Conventional insulin concentration units (IU/mL or just U/mL) are bioefficacy based, whereas the Système International (SI) units (pmol/L) are mass based. In converting between these two different ...approaches, there are at least 2 well-accepted conversion factors, where there should be only 1. The correct value is not the most-used or well-accepted using online calculators, some journal styles, laboratory reports, and published articles. In short, an incorrect insulin conversion factor is widely used which underreports insulin concentrations by ~15%, with potentially significant research and clinical implications. This short commentary describes the history of insulin IU definitions and conversion factors, and highlights the widespread nature of conversion factor misuse, to provoke deeper interest and thought regarding numbers we so often use without thinking.
Background:
Adsorption of insulin to infusion sets impacts patient therapeutic outcomes and, unaccounted for, may exacerbate persistent hyperglycemia or result in therapy-induced hypoglycemia. This ...article aims to provide recommendations for clinicians involved in intensive care and/or outpatient pump therapy contexts.
Methods:
A dynamic adsorption model is used to evaluate time-varying insulin concentration in the infusion set outflow. Hourly and daily percentage insulin loss to adsorption is examined for neonatal, pediatric, and adult intensive care patients, as well as outpatient children and adults weighing 30, 50, and 80 kg. A short review of preconditioning methods is included.
Results:
Insulin adsorption in outpatient pump therapy is most pronounced in the first hour, where as much as 80% of the intended insulin dose may be lost to adsorption. Subsequently, insulin adsorptive loss is typically negligible. Overall, extra care should be taken in the first 1-6 h of a new infusion set, particularly in children or teenagers. Typically, insulin adsorption in the adult intensive care unit is negligible unless infused at low flow rates (<2 mL/h). Insulin adsorption is significant in pediatric and neonatal intensive care, resulting in delivery concentrations as low as 5%-50% of that intended. Thus, it is recommended that preconditioning of insulin delivery lines be carried out prior to infusion initiation in this context. However, no preconditioning method completely removes adsorption, and care should still be taken in the first 1-6 h of insulin dosing.
Conclusions:
Recommendations made in this article are dependent on the insulin concentration and flow rate used in each clinical context.
(1) Background: Technically, a simple, inexpensive, and non-invasive method of ascertaining volume changes in thoracic and abdominal cavities are required to expedite the development and validation ...of pulmonary mechanics models. Clinically, this measure enables the real-time monitoring of muscular recruitment patterns and breathing effort. Thus, it has the potential, for example, to help differentiate between respiratory disease and dysfunctional breathing, which otherwise can present with similar symptoms such as breath rate. Current automatic methods of measuring chest expansion are invasive, intrusive, and/or difficult to conduct in conjunction with pulmonary function testing (spontaneous breathing pressure and flow measurements). (2) Methods: A tape measure and rotary encoder band system developed by the authors was used to directly measure changes in thoracic and abdominal circumferences without the calibration required for analogous strain-gauge-based or image processing solutions. (3) Results: Using scaling factors from the literature allowed for the conversion of thoracic and abdominal motion to lung volume, combining motion measurements correlated to flow-based measured tidal volume (normalised by subject weight) with R
= 0.79 in data from 29 healthy adult subjects during panting, normal, and deep breathing at 0 cmH
O (ZEEP), 4 cmH
O, and 8 cmH
O PEEP (positive end-expiratory pressure). However, the correlation for individual subjects is substantially higher, indicating size and other physiological differences should be accounted for in scaling. The pattern of abdominal and chest expansion was captured, allowing for the analysis of muscular recruitment patterns over different breathing modes and the differentiation of active and passive modes. (4) Conclusions: The method and measuring device(s) enable the validation of patient-specific lung mechanics models and accurately elucidate diaphragmatic-driven volume changes due to intercostal/chest-wall muscular recruitment and elastic recoil.
Background:
Insulin adsorption to clinical materials has been well observed, but not well quantified. Insulin adsorption reduces expected and actual insulin delivery and is unaccounted for in insulin ...therapy or glycemic control. It may thus contribute to poor control and high glycemic variability. This research quantifies the problem in the context of clinical use.
Method:
Experimental insulin adsorption data from literature is used to calculate insulin delivery and total insulin adsorption capacities for polyethylene (PE) and polyvinal chloride (PVC) lines at clinically relevant flow rates and concentrations.
Results:
Insulin adsorption capacity decreased hyperbolically with flow rate for both PE and PVC, where low flow scenarios result in greater insulin adherence to infusion lines. When the infusion flow rate was halved from 1 to 0.5 mL/h, twice as much insulin adsorbed to the line. Insulin loss to adsorption resulted in up to ~50% of intended insulin not delivered over 24 hours in a low flow and low concentration context.
Conclusion:
Material capacity for insulin adsorption is not constant, but increases with decreasing flow. Different materials have different adsorption capacities. In low flow and low concentration contexts, such as in neonatal or pediatric intensive care, insulin loss to adsorption represents a significant proportion of daily insulin delivery, which needs to be accounted for.
•Modelling spontaneous breathing effort and lung mechanics from bedside waveforms.•Spontaneous breathing has patient-specific regulation with volume and/or pressure.•High peak pressures result in ...unloading of work of breathing onto the ventilator.
Optimisation of mechanical ventilation (MV) and weaning requires insight into underlying patient breathing effort. Current identifiable models effectively describe lung mechanics, such as elastance (E) and resistance (R) at the bedside in sedated patients, but are less effective when spontaneous breathing is present. This research derives and regularises a single compartment model to identify patient-specific inspiratory effort.
Constrained second-order b-spline basis functions (knot width 0.05 s) are used to describe negative inspiratory drive (Pp, cmH2O) as a function of time. Breath-breath Pp are identified with single E and R values over inspiration and expiration from n = 20 breaths for N = 22 patients on NAVA ventilation. Pp is compared to measured electrical activity of the diaphragm (Eadi) and published results.
Average per-patient root-mean-squared model fit error was (median interquartile range, IQR) 0.9 0.6–1.3 cmH2O, and average per-patient median Pp was -3.9 -4.5– -3.0 cmH2O, with range -7.9 – -1.9 cmH2O. Per-patient E and R were 16.4 13.6–21.8 cmH2O/L and 9.2 6.4–13.1 cmH2O.s/L, respectively. Most patients showed an inspiratory volume threshold beyond which Pp started to return to baseline, and Pp at peak Eadi (end-inspiration) was often strongly correlated with peak Eadi (R2=0.25–0.86). Similarly, average transpulmonary pressure was consistent breath-breath in most patients, despite differences in peak Eadi and thus peak airway pressure.
The model-based inspiratory effort aligns with electrical muscle activity and published studies showing neuro-muscular decoupling as a function of pressure and/or volume. Consistency in coupling/dynamics were patient-specific. Quantification of patient and ventilator work of breathing contributions may aid optimisation of MV modes and weaning.
Mechanical ventilation is a life-support therapy for intensive care patients suffering from respiratory failure. To reduce the current rate of ventilator-induced lung injury requires ventilator ...settings that are patient-, time-, and disease-specific. A common lung protective strategy is to optimise the level of positive end-expiratory pressure (PEEP) through a recruitment manoeuvre to prevent alveolar collapse at the end of expiration and to improve gas exchange through recruitment of additional alveoli. However, this process can subject parts of the lung to excessively high pressures or volumes. This research significantly extends and more robustly validates a previously developed pulmonary mechanics model to predict lung mechanics throughout recruitment manoeuvres. In particular, the process of recruitment is more thoroughly investigated and the impact of the inclusion of expiratory data when estimating peak inspiratory pressure is assessed. Data from the McREM trial and CURE pilot trial were used to test model predictive capability and assumptions. For PEEP changes of up to and including 14 cmH
2
O, the parabolic model was shown to improve peak inspiratory pressure prediction resulting in less than 10% absolute error in the CURE cohort and 16% in the McREM cohort. The parabolic model also better captured expiratory mechanics than the exponential model for both cohorts.
Background:
Stress-induced hyperglycemia is frequently experienced by critically ill patients and the use of glycemic control (GC) has been shown to improve patient outcomes. For model-based ...approaches to GC, it is important to understand and quantify model parameter assumptions. This study explores endogenous glucose production (EGP) and the use of a population-based parameter value in the intensive care unit context.
Method:
Hourly insulin sensitivity (SI) was fit to clinical data from 145 patients on the Specialized Relative Insulin and Nutrition Titration GC protocol for at least 24 hours. Constraint of SI at a lower bound was used to explore likely EGP variability due to stress response. Minimum EGP was estimated during times when the model SI was constrained, and time and duration of events were examined.
Results:
Constrained events occur for 1.6% of patient hours. About 70% of constrained events occur in the first 12 hours and most events (~80%) occur when there is no exogenous nutrition given. Enhanced EGP values ranged from 1.16 mmol/min (current population value) to 2.75 mmol/min, with most being below 1.5 mmol/min (21% increase).
Conclusion:
The frequency of constrained events is low and the current population value of 1.16 mmol/min is sufficient for more than 98% of patient hours, however, some patients experience significantly raised EGP probably due to an extreme stress response early in patient stay.
Positive end-expiratory pressure (PEEP) at minimum respiratory elastance during mechanical ventilation (MV) in patients with acute respiratory distress syndrome (ARDS) may improve patient care and ...outcome. The Clinical utilisation of respiratory elastance (CURE) trial is a two-arm, randomised controlled trial (RCT) investigating the performance of PEEP selected at an objective, model-based minimal respiratory system elastance in patients with ARDS.
The CURE RCT compares two groups of patients requiring invasive MV with a partial pressure of arterial oxygen/fraction of inspired oxygen (PaO2/FiO2) ratio ≤ 200; one criterion of the Berlin consensus definition of moderate (≤ 200) or severe (≤ 100) ARDS. All patients are ventilated using pressure controlled (bi-level) ventilation with tidal volume = 6-8 ml/kg. Patients randomised to the control group will have PEEP selected per standard practice (SPV). Patients randomised to the intervention will have PEEP selected based on a minimal elastance using a model-based computerised method. The CURE RCT is a single-centre trial in the intensive care unit (ICU) of Christchurch hospital, New Zealand, with a target sample size of 320 patients over a maximum of 3 years. The primary outcome is the area under the curve (AUC) ratio of arterial blood oxygenation to the fraction of inspired oxygen over time. Secondary outcomes include length of time of MV, ventilator-free days (VFD) up to 28 days, ICU and hospital length of stay, AUC of oxygen saturation (SpO
)/FiO
during MV, number of desaturation events (SpO
< 88%), changes in respiratory mechanics and chest x-ray index scores, rescue therapies (prone positioning, nitric oxide use, extracorporeal membrane oxygenation) and hospital and 90-day mortality.
The CURE RCT is the first trial comparing significant clinical outcomes in patients with ARDS in whom PEEP is selected at minimum elastance using an objective model-based method able to quantify and consider both inter-patient and intra-patient variability. CURE aims to demonstrate the hypothesized benefit of patient-specific PEEP and attest to the significance of real-time monitoring and decision-support for MV in the critical care environment.
Australian New Zealand Clinical Trial Registry, ACTRN12614001069640. Registered on 22 September 2014. (https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366838&isReview=true) The CURE RCT clinical protocol and data usage has been granted by the New Zealand South Regional Ethics Committee (Reference number: 14/STH/132).
Glucose intolerance and insulin resistance manifest as hyperglycaemia in intensive care, which is associated with mortality and morbidities. Glycaemic control (GC) may improve outcomes, though safe ...and effective control has proven elusive. Nutritional glucose intake affects blood glucose (BG) outcomes, but few protocols actively control it. This study aims to examine BG outcomes in the context of nutritional management during GC.
Retrospective cohort analysis of 5 glycaemic control cohorts spanning 4 years (n = 273) from Christchurch Hospital Intensive Care Unit (ICU). GC is delivered using a single model-based protocol (STAR), with default 4.4–8.0 mmol/L target range via. modulation of insulin and nutrition. Clinical adaptations/cohorts include variations on upper target (UL-9 with 9.0 mmol/L, reducing workload and nutrition responsiveness), and insulin only (IO) with clinically set nutrition at 3 glucose concentrations (71 g/L vs. 120 and 180 g/L in the TARGET study).
Percent of BG hours in the 4.4–8.0 mmol/L range highest under standard STAR conditions (78%), and was lower at 64% under UL-9, likely due to reduced time-responsiveness of nutrition-insulin changes. By comparison, IO only resulted in 64–69% BG in range across different nutrition types. A subset of patients receiving high glucose nutrition under IO were persistently hyperglycaemic, indicating patient-specific glucose tolerance.
STAR GC is most effective when nutrition and insulin are modulated together with timely responsiveness to persistent hyperglycaemia. Results imply modulation of nutrition alongside insulin improves GC, particularly in patients with persistent hyperglycaemia/low glucose tolerance.
Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of ...a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload. Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems.
This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.
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