Publications
551 results found
Rawson TM, Ahmad R, Toumazou C, et al., 2019, Artificial intelligence can improve decision-making in infection management, Nature Human Behaviour, Vol: 3, Pages: 543-545, ISSN: 2397-3374
Antibiotic resistance is an emerging global danger. Reaching responsible prescribing decisions requires the integration of broad and complex information. Artificial intelligence tools could support decision-making at multiple levels, but building them needs a transparent co-development approach to ensure their adoption upon implementation.
Chen C-H, Toumazou C, 2019, Personalized Expert Recommendation Systems for Optimized Nutrition, TRENDS IN PERSONALIZED NUTRITION, Editors: Galanakis, Publisher: ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD, Pages: 309-338, ISBN: 978-0-12-816403-7
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- Citations: 4
Zhao Z, Li K, Toumazou C, et al., 2019, A computational model for anti-cancer drug sensitivity prediction, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, ISSN: 2163-4025
Cavallo FR, Mirza KB, Toumazou C, 2018, Links Between DNA-Based Diet and Salivary Leptin Hormone Concentration, IEEE Biomedical Circuits and Systems Conference (BioCAS) - Advanced Systems for Enhancing Human Health, Publisher: IEEE, Pages: 547-550, ISSN: 2163-4025
Khwaja M, Kalofonou M, Toumazou C, 2018, A Deep Autoencoder System for Differentiation of Cancer Types Based on DNA Methylation State, arXiv preprint arXiv:1810.01243
Ma D, Rodriguez-Manzano J, Lopez SDM, et al., 2018, Adapting ISFETs for Epigenetics: An Overview, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 12, Pages: 1186-1201, ISSN: 1932-4545
Hernandez B, Herrero P, Rawson TM, et al., 2018, Enhancing antimicrobial surveillance: an automated, dynamic and interactive approach, 18th International Congress on Infectious Disease, Publisher: Elsevier, Pages: 122-122, ISSN: 1201-9712
Gantier M, Kalofonou M, Toumazou C, 2018, A trapped charge compensation scheme for ISFET based translinear circuits, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE
A trapped charge compensation scheme for ISFET based translinear circuits is presented, as part of a system for prediction of cancer risk, based on DNA methylation. Each pixel is able to measure a DNA methylation ratio through pH-based measurements and by using in-pixel comparison to a tunable threshold, to output a result which indicates percentage of methylation used as a cancer score. The developed system was designed in a 0.35 μm CMOS technology and uses a novel trapped charge compensation scheme for ISFETs used in translinear circuits. The output scheme was able to compensate trapped charge of up to 380mV, with a ratio error below 5%, in a range of ratios between 50% and 80% which is generated from pH-based DNA methylation reactions.
Kulasekeram N, Wildner K, Mirza KB, et al., 2018, Reconfigurable Low-noise Multichannel Amplifier for Neurochemical Recording, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
Wildner K, Kulasekeram N, Mirza KB, et al., 2018, Live Demo: Reconfigurable Low-noise Multichannel Amplifier for Neurochemical Recording, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
Mirza K, Alenda A, Eftekhar A, et al., 2018, Influence of cholecystokinin-8 on compound nerve action potentials from ventral gastric vagus in rats, International Journal of Neural Systems, Vol: 28, ISSN: 0129-0657
Objective:Vagus Nerve Stimulation (VNS) has shown great promise as a potential therapy for anumber of conditions, such as epilepsy, depression and forNeurometabolic Therapies, especially fortreating obesity. The objective of this study was to characterise the left ventral subdiaphragmaticgastric trunk of vagus nerve (SubDiaGVN) and to analyse the influence of intravenous injection of guthormone cholecystokinin octapeptide (CCK-8) on compound nerve action potential (CNAP) observedon the same branch, with the aim of understanding the impact of hormones on VNS and incorporatingthe methods and results into closed loop implant design.Methods:The cervical region of the left vagus nerve (CerVN) of male Wistar rats was stimulatedwith electric current and the elicited CNAPs were recorded on the SubDiaGVN under four differentconditions:Control(no injection),Saline,CCK1(100 pmol/kg) andCCK2(1000 pmol/kg) injections.Results:We identified the presence of Aδ, B, C1, C2, C3 and C4 fibres with their respective velocityranges. Intravenous administration of CCKin vivoresults in selective, statistically significant reductionof CNAP components originating from A and B fibres, but with no discernible effect on the C fibresinn=7animals. The affected CNAP components exhibit statistically significant (pSaline−CCK1= 0.02andpSaline−CCK2= 0.007) higher normalized stimulation thresholds.Conclusion:This approach of characterising the vagus nerve can be used in closed loop systems todeterminewhento initiate VNS and also to tune the stimulation dose, which is patient specific andchanges over time.
Rawson T, Moore L, Castro Sanchez E, et al., 2018, Development of a patient-centred intervention to improve knowledge and understanding of antibiotic therapy in secondary care, Antimicrobial Resistance and Infection Control, Vol: 7, ISSN: 2047-2994
Introduction: We developed a personalised antimicrobial information module co-designed with patients. This study aimed to evaluate the potential impact of this patient-centred intervention on short-term knowledge and understanding of antimicrobial therapy in secondary care. Methods:Thirty previous patients who had received antibiotics in hospital within 12 months were recruited to co-design an intervention to promote patient engagement with infection management. Two workshops, containing five focus-groups were held. These were audio-recorded. Data were analysed using a thematic framework developed deductively based on previous work. Line-by-line coding was performed with new themes added to the framework by two researchers. This was used to inform the development of a patient information module, embedded within an electronic decision support tool (CDSS). The intervention was piloted over a four-week period at Imperial College Healthcare NHS Trust on 30 in-patients. Pre- and post-intervention questionnaires were developed and implemented to assess short term changes in patient knowledge and understanding and provide feedback on the intervention. Data were analysed using SPSS and NVIVO software. Results: Within the workshops, there was consistency in identified themes. The participants agreed upon and co-designed a personalised PDF document that could be integrated into an electronic CDSS to be used by healthcare professionals at the point-of-care. Their aim for the tool was to provide individualised practical information, signpost to reputable information sources, and enhance communication between patients and healthcare professionals.Eighteen out of thirty in-patients consented to participant in the pilot evaluation with 15/18(83%) completing the study. Median (range) age was 66(22-85) years. The majority were male (10/15;66%). Pre-intervention, patients reported desiring further information regarding their infections and antibiotic therapy, including side effects
Chen C-H, Karvela M, Sohbati M, et al., 2018, PERSON - Personalized Expert Recommendation System for Optimized Nutrition, IEEE Transactions on Biomedical Circuits and Systems, Vol: 12, Pages: 151-160, ISSN: 1932-4545
The rise of personalized diets is due to the emergence of nutrigenetics and genetic tests services. However, the recommendation system is far from mature to provide personalized food suggestion to consumers for daily usage. The main barrier of connecting genetic information to personalized diets is the complexity of data and the scalability of the applied systems. Aiming to cross such barriers and provide direct applications, a personalized expert recommendation system for optimized nutrition is introduced in this paper, which performs direct to consumer personalized grocery product filtering and recommendation. Deep learning neural network model is applied to achieve automatic product categorization. The ability of scaling with unknown new data is achieved through the generalized representation of word embedding. Furthermore, the categorized products are filtered with a model based on individual genetic data with associated phenotypic information and a case study with databases from three different sources is carried out to confirm the system.
Cork SC, Eftekhar A, Mirza KB, et al., 2018, Extracellular pH monitoring for use in closed-loop vagus nerve stimulation, Journal of Neural Engineering, Vol: 15, Pages: 1-11, ISSN: 1741-2552
Objective: Vagal nerve stimulation (VNS) has shown potential benefits for obesity treatment; however, current devices lack physiological feedback, which limit their efficacy. Changes in extracellular pH (pHe) have shown to be correlated with neural activity, but have traditionally been measured with glass microelectrodes, which limit their in vivo applicability. Approach. Iridium oxide has previously been shown to be sensitive to fluctuations in pH and is biocompatible. Iridium oxide microelectrodes were inserted into the subdiaphragmatic vagus nerve of anaesthetised rats. Introduction of the gut hormone cholecystokinin (CCK) or distension of the stomach was used to elicit vagal nerve activity. Main results. Iridium oxide microelectrodes have sufficient pH sensitivity to readily detect changes in pHe associated with both CCK and gastric distension. Furthermore, a custom-made Matlab script was able to use these changes in pHe to automatically trigger an implanted VNS device. Significance. This is the first study to show pHe changes in peripheral nerves in vivo. In addition, the demonstration that iridium oxide microelectrodes are sufficiently pH sensitive as to measure changes in pHe associated with physiological stimuli means they have the potential to be integrated into closed-loop neurostimulating devices.
Hernandez Perez B, Herrero Viñas P, Miles Rawson T, et al., 2017, Supervised Learning for Infection Risk Inference Using Pathology Data, BMC Medical Informatics and Decision Making, Vol: 17, ISSN: 1472-6947
Background: Antimicrobial Resistance is threatening our ability to treat common infectious diseases and overuse of antimicrobials to treat human infections in hospitals is accelerating this process. Clinical Decision Support Systems (CDSSs) have been proven to enhance quality of care by promoting change in prescription practices through antimicrobial selection advice. However, bypassing an initial assessment to determine the existence of an underlying disease that justifies the need of antimicrobial therapy might lead to indiscriminate and often unnecessary prescriptions.Methods: From pathology laboratory tests, six biochemical markers were selected and combined with microbiology outcomes from susceptibility tests to create a unique dataset with over one and a half million daily profiles to perform infection risk inference. Outliers were discarded using the inter-quartile range rule and several sampling techniques were studied to tackle the class imbalance problem. The first phase selects the most effective and robust model during training using four-fold stratified cross-validation. The second phase evaluates the final model after isotonic calibration in scenarios with missing inputs and imbalanced class distributions. Results: More than 50\% of infected profiles have daily requested laboratory tests for the six biochemical markers with very promising infection inference results: area under the receiver operating characteristic curve (0.80-0.83), sensitivity (0.64-0.75) and specificity (0.92-0.97). Standardization consistently outperforms normalization and sensitivity is enhanced by using the SMOTE sampling technique. Furthermore, models operated without noticeable loss in performance if at least four biomarkers were available.Conclusion: The selected biomarkers comprise enough information to perform infection risk inference with a high degree of confidence even in the presence of incomplete and imbalanced data. Since they are commonly available in hospitals, Clini
Khwaja M, Kalofonou M, Toumazou C, 2017, A Deep Belief Network system for prediction of DNA methylation, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE
A Deep Belief Network architecture is proposed for prediction of DNA methylation characteristics across genetic regions. The proposed system uses an image analogous visualisation of DNA methylation features through an efficient mapping model. Implementation of this method has resulted in an accurate classification of DNA methylation for multiple CpG regions identified in cancer cell lines and has been designed to address variability in patterns found in a given human cell, regardless of their function or disease state. The proposed method is compared to time-tested supervised learning algorithms that include Support Vector Machine and Random Forest classifiers and has been validated using data from cancer cell lines. Using documented features, it achieves differentiation of DNA methylation states, while predicting distinct features with an average value of sensitivity 92%, specificity 99%, accuracy 95% and Matthew's Correlation Coefficient 0.91. The feature set coupled with the deep learning model makes the system efficient for DNA methylation prediction, while being independent of the data set used.
Moser N, Rodriguez-Manzano J, Yu L-S, et al., 2017, Live Demonstration: A CMOS-Based ISFET Array for Rapid Diagnosis of the Zika Virus, IEEE International Symposium on Circuits and Systems (ISCAS) 2017, ISSN: 2379-447X
We demonstrate a diagnostics platform which integrates an ISFET array and a temperature control loop for isothermal DNA detection. The controller maintains a temperature of 63◦C to perform nucleic acid amplification which is detected by the on-chip sensors. The 32x32 ISFET array is first calibrated to cancel trapped charge and then measures the change in the pH of the reaction. The sensor data is sent to a microcontroller and the reaction is monitored in real-time using a MATLAB interface. Experiments confirm a change of 0.9 pH when tested for the presence of RNA associated with the Zika virus.
Mirza KB, Zuliani C, Hou B, et al., 2017, Injection moulded microneedle sensor for real-time wireless pH monitoring, 39th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 189-192, ISSN: 1094-687X
This paper describes the development of an array of individually addressable pH sensitive microneedles using injection moulding and their integration within a portable device for real-time wireless recording of pH distributions in biological samples. The fabricated microneedles are subjected to gold patterning followed by electrodeposition of iridium oxide to sensitize them to 0.07 units of pH change. Miniaturised electronics suitable for the sensors readout, analog-to-digital conversion and wireless transmission of the potentiometric data are embodied within the device, enabling it to measure real-time pH of soft biological samples such as muscles. In this paper, real-time recording of the cardiac pH distribution, during ischemia followed by reperfusion cycles in cardiac muscles of male Wistar rats has been demonstrated by using the microneedle array.
Mirza KB, Kulasekeram N, Toumazou C, 2017, Current feedback neural amplifier with real time electrode offset suppression, International Midwest Symposium on Circuits and Systems (MWSCAS), Publisher: IEEE, Pages: 1077-1080, ISSN: 1548-3746
This paper describes a direct coupled neural amplifier with active electrode offset suppression in order to avoid large coupling capacitors and complex chopper circuits. It describes a novel feedback scheme, where a low pass current mode feedback is applied to a regulated telescopic cascode amplifier, at the cascode nodes by using a modified transconductance block. This solution leads to fully differential input-differential output direct coupled neural amplifier, achieving a DC offset suppression range of ±200 mV, a chip area of 0.078 mm 2 per channel and an input referred noise of 2.5 μV rms over 1 Hz-5kHz bandwidth.
Moser N, Panteli C, Ma D, et al., 2017, Improving the pH Sensitivity of ISFET Arrays withReactive Ion Etching, BioCAS 2017, Publisher: IEEE
In this paper, we report a method to improvesensitivity for CMOS ISFET arrays using Reactive Ion Etching(RIE) as a post-processing technique. The process etches awaythe passivation layers of the commercial CMOS process, using anoxygen (O2) and sulfur hexafluoride (SF6) plasma. The resultingattenuation and pH sensitivity are characterised for five diesetched for 0 to 15 minutes, and we demonstrate that capacitiveattenuation is reduced by 196% and pH sensitivity increasedby 260% compared to the non-etched equivalent. The spread oftrapped charge is also reduced which relaxes requirements on theanalogue front-end. The technique significantly improves the performanceof the fully-integrated sensing system for applicationssuch as DNA detection.
Herrero P, Bondia J, Adewuyi O, et al., 2017, Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability, Computer Methods and Programs in Biomedicine, Vol: 146, Pages: 125-131, ISSN: 0169-2607
Background and ObjectiveCurrent prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain.MethodsIn this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake.ResultsOverall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4 vs. 131.8 ± 4.2 mg/dl; perce
Mirza K, Zuliani C, Hou B, et al., 2017, An Individually Addressable Microneedle Device for Real-Time Wireless pH Monitoring, 9th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC'17)
This paper describes the development of an array of individually addressable pH sensitive microneedles using injection moulding and their integration within a portable device for real-time wireless recording of pH distributions in biological samples. The fabricated microneedles are subjected to gold pat- terning followed by electrodeposition of iridium oxide to sensitize them to 0.07 units of pH change. Miniaturised electronics suitable for the sensors readout, analog-to-digital conversion and wireless transmission of the potentiometric data are embodied within the device, enabling it to measure real-time pH of soft biological samples such as muscles. In this paper, real-time recording of the cardiac pH distribution, during ischemia followed by reperfusion cycles in cardiac muscles of male Wistar rats has been demonstrated by using the microneedle array.
Mirza K, Zuliani C, Hou B, et al., 2017, An Individually Addressable Microneedle Device for Real-Time Wireless pH Monitoring, 39th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC'17)
This paper describes the development of an array of individually addressable pH sensitive microneedles using injection moulding and their integration within a portable device for real-time wireless recording of pH distributions in biological samples. The fabricated microneedles are subjected to gold pat- terning followed by electrodeposition of iridium oxide to sensitize them to 0.07 units of pH change. Miniaturised electronics suitable for the sensors readout, analog-to-digital conversion and wireless transmission of the potentiometric data are embodied within the device, enabling it to measure real-time pH of soft biological samples such as muscles. In this paper, real-time recording of the cardiac pH distribution, during ischemia followed by reperfusion cycles in cardiac muscles of male Wistar rats has been demonstrated by using the microneedle array.
Herrero Vinas P, Pesl P, Reddy M, et al., 2017, Safety layer for an insulin delivery system, Advanced Technologies & Treatments for Diabetes, Publisher: Mary Ann Liebert, ISSN: 1520-9156
Pesl P, Herrero P, Reddy M, et al., 2017, Case-Based Reasoning for Insulin Bolus Advice., J Diabetes Sci Technol, Vol: 11, Pages: 37-42
BACKGROUND: Insulin bolus calculators assist people with Type 1 diabetes (T1D) to calculate the amount of insulin required for meals to achieve optimal glucose levels but lack adaptability and personalization. We have proposed enhancing bolus calculators by the means of case-based reasoning (CBR), an established problem-solving methodology, by individualizing and optimizing insulin therapy for various meal situations. CBR learns from experiences of past similar meals, which are described in cases through a set of parameters (eg, time of meal, alcohol, exercise). This work discusses the selection, representation and effect of case parameters used for a CBR-based Advanced Bolus Calculator for Diabetes (ABC4D). METHODS: We analyzed the usage and effect of selected parameters during a pilot study (n = 10), where participants used ABC4D for 6 weeks. Retrospectively, we evaluated the effect of glucose rate of change before the meal on the glycemic excursion. Feedback from study participants about the choice of parameters was obtained through a nonvalidated questionnaire. RESULTS: Exercise and alcohol were the most frequently used parameters, which was congruent with the feedback from study participants, who found these parameters most useful. Furthermore, cases including either exercise or alcohol as parameter showed a trend in reduction of insulin at the end of the study. A significant difference ( P < .01) was found in glycemic outcomes for meals where glucose rate of change was rising compared to stable rate of change. CONCLUSIONS: Results from the 6-week study indicate the potential benefit of including parameters exercise, alcohol and glucose-rate of change for insulin dosing decision support.
Mirza KB, Wildner K, Kulasekeram N, et al., 2017, Live Demo: Platform for Closed Loop Neuromodulation Based on Dual Mode Biosignals, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, ISSN: 2163-4025
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Cooke GS, Gurrala R, Harrison E, et al., 2016, Novel pH sensing semiconductor for point-of-care detection of HIV-1 viremia, Scientific Reports, Vol: 6, ISSN: 2045-2322
The timely detection of viremia in HIV-infected patients receiving antiviral treatment is key to ensuring effective therapy and preventing the emergence of drug resistance. In high HIV burden settings, the cost and complexity of diagnostics limit their availability. We have developed a novel complementary metal-oxide semiconductor (CMOS) chip based, pH-mediated, point-of-care HIV-1 viral load monitoring assay that simultaneously amplifies and detects HIV-1 RNA. A novel low-buffer HIV-1 pH-LAMP (loop-mediated isothermal amplification) assay was optimised and incorporated into a pH sensitive CMOS chip. Screening of 991 clinical samples (164 on the chip) yielded a sensitivity of 95% (in vitro) and 88.8% (on-chip) at >1000 RNA copies/reaction across a broad spectrum of HIV-1 viral clades. Median time to detection was 20.8 minutes in samples with >1000 copies RNA. The sensitivity, specificity and reproducibility are close to that required to produce a point-of-care device which would be of benefit in resource poor regions, and could be performed on an USB stick or similar low power device.
Moser N, Lande TS, Toumazou C, et al., 2016, ISFETs in CMOS and Emergent Trends in Instrumentation: A Review, IEEE Sensors Journal, Vol: 16, Pages: 6496-6514, ISSN: 1530-437X
Over the past decade, ion-sensitive field-effect transistors (ISFETs) have played a major role in enabling the fabrication of fully integrated CMOS-based chemical sensing systems. This has allowed several new application areas, with the most promising being the fields of ion imaging and full genome sequencing. This paper reviews the new trends in front-end topologies toward the design of ISFET sensing arrays in CMOS for these new applications. More than a decade after the review of the ISFET by Bergveld which summarized the state of the art in terms of device and early readout circuity, we describe the evolution in terms of device macromodel and identify the main sensor challenges for current designers. We analyze the techniques that have been reported for both ISFET instrumentation and compensation, and conclude that topologies are focusing on device adaptation for offset and drift cancellation, as opposed to system compensation which are often not as robust. Guidelines are provided to build a tailored CMOS ISFET array, emphasizing that the needs in terms of applications are the keys to selecting the right pixel architecture. Over the next few years, the race for the largest and densest array is likely to be put on hold to allow the research to focus on new pixel topologies, ultimately leading to the development of reliable and scalable arrays. A wide range of new applications are expected to motivate this paper for at least another decade.
Koutsos A, Kalofonou M, Sohbati M, et al., 2016, Epigenetic-IC: A fully integrated sensing platform for epigenetic reaction monitoring, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 325-328, ISSN: 2379-447X
This paper presents a pH-based System-on-Chip DNA methylation quantification platform for real time monitoring of DNA methylation ratio in target genes. The architecture forms a novel autonomous system, capable of providing diagnostic information on the progression of a disease, notably cancer. The system is equipped with drift and trapped charge compensation schemes based on differential measurements and an auto-calibration algorithm. The simulated system in 0.35μm CMOS technology achieves a power consumption of 0.997mW, with a DNA methylation ratio output sensitivity of 0.1%. The ISFET-based detection platform occupies a total of 901um2 and allows the calculation of DNA methylation ratio in pH-monitored DNA methylation based reactions.
Reddy M, Pesl P, Xenou M, et al., 2016, Clinical Safety and Feasibility of the Advanced Bolus Calculator for Type 1 Diabetes Based on Case-Based Reasoning: A 6-Week Nonrandomized Single-Arm Pilot Study, Diabetes Technology & Therapeutics, Vol: 18, Pages: 487-493, ISSN: 1557-8593
Background: The Advanced Bolus Calculator for Diabetes (ABC4D) is an insulin bolus dose decision support system based on case-based reasoning (CBR). The system is implemented in a smartphone application to provide personalized and adaptive insulin bolus advice for people with type 1 diabetes. We aimed to assess proof of concept, safety, and feasibility of ABC4D in a free-living environment over 6 weeks.Methods: Prospective nonrandomized single-arm pilot study. Participants used the ABC4D smartphone application for 6 weeks in their home environment, attending the clinical research facility weekly for data upload, revision, and adaptation of the CBR case base. The primary outcome was postprandial hypoglycemia.Results: Ten adults with type 1 diabetes, on multiple daily injections of insulin, mean (standard deviation) age 47 (17), diabetes duration 25 (16), and HbA1c 68 (16) mmol/mol (8.4 (1.5) %) participated. A total of 182 and 150 meals, in week 1 and week 6, respectively, were included in the analysis of postprandial outcomes. The median (interquartile range) number of postprandial hypoglycemia episodes within 6-h after the meal was 4.5 (2.0–8.2) in week 1 versus 2.0 (0.5–6.5) in week 6 (P = 0.1). No episodes of severe hypoglycemia occurred during the study.Conclusion: The ABC4D is safe for use as a decision support tool for insulin bolus dosing in self-management of type 1 diabetes. A trend suggesting a reduction in postprandial hypoglycemia was observed in the final week compared with week 1.
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