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

Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis

One of the main challenges of glucose control in patients with type 1 diabetes is identifying a control-oriented model that reliably predicts the behavior of glycemia. Here, a review is provided emphasizing the structural identifiability and observability properties, which surprisingly reveals that few of them are globally identifiable and observable at the same time. Thus, a general proposal was developed to encompass four linear models according to suitable assumptions and transformations. After the corresponding structural properties analysis, two minimal model structures are generated, which are globally identifiable and observable. Then, the practical identifiability is analyzed for this application showing that the standard collected data in many cases do not have the necessary quality to ensure a unique solution in the identification process even when a considerable amount of data is collected. The two minimal control-oriented models were identified using a standard identification procedure using data from 30 virtual patients of the UVA/Padova simulator and 77 diabetes care data from adult patients of a diabetes center. The identification was performed in two stages: calibration and validation. In the first stage, the average length was taken as two days (dictated by the practical identifiability). For both structures, the mean absolute error was 16.8 mg/dl and 9.9 mg/dl for virtual patients and 21.6 mg/dl and 21.5 mg/dl for real patients. For the second stage, a one-day validation window was considered long enough for future artificial pancreas applications. The mean absolute error was 23.9 mg/dl and 12.3 mg/dl for virtual patients and 39.2 mg/dl and 36.6 mg/dl for virtual and real patients. These results confirm that linear models can be used as prediction models in model-based control strategies as predictive control.Fil: Hoyos, J. D.. Universidad Nacional de Colombia. Sede Medellín; ColombiaFil: Villa Tamayo, M. F.. Universidad Nacional de Colombia. Sede Medellín; ColombiaFil: Builes Montano, C. E.. Universidad de Antioquia; ColombiaFil: Ramirez Rincon, A.. Universidad Pontificia Bolivariana; ColombiaFil: Godoy, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Garcia Tirado, J.. University of Virginia; Estados UnidosFil: Rivadeneira Paz, Pablo Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

Model-Based Closed-Loop Glucose Control in Critical Illness

Stress hyperglycemia is a common complication in critically ill patients and is associated with increased mortality and morbidity. Tight glucose control (TGC) has shown promise in reducing mean glucose levels in critically ill patients and may mitigate the harmful repercussions of stress hyperglycemia. Despite the promise of TGC, care must be taken to avoid hypoglycemia, which has been implicated in the failure of some previous clinical attempts at TGC using intensive insulin therapies. In fact, a single hypoglycemic event has been shown to result in worsened patient outcomes. The nature of tight glucose regulation lends itself to automatic monitoring and control, thereby reducing the burden on clinical staff. A blood glucose target range of 110-130 mg/dL has been identified in the High-Density Intensive Care (HIDENIC) database at the University of Pittsburgh Medical Center (UPMC). A control framework comprised of a zone model predictive controller (zMPC) with moving horizon estimation (MHE) is proposed to maintain euglycemia in critically ill patients. Using continuous glucose monitoring (CGM) the proposed control scheme calculates optimized insulin and glucose infusion to maintain blood glucose concentrations within the target zone. Results from an observational study employing continuous glucose monitors at UPMC are used to reconstruct blood glucose from noisy CGM data, identify a model of CGM error in critically ill patients, and develop an in silico virtual patient cohort. The virtual patient cohort recapitulates expected physiologic trends with respect to insulin sensitivity and glycemic variability. Furthermore, a mechanism is introduced utilizing proportional-integral-derivative (PID) to modulate basal pancreatic insulin secretion rates in virtual patients. The result is virtual patients who behave realistically in simulated oral glucose tolerance tests and insulin tolerance tests and match clinically observed responses. Finally, in silico trials are used to simulate clinical conditions and test the developed control system under realistic conditions. Under normal conditions the control system is able to tightly control glucose concentrations within the target zone while avoiding hypoglycemia. To safely counteract the effect of faulty CGMs a system to detect sensor error and request CGM recalibration is introduced. Simulated in silico tests of this system results in accurate detection of excessive error leading to higher quality control and hypoglycemia reduction

Next-generation, personalised, model-based critical care medicine : a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

© 2018 The Author(s). Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care

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

A model-based clinically-relevant chemotherapy scheduling algorithm for anticancer agents

Chemotherapy is the most commonly employed method for systemic cancer treatment of solid tumors and their metastases. The balance between cancer cell elimination and host toxicity minimization remains a challenge for clinicians when deploying chemotherapy treatments. Our approach explicitly incorporates treatment-induced toxicities into the schedule design. As a case study, we synthesize administration schedules for docetaxel, a widely used chemotherapeutic employed as a monoagent or in combination for the treatment of a variety of cancers. The primary adverse effect of docetaxel treatment is myelosuppression, characterized by neutropenia, a low plasma absolute neutrophil count (ANC). Through the use of model-based systems engineering tools, this thesis provides treatment schedules for docetaxel and its combination therapies that reduce toxic side effects and improve patient outcomes. The current algorithm employs models of tumor growth, drug pharmacokinetics, and pharmacodynamics for both anticancer effects and toxicity, as characterized by ANC. Also included is a toxicity-rescue therapy, with granulocyte colony stimulating factor (G-CSF) that serves to elevate ANC. The single-agent docetaxel chemotherapy schedule minimizes tumor volume over a multi-cycle horizon, subject to toxicity and logistical constraints imposed by clinical practice.This single-agent chemotherapy scheduling formulation is extended to combination chemotherapy using docetaxel-cisplatin or docetaxel-carboplatin drug pairs. The two platinum agents display different toxicities, with cisplatin exhibiting kidney function damage and carboplatin demonstrating the same myelosuppression effects as docetaxel. These case studies provide two different challenges to the algorithm: (i) cisplatin scheduling significantly increases the number of variables and constraints, thereby challenging the computational engine and formulation; (ii) carboplatin's overlapping toxicity tests the ability of the algorithm to schedule drugs with different mechanisms of action (they act in different phases of the cellular growth cycle) with the same toxic side effects. The simulated results demonstrate the algorithms flexibility, in scheduling both docetaxel and cisplatin or carboplatin treatments for effective tumor elimination and clinically acceptable toxicties. Overall, a clinically-relevant chemotherapy scheduling optimization algorithm is provided for designing single agent and combination chemotherapies, when toxicity and pharmacokinetic/pharmacodynamic information is available. Furthermore, the algorithm can be extended to patient-specfic treatment by updating the pharmacokinetic/pharmacodynamic models as data are collected during treatment

Microfluidics for Biosensing

There are 12 papers published with 8 research articles, 3 review articles and 1 perspective. The topics cover: Biomedical microfluidics Lab-on-a-chip Miniaturized systems for chemistry and life science (MicroTAS) Biosensor development and characteristics Imaging and other detection technologies Imaging and signal processing Point-of-care testing microdevices Food and water quality testing and control We hope this collection could promote the development of microfluidics and point-of-care testing (POCT) devices for biosensing

Multiscale Mathematical Modeling of the Absorptive and Mucociliary Pathophysiology of Cystic Fibrosis Lung Disease

Airway disease is the primary cause of mortality for the over 70,000 patients with Cystic Fibrosis (CF) worldwide. It is characterized by lung infection, inflammation, and impaired mucociliary clearance (MCC) arising from depletion of the airway surface liquid (ASL) at the organ-scale. Dysfunction in the CF transmembrane conductance regulator protein causes dysregulation in ion and liquid transport alone and via other transport-related proteins. Analysis of cell-system interactions is experimentally complex, however, and motivates the use of mechanistic mathematical models that can also be used to design and optimize treatments for the disease. Tc99m or In111-labeled DTPA (DTPA) are small-molecule radiological probes that allow for observation of paracellular liquid convection and solute transport at cellular and organ scales, respectively. Previous work has shown that DTPA is hyperabsorbed in CF in a manner that strongly correlates with ASL hyperabsorption. The models of this dissertation describe, in part, the mechanisms that underlie this correlation. At the lung-scale, a physiologically motivated pharmacokinetic model was developed to describe the action of hypertonic saline (HS) as an inhaled therapy in CF. This model predicts that MCC is reduced in patients with CF because they have a reduced fraction of functional ciliated airway -- a model parameter -- that is increased via HS-induced airway rehydration. This prediction was verified \textit{in vitro} in human bronchial epithelial (HBE) cultures. A separate, cell-scale model accurately characterizes transcellular liquid transport in HBE cultures using transport parameters that agree well with previously reported values, producing ion flux estimates from the model fit to ASL and DTPA absorption that were similar to known physiological values. It also implicates diminished constitutive Cl¬- secretion in ASL dehydration but suggests that reduced paracellular integrity is the predominant factor leading to hyperabsorption in CF. The cell- and lung-scale models were then used to analyze treatment failure and suggest modifications of a clinical trial, which is the first indication of the utility of airway transport models in designing treatments for patients with CF