Variation in critical care services across North America and Western Europe. Crit Care Med. 2008;36:2787-2793. [PMID: 18766102 DOI: 10.1097/ccm.0b013e318186aec8]

Use of Life Support and Outcomes Among Patients Admitted to Intensive Care Units

Importance

Nationwide data are unavailable regarding changes in intensive care unit (ICU) outcomes and use of life support over the past 10 years, limiting understanding of practice changes.

Objective

To portray the epidemiology of US critical care before, during, and after the COVID-19 pandemic.

Design, Setting, and Participants

Retrospective cohort study of adult patients admitted to an ICU for any reason, using data from the 54 US health systems continuously contributing to the Epic Cosmos database from 2014-2023.

Exposures

Patient demographics, COVID-19 status, and pandemic era.

Main Outcomes and Measures

In-hospital mortality unadjusted and adjusted for patient demographics, comorbidities, and illness severity; ICU length of stay; and receipt of life-support interventions, including mechanical ventilation and vasopressor medications.

Results

Of 3 453 687 admissions including ICU care, median age was 65 (IQR, 53-75) years. Patients were 55.3% male; 17.3% Black and 6.1% Hispanic or Latino; and overall in-hospital mortality was 10.9%. The adjusted in-hospital mortality was elevated during the pandemic in COVID-negative (adjusted odds ratio [aOR], 1.3 [95% CI, 1.2-1.3]) and COVID-positive (aOR, 4.3 [95% CI, 3.8-4.8]) patients and returned to baseline by mid-2022. The median ICU length of stay was 2.1 (IQR, 1.1-4.2) days, with increases during the pandemic among COVID-positive patients (difference for COVID-positive vs COVID-negative patients, 2.0 days [95% CI, 2.0-2.1]). Rates of invasive mechanical ventilation were 23.2% (95% CI, 23.1%-23.2%) before the pandemic, increased to 25.8% (95% CI, 25.8%-25.9%) during the pandemic, and declined below prepandemic baseline thereafter (22.0% [95% CI, 21.9%-22.2%]). The use of vasopressors increased from 7.2% to 21.6% of ICU stays.

Conclusions and Relevance

Pandemic-era increases in length of stay and adjusted in-hospital mortality among US ICU patients returned to recent historical baselines. Fewer patients are now receiving mechanical ventilation than prior to the pandemic, while more patients are administered vasopressor medications.

Mobilisation of post-ICU patients - a crucial teamwork between physiotherapists and nurses at surgical wards: a qualitative study

PURPOSE

To describe experiences of the ward nurse in relation to extended physiotherapy and mobilising of post-ICU patients.

METHODS

Individual semi-structured interviews were conducted with 17 registered nurses working on surgical wards in a Swedish regional hospital. Qualitative content analysis was used to analyse the data. The study was reported according to the consolidated criteria for reporting qualitative research (COREQ).

RESULTS

The study findings are presented in three categories: challenges to mobilising post-ICU patients, shared responsibility facilitates mobilisation, and extended physiotherapy is beneficial for patients' wellbeing. Nurses stated that they lacked knowledge and skills to perform the safe mobilisation of post-ICU patients due to their complex medical history and needs. Collaboration with physiotherapists was perceived to facilitate mobilisation and to be beneficial for patients' wellbeing outcome.

CONCLUSIONS

The study indicates that post-ICU patients are at risk of remaining immobilised because ward nurses find mobilisation too complex to conduct without support from physiotherapists. Shared responsibility through multi-professional teamwork regarding patient rehabilitation is perceived as contributing the knowledge required to achieve safe mobilisation that enhances autonomy and physical ability in post-ICU patients.

Emergency and postoperative access to critical and enhanced care: a multicentre prospective observational study

INTRODUCTION

The ability to admit patients to enhanced or critical care may be limited by bed availability. In a network with low provision of critical and enhanced care beds, we aimed to assess the proportion of patients having surgery with moderate (1%-< 5%) or high (≥ 5%) predicted risk of 30-day postoperative mortality and their postoperative care location. We also aimed to study referral and admission outcomes to critical care.

METHODS

This prospective, 7-day observational study was conducted across 19 acute hospital sites within the South West Critical Care Network. All adult inpatients having a procedure under the care of an anaesthetist (excluding cardiac and obstetric procedures) had a surgical outcome risk tool score calculated retrospectively, and their postoperative destination captured. Synchronously, all critical care referrals, admissions and refusal decisions were captured, along with critical care bed capacity.

RESULTS

Of 2222 eligible patients, 1728 (78%) were captured. Retrospective surgical outcome risk tool score calculation revealed 1060 (61%) patients had a low, 418 (24%) a moderate and 250 (15%) a high risk of postoperative mortality. In patients with a moderate predicted risk of postoperative morbidity, 72/418 (17%) received enhanced or critical care and 64/249 (26%) patients with a high predicted risk received critical care. All critical care referral and admission activity was captured; in total, 263/680 (39%) of patients referred were admitted to critical care. Referrals to critical care exceeded the available level 3-equivalent beds on 79% of occasions.

DISCUSSION

These data describe constraints in access to postoperative and emergency enhanced/critical care in the south-west of England. There is poor compliance with national guidance regarding the postoperative care location of patients with a moderate or high risk of postoperative mortality.

Intermediate Care Units in Internal Medicine

BACKGROUND

Intermediate Care Units (ImCU) have been historically described as an intermediate level of care between standard wards and intensive care units (ICU), and general medical ImCUs have evolved as specifically addressed to high care medical patients. The objective of this study is to explore designs, appropriateness criteria, and quality of care of general medical ImCUs.

METHODS

a comprehensive literature search was performed in electronic database (PubMed/Medline, Embase, Cochrane and Web of Science) up to July 30th 2024 and data about general medical ImCU denominations, settings, processes and outcomes were extracted.

RESULTS

34 studies were included in systematic analyses, the more used nomenclature was ImCU (70.6 %), followed by High Dependency Unit (20.6 %). The median number of beds was 8 [4-11], the nurse-to-patients ratio 1:3.1, and internists involved in comanagement in 40.0 %. Either a step-up from standard wards or a step-down from ICUs role were reported, with a median of 50.8 % [26.2-71.0] of patients directly admitted from Emergency Departments. The main distinctive activities were continuous monitoring and non-invasive ventilation. The median ICU transfer rate was 8.0 % [5.6-12.3], while in-ImCU and in-hospital mortality were 6.2 % [3.6-8.3] and 14.0 % [8.7-19.1], respectively.

CONCLUSIONS

general medical ImCUs are being increasingly recognized as the appropriate setting for high care medical patients but present to date a wide variability of formats. Activity-based admission criteria tailored on each hospital reality could be a process model for adequate patient flow, and quality of care key indicators should consider the functional general medical ImCU role in hospital macro-systems.

Prognostic Markers of Adverse Outcomes in Acute Heart Failure: Use of Machine Learning and Network Analysis with Real Clinical Data

Background: Acute heart failure (AHF) is one of the leading causes of admissions to the emergency department (ED). There is a need to develop an easy-to-use score that can be used in the ED to risk-stratify patients with AHF and in hospitalization decisions regarding cardiac wards or intensive care units (ICUs). Methods: A retrospective observational study was conducted at a city hospital. The data from the presentation of AHF patients at the ED were collected. The combined primary endpoint included death from any cause during hospitalization or transfer to an intensive care unit (ICU) for using inotropes/vasopressors. Feature selection was performed using artificial intelligence. Results: From August 2020 to August 2021, 908 patients were enrolled (mean age: 71.6 ± 13 years; 500 (55.1%) men). We found significant predictors of in-hospital mortality and ICU transfers for inotrope/vasopressor use and built two models to assess the need for ICU admission of patients from the ED. The first model included SpO2 < 90%, QTc duration, prior diabetes mellitus and HF diagnosis, serum chloride concentration, respiratory rate and atrial fibrillation on admission, blood urea nitrogen (BUN) levels, and any implanted devices. The second model included left ventricular end-diastolic size, systolic blood pressure, pulse blood pressure, BUN levels, right atrium size, serum chloride, sodium and uric acid concentrations, prior loop diuretic use, and pulmonary artery systolic blood pressure. Conclusions: We developed two models that demonstrated a high negative predictive value, which allowed us to distinguish patients with low risk and determine patients who can be hospitalized and sent from the ED to the floor. These easy-to-use models can be used at the ED.

Protocol for a meta-review of interventions to prevent and manage ICU delirium

INTRODUCTION

Intensive care unit (ICU) delirium is an acute brain dysfunction that affects up to 7 out of 10 patients admitted to ICUs. Patients who develop ICU delirium cannot think clearly, have trouble paying attention, do not understand what is happening around them and may see or hear things that are not there. ICU delirium increases the time patients spend in ICUs and hospitals and therefore healthcare costs. ICU delirium is also associated with increased mortality and dementia in the longer term. ICU delirium prevention and management strategies are likely to include both pharmacological and non-pharmacological components as part of a complex intervention, but it is unclear which components should be included. The objective of this meta-review is to systematically map the quantity and certainty of the available evidence from reviews and meta-analyses of randomised controlled trials (RCTs) of pharmacological and non-pharmacological interventions, which will be used to design a multicomponent intervention to prevent and manage ICU delirium.

METHODS AND ANALYSIS

A systematic search strategy was performed in MEDLINE (Ovid), Embase (Elsevier), Cochrane Database of Systematic Reviews, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO and Web of Science (from inception to 26 September 2023), as well as Epistemonikos (from inception to 19 July 2023). We will include all critically ill adults (aged≥18 years) and any ICU delirium prevention or management intervention (pharmacological or non-pharmacological). For pharmacological interventions, we will include reviews of RCTs. For non-pharmacological interventions, we will consider reviews of RCTs, quasi-experimental and cohort studies. We will use the International Consensus Study (Del-COrS) core outcome set for research evaluating interventions to prevent or manage ICU delirium and synthesise our findings using quantitative data description methods. We will involve our Patient and Public Involvement group of people who experienced ICU delirium to develop and comment on such aspects as the research question, methodology and which outcomes are most important.

ETHICS AND DISSEMINATION

No ethical approval is required for this study. The results of this meta-review will be disseminated through peer-reviewed publications and conferences. They will also form part of an evidence map and logic model for the prevention and management of ICU delirium.

PROSPERO REGISTRATION NUMBER

CRD42023473260.

Population Sepsis Incidence, Mortality, and Trends in Hong Kong Between 2009 and 2018 Using Clinical and Administrative Data

BACKGROUND

Sepsis surveillance using electronic health record (EHR)-based data may provide more accurate epidemiologic estimates than administrative data, but experience with this approach to estimate population-level sepsis burden is lacking.

METHODS

This was a retrospective cohort study including all adults admitted to publicly funded hospitals in Hong Kong between 2009 and 2018. Sepsis was defined as clinical evidence of presumed infection (clinical cultures and treatment with antibiotics) and concurrent acute organ dysfunction (≥2-point increase in baseline Sequential Organ Failure Assessment [SOFA] score). Trends in incidence, mortality, and case fatality risk (CFR) were modeled by exponential regression. Performance of the EHR-based definition was compared with 4 administrative definitions using 500 medical record reviews.

RESULTS

Among 13 540 945 hospital episodes during the study period, 484 541 (3.6%) had sepsis by EHR-based criteria with 22.4% CFR. In 2018, age- and sex-adjusted standardized sepsis incidence was 756 per 100 000 (relative change: +2.8%/y [95% CI: 2.0%-3.7%] between 2009 and 2018) and standardized sepsis mortality was 156 per 100 000 (relative change: +1.9%/y; 95% CI: .9%-2.8%). Despite decreasing CFR (relative change: -0.5%/y; 95% CI: -1.0%, -.1%), sepsis accounted for an increasing proportion of all deaths (relative change: +3.9%/y; 95% CI: 2.9%-4.8%). Medical record reviews demonstrated that the EHR-based definition more accurately identified sepsis than administrative definitions (area under the curve [AUC]: .91 vs .52-.55; P < .001).

CONCLUSIONS

An objective EHR-based surveillance definition demonstrated an increase in population-level standardized sepsis incidence and mortality in Hong Kong between 2009 and 2018 and was much more accurate than administrative definitions. These findings demonstrate the feasibility and advantages of an EHR-based approach for widescale sepsis surveillance.

Post-Intensive Care Syndrome-Mental Health

Intensive care unit (ICU) survivors experience longstanding psychological impairments that persist in the months to years following ICU discharge, regardless of severity of illness or extent of physical recovery. Risk factors for psychological problems following critical illness have been identified including early symptoms of acute stress. Assessment of psychological symptoms in ICU patients and survivors remains inconsistent and many do not receive appropriate psychological evaluation, diagnosis, or treatment. Screening patients for psychological impairments early and serially following hospitalization is crucial to addressing patients' needs and mitigating long-term distress, as is connecting patients to outpatient mental health follow-up for treatment.

Creating a Culture of an Awake and Walking Intensive Care Unit: In-Hospital Strategies to Mitigate Post-Intensive Care Syndrome

The ABCDEF bundle and Awake and Walking intensive care unit (ICU) approach aim to prevent the long-term consequences of critical illness (ie, post-intensive care syndrome) by promoting patient wakefulness, cognition, and mobility. Humanizing the ICU experience is the key, preserving patients' function and autonomy. Successful implementation requires cultivating an ICU culture focused on avoiding sedatives and initiating prompt mobilization, addressing organizational barriers through tailored strategies. Overall, these patient-centered, mobility-focused models offer a holistic solution to the complex challenge of preventing post-intensive care syndrome and supporting critical illness survivors.

Lessons Learned From Extracorporeal Life Support Practice and Outcomes During the COVID-19 Pandemic

Extracorporeal membrane oxygenation is increasingly being used to support patients with hypoxemic respiratory failure and cardiogenic shock. During the COVID-19 pandemic, consensus guidance recommended extracorporeal life support for patients with COVID-19-related cardiopulmonary disease refractory to optimal conventional therapy, prompting a substantial expansion in the use of this support modality. Extracorporeal membrane oxygenation was particularly integral to the bridging of COVID-19 patients to heart or lung transplantation. Limited human and physical resources precluded widespread utilization of mechanical support during the COVID-19 pandemic, necessitating careful patient selection and optimal management by expert healthcare teams for judicious extracorporeal membrane oxygenation use. This review outlines the evidence supporting the use of extracorporeal life support in COVID-19, describes the practice and outcomes of extracorporeal membrane oxygenation for COVID-19-related respiratory failure and cardiogenic shock, and proposes lessons learned for the implementation of extracorporeal membrane oxygenation as a bridge to transplantation in future public health emergencies.

Height status matters for risk of mortality in critically ill children

BACKGROUND

Anthropometric measurements are crucial in pediatric critical care, but the impact of height on ICU outcomes is underexplored despite a substantial number of short-for-age children in ICUs. Previous studies suggest that short stature increases the risk of poor clinical outcomes. This study examines the relationship between short stature and ICU outcomes.

METHODS

We conducted a retrospective cohort study using a Japanese nationwide database (the Japanese Intensive Care Patient Database; JIPAD), which included pediatric patients under 16 years admitted to ICUs from April 2015 to March 2020. Height standard deviation scores (SD scores) were calculated based on age and sex. Short-stature patients were defined as height SD score <  - 2. The primary outcome was all-cause ICU mortality, and the secondary outcome was the length of stay in ICU.

RESULTS

Out of 6,377 pediatric patients, 27.2% were classified as having short stature. The ICU mortality rate was significantly higher in the short-stature group compared to the normal-height group (3.6% vs. 1.4%, p < 0.01). Multivariable logistic regression showed that short stature was independently associated with increased ICU mortality (OR = 2.73, 95% CI 1.81-4.11). Additionally, the Fine-Gray subdistribution hazards model indicated that short stature was associated with a lower chance of ICU discharge for each additional day (HR 0.85, 95% CI 0.81-0.90, p < 0.01).

CONCLUSIONS

Short stature is a significant risk factor for increased ICU mortality and prolonged ICU stay in critically ill children. Height should be considered in risk assessments and management strategies in pediatric intensive care to improve outcomes.

Economic Indicators, Quantity and Quality of Health Care Resources Affecting Post-surgical Mortality

OBJECTIVE

to identify correlations between quality and quantity of health care resources, national economic indicators, and postoperative in-hospital mortality as reported in the EUSOS study.

METHODS

Different variables were identified from a series of publicly available database. Postoperative in-hospital mortality was identified as reported by EUSOS study. Spearman non-parametric and Coefficients of non-linear regression were calculated.

RESULTS

Quality of health care resources was strongly and negatively correlated to postoperative in-hospital mortality. Quantity of health care resources were negatively and moderately correlated to postoperative in-hospital mortality. National economic indicators were moderately and negatively correlated to postoperative in-hospital mortality. General mortality, as reported by WHO, was positively but very moderately correlated with postoperative in-hospital mortality.

CONCLUSIONS

Postoperative in-hospital mortality is strongly determined by quality of health care instead of quantity of health resources and health expenditures. We suggest that improving the quality of health care system might reduce postoperative in-hospital mortality.

Showcasing the Saudi e-referral system experience: the epidemiology and pattern of referrals utilising nationwide secondary data

Introduction

Referrals are an integral part of any healthcare system. In the Kingdom of Saudi Arabia (KSA) an electronic referral (e-referral) system known as the Saudi Medical Appointments and Referrals Centre (SMARC) began formally functioning in 2019. This study aims to showcase the Saudi experience of the e-referral system and explore the epidemiology of referrals nationally.

Methods

This retrospective descriptive study utilised secondary collected data between 2020 and 2021 from the SMARC system. Cross tabulations with significance testing and colour-coded maps were used to highlight the patterns across all regions.

Results

The study analysed over 600,000 referral requests. The mean age of patients was 40.70 ± 24.66 years. Males had a higher number of referrals (55.43%). Referrals in 2021 were higher than those in 2020 (56.21%). Both the Autumn and Winter seasons had the highest number of referrals (27.09% and 27.43%, respectively). The Surgical specialty followed by Medicine had the highest referrals (26.07% and 22.27%, respectively). Life-saving referrals in the Central region were more than double those in other regions (14.56%). Emergency referrals were also highest in the Southern regions (44.06%). The Central and Eastern regions had higher referrals due to unavailable sub-speciality (68.86% and 67.93%, respectively). The Southern regions had higher referrals due to both unavailable machine and unavailable beds (18.44% and 6.24%, respectively).

Conclusion

This study shows a unique system in which referrals are between secondary, tertiary, and specialised care. It also highlights areas of improvement for equitable resource allocation and specialised care in slightly problematic areas as well as the use of population density in future planning.

A Comparison of Different Intensive Care Unit Definitions Derived from the German Administrative Data Set: A Methodological, Real-World Data Analysis from 86 Helios Hospitals

Background: The intensive care unit (ICU) is a scarce resource in all health care systems, necessitating a well-defined utilization. Therefore, benchmarks are essential; and yet, they are limited due to heterogenous definitions of what an ICU is. This study analyzed the case distribution, patient characteristics, and hospital course and outcomes of 6,204,093 patients in the German Helios Hospital Group according to 10 derived ICU definitions. We aimed to set a baseline for the development of a nationwide, uniform ICU definition. Methods: We analyzed ten different ICU definitions: seven derived from the German administrative data set of claims data according to the German Hospital Remuneration Act, three definitions were taken from the Helios Hospital Group's own bed classification. For each ICU definition, the size of the respective ICU population was analyzed. Due to similar patient characteristics for all ten definitions, we selected three indicator definitions to additionally test statistically against IQM. Results: We analyzed a total of 5,980,702 completed hospital cases, out of which 913,402 referred to an ICU criterion (14.7% of all cases). A key finding is the significant variability in ICU population size, depending on definitions. The most restrictive definition of only mechanical ventilation (DOV definition) resulted in 111,966 (1.9%) cases; mechanical ventilation plus typical intensive care procedure codes (IQM definition) resulted in 210,147 (3.5%) cases; defining each single bed individually as ICU or IMC (ICUᴧIMC definition) resulted in 411,681 (6.9%) cases; and defining any coded length of stay at ICU (LOSi definition) resulted in 721,293 (12.1%) cases. Further testing results for indicator definitions are reported. Conclusions: The size of the population, utilization rates, outcomes, and capacity assumptions clearly depend on the definition of ICU. Therefore, the underlying ICU definition should be stated when making any comparisons. From previous studies, we anticipated that 25-30% of all ICU patients should be mechanically ventilated, and therefore, we conclude that the ICUᴧIMC definition is the most plausible approximation. We suggest a mandatory application of a clearly defined ICU term for all hospitals nationwide for improved benchmarking and data analysis.

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model

BACKGROUND

Clinical notes contain contextualized information beyond structured data related to patients' past and current health status.

OBJECTIVE

This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data.

METHODS

Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors.

RESULTS

The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments.

CONCLUSIONS

The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.

Quality Indicators in Adult Critical Care Medicine

Quality indicators are increasingly used in the intensive care unit (ICU) to compare and improve the quality of delivered healthcare. Numerous indicators have been developed and are related to multiple domains, most importantly patient safety, care timeliness and effectiveness, staff well-being, and patient/family-centered outcomes and satisfaction. In this review, we describe pertinent ICU quality indicators that are related to organizational structure (such as the availability of an intensivist 24/7 and the nurse-to-patient ratio), processes of care (such as ventilator care bundle), and outcomes (such as ICU-acquired infections and standardized mortality rate). We also present an example of a quality improvement project in an ICU indicating the steps taken to attain the desired changes in quality measures.

A Train-the-Trainer Simulation Program Implemented Between Two International Partners

With the expansion of global health initiatives focused on healthcare professional training, it is important to ensure that such training is scalable and sustainable. Simulation-based education (SBE) is a highly effective means to achieve these goals. Although SBE is widely used in the United States, its integration globally is limited, which can impact the potential of SBE in many countries. The purpose of this perspective piece is to demonstrate how a train-the-trainer program can help in the development of an international SBE program and specifically what unique issues must be considered in operationalizing this strategy.

Description of Intensive Care and Intermediate Care resources managed by Anaesthesiology Departments in Spain and their adaptation capacity during the COVID-19 pandemic

INTRODUCTION

It is essential to understand the strategic importance of intensive care resources in the sustainable organisation of healthcare systems. Our objective has been to identify the intensive and intermediate care beds managed by Anaesthesiology and Resuscitation Services (A-ICU and A-IMCU) in Spain, their human and technical resources, and the changes made to these resources during the COVID-19 pandemic.

MATERIAL AND METHODS

Prospective observational study performed between December 2020 and July 2021 to register the number and characteristics of A-ICU and A-IMCU beds in hospitals listed in the catalogue published by the Spanish Ministry of Health.

RESULTS

Data were obtained from 313 hospitals (98% of all hospitals with more than 500 beds, 70% of all hospitals with more than 100 beds). One hundred and forty seven of these hospitals had an A-ICU with a total of 1702 beds. This capacity increased to 2107 (124%) during the COVID-19 pandemic. Three hundred and eight hospitals had an A-IMCU with a total of 3470 beds, 52.9% (2089) of which provided long-term care. The hospitals had 1900 ventilators, at a ratio of 1.07 respirators per A-ICU; 1559 anaesthesiologists dedicated more than 40% of their working time to intensive care. The nurse-to-bed ratio in A-ICUs was 2.8.

DISCUSSION

A large proportion of fully-equipped ICU and IMCU beds in Spanish hospitals are managed by the anaesthesiology service. A-ICU and A-IMCUs have shown an extraordinary capacity to adapt their resources to meet the increased demand for intensive care during the COVID-19 pandemic.

Cost-effectiveness of intravenous resuscitation fluids in sepsis patients: a patient-level data analysis in Jordan

AIM

Albumin role as fluid resuscitation in sepsis remains understudied in low- and middle-income countries. This study aimed to evaluate the cost-effectiveness of intravenous (IV) Albumin compared to Crystalloids in sepsis patients using patient-level data in Jordan.

METHODS

This was a retrospective cohort study of sepsis patients aged 18 or older admitted to intensive care units (ICU) at two major tertiary hospitals during the period 2018-2019. Patients information, type of IV fluid, and clinical outcomes were retrieved from medical records, and charges were retrieved from the billing system. A 90-day partitioned survival model with two health states (alive and dead) was constructed to estimate the survival of sepsis patients receiving either Albumin or Crystalloids as IV fluids for resuscitation. Overall survival was predicted by fitting a Weibull model on the patient-level data from the current study. To further validate the results, and to support the assessment of uncertainty, time-dependent transition probabilities of death at each cycle were estimated and used to construct a state-transition patient-level simulation model with 10,000 microsimulation trials. Adopting the healthcare system perspective, incremental cost-effectiveness ratios(ICERs) of Albumin versus Crystalloids were calculated in terms of the probability to be discharged alive from the ICU. Uncertainty was explored using probabilistic sensitivity analysis.

RESULTS

In the partitioned survival model, Albumin was associated with an incremental cost of $1,007 per incremental1% in the probability of being discharged alive from the ICU. In the state-transition patient-level simulation model, ICER was $1,268 per incremental 1% in the probability of being discharged alive. Probabilistic sensitivity analysis showed that Albumin was favored at thresholds >$800 per incremental 1%in the probability of being discharged alive from the ICU.

CONCLUSION

IV Albumin use in sepsis patients might not be cost-effective from the healthcare perspective of Jordan. This has important implications for policymakers to readdress Albumin prescribing practice in sepsis patients.

International variation in ethics and contract approval processes for a low-risk observational study of mechanical ventilation discontinuation practices

OBJECTIVES

To describe international variation in ethics and contract processes, identify predictors of approval times, and reasons for nonparticipation in an international observational study of ventilation discontinuation practices.

STUDY DESIGN AND SETTING

A nested cross-sectional survey of research personnel at 111 participating sites (representing 142 intensive care units [ICUs]) from six geographic regions (Canada, India, the United Kingdom, Europe, Australia/New Zealand, and the United States).

RESULTS

We analyzed responses from 80 sites (72.1% response rate). A single local or central approval was required at 34/80 (42.5%) and 23/80 (28.75%), respectively. Of those requiring central ethics approval, 20/23 (87.0%) sites required an additional approval. Sites with central vs. other ethics approval processes had significantly longer times to ethics approval (176 vs. 42 days; P < 0.0001). The median time to contract execution was 140 days (range: 11-1,215) with sites in India and the United States having the shortest and longest times to contract execution, respectively. We did not identify independent predictors of approval times. Of 190 sites that initially agreed to participate, 78 (41%) sites (89 ICUs) were ultimately unable to participate.

CONCLUSION

International ethics and contract approval times were lengthy and highly variable. Central ethics review processes significantly increased approval times.

Patients' experiences of preparing for transfer from the intensive care unit to a hospital ward: A multicentre qualitative study

BACKGROUND

The transfer from an intensive care unit (ICU) to a regular ward often causes confusion and stress for patients and family members. However, little is known about the patients' perspective on preparing for the transfer.

AIM

The purpose of the study was to describe patients' experiences of preparing for transfer from an ICU to a ward.

STUDY DESIGN

Individual interviews with 14 former ICU patients from three urban hospitals in Stockholm, Sweden were conducted 3 months after hospital discharge. Qualitative content analysis was used to interpret the interview transcripts. Reporting followed the consolidated criteria for reporting qualitative research checklist.

RESULTS

The results showed that the three categories, the discharge decision, patient involvement, and practical preparations were central to the patients' experiences of preparing for the transition from the intensive care unit to the ward. The discharge decision was associated with a sense of relief, but also worry about what would happen on the ward. The patients felt that they were not involved in the decision about the discharge or the planning of their health care. To handle the situation, patients needed information about planned care and treatment. However, the information was often sparse, delivered from a clinician's perspective, and therefore not much help in preparing for transfer.

CONCLUSIONS

ICU patients experienced that they were neither involved in the process of forthcoming care nor adequately prepared for the transfer to the ward. Relevant and comprehensible information and sufficient time to prepare were needed to reduce stress and promote efficient recovery.

RELEVANCE TO CLINICAL PRACTICE

The study suggests that current transfer strategies are not optimal, and a more person-centred discharge procedure would be beneficial to support patients and family members in the transition from the ICU to the ward.

Racial & ethnic disparities in geographic access to critical care in the United States: A geographic information systems analysis

OBJECTIVE

It is important to identify gaps in access and reduce health outcome disparities, understanding access to intensive care unit (ICU) beds, especially by race and ethnicity, is crucial. Our objective was to evaluate the race and ethnicity-specific 60-minute drive time accessibility of ICU beds in the United States (US).

DESIGN

We conducted a cross-sectional study using road network analysis to determine the number of ICU beds within a 60-minute drive time, and calculated adult intensive care bed ratios per 100,000 adults. We evaluated the US population at the Census block group level and stratified our analysis by race and ethnicity and by urbanicity. We classified block groups into four access levels: no access (0 adult intensive care beds/100,000 adults), below average access (>0-19.5), average access (19.6-32.0), and above average access (>32.0). We calculated the proportion of adults in each racial and ethnic group within the four access levels.

SETTING

All 50 US states and the District of Columbia.

PARTICIPANTS

Adults ≥15 years old.

MAIN OUTCOME MEASURES

Adult intensive care beds/100,000 adults and percentage of adults national and state) within four access levels by race and ethnicity.

RESULTS

High variability existed in access to ICU beds by state, and substantial disparities by race and ethnicity. 1.8% (n = 5,038,797) of Americans had no access to an ICU bed, and 26.8% (n = 73,095,752) had below average access, within a 60-minute drive time. Racial and ethnic analysis showed high rates of disparities (no access/below average access): American Indians/Alaskan Native 12.6%/28.5%, Asian 0.7%/23.1%, Black or African American 0.6%/16.5%, Hispanic or Latino 1.4%/23.0%, Native Hawaiian and other Pacific Islander 5.2%/35.0%, and White 2.1%/29.0%. A higher percentage of rural block groups had no (5.2%) or below average access (41.2%), compared to urban block groups (0.2% no access, 26.8% below average access).

CONCLUSION

ICU bed availability varied substantially by geography, race and ethnicity, and by urbanicity, creating significant disparities in critical care access. The variability in ICU bed access may indicate inequalities in healthcare access overall by limiting resources for the management of critically ill patients.

Cognitive Impairment, Physical Impairment, and Psychological Symptoms in Intensive Care Unit Survivors

BACKGROUND

Post-intensive care syndrome (PICS) affects 25% to 50% of adults who survive an intensive care unit (ICU) stay. Although the compounding of PICS impairments (cognitive, physical, and psychological) could intensify the syndrome, research on relationships among impairments is limited, particularly in patients with delirium.

OBJECTIVES

To examine associations among PICS impairments and examine delirium status and its relationship to PICS impairments at ICU discharge and 1 month later.

METHODS

A descriptive, correlational study of adults who survived an ICU stay. Participants completed measures for depression, anxiety, posttraumatic stress, physical function, functional status, and cognition at ICU discharge and 1 month later. Relationships among PICS impairments were examined with Spearman correlations; differences in impairments by delirium status were assessed with t tests.

RESULTS

Of 50 enrolled participants, 46 were screened for PICS impairment at ICU discharge and 35 were screened 1 month later. Cognitive impairment was the most common impairment at both time points. A positive correlation was found between cognition and functional status at ICU discharge (ρ = 0.50, P = .001) and 1 month later (ρ = 0.54, P = .001). Cognition and physical functioning were positively correlated 1 month after discharge (ρ = 0.46, P = .006). The group with delirium had significantly lower functional status scores than the group without delirium at ICU discharge (P = .04).

CONCLUSIONS

The findings suggest a moderate correlation between cognitive and physical impairments. This relationship should be explored further; ICU survivors with undiagnosed cognitive impairment may have delayed physical recovery and greater risk for injury.

Cost comparisons and factors related to cost per stay in intensive care units in Belgium

BACKGROUND

Given the variability of intensive care unit (ICU) costs in different countries and the importance of this information for guiding clinicians to effective treatment and to the organisation of ICUs at the national level, it is of value to gather data on this topic for analysis at the national level in Belgium. The objectives of the study were to assess the total cost of ICUs and the factors that influence the cost of ICUs in hospitals in Belgium.

METHODS

This was a retrospective cohort study using data collected from the ICUs of 17 Belgian hospitals from January 01 to December 31, 2018. A total of 18,235 adult ICU stays were included in the study. The data set was a compilation of inpatient information from analytical cost accounting of hospitals, medical discharge summaries, and length of stay data. The costs were evaluated as the expenses related to the management of hospital stays from the hospital's point of view. The cost from the hospital perspective was calculated using a cost accounting analytical methodology in full costing. We used multivariate linear regression to evaluate factors associated with total ICU cost per stay. The ICU cost was log-transformed before regression and geometric mean ratios (GMRs) were estimated for each factor.

RESULTS

The proportion of ICU beds to ward beds was a median [p25-p75] of 4.7% [4.4-5.9]. The proportion of indirect costs to total costs in the ICU was 12.1% [11.4-13.3]. The cost of nurses represented 57.2% [55.4-62.2] of direct costs and this was 15.9% [12.0-18.2] of the cost of nurses in the whole hospital. The median cost per stay was €4,267 [2,050-9,658] and was €2,160 [1,545-3,221] per ICU day. The main factors associated with higher cost per stay in ICU were Charlson score, mechanical ventilation, ECMO, continuous hemofiltration, length of stay, readmission, ICU mortality, hospitalisation in an academic hospital, and diagnosis of coma/convulsions or intoxication.

CONCLUSIONS

This study demonstrated that, despite the small proportion of ICU beds in relation to all services, the ICU represented a significant cost to the hospital. In addition, this study confirms that nursing staff represent a significant proportion of the direct costs of the ICU. Finally, the total cost per stay was also important but highly variable depending on the medical factors identified in our results.

The burden of critical illness among adults in a Swedish region-a population-based point-prevalence study

BACKGROUND

Patients with critical illness have a high risk of mortality. Key decision-making in the health system affecting the outcomes of critically ill patients requires epidemiological evidence, but the burden of critical illness is largely unknown. This study aimed to estimate the prevalence of critical illness in a Swedish region. Secondary objectives were to estimate the proportion of hospital inpatients who are critically ill and to describe the in-hospital location of critically ill patients.

METHODS

A prospective, multi-center, population-based, point-prevalence study on specific days in 2017-2018. All adult (> 18 years) in-patients, regardless of admitting specially, in all acute hospitals in Sörmland, and the patients from Sörmland who had been referred to university hospitals, were included. Patients in the operating theatres, with a psychiatric cause of admission, women in active labor and moribund patients, were excluded. All participants were examined by trained data collectors. Critical illness was defined as "a state of ill health with vital organ dysfunction, a high risk of imminent death if care is not provided and a potential for reversibility". The presence of one or more severely deranged vital signs was used to classify critical illness. The prevalence of critical illness was calculated as the number of critically ill patients divided by the number of adults in the region.

RESULTS

A total of 1269 patients were included in the study. Median age was 74 years and 50% of patients were female. Critical illness was present in 133 patients, resulting in an adult population prevalence of critical illness per 100,000 people of 19.4 (95% CI 16.4-23.0). The proportion of patients in hospital who were critically ill was 10.5% (95% CI 8.8-12.3%). Among the critically ill, 125 [95% CI 94.0% (88.4-97.0%)] were cared for in general wards.

CONCLUSIONS

The prevalence of critical illness was higher than previous, indirect estimates. One in ten hospitalized patients were critically ill, the large majority of which were cared for in general wards. This suggests a hidden burden of critical illness of potential public health, health system and hospital management significance.

Association Between Levels of Intensive Care and In-Hospital Mortality in Patients Hospitalized for Sepsis Stratified by Sequential Organ Failure Assessment Scores

OBJECTIVES

To assess the association between levels of intensive care and in-hospital mortality in patients hospitalized for sepsis, stratified by Sequential Organ Failure Assessment (SOFA) score at admission.

DESIGN

A nationwide, propensity score-matched, retrospective cohort study.

SETTING

A Japanese national inpatient database with data on 70-75% of all ICU and high-dependency care unit (HDU) beds in Japan.

PATIENTS

Adult patients hospitalized for sepsis with SOFA scores greater than or equal to 2 on their day of admission between April 1, 2018, and March 31, 2021, were recruited. Propensity score matching was performed to compare in-hospital mortality, and patients were stratified into 10 groups according to SOFA scores.

INTERVENTIONS

Two exposure and control groups according to treatment unit on day of admission: 1) ICU + HDU versus general ward and 2) ICU versus HDU.

MEASUREMENTS AND MAIN RESULTS

Of 97,070 patients, 19,770 (20.4%), 23,066 (23.8%), and 54,234 (55.9%) were treated in ICU, HDU, and general ward, respectively. After propensity score matching, the ICU + HDU group had significantly lower in-hospital mortality than the general ward group, among cohorts with SOFA scores greater than or equal to 6. There were no significant differences in in-hospital mortality among cohorts with SOFA scores 3-5. The ICU + HDU group had significantly higher in-hospital mortality than the general ward among cohorts with SOFA scores of 2. The ICU group had lower in-hospital mortality than the HDU group among cohorts with SOFA scores greater than or equal to 12. There were no significant differences in in-hospital mortality among cohorts with SOFA scores 5-11. The ICU group had significantly higher in-hospital mortality than the general ward group among cohorts with SOFA scores less than or equal to 4.

CONCLUSIONS

Patients hospitalized for sepsis with SOFA scores greater than or equal to 6 in the ICU or HDU had lower in-hospital mortality than those in the general ward, as did those with SOFA scores greater than or equal to 12 in the ICU versus HDU.

Comparing Outcomes of Critically Ill Patients in Intensive Care Units and General Wards: A Comprehensive Analysis

Background

The admission of critically ill patients to intensive care unit (ICU) plays a crucial role in reducing mortality. However, the scarcity of available ICU beds presents a significant challenge. In resource-limited settings, the outcomes of critically ill patients, particularly those who are not accepted for ICU admission, have been a topic of ongoing debate and contention.

Objective

This study aimed to explore the outcomes and factors associated with ICU admission and mortality among critically ill patients in Thailand.

Methods

This prospective cohort study enrolled critically ill adults indicated for medical ICU admission. Patients were followed for 28 days regardless of whether they were admitted to an ICU. Data on mortality, hospital length of stay, duration of organ support, and factors associated with mortality and ICU admission were collected.

Results

Of the 180 patients enrolled, 72 were admitted to ICUs, and 108 were cared for in general wards. The ICU group had a higher 28-day mortality rate (44.4% vs 20.4%; P=0.001), but other outcomes of interest were comparable. Multivariate analysis identified alteration of consciousness, norepinephrine use, and epinephrine use as independent predictors of 28-day mortality. Higher body mass index (BMI), higher APACHE II score, and acute kidney injury were predictive factors associated with ICU acceptance.

Conclusion

Among patients indicated for ICU admission, those who were admitted had a higher 28-day mortality rate. Higher mortality was associated with alteration of consciousness and vasopressor use. Patients who were sicker and had higher BMI were more likely to be admitted to an ICU.

Dendrogram of transparent feature importance machine learning statistics to classify associations for heart failure: A reanalysis of a retrospective cohort study of the Medical Information Mart for Intensive Care III (MIMIC-III) database

BACKGROUND

There is a continual push for developing accurate predictors for Intensive Care Unit (ICU) admitted heart failure (HF) patients and in-hospital mortality.

OBJECTIVE

The study aimed to utilize transparent machine learning and create hierarchical clustering of key predictors based off of model importance statistics gain, cover, and frequency.

METHODS

Inclusion criteria of complete patient information for in-hospital mortality in the ICU with HF from the MIMIC-III database were randomly divided into a training (n = 941, 80%) and test (n = 235, 20%). A grid search was set to find hyperparameters. Machine Learning with XGBoost were used to predict mortality followed by feature importance with Shapely Additive Explanations (SHAP) and hierarchical clustering of model metrics with a dendrogram and heat map.

RESULTS

Of the 1,176 heart failure ICU patients that met inclusion criteria for the study, 558 (47.5%) were males. The mean age was 74.05 (SD = 12.85). XGBoost model had an area under the receiver operator curve of 0.662. The highest overall SHAP explanations were urine output, leukocytes, bicarbonate, and platelets. Average urine output was 1899.28 (SD = 1272.36) mL/day with the hospital mortality group having 1345.97 (SD = 1136.58) mL/day and the group without hospital mortality having 1986.91 (SD = 1271.16) mL/day. The average leukocyte count in the cohort was 10.72 (SD = 5.23) cells per microliter. For the hospital mortality group the leukocyte count was 13.47 (SD = 7.42) cells per microliter and for the group without hospital mortality the leukocyte count was 10.28 (SD = 4.66) cells per microliter. The average bicarbonate value was 26.91 (SD = 5.17) mEq/L. Amongst the group with hospital mortality the average bicarbonate value was 24.00 (SD = 5.42) mEq/L. Amongst the group without hospital mortality the average bicarbonate value was 27.37 (SD = 4.98) mEq/L. The average platelet value was 241.52 platelets per microliter. For the group with hospital mortality the average platelet value was 216.21 platelets per microliter. For the group without hospital mortality the average platelet value was 245.47 platelets per microliter. Cluster 1 of the dendrogram grouped the temperature, platelets, urine output, Saturation of partial pressure of Oxygen (SPO2), Leukocyte count, lymphocyte count, bicarbonate, anion gap, respiratory rate, PCO2, BMI, and age as most similar in having the highest aggregate gain, cover, and frequency metrics.

CONCLUSION

Machine Learning models that incorporate dendrograms and heat maps can offer additional summaries of model statistics in differentiating factors between in patient ICU mortality in heart failure patients.

Clinical encounter heterogeneity and methods for resolving in networked EHR data: a study from N3C and RECOVER programs

OBJECTIVE

Clinical encounter data are heterogeneous and vary greatly from institution to institution. These problems of variance affect interpretability and usability of clinical encounter data for analysis. These problems are magnified when multisite electronic health record (EHR) data are networked together. This article presents a novel, generalizable method for resolving encounter heterogeneity for analysis by combining related atomic encounters into composite "macrovisits."

MATERIALS AND METHODS

Encounters were composed of data from 75 partner sites harmonized to a common data model as part of the NIH Researching COVID to Enhance Recovery Initiative, a project of the National Covid Cohort Collaborative. Summary statistics were computed for overall and site-level data to assess issues and identify modifications. Two algorithms were developed to refine atomic encounters into cleaner, analyzable longitudinal clinical visits.

RESULTS

Atomic inpatient encounters data were found to be widely disparate between sites in terms of length-of-stay (LOS) and numbers of OMOP CDM measurements per encounter. After aggregating encounters to macrovisits, LOS and measurement variance decreased. A subsequent algorithm to identify hospitalized macrovisits further reduced data variability.

DISCUSSION

Encounters are a complex and heterogeneous component of EHR data and native data issues are not addressed by existing methods. These types of complex and poorly studied issues contribute to the difficulty of deriving value from EHR data, and these types of foundational, large-scale explorations, and developments are necessary to realize the full potential of modern real-world data.

CONCLUSION

This article presents method developments to manipulate and resolve EHR encounter data issues in a generalizable way as a foundation for future research and analysis.

CHARACTERISTICS AND PREDICTORS OF PATIENTS WITH SEPSIS WHO ARE CANDIDATES FOR MINIMALLY INVASIVE APPROACH OUTSIDE OF INTENSIVE CARE UNIT

Objective: To identify and describe characteristics of patients with sepsis who could be treated with minimally invasive sepsis (MIS) approach without intensive care unit (ICU) admission and to develop a prediction model to select candidates for MIS approach. Methods: A secondary analysis of the electronic database of patients with sepsis at Mayo Clinic, Rochester, MN. Candidates for the MIS approach were adults with septic shock and less than 48 hours of ICU stay, who did not require advanced respiratory support and were alive at hospital discharge. Comparison group consisted of septic shock patients with an ICU stay of more than 48 hours without advanced respiratory support at the time of ICU admission. Results: Of 1795 medical ICU admissions, 106 patients (6%) met MIS approach criteria. Predictive variables (age >65 years, oxygen flow >4 L/min, temperature <37°C, creatinine >1.6 mg/dL, lactate >3 mmol/L, white blood cells >15 × 10 9 /L, heart rate >100 beats/min, and respiration rate >25 breaths/min) selected through logistic regression were translated into an 8-point score. Model discrimination yielded the area under the receiver operating characteristic curve of 79% and was well fitted (Hosmer-Lemeshow P = 0.94) and calibrated. The MIS score cutoff of 3 resulted in a model odds ratio of 0.15 (95% confidence interval, 0.08-0.28) and a negative predictive value of 91% (95% confidence interval, 88.69-92.92). Conclusions: This study identifies a subset of low-risk septic shock patients who can potentially be managed outside the ICU. Once validated in an independent, prospective sample our prediction model can be used to identify candidates for MIS approach.

Hospital factors that influence ICU admission decision-making: a qualitative study of eight hospitals

PURPOSE

Some hospitals in the United States (US) use intensive care 20 times more than others. Since intensive care is lifesaving for some but potentially harmful for others, there is a need to understand factors that influence how intensive care unit (ICU) admission decisions are made.

METHODS

A qualitative analysis of eight US hospitals was conducted with semi-structured, one-on-one interviews supplemented by site visits and clinical observations.

RESULTS

A total of 87 participants (24 nurses, 52 physicians, and 11 other staff) were interviewed, and 40 h were spent observing ICU operations across the eight hospitals. Four hospital-level factors were identified that influenced ICU admission decision-making. First, availability of intermediate care led to reallocation of patients who might otherwise be sent to an ICU. Second, participants stressed the importance of ICU nurse availability as a key modifier of ICU capacity. Patients cared for by experienced general care physicians and nurses were less likely to receive ICU care. Third, smaller or rural hospitals opted for longer emergency department patient-stays over ICU admission to expedite interhospital transfer of critically ill patients. Fourth, lack of clarity in ICU admission policies led clinicians to feel pressured to use ICU care for patients who might otherwise not have received it.

CONCLUSION

Health care systems should evaluate their use of ICU care and establish institutional patterns that ensure ICU admission decisions are patient-centered but also account for resources and constraints particular to each hospital.

The Nordic perioperative and intensive care registries-Collaboration and research possibilities

BACKGROUND

The Nordic perioperative and intensive care registries have been built up during the last 25 years to improve quality in intensive and perioperative care. We aimed to describe the Nordic perioperative and intensive care registries and to highlight possibilities and challenges in future research collaboration between these registries.

MATERIAL AND METHOD

We present an overview of the following Nordic registries: Swedish Perioperative Registry (SPOR), the Danish Anesthesia Database (DAD), the Finnish Perioperative Database (FIN-AN), the Icelandic Anesthesia Database (IS-AN), the Danish Intensive Care Database (DID), the Swedish Intensive Care Registry (SIR), the Finnish Intensive Care Consortium, the Norwegian Intensive Care and Pandemic Registry (NIPaR), and the Icelandic Intensive Care Registry (IS-ICU).

RESULTS

Health care systems and patient populations are similar in the Nordic countries. Despite certain differences in data structure and clinical variables, the perioperative and intensive care registries have enough in common to enable research collaboration. In the future, even a common Nordic registry could be possible.

CONCLUSION

Collaboration between the Nordic perioperative and intensive care registries is both possible and likely to produce research of high quality. Research collaboration between registries may have several add-on effects and stimulate international standardization regarding definitions, scoring systems, and benchmarks, thereby improving overall quality of care.

Patterns of admission and outcome of patients admitted to the intensive care unit of Addis Ababa Burn Emergency and Trauma Hospital

Data on patterns of intensive care unit (ICU) admission including age, and severity of illness is essential in developing better strategies for resource allocation to improve outcomes. A 2-year cross-sectional study of 268 patients using a systematic random sampling and structured questionnaire obtained from the database was conducted with the aim of examining patterns of admission among patients admitted to the ICU of Addis Ababa burn emergency and trauma (AaBET) hospital. Data were entered into Epi-Info version 3.5.3 and exported to SPSS version 24 for analysis. Bivariate and multivariate logistic regression were used for association. A P-value of 0.05 at a 95% confidence interval was declared clinically significant. Of the 268 charts reviewed, 193 (73.5%) of them were men with a mean age of 32.6 years. Trauma accounted for 163 (53.4%) of admissions. Burn admission category, Glasgow coma score of 3-8, and not receiving pre-referral treatment were found to be substantially correlated with mortality in both bivariate and multivariate analysis. Trauma constituted a sizeable cause of ICU admission. Road traffic accidents of traumatic brain injuries were the major causes of admission. Developing good pre-referral care equipped with manpower and ambulance services will improve the outcome.

Resource allocation

PURPOSE OF REVIEW

The coronavirus disease 2019 pandemic and recent global recessions have brought to the forefront of the medical-political discussion the fact that medical resources are finite and have focused a spotlight on fair allocation and prioritization of healthcare resources describe why this review is timely and relevant.

RECENT FINDINGS

This review presents past and present concepts related to the ethics of resource allocation. Included are discussions regarding the topics of who should determine resource allocation, what types of research require allocation, methods currently in use to determine what resources are appropriate and which should be prioritized.describe the main themes in the literature covered by the article.

SUMMARY

Models for resource allocation must differentiate between different types of resources, some of which may require early preparation or distribution. Local availability of specific resources, supplies and infrastructure must be taken into consideration during preparation. When planning for long durations of limited resource availability, the limitations of human resilience must also be considered. Preparation also requires information regarding the needs of the specific population at hand (e.g. age distributions, disease prevalence) and societal preferences must be acknowledged within possible limits.

Delirium in the intensive care unit and its importance in the post-operative context: A review

The burden of delirium in the intensive care setting is a global priority. Delirium affects up to 80% of patients in intensive care units; an episode of delirium is often distressing to patients and their families, and delirium in patients within, or outside of, the intensive care unit (ICU) setting is associated with poor outcomes. In the short term, such poor outcomes include longer stay in intensive care, longer hospital stay, increased risk of other hospital-acquired complications, and increased risk of hospital mortality. Longer term sequelae include cognitive impairment and functional dependency. While medical category of admission may be a risk factor for poor outcomes in critical care populations, outcomes for surgical ICU admissions are also poor, with dependency at hospital discharge exceeding 30% and increased risk of in-hospital mortality, particularly in vulnerable groups, with high-risk procedures, and resource-scarce settings. A practical approach to delirium prevention and management in the ICU setting is likely to require a multi-faceted approach. Given the good evidence for the prevention of delirium among older post-operative outside of the intensive care setting, simple non-pharmacological interventions should be effective among older adults post-operatively who are cared for in the intensive care setting. In response to this, the future ICU environment will have a range of organizational and distinct environmental characteristics that are directly targeted at preventing delirium.

A 7-year review of medical admission profile for clinical diseases in an intensive care unit of a low-resource setting

Objective

Various patients needing organ or systemic support and close monitoring are routinely managed in the intensive care unit. This includes patients that emanate from various sources, like the trauma unit, emergency department, inpatient wards, and post-anesthesia care unit. Admissions into the intensive care unit due to medical conditions have not been analyzed in our environment to determine the common indications and the outcome. We aimed to determine the pattern of medical admissions and outcomes in the intensive care unit.

Method

A retrospective study of all patients admitted to the intensive care unit of Nnamdi Azikiwe University Teaching Hospital Nnewi, Anambra State, Nigeria, from January 1, 2014 to December 31, 2020, with medical diagnosis was conducted. Data were retrieved from the intensive care unit admission and discharge registers and analyzed using the Statistical Package for Social Sciences (SPSS) Version 20 (IBM Corp., Chicago, Illinois, USA).

Results

Eighty-nine medical patients were admitted, which accounted for 7.63% of the total intensive care unit admissions of 1167 patients during the period, with a preponderance of males (57.3%). The most common medical condition for intensive care unit admission (31.5%) was a cerebrovascular accident. The mean length of stay was found to be 5.13 ± 3.42 days. Mortality following medical intensive care unit admission was 56.18%, which contributed to about 11.4% of the total ICU mortality.

Conclusion

When compared to all other reasons for admission to a general intensive care unit, medical conditions account for a small percentage. The most frequent illness was a cerebrovascular accident.

Overtriage, Undertriage, and Value of Care after Major Surgery: An Automated, Explainable Deep Learning-Enabled Classification System

BACKGROUND

In single-institution studies, overtriaging low-risk postoperative patients to ICUs has been associated with a low value of care; undertriaging high-risk postoperative patients to general wards has been associated with increased mortality and morbidity. This study tested the reproducibility of an automated postoperative triage classification system to generating an actionable, explainable decision support system.

STUDY DESIGN

This longitudinal cohort study included adults undergoing inpatient surgery at two university hospitals. Triage classifications were generated by an explainable deep learning model using preoperative and intraoperative electronic health record features. Nearest neighbor algorithms identified risk-matched controls. Primary outcomes were mortality, morbidity, and value of care (inverted risk-adjusted mortality/total direct costs).

RESULTS

Among 4,669 ICU admissions, 237 (5.1%) were overtriaged. Compared with 1,021 control ward admissions, overtriaged admissions had similar outcomes but higher costs ($15.9K [interquartile range $9.8K to $22.3K] vs $10.7K [$7.0K to $17.6K], p < 0.001) and lower value of care (0.2 [0.1 to 0.3] vs 1.5 [0.9 to 2.2], p < 0.001). Among 8,594 ward admissions, 1,029 (12.0%) were undertriaged. Compared with 2,498 control ICU admissions, undertriaged admissions had longer hospital length-of-stays (6.4 [3.4 to 12.4] vs 5.4 [2.6 to 10.4] days, p < 0.001); greater incidence of hospital mortality (1.7% vs 0.7%, p = 0.03), cardiac arrest (1.4% vs 0.5%, p = 0.04), and persistent acute kidney injury without renal recovery (5.2% vs 2.8%, p = 0.002); similar costs ($21.8K [$13.3K to $34.9K] vs $21.9K [$13.1K to $36.3K]); and lower value of care (0.8 [0.5 to 1.3] vs 1.2 [0.7 to 2.0], p < 0.001).

CONCLUSIONS

Overtriage was associated with low value of care; undertriage was associated with both low value of care and increased mortality and morbidity. The proposed framework for generating automated postoperative triage classifications is reproducible.

Laparoscopy in high-risk emergency general surgery reduces intensive care stay, length of stay and mortality

PURPOSE

Emergency general surgery patients undergoing laparoscopic surgery are at reduced risk of mortality and may require reduced length of critical care stay. This study investigated the effect of laparoscopy on high-risk patients' post-operative care requirements.

METHODS

Data were retrieved for all patients entered into the NELA database between 2013 and 2018. Only high-risk surgical patients (P-POSSUM predicted mortality risk of ≥ 5%) were included. Patients undergoing laparoscopic and open emergency general surgical procedures were compared using a propensity score weighting approach. Outcome measures included total length of critical care (level 3) stay, overall length of stay and inpatient mortality.

RESULTS

A total of 66,517 high-risk patients received emergency major abdominal surgery. A laparoscopic procedure was attempted in 6998 (10.5%); of these, the procedure was competed laparoscopically in 3492 (49.9%) and converted to open in 3506 (50.1%). Following inverse probability treatment weighting adjustment for patient disease and treatment characteristics, high-risk patients undergoing laparoscopic surgery had a shorter median ICU stay (1 day vs 2 days p < 0.001), overall hospital length of stay (11 days vs 14 days p < 0.001) and a lower inpatient mortality (16.0% vs 18.8%, p < 0.001). They were also less likely to have a prolonged ICU stay with an OR of 0.78 (95% CI 0.74-0.83, p < 0.001).

CONCLUSION

The results of this study suggest that in patients at high risk of post-operative mortality, laparoscopic emergency bowel surgery leads to a reduced length of critical care stay, overall length of stay and inpatient mortality compared to traditional laparotomy.

ICU-Managed Patients' Epidemiology, Characteristics, and Outcomes: A Retrospective Single-Center Study

Background

Resources are limited, and it is exceedingly difficult to provide intensive care in developing nations. In Somalia, intensive care unit (ICU) care was introduced only a few years ago.

Purpose

In this study, we aimed to determine the epidemiology, characteristics, and outcome of ICU-managed patients in a tertiary hospital in Mogadishu.

Methods

We retrospectively evaluated the files of 1082 patients admitted to our ICU during the year 2021.

Results

The majority (39.7%) of the patients were adults (aged between 20 and 39 years), and 67.8% were male patients. The median ICU length of stay was three days (IQR = 5 days), and nonsurvivors had shorter stays, one day. The mortality rate was 45.1%. The demand for critical care services in low-income countries is high.

Conclusion

The country has a very low ICU bed capacity. Critical care remains a neglected area of health service delivery in this setting, with large numbers of patients with potentially treatable conditions not having access to such services.

Intensive Care Unit Occupancy in Japan, 2015-2018: A Nationwide Inpatient Database Study

BACKGROUND

Detailed data on intensive care unit (ICU) occupancy in Japan are lacking. Using a nationwide inpatient database in Japan, we aimed to assess ICU bed occupancy to guide critical care utilization planning.

METHODS

We identified all ICU patients admitted from January 1, 2015 to December 31, 2018 to ICU-equipped hospitals participating in the Japanese Diagnosis Procedure Combination inpatient database. We assessed the trends in daily occupancy by counting the total number of occupied ICU beds on a given day divided by the total number of licensed ICU beds in the participating hospitals. We also assessed ICU occupancy for patients with mechanical ventilation, patients with extracorporeal membrane oxygenation, and patients without life-supportive therapies.

RESULTS

Over the 4 study years, 1,379,618 ICU patients were admitted to 495 hospitals equipped with 5,341 ICU beds, accounting for 75% of all ICU beds in Japan. Mean ICU occupancy on any given day was 60%, with a range of 45.0% to 72.5%. Mean ICU occupancy did not change over the 4 years. Mean ICU occupancy was about 9% higher on weekdays than on weekends and about 5% higher in the coldest season than in the warmest season. For patients with mechanical ventilation, patients with extracorporeal membrane oxygenation, and patients without life-supportive therapies, mean ICU occupancy was 24%, 0.5%, and 30%, respectively.

CONCLUSION

Only one-fourth of ICU beds were occupied by mechanically ventilated patients, suggesting that the critical care system in Japan has substantial surge capacity under normal temporal variation to care for critically ill patients.

Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases

Background

Heart failure (HF) combined with hypertension is an extremely important cause of in-hospital mortality, especially for the intensive care unit (ICU) patients. However, under intense working pressure, the medical staff are easily overwhelmed by the large number of clinical signals generated in the ICU, which may lead to treatment delay, sub-optimal care, or even wrong clinical decisions. Individual risk stratification is an essential strategy for managing ICU patients with HF combined with hypertension. Artificial intelligence, especially machine learning (ML), can develop superior models to predict the prognosis of these patients. This study aimed to develop a machine learning method to predict the 28-day mortality for ICU patients with HF combined with hypertension.

Methods

We enrolled all critically ill patients with HF combined with hypertension in the Medical Information Mart for IntensiveCare Database-IV (MIMIC-IV, v.1.4) and the eICU Collaborative Research Database (eICU-CRD) from 2008 to 2019. Subsequently, MIMIC-IV was divided into training cohort and testing cohort in an 8:2 ratio, and eICU-CRD was designated as the external validation cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression with internal tenfold cross-validation was used for data dimension reduction and identifying the most valuable predictive features for 28-day mortality. Based on its accuracy and area under the curve (AUC), the best model in the validation cohort was selected. In addition, we utilized the Shapley Additive Explanations (SHAP) method to highlight the importance of model features, analyze the impact of individual features on model output, and visualize an individual’s Shapley values.

Results

A total of 3,458 and 6582 patients with HF combined with hypertension in MIMIC-IV and eICU-CRD were included. The patients, including 1,756 males, had a median (Q1, Q3) age of 75 (65, 84) years. After selection, 22 out of a total of 58 clinical parameters were extracted to develop the machine-learning models. Among four constructed models, the Neural Networks (NN) model performed the best predictive performance with an AUC of 0.764 and 0.674 in the test cohort and external validation cohort, respectively. In addition, a simplified model including seven variables was built based on NN, which also had good predictive performance (AUC: 0.741). Feature importance analysis showed that age, mechanical ventilation (MECHVENT), chloride, bun, anion gap, paraplegia, rdw (RDW), hyperlipidemia, peripheral capillary oxygen saturation (SpO₂), respiratory rate, cerebrovascular disease, heart rate, white blood cell (WBC), international normalized ratio (INR), mean corpuscular hemoglobin concentration (MCHC), glucose, AIDS, mean corpuscular volume (MCV), N-terminal pro-brain natriuretic peptide (Npro. BNP), calcium, renal replacement therapy (RRT), and partial thromboplastin time (PTT) were the top 22 features of the NN model with the greatest impact. Finally, after hyperparameter optimization, SHAP plots were employed to make the NN-based model interpretable with an analytical description of how the constructed model visualizes the prediction of death.

Conclusion

We developed a predictive model to predict the 28-day mortality for ICU patients with HF combined with hypertension, which proved superior to the traditional logistic regression analysis. The SHAP method enables machine learning models to be more interpretable, thereby helping clinicians to better understand the reasoning behind the outcome and assess in-hospital outcomes for critically ill patients.

Variation in the risk of death due to COVID-19: An international multicenter cohort study of hospitalized adults

BACKGROUND

There is wide variation in mortality among patients hospitalized with COVID-19. Whether this is related to patient or hospital factors is unknown.

OBJECTIVE

To compare the risk of mortality for patients hospitalized with COVID-19 and to determine whether the majority of that variation was explained by differences in patient characteristics across sites.

DESIGN, SETTING, AND PARTICIPANTS

An international multicenter cohort study of hospitalized adults with laboratory-confirmed COVID-19 enrolled from 10 hospitals in Ontario, Canada and 8 hospitals in Copenhagen, Denmark between January 1, 2020 and November 11, 2020.

MAIN OUTCOMES AND MEASURES

Inpatient mortality. We used a multivariable multilevel regression model to compare the in-hospital mortality risk across hospitals and quantify the variation attributable to patient-level factors.

RESULTS

There were 1364 adults hospitalized with COVID-19 in Ontario (n = 1149) and in Denmark (n = 215). In Ontario, the absolute risk of in-hospital mortality ranged from 12.0% to 39.8% across hospitals. Ninety-eight percent of the variation in mortality in Ontario was explained by differences in the characteristics of the patients. In Denmark, the absolute risk of inpatients ranged from 13.8% to 20.6%. One hundred percent of the variation in mortality in Denmark was explained by differences in the characteristics of the inpatients.

CONCLUSION

There was wide variation in inpatient COVID-19 mortality across hospitals, which was largely explained by patient-level factors, such as age and severity of presenting illness. However, hospital-level factors that could have affected care, including resource availability and capacity, were not taken into account. These findings highlight potential limitations in comparing crude mortality rates across hospitals for the purposes of reporting on the quality of care.

External validation of the ProVent score for prognostication of 1-year mortality of critically ill patients with prolonged mechanical ventilation: a single-centre, retrospective observational study in Austria

OBJECTIVES

In critically ill patients requiring mechanical ventilation for at least 21 days, 1-year mortality can be estimated using the ProVent score, calculated from four variables (age, platelet count, vasopressor use and renal replacement therapy). We aimed to externally validate discrimination and calibration of the ProVent score and, if necessary, to update its underlying regression model.

DESIGN

Retrospective, observational, single-centre study.

SETTING

11 intensive care units at one tertiary academic hospital.

PATIENTS

780 critically ill adult patients receiving invasive mechanical ventilation for at least 21 days.

PRIMARY OUTCOME MEASURE

1-year mortality after intensive care unit discharge.

RESULTS

380 patients (49%) had died after 1 year. One-year mortality for ProVent scores from 0 to 5 were: 15%, 27%, 57%, 66%, 72% and 76%. Area under the receiver operating characteristic curve of the ProVent probability model was 0.76 (95% CI 0.72 to 0.79), calibration intercept was -0.43 (95% CI -0.59 to -0.27) and calibration slope was 0.76 (95% CI 0.62 to 0.89). Model recalibration and extension by inclusion of three additional predictors (total bilirubin concentration, enteral nutrition and surgical status) improved model discrimination and calibration. Decision curve analysis demonstrated that the original ProVent model had negative net benefit, which was avoided with the extended ProVent model.

CONCLUSIONS

The ProVent probability model had adequate discrimination but was miscalibrated in our patient cohort and, as such, could potentially be harmful. Use of the extended ProVent score developed by us could possibly alleviate this concern.

Towards definitions of critical illness and critical care using concept analysis

OBJECTIVE

As 'critical illness' and 'critical care' lack consensus definitions, this study aimed to explore how the concepts' are used, describe their defining attributes, and propose potential definitions.

DESIGN AND METHODS

We used the Walker and Avant approach to concept analysis. The uses and definitions of the concepts were identified through a scoping review of the literature and an online survey of 114 global clinical experts. We used the Arksey and O'Malley framework for scoping reviews and searched in PubMed and Web of Science with a strategy including terms around critical illness/care and definitions/etymologies limited to publications in English between 1 January 2008 and 1 January 2020. The experts were selected through purposive sampling and snowballing, with 36.8% in Africa, 25.4% in Europe, 22.8% in North America, 10.5% in Asia, 2.6% in South America and 1.8% in Australia. They worked with anaesthesia or intensive care 59.1%, emergency care 15.8%, medicine 9.5%, paediatrics 5.5%, surgery 4.7%, obstetrics and gynaecology 1.6% and other specialties 3.9%. Through content analysis of the data, we extracted codes, categories and themes to determine the concepts' defining attributes and we proposed potential definitions. To assist understanding, we developed model, related and contrary cases concerning the concepts, we identified antecedents and consequences to the concepts, and defined empirical referents.

RESULTS

Nine and 13 articles were included in the scoping reviews of critical illness and critical care, respectively. A total of 48 codes, 14 categories and 4 themes were identified in the uses and definitions of critical illness and 60 codes, 13 categories and 5 themes for critical care. The defining attributes of critical illness were a high risk of imminent death; vital organ dysfunction; requirement for care to avoid death; and potential reversibility. The defining attributes of critical care were the identification, monitoring and treatment of critical illness; vital organ support; initial and sustained care; any care of critical illness; and specialised human and physical resources. The defining attributes led to our proposed definitions of critical illness as, 'a state of ill health with vital organ dysfunction, a high risk of imminent death if care is not provided and the potential for reversibility', and of critical care as, 'the identification, monitoring and treatment of patients with critical illness through the initial and sustained support of vital organ functions.'

CONCLUSION

The concepts critical illness and critical care lack consensus definitions and have varied uses. Through concept analysis of uses and definitions in the literature and among experts, we have identified the defining attributes of the concepts and proposed definitions that could aid clinical practice, research and policy-making.

Survivorship After Critical Illness and Post-Intensive Care Syndrome

Improvements in critical care medicine have led to a marked increase in survivors of the intensive care unit (ICU). These survivors encounter many difficulties following ICU discharge. The term post -intensive care syndrome (PICS) provides a framework for identifying the most common symptoms which fall into three domains: cognitive, physical, and mental health. There are numerous risk factors for the development of PICS including premorbid conditions and specific elements of ICU hospitalizations. Management is complex and should take an individualized approach with interdisciplinary care. Future research should focus on prevention, identification, and treatment of this unique population.