Validation and implementation of low-cost dynamic insulin sensitivity tests

DTM2011 handbook/programme is given in files and also available as a hard copyObjective: Insulin sensitivity (SI) tests can provide important information for type 2 diabetes risk assessment and investigations of metabolism or pre-diabetes. Our group previously presented the dynamic insulin sensitivity and secretion test (DISST) and the real-time quick DISST (DISTq) as low-cost, low-burden and accurate alternatives to established tests. The DISST provides concurrent SI and endogenous insulin secretion (UN) metrics, the DISTq does not require insulin or C-peptide assays for SI identification, but can return an immediate result. This study validates the DISST and DISTq in comparison to the euglycemic, hyperinsulinemic clamp (EIC) Method: Fifty participants (with 10 BMI>30; 10 BMI>25, <30; and 5 BMI<25 of each gender) underwent the EIC and DISST. The DISST protocol requires 5 samples from a 30 minute protocol similar to the IM-IVGTT. Data from the DISST protocol was sufficient to identify SI using both the DISST and DISTq parameter identification methods and UN from the DISST. Result: DISST and DISTq SI values correlated well to the EIC (R=0.81 and R=0.76, respectively) and each other (R=0.84). UN values obtained during the DISST showed clinically relevant distinctions between participants, and clearly differentiated the beta-cell function of impaired glucose tolerant participants who had the same EIC SI. Participant acceptance of the protocol was high with very minor reported adverse effects. Conclusion: The DISST and DISTq correlated well against the EIC compared to most established insulin sensitivity tests. The DISST can better differentiate patients as it provides UN metrics that the EIC does not. A computer program makes uptake and use of the model-based DISST and DISTq tests straightforward for clinicians and researchers

A subcutaneous insulin pharmacokinetic model for insulin Detemir

Background and Objective: Type 2 diabetes (T2D) is rapidly increasing in incidence and has significant social and economic costs. Given the increasing cost of complications, even relatively short delays in the onset of T2D can significantly reduce long-term complications and costs. Equally, recent studies have shown the onset of T2D can be delayed by use of long-acting insulin, despite the risk and concomitant low adherence. Thus, there is a strong potential motivation to develop models of long-acting insulin analogues to enable safe, effective use in model-based dosing systems. In particular, there are no current models of long-acting insulin Detemir and its unique action for model-based control. The objective of this work is to develop a first model of insulin Detemir and its unique action, and validate it against existing data in the literature. Methods: This study develops a detailed compartment model for insulin Detemir. Model specific parameters are identified using data from a range of published clinical studies on the pharmacokinetic of insulin Detemir. Model validity and robustness are assessed by identifying the model for each study and using average identified parameters over several dose sizes and study cohorts. Comparisons to peak concentration, time of peak concentration and overall error versus measured plasma concentrations are used to assess model accuracy and validity. Results: Almost all studies and cohorts fit literature data to within one standard deviation of error, even when using averaged identified model parameters. However, there appears to be a noticeable dose dependent dynamic not included in this first model, nor reported in the literature studies. Conclusions: A first model of insulin Detemir including its unique albumin binding kinetics is derived and provisionally validated against clinical pharmacokinetic data. The pharmacokinetic curves are suitable for model-based control and general enough for use. While there are limitations in the studies used for validation that prevent a more complete understanding, the results provide an effective first model and justify the design and implementation of further, more precise human trials

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

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

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

Increased insulin resistance in intensive care: longitudinal retrospective analysis of glycaemic control patients in a New Zealand ICU

Background: Critical care populations experience demographic shifts in response to trends in population and healthcare, with increasing severity and/or complexity of illness a common observation worldwide. Inflammation in critical illness impacts glucose–insulin metabolism, and hyperglycaemia is associated with mortality and morbidity. This study examines longitudinal trends in insulin sensitivity across almost a decade of glycaemic control in a single unit. Methods: A clinically validated model of glucose–insulin dynamics is used to assess hour–hour insulin sensitivity over the first 72 h of insulin therapy. Insulin sensitivity and its hour–hour percent variability are examined over 8 calendar years alongside severity scores and diagnostics. Results: Insulin sensitivity was found to decrease by 50–55% from 2011 to 2015, and remain low from 2015 to 2018, with no concomitant trends in age, severity scores or risk of death, or diagnostic category. Insulin sensitivity variability was found to remain largely unchanged year to year and was clinically equivalent (95% confidence interval) at the median and interquartile range. Insulin resistance was associated with greater incidence of high insulin doses in the effect saturation range (6–8 U/h), with the 75th percentile of hourly insulin doses rising from 4–4.5 U/h in 2011–2014 to 6 U/h in 2015–2018. Conclusions: Increasing insulin resistance was observed alongside no change in insulin sensitivity variability, implying greater insulin needs but equivalent (variability) challenge to glycaemic control. Increasing insulin resistance may imply greater inflammation and severity of illness not captured by existing severity scores. Insulin resistance reduces glucose tolerance, and can cause greater incidence of insulin saturation and resultant hyperglycaemia. Overall, these results have significant clinical implications for glycaemic control and nutrition management

Medical innovation : using mechatronics engineering to reduce inequities in healthcare.

Medical device innovation provides access to healthcare. Innovations come about be- cause of pressures, in particular financial pressures, and access to care. With increasing interoperability of devices, distinction is made between devices with specific interoperability (SIO) only able to communicate with a pre-determined range of other devices, and non-specific interoperability (NSIO). Devices with NSIO pose substantially greater potential benefits by allowing long-term system wide innovations. Scales of innovation are discussed, where short-term innovations meet an immediate need, such as the inundation of intensive care units (ICUs) in the COVID-19 pandemic. Medium-term innovations see either incremental increase in efficiencies, or an increase in interoperability which enables subsequent innovation. Long-term innovations are disruptive, systemic changes, often enabled through the use of increasing interoperability. The uptake of innovation is often lacking, but through the use of a framework such as Tech-ISM the chance of adoption is increased. This framework sees establishment and fostering of close relationships with a range of end users, decision makers, and industry partners. Diabetes technologies are presented as examples of innovation. Insulin pumps are an effective method of delivering insulin, and see considerable benefit in control. Widespread adoption of insulin pumps is posed through the development of an ultra-low cost (ULC) insulin pump, made possible by the separation of hardware and computation, and costing 12 × −20× less than currently-available devices, both for a traditional-style insulin pump, and also a novel spring-driven design. Initial results show similar accuracy to current commercially-available insulin pumps, with a mean error of 0.64%, the same as the MiniMed™640G (Medtronic, Dublin, Ire- land) for 1 U boluses, and mean error of 0.06% for 10 U boluses. Basal windows of 1 hour are similarly accurate, with 100% within ±15%, 92% within ±10%, and 84% within ±5%, again very similar to the MiniMed™640G. The ULC insulin pump is a solution to the economic infeasibility of insulin pumps for the majority of New Zealanders. System-wide adoption of insulin pumps would see considerable economic benefit for New Zealand, in particular with a patch pump. Several possible adoption scenarios are presented. Annually, direct savings associated with less insulin use and current public investment in insulin pumps is expected to total $6.6M -$25.3M, indirect savings from reduction of expensive complications are expected to save $2.5M -$25.5M, with direct costs of $0.8M -$25.7M. Projections are for a total overall system saving of $8.3M with no additional uptake of insulin pumps, but only replacing current insulin pumps with the ULC alternative, to$25.0M with widespread adoption. These figures do not account for additional savings made possible through future long-term development of smart, automated healthcare systems. A continuous glucose monitor (CGM) is a device that estimates blood glucose (BG) every 1-5 minutes, replacing discrete, invasive self-monitored blood glucose (SMBG) measure- ments as required four to ten per day currently for approximately 40,000 - 60,000 New Zealanders with diabetes who administer insulin. Current CGM use is limited, but rel- atively unknown, due to no public funding, with expert estimates at 2-8% prevalence among individuals with type-one diabetes. A low-cost alternative is presented in the form of the blood optical biosensor CGM (BOB CGM) at an annual cost 10 × −20× less expensive than current devices. Initial, un-calibrated results show promise, with 91% of BG results deemed clinically accurate, and a further 8% sufficiently accurate to not cause treatment error. Fundamentally, cost savings arise from allowing access to otherwise inaccessible data, and thus turning the current data monopoly into a data market. Substantial economic benefit is seen from direct savings from current monitoring of diabetes disease progressions with SMBG and glycated haemoglobin (HbA1c), and also indirect savings from earlier identification of worsening diabetes control. Various adoption scenarios are presented, with overall annual economic savings of $1.9M -$25.1M. Another medical innovation is presented in the form of the actuated, closed-loop, time- series inspiratory valve (ACTIV) dual ventilation system. This innovation is a short- term example, developed under pressure of inundation of the healthcare system due to the novel coronavirus disease (COVID-19). The basic operating premise is ventilatory effort from a single mechanical ventilator is delivered first to one patient, and subsequent to a valve switching state, to a second patient. The system is a solution that addresses valid concern for multiple ventilation from a consensus of oversight bodies for ICU treatment, in particular personalised therapy and monitoring, especially in the case of changing pathology. The system is designed to be low-cost, robust, portable, and readily manufactured in low-resource environments. Thus, it has an Arduino (Arduino, Massachusetts, USA) controller, and requires a 5.0 V power supply. The system requires a flow and pressure sensor for detection of inspiration, and sub- sequent valve switching. A custom-made 3D-printed Venturi interfaced with simple electronics with an analogue 0.0 − 5.0 V output signal is presented. The sensor is validated against data from mechanical ventilation devices to be accurate over the range of 5 − 75L · min−1, with a Pearson Correlation ≥ 0.95 for flow and pressure, typically ≥ 0.97 in 5 S bins at fs = 50 Hz. Additional components are a 3-D printed pressure drop device in the form of the PANDAPeep Gen2 Inline valve, and off-the-shelf one-way valves, airway filters, and 22 mm⊘ tubing. The switching ACTIV valve is another 3D-printed component, and uses a common HXT12K servo motor, or similar, for interoperability. The Arduino-based control system is a basic finite state machine (FSM) relying on low-pass filtered flow sensor data, implemented through a circular buffer, for state changes. These state changes, in com- bination with various necessary delays for safety, dictate the change of state of the ACTIV valve, and thus to which patient ventilation effort is delivered. An example of two considerably unbalance patients, with compliance C1 = 0.10 L · cmH2O−1and C2 = 0.05 L · cmH2O−1, being safely and efficiently balanced to achieve equal tidal volume is demonstrated to show individualised therapy and monitoring. Without innovations such as the diabetes technologies and ACTIV system, care will become increasingly rationed. Rationing of diabetes devices with high effectiveness, but also high cost, is already seen in the lack of public funding for CGM devices, and funding for only 8-10% of individuals with type-one diabetes to access insulin pumps, despite significant proven benefits of both of these devices. Since 2000, increases in direct out- of-pocket expenditure have grown an average of 4.3% per annum, compared to median wage growth of 3.2%, and inflation of 2.6%. These trends show that the rationing of healthcare is being seen in a reduction of access to publicly-funded services. Given an average annual wage increase of only 1.6% for the lowest 20th centile, individuals who are least well off are less able to afford the required personal expenditure to attain the same access the healthcare. Therefore, access to healthcare is seeing worsening equity of access. With increasing demand for healthcare, and a taxation base stagnant at best, relying only on intrinsic changes, New Zealand faces significant taxation increases, or drastic reductions in healthcare services. The alternative is to increase the efficiency of healthcare delivery methods, using extrinsic, disruptive changes. These changes are only made possible through innovation informed by strong clinical insight, developing mechatronic devices with broad, non-specific interoperability, if not open-source design. This approach provides equitable access to care, and provides the necessary framework for automation of healthcare services, including diagnostics, prognostics, and personalised care models under a one-method-fits all approach. This widespread technological innovation and adoption poses significant increase of access to care, combating current inequities

Independent cohort cross-validation of the real-time DISTq estimation of insulin sensitivity

Invited. Available online 25 August 2010Insulin sensitivity (SI) is useful in the diagnosis, screening and treatment of diabetes. However, most current tests cannot provide an accurate, immediate or real-time estimate. The DISTq method does not require insulin or C-peptide assays like most SI tests, thus enabling real-time, low-cost SI estimation. The method uses a-posteriori parameter estimations in the absence of insulin or C-peptide assays to simulate accurate, patient-specific, insulin concentrations that enable SI identification. Mathematical functions for the a-posteriori parameter estimates were generated using data from 46 fully sampled DIST tests (glucose, insulin and C-peptide). SI values found using the DISTq from the 46 test pilot cohort and a second independent 218 test cohort correlated R=0.890 and R=0.825, respectively, to the fully sampled (including insulin and C-peptide assays) DIST SI metrics. When the a-posteriori insulin estimation functions were derived using the second cohort, correlations for the pilot and second cohorts reduced to 0.765 and 0.818, respectively. These results show accurate SI estimation is possible in the absence of insulin or C-peptide assays using the proposed method. Such estimates may only need to be generated once and then used repeatedly in the future for isolated cohorts. The reduced correlation using the second cohort was due to this cohort’s bias towards low SI insulin resistant subjects, limiting the dataset’s ability to generalise over a wider range. All the correlations remain high enough for the DISTq to be a useful test for a number of clinical applications. The unique real-time results can be generated within minutes of testing as no insulin and C-peptide assays are required and may enable new clinical applications