Using a causal smoothing to improve the performance of an on-line neural network glucose prediction algorithm

This work evaluates a spline-based smoothing method applied to the output of a glucose predictor. Methods:Our on-line prediction algorithm is based on a neural network model (NNM). We trained/validated the NNM with a prediction horizon of 30 minutes using 39/54 profiles of patients monitored with the Guardian® Real-Time continuous glucose monitoring system The NNM output is smoothed by fitting a causal cubic spline. The assessment parameters are the error (RMSE), mean delay (MD) and the high-frequency noise (HFCrms). The HFCrms is the root-mean-square values of the high-frequency components isolated with a zero-delay non-causal filter. HFCrms is 2.90±1.37 (mg/dl) for the original profiles

Artificial pancreas using a personalized rule-based controller achieves overnight normoglycemia in patients with type 1 diabetes

Objective: This study assessed the efficacy of a closed-loop (CL) system consisting of a predictive rule-based algorithm (pRBA) on achieving nocturnal and postprandial normoglycemia in patients with type 1 diabetes mellitus (T1DM). The algorithm is personalized for each patient’s data using two different strategies to control nocturnal and postprandial periods. Research Design and Methods: We performed a randomized crossover clinical study in which 10 T1DM patients treated with continuous subcutaneous insulin infusion (CSII) spent two nonconsecutive nights in the research facility: one with their usual CSII pattern (open-loop [OL]) and one controlled by the pRBA (CL). The CL period lasted from 10 p.m. to 10 a.m., including overnight control, and control of breakfast. Venous samples for blood glucose (BG) measurement were collected every 20 min. Results: Time spent in normoglycemia (BG, 3.9–8.0 mmol/L) during the nocturnal period (12 a.m.–8 a.m.), expressed as median (interquartile range), increased from 66.6% (8.3–75%) with OL to 95.8% (73–100%) using the CL algorithm (P<0.05). Median time in hypoglycemia (BG, <3.9 mmol/L) was reduced from 4.2% (0–21%) in the OL night to 0.0% (0.0–0.0%) in the CL night (P<0.05). Nine hypoglycemic events (<3.9 mmol/L) were recorded with OL compared with one using CL. The postprandial glycemic excursion was not lower when the CL system was used in comparison with conventional preprandial bolus: time in target (3.9–10.0 mmol/L) 58.3% (29.1–87.5%) versus 50.0% (50–100%). Conclusions: A highly precise personalized pRBA obtains nocturnal normoglycemia, without significant hypoglycemia, in T1DM patients. There appears to be no clear benefit of CL over prandial bolus on the postprandial glycemi

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Propuesta de un algoritmo de control en lazo cerrado para la diabetes tipo 1

La diabetes mellitus es un trastorno del metabolismo de los carbohidratos producido por la insuficiente o nula producción de insulina o la reducida sensibilidad a esta hormona. Es una enfermedad crónica con una mayor prevalencia en los países desarrollados debido principalmente a la obesidad y la vida sedentaria. La diabetes Tipo 1 es una enfermedad autoinmune en la que son destruidas las células beta del páncreas, que producen la insulina, y por tanto es necesaria la administración de insulina exógena. Por tanto, el paciente diabético Tipo 1 debe seguir una terapia con insulina administrada por la vía subcutánea que debe estar adaptada a sus necesidades metabólicas y a sus hábitos de vida. El paciente es el principal responsable de su control metabólico y debe ajustar las dosis de insulina para adecuarlas a sus hábitos diarios. Su experiencia y conocimiento de la enfermedad son las claves para mantener la glucosa en valores similares a las personas sanas, pero es una tarea compleja ya que la concentración de glucosa en sangre se ve afectada por la ingestión de carbohidratos, el ejercicio físico, variaciones diarias de la sensibilidad a la insulina, el ciclo menstrual, la fiebre, la interacción con fármacos, etc. La investigación médica en la cura de la diabetes Tipo 1 está explorando la terapia celular, la medicina regenerativa y el trasplante de páncreas. Hasta que exista una cura médica eficaz, la tecnología actual permite abordar el desarrollo del denominado “páncreas endocrino artificial”. Este sistema consta de un sensor continuo de glucosa, una bomba de infusión de insulina y un algoritmo de control en lazo cerrado, la Tesis Doctoral se centra en la propuesta de un controlador. El objetivo del controlador es mantener la concentración de glucosa en sangre en rango de normoglucemia, con una mínima variación e independiente de las perturbaciones. El algoritmo propuesto se ha definido mediante la inversión de un modelo matemático de la dinámica insulina-glucosa. El retardo del sensor de glucosa subcutánea, debido al tiempo de transporte de la glucosa en plasma al líquido intersticial, y el retardo debido al tiempo de absorción de la insulina administrada por vía subcutánea, desvían el comportamiento del controlador del esperado, aumentando las variaciones de la glucemia y prolongando los periodos transitorios. Para reducir estas desviaciones, en este trabajo de investigación se han propuesto métodos complementarios como son la adaptación del controlador al paciente, la utilización de un sistema de predicción y la definición de un modo de funcionamiento en lazo semicerrado. La validación del algoritmo se ha realizado principalmente mediante experimentos en simulación utilizando una población de pacientes sintéticos. Para evaluar los experimentos se han empleado estadísticos de primer orden, tiempo en zona de normalidad y algunos más específicos como es el índice de riesgo de Kovatchev. Se han realizado dos experiencias clínicas en el Hospital Sant Pau de Barcelona que han permitido aprender de la puesta en práctica del algoritmo con un paciente en condiciones reales. Los resultados en simulación demuestran que el controlador inverso cuando funciona en lazo cerrado completo, al igual que otros métodos de control planteados en la comunidad científica, tiene dificultades para controlar la glucosa postprandial pero muestra un buen control interprandial. En el modo lazo semicerrado, en el que el paciente anticipa una parte de la dosis prandial, los picos glucémicos postprandiales se reducen aumentando la estabilidad metabólica. Cuando se dota al algoritmo de un sistema de adaptación, disminuyen tanto el tiempo en hiperglucemia, como la glucemia media y la dispersión de la glucosa en la población. La introducción adicional en el algoritmo de un sistema predictor mejora su comportamiento y lo acerca a la terapia convencional con bomba optimizada manualmente. Las aportaciones más relevantes de la Tesis son: la metodología de la inversión de un modelo compartimental de la dinámica glucosa-insulina para la definición de un controlador en lazo cerrado de la diabetes Tipo 1; el método de adaptación utilizando un modelo de referencia y la regla del MIT sobre un modelo compartimental; los métodos de seguridad basados en histéresis y tendencia que fuerzan la suspensión de la bomba para evitar hipoglucemia; el método de inicialización del controlador utilizando la información disponible de la terapia manual; la metodología de la inversión de un modelo paramétrico de la dinámica glucosa-insulina identificada con un modelo autorregresivo con variable exógena y el método de predicción mediante el uso de un modelo paramétrico que modela al sistema glucorregulatorio. SUMMARY The diabetes mellitus is a metabolic disorder caused by a poor or null insulin secretion or a reduced sensibility to this hormone. Diabetes is a chronic disease with a higher prevalence in the industrialized countries, mainly due to obesity and the sedentary life. Type 1 diabetes is a self-immune disease where the beta cells of the pancreas, which are the responsible of secreting insulin, are damaged. Therefore, it is necessary an exogenous delivery of insulin. The Type 1 diabetic patient has to follow an insulin therapy which should be adjusted to his/her metabolic needs and life style. Patients are the main responsible of their metabolic control and they must adjust the insulin doses to their daily habits. Their experience and their knowledge about the disease are the keys to keep the glucose within normoglycemic ranges. But decisions are complex because the plasma glucose concentration is modified not only by the carbohydrate intakes, but also by the physical activity, circadian variations of insulin sensitivity, the menstruation cycle, illness, some drugs, etc. The medical research to cure Type 1 diabetes is focused in cellular therapy, regenerative medicine and pancreas transplantation. Until a medical cure was efficient, the current technology enables the development of the named “endocrine artificial pancreas”, which is composed by a continuous glucose sensor, an insulin infusion pump and a close loop control algorithm. This Doctoral Thesis is focused in the design and evaluation of a closed-loop controller. The controller goal is to keep the blood glucose concentration into a normoglycemic range, with minimal deviations and independently of disturbances. The proposed algorithm is defined by the inversion of a dynamic insulin-glucose mathematical model. Glucose sensing and insulin absorption delays deviate the controller behavior from the expected one, increasing glucose variability and the transients periods. The subcutaneous glucose sensor delay is due to the transport time of the plasma glucose to interstitial fluid. In order to decrease the delays’ effects, in this Doctoral Thesis we propose the use of complementary methods, such as: the adaption of the controller to the patient, the inclusion of a prediction system and the definition of a semiclosed loop operation mode. The algorithm assessment has been mainly made by in silico experiments using a population of synthetic patients. To assess the results we used fist order statistical parameters, time in normoglycemic and other more specific parameters such as the Kovatchev’s risk index. Two clinical experiments have been carried out at the Sant Pau Hospital (Barcelona) that have allowed us to learn the real controller operation when it is applied to a real patient. The simulation results demonstrate that the full closed loop inverse controller has difficulties to control the postprandial glucose, as reported by other methods presented in the literature, but it shows a good interprandial control. In semiclosed loop operation the postprandial glucose peaks are reduced, increasing the metabolic stability. When the adaption is used, we observe a decrease in the time in hyperglycemia, the glucose average and the population dispersion. The addition of a predictor algorithm improves the controller behavior, obtaining a glucose control close to the one of a conventional insulin pump therapy that is manually optimized. The main contributions of this doctoral thesis are: the methodology for the inversion of a insulin-glucose dynamics compartmental model for the definition of a closed loop controller in diabetes Type 1; the adaptive method based on a reference model and the MIT rule applied to a compartmental model; the pump suspension methods to avoid hypoglycemia; the controller initialization method using the information available before the close loop starts; the parametric model inversion methodology that uses an autorregresive model with exogenous variable identified with glucose and insulin data; and the parametric prediction method based on a identified model

Luenberger observer with nonlinear structure applied to diabetes type 1

In this work a Luenberger observer (LO) for type 1 diabetes is established using the Hovorka?s model (HM). The HM is linearized around an operating point and the eigenvalues are calculated. The LO is designed relocating the HM eigenvalues through the Ackermann?s methodology for linear observers where the proposed LO keeps the nonlinear structure of the model system. The LO is parameterized and tuned with the mean from six virtual patients of HM. Once the observer performance is reliable estimating the state space variables for HM, the virtual patients are changed by patients of Bergman?s model in order to test the observer behavior under unknown dynamics. These estimated variables constitute the ones corresponding to HM. The variables are estimated by the data computational processing which correspond to the insulin (input) and glucose (output) of the virtual patients. The estimated variables by the LO are very similar for virtual patients generated by both models, where the parameter FIT is used to quantify the performance of the observer. The computational implementation of the LO is useful tool to estimate the unmeasured variables in diabetic patients so they can be used in the artificial pancreas

Linear time-varying Luenberger observer applied to diabetes

We present a linear time-varying Luenberger observer (LTVLO) using compartmental models to estimate the unmeasurable states in patients with type 1 diabetes. The LTVLO proposed is based on the linearization in an operation point of the virtual patient (VP), where a linear time-varying system is obtained. LTVLO gains are obtained by selection of the asymptotic eigenvalues where the observability matrix is assured. The estimation of the unmeasurable variables is done using Ackermann’s methodology. The Lyapunov approach is used to prove the stability of the time-varying proposal. In order to evaluate the proposed methodology, we designed three experiments: A) VP obtained with Bergman’s minimal model, B) VP obtained with Hovorka’s model, and C) real patient data set. For both experiments A) and B), it is applied a meal plan to the VP, where the dynamic response of each state model is compared to the response of each variable of the time-varying observer. Once the observer is obtained in experiment B), the proposal is applied to experiment C) with data extracted from real patients and the unmeasurable state space variables are obtained with the LTVLO. LTVLO methodology has the feature of being updated each time instant to estimate the states under a known structure. The results are obtained using simulation with M atlabTM and SimulinkTM. The LTVLO estimates the unmeasurable states from in silico patients with high accuracy by means of the update of Luenberger gains at each iteration. The accuracy of the estimated state space variables is validated through the fit parameter

Luenberger observer with nonlinear structure applied to diabetes type 1

In this work a Luenberger observer (LO) for type 1 diabetes is established using the Hovorka?s model (HM). The HM is linearized around an operating point and the eigenvalues are calculated. The LO is designed relocating the HM eigenvalues through the Ackermann?s methodology for linear observers where the proposed LO keeps the nonlinear structure of the model system. The LO is parameterized and tuned with the mean from six virtual patients of HM. Once the observer performance is reliable estimating the state space variables for HM, the virtual patients are changed by patients of Bergman?s model in order to test the observer behavior under unknown dynamics. These estimated variables constitute the ones corresponding to HM. The variables are estimated by the data computational processing which correspond to the insulin (input) and glucose (output) of the virtual patients. The estimated variables by the LO are very similar for virtual patients generated by both models, where the parameter FIT is used to quantify the performance of the observer. The computational implementation of the LO is useful tool to estimate the unmeasured variables in diabetic patients so they can be used in the artificial pancreas

Screening policies, preventive measures and in-hospital infection of COVID-19 in global surgical practices

Background: In a surgical setting, COVID-19 patients may trigger in-hospital outbreaks and have worse postoperative outcomes. Despite these risks, there have been no consistent statements on surgical guidelines regarding the perioperative screening or management of COVID-19 patients, and we do not have objective global data that describe the current conditions surrounding this issue. This study aimed to clarify the current global surgical practice including COVID-19 screening, preventive measures and in-hospital infection under the COVID-19 pandemic, and to clarify the international gaps on infection control policies among countries worldwide. Methods: During April 2-8, 2020, a cross-sectional online survey on surgical practice was distributed to surgeons worldwide through international surgical societies, social media and personal contacts. Main outcome and measures included preventive measures and screening policies of COVID-19 in surgical practice and centers' experiences of in-hospital COVID-19 infection. Data were analyzed by country's cumulative deaths number by April 8, 2020 (high risk, &gt;5000; intermediate risk, 100-5000; low risk, &lt;100). Results: A total of 936 centers in 71 countries responded to the survey (high risk, 330 centers; intermediate risk, 242 centers; low risk, 364 centers). In the majority (71.9%) of the centers, local guidelines recommended preoperative testing based on symptoms or suspicious radiologic findings. Universal testing for every surgical patient was recommended in only 18.4% of the centers. In-hospital COVID-19 infection was reported from 31.5% of the centers, with higher rates in higher risk countries (high risk, 53.6%; intermediate risk, 26.4%; low risk, 14.8%; P &lt; 0.001). Of the 295 centers that experienced in-hospital COVID-19 infection, 122 (41.4%) failed to trace it and 58 (19.7%) reported the infection originating from asymptomatic patients/staff members. Higher risk countries adopted more preventive measures including universal testing, routine testing of hospital staff and use of dedicated personal protective equipment in operation theatres, but there were remarkable discrepancies across the countries. Conclusions: This large international survey captured the global surgical practice under the COVID-19 pandemic and highlighted the insufficient preoperative screening of COVID-19 in the current surgical practice. More intensive screening programs will be necessary particularly in severely affected countries/institutions