Computer methods and programs in biomedicine
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Comput Methods Programs Biomed · Nov 2011
Increased variation of the response index of nociception during noxious stimulation in patients during general anaesthesia.
Analgesia is an important part of general anaesthesia, but no direct indicators of nociceptive-anti-nociceptive balance have been validated in detail. The Response Index of Nociception (RN) is a multiparameter approach which combines photoplethysmographic waveform (PPG), State Entropy (SE), Response Entropy (RE), and heart rate variability (HRV). We aimed at evaluating RN during general anaesthesia; especially we wanted to compare pre- and post-index values of certain noxious stimuli to the average index values. Our assumption was that RN could be a useful indicator of nociceptive-anti-nociceptive balance during the surgery. ⋯ Changes in RN can be used to detect noxious stimuli during surgery. RN also predicted movement in our patients under propofol-remifentanil anaesthesia.
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Comput Methods Programs Biomed · Sep 2011
Using "off-the-shelf" tools for terabyte-scale waveform recording in intensive care: computer system design, database description and lessons learned.
Until now, the creation of massive (long-term and multichannel) waveform databases in intensive care required an interdisciplinary team of clinicians, engineers and informaticians and, in most cases, also design-specific software and hardware development. Recently, several commercial software tools for waveform acquisition became available. Although commercial products and even turnkey systems are now being marketed as simple and effective, the performance of those solutions is not known. ⋯ Up to six ECG leads, all other monitored waveforms, and all monitored numeric data were recorded in most of the cases. We describe the details of building blocks of our system, provide description of three datasets exported from our VSDB and compare the contents of our VSDB with other available waveform databases. Finally, we summarize lessons learned during recording, storage, and pre-processing of physiologic signals.
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Comput Methods Programs Biomed · May 2011
ReviewTight glycemic control in critical care--the leading role of insulin sensitivity and patient variability: a review and model-based analysis.
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. ⋯ 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.
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Comput Methods Programs Biomed · May 2011
Development of a model-based clinical sepsis biomarker for critically ill patients.
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 (S(I)) is known to decrease with worsening condition and could thus be used to aid diagnosis. ⋯ Multivariate clinical biomarker combining S(I), 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, the multivariate 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.
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Comput Methods Programs Biomed · May 2011
A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients.
Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. ⋯ It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care.