Development of a model-based clinical sepsis biomarker for critically ill patients

Invited. online 15 May 2010.Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48 h. Insulin sensitivity (SI) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time. Hourly model-based insulin sensitivity SI values were calculated from glycemic control data of 36 patients with sepsis. The hourly SI is compared to the hourly sepsis score (ss) for these patients (ss = 0–4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss=2) are created for both SI and the multivariate clinical biomarker. Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an SI cut-off value of 0.00013 L/mU/min. Multivariate clinical biomarker combining SI, temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, themultivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score shows potential avenues to improve the positive predictive value

The SF3B1 inhibitor spliceostatin A (SSA) elicits apoptosis in chronic lymphocytic leukemia cells through downregulation of Mcl-1

The pro-survival Bcl-2 family member Mcl-1 is expressed in chronic lymphocytic leukemia (CLL), with high expression correlated with progressive disease. The spliceosome inhibitor spliceostatin A (SSA), is known to regulate Mcl-1 and so here we assessed the ability of SSA to elicit apoptosis in CLL. SSA induced apoptosis of CLL cells at low nanomolar concentrations in a dose- and time-dependent manner, but independently of SF3B1 mutational status, IGHV status and CD38 or ZAP70 expression. However, normal B and T cells were less sensitive than CLL cells (P=0.006 and P<0.001, respectively). SSA altered the splicing of anti-apoptotic MCL-1L to MCL-1s in CLL cells coincident with induction of apoptosis. Overexpression studies in Ramos cells suggested Mcl-1 was important for SSA-induced killing since its expression inversely correlated with apoptosis (P=0.001). IL4 and CD40L, present in patient lymph nodes, are known to protect tumor cells from apoptosis and significantly inhibited SSA, ABT-263 and ABT-199 induced killing following administration to CLL cells (P=0.008). However, by combining SSA with the Bcl-2/Bcl-xL antagonists ABT-263 or ABT-199, we were able to overcome this pro-survival effect. We conclude that SSA combined with Bcl-2/Bcl-xL antagonists may have therapeutic utility for CL

The free-energy self:A predictive coding account of self-recognition

Recognising and representing one's self as distinct from others is a fundamental component of self-awareness. However, current theories of self-recognition are not embedded within global theories of cortical function and therefore fail to provide a compelling explanation of how the self is processed. We present a theoretical account of the neural and computational basis of self-recognition that is embedded within the free-energy account of cortical function. In this account one's body is processed in a Bayesian manner as the most likely to be "me". Such probabilistic representation arises through the integration of information from hierarchically organised unimodal systems in higher-level multimodal areas. This information takes the form of bottom-up "surprise" signals from unimodal sensory systems that are explained away by top-down processes that minimise the level of surprise across the brain. We present evidence that this theoretical perspective may account for the findings of psychological and neuroimaging investigations into self-recognition and particularly evidence that representations of the self are malleable, rather than fixed as previous accounts of self-recognition might suggest

Whole-genome sequencing of chronic lymphocytic leukemia identifies subgroups with distinct biological and clinical features

The value of genome-wide over targeted driver analyses for predicting clinical outcomes of cancer patients is debated. Here, we report the whole-genome sequencing of 485 chronic lymphocytic leukemia patients enrolled in clinical trials as part of the United Kingdom’s 100,000 Genomes Project. We identify an extended catalog of recurrent coding and noncoding genetic mutations that represents a source for future studies and provide the most complete high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and genomic complexity. We demonstrate the relationship of these features with clinical outcome and show that integration of 186 distinct recurrent genomic alterations defines five genomic subgroups that associate with response to therapy, refining conventional outcome prediction. While requiring independent validation, our findings highlight the potential of whole-genome sequencing to inform future risk stratification in chronic lymphocytic leukemia

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

Background: Many patients with COVID-19 have been treated with plasma containing anti-SARS-CoV-2 antibodies. We aimed to evaluate the safety and efficacy of convalescent plasma therapy in patients admitted to hospital with COVID-19. Methods: This randomised, controlled, open-label, platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]) is assessing several possible treatments in patients hospitalised with COVID-19 in the UK. The trial is underway at 177 NHS hospitals from across the UK. Eligible and consenting patients were randomly assigned (1:1) to receive either usual care alone (usual care group) or usual care plus high-titre convalescent plasma (convalescent plasma group). The primary outcome was 28-day mortality, analysed on an intention-to-treat basis. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936. Findings: Between May 28, 2020, and Jan 15, 2021, 11558 (71%) of 16287 patients enrolled in RECOVERY were eligible to receive convalescent plasma and were assigned to either the convalescent plasma group or the usual care group. There was no significant difference in 28-day mortality between the two groups: 1399 (24%) of 5795 patients in the convalescent plasma group and 1408 (24%) of 5763 patients in the usual care group died within 28 days (rate ratio 1·00, 95% CI 0·93–1·07; p=0·95). The 28-day mortality rate ratio was similar in all prespecified subgroups of patients, including in those patients without detectable SARS-CoV-2 antibodies at randomisation. Allocation to convalescent plasma had no significant effect on the proportion of patients discharged from hospital within 28 days (3832 [66%] patients in the convalescent plasma group vs 3822 [66%] patients in the usual care group; rate ratio 0·99, 95% CI 0·94–1·03; p=0·57). Among those not on invasive mechanical ventilation at randomisation, there was no significant difference in the proportion of patients meeting the composite endpoint of progression to invasive mechanical ventilation or death (1568 [29%] of 5493 patients in the convalescent plasma group vs 1568 [29%] of 5448 patients in the usual care group; rate ratio 0·99, 95% CI 0·93–1·05; p=0·79). Interpretation: In patients hospitalised with COVID-19, high-titre convalescent plasma did not improve survival or other prespecified clinical outcomes. Funding: UK Research and Innovation (Medical Research Council) and National Institute of Health Research

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

A Benchmark Data Set for Model-Based Glycemic Control in Critical Care

Comes with free download data set file ( http://www.journalofdst.org/Journal/pdf/July2008/VOL-2-4-ORG4-CHASE-DATA-SUPPLEMENT-DS1.XLS ) and was made free immediately by the journal as a resultBackground: Hyperglycemia is prevalent in critical care. That tight control saves lives is becoming more clear, but the “how” and “for whom” in repeating the initial results remain elusive. Model-based methods can provide tight, patient-specific control, as well as providing significant insight into the etiology and evolution of this condition. However, it is still often difficult to compare results due to lack of a common benchmark. This article puts forward a benchmark data set for critical care glycemic control in a medical intensive care unit (ICU). Based on clinical patient data from SPecialized Relative Insulin and Nutrition Tables (SPRINT) studies, it provides a benchmark for comparing and analyzing performance in model-based glycemic control. Methods: Data from 20 of the first 150 postpilot patients treated under SPRINT are presented. All patients had longer than a 5-day length of stay (LoS) in the Christchurch ICU. The benchmark data set matches overall patient data and glycemic control results for the entire cohort and this particular LoS >5-day group. The mortality outcome (n = 3, 15%) also matches SPRINT results for this patient group. Results: Data cover 20 patients and 6372 total patient hours with an average of 339.4 hours per patient. It includes insulin and nutrition inputs along with 4182 blood glucose measurements at an average of 224.3 measurements per patient, averaging a measurement approximately every 1.5 hours (16 per day). Data are available via download in a Microsoft Excel format. A series of cumulative distribution functions and tables are used to summarize data in this article. Conclusion: Model-based methods can provide tighter, more adaptable “one method fits all” solutions using methods that enable patient-specific modeling and control. A benchmark data set will enable easier model and protocol development for groups lacking clinical data, as well as providing a benchmark to compare results of different protocols on a single (virtual) cohort based on real clinical data

Model-Based Insulin Sensitivity as a Sepsis Diagnostic in Critical Care

Background: Timely diagnosis and treatment of sepsis in critical care require significant clinical effort, experience, and resources. Insulin sensitivity is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to identify insulin sensitivity in real time. Methods: Receiver operating characteristic curves and cutoff insulin sensitivity values for diagnosing sepsis were calculated for model-based insulin sensitivity (SI) and a simpler metric (SSI) that was estimated from glycemic control data of 30 patients with sepsis and can be calculated in real time without use of a computer. Results were compared to the insulin sensitivity profiles of a general intensive care unit population of 113 patients without sepsis and 30 patients with sepsis, comprising a total of 26,453 patient hours. Patients with sepsis were identified as having sepsis based on a sepsis score (ss) of 3 or higher (ss = 0–4 for increasing severity). Patients with type I or type II diabetes were excluded. Ethics approval for this study was granted by the South Island Regional Ethics Committee. Results: Receiver operating characteristic cutoff values of SI = 8 × 10-5 liter mU-1 min-1 and SSI = 2.8 × 10-4 liter mU-1 min-1 were determined for ss = 3. The model-based SI fell below this value in 15% of all patient hours. The SI test had a negative predictive value of 99.8%. The test sensitivity was 78% and specificity was 82%. However, the positive predictor value was 2.8%. Slightly lower sensitivity (68.8%) and specificity (81.7%), but equally good negative prediction (99.7%), were obtained for the estimated SSI. Conclusions: Insulin sensitivity provides a negative predictive diagnostic for sepsis. High insulin sensitivity rules out sepsis for the majority of patient hours and may be determined noninvasively in real time from glycemic control protocol data. Low insulin sensitivity is not an effective diagnostic, as it can equally mark the presence of sepsis or other conditions