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Extrapolation of a non-linear autoregressive model of pulmonary mechanics

Langdon R, Docherty PD, Chiew YS, Chase JG

Math Biosci, 2017 02;284:32-39.
PMID: 27513728 DOI: 10.1016/j.mbs.2016.08.001

For patients with acute respiratory distress syndrome (ARDS), mechanical ventilation (MV) is an essential therapy in the intensive care unit (ICU). Suboptimal PEEP levels in MV can cause ventilator induced lung injury, which is associated with increased mortality, extended ICU stay, and high cost. The ability to predict the outcome of respiratory mechanics in response to changes in PEEP would thus provide a critical advantage in personalising and improving care. Testing the potentially dangerous high pressures would not be required to assess their impact. A nonlinear autoregressive (NARX) model was used to predict airway pressure in 19 data sets from 10 mechanically ventilated ARDS patients. Patient-specific NARX models were identified from pressure and flow data over one, two, three, or four adjacent PEEP levels in a recruitment manoeuvre. Extrapolation of NARX model elastance functions allowed prediction of patient responses to PEEP changes to higher or lower pressures. NARX model predictions were more successful than those using a well validated first order model (FOM). The most clinically important results were for extrapolation up one PEEP step of 2cmH2O from the highest PEEP in the training data. When the NARX model was trained on one PEEP level, the mean RMS residual for the extrapolation PEEP level was 0.52 (90% CI: 0.47-0.57) cmH2O, compared to 1.50 (90% CI: 1.38-1.62) cmH2O for the FOM. When trained on four PEEP levels, the NARX result was 0.50 (90% CI: 0.42-0.58) cmH2O, and was 1.95 (90% CI: 1.71-2.19) cmH2O for the FOM. The results suggest that a full recruitment manoeuvre may not be required for the NARX model to obtain a useful estimate of the pressure waveform at higher PEEP levels. The methodology could thus allow clinicians to make informed decisions about ventilator PEEP settings while reducing the risk associated with high PEEP, and subsequent high peak airway pressures.
Impact of Haemodialysis on Insulin Kinetics of Acute Kidney Injury Patients in Critical Care

Jamaludin UK, Docherty PD, Geoffrey Chase J, Shaw GM

J Med Biol Eng, 2015 02 03;35(1):125-133.
PMID: 25750607

Critically ill patients are occasionally associated with an abrupt decline in renal function secondary to their primary diagnosis. The effect and impact of haemodialysis (HD) on insulin kinetics and endogenous insulin secretion in critically ill patients remains unclear. This study investigates the insulin kinetics of patients with severe acute kidney injury (AKI) who required HD treatment and glycaemic control (GC). Evidence shows that tight GC benefits the onset and progression of renal involvement in precocious phases of diabetic nephropathy for type 2 diabetes. The main objective of GC is to reduce hyperglycaemia while determining insulin sensitivity. Insulin sensitivity (S
I
) is defined as the body response to the effects of insulin by lowering blood glucose levels. Particularly, this study used S
I
to track changes in insulin levels during HD therapy. Model-based insulin sensitivity profiles were identified for 51 critically ill patients with severe AKI on specialized relative insulin nutrition titration GC during intervals on HD (OFF/ON) and after HD (ON/OFF). The metabolic effects of HD were observed through changes in S
I
over the ON/OFF and OFF/ON transitions. Changes in model-based S
I
at the OFF/ON and ON/OFF transitions indicate changes in endogenous insulin secretion and/or changes in effective insulin clearance. Patients exhibited a median reduction of -29 % (interquartile range (IQR): [-58, 6 %], p = 0.02) in measured S
I
after the OFF/ON dialysis transition, and a median increase of +9 % (IQR -15 to 28 %, p = 0.7) after the ON/OFF transition. Almost 90 % of patients exhibited decreased S
I
at the OFF/ON transition, and 55 % exhibited increased S
I
at the ON/OFF transition. Results indicate that HD commencement has a significant effect on insulin pharmacokinetics at a cohort and per-patient level. These changes in metabolic behaviour are most likely caused by changes in insulin clearance or/and endogenous insulin secretion.
Fulltext The Need to Calculate Target Glucose Levels When Measuring Changes in Insulin Sensitivity During Interventions for Individuals With Type 2 Diabetes

Othman NA, Docherty PD, Krebs JD, Bell DA, Chase JG

J Diabetes Sci Technol, 2018 05;12(3):665-672.
PMID: 29295634 DOI: 10.1177/1932296817750402

BACKGROUND: Physiological models that are used with dynamic test data to assess insulin sensitivity (SI) assume that the metabolic target glucose concentration ( GTARGET) is equal to fasting glucose concentration ( G0). However, recent research has implied that irregularities in G0 in diabetes may cause erroneous SI values. This study quantifies the magnitude of these errors.
METHODS: A clinically validated insulin/glucose model was used to calculate SI with the standard fasting assumption (SFA) G0 = GTARGET. Then GTARGET was treated as a variable in a second analysis (VGT). The outcomes were contrasted across twelve participants with established type 2 diabetes mellitus that were recruited to take part in a 24-week dietary intervention. Participants underwent three insulin-modified intravenous glucose tolerance tests (IM-IVGTT) at 0, 12, and 24 weeks.
RESULTS: SIVGT had a median value of 3.36×10-4 L·mU-1·min-1 (IQR: 2.30 - 4.95×10-4) and were significantly lower ( P < .05) than the median SISFA (6.38×10-4 L·mU-1·min-1, IQR: 4.87 - 9.39×10-4). The VGT approach generally yielded lower SI values in line with expected participant physiology and more effectively tracked changes in participant state over the 24-week trial. Calculated GTARGET values were significantly lower than G0 values (median GTARGET = 5.48 vs G0 = 7.16 mmol·L-1 P < .001) and were notably higher in individuals with longer term diabetes.
CONCLUSIONS: Typical modeling approaches can overestimate SI when GTARGET does not equal G0. Hence, calculating GTARGET may enable more precise SI measurements in individuals with type 2 diabetes, and could imply a dysfunction in diabetic metabolism.
Assessing respiratory mechanics using pressure reconstruction method in mechanically ventilated spontaneous breathing patient

Damanhuri NS, Chiew YS, Othman NA, Docherty PD, Pretty CG, Shaw GM, et al.

Comput Methods Programs Biomed, 2016 Jul;130:175-85.
PMID: 27208532 DOI: 10.1016/j.cmpb.2016.03.025

BACKGROUND: Respiratory system modelling can aid clinical decision making during mechanical ventilation (MV) in intensive care. However, spontaneous breathing (SB) efforts can produce entrained "M-wave" airway pressure waveforms that inhibit identification of accurate values for respiratory system elastance and airway resistance. A pressure wave reconstruction method is proposed to accurately identify respiratory mechanics, assess the level of SB effort, and quantify the incidence of SB effort without uncommon measuring devices or interruption to care.
METHODS: Data from 275 breaths aggregated from all mechanically ventilated patients at Christchurch Hospital were used in this study. The breath specific respiratory elastance is calculated using a time-varying elastance model. A pressure reconstruction method is proposed to reconstruct pressure waves identified as being affected by SB effort. The area under the curve of the time-varying respiratory elastance (AUC Edrs) are calculated and compared, where unreconstructed waves yield lower AUC Edrs. The difference between the reconstructed and unreconstructed pressure is denoted as a surrogate measure of SB effort.
RESULTS: The pressure reconstruction method yielded a median AUC Edrs of 19.21 [IQR: 16.30-22.47]cmH2Os/l. In contrast, the median AUC Edrs for unreconstructed M-wave data was 20.41 [IQR: 16.68-22.81]cmH2Os/l. The pressure reconstruction method had the least variability in AUC Edrs assessed by the robust coefficient of variation (RCV)=0.04 versus 0.05 for unreconstructed data. Each patient exhibited different levels of SB effort, independent from MV setting, indicating the need for non-invasive, real time assessment of SB effort.
CONCLUSION: A simple reconstruction method enables more consistent real-time estimation of the true, underlying respiratory system mechanics of a SB patient and provides the surrogate of SB effort, which may be clinically useful for clinicians in determining optimal ventilator settings to improve patient care.