Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions.

Accurate blood glucose (BG) forecasting is key in diabetes management, as it allows preventive actions to mitigate harmful hypoglycemic/hyperglycemic episodes. Considering the encouraging results obtained by seasonal stochastic models in proof-of-concept studies, this work assesses the methodology in two datasets (open-loop and closed-loop) recorded in free-living conditions. First, similar postprandial glycemic profiles are grouped together with fuzzy C-means clustering. Then, a seasonal stochastic model is identified for each cluster. Finally, real-time BG forecasting is performed by weighting each model's prediction. The proposed methodology (named C-SARIMA) is compared to other linear and nonlinear black-box methods: autoregressive integrated moving average (ARIMA), its variant with input (ARIMAX), a feed-forward neural network (NN), and its modified version (NN-X) fed by BG, insulin, and carbohydrates (timing and dosing) information for several prediction horizons (PHs). In the open-loop dataset, C-SARIMA grants a median root-mean-squared error (RMSE) of 20.13 mg/dL (PH = 30) and 27.23 mg/dL (PH = 45), not significantly different from ARIMA and NN. Over a longer PH, C-SARIMA achieves an RMSE = 31.96 mg/dL (PH = 60) and RMSE = 33.91 mg/dL (PH = 75), significantly outperforming the ARIMA and NN, without significant differences from the ARIMAX for PH ≥ 45 and the NN-X for PH ≥ 60. Similar results hold on the closed-loop dataset: for PH = 30 and 45 min, the C-SARIMA achieves an RMSE = 21.63 mg/dL and RMSE = 29.67 mg/dL, not significantly different from the ARIMA and NN. On longer PH, the C-SARIMA outperforms the ARIMA for PH > 45 and the NN for PH > 60 without significant differences from the ARIMAX for PH ≥ 45. Although using less input information, the C-SARIMA achieves similar performance to other prediction methods such as the ARIMAX and NN-X and outperforming the CGM-only approaches on PH > 45min.

Impact of diabetes in patients waiting for invasive cardiac procedures during COVID-19 pandemic.

BACKGROUND: During COVID-19 pandemic, elective invasive cardiac procedures (ICP) have been frequently cancelled or postponed. Consequences may be more evident in patients with diabetes. OBJECTIVES: The objective was to identify the peculiarities of patients with DM among those in whom ICP were cancelled or postponed due to the COVID-19 pandemic, as well as to identify subgroups in which the influence of DM has higher impact on the clinical outcome. METHODS: We included 2,158 patients in whom an elective ICP was cancelled or postponed during COVID-19 pandemic in 37 hospitals in Spain. Among them, 700 (32.4%) were diabetics. Patients with and without diabetes were compared. RESULTS: Patients with diabetes were older and had a higher prevalence of other cardiovascular risk factors, previous cardiovascular history and co-morbidities. Diabetics had a higher mortality (3.0% vs. 1.0%; p = 0.001) and cardiovascular mortality (1.9% vs. 0.4%; p = 0.001). Differences were especially important in patients with valvular heart disease (mortality 6.9% vs 1.7% [p < 0.001] and cardiovascular mortality 4.9% vs 0.9% [p = 0.002] in patients with and without diabetes, respectively). In the multivariable analysis, diabetes remained as an independent risk factor both for overall and cardiovascular mortality. No significant interaction was found with other clinical variables. CONCLUSION: Among patients in whom an elective invasive cardiac procedure is cancelled or postponed during COVID-19 pandemic, mortality and cardiovascular mortality is higher in patients with diabetes, irrespectively on other clinical conditions. These procedures should not be cancelled in patients with diabetes.

Consequences of canceling elective invasive cardiac procedures during Covid-19 outbreak.

BACKGROUND: During COVID-19 pandemic in Spain, elective procedures were canceled or postponed, mainly due to health care systems overwhelming. OBJECTIVE: The objective of this study was to evaluate the consequences of interrupting invasive procedures in patients with chronic cardiac diseases due to the COVID-19 outbreak in Spain. METHODS: The study population is comprised of 2,158 patients that were pending on elective cardiac invasive procedures in 37 hospitals in Spain on the 14th of March 2020, when a state of alarm and subsequent lockdown was declared in Spain due to the COVID-19 pandemic. These patients were followed-up until April 31th. RESULTS: Out of the 2,158 patients, 36 (1.7%) died. Mortality was significantly higher in patients pending on structural procedures (4.5% vs. 0.8%, respectively; p < .001), in those >80 year-old (5.1% vs. 0.7%, p < .001), and in presence of diabetes (2.7% vs. 0.9%, p = .001), hypertension (2.0% vs. 0.6%, p = .014), hypercholesterolemia (2.0% vs. 0.9%, p = .026) [Correction added on December 23, 2020, after first online publication: as per Dr. Moreno's request changes in p-values were made after original publication in Abstract.], chronic renal failure (6.0% vs. 1.2%, p < .001), NYHA > II (3.8% vs. 1.2%, p = .001), and CCS > II (4.2% vs. 1.4%, p = .013), whereas was it was significantly lower in smokers (0.5% vs. 1.9%, p = .013). Multivariable analysis identified age > 80, diabetes, renal failure and CCS > II as independent predictors for mortality. CONCLUSION: Mortality at 45 days during COVID-19 outbreak in patients with chronic cardiovascular diseases included in a waiting list due to cancellation of invasive elective procedures was 1.7%. Some clinical characteristics may be of help in patient selection for being promptly treated when similar situations happen in the future.

How often should ring pessaries be removed or changed in women with advanced POP? A prospective observational study.

INTRODUCTION AND HYPOTHESIS: This study aimed to evaluate the efficacy and safety of ring pessaries under continuous use for > 2 years. Our starting hypothesis was that their use without periodic removal, cleaning or replacement for between 24 to 48 months after insertion is safe and effective. METHODS: This was a prospective observational and descriptive study. One hundred one women who successfully completed the 24 first months of continuous use of a ring pessary were included and monitored for another 24 months. The objectives were to establish the percentage of patients maintaining its use 48 months after insertion, the reasons for discontinuation and the adverse events. Another purpose of this study was to determine the timing of replacement of the vaginal pessary in long-term users. RESULTS: Of the women, 92.1% (93/101) had successful pessary use, and it was discontinued by three patients (2.9%, 3/101); 76.2% (77/101) of the women continued pessary use after the end of the study, and in 16 (15.8%, 16/101) patients, after pessary removal, the prolapse disappeared and did not recur. Forty-five women (48.4%, 45/93) presented some adverse events that required temporary pessary removal. The most common one was an increase in vaginal discharge (73.3%, 33/45). In four women (8.9%, 4/45), the ring pessary was detected embedded in the vaginal epithelium. CONCLUSIONS: Continuous use of a ring pessary can be recommended for 2 years in hysterectomized women and for 4 years in non-hysterectomized women if there are no complications.

Glucose Prediction under Variable-Length Time-Stamped Daily Events: A Seasonal Stochastic Local Modeling Framework.

Accurate glucose prediction along a long-enough time horizon is a key component for technology to improve type 1 diabetes treatment. Subjects with diabetes might benefit from supervision and control systems that accurately predict risks and trigger corrective actions early enough with improved mitigation. However, large intra-patient variability poses big challenges to glucose prediction. In previous works by the authors, clustering and local modeling techniques with seasonal stochastic models proved to be efficient, allowing for good glucose prediction accuracy for long prediction horizons. Continuous glucose monitoring (CGM) data were partitioned into fixed-length postprandial time subseries and clustered with Fuzzy C-Means to collect similar behaviors, enforcing seasonality at each cluster after subseries concatenation. Then, seasonal stochastic models were identified for each cluster and local predictions were integrated into a global prediction. However, free-living conditions do not support the fixed-length partition of CGM data since daily events duration is variable. In this work, a new algorithm is provided to overcome this constraint, allowing better coping with patient's variability under variable-length time-stamped daily events in supervision and control applications. Besides predicted glucose, two real-time indices are additionally provided-a crispness index, indicating good representation of current glucose behavior by a single model, and a normality index, allowing for the detection of an abnormal glucose behavior (unusual according to registered historical data). The framework is tested in a proof-of-concept in silico study with ten patients over four month training data and two independent two month validation datasets, with and without abnormal behaviors, from the distribution version of the UVA/Padova simulator extended with diverse sources of intra-patient variability.

A Comprehensive Review of Continuous Glucose Monitoring Accuracy during Exercise Periods.

Continuous Glucose Monitoring (CGM) has been a springboard of new diabetes management technologies such as integrated sensor-pump systems, the artificial pancreas, and more recently, smart pens. It also allows patients to make better informed decisions compared to a few measurements per day from a glucometer. However, CGM accuracy is reportedly affected during exercise periods, which can impact the effectiveness of CGM-based treatments. In this review, several studies that used CGM during exercise periods are scrutinized. An extensive literature review of clinical trials including exercise and CGM in type 1 diabetes was conducted. The gathered data were critically analysed, especially the Mean Absolute Relative Difference (MARD), as the main metric of glucose accuracy. Most papers did not provide accuracy metrics that differentiated between exercise and rest (non-exercise) periods, which hindered comparative data analysis. Nevertheless, the statistic results confirmed that CGM during exercise periods is less accurate.

Is it safe and effective to maintain the vaginal pessary without removing it for 2 consecutive years?

INTRODUCTION AND HYPOTHESIS: The study was aimed at evaluating the safety and efficacy of ring pessaries without support under continuous use without periodic removal or replacement for the treatment of advanced pelvic organ prolapse (POP) in women for 2 consecutive years. METHODS: This study was a prospective observational study. A total of 123 women were recruited in a tertiary hospital from January 2013 to January 2016. The primary objective was the percentage of patients maintaining the use of the pessary after 24 months. The secondary objectives were the reasons for discontinuation and the adverse events in patients with successful fittings. RESULTS: A total of 115 patients (93.5%) had a successful fitting. Four patients died of non-pessary-related causes during the study and, one patient dropped out the follow-up so that finally, 110 patients were included in the efficacy analysis. Pessary use was maintained by 91.8% of the women at the end of the study. The adverse events rate was low (27.0%). The two main factors of interruption in the pessary use were: age (OR 0.93; 95% CI 0.87-0.99) and history of urinary urge incontinence (OR 0.33; 95% CI 0.11-0.96]). CONCLUSIONS: A high success rate and low adverse events rate were achieved in patients with advanced-stage POP with continuous pessary use for 24 months, indicating that a ring pessary could also be used without periodic removal for at least the first 2 years. This practice could reduce the number of control visits.

Effectiveness of ring pessaries versus vaginal hysterectomy for advanced pelvic organ prolapse. A cohort study.

INTRODUCTION AND HYPOTHESIS: This study aimed to evaluate the efficacy of the ring pessary compared with surgery as a primary treatment for advanced pelvic organ prolapse (POP) in non-hysterectomized, postmenopausal women. Our starting hypothesis was that the pessary is as effective as and less risky than surgery. METHODS: This study was a prospective observational study, which recruited 171 women with symptomatic advanced POP in a tertiary hospital for 30 months. They were treated according their preference with either surgery [77/171 (45.0%)] or vaginal ring pessary without support [94/171 (55.0%)]. The primary outcomes included the discontinuation of pessary use and the incidence of recurrent prolapse throughout the study. Secondary outcomes included complications categorized according to Clavien-Dindo classification. Descriptive statistics were used for demographic data. The mean and standard deviation were calculated for continuous variables, and continuity correction tests, Mann-Whitney U tests, and Fisher's exact tests were used for categorical variables. RESULTS: There was successful use of a pessary in 84.4% (76/90) of cases, and 89.6% (69/77) of patients did not have prolapse recurrence in the surgical group (>POP-Q 2). In the pessary group, the adverse event rate was 31.6%, and all were Clavien-Dindo grade I. Thirty patients [30/77 (39.0%)] had complications in the surgery group: 14.3% were Clavien-Dindo grade I (11/77), 10.4% were grade II (8/77), and 14.3% were grade III (11/77). CONCLUSIONS: The pessary is effective and has mild adverse events in non-hysterectomized, postmenopausal women with advanced POP.

Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration.

Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are "Mets" (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only "Mets" is also viable for a more immediate implementation of this correction into market devices.

Severe liver toxicity in a chronic myeloid leukemia patient probably induced by a drug interaction between imatinib and sertraline.

Our objective is to describe a chronic myeloid leukemia patient with a severe liver toxicity likely due to a drug-drug interaction between imatinib and sertraline. The patient started treatment with sertraline three months after starting imatinib. From the beginning of sertraline treatment, the patient developed vomiting, and five weeks later she developed a severe hepatic failure and was admitted to the hospital. The Naranjo nomogram showed a probable correlation between this adverse effect and the interaction between imatinib and sertraline. This interaction is extremely rare and the mechanism of action is not clear; it could be a mix of pharmacokinetic and pharmacodynamic processes. To our knowledge, this is the first case in medical literature of a severe liver toxicity due to an interaction between imatinib and sertraline. This interaction is also not described in the main secondary data sources, such as Lexicomp® and Micromedex®. However, due to the severity of this event, the hepatic function should be carefully monitored in patients treated with imatinib and sertraline.

Preventing parastomal hernias with systematic intraperitoneal specifically designed mesh.

BACKGROUND: Parastomal hernia is a very common complication after stoma formation. Current surgical techniques for repairing parastomal hernia have unsatisfactory results. We aim to assess our preliminary experience with prophylactic mesh placement at the time of stoma formation. METHODS: Data were prospectively recorded. A specifically designed mesh made of polyvinyl fluoride with central conduit (Dynamesh IPST®) was fixed using an intra-peritoneal onlay technique. Safety was evaluated by means of surgical data and frequency of mesh-related complications, efficacy by the rate of parastomal hernias. RESULTS: Thirty-four patients were included in the study. Three of them died before a year of follow up (not related to the stoma), so they were excluded. The other 31 patients (11 women and 20 men) were prospectively followed up after different pathologies resulting in a permanent colostomy. Twelve months after surgery CT-Scan imaging revealed two (6.4%) parastomal hernias, one of them already clinically suspected. During the follow up, 29% of the patients (n = 9) developed another type of hernia (incisional, inguinal or both). In five patients (16.1%) a light stomal retraction of the otherwise slightly prominent ostomy was observed. Median clinical follow-up was 17.5 months (range 12-34). CONCLUSION: Prophylactic parastomal mesh placement might be a safe and effective procedure with a potential to reduce the risk of parastomal hernia. Routine use of this technique should be further analysed.

GLP-1 Receptor Agonists Models for Type 1 Diabetes: A Narrative Review.

BACKGROUND: Glucagon-like peptide 1 (GLP-1) is a hormone that promotes insulin secretion, delays gastric emptying, and inhibits glucagon secretion. The GLP-1 receptor agonists have been developed as adjunctive therapies for type 2 diabetes to improve glucose control. Recently, there has been an interest in introducing GLP-1 receptor agonists as adjunctive therapies in type 1 diabetes alongside automatic insulin delivery systems. The preclinical validation of these systems often relies on mathematical simulators that replicate the glucose dynamics of a person with diabetes. This review aims to explore mathematical models available in the literature to describe GLP-1 effects to be used in a type 1 diabetes simulator. METHODS: Three databases were examined in the search for GLP-1 mathematical models. More than 1500 works were found after searching for specific keywords that were narrowed down to 39 works for full-text assessment. RESULTS: A total of 23 works were selected describing GLP-1 pharmacokinetics and pharmacodynamics. However, none of the found models was designed for type 1 diabetes. An analysis is included of the available models' features that could be translated into a GLP-1 receptor agonist model for type 1 diabetes. CONCLUSION: There is a gap in research in GLP-1 receptor agonists mathematical models for type 1 diabetes, which could be incorporated into type 1 diabetes simulators, providing a safe and inexpensive tool to carry out preclinical validations using these therapies.

A model of subcutaneous pramlintide pharmacokinetics and its effect on gastric emptying: Proof-of-concept based on populational data.

Pramlintide, an amylin analog, has been coming up as an agent in type 1 diabetes dual-hormone therapies (insulin/pramlintide). Since pramlintide slows down gastric emptying, it allows for easing glucose control and reducing the burden of meal announcements. Pre-clinical in silico evaluations are a key step in the development of any closed-loop strategy. However, mathematical models are needed, and pramlintide models in the literature are scarce. This work proposes a proof-of-concept pramlintide model, describing its subcutaneous pharmacokinetics (PK) and its effect on gastric emptying (PD). The model is validated with published populational (clinical) data. The model development is divided into three stages: intravenous PK, subcutaneous PK, and PD modeling. In each stage, a set of model structures are proposed, and their performance is assessed using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). In order to evaluate the modulation of the rate of gastric emptying, a literature meal model was used. The final pramlintide model comprises four compartments and a function that modulates gastric emptying depending on plasma pramlintide. Results show an appropriate fit for the data. Some aspects are left as open questions due to the lack of specific data (e.g., the influence of meal composition on the pramlintide effect). Moreover, further validation with individual data is necessary to propose a virtual cohort of patients.

Accuracy of Two Continuous Glucose Monitoring Devices During Aerobic and High-Intensity Interval Training in Individuals with Type 1 Diabetes.

Background: This study aimed to evaluate the accuracy of Dexcom G6 (DG6) and FreeStyle Libre-2 (FSL2) during aerobic training and high-intensity interval training (HIIT) in individuals with type 1 diabetes. Methods: Twenty-six males (mean age 29.3 ± 6.3 years and mean duration of diabetes 14.9 ± 6.1 years) participated in this study. Interstitial glucose levels were measured using DG6 and FSL2, while plasma glucose levels were measured every 10 min using YSI 2500 as the reference for glucose measurements in this study. The measurements began 20 min before the start of exercise and continued for 20 min after exercise. Seven measurements were taken for each subject and exercise. Results: Both DG6 and FSL2 devices showed significant differences compared to YSI glucose data for both aerobic and HIIT exercises. Continuous glucose monitoring (CGM) devices exhibited superior performance during HIIT than aerobic training, with DG6 showing a mean absolute relative difference of 14.03% versus 31.98%, respectively. In the comparison between the two devices, FSL2 demonstrated significantly higher effectiveness in aerobic training, yet its performance was inferior to DG6 during HIIT. According to the 40/40 criteria, both sensors performed similarly, with marks over 93% for all ranges and both exercises, and above 99% for HIIT and in the >180 mg/dL range, which is in accordance with FDA guidelines. Conclusions: The findings suggest that the accuracy of DG6 and FSL2 deteriorates during and immediately after exercise but remains acceptable for both devices during HIIT. However, accuracy is compromised with DG6 during aerobic exercise. This study is the first to compare the accuracy of two CGMs, DG6, and FSL2, during two exercise modalities, using plasma glucose YSI measurements as the gold standard for comparisons. It was registered at clinicaltrials.gov (NCT06080542).

Modeling the effect of glucagon on endogenous glucose production in type 1 diabetes: On the role of glucagon receptor dynamics.

This paper validates a glucoregulatory model including glucagon receptors dynamics in the description of endogenous glucose production (EGP). A set of models from literature are selected for a head-to-head comparison in order to evaluate the role of glucagon receptors. Each EGP model is incorporated into an existing glucoregulatory model and validated using a set of clinical data, where both insulin and glucagon are administered. The parameters of each EGP model are identified in the same optimization problem, minimizing the root mean square error (RMSE) between the simulation and the clinical data. The results show that the RMSE for the proposed receptors-based EGP model was lower when compared to each of the considered models (Receptors approach: 7.13±1.71 mg/dl vs. 7.76±1.45 mg/dl (p=0.066), 8.45±1.38 mg/dl (p=0.011) and 8.99±1.62 mg/dl (p=0.007)). This raises the possibility of considering glucagon receptors dynamics in type 1 diabetes simulators.

Changes in the Treatment Strategy Following Intracoronary Pressure Wire in a Contemporaneous Real-Life Cohort of Patients With Intermediate Coronary Stenosis. Results From a Nationwide Registry.

BACKGROUND: Intracoronary pressure wire is useful to guide revascularization in patients with coronary artery disease. AIMS: To evaluate changes in diagnosis (coronary artery disease extent), treatment strategy and clinical results after intracoronary pressure wire study in real-life patients with intermediate coronary artery stenosis. METHODS: Observational, prospective and multicenter registry of patients in whom pressure wire was performed. The extent of coronary artery disease and the treatment strategy based on clinical and angiographic criteria were recorded before and after intracoronary pressure wire guidance. 12-month incidence of MACE (cardiovascular death, non-fatal myocardial infarction or new revascularization of the target lesion) was assessed. RESULTS: 1414 patients with 1781 lesions were included. Complications related to the procedure were reported in 42 patients (3.0 %). The extent of coronary artery disease changed in 771 patients (54.5 %). There was a change in treatment strategy in 779 patients (55.1 %) (18.0 % if medical treatment; 68.8 % if PCI; 58.9 % if surgery (p < 0.001 for PCI vs medical treatment; p = 0.041 for PCI vs CABG; p < 0.001 for medical treatment vs CABG)). In patients with PCI as the initial strategy, the change in strategy was associated with a lower rate of MACE (4.6 % vs 8.2 %, p = 0.034). CONCLUSIONS: The use of intracoronary pressure wire was safe and led to the reclassification of the extent of coronary disease and change in the treatment strategy in more than half of the cases, especially in patients with PCI as initial treatment.

Internal model control based module for the elimination of meal and exercise announcements in hybrid artificial pancreas systems.

BACKGROUND AND OBJECTIVES: Hybrid artificial pancreas systems outperform current insulin pump therapies in blood glucose regulation in type 1 diabetes. However, subjects still have to inform the system about meals intake and exercise to achieve reasonable control. These patient announcements may result in overburden and compromise controller performance if not provided timely and accurately. Here, a hybrid artificial pancreas is extended with an add-on module that releases subjects from meals and exercise announcements. METHODS: The add-on module consists of an internal-model controller that generates a "virtual" control action to compensate for disturbances. This "virtual" action is converted into insulin delivery, rescue carbohydrates suggestions, or insulin-on-board limitations, depending on a switching logic based on glucose measurements and predictions. The controller parameters are tuned by optimization and then related to standard parameters from the open-loop therapy. This module is implemented in a hybrid artificial pancreas system proposed by our research group for validation. This hybrid system extended with the add-on module is compared with the hybrid controller with carbohydrate counting errors (hybrid) and the hybrid controller with an alternative unannounced meal compensation module based on a meal detection algorithm (meal detector). The validation used the educational version of the UVa/Padova simulator to simulate the three controllers under two scenarios: one with only meals and another with meals and exercise. The exercise was modeled as a temporal increase of the insulin sensitivity resulting in the glucose drop usually related to an aerobic exercise. RESULTS: For the scenario with only meals, the three controllers achieved similar time in range (proposed: 85.1 [77.9,88.1]%, hybrid: 84.0 [75.9,86.4]%, meal detector: 81.9 [79.3,83.8]%, median [interquartile range]) with low time in moderate hypoglycemia. Under the scenario with meals and exercise, the proposed module reduces 4.61% the time in hypoglycemia achieved with the other controllers, suggesting an acceptable amount of rescues (27.2 [23.7, 31.0] g). CONCLUSIONS: The proposed add-on module achieved promising results: it outperformed the meal-detector-based controller, even achieving a postprandial performance as good as the hybrid controller (with carbohydrate counting errors). Also, the rescue suggestion feature of the module mitigated exercise-induced hypoglycemia with admissible rescue amounts.

Comparison of Artificial Intelligence Control Strategies for a Peristaltically Pumped Low-Pressure Driven Membrane Process.

Peristaltic pumping is used in membrane applications where high and sterile sealing is required. However, control is difficult due to the pulsating pump characteristics and the time-varying properties of the system. In this work, three artificial intelligence control strategies (artificial neural networks (ANN), fuzzy logic expert systems, and fuzzy-integrated local models) were used to regulate transmembrane pressure and crossflow velocity in a microfiltration system under high fouling conditions. A pilot plant was used to obtain the necessary data to identify the AI models and to test the controllers. Humic acid was employed as a foulant, and cleaning-in-place with NaOH was used to restore the membrane state. Several starting operating points were studied and setpoint changes were performed to study the plant dynamics under different control strategies. The results showed that the control approaches were able to control the membrane system, but significant differences in the dynamics were observed. The ANN control was able to achieve the specifications but showed poor dynamics. Expert control was fast but showed problems in different working areas. Local models required less data than ANN, achieving high accuracy and robustness. Therefore, the technique to be used will depend on the available information and the application dynamics requirements.

Percutaneous Mitral Valve Repair: Outcome Improvement with Operator Experience and a Second-Generation Device.

BACKGROUND AND AIM: Recent randomized data comparing percutaneous mitral valve repair (PMVR) versus optimal medical treatment in patients with functional MR (FMR) seemed to highlight the importance of the learning curve not only for procedural outcomes but also for patient selection. The aim of the study was to compare a contemporary series of patients undergoing PMVR using a second-generation Mitraclip device (Mitraclip NT) with previous cohorts treated with a first-generation system. METHODS: This multicenter study collected individual data from 18 centers between 2012 and 2017. The cohort was divided into three groups according to the use of the first-generation Mitraclip during the first (control-1) or second half (control-2) or the Mitraclip NT system. RESULTS: A total of 545 consecutive patients were included in the study. Among all, 182 (33.3%), 183 (33.3%), and 180 (33.3%) patients underwent mitral repair in the control-1, control-2, and NT cohorts, respectively. Procedural success was achieved in 93.3% of patients without differences between groups. Major adverse events did not statistically differ among groups, but there was a higher rate of pericardial effusion in the control-1 group (4.3%, 0.6%, and 2.6%, respectively; p = 0.025). The composite endpoint of death, surgery, and admission for congestive heart failure (CHF) at 12 months was lower in the NT group (23.5% in control-1, 22.5% in control-2, and 8.3% in the NT group; p = 0.032). CONCLUSIONS: The present paper shows that contemporary clinical outcomes of patients undergoing PMVR with the Mitraclip system have improved over time.

Seasonal Local Models for Glucose Prediction in Type 1 Diabetes.

Linear empirical dynamic models have been widely used for blood glucose prediction and risks prevention in people with type 1 diabetes. More accurate blood glucose prediction models with longer prediction horizon (PH) are desirable to enable warnings to patients about imminent blood glucose changes with enough time to take corrective actions. In this study, a blood glucose prediction method is developed by integrating the predictions of a set of seasonal local models (each of them corresponding to different glucose profiles observed along historical data). In the modeling step, the number of sets and their corresponding glucose profiles characteristics are obtained by clustering techniques (Fuzzy C-Means). Then, Box-Jenkins methodology is used to identify a seasonal model for each set. Finally, blood glucose predictions of local models are integrated using different techniques. The proposed method is tested by using 18 60-h closed-loop experiments (including different exercise types and artificial pancreas strategies) and achieving mean absolute percentage error (MAPE) of 2.94%, 3.89%, 5.41%, 6.29% and 8.66% for 15-, 30-, 45-, 60-, and 90-min PHs, respectively.