Usability study of a new tool for nutritional and glycemic management in adult intensive care: Glucosafe 2.

The new decision support tool Glucosafe 2 (GS2) is based on a mathematical model of glucose and insulin dynamics, designed to assist caregivers in blood glucose control and nutrition. This study aims to assess end-user acceptance and usability of this bedside decision support tool in an adult intensive care setting. Caregivers were first trained and then invited to trial GS2 prototype on bedside computers. Data for qualitative analysis were collected through semi-structured interviews from twenty users after minimum three trial days. Most caregivers (70%) rated GS2 as convenient and believed it would help improving adherence to current guidelines (85%). Moreover, most nurses (80%) believed that GS2 would be timesaving. Nurses' risk perceptions and manual data entry emerged as central barriers to use GS2 in routine practice. Issues emerged from the caregivers were compiled into a list of 12 modifications of the GS2 prototype to increase end-user acceptance and usability. This usability study showed that GS2 was considered by ICU caregivers as helpful in daily clinical practice, allowing time-saving and better standardization of ICU patient's care. Important issues were raised by the users with implications for the development and deployment of GS2. Integrating the technology into existing IT infrastructure may facilitate caregivers' acceptance. Further clinical studies of the performance and potential health outcomes are warranted.

Decision support for optimized blood glucose control and nutrition in a neurotrauma intensive care unit: preliminary results of clinical advice and prediction accuracy of the Glucosafe system.

Assessment of glycemic control with model-based decision support ("Glucosafe") in neurotrauma intensive care patients in an ongoing randomized controlled trial with a blood glucose (BG) target of 5-8 mmol/L. Assessment of BG prediction accuracy of the model and assessment of the effect that two potential model extensions would have on prediction accuracy in this trial. In the intervention group insulin infusion rates and nutrition are varied based on Glucosafe's decision support. In the control group, the caloric target is 25-30 kcal/kg per day and insulin is regulated according to department rules. BG concentrations, insulin infusion rates, and feed rates are compared from the data of 12 consecutive patients. BG measurements are predicted retrospectively and the mean relative prediction error is calculated using (1) the current model from the trial, (2) the current model modified by using a BG-dependent variable endogenous insulin appearance rate, (3) the current model modified by a patient-specific carbohydrate absorption factor. BG control was improved by Glucosafe. 76 % of BG measurements in Glucosafe patients were in the 5-8 mmol/L band (Controls: 51 %). BG means (log-normal) ± SD were 7.0 ± 1.19 mmol/L in Glucosafe patients compared to 8.0 ± 1.24 mmol/L in controls (P = 0.05). Mean caloric intake was 93.5 ± 15 % of resting energy expenditure in Glucosafe patients (Controls: 129 ± 29 %). The BG-dependent variable insulin appearance rate had no measurable effect on prediction accuracy. The patient-specific carbohydrate absorption factor improved prediction accuracy significantly (P = 0.001). Glucosafe advice reduces hyperglycemia in neurotrauma intensive care patients. Further parameterization can improve model prediction accuracy.

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy.

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions.

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

Reporting of adverse events in randomized controlled trials of highly active antiretroviral therapy: systematic review.

OBJECTIVES: Our objectives were to systematically assess the quality of reporting of adverse events (AEs) in publications of randomized trials of highly active antiretroviral therapy (HAART), and to examine whether reporting quality affects the effect estimates reported for AEs. METHODS: We searched the PubMed, Cochrane library and EMBASE electronic databases up to December 2008. We included all published randomized controlled trials assessing HAART for treatment-naive adult HIV-infected individuals, with 48 weeks' follow-up. The quality of AE reporting was extracted according to CONSORT guidelines. We pooled the relative risks for AEs and compared results by sponsorship and different reporting methods. RESULTS: Forty-nine trials, including 19 882 patients, published between 2000 and 2008, met the inclusion criteria. Only one of the trials reported on AE collection methods. Twenty-six trials reported only AEs attributed to drugs, 17 of which did not refer to the attribution methods. AE reporting was nearly always selective and selection criteria were highly variable, based on severity grading or occurrence threshold. Presentation of AEs above an occurrence threshold was more common in studies sponsored by industry (30/31) than in studies sponsored by non-profit organizations (3/18). Moreover, we showed that differences in the methods of reporting AEs may affect the results reported for AEs. No significant improvement in AE reporting was seen over this period. CONCLUSIONS: We found substantial variability in AE reporting. Variability was influenced by sponsor identity and affected outcomes. These facts obstruct our ability to choose HAART based on currently published data.

Energy expenditure in critically ill patients estimated by population-based equations, indirect calorimetry and CO2-based indirect calorimetry.

BACKGROUND: Indirect calorimetry (IC) is the reference method for measurement of energy expenditure (EE) in mechanically ventilated critically ill patients. When IC is unavailable, EE can be calculated by predictive equations or by VCO2-based calorimetry. This study compares the bias, quality and accuracy of these methods. METHODS: EE was determined by IC over a 30-min period in patients from a mixed medical/postsurgical intensive care unit and compared to seven predictive equations and to VCO2-based calorimetry. The bias was described by the mean difference between predicted EE and IC, the quality by the root mean square error (RMSE) of the difference and the accuracy by the number of patients with estimates within 10 % of IC. Errors of VCO2-based calorimetry due to choice of respiratory quotient (RQ) were determined by a sensitivity analysis, and errors due to fluctuations in ventilation were explored by a qualitative analysis. RESULTS: In 18 patients (mean age 61 ± 17 years, five women), EE averaged 2347 kcal/day. All predictive equations were accurate in less than 50 % of the patients with an RMSE ≥ 15 %. VCO2-based calorimetry was accurate in 89 % of patients, significantly better than all predictive equations, and remained better for any choice of RQ within published range (0.76-0.89). Errors due to fluctuations in ventilation are about equal in IC and VCO2-based calorimetry, and filtering reduced these errors. CONCLUSIONS: This study confirmed the inaccuracy of predictive equations and established VCO2-based calorimetry as a more accurate alternative. Both IC and VCO2-based calorimetry are sensitive to fluctuations in respiration.

An in silico method to identify computer-based protocols worthy of clinical study: An insulin infusion protocol use case.

OBJECTIVE: Develop an efficient non-clinical method for identifying promising computer-based protocols for clinical study. An in silico comparison can provide information that informs the decision to proceed to a clinical trial. The authors compared two existing computer-based insulin infusion protocols: eProtocol-insulin from Utah, USA, and Glucosafe from Denmark. MATERIALS AND METHODS: The authors used eProtocol-insulin to manage intensive care unit (ICU) hyperglycemia with intravenous (IV) insulin from 2004 to 2010. Recommendations accepted by the bedside clinicians directly link the subsequent blood glucose values to eProtocol-insulin recommendations and provide a unique clinical database. The authors retrospectively compared in silico 18,984 eProtocol-insulin continuous IV insulin infusion rate recommendations from 408 ICU patients with those of Glucosafe, the candidate computer-based protocol. The subsequent blood glucose measurement value (low, on target, high) was used to identify if the insulin recommendation was too high, on target, or too low. RESULTS: Glucosafe consistently provided more favorable continuous IV insulin infusion rate recommendations than eProtocol-insulin for on target (64% of comparisons), low (80% of comparisons), or high (70% of comparisons) blood glucose. Aggregated eProtocol-insulin and Glucosafe continuous IV insulin infusion rates were clinically similar though statistically significantly different (Wilcoxon signed rank test P = .01). In contrast, when stratified by low, on target, or high subsequent blood glucose measurement, insulin infusion rates from eProtocol-insulin and Glucosafe were statistically significantly different (Wilcoxon signed rank test, P < .001), and clinically different. DISCUSSION: This in silico comparison appears to be an efficient nonclinical method for identifying promising computer-based protocols. CONCLUSION: Preclinical in silico comparison analytical framework allows rapid and inexpensive identification of computer-based protocol care strategies that justify expensive and burdensome clinical trials.

Glucose Control in the ICU: A Continuing Story.

In the present era of near-continuous glucose monitoring (CGM) and automated therapeutic closed-loop systems, measures of accuracy and of quality of glucose control need to be standardized for licensing authorities and to enable comparisons across studies and devices. Adequately powered, good quality, randomized, controlled studies are needed to assess the impact of different CGM devices on the quality of glucose control, workload, and costs. The additional effects of continuing glucose control on the general floor after the ICU stay also need to be investigated. Current algorithms need to be adapted and validated for CGM, including effects on glucose variability and workload. Improved collaboration within the industry needs to be encouraged because no single company produces all the necessary components for an automated closed-loop system. Combining glucose measurement with measurement of other variables in 1 sensor may help make this approach more financially viable.

A simulation model of insulin saturation and glucose balance for glycemic control in ICU patients.

Consistent tight blood sugar control in critically ill patients has proven elusive. Properly accounting for the saturation of insulin action and reducing the need for frequent measurements are important aspects in intensive insulin therapy. This paper presents a composite metabolic model, 'Glucosafe', that integrates models and parameters from normal physiology and accounts for the reduced rate of glucose gut absorption and saturation of insulin action in patients with reduced insulin sensitivity. Particularly, two different sites of reduced insulin sensitivity, before and after the non-linearity of insulin action, are explored with this model. These approaches are assessed based on the model's accuracy in retrospectively predicting blood glucose measurements of 10 randomly chosen, hyperglycemic intensive care patients. For each patient, median absolute percent error is <25% for prediction times < or = 270min and modelling reduced insulin sensitivity after the non-linearity, compared to <29% for modelling reduced insulin sensitivity before the non-linearity. Scaling the insulin effect (after the non-linearity) is a suitable assumption in this model structure. These results are preliminary and subject to further and more extensive validation of the model's capability to predict the longer term (>2h) blood glucose excursion in critically ill patients.

The Glucosafe system for tight glycemic control in critical care: a pilot evaluation study.

PURPOSE: "Glucosafe' is a new model-based decision support system for glycemic control in critical care. Safety and achievement of glycemic goals using the system are tested prospectively. METHODS: Four penalty functions were developed to balance regimens of nutrition and insulin therapy against model-predicted glycemic outcome. The system advises the regimen where the penalty sum is minimal. An interactive interface allows advice alterations. Ten hyperglycemic patients (median Acute Physiology and Chronic Health Evaluation II, 12.5; interquartile range, 7.5-16.3) from a neuro and trauma intensive care unit were included for pilot testing using Glucosafe for 12 to 14 hours. Glycemic outcomes were compared to the 24-hour intervals before and after intervention. RESULTS: Hypoglycemia (blood glucose [BG] <3.5 mmol/L) was not observed. Mean log-normal BG +/- standard deviation was reduced from 8.6 +/- 2.4 mmol/L preintervention to 7.0 +/- 1.1 mmol/L during the intervention. Nine patients reached the 4.4- to 6.1-mmol/L band after a mean 5 hours. At 5 hours intervention, mean log-normal BG was 6.7 mmol/L, 40% of measurements were in the 4.4- to 6.1-mmol/L band, and 84% were in the 4.4- to 7.75-mmol/L band. CONCLUSIONS: Safety was demonstrated with the developed penalty functions. The low BG variance achieved may permit minor adjustments of the penalty function values to reduce average BG if desired.