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See detailModel-Based Weaning Tests for VA-ECLS Therapy
Habran, Simon; Desaive, Thomas ULiege; MORIMONT, Philippe ULiege et al

in Computational and Mathematical Methods in Medicine (2020), 2020

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See detailRisk and Reward: Extending stochastic glycaemic control intervals to reduce workload
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; Shaw, Geoffrey M. et al

in BioMedical Engineering OnLine (2020), 19

Background 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 ... [more ▼]

Background 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. [less ▲]

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See detailStochastic TARgeted (STAR) glycaemic control: improved performances and safety for all
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; PIROTTE, Marc ULiege et al

Poster (2020, January 07)

Rationale: Whether more intensive glycemic control (GC) is beneficial or harmful for critically ill patient has been debated over the last decades. GC has been shown hard to achieve safely and effectively ... [more ▼]

Rationale: Whether more intensive glycemic control (GC) is beneficial or harmful for critically ill patient has been debated over the last decades. GC has been shown hard to achieve safely and effectively in intensive care. The associated increased hypoglycemia and glycemic variability is associated with worsened outcomes. However, model-based risk-based dosing approach have recently shown potential benefits, improving significantly GC safety and performances. Objective: The Stochastic TARgeted (STAR) GC framework is a model-based controller using a unique risk-based dosing approach. STAR identifies model-based patient-specific insulin sensitivity and assesses its potential variability over the next hours. These predictions are used to assess hypoglycemic risks associated with a specific insulin and/or nutrition intervention to reach a specific target band. This study analyzes preliminary clinical trial results of STAR in a Belgian ICU compared to the local standard protocol (SP). Patients and Methods: Ethics approval was granted by the local University Hospital Ethics Committee. Patient are included if two BG measurements > 145 mg/dL. STAR target band is 80-145 mg/dL compared to 100-150mg/dL for the SP. Nutrition is administered enterally, and insulin infusion intra-venously. GC is stopped if BG is stable (6 hours in target band) or after 72 hours of control. Safety is assessed by %BG <80mg/dL and %BG >180 mg/dL. Performance is assessed by %BG in target band. Clinical data from 10 patients is used and compared to 20 retrospective patients under the SP. Results: STAR outperformed the SP. Results summary is presented in Table 1. Despite the lower BG target, STAR safety was improved with lower %BG<80mg/dL (0.5% vs. 1%), and significantly lower %BG>145 mg/dL (11% vs. 44%) and %BG>180mg/dL (2% vs 13%). STAR was highly effective with 89% BG in target band compared to 54% for the SP. Median [IQR] BG and nutrition rates achieved were lower for STAR (118 [109 129] vs. 139 [117 160] mg/dL and 7.0 [4.7 8.2] vs. 9.8 [8.6 11.5] g/h), while higher insulin rates were administered in STAR (3.0 [2.0 4.0] vs. 2.5 [2.0 3.0] U/h). However, workload was increased under STAR (12 vs. 7 measurements per day), as expected from measurement interval difference between STAR (3-hourly) and the SP (4-hourly). Conclusion: This unique patient-specific risk-based dosing approach GC framework was successful in controlling all patients safely and effectively. These preliminary results are encouraging and show GC can be achieved safely and effectively at lower target bands. In turns, these improved GC outcomes could improve patient outcomes. [less ▲]

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See detailHigher Insulin Resistance In Female ICU Patients
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; Shaw, Geoffrey M. et al

Poster (2020)

Introduction: Sex differences in the metabolic response to critical illness are unknown. This retrospective analysis examines potential differences in the evolution of insulin sensitivity (SI) and its ... [more ▼]

Introduction: Sex differences in the metabolic response to critical illness are unknown. This retrospective analysis examines potential differences in the evolution of insulin sensitivity (SI) and its variability (%ΔSI) between sexes. Significant differences would suggest differences in the metabolic stress response and glycaemic response to insulin therapy, and, thus, the need for more personalised glycaemic control (GC). Methods: Retrospective data from 145 ICU patients (N=8710 hours) are used to hourly identify hourly model-based SI and its rate of change %ΔSI in 6-hour blocks from ICU admission to 72 hours. The evolution of SI and %ΔSI are compared for males and females. Hypothesis testing (95% confidence interval (CI) bootstrapped difference in medians) assesses if differences are significant, and equivalence testing assesses if differences are clinically equivalent. Results: Females have significantly lower SI levels than males (p<0.05), and this difference is not clinically equivalent (Figure 1; top). Differences in %ΔSI are not significant (p>0.05), and these differences are clinically equivalent (Figure 1; bottom). Conclusion: Given significantly lower SI levels, but equivalent variability, for women, equally safe and effective GC should be achievable for both sexes. However, women may require more insulin to achieve these goals. GC protocol designs should thus account for these differences in the future. [less ▲]

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See detailReduced Workload in the STAR Glycaemic Control Framework: Quantifying the Safety Trade-Off
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; Shaw, Geoffrey M. et al

Poster (2020)

Introduction: The Stochastic Targeted (STAR) glycaemic control (GC) framework is a model-based, risk-based insulin and nutrition protocol providing safe, effective GC to nearly all patients. STAR ... [more ▼]

Introduction: The Stochastic Targeted (STAR) glycaemic control (GC) framework is a model-based, risk-based insulin and nutrition protocol providing safe, effective GC to nearly all patients. STAR currently uses 1-3 hourly measurement intervals, averaging 12 measurements per day. This study assesses the impact of increasing its measurement interval up to 6-hourly on GC safety and efficacy. Methods: STAR identifies patient-specific model-based insulin sensitivity (SI) using clinical data and forecasts its future variability to obtain an insulin and nutrition intervention minimising hypoglycaemic risk. STAR also modulates nutrition intervention to reduce persistent hyperglycaemia for highly insulin resistant patients. STAR is adapted to allow 1-6 hourly measurement interventions. Validated virtual trials assess GC outcomes of the original STAR (STAR-3H) and the STAR 1-6 hourly (STAR-6H) protocols on the same 681 underlying virtual patients based on retrospective clinical data. Results: Results are presented in Table 1. As expected, STAR-6H results in lower workload than STAR-3H (8 vs 12 measures per day). The resulting median [IQR] BG is higher in STAR-6H (124 [113 139] mg/dL) compared to STAR-3H (117 [106 131] mg/dL), using significantly lower insulin (2.5 [1.5 3.0] vs 3.2 [2.0 5.0] U/h). Overall, there is a slight decrease in the %BG in target band (80-145 mg/dL) for STAR-6H (80% vs 83%), and slightly higher % BG above target (18% vs 15%). Importantly, the incidence of moderate hypoglycaemia is similar (1.6%), but STAR-6H has higher incidence of severe hypoglycaemia (19 (2.8%) vs 14 (2.1%) patients). These results are achieved with slightly lower nutrition for STAR-6H (90 [75 100] vs. 100 [85 100] %goal). Conclusion: The risks associated with the reward of reducing workload are slightly reduced safety, performance, and nutrition rates. Overall, despite using these longer measurements intervals, STAR still managed to provide highly safe, effective GC for nearly all patients. [less ▲]

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See detailImproved Risk-Based Glycaemic Control for Critically Ill Patients: the First 200 Patients
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; Shaw, Geoffrey M. et al

Poster (2020)

Introduction: Stochastic TARgeted (STAR) is unique patient-specific, model-based, and risk-based glycaemic control (GC) framework providing safe, effective control for virtually all patients, targeting ... [more ▼]

Introduction: Stochastic TARgeted (STAR) is unique patient-specific, model-based, and risk-based glycaemic control (GC) framework providing safe, effective control for virtually all patients, targeting the 80-145 mg/dL range. STAR uses a proven physiological model and a stochastic model to identify patient-specific insulin sensitivity (SI) from clinical data and its likely future evolution in the next 1-3 hours. These predicted SI ranges can be used to predict blood glucose (BG) outcomes for a given insulin and nutrition treatment to maximise nutrition and minimise (mild) hypoglycaemic risk. This study presents an initial safety and performance analysis of STAR using an enhanced 3D (STAR-3D) riskprediction model from use as the new standard of care at Christchurch Hospital, New Zealand. Methods: STAR is unique as it modulates both insulin and nutrition inputs. In total, 200 GC episodes are analysed for performance and safety. This data audit and analysis was approved by the New Zealand Health and Disability Ethics Committee Upper South Regional Ethics Committee B (Ref: URB/07/15/EXP). Results: Results are presented in Table 1. STAR provided high GC efficacy with median [IQR] per-patient %BG in target band of 81 [65 92]%, with minimal incidence of mild hypoglycaemia overall 0.3%BG<80mg/dL and no incidence of severe hypoglycaemia (BG<40 mg/dL). The resulting median [IQR] 119 [112 130] mg/dL per-patient median BG is achieved with median [IQR] 3.5 [2.5 5.0] U/h of median insulin and 97 [80 100] median % goal feed. Considering results only once the target band is reached (145 mg/dL) to avoid bias from different initial starting BG, both performance and safety are further improved (Table 1). Conclusion: STAR using this new 3D predictive model provides safe, effective control for all patients, with no incidence of severe hypoglycaemia, despite targeting normoglycaemic ranges. Results are slightly better than the current standard of care version of STAR. [less ▲]

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See detailLetter to the Editor in response to “COVID-19: desperate times call for desperate measures
Chase, JG; Chiew, YS; LAMBERMONT, Bernard ULiege et al

in Critical Care (2020)

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See detailSafe doubling of ventilator capacity: A last resort proposal for last resorts
Chase, J. G.; Chiew, Y. S.; Lambermont, Bernard ULiege et al

in Critical Care (2020), 24(1),

[No abstract available]

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See detailIncorporating pulse wave velocity into model-based pulse contour analysis method for estimation of cardiac stroke volume
Smith, R.; Balmer, J.; Pretty, C. G. et al

in Computer Methods and Programs in Biomedicine (2020), 195

Background and Objectives:Stroke volume (SV) and cardiac output (CO) are important metrics for hemodynamic management of critically ill patients. Clinically available devices to continuously monitor these ... [more ▼]

Background and Objectives:Stroke volume (SV) and cardiac output (CO) are important metrics for hemodynamic management of critically ill patients. Clinically available devices to continuously monitor these metrics are invasive, and less invasive methods perform poorly during hemodynamic instability. Pulse wave velocity (PWV) could potentially improve estimation of SV and CO by providing information on changing vascular tone. This study investigates whether using PWV for parameter identification of a model-based pulse contour analysis method improves SV estimation accuracy. Methods: Three implementations of a 3-element windkessel pulse contour analysis model are compared: constant-Z, water hammer, and Bramwell-Hill methods. Each implementation identifies the characteristic impedance parameter (Z) differently. The first method identifies Z statically and does not use PWV, and the latter two methods use PWV to dynamically update Z. Accuracy of SV estimation is tested in an animal trial, where interventions induce severe hemodynamic changes in 5 pigs. Model-predicted SV is compared to SV measured using an aortic flow probe. Results: SV percentage error had median bias and [(IQR); (2.5th, 97.5th percentiles)] of -0.5% [(-6.1%, 4.7%); (-50.3%, +24.1%)] for the constant-Z method, 0.6% [(-4.9%, 6.2%); (-43.4%, +29.3%)] for the water hammer method, and 0.8% [(-6.5, 8.6); (-37.1%, +47.6%)] for the Bramwell-Hill method. Conclusion: Incorporating PWV for dynamic Z parameter identification through either the Bramwell-Hill equation or the water hammer equation does not appreciably improve the 3-element windkessel pulse contour analysis model's prediction of SV during hemodynamic changes compared to the constant-Z method. © 2020 [less ▲]

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See detailMeasuring lung mechanics of expiratory tidal breathing with non-invasive breath occlusion
Howe, S. L.; März, M.; Krüger-Ziolek, S. et al

in BioMedical Engineering OnLine (2020), 19(1), 32

BACKGROUND AND OBJECTIVE: Lung mechanics measurements provide clinically useful information about disease progression and lung health. Currently, there are no commonly practiced methods to non-invasively ... [more ▼]

BACKGROUND AND OBJECTIVE: Lung mechanics measurements provide clinically useful information about disease progression and lung health. Currently, there are no commonly practiced methods to non-invasively measure both resistive and elastic lung mechanics during tidal breathing, preventing the important information provided by lung mechanics from being utilised. This study presents a novel method to easily assess lung mechanics of spontaneously breathing subjects using a dynamic elastance, single-compartment lung model. METHODS: A spirometer with a built-in shutter was used to occlude expiration during tidal breathing, creating exponentially decaying flow when the shutter re-opened. The lung mechanics measured were respiratory system elastance and resistance, separated from the exponentially decaying flow, and interrupter resistance calculated at shutter closure. Progressively increasing resistance was added to the spirometer mouthpiece to simulate upper airway obstruction. The lung mechanics of 17 healthy subjects were successfully measured through spirometry. RESULTS: N = 17 (8 female, 9 male) healthy subjects were recruited. Measured decay rates ranged from 5 to 42/s, subjects with large variation of decay rates showed higher muscular breathing effort. Lung elastance measurements ranged from 3.9 to 21.2 cmH[Formula: see text]O/L, with no clear trend between change in elastance and added resistance. Resistance calculated from decay rate and elastance ranged from 0.15 to 1.95 cmH[Formula: see text]Os/L. These very small resistance values are due to the airflow measured originating from low-resistance areas in the centre of airways. Occlusion resistance measurements were as expected for healthy subjects, and increased as expected as resistance was added. CONCLUSIONS: This test was able to identify reasonable dynamic lung elastance and occlusion resistance values, providing new insight into expiratory breathing effort. Clinically, this lung function test could impact current practice. It does not require high levels of cooperation from the subject, allowing a wider cohort of patients to be assessed more easily. Additionally, this test can be simply implemented in a small standalone device, or with standard lung function testing equipment. [less ▲]

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See detailPatient-Specific Monitoring and Trend Analysis of Model-Based Markers of Fluid Responsiveness in Sepsis: A Proof-of-Concept Animal Study
Murphy, L.; Davidson, S.; Chase, J. G. et al

in Annals of Biomedical Engineering (2020), 48(2), 682-694

Total stressed blood volume (SBVT) and arterial elastance (Ea) are two potentially important, clinically applicable metrics for guiding treatment in patients with altered hemodynamic states. Defined as ... [more ▼]

Total stressed blood volume (SBVT) and arterial elastance (Ea) are two potentially important, clinically applicable metrics for guiding treatment in patients with altered hemodynamic states. Defined as the total pressure generating blood in the circulation, SBVT is a potential direct measurement of tissue perfusion, a critical component in treatment of sepsis. Ea is closely related to arterial tone thus provides insight into cardiac efficiency. However, it is not clinically feasible or ethical to measure SBVT in patients, so a three chambered cardiovascular system model using measured left ventricle pressure and volume, aortic pressure and central venous pressure is implemented to identify SBVT and Ea from clinical data. SBVT and Ea are identified from clinical data from six (6) pigs, who have undergone clinical procedures aimed at simulating septic shock and subsequent treatment, to identify clinically relevant changes. A novel, validated trend analysis method is used to adjudge clinically significant changes in state in the real-time Ea and SBVT traces. Results matched hypothesised increases in SBVT during fluid therapy, with a mean change of + 21% during initial therapy, and hypothesised decreases during endotoxin induced sepsis, with a mean change of − 29%. Ea displayed the hypothesised reciprocal behaviour with a mean changes of − 12 and + 30% during initial therapy and endotoxin induced sepsis, respectively. The overall results validate the efficacy of SBVT in tracking changes in hemodynamic state in septic shock and fluid therapy. © 2019, Biomedical Engineering Society. [less ▲]

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See detailModel-based PEEP titration versus standard practice in mechanical ventilation: A randomised controlled trial
Kim, K. T.; Morton, S.; Howe, S. et al

in Trials (2020), 21(1),

Background: 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 ... [more ▼]

Background: 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. Methods and design: 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 (SpO2)/FiO2 during MV, number of desaturation events (SpO2 < 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. Discussion: 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. Trial registration: 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). © 2020 The Author(s). [less ▲]

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See detailClinically applicable model-based method, for physiologically accurate flow waveform and stroke volume estimation
Balmer, J.; Pretty, C. G.; Davidson, S. et al

in Computer Methods and Programs in Biomedicine (2020), 185

Background and Objectives: Cardiovascular dysfunction can be more effectively monitored and treated, with accurate, continuous, stroke volume (SV) and/or cardiac output (CO) measurements. Since direct ... [more ▼]

Background and Objectives: Cardiovascular dysfunction can be more effectively monitored and treated, with accurate, continuous, stroke volume (SV) and/or cardiac output (CO) measurements. Since direct measurements of SV/CO are highly invasive, clinical measures are often discrete, or if continuous, can require recalibration with a discrete SV measurement after hemodynamic instability. This study presents a clinically applicable, non-additionally invasive, physiological model-based, SV and CO measurement method, which does not require recalibration during or after hemodynamic instability. Methods and Results: The model's ability to predict flow profiles and SV is assessed in an animal trial, using endotoxin to induce sepsis in 5 pigs. Mean percentage error between beat-to-beat SV measured from an aortic flow probe and estimated by the model was −2%, while 90% of estimations fell within −24.2% and +27.9% error. Error between estimated and measured changes in mean SV following interventions was less than 30% for 4 out of the 5 pigs. Correlations between model estimated and probe measured flow, for each pig and hemodynamic interventions, was r2 = 0.58 − 0.96, with 21 of the 25 pig intervention stages having r2 > 0.80. Conclusion: The results demonstrate the model accurately estimates and tracks changes in flow profiles and resulting SV, without requiring model recalibration. © 2019 [less ▲]

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See detailInspiratory respiratory mechanics estimation by using expiratory data for reverse-triggered breathing cycles
Howe, S. L.; Chase, J. G.; Redmond, D. P. et al

in Computer Methods and Programs in Biomedicine (2020), 186

Background and objective: Model-based lung mechanics monitoring can provide clinically useful information for guiding mechanical ventilator treatment in intensive care. However, many methods of measuring ... [more ▼]

Background and objective: Model-based lung mechanics monitoring can provide clinically useful information for guiding mechanical ventilator treatment in intensive care. However, many methods of measuring lung mechanics are not appropriate for both fully and partially sedated patients, and are unable provide lung mechanics metrics in real-time. This study proposes a novel method of using lung mechanics identified during passive expiration to estimate inspiratory lung mechanics for spontaneously breathing patients. Methods: Relationships between inspiratory and expiratory modeled lung mechanics were identified from clinical data from 4 fully sedated patients. The validity of these relationships were assessed using data from a further 4 spontaneously breathing patients. Results: For the fully sedated patients, a linear relationship was identified between inspiratory and expiratory elastance, with slope 1.04 and intercept 1.66. The r value of this correlation was 0.94. No cohort-wide relationship was determined for airway resistance. Expiratory elastance measurements in spontaneously breathing patients were able to produce reasonable estimates of inspiratory elastance after adjusting for the identified difference between them. Conclusions: This study shows that when conventional methods fail, typically ignored expiratory data may be able to provide clinicians with the information needed about patient condition to guide MV therapy. © 2019 Elsevier B.V. [less ▲]

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See detailAccurate end systole detection in dicrotic notch-less arterial pressure waveforms
Balmer, J.; Smith, R.; Pretty, C. G. et al

in Journal of Clinical Monitoring and Computing (2020)

Identification of end systole is often necessary when studying events specific to systole or diastole, for example, models that estimate cardiac function and systolic time intervals like left ventricular ... [more ▼]

Identification of end systole is often necessary when studying events specific to systole or diastole, for example, models that estimate cardiac function and systolic time intervals like left ventricular ejection duration. In proximal arterial pressure waveforms, such as from the aorta, the dicrotic notch marks this transition from systole to diastole. However, distal arterial pressure measures are more common in a clinical setting, typically containing no dicrotic notch. This study defines a new end systole detection algorithm, for dicrotic notch-less arterial waveforms. The new algorithm utilises the beta distribution probability density function as a weighting function, which is adaptive based on previous heartbeats end systole locations. Its accuracy is compared with an existing end systole estimation method, on dicrotic notch-less distal pressure waveforms. Because there are no dicrotic notches defining end systole, validating which method performed better is more difficult. Thus, a validation method is developed using dicrotic notch locations from simultaneously measured aortic pressure, forward projected by pulse transit time (PTT) to the more distal pressure signal. Systolic durations, estimated by each of the end systole estimates, are then compared to the validation systolic duration provided by the PTT based end systole point. Data comes from ten pigs, across two protocols testing the algorithms under different hemodynamic states. The resulting mean difference ± limits of agreement between measured and estimated systolic duration, of -8.7±26.6ms versus -23.2±37.7ms, for the new and existing algorithms respectively, indicate the new algorithms superiority. © 2020, Springer Nature B.V. [less ▲]

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See detailVirtual patient trials of a multi-input stochastic model for tight glycaemic control using insulin sensitivity and blood glucose data
Davidson, Shaun M.; Uyttendaele, Vincent ULiege; Pretty, Christopher et al

in Biomedical Signal Processing and Control (2020)

Objective Safe, effective glycaemic control (GC) requires accurate prediction of future patient insulin sensitivity (SI), balancing the risk of hyper- and hypo-glycaemia. The stochastic targeted (STAR ... [more ▼]

Objective Safe, effective glycaemic control (GC) requires accurate prediction of future patient insulin sensitivity (SI), balancing the risk of hyper- and hypo-glycaemia. The stochastic targeted (STAR) protocol combines a clinically validated metabolic model and SI metric with a risk-based stochastic approach to optimise patient specific insulin and feed rates. Validated virtual trials comparing a novel 3D stochastic model for prediction of future patient SI using current patient SI and current blood glucose (BG) to an existing 2D stochastic model for SI prediction were conducted. Methods The virtual trials involved 1477 retrospective patients across two hospitals and two GC protocols. They were conducted using five-fold cross-validation to build each stochastic model, ensuring independent test data. Results The 3D stochastic model shifted BG from the 4.4–8.0 mmol/L target band towards the lower 4.4–6.5 mmol/L band, providing a decrease from 12.31 % to 11.19 % in hyperglycaemic hours (BG > 8.0 mmol/L), but only a 0.24 % increase, from 1.01 % to 1.25 %, in light hypoglycaemic hours (BG < 4.0 mmol/L). Simultaneously, the 3D stochastic model enabled greater patient nutrition, and required negligible increase in computational or clinical workload. Conclusions The 3D stochastic model provided greater personalisation and better realised STAR’s design philosophy of minimising hyperglycaemic events for an acceptable clinical risk of 5.0 % BG < 4.4 mmol/L. Thus, this model could provide better clinical conformity to design targets if implemented within the STAR protocol. [less ▲]

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See detailMulti-input stochastic prediction of insulin sensitivity for tight glycaemic control using insulin sensitivity and blood glucose data
Davidson, Shaun; Pretty, Christopher ULiege; Uyttendaele, Vincent ULiege et al

in Computer Methods and Programs in Biomedicine (2019), 182

Background: Glycaemic control in the intensive care unit is dependent on effective prediction of patient insulin sensitivity (SI). The stochastic targeted (STAR) controller uses a 2D stochastic model for ... [more ▼]

Background: Glycaemic control in the intensive care unit is dependent on effective prediction of patient insulin sensitivity (SI). The stochastic targeted (STAR) controller uses a 2D stochastic model for prediction, with current SI as an input and future SI as an output. Methods: This paper develops an extension of the STAR 2D stochastic model into 3D by adding blood glucose (G) as an input. The performance of the 2D and 3D stochastic models is compared over a retrospective cohort of 65,269 data points across 1,525 patients. Results: Under five-fold cross-validation, the 3D model was found to better match the expected potion of data points within, above and below various credible intervals, suggesting it provided a better representation of the underlying probability field. The 3D model was also found to provide an 18.1% narrower 90% credible interval on average, and a narrower 90% credible interval in 96.4% of cases, suggesting it provided more accurate predictions of future SI. Additionally, the 3D stochastic model was found to avoid the undesirable tendency of the 2D model to overestimate SI for patients with high G, and underestimate SI for patients with low G. Conclusions: Overall, the 3D stochastic model is shown to provide clear potential benefits over the 2D model for minimal clinical cost or effort, though further exploration into whether these improvements in SI prediction translate into improved clinical outcomes is required. [less ▲]

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See detailRisk-based Dosing of Insulin and Nutrition Improves Glycaemic Control Outcomes
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; PIROTTE, Marc ULiege et al

Poster (2019, November)

Objective: Hyperglycaemia and insulin resistance are common in critically ill patients and associated with worsened outcomes. STAR (Stochastic TARgeted) glycaemic control (GC) has proven effective over ... [more ▼]

Objective: Hyperglycaemia and insulin resistance are common in critically ill patients and associated with worsened outcomes. STAR (Stochastic TARgeted) glycaemic control (GC) has proven effective over different units and clinical practices. Unlike many protocols, STAR also modulates nutrition with insulin, using a patient-specific risk-based dosing approach to provide greater flexibility in control. This study compares and assesses safety and efficacy of the ongoing STAR clinical trial results at the University Hospital of Liège, Belgium. Method: Two arms are compared: the first uses an insulin only version of STAR (STAR-IO), and the second the full insulin+nutrition version of STAR. The target band is 80-145mg/dL. Insulin is administered IV and nutrition is administered enterally. GC was stopped after 72h or if BG was stable at insulin rate ≤2U/h. Safety is assessed by %BG <80mg/dL below target and hyperglycaemia (%BG>180mg/dL). Performance is evaluated by %BG within target band and median BG. Clinical data from 11 patients on STAR-IO and 10 patients on STAR totalling 1100 hours of control is used. Ethics approval was granted by the University Hospital of Liège Ethics Committee. Results: STAR performance is statistically significantly better compared to STAR-IO (89% vs. 78% for %BG in target band, p<0.01 using Fisher Exact test). Median [IQR] BG is similar but tighter in STAR (118[109 129] vs. 120[107 138]mg/dL, p=0.19 using Wilcoxon rank sum test). STAR is also safer compared to STAR-IO with 0.7% vs. 1.4% for %BG<80 mg/dL and only 2.0% vs. 9.8% for %BG>180mg/dL. This outcome was achieved using less insulin and nutrition rates for STAR vs STAR-IO (3.0[2.0 4.0] U/h vs. 3.5[1.5 6]U/h and 7.0[4.7 8.2] vs. 8.1[4.9 9.2]g/h). Conclusions: Modulating nutrition in addition to insulin can significantly improve GC outcomes, especially by reducing nutrition rates for highly resistive patients. [less ▲]

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See detailWomen have greater (Metabolic) Stress Response than Men
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; Shaw, Geoff M. et al

Poster (2019, November)

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 ... [more ▼]

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. [less ▲]

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See detail3D kernel-density stochastic model for more personalized glycaemic control: development and in-silico validation
Uyttendaele, Vincent ULiege; Knopp, Jennifer L.; Davidson, Shaun et al

in BioMedical Engineering OnLine (2019), 18(1), 102

Background: 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 ... [more ▼]

Background: 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. [less ▲]

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