Validation of a virtual patient and virtual trials method for accurate prediction of tight glycemic control protocol performance

Peer reviewe

Variability of insulin sensitivity during the first 4 days of critical illness

1-pageSafe, effective tight glycaemic control (TGC) can improve outcomes in critical care patients, but is difficult to achieve consistently. Insulin sensitivity defines the metabolic balance between insulin concentration and insulin mediated glucose disposal. Hence, variability of insulin sensitivity can cause variable glycaemia. This study investigates the daily evolution of model-based insulin sensitivity level and variability for critical care patients receiving TGC during the first four days of their ICU stay

Does tight glycemic control positively impact on patient mortality?

peer reviewe

Year in review in Intensive Care Medicine 2012. II: Pneumonia and infection, sepsis, coagulation, hemodynamics, cardiovascular and microcirculation, critical care organization, imaging, ethics and legal issues.

Journal ArticleSCOPUS: re.jSCOPUS: re.jinfo:eu-repo/semantics/publishe

Enhanced insulin sensitivity variability in the first 3 days of ICU stay: implications for tight glycemic control

Effective tight glycemic control (TGC) can improve outcomes, particularly in cardiovascular surgery, but is difficult to achieve. Variability in insulin sensitivity/resistance resulting from the level and evolution of stress response, particularly early in a patient’s stay, can lead to hyperglycemia and variability, which are associated with mortality. This study quantifies the daily evolution of the variability of insulin sensitivity for cardiovascular surgical and all other ICU patients

Mild hypoglycemia is independently associated with increased mortality in the critically ill

Introduction: Severe hypoglycemia (blood glucose concentration (BG) < 40 mg/dL) is independently associated with an increased risk of mortality in critically ill patients. The association of milder hypoglycemia (BG < 70 mg/dL) with mortality is less clear.Methods: Prospectively collected data from two observational cohorts in the USA and in The Netherlands, and from the prospective GLUCONTROL trial were analyzed. Hospital mortality was the primary endpoint.Results: We analyzed data from 6,240 patients: 3,263 admitted to Stamford Hospital (ST), 2,063 admitted to three institutions in The Netherlands (NL) and 914 who participated in the GLUCONTROL trial (GL). The percentage of patients with hypoglycemia varied from 18% to 65% among the different cohorts. Patients with hypoglycemia experienced higher mortality than did those without hypoglycemia even after stratification by severity of illness, diagnostic category, diabetic status, mean BG during intensive care unit (ICU) admission and coefficient of variation (CV) as a reflection of glycemic variability. The relative risk (RR, 95% confidence interval) of mortality associated with minimum BG < 40, 40 to 54 and 55 to 69 mg/dL compared to patients with minimum BG 80 to 109 mg/dL was 3.55 (3.02 to 4.17), 2.70 (2.31 to 3.14) and 2.18 (1.87 to 2.53), respectively (all P < 0.0001). The RR of mortality associated with any hypoglycemia < 70 mg/dL was 3.28 (2.78 to 3.87) (P < 0.0001), 1.30 (1.12 to 1.50) (P = 0.0005) and 2.11 (1.62 to 2.74) (P < 0.0001) for the ST, NL and GL cohorts, respectively. Multivariate regression analysis demonstrated that minimum BG < 70 mg/dL, 40 to 69 mg/dL and < 40 mg/dL were independently associated with increased risk of mortality for the entire cohort of 6,240 patients (odds ratio (OR) (95% confidence interval (CI)) 1.78 (1.39 to 2.27) P < 0.0001), 1.29 (1.11 to 1.51) P = 0.0011 and 1.87 (1.46 to 2.40) P < 0.0001) respectively.Conclusions: Mild hypoglycemia was associated with a significantly increased risk of mortality in an international cohort of critically ill patients. Efforts to reduce the occurrence of hypoglycemia in critically ill patients may reduce mortality. © 2011 Krinsley et al. licensee BioMed Central Ltd.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Changes in Parasite Virulence Induced by the Disruption of a Single Member of the 235 kDa Rhoptry Protein Multigene Family of Plasmodium yoelii

Invasion of the erythrocyte by the merozoites of the malaria parasite is a complex process involving a range of receptor-ligand interactions. Two protein families termed Erythrocyte Binding Like (EBL) proteins and Reticulocyte Binding Protein Homologues (RH) play an important role in host cell recognition by the merozoite. In the rodent malaria parasite, Plasmodium yoelii, the 235 kDa rhoptry proteins (Py235) are coded for by a multigene family and are members of the RH. In P. yoelii Py235 as well as a single member of EBL have been shown to be key mediators of virulence enabling the parasite to invade a wider range of host erythrocytes. One member of Py235, PY01365 is most abundantly transcribed in parasite populations and the protein specifically binds to erythrocytes and is recognized by the protective monoclonal antibody 25.77, suggesting a key role of this particular member in virulence. Recent studies have indicated that overall levels of Py235 expression are essential for parasite virulence. Here we show that disruption of PY01365 in the virulent YM line directly impacts parasite virulence. Furthermore the disruption of PY01365 leads to a reduction in the number of schizonts that express members of Py235 that react specifically with the mcAb 25.77. Erythrocyte binding assays show reduced binding of Py235 to red blood cells in the PY01365 knockout parasite as compared to YM. While our results identify PY01365 as a mediator of parasite virulence, they also confirm that other members of Py235 are able to substitute for PY01365

A systematic review of techniques and interventions for improving adherence to inclusion and exclusion criteria during enrolment into randomised controlled trials

<p>Abstract</p> <p>Background</p> <p>Enrolment of patients into a randomised controlled trial (RCT) in violation of key inclusion or exclusion criteria, may lead to excess avoidable harm. The purpose of this paper was to systematically identify and review techniques and interventions proven to prevent or avoid inappropriate enrolment of patients into RCTs.</p> <p>Methods</p> <p>EMBASE, MEDLINE, Cochrane Database of Systematic Reviews, Cochrane Methodology Register, online abstract repositories, and conference websites were searched. Experts were contacted and bibliographies of retrieved papers hand-searched. The search cut-off date was 31 August 2009.</p> <p>Results</p> <p>No primary publications were found. We identified one study in the grey literature (conference abstracts and presentations) reporting the results of an evaluation of the effectiveness of an intervention designed to prevent or avoid inappropriate enrolment of patients into an RCT. In the context of a multicentre trial, use of a dummy enrolment run-in phase was shown to reduce enrolment errors significantly (<it>P </it>< 0.001), from 16.1% during the run-in phase to < 1% after trial initiation.</p> <p>Conclusions</p> <p>Our systematic search yielded only one technique or intervention shown to improve adherence to eligibility criteria during enrolment into RCTs. Given the potential harm involved in recruiting patients into a clinical trial in violation of key eligibility criteria, future research is needed to better inform those conducting clinical trials of how best to prevent enrolment errors</p

Next-generation, personalised, model-based critical care medicine : a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

© 2018 The Author(s). Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care