Explore the vast range of titles available.

Background Prognosticating the course of diseases to inform decision-making is a key component of intensive care medicine. For several applications in medicine, new methods from the field of artificial intelligence (AI) and machine learning have already outperformed conventional prediction models. Due to their technical characteristics, these methods will present new ethical challenges to the intensivist. Results In addition to the standards of data stewardship in medicine, the selection of datasets and algorithms to create AI prognostication models must involve extensive scrutiny to avoid biases and, consequently, injustice against individuals or groups of patients. Assessment of these models for compliance with the ethical principles of beneficence and non-maleficence should also include quantification of predictive uncertainty. Respect for patients’ autonomy during decision-making requires transparency of the data processing by AI models to explain the predictions derived from these models. Moreover, a system of continuous oversight can help to maintain public trust in this technology. Based on these considerations as well as recent guidelines, we propose a pathway to an ethical implementation of AI-based prognostication. It includes a checklist for new AI models that deals with medical and technical topics as well as patient- and system-centered issues. Conclusion AI models for prognostication will become valuable tools in intensive care. However, they require technical refinement and a careful implementation according to the standards of medical ethics.

There is ongoing demographic ageing and increasing longevity of the population, with previously devastating and often-fatal diseases now transformed into chronic conditions. This is turning multi-morbidity into a major challenge in the world of critical care. After many years of research and innovation, mainly in geriatric care, the concept of multi-morbidity now requires fine-tuning to support decision-making for patients along their whole trajectory in healthcare, including in the intensive care unit (ICU). This article will discuss current challenges and present approaches to adapt critical care services to the needs of these patients.

Critically-ill patients are at increased risk for cytomegalovirus (CMV) reactivation, associated with adverse clinical outcomes. Given the surge in intensive care unit (ICU) admissions during the COVID-19 pandemic and the continued burden of critical illness associated with the ongoing circulation of SARS-CoV-2, we sought to resolve risk factors for CMV reactivation and disease within the broader ICU patient population including those with and without COVID-19, to identify common and potentially distinct contributors to CMV reactivation and disease in this vulnerable setting. This prospective study included 208 adult ICU (85 COVID-19, and 123 concomitant non-COVID-19) patients, monitored weekly for CMV DNAemia. CMV reactivation was categorized as any detectable DNAemia or as clinically-significant reactivation characterized by high-level DNAemia (≥ 1000 IU/mL) and/or CMV disease. Overall, 29.8% of ICU patients experienced CMV reactivation, with 10.6% exhibiting clinically-significant reactivation. COVID-19 ICU patients had significantly higher rates of any CMV reactivation (40% vs. 23%, p  = 0.009), high-level DNAemia (18% vs. 2%, p  = 0.001), and CMV disease (12% vs. 1%, p  = 0.001) compared to concomitant non-COVID-19 patients. Risk factors associated with clinically-significant CMV reactivation in ICU patients included septic shock, lower absolute lymphocyte count, high-dose steroid use, multiple blood transfusions, and COVID-19. CMV reactivation correlated with prolonged ventilation, hospitalization, and ICU stay, and increased in-hospital mortality. The high rates of clinically-significant CMV reactivation in both COVID-19 and non-COVID-19 ICU patients and the identified risk factors, along with the worse clinical outcomes linked to CMV reactivation, highlight the need for vigilant monitoring of CMV reactivation and for consideration of early antiviral treatment in ICU patients at risk, and support future interventional trials.

In this narrative review, we describe the most important age-related “syndromes” found in the old ICU patients. The syndromes are frailty, comorbidity, cognitive decline, malnutrition, sarcopenia, loss of functional autonomy, immunosenescence and inflam-ageing. The underlying geriatric condition, together with the admission diagnosis and the acute severity contribute to the short-term, but also to the long-term prognosis. Besides mortality, functional status and quality of life are major outcome variables. The geriatric assessment is a key tool for long-term qualitative outcome, while immediate severity accounts for acute mortality. A poor functional baseline reduces the chances of a successful outcome following ICU. This review emphasises the importance of using a geriatric assessment and considering the older patient as a whole, rather than the acute illness in isolation, when making decisions regarding intensive care treatment.

BackgroundThis large-scale analysis pools individual data about the Clinical Frailty Scale (CFS) to predict outcome in the intensive care unit (ICU).MethodsA systematic search identified all clinical trials that used the CFS in the ICU (PubMed searched until 24th June 2020). All patients who were electively admitted were excluded. The primary outcome was ICU mortality. Regression models were estimated on the complete data set, and for missing data, multiple imputations were utilised. Cox models were adjusted for age, sex, and illness acuity score (SOFA, SAPS II or APACHE II).Results12 studies from 30 countries with anonymised individualised patient data were included (n = 23,989 patients). In the univariate analysis for all patients, being frail (CFS ≥ 5) was associated with an increased risk of ICU mortality, but not after adjustment. In older patients (≥ 65 years) there was an independent association with ICU mortality both in the complete case analysis (HR 1.34 (95% CI 1.25–1.44), p < 0.0001) and in the multiple imputation analysis (HR 1.35 (95% CI 1.26–1.45), p < 0.0001, adjusted for SOFA). In older patients, being vulnerable (CFS 4) alone did not significantly differ from being frail. After adjustment, a CFS of 4–5, 6, and ≥ 7 was associated with a significantly worse outcome compared to CFS of 1–3.ConclusionsBeing frail is associated with a significantly increased risk for ICU mortality in older patients, while being vulnerable alone did not significantly differ. New Frailty categories might reflect its “continuum” better and predict ICU outcome more accurately.Trial registration: Open Science Framework (OSF: https://osf.io/8buwk/).

[...]the absence of such information in current published COVID-19 studies on outcome is surprising, especially in critically ill old patients. [...]we have not found any recent major studies on outcome during the present pandemic where a detailed description of the use of limitation of LST is given [4,5]. [...]the study was funded by a grant from the European Open Science Cloud (EOSC), EOSCsecretariat.eu has received funding from the European Union's Horizon Programme call H2020-INFRAEOSC-05-2018-2019, grant Agreement number 831644.Declaration of Competing Interest All authors declare no conflict of interest.Acknowlegdement The COVIP study group including all national and local investigators from 138 ICUs are thanked for their continuing support during the present pandemic.

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

Sepsis has no proven specific pharmacologic treatment and reported mortality ranges from 30%-45%. The primary aim of this phase IB study was to determine the safety profile of Allocetra™-OTS (early apoptotic cell) infusion in subjects presenting to the emergency room with sepsis. The secondary aims were to measure organ dysfunction, intensive care unit (ICU) and hospital stays, and mortality. Exploratory endpoints included measuring immune modulator agents to elucidate the mechanism of action. Ten patients presenting to the emergency room at the Hadassah Medical Center with sepsis were enrolled in this phase Ib clinical study. Enrolled patients were males and females aged 51-83 years, who had a Sequential Organ Failure Assessment (SOFA) score ≥2 above baseline and were septic due to presumed infection. Allocetra™-OTS was administered as a single dose (day +1) or in two doses of 140×10 cells/kg on (day +1 and +3), following initiation of standard-of-care (SOC) treatment for septic patients. Safety was evaluated by serious adverse events (SAEs) and adverse events (AEs). Organ dysfunction, ICU and hospital stays, and mortality, were compared to historical controls. Immune modulator agents were measured using Luminex multiplex analysis. All 10 patients had mild-to-moderate sepsis with SOFA scores ranging from 2-6 upon entering the study. No SAEs and no related AEs were reported. All 10 study subjects survived, while matched historical controls had a mortality rate of 27%. The study subjects exhibited rapid resolution of organ dysfunction and had significantly shorter ICU stays compared to matched historical controls (p<0.0001). All patients had both elevated pro- and anti-inflammatory cytokines, chemokines, and additional immune modulators that gradually decreased following treatment. Administration of apoptotic cells to patients with mild-to-moderate sepsis was safe and had a significant immuno-modulating effect, leading to early resolution of the cytokine storm. ClinicalTrials.gov Identifier: NCT03925857. (https://clinicaltrials.gov/ct2/show/study/NCT03925857).

Very old critically ill patients pose a growing challenge for intensive care. Critical illness and the burden of treatment in the intensive care unit (ICU) can lead to a long-lasting decline of functional and cognitive abilities, especially in very old patients. Multi-complexity and increased vulnerability to stress in these patients may lead to new and worsening disabilities, requiring careful assessment, prevention and rehabilitation. The potential for rehabilitation, which is crucial for optimal functional outcomes, requires a systematic, multi-disciplinary approach and careful long-term planning during and following ICU care. We describe this process and provide recommendations and checklists for comprehensive and timely assessments in the context of transitioning patients from ICU to post-ICU and acute hospital care, and review the barriers to the provision of good functional outcomes.