelines have been developed elsewhere. This paper discusses the ethical implications of such rationing and argues that this is an unproven and ethically problematic form of health care rationing. It specifically discusses: (1) how the frailty ascription becomes a self-fulfilling prophecy, (2) the problematic use of "frailty" in COVID-19 "triage," (3) the circularity of the link between age and frailty, (4) indirect discrimination because of the use of a seemingly neutral criterion in health care rationing, and (5) the difficult link between comorbidities and frailty. It is found that there was no research into the use of global frailty scores as a criterion for access to acute treatment before January 2020 and so it is concerning how readily frailty scoring has been adopted to ration access to potentially lifesaving treatments. Existing gerontological frailty scoring systems have not been developed for this purpose, and repurposing them creates significant ethical issues.

Factors Associated with Mortality in Patients with COVID-19 Receiving Prolonged Ventilatory Support

Background: Since its emergence in early 2020, coronavirus disease 2019 (COVID-19)-associated pneumonia has caused a global strain on intensive care unit (ICU) resources with many intubated patients requiring prolonged ventilatory support. Outcomes for patients with COVID-19 who receive prolonged intubation (>21 days) and possible predictors of mortality in this group are not well established. Patients and Methods: Data were prospectively collected from adult patients with COVID-19 requiring mechanical ventilation from March 2020 through December 2021 across a system of 11 hospitals. The primary end point was in-hospital mortality. Factors associated with mortality were evaluated using univariable and multivariable logistic regression analyses. Results: Six hundred six patients were placed on mechanical ventilation for COVID-19 pneumonia during the study period, with in-hospital mortality of 40.3% (n = 244). Increased age (odds ratio [OR], 1.06; 95% confidence interval [CI], 1.03-1.09), increased creatinine (OR, 1.40; 95% CI, 1.08-1.82), and receiving corticosteroids (OR, 2.68; 95% CI, 1.20-5.98) were associated with mortality. Intubations lasting longer than 21 days (n = 140) had a lower in-hospital mortality of 25.7% (n = 36; p < 0.001). Increasing Elixhauser comorbidity index (OR, 1.12; 95% CI, 1.04-1.19) and receiving corticosteroids (OR, 1.92; 95% CI, 1.06-3.47) were associated with need for prolonged ventilation. In this group, increased age (OR, 1.06; 95% CI, 1.01-1.08) and non-English speaking (OR, 3.74; 95% CI, 1.13-12.3) were associated with mortality. Conclusions: In-hospital mortality in mechanically ventilated patients with COVID-19 pneumonia occurs primarily in the first 21 days after intubation, possibly related to the early active inflammatory process. In patients on prolonged mechanical ventilation, increased age and being non-English speaking were associated with mortality.

Long-term health-related quality of life, healthcare utilisation and back-to-work activities in intensive care unit survivors: Prospective confirmatory study from the Frisian aftercare cohort

Purpose

More substantial information on recovery after Intensive Care Unit (ICU) admission is urgently needed. In a previous retrospective study, the proportion of non-recovery patients was 44%. The aim of this prospective follow-up study was to evaluate changes in Health-Related Quality of Life (HRQoL) in the first year after ICU-admission.

Methods

Long-stay adult ICU-patients (≥ 48 hours) were included. HRQoL was evaluated with the Dutch translation of the RAND-36 item Health Survey (RAND-36) at baseline via proxy measurement, and at three, six, and twelve months after ICU admission. Subsequently, the relation between physical functioning, healthcare utilisation, and work activities was explored.

Results

A total of 81 patients were included in this study. Fifty-five percent of patients did not meet criteria for full recovery and were allocated to the Non Recovery (NR)-group (Physical Functioning domain-score: 35 [15–55]). Baseline physical HRQoL differed significantly between the Recovery (R) and NR-group. Patients in the NR-group received home care more often and had higher healthcare utilisation (44 versus 17% in the first three months post-ICU, p = 0.013). Only fourteen percent of NR-patients were able to participate in work activities. Moreover, NR-patients persistently showed impaired overall HRQoL throughout the year after critical illness.

Conclusions

Limited recovery in ICU survivors is reflected in overall impaired HRQoL, as well as in far-reaching consequences for patients’ healthcare needs and their ability to reintegrate into society. In our study, baseline HRQoL appeared to be an important predictor of long-term outcomes, but not Clinical Frailty Scale (CFS) score. And, (proxy-derived) HRQoL may help to identify patients at risk of long-term non-recovery.

A methodology to facilitate critical care research using multiple linked electronic, clinical and administrative health records at population scale

Introduction

Critical Care is a specialty in medicine providing a service for severely ill and high-risk patients who, due to the nature of their condition, may require long periods recovering after discharge. Consequently, focus on the routine data collection carried out in Intensive Care Units (ICUs) leads to reporting that is confined to the critical care episode and is typically insensitive to variation in individual patient pathways through critical care to recovery.A resource which facilitates efficient research into interactions with healthcare services surrounding critical admissions, capturing the complete patient's healthcare trajectory from primary care to non-acute hospital care prior to ICU, would provide an important longer-term perspective for critical care research.

Objective

To describe and apply a reproducible methodology that demonstrates how both routine administrative and clinically rich critical care data sources can be integrated with primary and secondary healthcare data to create a single dataset that captures a broader view of patient care.

Method

To demonstrate the INTEGRATE methodology, it was applied to routine administrative and clinical healthcare data sources in the Secure Anonymised Data Linking (SAIL) Databank to create a dataset of patients' complete healthcare trajectory prior to critical care admission. SAIL is a national, data safe haven of anonymised linkable datasets about the population of Wales.

Results

When applying the INTEGRATE methodology in SAIL, between 2010 and 2019 we observed 91,582 critical admissions for 76,019 patients. Of these, 90,632 (99%) had an associated non-acute hospital admission, 48,979 (53%) had an emergency admission, and 64,832 (71%) a primary care interaction in the week prior to the critical care admission.

Conclusion

This methodology, at population scale, integrates two critical care data sources into a single dataset together with data sources on healthcare prior to critical admission, thus providing a key research asset to study critical care pathways.

Chronic illness and critical care-A qualitative exploration of family experience and need

BACKGROUND

People with chronic illnesses such as cancer and cardiovascular disease are living longer and often require the support of critical care services. Current health care provision means patients may be discharged home once clinically stable despite still having high care demands including social, emotional, or physical needs. Families are often required to assume caregiving roles. Research into family burden using quantitative methods has increased awareness, however, little qualitative work exists and the development of support interventions for families is required.

AIMS

To explore the experience and needs of family members of people with an existing chronic illness who are admitted to the Critical Care Unit (CCU), and to identify the desired components of a family support intervention in the form of a resource toolkit.

STUDY DESIGN

A qualitative exploration of family experience and need, and content development for a resource toolkit using focus group methodology. Two focus groups and one face-to-face interview were conducted involving nine adult (≥18 years) family members of adult patients with chronic illness admitted to critical care in the preceding 9 months across two specialist hospitals in the UK. These were digitally recorded, transcribed, and thematically analysed.

FINDINGS

Four themes were identified: importance of communication, need for support, trauma of chronic illness, and having to provide "Do-it-Yourself" care. The immense responsibility of families to provide care throughout the illness trajectory is highlighted. Understandable information is essential for a family support toolkit.

CONCLUSION

Family members often view a critical care episode broadly from diagnosis through to recovery/rehabilitation. Basic communication training skills within critical care should be ensured, alongside coordination of simple solutions. The potential traumatic impact on families should be highlighted early within the pathway, and positive aspects used to harness essential family support. A simple and coordinated approach to a toolkit is preferred.

RELEVANCE TO CLINICAL PRACTICE

This study highlights that a critical care experience may impact broadly beyond CCU, and the importance of informing patients and families of this potential experience, prior to or on admission, to aid preparation. Further highlighted is the need for contemporaneous and accurate information from clinicians involved in care. Families report a better experience when there is good collaboration across critical care services and admitting clinical teams. Early involvement of families in overall discharge planning is essential to allow patients and families to adjust and plan for recovery.

Development and Validation of a Two-Step Predictive Risk Stratification Model for Coronavirus Disease 2019 In-hospital Mortality: A Multicenter Retrospective Cohort Study

Objectives

An accurate prognostic score to predict mortality for adults with COVID-19 infection is needed to understand who would benefit most from hospitalizations and more intensive support and care. We aimed to develop and validate a two-step score system for patient triage, and to identify patients at a relatively low level of mortality risk using easy-to-collect individual information.

Design

Multicenter retrospective observational cohort study.

Setting

Four health centers from Virginia Commonwealth University, Georgetown University, the University of Florida, and the University of California, Los Angeles.

Patients

Coronavirus Disease 2019-confirmed and hospitalized adult patients.

Measurements and Main Results

We included 1,673 participants from Virginia Commonwealth University (VCU) as the derivation cohort. Risk factors for in-hospital death were identified using a multivariable logistic model with variable selection procedures after repeated missing data imputation. A two-step risk score was developed to identify patients at lower, moderate, and higher mortality risk. The first step selected increasing age, more than one pre-existing comorbidities, heart rate >100 beats/min, respiratory rate ≥30 breaths/min, and SpO₂ <93% into the predictive model. Besides age and SpO₂, the second step used blood urea nitrogen, absolute neutrophil count, C-reactive protein, platelet count, and neutrophil-to-lymphocyte ratio as predictors. C-statistics reflected very good discrimination with internal validation at VCU (0.83, 95% CI 0.79-0.88) and external validation at the other three health systems (range, 0.79-0.85). A one-step model was also derived for comparison. Overall, the two-step risk score had better performance than the one-step score.

Conclusions

The two-step scoring system used widely available, point-of-care data for triage of COVID-19 patients and is a potentially time- and cost-saving tool in practice.

Hospital re-admission after critical care survival: a systematic review and meta-analysis

Survivors of critical illness frequently require increased healthcare resources after hospital discharge. We undertook a systematic review and meta-analysis to assess hospital re-admission rates following critical care admission and to explore potential re-admission risk factors. We searched the MEDLINE, Embase and CINAHL databases on 05 March 2020. Our search strategy incorporated controlled vocabulary and text words for hospital re-admission and critical illness, limited to the English language. Two reviewers independently applied eligibility criteria and assessed quality using the Newcastle Ottawa Score checklist and extracted data. The primary outcome was acute hospital re-admission in the year after critical care discharge. Of the 8851 studies screened, 87 met inclusion criteria and 41 were used within the meta-analysis. The analysis incorporated data from 3,897,597 patients and 741,664 re-admission episodes. Pooled estimates for hospital re-admission after critical illness were 16.9% (95%CI: 13.3-21.2%) at 30 days; 31.0% (95%CI: 24.3-38.6%) at 90 days; 29.6% (95%CI: 24.5-35.2%) at six months; and 53.3% (95%CI: 44.4-62.0%) at 12 months. Significant heterogeneity was observed across included studies. Three risk factors were associated with excess acute care rehospitalisation one year after discharge: the presence of comorbidities; events during initial hospitalisation (e.g. the presence of delirium and duration of mechanical ventilation); and subsequent infection after hospital discharge. Hospital re-admission is common in survivors of critical illness. Careful attention to the management of pre-existing comorbidities during transitions of care may help reduce healthcare utilisation after critical care discharge. Future research should determine if targeted interventions for at-risk critical care survivors can reduce the risk of subsequent rehospitalisation.

Long-term outcomes of hospital survivors following an ICU stay: A multi-centre retrospective cohort study

Background

The focus of much Intensive Care research has been on short-term survival, which has demonstrated clear improvements over time. Less work has investigated long-term survival, and its correlates. This study describes long-term survival and identifies factors associated with time to death, in patients who initially survived an Intensive Care admission in Victoria, Australia.

Methods

We conducted a retrospective cohort study of adult patients discharged alive from hospital following admission to all Intensive Care Units (ICUs) in the state of Victoria, Australia between July 2007 and June 2018. Using the Victorian Death Registry, we determined survival of patients beyond hospital discharge. Comparisons between age matched cohorts of the general population were made. Cox regression was employed to investigate factors associated with long-term survival.

Results

A total of 130,775 patients from 23 ICUs were included (median follow-up 3.6 years post-discharge). At 1-year post-discharge, survival was 90% compared to the age-matched cohort of 98%. All sub-groups had worse long-term survival than their age-matched general population cohort, apart from elderly patients admitted following cardiac surgery who had better or equal survival. Multiple demographic, socio-economic, diagnostic, acute and chronic illness factors were associated with long-term survival.

Conclusions

Australian patients admitted to ICU who survive to discharge have worse long-term survival than the general population, except for the elderly admitted to ICU following cardiac surgery. These findings may assist during goal-of-care discussions with patients during an ICU admission.

The complications of opioid use during and post-intensive care admission: A narrative review

Opioids are a commonly administered analgesic medication in the intensive care unit, primarily to facilitate invasive mechanical ventilation. Consensus guidelines advocate for an opioid-first strategy for the management of acute pain in ventilated patients. As a result, these patients are potentially exposed to high opioid doses for prolonged periods, increasing the risk of adverse effects. Adverse effects relevant to these critically ill patients include delirium, intensive care unit-acquired infections, acute opioid tolerance, iatrogenic withdrawal syndrome, opioid-induced hyperalgesia, persistent opioid use, and chronic post-intensive care unit pain. Consequently, there is a challenge of optimising analgesia while minimising these adverse effects. This narrative review will discuss the characteristics of opioid use in the intensive care unit, outline the potential short-term and long-term adverse effects of opioid therapy in critically ill patients, and outline a multifaceted strategy for opioid minimisation.

Anesthesia workforce capacity in Thailand: A multicenter study

Background

Workforce management in anesthesia services is crucial for service quality. However, the data associated with this are lacking. Therefore, this study was done to analyze workforce and workload and to compare differences among hospital clusters in Thailand.

Materials and Methods

We conducted a cross-sectional study in multilevel hospitals that were classified by location, the population cared for, and the categorization of physicians. Stratified randomization from all health service regions across Thailand was done. The profile of hospitals, number of anesthesia staffs, their capabilities, and ratio of anesthesia personnel to the service provided during the 5 workdays and 1 weekend period were analyzed.

Results

A total of 18 hospitals, ranging from secondary to super-tertiary referral centers, were included in the study. The mean number of personnel ranged from 2.0 ± 1.2 to 12.0 ± 0 for anesthesiologists and 7.5 ± 2.9 to 42.3 ± 19.3 for nurse anesthetists from each hospital cluster, which vary in terms of capabilities and the number of staff. The average number of anesthesia service units was 9.1 ± 4.2 to 31.9 ± 16.4, while the number of operating theaters was 6.9 ± 2.2 to 22.7 ± 8.3. However, the ratio of anesthesia personnel to one anesthesia service unit and the ratio of these personnel to an operating theater were not significantly different among the participating hospitals, with a mean of 0.94 ± 0.45 and 1.34 ± 0.38, respectively.

Conclusion

The overall number of anesthesia service units was above the designated operating theater capacity, while the ratio of anesthesiologists was 0.8-1.3 and nurse anesthetists was 2.4-6.5 per 100,000 people on an average, with a disproportionate responsibility ratio of anesthesia personnel to anesthesia service units during that time.

The Use of Different Sepsis Risk Stratification Tools on the Wards and in Emergency Departments Uncovers Different Mortality Risks: Results of the Three Welsh National Multicenter Point-Prevalence Studies

Supplemental Digital Content is available in the text.

To compare the performance of Sequential Organ Failure Assessment, systemic inflammatory response syndrome, Red Flag Sepsis, and National Institute of Clinical Excellence sepsis risk stratification tools in the identification of patients at greatest risk of mortality from sepsis in nonintensive care environments.

Frailty assessed by administrative tools and mortality in patients with pneumonia admitted to the hospital and ICU in Wales

The ideal method of identifying frailty is uncertain, and data on long-term outcomes is relatively limited. We examined frailty indices derived from population-scale linked data on Intensive Care Unit (ICU) and hospitalised non-ICU patients with pneumonia to elucidate the influence of frailty on mortality. Longitudinal cohort study between 2010-2018 using population-scale anonymised data linkage of healthcare records for adults admitted to hospital with pneumonia in Wales. Primary outcome was in-patient mortality. Odds Ratios (ORs [95% confidence interval]) for age, hospital frailty risk score (HFRS), electronic frailty index (eFI), Charlson comorbidity index (CCI), and social deprivation index were estimated using multivariate logistic regression models. The area under the receiver operating characteristic curve (AUC) was estimated to determine the best fitting models. Of the 107,188 patients, mean (SD) age was 72.6 (16.6) years, 50% were men. The models adjusted for the two frailty indices and the comorbidity index had an increased odds of in-patient mortality for individuals with an ICU admission (ORs for ICU admission in the eFI model 2.67 [2.55, 2.79], HFRS model 2.30 [2.20, 2.41], CCI model 2.62 [2.51, 2.75]). Models indicated advancing age, increased frailty and comorbidity were also associated with an increased odds of in-patient mortality (eFI, baseline fit, ORs: mild 1.09 [1.04, 1.13], moderate 1.13 [1.08, 1.18], severe 1.17 [1.10, 1.23]. HFRS, baseline low, ORs: intermediate 2.65 [2.55, 2.75], high 3.31 [3.17, 3.45]). CCI, baseline < 1, ORs: '1-10' 1.15 [1.11, 1.20], > 10 2.50 [2.41, 2.60]). For predicting inpatient deaths, the CCI and HFRS based models were similar, however for longer term outcomes the CCI based model was superior. Frailty and comorbidity are significant risk factors for patients admitted to hospital with pneumonia. Frailty and comorbidity scores based on administrative data have only moderate ability to predict outcome.

Long-term kidney function of patients discharged from hospital after an intensive care admission: observational cohort study

The long-term trajectory of kidney function recovery or decline for survivors of critical illness is incompletely understood. Characterising changes in kidney function after critical illness and associated episodes of acute kidney injury (AKI), could inform strategies to monitor and treat new or progressive chronic kidney disease. We assessed changes in estimated glomerular filtration rate (eGFR) and impact of AKI for 1301 critical care survivors with 5291 eGFR measurements (median 3 [IQR 2, 5] per patient) between hospital discharge (2004-2008) and end of 7 years of follow-up. Linear mixed effects models showed initial decline in eGFR over the first 6 months was greatest in patients without AKI (- 9.5%, 95% CI - 11.5% to - 7.4%) and with mild AKI (- 12.3%, CI - 15.1% to - 9.4%) and least in patients with moderate-severe AKI (- 4.3%, CI - 7.0% to - 1.4%). However, compared to patients without AKI, hospital discharge eGFR was lowest for the moderate-severe AKI group (median 61 [37, 96] vs 101 [78, 120] ml/min/1.73m2) and two thirds (66.5%, CI 59.8-72.6% vs 9.2%, CI 6.8-12.4%) had an eGFR of < 60 ml/min/1.73m2 through to 7 years after discharge. Kidney function trajectory after critical care discharge follows a distinctive pattern of initial drop then sustained decline. Regardless of AKI severity, this evidence suggests follow-up should incorporate monitoring of eGFR in the early months after hospital discharge.

Mobilization During Critical Illness: A Higher Level of Mobilization Improves Health Status at 6 Months, a Secondary Analysis of a Prospective Cohort Study

OBJECTIVES

To determine the influence of active mobilization during critical illness on health status in survivors 6 months post ICU admission.

DESIGN

Post hoc secondary analysis of a prospective cohort study conducted between November 2013 and March 2015.

SETTING

Two tertiary hospital ICU's in Victoria, Australia.

PATIENTS

Of 194 eligible patients admitted, mobility data for 186 patients were obtained. Inclusion and exclusion criteria were as per the original trial.

INTERVENTIONS

The dosage of mobilization in ICU was measured by 1) the Intensive Care Mobility Scale where a higher Intensive Care Mobility Scale level was considered a higher intensity of mobilization or 2) the number of active mobilization sessions performed during the ICU stay. The data were extracted from medical records and analyzed against Euro-quality of life-5D-5 Level version answers obtained from phone interviews with survivors 6 months following ICU admission. The primary outcome was change in health status measured by the Euro-quality of life-5D-5 Level utility score, with change in Euro-quality of life-5D-5 Level mobility domain a secondary outcome.

MEASUREMENTS AND MAIN RESULTS

Achieving higher levels of mobilization (as per the Intensive Care Mobility Scale) was independently associated with improved outcomes at 6 months (Euro-quality of life-5D-5 Level utility score unstandardized regression coefficient [β] 0.022 [95% CI, 0.002-0.042]; p = 0.033; Euro-quality of life-5D-5 Level mobility domain β = 0.127 [CI, 0.049-0.205]; p = 0.001). Increasing the number of active mobilization sessions was not found to independently influence health status. Illness severity, total comorbidities, and admission diagnosis also independently influenced health status.

CONCLUSIONS

In critically ill survivors, achieving higher levels of mobilization, but not increasing the number of active mobilization sessions, improved health status 6 months after ICU admission.

Long-term trends in critical care admissions in Wales

As national populations age, demands on critical care services are expected to increase. In many healthcare settings, longitudinal trends indicate rising numbers and proportions of patients admitted to ICU who are older; elsewhere, including some parts of the UK, a decrease has raised concerns with regard to rationing according to age. Our aim was to investigate admission trends in Wales, where critical care capacity has not risen in the last decade. We used the Secure Anonymised Information Linkage Databank to identify and characterise critical care admissions in patients aged ≥ 18 years from 1 January 2008 to 31 December 2017. We categorised 85,629 ICU admissions as youngest (18-64 years), older (65-79 years) and oldest (≥ 80 years). The oldest group accounted for 15% of admissions, the older age group 39% and the youngest group 46%. Relative to the national population, the incidence of admission rates per 10,000 population in the oldest group decreased significantly over the study period from 91.5/10,000 in 2008 to 77.5/10,000 (a relative decrease of 15%), and among the older group from 89.2/10,000 in 2008 to 75.3/10,000 in 2017 (a relative decrease of 16%). We observed significant decreases in admissions with high comorbidity (modified Charlson comorbidity index); increases in the proportion of older patients admitted who were considered 'fit' rather than frail (electronic frailty index); and decreases in admissions with a medical diagnosis. In contrast to other healthcare settings, capacity constraints and surgical imperatives appear to have contributed to a relative exclusion of older patients presenting with acute medical illness.

Association Between Incident Delirium Treatment With Haloperidol and Mortality in Critically Ill Adults

OBJECTIVES

Haloperidol is commonly administered in the ICU to reduce the burden of delirium and its related symptoms despite no clear evidence showing haloperidol helps to resolve delirium or improve survival. We evaluated the association between haloperidol, when used to treat incident ICU delirium and its symptoms, and mortality.

DESIGN

Post hoc cohort analysis of a randomized, double-blind, placebo-controlled, delirium prevention trial.

SETTING

Fourteen Dutch ICUs between July 2013 and December 2016.

PATIENTS

One-thousand four-hundred ninety-five critically ill adults free from delirium at ICU admission having an expected ICU stay greater than or equal to 2 days.

INTERVENTIONS

Patients received preventive haloperidol or placebo for up to 28 days until delirium occurrence, death, or ICU discharge. If delirium occurred, treatment with open-label IV haloperidol 2 mg tid (up to 5 mg tid per delirium symptoms) was administered at clinician discretion.

MEASUREMENTS AND MAIN RESULTS

Patients were evaluated tid for delirium and coma for 28 days. Time-varying Cox hazards models were constructed for 28-day and 90-day mortality, controlling for study-arm, delirium and coma days, age, Acute Physiology and Chronic Health Evaluation-II score, sepsis, mechanical ventilation, and ICU length of stay. Among the 1,495 patients, 542 (36%) developed delirium within 28 days (median [interquartile range] with delirium 4 d [2-7 d]). A total of 477 of 542 (88%) received treatment haloperidol (2.1 mg [1.0-3.8 mg] daily) for 6 days (3-11 d). Each milligram of treatment haloperidol administered daily was associated with decreased mortality at 28 days (hazard ratio, 0.93; 95% CI, 0.91-0.95) and 90 days (hazard ratio, 0.97; 95% CI, 0.96-0.98). Treatment haloperidol administered later in the ICU course was less protective of death. Results were stable by prevention study-arm, predelirium haloperidol exposure, and haloperidol treatment protocol adherence.

CONCLUSIONS

Treatment of incident delirium and its symptoms with haloperidol may be associated with a dose-dependent improvement in survival. Future randomized trials need to confirm these results.

Health care use before and after intensive care unit admission-A nationwide register-based study

BACKGROUND

The aim of this study was to describe healthcare utilization of patients admitted to ICU before and after ICU admission.

METHODS

Register-based study including adult patients discharged from ICU between January 1st, 2011 and December 31st, 2014. Reference group was a sex- and age-matched population not admitted to an ICU in the study period. Outcomes were hospital admissions, contacts to general practitioner or emergency services and municipality services from 1 year before ICU admission and up to 3 years after.

RESULTS

The study included 82 384 patients and an equal number of reference persons. Of patients with ICU admission, 48% were married (reference group 57%), 48% had elementary school education (reference group 38%) and 18% had a Charlson co-morbidity score of 5+ (4% in reference group). We found that 51% of patients with an ICU admission had been admitted to hospital in the year before ICU admission (reference group 15%) and 97% had a contact to a general practitioner (reference group 89%) in the same period.

CONCLUSIONS

Patients admitted to an ICU had increased use of both primary and secondary health care both before and for years after ICU treatment, even after adjustment for comorbidities and socio-economic factors.

Protocol for the development of the Wales Multimorbidity e-Cohort (WMC): data sources and methods to construct a population-based research platform to investigate multimorbidity

INTRODUCTION

Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence.We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity.

METHODS AND ANALYSIS

The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation.

ETHICS AND DISSEMINATION

The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

Development of a structured process for fair allocation of critical care resources in the setting of insufficient capacity: a discussion paper

Early in the COVID-19 pandemic there was widespread concern that healthcare systems would be overwhelmed, and specifically, that there would be insufficient critical care capacity in terms of beds, ventilators or staff to care for patients. In the UK, this was avoided by a threefold approach involving widespread, rapid expansion of critical care capacity, reduction of healthcare demand from non-COVID-19 sources by temporarily pausing much of normal healthcare delivery, and by governmental and societal responses that reduced demand through national lockdown. Despite high-level documents designed to help manage limited critical care capacity, none provided sufficient operational direction to enable use at the bedside in situations requiring triage. We present and describe the development of a structured process for fair allocation of critical care resources in the setting of insufficient capacity. The document combines a wide variety of factors known to impact on outcome from critical illness, integrated with broad-based clinical judgement to enable structured, explicit, transparent decision-making founded on robust ethical principles. It aims to improve communication and allocate resources fairly, while avoiding triage decisions based on a single disease, comorbidity, patient age or degree of frailty. It is designed to support and document decision-making. The document has not been needed to date, nor adopted as hospital policy. However, as the pandemic evolves, the resumption of necessary non-COVID-19 healthcare and economic activity mean capacity issues and the potential need for triage may yet return. The document is presented as a starting point for stakeholder feedback and discussion.

Prevalence and Outcomes of Acute Hypoxaemic Respiratory Failure in Wales: The PANDORA-WALES Study

BACKGROUND

We aimed to identify the prevalence of acute hypoxaemic respiratory failure (AHRF) in the intensive care unit (ICU) and its associated mortality. The secondary aim was to describe ventilatory management as well as the use of rescue therapies.

METHODS

Multi-centre prospective study in nine hospitals in Wales, UK, over 2-month periods. All patients admitted to an ICU were screened for AHRF and followed-up until discharge from the ICU. Data were collected from patient charts on patient demographics, clinical characteristics, management and outcomes.

RESULTS

Out of 2215 critical care admissions, 886 patients received mechanical ventilation. A total of 197 patients met inclusion criteria and were recruited. Seventy (35.5%) were non-survivors. Non-survivors were significantly older, had higher SOFA scores and received more vasopressor support than survivors. Twenty-five (12.7%) patients who fulfilled the Berlin definition of acute respiratory distress syndrome (ARDS) during the ICU stay without impact on overall survival. Rescue therapies were rarely used. Analysis of ventilation showed that median Vt was 7.1 mL/kg PBW (IQR 5.9-9.1) and 21.3% of patients had optimal ventilation during their ICU stay.

CONCLUSIONS

One in four mechanically ventilated patients have AHRF. Despite advances of care and better, but not optimal, utilisation of low tidal volume ventilation, mortality remains high.

Understanding and responding to COVID-19 in Wales: protocol for a privacy-protecting data platform for enhanced epidemiology and evaluation of interventions

INTRODUCTION

The emergence of the novel respiratory SARS-CoV-2 and subsequent COVID-19 pandemic have required rapid assimilation of population-level data to understand and control the spread of infection in the general and vulnerable populations. Rapid analyses are needed to inform policy development and target interventions to at-risk groups to prevent serious health outcomes. We aim to provide an accessible research platform to determine demographic, socioeconomic and clinical risk factors for infection, morbidity and mortality of COVID-19, to measure the impact of COVID-19 on healthcare utilisation and long-term health, and to enable the evaluation of natural experiments of policy interventions.

METHODS AND ANALYSIS

Two privacy-protecting population-level cohorts have been created and derived from multisourced demographic and healthcare data. The C20 cohort consists of 3.2 million people in Wales on the 1 January 2020 with follow-up until 31 May 2020. The complete cohort dataset will be updated monthly with some individual datasets available daily. The C16 cohort consists of 3 million people in Wales on the 1 January 2016 with follow-up to 31 December 2019. C16 is designed as a counterfactual cohort to provide contextual comparative population data on disease, health service utilisation and mortality. Study outcomes will: (a) characterise the epidemiology of COVID-19, (b) assess socioeconomic and demographic influences on infection and outcomes, (c) measure the impact of COVID-19 on short -term and longer-term population outcomes and (d) undertake studies on the transmission and spatial spread of infection.

ETHICS AND DISSEMINATION

The Secure Anonymised Information Linkage-independent Information Governance Review Panel has approved this study. The study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

Epidemiology of the First Wave of COVID-19 ICU Admissions in South Wales-The Interplay Between Ethnicity and Deprivation

On the 9th March 2020, the first patient with COVID-19 was admitted to ICU in the Royal Gwent Hospital (RGH), Newport, Wales. We prospectively recorded the rate of ICU admissions of 52 patients with COVID-19 over 60 days, focusing on the epidemiology of ethnicity and deprivation because these factors have emerged as significant risk factors. Patients were 65% (34 of 52) male and had a median (IQR) age of 55 (48–62) years. Prevalent comorbidities included obesity (52%); diabetes (33%), and asthma (23%). COVID-19 hospital and ICU inpatient numbers peaked on days 23 and 39, respectively—a lag of 16 days. The ICU mortality rate was 33% (17 of 52). People of black, Asian, and minority ethnic descent (BAME group) represented 35% of ICU COVID-19 admissions (18 of 52) and 35% of deaths (6 of 17). Amongst the BAME group, 72% (13 of 18) of patients were found to reside in geographical areas representing the 20% most deprived in Wales, vs. 27% of patients in the Caucasian group (9 of 33). Less than 5% of the population within the area covered by RGH are of BAME descent, yet this group had a disproportionately high ICU admission and mortality rate from COVID-19. The interplay between ethnicity and deprivation, which is complex, may be a factor in our findings. This in turn could be related to an increased prevalence of co-morbidities; higher community exposure; larger proportion of lower band key worker roles; or genetic polymorphisms.

Discrete-Event Simulation Modeling of Critical Care Flow: New Hospital, Old Challenges

Supplemental Digital Content is available in the text.

Objectives:

As the demand for critical care beds rises each year, hospitals must be able to adapt. Delayed transfer of care reduces available critical care capacity and increases occupancy. The use of mathematic modeling within healthcare systems has the ability to aid planning of resources. Discrete-event simulation models can determine the optimal number of critical care beds required and simulate different what-if scenarios.

Design:

Complex discrete-event simulation model was developed using a warm-up period of 30 days and ran for 30 trials against a 2-year period with the mean calculated for the runs. A variety of different scenarios were investigated to determine the effects of increasing capacity, increasing demand, and reduction of proportion and length of delayed transfer of care out of the ICU.

Setting:

Combined data from two ICUs in United Kingdom.

Patients:

The model was developed using 1,728 patient records and was validated against an independent dataset of 2,650 patients.

Interventions:

None.

Measurements and Main Results:

During model validation, the average bed utilization and admittance rate were equal to the real-world data. In the what-if scenarios, we found that increasing bed numbers from 23 to 28 keeping the arrival rate stable reduces the average occupancy rate to 70%. We found that the projected 4% yearly increase in admissions could overwhelm even the 28-bedded unit, without change in the delayed transfer of care episodes. Reduction in the proportion of patients experiencing delayed transfer of care had the biggest effect on occupancy rates, time spent at full capacity, and average bed utilization.

Conclusions:

Using discrete-event simulation of commonly available baseline patient flow and patient care data produces reproducible models. Reducing the proportion of patients with delayed transfer of care had a greater effect in reducing occupancy levels than simply increasing bed numbers even when demand is increased.

Early and Late Mortality Following Discharge From the ICU: A Multicenter Prospective Cohort Study

OBJECTIVES

To identify the frequency, causes, and risk factors of early and late mortality among general adult patients discharged from ICUs.

DESIGN

Multicenter, prospective cohort study.

SETTING

ICUs of 10 tertiary hospitals in Brazil.

PATIENTS

One-thousand five-hundred fifty-four adult ICU survivors with an ICU stay greater than 72 hours for medical and emergency surgical admissions or greater than 120 hours for elective surgical admissions.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The main outcomes were early (30 d) and late (31 to 365 d) mortality. Causes of death were extracted from death certificates and medical records. Twelve-month cumulative mortality was 28.2% (439 deaths). The frequency of early mortality was 7.9% (123 deaths), and the frequency of late mortality was 22.3% (316 deaths). Infections were the leading cause of death in both early (47.2%) and late (36.4%) periods. Multivariable analysis identified age greater than or equal to 65 years (hazard ratio, 1.65; p = 0.01), pre-ICU high comorbidity (hazard ratio, 1.59; p = 0.02), pre-ICU physical dependence (hazard ratio, 2.29; p < 0.001), risk of death at ICU admission (hazard ratio per 1% increase, 1.008; p = 0.03), ICU-acquired infections (hazard ratio, 2.25; p < 0.001), and ICU readmission (hazard ratio, 3.76; p < 0.001) as risk factors for early mortality. Age greater than or equal to 65 years (hazard ratio, 1.30; p = 0.03), pre-ICU high comorbidity (hazard ratio, 2.28; p < 0.001), pre-ICU physical dependence (hazard ratio, 2.00; p < 0.001), risk of death at ICU admission (hazard ratio per 1% increase, 1.010; p < 0.001), and ICU readmission (hazard ratios, 4.10, 4.17, and 1.82 for death between 31 and 60 days, 61 and 90 days, and greater than 90 days after ICU discharge, respectively; p < 0.001 for all comparisons) were associated with late mortality.

CONCLUSIONS

Infections are the main cause of death after ICU discharge. Older age, pre-ICU comorbidities, pre-ICU physical dependence, severity of illness at ICU admission, and ICU readmission are associated with increased risk of early and late mortality, while ICU-acquired infections are associated with increased risk of early mortality.

The performance of acute versus antecedent patient characteristics for 1-year mortality prediction during intensive care unit admission: a national cohort study

Background

Multiple factors contribute to mortality after ICU, but it is unclear how the predictive value of these factors changes during ICU admission. We aimed to compare the changing performance over time of the acute illness component, antecedent patient characteristics, and ICU length of stay (LOS) in predicting 1-year mortality.

Methods

In this retrospective observational cohort study, the discriminative value of four generalized mixed-effects models was compared for 1-year and hospital mortality. Among patients with increasing ICU LOS, the models included (a) acute illness factors and antecedent patient characteristics combined, (b) acute component only, (c) antecedent patient characteristics only, and (d) ICU LOS. For each analysis, discrimination was measured by area under the receiver operating characteristics curve (AUC), calculated using the bootstrap method. Statistical significance between the models was assessed using the DeLong method (p value < 0.05).

Results

In 400,248 ICU patients observed, hospital mortality was 11.8% and 1-year mortality 21.8%. At ICU admission, the combined model predicted 1-year mortality with an AUC of 0.84 (95% CI 0.84–0.84). When analyzed separately, the acute component progressively lost predictive power. From an ICU admission of at least 3 days, antecedent characteristics significantly exceeded the predictive value of the acute component for 1-year mortality, AUC 0.68 (95% CI 0.68–0.69) versus 0.67 (95% CI 0.67–0.68) (p value < 0.001). For hospital mortality, antecedent characteristics outperformed the acute component from a LOS of at least 7 days, comprising 7.8% of patients and accounting for 52.4% of all bed days. ICU LOS predicted 1-year mortality with an AUC of 0.52 (95% CI 0.51–0.53) and hospital mortality with an AUC of 0.54 (95% CI 0.53–0.55) for patients with a LOS of at least 7 days.

Conclusions

Comparing the predictive value of factors influencing 1-year mortality for patients with increasing ICU LOS, antecedent patient characteristics are more predictive than the acute component for patients with an ICU LOS of at least 3 days. For hospital mortality, antecedent patient characteristics outperform the acute component for patients with an ICU LOS of at least 7 days. After the first week of ICU admission, LOS itself is not predictive of hospital nor 1-year mortality.

U.K. Intensivists' Preferences for Patient Admission to ICU: Evidence From a Choice Experiment

Supplemental Digital Content is available in the text.

Deciding whether to admit a patient to the ICU requires considering several clinical and nonclinical factors. Studies have investigated factors associated with the decision but have not explored the relative importance of different factors, nor the interaction between factors on decision-making. We examined how ICU consultants prioritize specific factors when deciding whether to admit a patient to ICU.

Impact of Critical Illness on Resource Utilization: A Comparison of Use in the Year Before and After ICU Admission

Supplemental Digital Content is available in the text.

Increasingly, patients admitted to an ICU survive to hospital discharge; many with ongoing medical needs. The full impact of an ICU admission on an individual’s resource utilization and survivorship trajectory in the United States is not clear. We sought to compare healthcare utilization among ICU survivors in each year surrounding an ICU admission.

Historic review of long-term outcomes research

PURPOSE OF REVIEW

This review summarizes the results from long-term intensive care outcome research over the past 50 years. Key findings from early studies are reflected in citations of contemporary research.

RECENT FINDINGS

The postintensive care syndrome (PICS) is a multifaceted entity of residual disability and complications burdening survivors of critical illness. Some interventions applied early in the history of outcomes research have now been confirmed as effective in counteracting specific PICS components.

SUMMARY

Interest in patient-centred outcomes has been present since the beginning of modern intensive care. Findings from early long-term studies remain valid even in the face of contemporary large registries that facilitate follow-up of larger cohorts. A further understanding of the mechanisms leading to experienced physical and psychological impairment of PICS will be essential to the design of future intervention trials.

Long-term mortality and outcome in hospital survivors of septic shock, sepsis, and severe infections: The importance of aftercare

Patients with severe infections and especially sepsis have a high in-hospital mortality, but even hospital survivors face long-term sequelae, decreased health-related quality of life, and high risk of death, suggesting a great need for specialized aftercare. However, data regarding a potential benefit of post-discharge rehabilitation in these patients are scarce. In this retrospective matched cohort study the claim data of a large German statutory health care insurer was analyzed. 83,974 hospital survivors having suffered from septic shock, sepsis, and severe infections within the years 2009–2016 were identified using an ICD abstraction strategy closely matched to the current Sepsis-3 definition. Cases were analyzed and compared with their matched pairs to determine their 5-year mortality and the impact of post-discharge rehabilitation. Five years after hospital discharge, mortality of initial hospital survivors were still increased after septic shock (HR_adj 2.03, 95%-CI 1.87 to 2.19; P<0.001), sepsis (HR_adj 1.73, 95%-CI 1.71 to 1.76; P<0.001), and also in survivors of severe infections without organ dysfunction (HR_adj 1.70, 95%-CI 1.65 to 1.74; P<0.001) compared to matched controls without infectious diseases. Strikingly, patients treated in rehabilitation facilities showed a significantly improved 5-year survival after suffering from sepsis or septic shock (HR_adj 0.81, 95%-CI 0.77 to 0.85; P<0.001) as well as severe infections without organ dysfunction (HR_adj 0.81, 95%-CI 0.73 to 0.90; P<0.001) compared to matched patients discharged to home or self-care. Long-term mortality and morbidity of hospital survivors are markedly increased after septic shock, sepsis and severe infections without organ dysfunction, but best 5-year survival was recorded in patients discharged to a rehabilitation facility in all three groups. Thus, our data suggest that specialized aftercare programs may help to improve long-term outcome in these patients and warrants more vigilance in future investigations.

Healthcare trajectories before and after critical illness: population-based insight on diverse patients clusters

BACKGROUND

The post intensive care syndrome (PICS) gathers various disabilities, associated with a substantial healthcare use. However, patients' comorbidities and active medical conditions prior to intensive care unit (ICU) admission may partly drive healthcare use after ICU discharge. To better understand retative contribution of critical illness and PICS-compared to pre-existing comorbidities-as potential determinant of post-critical illness healthcare use, we conducted a population-based evaluation of patients' healthcare use trajectories.

RESULTS

Using discharge databases in a 2.5-million-people region in France, we retrieved, over 3 years, all adult patients admitted in ICU for septic shock or acute respiratory distress syndrome (ARDS), intubated at least 5 days and discharged alive from hospital: 882 patients were included. Median duration of mechanical ventilation was 11 days (interquartile ranges [IQR] 8;20), mean SAPS2 was 49, and median hospital length of stay was 42 days (IQR 29;64). Healthcare use (days spent in healthcare facilities) was analyzed 2 years before and 2 years after ICU admission. Prior to ICU admission, we observed, at the scale of the whole study population, a progressive increase in healthcare use. Healthcare trajectories were then explored at individual level, and patients were assembled according to their individual pre-ICU healthcare use trajectory by clusterization with the K-Means method. Interestingly, this revealed diverse trajectories, identifying patients with elevated and increasing healthcare use (n = 126), and two main groups with low (n = 476) or no (n = 251) pre-ICU healthcare use. In ICU, however, SAPS2, duration of mechanical ventilation and length of stay were not different across the groups. Analysis of post-ICU healthcare trajectories for each group revealed that patients with low or no pre-ICU healthcare (which represented 83% of the population) switched to a persistent and elevated healthcare use during the 2 years post-ICU.

CONCLUSION

For 83% of ARDS/septic shock survivors, critical illness appears to have a pivotal role in healthcare trajectories, with a switch from a low and stable healthcare use prior to ICU to a sustained higher healthcare recourse 2 years after ICU discharge. This underpins the hypothesis of long-term critical illness and PICS-related quantifiable consequences in healthcare use, measurable at a population level.

The association of intensive care with utilization and costs of outpatient healthcare services and quality of life

Background

Little is known about outpatient health services use following critical illness and intensive care. We examined the association of intensive care with outpatient consultations and quality of life in a population-based sample.

Methods

Cross-sectional analysis of data from 6,686 participants of the Study of Health in Pomerania (SHIP), which consists of two independent population-based cohorts. Statistical modeling was done using Poisson regression, negative binomial and generalized linear models for consultations, and a fractional response model for quality of life (EQ-5D-3L index value), with results expressed as prevalence ratios (PR) or percent change (PC). Entropy balancing was used to adjust for observed confounding.

Results

ICU treatment in the previous year was reported by 139 of 6,686 (2,1%) participants, and was associated with a higher probability (PR 1.05 [CI:1.03;1.07]), number (PC +58.0% [CI:22.8;103.2]) and costs (PC +64.1% [CI:32.0;103.9]) of annual outpatient consultations, as well as with a higher number of medications (PC +37.8% [CI:17.7;61.5]). Participants with ICU treatment were more likely to visit a specialist (PR 1.13 [CI:1.09; 1.16]), specifically internal medicine (PR 1.67 [CI:1.45;1.92]), surgery (PR 2.42 [CI:1.92;3.05]), psychiatry (PR 2.25 [CI:1.30;3.90]), and orthopedics (PR 1.54 [CI:1.11;2.14]). There was no significant effect regarding general practitioner consultations. ICU treatment was also associated with lower health-related quality of life (EQ-5D index value: PC -13.7% [CI:-27.0;-0.3]). Furthermore, quality of life was inversely associated with outpatient consultations in the previous month, more so for participants with ICU treatment.

Conclusions

Our findings suggest that ICU treatment is associated with an increased utilization of outpatient specialist services, higher medication intake, and impaired quality of life.

The needs of patients with post-intensive care syndrome: A prospective, observational study

BACKGROUND

The needs of critical illness survivors and how best to address these are unclear.

OBJECTIVES

The objective of this study was to identify critical illness survivors who had developed post-intensive care syndrome and to explore their use of community healthcare resources, the socioeconomic impact of their illness, and their self-reported unmet healthcare needs.

METHODS

Patients from two intensive care units (ICUs) in Western Australia who were mechanically ventilated for 5 days or more and/or had a prolonged ICU admission were included in this prospective, observational study. Questionnaires were used to assess participants' baseline health and function before admission, which were then repeated at 1 and 3 months after ICU discharge.

RESULTS

Fifty participants were enrolled. Mean Functional Activities Questionnaire scores increased from 1.8 out of 30 at baseline (95% confidence interval [CI]: 0-3.5) to 8.9 at 1 month after ICU discharge (95% CI: 6.5-11.4; P = <0.001) and 7.0 at 3 months after ICU discharge (95% CI: 4.9-9.1; P = < 0.001). Scores indicating functional dependence increased from 8% at baseline to 54% and 33% at 1 and 3 months after ICU discharge, respectively. Statistically significant declines in health-related quality of life were identified in the domains of Mobility, Personal Care, Usual Activities, and Pain/Discomfort at 1 month after ICU discharge and in Mobility, Personal Care, Usual Activities, and Anxiety/Depression at 3 months after ICU discharge. An increase in healthcare service use was identified after ICU discharge. Participants primarily identified mental health services as the service that they felt they would benefit from but were not accessing. Very low rates of return to work were observed, with 35% of respondents at 3 months, indicating they were experiencing financial difficulty as a result of their critical illness.

CONCLUSIONS

Study participants developed impairments consistent with post-intensive care syndrome, with associated negative socioeconomic ramifications, and identified mental health as an area they need more support in.