Assessing the safety of physical rehabilitation in critically ill patients: a Delphi study.

BACKGROUND: Physical rehabilitation of critically ill patients is implemented to improve physical outcomes from an intensive care stay. However, before rehabilitation is implemented, a risk assessment is essential, based on robust safety data. To develop this information, a uniform definition of relevant adverse events is required. The assessment of cardiovascular stability is particularly relevant before physical activity as there is uncertainty over when it is safe to start rehabilitation with patients receiving vasoactive drugs. METHODS: A three-stage Delphi study was carried out to (a) define adverse events for a general ICU cohort, and (b) to define which risks should be assessed before physical rehabilitation of patients receiving vasoactive drugs. An international group of intensive care clinicians and clinician researchers took part. Former ICU patients and their family members/carers were involved in generating consensus for the definition of adverse events. Round one was an open round where participants gave their suggestions of what to include. In round two, participants rated their agreements with these suggestions using a five-point Likert scale; a 70% consensus agreement threshold was used. Round three was used to re-rate suggestions that had not reached consensus, whilst viewing anonymous feedback of participant ratings from round two. RESULTS: Twenty-four multi-professional ICU clinicians and clinician researchers from 10 countries across five continents were recruited. Average duration of ICU experience was 18 years (standard deviation 8) and 61% had publications related to ICU rehabilitation. For the adverse event definition, five former ICU patients and one patient relative were recruited. The Delphi process had a 97% response rate. Firstly, 54 adverse events reached consensus; an adverse event tool was created and informed by these events. Secondly, 50 risk factors requiring assessment before physical rehabilitation of patients receiving vasoactive drugs reached consensus. A second tool was created, informed by these suggestions. CONCLUSIONS: The adverse event tool can be used in studies of physical rehabilitation to ensure uniform measurement of safety. The risk assessment tool can be used to inform clinical practise when risk assessing when to start rehabilitation with patients receiving vasoactive drugs. Trial registration This study protocol was retrospectively registered on https://www.researchregistry.com/ (researchregistry2991).

Patient stratification using plasma cytokines and their regulators in sepsis: relationship to outcomes, treatment effect and leucocyte transcriptomic subphenotypes.

RATIONALE: Heterogeneity of the host response within sepsis, acute respiratory distress syndrome (ARDS) and more widely critical illness, limits discovery and targeting of immunomodulatory therapies. Clustering approaches using clinical and circulating biomarkers have defined hyper-inflammatory and hypo-inflammatory subphenotypes in ARDS associated with differential treatment response. It is unknown if similar subphenotypes exist in sepsis populations where leucocyte transcriptomic-defined subphenotypes have been reported. OBJECTIVES: We investigated whether inflammatory clusters based on cytokine protein abundance were seen in sepsis, and the relationships with previously described transcriptomic subphenotypes. METHODS: Hierarchical cluster and latent class analysis were applied to an observational study (UK Genomic Advances in Sepsis (GAinS)) (n=124 patients) and two clinical trial datasets (VANISH, n=155 and LeoPARDS, n=484) in which the plasma protein abundance of 65, 21, 11 circulating cytokines, cytokine receptors and regulators were quantified. Clinical features, outcomes, response to trial treatments and assignment to transcriptomic subphenotypes were compared between inflammatory clusters. MEASUREMENTS AND MAIN RESULTS: We identified two (UK GAinS, VANISH) or three (LeoPARDS) inflammatory clusters. A group with high levels of pro-inflammatory and anti-inflammatory cytokines was seen that was associated with worse organ dysfunction and survival. No interaction between inflammatory clusters and trial treatment response was found. We found variable overlap of inflammatory clusters and leucocyte transcriptomic subphenotypes. CONCLUSIONS: These findings demonstrate that differences in response at the level of cytokine biology show clustering related to severity, but not treatment response, and may provide complementary information to transcriptomic sepsis subphenotypes. TRIAL REGISTRATION NUMBER: ISRCTN20769191, ISRCTN12776039.

Machine learning to predict antimicrobial resistance: future applications in clinical practice?

INTRODUCTION: Machine learning (ML) is increasingly being used to predict antimicrobial resistance (AMR). This review aims to provide physicians with an overview of the literature on ML as a means of AMR prediction. METHODS: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, ACM Digital Library, and IEEE Xplore Digital Library up to December 2023. RESULTS: Thirty-six studies were included in this review. Thirty-two studies (32/36, 89 %) were based on hospital data and four (4/36, 11 %) on outpatient data. The vast majority of them were conducted in high-resource settings (33/36, 92 %). Twenty-four (24/36, 67 %) studies developed systems to predict drug resistance in infected patients, eight (8/36, 22 %) tested the performances of ML-assisted antibiotic prescription, two (2/36, 6 %) assessed ML performances in predicting colonization with carbapenem-resistant bacteria and, finally, two assessed national and international AMR trends. The most common inputs were demographic characteristics (25/36, 70 %), previous antibiotic susceptibility testing (19/36, 53 %) and prior antibiotic exposure (15/36, 42 %). Thirty-three (92 %) studies targeted prediction of Gram-negative bacteria (GNB) resistance as an output (92 %). The studies included showed moderate to high performances, with AUROC ranging from 0.56 to 0.93. CONCLUSION: ML can potentially provide valuable assistance in AMR prediction. Although the literature on this topic is growing, future studies are needed to design, implement, and evaluate the use and impact of ML decision support systems.

A Transcriptomic Approach to Understand Patient Susceptibility to Pneumonia After Abdominal Surgery.

OBJECTIVE: To describe immune pathways and gene networks altered following major abdominal surgery and to identify transcriptomic patterns associated with postoperative pneumonia. BACKGROUND: Nosocomial infections are a major healthcare challenge, developing in over 20% of patients aged 45 or over undergoing major abdominal surgery, with postoperative pneumonia associated with an almost 5-fold increase in 30-day mortality. METHODS: From a prospective consecutive cohort (n=150) undergoing major abdominal surgery, whole-blood RNA was collected preoperatively and at 3 time-points postoperatively (2-6, 24, and 48 h). Twelve patients diagnosed with postoperative pneumonia and 27 matched patients remaining infection-free were identified for analysis with RNA-sequencing. RESULTS: Compared to preoperative sampling, 3639 genes were upregulated and 5043 downregulated at 2 to 6 hours. Pathway analysis demonstrated innate-immune activation with neutrophil degranulation and Toll-like-receptor signaling upregulation alongside adaptive-immune suppression. Cell-type deconvolution of preoperative RNA-sequencing revealed elevated S100A8/9-high neutrophils alongside reduced naïve CD4 T-cells in those later developing pneumonia. Preoperatively, a gene-signature characteristic of neutrophil degranulation was associated with postoperative pneumonia acquisition ( P =0.00092). A previously reported Sepsis Response Signature (SRSq) score, reflecting neutrophil dysfunction and a more dysregulated host response, at 48 hours postoperatively, differed between patients subsequently developing pneumonia and those remaining infection-free ( P =0.045). Analysis of the novel neutrophil gene-signature and SRSq scores in independent major abdominal surgery and polytrauma cohorts indicated good predictive performance in identifying patients suffering later infection. CONCLUSIONS: Major abdominal surgery acutely upregulates innate-immune pathways while simultaneously suppressing adaptive-immune pathways. This is more prominent in patients developing postoperative pneumonia. Preoperative transcriptomic signatures characteristic of neutrophil degranulation and postoperative SRSq scores may be useful predictors of subsequent pneumonia risk.

Patients with cancer and sepsis trials: an unfair representation?

Approximately 20% of sepsis cases are thought to occur in patients with cancer. Thus, such patients are an important cohort to be represented and characterised among sepsis trials. However, patients with cancer are commonly excluded from sepsis trials, although the extent to which is unknown. In this opinion article, we discuss our findings that suggest that patients with cancer are being under-represented in sepsis trials, often with an unclear rationale. We question the validity of generalising results from sepsis trials to heterogenous cancer populations and call for wider inclusion of patients with cancer to bridge this knowledge gap in sepsis management.

Management of Bacterial and Fungal Infections in the ICU: Diagnosis, Treatment, and Prevention Recommendations.

Bacterial and fungal infections are common issues for patients in the intensive care unit (ICU). Large, multinational point prevalence surveys have identified that up to 50% of ICU patients have a diagnosis of bacterial or fungal infection at any one time. Infection in the ICU is associated with its own challenges. Causative organisms often harbour intrinsic and acquired mechanisms of drug-resistance, making empiric and targeted antimicrobial selection challenging. Infection in the ICU is associated with worse clinical outcomes for patients. We review the epidemiology of bacterial and fungal infection in the ICU. We discuss risk factors for acquisition, approaches to diagnosis and management, and common strategies for the prevention of infection.

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.

Machine learning and synthetic outcome estimation for individualised antimicrobial cessation.

The decision on when it is appropriate to stop antimicrobial treatment in an individual patient is complex and under-researched. Ceasing too early can drive treatment failure, while excessive treatment risks adverse events. Under- and over-treatment can promote the development of antimicrobial resistance (AMR). We extracted routinely collected electronic health record data from the MIMIC-IV database for 18,988 patients (22,845 unique stays) who received intravenous antibiotic treatment during an intensive care unit (ICU) admission. A model was developed that utilises a recurrent neural network autoencoder and a synthetic control-based approach to estimate patients' ICU length of stay (LOS) and mortality outcomes for any given day, under the alternative scenarios of if they were to stop vs. continue antibiotic treatment. Control days where our model should reproduce labels demonstrated minimal difference for both stopping and continuing scenarios indicating estimations are reliable (LOS results of 0.24 and 0.42 days mean delta, 1.93 and 3.76 root mean squared error, respectively). Meanwhile, impact days where we assess the potential effect of the unobserved scenario showed that stopping antibiotic therapy earlier had a statistically significant shorter LOS (mean reduction 2.71 days, p -value <0.01). No impact on mortality was observed. In summary, we have developed a model to reliably estimate patient outcomes under the contrasting scenarios of stopping or continuing antibiotic treatment. Retrospective results are in line with previous clinical studies that demonstrate shorter antibiotic treatment durations are often non-inferior. With additional development into a clinical decision support system, this could be used to support individualised antimicrobial cessation decision-making, reduce the excessive use of antibiotics, and address the problem of AMR.

Sepsis biomarkers and diagnostic tools with a focus on machine learning.

Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers.

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.

Application of therapeutic drug monitoring to the treatment of bacterial central nervous system infection: a scoping review.

BACKGROUND: Bacterial central nervous system (CNS) infection is challenging to treat and carries high risk of recurrence, morbidity, and mortality. Low CNS penetration of antibiotics may contribute to poor clinical outcomes from bacterial CNS infections. The current application of therapeutic drug monitoring (TDM) to management of bacterial CNS infection was reviewed. METHODS: Studies were included if they described adults treated for a suspected/confirmed bacterial CNS infection and had antibiotic drug concentration(s) determined that affected individual treatment. RESULTS: One-hundred-and-thirty-six citations were retrieved. Seventeen manuscripts were included describing management of 68 patients. TDM for vancomycin (58/68) and the beta-lactams (29/68) was most common. Timing of clinical sampling varied widely between studies and across different antibiotics. Methods for setting individual PK-PD targets, determining parameters and making treatment changes varied widely and were sometimes unclear. DISCUSSION: Despite increasing observational data showing low CNS penetration of various antibiotics, there are few clinical studies describing practical implementation of TDM in management of CNS infection. Lack of consensus around clinically relevant CSF PK-PD targets and protocols for dose-adjustment may contribute. Standardised investigation of TDM as a tool to improve treatment is required, especially as innovative drug concentration-sensing and PK-PD modelling technologies are emerging. Data generated at different centres offering TDM should be open access and aggregated to enrich understanding and optimize application.

Patient Stratification in Sepsis: Using Metabolomics to Detect Clinical Phenotypes, Sub-Phenotypes and Therapeutic Response.

Infections are common and need minimal treatment; however, occasionally, due to inappropriate immune response, they can develop into a life-threatening condition known as sepsis. Sepsis is a global concern with high morbidity and mortality. There has been little advancement in the treatment of sepsis, outside of antibiotics and supportive measures. Some of the difficulty in identifying novel therapies is the heterogeneity of the condition. Metabolic phenotyping has great potential for gaining understanding of this heterogeneity and how the metabolic fingerprints of patients with sepsis differ based on survival, organ dysfunction, disease severity, type of infection, treatment or causative organism. Moreover, metabolomics offers potential for patient stratification as metabolic profiles obtained from analytical platforms can reflect human individuality and phenotypic variation. This article reviews the most relevant metabolomic studies in sepsis and aims to provide an overview of the metabolic derangements in sepsis and how metabolic phenotyping has been used to identify sub-groups of patients with this condition. Finally, we consider the new avenues that metabolomics could open, exploring novel phenotypes and untangling the heterogeneity of sepsis, by looking at advances made in the field with other -omics technologies.

A Novel Role for Cytochrome P450 Epoxygenase Metabolites in Septic Shock.

This is the largest study describing the role of P450 epoxygenase metabolites in septic shock in humans and suggests a novel therapeutic target. OBJECTIVES: Oxylipins are oxidative breakdown products of cell membrane fatty acids. Animal models have demonstrated that oxylipins generated by the P450 epoxygenase pathway may be implicated in septic shock pathology. However, these mediators are relatively unexplored in humans with septic shock. We aimed to determine if there were patterns of oxylipins that were associated with 28-day septic shock mortality and organ dysfunction. DESIGN: Retrospective analysis of samples collected during the Vasopressin versus Norepinephrine as Initial Therapy in Septic Shock trial. SETTING: ICUs in the United Kingdom. PARTICIPANTS: Adults recruited within 6 hours of onset of septic shock. MAIN OUTCOMES AND MEASURES: Oxylipin profiling was performed on 404 serum samples from 152 patients using liquid chromatography-mass spectrometry. RESULTS: Nonsurvivors were found to have higher levels of 14,15-dihydroxyeicosatrienoic acid (DHET) at baseline than survivors (p = 0.02). Patients with 14,15-DHET levels above the lower limit of quantification of the assay were more likely to die than patients with levels below this limit (hazard ratio, 2.3; 95% CI, 1.2-4.5). Patients with measurable 14,15-DHET had higher levels of organ dysfunction and fewer renal failure-free days than those in whom it was unmeasurable. Considering samples collected over the first week of intensive care stay, measurable levels of DHET species were associated with higher daily Sequential Organ Failure Assessment scores that appeared to be accounted for predominantly by the liver component. Measurable 14,15-DHET showed positive correlation with bilirubin (r s = 0.38; p < 0.001) and lactate (r s = 0.27; p = 0.001). CONCLUSIONS AND RELEVANCE: The P450 epoxygenase-derived DHET species of oxylipins were associated with organ, particularly liver, dysfunction in septic shock and 14,15-DHET was associated with septic shock mortality. These results support further investigation into the role of the P450 epoxygenase-derived oxylipins in sepsis and suggest that this pathway may offer a novel therapeutic strategy in septic shock.

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.

Levosimendan in septic shock in patients with biochemical evidence of cardiac dysfunction: a subgroup analysis of the LeoPARDS randomised trial.

PURPOSE: Myocardial dysfunction is common in sepsis but optimal treatment strategies are unclear. The inodilator, levosimendan was suggested as a possible therapy; however, the levosimendan to prevent acute organ dysfunction in Sepsis (LeoPARDS) trial found it to have no benefit in reducing organ dysfunction in septic shock. In this study we evaluated the effects of levosimendan in patients with and without biochemical cardiac dysfunction and examined its non-inotropic effects. METHODS: Two cardiac biomarkers, troponin I (cTnI) and N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and five inflammatory mediators were measured in plasma from patients recruited to the LeoPARDS trial at baseline and over the first 6 days. Mean total Sequential Organ Failure Assessment (SOFA) score and 28-day mortality were compared between patients with normal and raised cTnI and NT-proBNP values, and between patients above and below median values. RESULTS: Levosimendan produced no benefit in SOFA score or 28-day mortality in patients with cardiac dysfunction. There was a statistically significant treatment by subgroup interaction (p = 0.04) in patients with NT-proBNP above or below the median value. Those with NT-proBNP values above the median receiving levosimendan had higher SOFA scores than those receiving placebo (mean daily total SOFA score 7.64 (4.41) vs 6.09 (3.88), mean difference 1.55, 95% CI 0.43-2.68). Levosimendan had no effect on the rate of decline of inflammatory biomarkers. CONCLUSION: Adding levosimendan to standard care in septic shock was not associated with less severe organ dysfunction nor lower mortality in patients with biochemical evidence of cardiac dysfunction.

Transcriptomic Signatures in Sepsis and a Differential Response to Steroids. From the VANISH Randomized Trial.

RATIONALE: There remains uncertainty about the role of corticosteroids in sepsis with clear beneficial effects on shock duration, but conflicting survival effects. Two transcriptomic sepsis response signatures (SRSs) have been identified. SRS1 is relatively immunosuppressed, whereas SRS2 is relatively immunocompetent. OBJECTIVES: We aimed to categorize patients based on SRS endotypes to determine if these profiles influenced response to either norepinephrine or vasopressin, or to corticosteroids in septic shock. METHODS: A post hoc analysis was performed of a double-blind, randomized clinical trial in septic shock (VANISH [Vasopressin vs. Norepinephrine as Initial Therapy in Septic Shock]). Patients were included within 6 hours of onset of shock and were randomized to receive norepinephrine or vasopressin followed by hydrocortisone or placebo. Genome-wide gene expression profiling was performed and SRS endotype was determined by a previously established model using seven discriminant genes. MEASUREMENTS AND MAIN RESULTS: Samples were available from 176 patients: 83 SRS1 and 93 SRS2. There was no significant interaction between SRS group and vasopressor assignment (P = 0.50). However, there was an interaction between assignment to hydrocortisone or placebo, and SRS endotype (P = 0.02). Hydrocortisone use was associated with increased mortality in those with an SRS2 phenotype (odds ratio = 7.9; 95% confidence interval = 1.6-39.9). CONCLUSIONS: Transcriptomic profile at onset of septic shock was associated with response to corticosteroids. Those with the immunocompetent SRS2 endotype had significantly higher mortality when given corticosteroids compared with placebo. Clinical trial registered with www.clinicaltrials.gov (ISRCTN 20769191).