Escalation to intensive care for the older patient. An exploratory qualitative study of patients aged 65 years and older and their next of kin during the COVID-19 pandemic: the ESCALATE study.

BACKGROUND: older people comprise the majority of hospital medical inpatients so decision-making regarding admission of this cohort to the intensive care unit (ICU) is important. ICU can be perceived by clinicians as overly burdensome for patients and loved ones, and long-term impact on quality of life considered unacceptable, effecting potential bias against admitting older people to ICU. The COVID-19 pandemic highlighted the challenge of selecting those who could most benefit from ICU. OBJECTIVE: this qualitative study aimed to explore the views and recollections of escalation to ICU from older patients (aged ≥ 65 years) and next of kin (NoK) who experienced a COVID-19 ICU admission. SETTING: the main site was a large NHS Trust in London, which experienced a high burden of COVID-19 cases. SUBJECTS: 30 participants, comprising 12 patients, 7 NoK of survivor and 11 NoK of deceased. METHODS: semi-structured interviews with thematic analysis using a framework approach. RESULTS: there were five major themes: inevitability, disconnect, acceptance, implications for future decision-making and unique impact of the COVID-19 pandemic. Life was highly valued and ICU perceived to be the only option. Prior understanding of ICU and admission decision-making explanations were limited. Despite benefit of hindsight, having experienced an ICU admission and its consequences, most could not conceptualise thresholds for future acceptable treatment outcomes. CONCLUSIONS: in this study of patients ≥65 years and their NoK experiencing an acute ICU admission, survival was prioritised. Despite the ordeal of an ICU stay and its aftermath, the decision to admit and sequelae were considered acceptable.

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression.

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.

Natural history, trajectory, and management of mechanically ventilated COVID-19 patients in the United Kingdom.

PURPOSE: The trajectory of mechanically ventilated patients with coronavirus disease 2019 (COVID-19) is essential for clinical decisions, yet the focus so far has been on admission characteristics without consideration of the dynamic course of the disease in the context of applied therapeutic interventions. METHODS: We included adult patients undergoing invasive mechanical ventilation (IMV) within 48 h of intensive care unit (ICU) admission with complete clinical data until ICU death or discharge. We examined the importance of factors associated with disease progression over the first week, implementation and responsiveness to interventions used in acute respiratory distress syndrome (ARDS), and ICU outcome. We used machine learning (ML) and Explainable Artificial Intelligence (XAI) methods to characterise the evolution of clinical parameters and our ICU data visualisation tool is available as a web-based widget ( https://www.CovidUK.ICU ). RESULTS: Data for 633 adults with COVID-19 who underwent IMV between 01 March 2020 and 31 August 2020 were analysed. Overall mortality was 43.3% and highest with non-resolution of hypoxaemia [60.4% vs17.6%; P < 0.001; median PaO2/FiO2 on the day of death was 12.3(8.9-18.4) kPa] and non-response to proning (69.5% vs.31.1%; P < 0.001). Two ML models using weeklong data demonstrated an increased predictive accuracy for mortality compared to admission data (74.5% and 76.3% vs 60%, respectively). XAI models highlighted the increasing importance, over the first week, of PaO2/FiO2 in predicting mortality. Prone positioning improved oxygenation only in 45% of patients. A higher peak pressure (OR 1.42[1.06-1.91]; P < 0.05), raised respiratory component (OR 1.71[ 1.17-2.5]; P < 0.01) and cardiovascular component (OR 1.36 [1.04-1.75]; P < 0.05) of the sequential organ failure assessment (SOFA) score and raised lactate (OR 1.33 [0.99-1.79]; P = 0.057) immediately prior to application of prone positioning were associated with lack of oxygenation response. Prone positioning was not applied to 76% of patients with moderate hypoxemia and 45% of those with severe hypoxemia and patients who died without receiving proning interventions had more missed opportunities for prone intervention [7 (3-15.5) versus 2 (0-6); P < 0.001]. Despite the severity of gas exchange deficit, most patients received lung-protective ventilation with tidal volumes less than 8 mL/kg and plateau pressures less than 30cmH2O. This was despite systematic errors in measurement of height and derived ideal body weight. CONCLUSIONS: Refractory hypoxaemia remains a major association with mortality, yet evidence based ARDS interventions, in particular prone positioning, were not implemented and had delayed application with an associated reduced responsiveness. Real-time service evaluation techniques offer opportunities to assess the delivery of care and improve protocolised implementation of evidence-based ARDS interventions, which might be associated with improvements in survival.