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Low-Dose Computed Tomography (LDCT) screening for lung cancer can result in several potential outcomes of varying significance. Communication methods used in Lung Cancer Screening (LCS) programmes must, therefore, ensure that participants are prepared for the range of possible results and follow-up. Here, we assess perceptions of a written preparatory information booklet provided to participants in a large LCS cohort designed to convey this information.All participants in the SUMMIT Study (NCT03934866) were provided with a results preparation information booklet, entitled 'The SUMMIT Study: Next Steps' at their baseline appointment which outlined potential results, their significance, and timelines for follow up. Results from the LDCT scan and Lung Health Check were subsequently sent by letter. Perceptions of this booklet were assessed among participants with indeterminate pulmonary findings when they attended a face-to-face appointment immediately before their three-month interval scan. Specifically, questions assessed the perceived usefulness of the booklet and the amount of information contained in it.70.1% (n = 1,412/2,014) participants remembered receiving the booklet at their appointment. Of these participants, 72.0% (n = 1,017/1,412) found it quite or very useful and 68.0% (n = 960/1,412) reported that it contained the right amount of information. Older participants, those from the least deprived socioeconomic quintile and those of Black ethnicity were less likely to report finding the booklet either quite or very useful, or that it contained the right amount of information. Participants who remembered receiving the booklet were more likely to be satisfied with the process of results communication by letter.Providing written information that prepares participants for possible LDCT results and their significance appears to be a useful resource and a helpful adjunct to a written method of results communication for large scale LCS programmes.

Lung cancer screening (LCS) eligibility is largely determined by tobacco consumption. Primary care smoking data could guide LCS invitation and eligibility assessment. We present observational data from the SUMMIT Study, where individual self-reported smoking status was concordant with primary care records in 75.3%. However, 10.3% demonstrated inconsistencies between historic and most recent smoking status documentation. Quantified tobacco consumption was frequently missing, precluding direct LCS eligibility assessment. Primary care recorded "ever-smoker" status, encompassing both recent and historic documentation, can be used to target LCS invitation. Identifying those with missing or erroneous "never-smoker" smoking status is crucial for equitable invitation to LCS.

Objectives Successful lung cancer screening delivery requires sensitive, timely reporting of low-dose computed tomography (LDCT) scans, placing a demand on radiology resources. Trained non-radiologist readers and computer-assisted detection (CADe) software may offer strategies to optimise the use of radiology resources without loss of sensitivity. This report examines the accuracy of trained reporting radiographers using CADe support to report LDCT scans performed as part of the Lung Screen Uptake Trial (LSUT). Methods In this observational cohort study, two radiographers independently read all LDCT performed within LSUT and reported on the presence of clinically significant nodules and common incidental findings (IFs), including recommendations for management. Reports were compared against a ‘reference standard’ (RS) derived from nodules identified by study radiologists without CADe, plus consensus radiologist review of any additional nodules identified by the radiographers. Results A total of 716 scans were included, 158 of which had one or more clinically significant pulmonary nodules as per our RS. Radiographer sensitivity against the RS was 68–73.7%, with specificity of 92.1–92.7%. Sensitivity for detection of proven cancers diagnosed from the baseline scan was 83.3–100%. The spectrum of IFs exceeded what could reasonably be covered in radiographer training. Conclusion Our findings highlight the complexity of LDCT reporting requirements, including the limitations of CADe and the breadth of IFs. We are unable to recommend CADe-supported radiographers as a sole reader of LDCT scans, but propose potential avenues for further research including initial triage of abnormal LDCT or reporting of follow-up surveillance scans. Key Points • Successful roll-out of mass screening programmes for lung cancer depends on timely, accurate CT scan reporting, placing a demand on existing radiology resources. • This observational cohort study examines the accuracy of trained radiographers using computer-assisted detection (CADe) software to report lung cancer screening CT scans, as a potential means of supporting reporting workflows in LCS programmes. • CADe-supported radiographers were less sensitive than radiologists at identifying clinically significant pulmonary nodules, but had a low false-positive rate and good sensitivity for detection of confirmed cancers.

Pulmonary nodules are commonly found in Lung Cancer Screening (LCS), with results typically communicated by face-to-face or telephone consultation. Providing LCS on a population basis requires resource efficient and scalabe communication methods. Written communication provides one such method. Here, we assess participant satisfaction with this approach in a LCS setting and investigate characteristics associated with dissatisfaction.The SUMMIT Study is a prospective observational cohort study which aims to assess the implementation of Low-Dose Computed Tomography (LDCT) scanning for LCS in a high-risk population and validate a multi-cancer early detection blood test (NCT03934866). Participants with indeterminate pulmonary nodules requiring a three-month interval LDCT were informed of their result by postal letter and given a face-to-face appointment with a study practitioner at their interval LDCT appointment. At this appointment, having previously received their results letter, participants were verbally asked questions to assess their satisfaction with, and preferences for, methods of results communication.1,900 participants were included in the analysis. 82.8% (n = 1573) were satisfied with receiving their results by letter, with 2.9% (n = 55) reporting dissatisfaction. 86.3% (n = 1640) stated it was their preferred communication method and 77.3% (n = 1469) reported that their letter contained the right amount of information. Participants from less deprived socioeconomic quintiles were more likely to report that the letter contained insufficient information and individuals aged ≥ 70 years were less likely to do so. Although 13.7% (n = 261) participants had discussed their results with their General Practitioner (GP) prior to the study visit, 83.9% (n = 219) of these participants were satisfied with receiving results by letter, with the same proportion preferring this communication method.We report high participant satisfaction with the reporting of pulmonary nodule results by letter in a LCS setting. We believe this provides a feasible route forward for large-scale screening programmes.

BackgroundCOPD is a major comorbidity in lung cancer screening (LCS) cohorts, with a high prevalence of undiagnosed COPD. Combining symptom assessment with spirometry in this setting may enable earlier diagnosis of clinically significant COPD and facilitate increased understanding of lung cancer risk in COPD. In this study, we wished to understand the prevalence, severity, clinical phenotype and lung cancer risk of individuals with symptomatic undiagnosed COPD in a LCS cohort.Methods16 010 current or former smokers aged 55–77 years attended a lung health check as part of the SUMMIT Study. A respiratory consultation and spirometry were performed alongside LCS eligibility assessment. Those with symptoms, no previous COPD diagnosis and airflow obstruction were labelled as undiagnosed COPD. Baseline low-dose computed tomography (LDCT) was performed in those at high risk of lung cancer (PLCOm2012score ≥1.3% and/or meeting USPSTF 2013 criteria).ResultsNearly one in five (19.7%) met criteria for undiagnosed COPD. Compared with those previously diagnosed, those undiagnosed were more likely to be male (59.1%versus53.2%; pversus47.6%; pversus68.4%; pversus77.9%; pConclusionsTargeted case-finding within LCS detects high rates of undiagnosed symptomatic COPD in those most at risk. Individuals with undiagnosed COPD are at high risk for lung cancer.

Mechanical ventilation is a primary therapy for patients with respiratory failure. However, incorrect ventilator settings can cause lung damage. Optimising ventilation while minimising risk is complex in practice. A common lung protective strategy is to titrate positive end-expiratory pressure (PEEP) to the point of minimum elastance. This process can result in additional available lung volume due to alveolar recruitment but comes with the risk of subjecting the lungs to excessive pressure and lung damage. Predictive elastance models can mitigate this risk by estimating airway pressure at a higher PEEP level. Due to the increased risk of barotrauma during inspiration, many models exclude expiration data. However, this section of the breath can include useful information about lung mechanics. This research investigates the impact that including expiration data into the fitting of a validated predictive elastance model will have on its ability to predict peak inspiratory pressure. Results showed that expiration data did not improve the efficacy of the model in this case with an increase in error (median (%)) of predicting peak inspiratory pressure through an increase in PEEP of 8 cmH2O from 6% to 16%.

Background : Elevated blood glucose (BG) concentrations (Hyperglycaemia) are a common complication in critically ill patients. Insulin therapy is commonly used to treat hyperglycaemia, but metabolic variability often results in poor BG control and low BG (hypoglycaemia). Objective: This paper presents a model-based virtual trial method for glycaemic control protocol design, and evaluates its generalisability across different populations. Methods: Model-based insulin sensitivity (SI) was used to create virtual patients from clinical data from three different ICUs in New Zealand, Hungary, and Belgium. Glycaemic results from simulation of virtual patients under their original protocol (self-simulation) and protocols from other units (cross simulation) were compared. Results: Differences were found between the three cohorts in median SI and inter-patient variability in SI. However, hour-to-hour intra-patient variability in SI was found to be consistent between cohorts. Self and cross-simulation results were found to have overall similarity and consistency, though results may differ in the first 24–48 h due to different cohort starting BG and underlying SI. Conclusions and Significance: Virtual patients and the virtual trial method were found to be generalisable across different ICUs. This virtual trial method is useful for in silico protocol design and testing, given an understanding of the underlying assumptions and limitations of this method.

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., SCOPUS: re.j, info:eu-repo/semantics/published

Hyperglycaemia, hypoglycaemia and glycaemic variability in critically ill patients are associated with increased mortality and adverse outcomes. Some studies have shown insulin therapy to control glycaemia has improved outcomes, but have proven difficult to repeat or achieve safely. STAR (Stochastic Targeted) is a model-based glycaemic control protocol using a stochastic model to forecast future distributions of insulin sensitivity (SI) based on its current value, to predict the range of future blood glucose outcomes for a given intervention. This study presents an improved 3D stochastic model, forecasting future distributions of SI based on its current value and prior variation. The percentage difference in the 5th, 50th, and 95th percentiles between the current 2D and new 3D models are compared. Results show the original 2D stochastic model is over-conservative for around 77% of the data, predominantly where prior variability was low. For higher prior variation (more than ±25% change in SI), the 3D stochastic model prediction range of future SI is wider. The new 3D model was found to have overall narrower 5th – 95th prediction ranges in SI, but to retain a similar per-patient (60 – 100%) and overall (92%) percentage of SI outcomes correctly predicted within these ranges. These results suggest the new 3D model is more patient-specific and will enable more optimal dosing, to increase both safety and performance. This improvement in forecasting may result in tighter and safer glycaemic control, improving performance within the STAR framework.

Specific pulmonary compliance is a widely used metric in neonatal mechanical ventilation, capturing the intrinsic ability of the lung to expand with pressure normalized by the functional residual capacity of the lung. Previous studies have found specific compliance to be similar for both neonates and adults. However, it is unclear what this implies about the structure and growth of the lung. To model the factors affecting specific compliance, a simple model is developed separating respiratory compliance into material-dependent and volume-dependent components. It provides reasonable estimates of compliance for experimental clinical data. The model demonstrates specific compliance is dependent on the alveolar tissue elastic modulus, Poisson’s ratio, septal thickness to alveolar radius ratio, and surface tension effects. The model also indicates specific compliance may not be entirely independent to lung volume as the lung grows, due to surface tension effects varying with alveolar diameter, and the possibility of a varying alveolar wall thickness to diameter ratio. To experimentally evaluate trends in specific compliance, data from 9 infants and 8 adult mechanical ventilation patients was recorded. Median compliance values were 0.51 mL/cmH2O/kg for adult patients and 0.78 mL/cmH2O/kg for neonatal patients (p = 0.098). This result demonstrates mechanically ventilated neonates and adults have statistically similar values of specific compliance, in agreement with prior studies. Treating specific compliance as a constant and using a least-squares fit, the trend of neonatal compliance increasing with body weight can be used to predict typical ranges of adult compliance values, indicating the non-linearity of compliance varying with volume is likely not significant in reality, supporting the use of specific compliance as a measure of the underlying tissue distensibility. However, the variation in values for each cohort suggest that specific compliance, like lung elastance, can vary significantly, and while overall trends hold, central values are not necessarily indicative. Overall, the model provides a unique analysis and insight using a simplified, mechanics relevant model, which is could be generalized to other studies.

Introduction: Hyperglycaemia is common in intensive care units (ICU’s) and is associated with negative clinical outcomes. One ICU aim is to provide the highest nutrition possible, while achieving normo-glycaemic blood glucose (BG) levels. The best nutrition delivery method and amount is controversial and highly debated. STAR is a proven glucose control (GC) method providing nutrition at or near to that achieved by the best ICU’s in the world, while also achieving safe and effective GC. This study analyses the key outcomes of STAR’s modulated insulin and nutrition protocol (STAR), and a clinically set nutrition protocol with STAR’s insulin only control (STAR-IO). This comparison separates the effect of controlling insulin and nutrition, vs using insulin alone to control glucose. Methods: BG measurements for patients treated with STAR (N = 221; 2011 - 2015) and STAR-IO (N = 194; 2016 - 2017) are evaluated for GC performance, safety, and workload. Mean nutrition delivery over the first 7 days of ICU stay for both cohorts was analysed, including assessment of unfed hours. Results: Patient cohorts were similar in age, sex and length of GC (median ~ 2days). The BG levels achieved by STAR were significantly lower than STAR-IO (median 6.8 vs 7.9 mmol/L, P Conclusions: STAR’s modulated insulin and nutrition protocol resulted in better GC performance, and equivalent safety, with far lower workload. It also delivered significantly higher nutrition on the first 2 days of ICU stay, where median time on GC is 2.2 days and STAR had 55% less unfed hours than STAR-IO.

Continuous glucose monitoring (CGM) devices have the potential to reduce nurse workload in the ICU while improving glycaemic control (GC). However, larger point accuracy errors that are inherent in these devices can lead to adverse consequences for GC performance and safety. There is a need to characterize this error, caused by drift and sensor noise, to evaluate the impact it would have on GC when using CGM devices instead of intermittent blood glucose (BG) measurements. This paper presents an auto-regressive model to model the drift and sensor noise, which can then be used to simulate further CGM sensor traces. Validation by comparison between the original clinical data and the simulated sensor data is used to provide qualitative and quantitative assurance of model accuracy. The auto-regressive model and simulated CGM sensor traces are found to represent the Glysure CGM sensor well, and capture all necessary sensor behaviors.

Hypoglycaemia, hyperglycaemia and blood glucose (BG) variability are associated with worsened outcomes in critical care. However, NICE-SUGAR trial showed no clinical benefit from intensive insulin therapy. This study compares the table-based NICE-SUGAR and model-based STAR protocols to assess their relative capability to achieve safe, effective control for all patients. Validated virtual patients (n=443) were used to simulate glycaemic outcomes of the NICE-SUGAR and STAR protocols. Key outcomes evaluate tightness and safety of control for all patients: %BG in 80–144 mg/dL range (PTR); Per-Patient Mean BG (PPM_BG); and Incidence of Hypoglycaemia (BG Per-protocol, STAR provided tight control, with higher PTR (90.7% vs. 78.3%) and tighter median [IQR] PPM_BG (112[106-119] vs. 117[106–137] mg/dL), and greater safety from hypoglycaemia (5 (1%) vs. 10 patients (2.5%)) compared to NICE-SUGAR simulations as per protocol. The 5-95th percentile range PPM_BG for NICE-SUGAR (97–185 mg/dL) showed ~5% of NICE-SUGAR patients had mean BG above 180mg/dL matching clinically reported performance. STAR’s 5th-90th PPM_BG percentile range was (97– 146 mg/dL). Measuring as recorded clinically, NICE-SUGAR had PTR of 77%, PPM_BG of 122 [110-140] mg/dL and 24(6%) of patients experienced hypoglycaemia. These results match clinically reported values well (mean BG 115 vs. 118 mg/dL clinically vs. simulation, clinically 7% of patients had a hypoglycaemic event). Glycaemic control protocols need to be both safe and effective for all patients before potential clinical benefits can be assessed. NICE-SUGAR clinical results do not match results expected from their protocol, and show reduced safety and performance in comparison to STAR.

Before continuous glucose monitoring (CGM) can be safely used to guide glycaemic control (GC) protocols the impact of suboptimal accuracy resulting from error or delay in calibration measurement, sensor drift, and delayed glucose diffusion must first be characterised. Characterising this error allows models to be formed so in-silico simulations can test the performance and safety of CGM driven glycaemic control protocols and examine best and worst scenarios. Existing models of CGM dynamics are now 10 years old and significant advances in sensor technology mean the level of error produced by these models no longer characterises the dynamics of more recent CGM devices. Therefore, this paper presents and validates a simple CGM error model based on the latest available CGM devices, as well as a generalisable sensor modeling approach. The model was created using 28 data sets from an observational pilot study of CGM in patients admitted to the Christchurch Hospital ICU during 2014-15. The model was characterised by empirical models of drift and noise. Autocorrelation was then used to validate the modelled data with the measured data. The median absolute difference between modelled and measured SG autocorrelation values was 0.007 with a range of 0 - 0.13. Hence, the model is judged to be suitable for use in simulation to provide better insight into using CGM to guide GC will effect control and its safety and performance. The overall modelling process is data driven and readily generalised to any other device.

Background Hyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often failed to replicate these results, and they were often unable to achieve consistent, safe control, raising questions about the benefit or harm of GC as well as the nature of the association of glycaemia with mortality and clinical outcomes. In this study, we evaluated if non-survivors are harder to control than survivors and determined if glycaemic outcome is a function of patient condition and eventual outcome or of the glycaemic control provided. Methods Clinically validated, model-based, hour-to-hour insulin sensitivity (SI) and its hour-to-hour variability (%ΔSI) were identified over the first 72 h of therapy in 145 patients (119 survivors, 26 non-survivors). In hypothesis testing, we compared distributions of SI and %ΔSI in 6-hourly blocks for survivors and non-survivors. In equivalence testing, we assessed if differences in these distributions, based on blood glucose measurement error, were clinically significant. Results SI level was never equivalent between survivors and non-survivors (95% CI of percentage difference in medians outside ±12%). Non-survivors had higher SI, ranging from 9% to 47% higher overall in 6-h blocks, and this difference became statistically significant as glycaemic control progressed. %ΔSI was equivalent between survivors and non-survivors for all 6-hourly blocks (95% CI of difference in medians within ±12%) and decreased in general over time as glycaemic control progressed. Conclusions Whereas non-survivors had higher SI levels, variability was equivalent to that of survivors over the first 72 h. These results indicate survivors and non-survivors are equally controllable, given an effective glycaemic control protocol, suggesting that glycaemia level and variability, and thus the association between glycaemia and outcome, are essentially determined by the control provided rather than by underlying patient or metabolic condition. Electronic supplementary material The online version of this article (doi:10.1186/s13054-017-1725-y) contains supplementary material, which is available to authorized users.

Background Models of human glucose–insulin physiology have been developed for a range of uses, with similarly different levels of complexity and accuracy. STAR (Stochastic Targeted) is a model-based approach to glycaemic control. Elevated blood glucose concentrations (hyperglycaemia) are a common complication of stress and prematurity in very premature infants, and have been associated with worsened outcomes and higher mortality. This research identifies and validates the model parameters for model-based glycaemic control in neonatal intensive care. Methods C-peptide, plasma insulin, and BG from a cohort of 41 extremely pre-term (median age 27.2 [26.2–28.7] weeks) and very low birth weight infants (median birth weight 839 [735–1000] g) are used alongside C-peptide kinetic models to identify model parameters associated with insulin kinetics in the NICING (Neonatal Intensive Care Insulin–Nutrition–Glucose) model. A literature analysis is used to determine models of kidney clearance and body fluid compartment volumes. The full, final NICING model is validated by fitting the model to a cohort of 160 glucose, insulin, and nutrition data records from extremely premature infants from two different NICUs (neonatal intensive care units). Results Six model parameters related to insulin kinetics were identified. The resulting NICING model is more physiologically descriptive than prior model iterations, including clearance pathways of insulin via the liver and kidney, rather than a lumped parameter. In addition, insulin diffusion between plasma and interstitial spaces is evaluated, with differences in distribution volume taken into consideration for each of these spaces. The NICING model was shown to fit clinical data well, with a low model fit error similar to that of previous model iterations. Conclusions Insulin kinetic parameters have been identified, and the NICING model is presented for glycaemic control neonatal intensive care. The resulting NICING model is more complex and physiologically relevant, with no loss in bedside-identifiability or ability to capture and predict metabolic dynamics.

Triplet lambs have reduced survival and most deaths occur due to starvation and exposure, but there are a few simple and safe interventions. We hypothesised that buccal dextrose gel would increase blood glucose concentration, vigour, survival and early feeding in less vigorous triplet lambs. Triplet lambs meeting criteria for decreased vigour were randomised to 40% dextrose or placebo gel 1 mL/kg via the buccal mucosa at 1 hour of age. Primary outcome was survival at 3 hours. An additional study exploring the effect of gel on interstitial glucose concentrations was assessed by continuous glucose monitoring in twin lambs. Lambs randomised to dextrose gel did not have higher blood glucose concentrations or better survival than those randomised to placebo. Low temperature at 1 hour after birth, rather than birthweight or blood glucose concentrations, was associated with decreased survival. Interventions to address hypothermia rather than hypoglycaemia may be most effective in improving survival in le...

Hyperglycaemia is a common complication of prematurity in neonatal intensive care. Physiological model-based protocols offer great potential for allowing patient-specific insulin therapy. One aspect of such models is the appearance of glucose in the bloodstream from enteral feeds. A two compartment model is used to capture glucose transport in the stomach and gut. The rate of transfer of glucose from the stomach compartment to the gut can be determined from gastric emptying, which in term infants can be modelled with a half life of around 20 minutes (d 1 =0.035 min −1 ). In premature infants gastric emptying is slower, so can be modelled with a half life of around 40 minutes (d 1 =0.017 min −1 ). CGM data showing the appearance of glucose from enteral feeds in term and premature infants was used to estimate the rate constant for glucose absorption from the gut to the bloodstream. This absorption rate constant was found to vary between infants and over time. The median absorption constant was found to be d 2 =0.014 min −1 , which corresponds to an absorption half life of 50 minutes. If fitting error that fell within blood glucose measurement error was considered, the range of possible absorption half lives of absorption was 30-63 minutes.

Inability to maintain blood glucose concentrations (BG) to a normal range is a common complication of prematurity and stress in neonatal intensive care units (NICUs). STAR (Stochastic TARgeted) glycaemic control uses physiological models to capture and predict patient glycaemic behaviour, and thus dose insulin safely and effectively for tight glycaemic control. STAR in the NICU has in past been based on a simple NICU model. In this study, clinical data is used to identify and validate insulin kinetic parameters of the more physiologically descriptive NICING (Neonatal Intensive Care Insulin-Nutrition-Glucose) model. C-peptide, plasma insulin and BG from a cohort of 41 extremely pre-term (median age 27.2 [26.2 - 28.7] weeks) and very low birth-weight infants (median birth weight 839 [735 - 1000] g) was used alongside C-peptide kinetic models to identify model parameters associated with insulin kinetics in the NICING model. These kinetic parameters in the NICING model were validated by fitting the model to a cohort of 160 glucose, insulin, and nutrition data records from extremely premature infants from two different NICUs. The model fits data and predicts changes in BG in a manner similar to the previous NICU model, indicating that it will perform safely and effectively in the clinical setting. However, the NICING model is more physiologically descriptive, capturing more insulin kinetics and allowing consistency with existing adult ICU glycaemic control protocols and other diabetes models.

Stress-induced hyperglycemia is prevalent in critical care, even in patients with no history of diabetes. Control of blood glucose level with tight insulin therapy has been shown to reduce incidences of hyperglycemia leading to reduced mortality and improved clinical outcomes. STAR is a tablet-based glucose control protocol with a specialized user interface into which insulin and nutrition information can be entered and predicted. This research describes the first clinical pilot trial of STAR approach in International Islamic University Hospital, Kuantan, Malaysia. The clinically specified target for blood glucose level is between 4.4 and 8.0 mmol/L. Seven episodes (of 359 h) were recruited based on the need for glucose control. Overall, 43.93% of measurement are in the range of 4.4–8.0 mmol/L band. The blood glucose median is 8.30 [6.32–10.00] mmol/L with only 1 patient having below than 2.22 mmol/L which is the guaranteed minimum risk level. This pilot study shows that STAR protocol is a patient specific approach that provides a good glycemic control in critically ill patients. Nevertheless, its implementation in Malaysian intensive care environments requires modifications and improvements in certain areas.

Continuous glucose monitoring (CGM) is well established in the management of diabetes mellitus, but its role in neonatal glycaemic control is less clear. CGM has provided important insights about neonatal glucose metabolism, and there is increasing interest in its clinical use, particularly in preterm neonates and in those in whom glucose control is difficult. Neonatal glucose instability, including hypoglycaemia and hyperglycaemia, has been associated with poorer neurodevelopment, and CGM offers the possibility of adjusting treatment in real time to account for individual metabolic requirements while reducing the number of blood tests required, potentially improving long-term outcomes. However, current devices are optimised for use at relatively high glucose concentrations, and several technical issues need to be resolved before real-time CGM can be recommended for routine neonatal care. These include: 1) limited point accuracy, especially at low or rapidly changing glucose concentrations; 2) calibration methods that are designed for higher glucose concentrations of children and adults, and not for neonates; 3) sensor drift, which is under-recognised; and 4) the need for dynamic and integrated metrics that can be related to long-term neurodevelopmental outcomes. CGM remains an important tool for retrospective investigation of neonatal glycaemia and the effect of different treatments on glucose metabolism. However, at present CGM should be limited to research studies, and should only be introduced into routine clinical care once benefit is demonstrated in randomised trials.

Accurate glycaemic control (AGC) has been shown to be beneficial to the outcomes of critically ill patients. These benefits may also extend to patients in less acute wards, particularly those with existing diabetes. However, the clinical demands of an intensive care glycaemic control protocol are not appropriate for the general wards where the nurse-to-patient ratio is much lower and patients do not typically have an intravenous line available for insulin delivery. Thus, there is a need for a safe, effective glycemic control protocol tailored to the needs of general wards to enable appropriate care for diabetic patients and further testing of the benefits of glycaemic control for this cohort. This paper presents the development and testing of such a protocol for glycaemic control in the general wards. The DRAGONS protocol (Dynamic Regulation for Accurate Glycaemic-control Optimising iNsulin Subcutaneously) was designed to use subcutaneous insulin and only require blood glucose (BG) measurements every four hours, while maintaining BG concentrations within the range 4.4-8.0 mmol/L. Virtual trial simulation indicated an expected time in the target band of 73.0%, with

Elevated blood glucose concentrations (BG) (Hyperglycaemia) are a common complication of prematurity in extremely low birth weight neonates in the neonatal intensive care unit (NICU), and are associated with increased mortality and morbidity. Insulin therapy allows glucose tolerance and weight gain to be increased. However, insulin therapy is commonly associated with a significant increase in low BG events (hypoglycaemia), which is also associated with adverse outcomes. Controlling BG levels via nutrition restriction reduces infant growth and is thus undesirable. STAR (Stochastic TARgeted) is a model-based glycaemic framework that mitigates the risks of hypoglycaemia through quantification of current insulin sensitivity and future variability. From August 2008 to December 2012 40 patients totaling 61 glycaemic episodes were treated with STAR in the NICU (STAR-NICU). Percentage time in the clinically targeted 4.0-8.0 mmol/L band was 62%, a 14% increase compared to retrospective data and hyperglycaemia (BG>10.0mmol/L) was halved. Overall incidence of severe hypoglycemia (BG

Background: STAR (stochastic targeted) is a glycemic control model-based framework for critically ill neonates that has shown benefits in reducing hypoglycemia and hyperglycemia. STAR uses a stochastic matrix method to forecast future changes in a patient's insulin sensitivity and then applies this result to a physiological model to select an optimal insulin treatment. Nasogastric aspiration may be used as an indicator to suggest periods of care when enteral feed absorption is compromised, improving the performance of glycemic control. An analysis has been carried out to investigate the effect of poorly absorbed feeds on glycemic control. Method: Clinical data were collected from eight patients on insulin therapy and enteral feed, which included large or significantly milky aspirates. Patients had a median gestational age of 25 weeks and postnatal age of 5.5 days. Virtual patients were created using the NICING model, and insulin sensitivity ( SI) profiles were fit. Alternative feed profiles were generated whereby enteral feed absorption was redistributed with time to account for poor feed absorption. The effect of poor feed absorption, as indicated by aspirates, is investigated. Results: The average percentage change of SI 4 h before a significant aspirate was 1.16%, and 1.49% in the 4 h following the aspirate. No distinct relationship was found between the fractional change in SI and the volume of the aspirate. Accounting for aspirates had a clinically negligible impact on glycemic control in virtual trials. Conclusion: Accounting for aspirates by manipulating enteral feed profiles had a minimal influence on both modeling and controlling glycemia in neonates. The impact of this method is clinically insignificant, suggesting that a population constant for the rate of glucose absorption in the gut adequately models feed absorption within the STAR framework.

Hyperglycaemia is a common complication of prematurity and stress in neonatal intensive care units (NICUs). It has been linked to worsened outcomes and mortality. There is currently no universally accepted best practice glycaemic control method, with many protocols lacking patient specificity or relying heavily on ad hoc clinical judgment from clinical staff who may be caring or overseeing care for several patients at once. The result is persistent hypoglycaemia and poor control. This research presents the virtual trial design and optimisation of a stochastic targeted (STAR) approach to improve performance and reduce hypoglycaemia. Clinically validated virtual trials based on NICU patient data (N = 61 patients, 7006 h) are used to develop and optimise a STAR protocol that improves on current STAR-NICU performance and reduce hypoglycaemia. Five approaches are used to maximise the stochastic range of BG outcomes within 4.0–8.0 mmol/L, and are designed based on an overall cohort risk to provide clinically specified risk (5%) of BG above or below a clinically specified level. The best protocol placed the 5th percentile BG outcome for an intervention on 4.0 mmol/L band. The optimised protocol increased %BG in the 4.0–8.0 mmol/L band by 3.5% and the incidence of BG

Transplant recipients require immunosuppression to prevent allograft rejection, placing them at risk of opportunistic infections including fungal infection. Difficulties in managing fungal infections include: establishing diagnosis, poor treatment response, drug interactions and toxicity. We report our single centre experience of treating fungal infections using systemic non-Amphotericin current generation antifungals. Patients receiving inpatient antifungal therapy from September 2005 to December 2010 were identified from pharmacy records. Fungal infections were retrospectively classified according to European Organization for Research and Treatment of Cancer (EORTC) criteria. Treatment outcomes were classified in a manner similar to those used in clinical trials. Two hundred and forty-nine recipients received antifungal treatment, 204 lungs and 45 hearts. One hundred and one patients received Voriconazole, 82 Caspofungin and 65 received both agents. One patient was unsuccessfully treated with additional Amphotericin. Treatment duration varied from 1.5 to 12 weeks. One hundred and sixty-five patients had a complete response, 24 had a partial response and in 60 patients treatment was unsuccessful. The response to systemic non-Amphotericin based antifungal therapy was high. We propose that diagnostic criteria without positive identification of a fungus allow treatment to be started early with few clinically relevant side effects.

Hyperglycaemia is a common complication of prematurity and stress in neonatal intensive care units (NICUs). It has been linked to worsened outcomes and mortality. There is currently no universally accepted best practice glycaemic control method, with many protocols lacking patient specificity and relying heavily on clinical judgment. The result is persistent hypoglycaemia and poor control. This research presents the virtual trial design and optimisation of a stochastic targeted (STAR) approach to improve performance and reduce hypoglycaemia. Clinically validated virtual trials based on NICU patient data (N = 61 patients, 7006 hours) are used to develop and optimise a STAR protocol that improves on current STAR-NICU performance and reduce hypoglycaemia. Five approaches are used to maximize the stochastic range of BG outcomes within 4.0-8.0mmol/L, and are designed based on an overall cohort risk to provide clinically specified risk (5%) of BG above or below a clinically specified level. The best protocol placed the 5th percentile BG outcome for an intervention on 4.0mmol/L band. The optimised protocol increased %BG in the 4.0-8.0mmol/L band by 3.5% and decreased the incidence of BG

Hyperglycaemia is a prevalent complication in the neonatal intensive care unit (NICU) and is associated with worsened outcomes. It occurs as a result of prematurity, under-developed endogenous glucose regulatory systems, and clinical stress. The stochastic targeting (STAR) framework provides patient-specific, model-based glycaemic control with a clinically proven level of confidence on the outcome of treatment interventions, thus directly managing the risk of hypo- and hyper-glycaemia. However, stochastic models that are over conservative can limit control performance. Retrospective clinical data from 61 episodes (25 retrospective to STAR, and 36 from a prospective-STAR blood glucose control study) of insulin therapy in very-low birth weight (VLBW) and extremely-low birth weight (ELBW) neonates are used to create a new stochastic model of model-based insulin sensitivity ( S I [L/mU/min]). Sub-cohort models based on gestational age (GA) and birth weight (BW) are also created. Performance is assessed by the percentage of patients who have 90% of actual intra-patient variability in S I captured by the 90% confidence bands of the cohort based (inter-patient) stochastic variability model created. This assessment measures per-patient accuracy for any given cohort model. Per-patient coverage trends were very similar between prospective and retrospective cohorts, providing a measure of external validation of cohort similarity. Per-patient coverage was improved through the use of BW and GA dependent stochastic models, which ensures that the stochastic models more accurately capture both inter- and intra-patient variability. Stochastic models based on insulin sensitivities during insulin treatment periods are tighter, and give better and safer glycaemic control. Overall it seems that inter-patient variation is more significant than intra-patient variation as a limiting factor in this stochastic forecasting model, and a small number of patients are essentially different in behaviour. More patient specific methods, particularly in the modelling of endogenous insulin and glucose production, will be required to further improve forecasting and glycaemic control.

Background: Hyperglycaemia is a common complication of prematurity and is associated with neonatal mortality and morbidity, yet the aetiology is incompletely understood. C-peptide has been used in adults to estimate endogenous insulin secretion due to its simple clearance kinetics. Objective: To determine insulin secretion calculated from plasma C-peptide concentrations in hyperglycaemic preterm babies. Methods: This is a retrospective analysis of a cohort of 41 very preterm babies with a median gestational age of 27.2 weeks (26.2-28.7) enrolled in a randomised controlled trial of tight glycaemic control when they developed hyperglycaemia (2 consecutive blood glucose concentrations, BGC, > 8.5 mmol·l-1). Insulin secretion was determined using a steady state analysis of a 2-compartment C-peptide kinetic model. Results: BGC, plasma insulin concentration, plasma C-peptide concentrations, and insulin secretion were higher at randomisation than 1-2 weeks following randomisation (p ≤ 0.02). Insulin secretion was higher in girls at 11.7 mU·l-1·kg-1·min-1 (5.3-18.7) vs. 4.7 mU·l-1·kg-1·min-1 (2.1-8.3; p < 0.005), with no difference in clinical characteristics, BGC, plasma insulin concentration, or nutrition between the sexes (p > 0.25). Insulin secretion was lower in samples taken during exogenous insulin delivery at 3.7 mU·l-1·kg-1·min-1 (1.8-6.9) vs. 9.8 mU·l-1·kg-1·min-1 (4.7-17.8; p = 0.02). Conclusions: Insulin secretion was higher when babies had higher BGC, indicating that endogenous insulin secretion is sensitive to BGC. Girls had higher insulin secretion, at similar blood glucose and plasma insulin concentrations, than boys.

Hyperglycaemia is a common complication of stress and prematurity in extremely low-birth-weight infants. Model-based insulin therapy protocols have the ability to safely improve glycaemic control for this group. Estimating non-insulin-mediated brain glucose uptake by the central nervous system in these models is typically done using population-based body weight models, which may not be ideal.A head circumference-based model that separately treats small-for-gestational-age (SGA) and appropriate-for-gestational-age (AGA) infants is compared to a body weight model in a retrospective analysis of 48 patients with a median birth weight of 750g and median gestational age of 25 weeks. Estimated brain mass, model-based insulin sensitivity (SI) profiles, and projected glycaemic control outcomes are investigated. SGA infants (5) are also analyzed as a separate cohort.Across the entire cohort, estimated brain mass deviated by a median 10% between models, with a per-patient median difference in SI of 3.5%. For the SGA group, brain mass deviation was 42%, and per-patient SI deviation 13.7%. In virtual trials, 87-93% of recommended insulin rates were equal or slightly reduced (Δ0.16mU/h) under the head circumference method, while glycaemic control outcomes showed little change.The results suggest that body weight methods are not as accurate as head circumference methods. Head circumference-based estimates may offer improved modelling accuracy and a small reduction in insulin administration, particularly for SGA infants.

BACKGROUND Both stress and prematurity can induce hyperglycemia in the neonatal intensive care unit, which, in turn, is associated with worsened outcomes. Endogenous glucose production (EGP) is the formation of glucose by the body from substrates and contributes to blood glucose (BG) levels. Due to the inherent fragility of the extremely low birth weight (ELBW) neonates, true fasting EGP cannot be explicitly determined, introducing uncertainty into glycemic models that rely on quantifying glucose sources. Stochastic targeting, or STAR, is one such glycemic control framework. METHODS A literature review was carried out to gather metabolic and EGP values on preterm infants with a gestational age (GA)

Transplant recipients require immunosuppression to prevent allograft rejection, placing them at risk of opportunistic infections including fungal infection. Difficulties in managing fungal infections include: establishing diagnosis, poor treatment response, drug interactions and toxicity. We report our single centre experience of treating fungal infections using systemic non-Amphotericin current generation antifungals. Patients receiving inpatient antifungal therapy from September 2005 to December 2010 were identified from pharmacy records. Fungal infections were retrospectively classified according to European Organization for Research and Treatment of Cancer (EORTC) criteria. Treatment outcomes were classified in a manner similar to those used in clinical trials. Two hundred and forty-nine recipients received antifungal treatment, 204 lungs and 45 hearts. One hundred and one patients received Voriconazole, 82 Caspofungin and 65 received both agents. One patient was unsuccessfully treated with additional Amphotericin. Treatment duration varied from 1.5 to 12 weeks. One hundred and sixty-five patients had a complete response, 24 had a partial response and in 60 patients treatment was unsuccessful. The response to systemic non-Amphotericin based antifungal therapy was high. We propose that diagnostic criteria without positive identification of a fungus allow treatment to be started early with few clinically relevant side effects.