Does Facemask Impact Diagnostic During Pulmonary Auscultation?

peer reviewedFacemasks have been widely used in hospitals, especially since the emergence of the coronavirus 2019 (COVID-19) pandemic, often severely affecting respiratory functions. Masks protect patients from contagious airborne transmission, and are thus more specifically important for chronic respiratory disease (CRD) patients. However, masks also increase air resistance and thus work of breathing, which may impact pulmonary auscultation and diagnostic acuity, the primary respiratory examination. This study is the first to assess the impact of facemasks on clinical auscultation diagnostic. Lung sounds from 29 patients were digitally recorded using an electronic stethoscope. For each patient, one recording was taken wearing a surgical mask and one without. Recorded signals were segmented in breath cycles using an autocorrelation algorithm. In total, 87 breath cycles were identified from sounds with mask, and 82 without mask. Time-frequency analysis of the signals was used to extract comparison features such as peak frequency, median frequency, band power, or spectral integration. All the features extracted in frequency content, its evolution, or power did not significantly differ between respiratory cycles with or without mask. This early stage study thus suggests minor impact on clinical diagnostic outcomes in pulmonary auscultation. However, further analysis is necessary such as on adventitious sounds characteristics differences with or without mask, to determine if facemask could lead to no discernible diagnostic outcome in clinical practice

Risk and Reward: Extending stochastic glycaemic control intervals to reduce workload

peer reviewedBackground STAR is a model-based, personalised, risk-based dosing approach for glycaemic control (GC) in critically ill patients. STAR provides safe, effective control to nearly all patients, using 1-3 hourly measurement and intervention intervals. However, the average 11-12 measurements per day required can be a clinical burden in many intensive care units. This study aims to significantly reduce workload by extending STAR 1-3 hourly intervals to 1 to 4-, 5-, and 6- hourly intervals, and evaluate the impact of these longer intervals on GC safety and efficacy, using validated in silico virtual patients and trials methods. A Standard STAR approach was used which allowed more hyperglycaemia over extended intervals, and a STAR Upper Limit Controlled approach limited nutrition to mitigate hyperglycaemia over longer intervention intervals. Results Extending STAR from 1-3 hourly to 1-6 hourly provided high safety and efficacy for nearly all patients in both approaches. For STAR Standard, virtual trial results showed lower % blood glucose (BG) in the safe 4.4-8.0 mmol/L target band (from 83% to 80%) as treatment intervals increased. Longer intervals resulted in increased risks of hyper- (15% to 18% BG > 8.0 mmol/L) and hypo- (2.1% to 2.8% of patients with min. BG < 2.2 mmol/L) glycaemia. These results were achieved with slightly reduced insulin (3.2 [2.0 5.0] to 2.5 [1.5 3.0] U/h) and nutrition (100 [85 100] to 90 [75 100] % goal feed) rates, but most importantly, with significantly reduced workload (12 to 8 measurements per day). The STAR Upper Limit Controlled approach mitigated hyperglycaemia and had lower insulin and significantly lower nutrition administration rates. Conclusions The modest increased risk of hyper- and hypo- glycaemia, and the reduction in nutrition delivery associated with longer treatment intervals represent a significant risk and reward trade-off in GC. However, STAR still provided highly safe, effective control for nearly all patients regardless of treatment intervals and approach, showing this unique risk-based dosing approach, modulating both insulin and nutrition, to be robust in its design. Clinical pilot trials using STAR with different measurement timeframes should be undertaken to confirm these results clinically

Women have greater (Metabolic) Stress Response than Men

Objective: Stress hyperglycaemia is frequent in intensive care unit (ICU) patients and associated with increased morbidity and mortality. Glycemic control (GC) has proven difficult due to high levels of inter- and intra- patient variability in response to insulin. However, despite anecdotes, no one has studied if males and females are easier/harder to control. This study examines differences in clinically validated insulin sensitivity (SI) and its variability between males and females as surrogates of control difficulty. Method: Data from N=145 SPRINT GC patients is analysed for the first 72hours of stay. Demographic characteristics of the male (N=91) and female (N=54) sub-cohorts are similar (age, mortality, injury severity, ICU length of stay, GC duration), as well as GC outcomes (median BG, %BG in/out target band, workload). SI is identified hourly and its hour-to-hour percentage variability is computed (%ΔSI). Due to large data samples, the 95%CI of difference in bootstrapped medians in SI and %ΔSI is used for hypothesis testing to a significance level of p<0.05. Equivalence testing is used to determine whether this difference is clinically significant. Results: Females are more insulin resistant (lower SI) than males (2.5e-4[1.5e-4 4.0e-4] vs. 3.1 e-4[1.7e-4 5.5e-4] L/mU/min). This difference is statistically different and clinically not equivalent. Conversely, %ΔSI is not significantly different (2[-17 22]% vs. 3[-14 25]%), and any difference can be considered clinically equivalent. These observations are also true when data is analysed over 6-h blocks. Conclusions: Females are more insulin resistant than males but have equivalent SI variability. The difference in SI levels suggests either higher endogenous glucose production and/or lower insulin secretion rates for females. Since severity of injury and glycemic outcomes are similar across both groups, the results suggest a stronger stress response to injury for female patients

Improved 3D Stochastic Modelling of Insulin Sensitivity Variability for Improved Glycaemic Control

Glycaemic control in intensive care unit has been associated with improved outcomes. Metabolic variability is one of the main factors making glycaemic control hard to achieve safely. STAR (Stochastic Targeted) is a model-based glycaemic control protocol using a stochastic model to predict likely distributions of future insulin sensitivity based on current patient-specific insulin sensitivity, enabling unique risk-based dosing. This study aims to improve insulin sensitivity forecasting by presenting a new 3D stochastic model, using current and previous insulin sensitivity levels. The predictive power and the percentage difference in the 5th-95th percentile prediction width are compared between the two models. Results show the new model accurately predicts insulin sensitivity variability, while having a median 21.7% reduction of the prediction range for more than 73% of the data, which will safely enable tighter control. The new model also shows trends in insulin sensitivity variability. For previous stable or low insulin sensitivity changes, future insulin sensitivity tends to remain more stable (tighter prediction ranges), whereas for higher previous variation of insulin sensitivity, higher potential future variation of insulin sensitivity is more likely (wider prediction ranges). These results offer the opportunity to better assess and predict future evolution of insulin sensitivity, enabling more optimal risk-based dosing approach, potentially resulting in tighter and safer glycaemic control using the STAR framework

Insulin sensitivity in critically ill patients: are women more insulin resistant?

peer reviewedGlycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care. Methods Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (P < 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal. Results Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent. Conclusion Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.NZ National Science Challenge 7; MedTech CoRE program; EU H2020 R&I programme (MSCA-RISE-2019 call, #872488)–DCP

3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation

peer reviewedBackground: The challenges of glycaemic control in critically ill patients have been debated for 20 years. While glycaemic control shows benefits, inter- and intra-patient metabolic variability results in increased hypoglycaemia and glycaemic variability, both increasing morbidity and mortality. Hence, current recommendations for glycaemic control target higher glycaemic ranges, guided by the fear of harm. Lately, studies have proven the ability to provide safe, effective control for lower, normoglycaemic, ranges, using model-based computerised methods. Such methods usually identify patient-specific physiological parameters to personalize titration of insulin and/or nutrition. The Stochastic-Targeted (STAR) glycaemic control framework uses patient-specific insulin sensitivity and a stochastic model of its future variability to directly account for both inter- and intra-patient variability in a risk-based insulin-dosing approach. Results: In this study, a more personalized and specific 3D version of the stochastic model used in STAR is compared to the current 2D stochastic model, both built using kernel-density estimation methods. Fivefold cross validation on 681 retrospective patient glycaemic control episodes, totalling over 65,000 h of control, is used to determine whether the 3D model better captures metabolic variability, and the potential gain in glycaemic outcome is assessed using validated virtual trials. Results show that the 3D stochastic model has similar forward predictive power, but provides significantly tighter, more patient-specific, prediction ranges, showing the 2D model overconservative > 70% of the time. Virtual trial results show that overall glycaemic safety and performance are similar, but the 3D stochastic model reduced median blood glucose levels (6.3 [5.7, 7.0] vs. 6.2 [5.6, 6.9]) with a higher 61% vs. 56% of blood glucose within the 4.4–6.5 mmol/L range. Conclusions: This improved performance is achieved with higher insulin rates and higher carbohydrate intake, but no loss in safety from hypoglycaemia. Thus, the 3D stochastic model developed better characterises patient-specific future insulin sensitivity dynamics, resulting in improved simulated glycaemic outcomes and a greater level of personalization in control. The results justify inclusion into ongoing clinical use of STAR

Patient-specific metabolic variability and precision glycaemic control in critical care.

Critically ill patients often experience stress-induced hyperglycaemia. Elevated blood glucose levels are associated with increased morbidity and mortality. Glycaemic control demonstrated improved outcomes for these patients. However, other studies failed to replicate the results, primarily blaming the increased risk of hypoglycaemia and glycaemic variability, both associated with worse outcomes. These confounding outcomes have resulted in acceptance of hyperglycaemia and reduced outcomes, causing ongoing debate on glycaemic control. The goal of the thesis is to define what makes glycaemic control hard to achieve safely, prove safe, effective control impacts patient outcome, and demonstrate it is possible to achieve safe, effective control for all patients, despite targeting lower glycaemic ranges. Metabolic variability is the main factor making glycaemic control hard to achieve safely. More specifically, sudden changes in patient-specific response to insulin (intra-patient variability) can lead to severe hyper- and hypo- glycaemia. Novel analysis of model-based insulin sensitivity and its variability clearly showed while inter-patient variability can be significantly different across patients, intra -patient variability is equivalent. Therefore, no patient is harder nor easier to control, and thus all patients should be able to benefit from similar quality of control. In turn, conclusions on glycaemic control from studies failing to do so may be biased due to poor protocol design, rather than physiological factors related to severity and outcome. Intra-patient variability is still very large, and it is not possible to discriminate more and less variable patients, reducing the quality of control deliverable in practical clinical scenarios. This research developed a novel 3D stochastic model to optimally segregate more and less variable patients based on prior behaviours. This approach enabled significantly improved, and tighter prediction of risks associated with a given insulin and/or nutrition intervention. Clinical trial results in NZ have shown improved control and safety using this new 3D stochastic model. To demonstrate these outcomes, a clinical trial using STAR, a model-based, patient-specific glycaemic control framework, was designed and implemented at the University Hospital of Liège. Results showed STAR succeeded in providing safe, effective control to virtually all patients, despite targeting lower target bands associated with better outcomes. However, increased workload compared to the standard protocol was identified as a limitation. Finally, this thesis develops a means to dramatically increase the STAR measurement interval from 1 - 3 hourly to 1-6 hourly without significantly degrading performance or safety. Virtual trials clearly defined the risk and reward trade-off between control performance, patient safety, workload, and nutrition. This result allows clinical staff to choose from a far wider range of options and approaches to provide safe, effective control, with clearly defined risk trade-offs. Overall, a series of analyses and clinical trials have shown safe, effective control is necessary to improve outcomes, and can be achieved for all patients. These outcomes are possible using patient -specific, model-based glycaemic control protocols developed in this thesis, which directly account for both intra- and inter- patient variability and reduce workload

Patient-Specific Metabolic Variability and Precision Glycaemic Control in Critical Care

Critically ill patients often experience stress-induced hyperglycaemia. Elevated blood glucose levels are associated with increased morbidity and mortality. Glycaemic control demonstrated improved outcomes for these patients. However, other studies failed to replicate the results, primarily blaming the increased risk of hypoglycaemia and glycaemic variability, both associated with worse outcomes. These confounding outcomes have resulted in acceptance of hyperglycaemia and reduced outcomes, causing ongoing debate on glycaemic control. The goal of the thesis is to define what makes glycaemic control hard to achieve safely, prove safe, effective control impacts patient outcome, and demonstrate it is possible to achieve safe, effective control for all patients, despite targeting lower glycaemic ranges. Metabolic variability is the main factor making glycaemic control hard to achieve safely. More specifically, sudden changes in patient-specific response to insulin (intra-patient variability) can lead to severe hyper- and hypo- glycaemia. Novel analysis of model-based insulin sensitivity and its variability clearly showed while inter-patient variability can be significantly different across patients, intra-patient variability is equivalent. Therefore, no patient is harder nor easier to control, and thus all patients should be able to benefit from similar quality of control. In turn, conclusions on glycaemic control from studies failing to do so may be biased due to poor protocol design, rather than physiological factors related to severity and outcome. Intra-patient variability is still very large, and it is not possible to discriminate more and less variable patients, reducing the quality of control deliverable in practical clinical scenarios. This research developed a novel 3D stochastic model to optimally segregate more and less variable patients based on prior behaviours. This approach enabled significantly improved, and tighter prediction of risks associated with a given insulin and/or nutrition intervention. Clinical trial results in NZ have shown improved control and safety using this new 3D stochastic model. To demonstrate these outcomes, a clinical trial using STAR, a model-based, patient-specific glycaemic control framework, was designed and implemented at the University Hospital of Liège. Results showed STAR succeeded in providing safe, effective control to virtually all patients, despite targeting lower target bands associated with better outcomes. However, increased workload compared to the standard protocol was identified as a limitation. Finally, this thesis develops a means to dramatically increase the STAR measurement interval from 1-3 hourly to 1-6 hourly without significantly degrading performance or safety. Virtual trials clearly defined the risk and reward trade-off between control performance, patient safety, workload, and nutrition. This result allows clinical staff to choose from a far wider range of options and approaches to provide safe, effective control, with clearly defined risk trade-offs. Overall, a series of analyses and clinical trials have shown safe, effective control is necessary to improve outcomes, and can be achieved for all patients. These outcomes are possible using patient-specific, model-based glycaemic control protocols developed in this thesis, which directly account for both intra- and inter- patient variability and reduce workload

Insulin Sensitivity Profile as a Marker for Reduced Outcome in the Neonatal Intensive Care Unit

Objective: Hyperglycemia in neonatal intensive care units is associated with mortality and morbidity. This trial aims to use machine learning methods to provide a prediction of outcomes in hyperglycemic neonates, based on model-based metabolic (glycemic control) data as a non-invasive marker. Method: Glycemic control data from 44 patients (4499 hours) under the STAR-NICU or STAR-GRYPHON model-based glycemic controllers from Christchurch Women’s Hospital were used. Predictive models were built using attributes from hourly, patient-specific, model-based insulin sensitivity. Among these patients, 12 contracted sepsis, 8 suffered from intraventricular hemorrhage (IVH), and 8 died. The methods used were classification trees and K-nearest neighbors. The efficacy of the models was assessed evaluating sensitivity, specificity and accuracy. Result: Mean insulin sensitivity was different among different sub-groups: 7.51×10-4, 5.47×10-4, 2.42×10-4, and 7.50×10-4L/mU/min for patients who were septic, had IVH>grade 1, non-survivors, and survivors respectively. Variability assessed as IQR range was also different between groups, with 1.00×10-4, 4.99×10-5, 4.22×10-5, and 9.09×10-5L/mU/min respectively. It was possible to predict mortality with 85% sensitivity after the first 15 hours, and (later proven) sepsis with sensitivity of 80% within 20 hours. Conclusion: A clinically validated model-based insulin sensitivity measure and its variability, may provide information about patient condition and possible outcome, despite modeling limitations. This study emphasized the potential of machine learning to provide information on degrading patient condition and worsened outcome, as an alert to provide more intensive care

Improved Blood Glucose Forecasting Models using Changes in Insulin Sensitivity in Intensive Care Patients

Introduction: Hyperglycaemia, hypoglycaemia and glycaemic variability are associated with worsened outcomes and increased mortality in intensive care units. Glycaemic control (GC) using insulin therapy has shown improved outcomes, but have been proven difficult to repeat or achieve safely. STAR (Stochastic TARgeted) is a model-based glycaemic control protocol using a stochastic model to forecast distributions of likely future changes in insulin sensitivity (SI) based on its current value. This can be used to determine likely future blood glucose (BG) levels for a given intervention, enabling the most optimal dose selection that best overlaps a clinically defined BG target band. This study presents a novel 3D model capable to predict likely future distribution of SI using both current SI and its prior variability (%ΔSI). Methods: Metabolic data from 3 clinical ICU cohorts totalling 819 episodes and 68629 hours of treatment under STAR and SPRINT protocols are used in this study. Data triplets (%ΔSIn, SIn, SIn+1) are created and binned together in a range of %ΔSI = [-100%, 200%] and SIn = [1.0e-7, 2.1e-3] in bin sizes of %ΔSI = 10% and SIn = 0.5e-4. The 5th, 50th, and 95th percentile of SIn+1 are determined for each bin where data density is high enough (>100 triplets) and compared to the previous stochastic model. The predictive power of the two models are compared by computing median [IQR] per-patient percentage prediction of SI within the 5th-95th and 25th-75th percentile ranges of model predictions. Results: Results show the previous model is over-conservative for ~77% of the data, mainly where %ΔSI is within an absolute 25% change. The percentage change in the 90% CI width in this region is reduced by ~25-40%. Conversely, non-conservative regions are also identified, with 90% CI width increased up to ~80%. Predictive power is similar for both model (60.3% [47.8%, 71.5%] vs. 51.2 [42.9%, 59.2%] within 25th-75th and 93.6% [85.7%, 97.3%] vs. 90.7% [84.4%, 94.6%] within 5th-95th range). Conclusions: The new 3D model achieved similar predictive power as the previous model by reducing the 5th-95th percentile prediction range for 77% of the data, predominantly where SI is stable. If the conservatism of the previous model reduces risk of hypoglycaemia, it also inhibits the controller’s ability to reduce BG to the normal range by safely using more aggressive dosing. The 3D new model thus better characterises patient-specific response to insulin, and allows more optimal dosing, increasing performance and safety