Cumulative time in band (cTIB): glycemic level, variability and patient outcome all in one

Conference (2012, October 15)

Second pilot trials of the STAR-Liege protocol for tight glycemic control in critically ill patients

in BioMedical Engineering OnLine (2012)

Procalcitonin usefulness for the initiation of antibiotic treatment in intensive care unit patients*.

in Critical Care Medicine (2012), 40(8), 2304-9

OBJECTIVES: : To test the usefulness of procalcitonin serum level for the reduction of antibiotic consumption in intensive care unit patients. DESIGN: : Single-center, prospective, randomized controlled ... [more ▼]

OBJECTIVES: : To test the usefulness of procalcitonin serum level for the reduction of antibiotic consumption in intensive care unit patients. DESIGN: : Single-center, prospective, randomized controlled study. SETTING: : Five intensive care units from a tertiary teaching hospital. PATIENTS: : All consecutive adult patients hospitalized for > 48 hrs in the intensive care unit during a 9-month period. INTERVENTIONS: : Procalcitonin serum level was obtained for all consecutive patients suspected of developing infection either on admission or during intensive care unit stay. The use of antibiotics was more or less strongly discouraged or recommended according to the Muller classification. Patients were randomized into two groups: one using the procalcitonin results (procalcitonin group) and one being blinded to the procalcitonin results (control group). The primary end point was the reduction of antibiotic use expressed as a proportion of treatment days and of daily defined dose per 100 intensive care unit days using a procalcitonin-guided approach. Secondary end points included: a posteriori assessment of the accuracy of the infectious diagnosis when using procalcitonin in the intensive care unit and of the diagnostic concordance between the intensive care unit physician and the infectious-disease specialist. MEASUREMENTS AND MAIN RESULTS: : There were 258 patients in the procalcitonin group and 251 patients in the control group. A significantly higher amount of withheld treatment was observed in the procalcitonin group of patients classified by the intensive care unit clinicians as having possible infection. This, however, did not result in a reduction of antibiotic consumption. The treatment days represented 62.6 +/- 34.4% and 57.7 +/- 34.4% of the intensive care unit stays in the procalcitonin and control groups, respectively (p = .11). According to the infectious-disease specialist, 33.8% of the cases in which no infection was confirmed, had a procalcitonin value >1microg/L and 14.9% of the cases with confirmed infection had procalcitonin levels <0.25 microg/L. The ability of procalcitonin to differentiate between certain or probable infection and possible or no infection, upon initiation of antibiotic treatment was low, as confirmed by the receiving operating curve analysis (area under the curve = 0.69). Finally, procalcitonin did not help improve concordance between the diagnostic confidence of the infectious-disease specialist and the ICU physician. CONCLUSIONS: : Procalcitonin measuring for the initiation of antimicrobials did not appear to be helpful in a strategy aiming at decreasing the antibiotic consumption in intensive care unit patients. [less ▲]

Enhanced insulin sensitivity variability in the first 3 days of ICU stay: Implications for TGC

in Critical Care (2011, March)

Tight glycemic control in critical care - The leading role of insulin sensitivity and patient variability: A review and model-based analysis.

in Computer Methods & Programs in Biomedicine (2011)

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials ... [more ▼]

Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts. This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols. A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric (S(I)). Variation in S(I) provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/S(I)) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration. [less ▲]

Pilot Trials of STAR Target to Range Glycemic Control

in Intensive Care Medicine (2011), 37 (Suppl 1)

Variability of insulin sensitivity for diabetics and non-diabetics during the first 3 days of ICU stay

Poster (2011)

Glycemic Variability, Hypoglycemia and Organ Failure in the Glucontrol Study

in 10th Belgian Day on Biomedical Engineering (2011)

Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice?

in Annals of Intensive Care (2011), 1:11

Validation of a model-based virtual trials method for tight glycemic control in intensive care.

in BioMedical Engineering OnLine (2010), 9

BACKGROUND: In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective tight glycemic control (TGC) protocols. However, no such method has fully ... [more ▼]

BACKGROUND: In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective tight glycemic control (TGC) protocols. However, no such method has fully validated the independence of virtual patients (or resulting clinical trial predictions) from the data used to create them. This study uses matched cohorts from a TGC clinical trial to validate virtual patients and in-silico virtual trial models and methods. METHODS: Data from a 211 patient subset of the Glucontrol trial in Liege, Belgium. Glucontrol-A (N = 142) targeted 4.4-6.1 mmol/L and Glucontrol-B (N = 69) targeted 7.8-10.0 mmol/L. Cohorts were matched by APACHE II score, initial BG, age, weight, BMI and sex (p > 0.25). Virtual patients are created by fitting a clinically validated model to clinical data, yielding time varying insulin sensitivity profiles (SI(t)) that drives in-silico patients.Model fit and intra-patient (forward) prediction errors are used to validate individual in-silico virtual patients. Self-validation (tests A protocol on Group-A virtual patients; and B protocol on B virtual patients) and cross-validation (tests A protocol on Group-B virtual patients; and B protocol on A virtual patients) are used in comparison to clinical data to assess ability to predict clinical trial results. RESULTS: Model fit errors were small (<0.25%) for all patients, indicating model fitness. Median forward prediction errors were: 4.3, 2.8 and 3.5% for Group-A, Group-B and Overall (A+B), indicating individual virtual patients were accurate representations of real patients. SI and its variability were similar between cohorts indicating they were metabolically similar.Self and cross validation results were within 1-10% of the clinical data for both Group-A and Group-B. Self-validation indicated clinically insignificant errors due to model and/or clinical compliance. Cross-validation clearly showed that virtual patients enabled by identified patient-specific SI(t) profiles can accurately predict the performance of independent and different TGC protocols. CONCLUSIONS: This study fully validates these virtual patients and in silico virtual trial methods, and clearly shows they can accurately simulate, in advance, the clinical results of a TGC protocol, enabling rapid in silico protocol design and optimization. These outcomes provide the first rigorous validation of a virtual in-silico patient and virtual trials methodology. [less ▲]