Desempeño de la orientación de la cadena de abastecimiento en armonia con operaciones y mercadeo en empresas del sector electrico en bogotá

This article offers a descriptive analysis about the performance factor of supply chain orientation (SCOP) based on the harmonization between marketing strategies in several companies in the electrical and electronic sector, through the evaluation of three fundamental constructs: the alignment of the marketing strategy, the performance of the orientation to the supply chain and the organizational performance, fundamental components for the optimal flow of the operational processes in order to satisfy the needs of the stakeholders. An instrument was applied as a survey to 121 people with different macro environments and positions within their organizations. In the research process, there was a misalignment between the logistics and marketing strategies due to the lack of articulation between the functional processes in addition to their respective areas, from the strategic, tactical and operational plans.El presente artículo ofrece un análisis descriptivo acerca del factor desempeño de la orientación a la cadena de abastecimiento (DOGCA) en empresas del sector eléctrico, mediante la armonización entre las estrategias de mercadeo en varias empresas del sector eléctrico y electrónico, mediante la evaluación de tres constructos fundamentales: la alineación de la estrategia de mercadeo, el rendimiento de la orientación a la cadena de abastecimiento y el rendimiento organizacional, componentes fundamentales para el óptimo flujo de los procesos operacionales con el fin de satisfacer las necesidades de los grupos de interés. Se aplicó un instrumento como encuesta a 121 personas con diferentes macro ambientes y posiciones dentro de sus organizaciones. En el proceso investigativo se encontró una desalineación entre las estrategias de logística y mercadeo debido a la falta de articulación entre los procesos funcionales además de sus respectivas áreas, desde los planes estratégicos, tácticos y operacionales

Different indexes of glycemic variability as identifiers of patients with risk of hypoglycemia in type 2 diabetes mellitus

Q1Artículo original1007-1015Introduction: Recent publications frequently introduce new indexes to measure glycemic variability (GV), quality of glycemic control, or glycemic risk; however, there is a lack of evidence supporting the use of one particular parameter, especially in clinical practice. Methods: A cohort of type 2 diabetes mellitus (T2DM) patients in ambulatory care were followed using continuous glucose monitoring sensors (CGM). Mean glucose (MG), standard deviation, coefficient of variation (CV), interquartile range, CONGA1, 2, and 4, MAGE, M value, J index, high blood glucose index, and low blood glucose index (LBGI) were estimated. Hypoglycemia incidence (<54 mg/dl) was calculated. Area under the curve (AUC) was determined for different indexes as identifiers of patients with risk of hypoglycemia (IRH). Optimal cutoff thresholds were determined from analysis of the receiver operating characteristic curves. Results: CGM data for 657 days from 140 T2DM patients (4.69 average days per patient) were analyzed. Hypoglycemia was present in 50 patients with 144 hypoglycemic events in total (incidence rate of 0.22 events per patient/day). In the multivariate analysis, both CV (OR 1.20, 95% CI 1.12-1.28, P < .001) and LBGI (OR 4.83, 95% CI 2.41-9.71, P < .001) were shown to have a statistically significant association with hypoglycemia. The highest AUC were for CV (0.84; 95% CI 0.77-0.91) and LBGI (0.95; 95% CI 0.92-0.98). The optimal cutoff threshold for CV as IRH was 34%, and 3.4 for LBGI. Conclusion: This analysis shows that CV can be recommended as the preferred parameter of GV to be used in clinical practice for T2DM patients. LBGI is the preferred IRH between glycemic risk indexes

Numerical and clinical precision in hypoglycemia of the intermittent FreeStyle Libre glucose monitoring through an NFC-Bluetooth transmitter associated with the xDrip+ algorithm in diabetic patients under insulin therapy

Métodos Estudio de pruebas diagnósticas. Se evaluó la exactitud numérica mediante la diferencia absoluta en los valores con respecto a la glucometría capilar (norma ISO 15197:2015) y la exactitud clínica, mediante las gradillas de error de Clarke y Parkes (Consensus), para mediciones de glucosa inferiores a 70 mg/dl realizadas con el sistema FreeStyle Libre y con la aplicación xDrip+ de estimación digital, en personas con diabetes con insulinoterapia. Resultados Se incluyó a 27 pacientes (TIR 73,4%, TBR70 5,6%), quienes aportaron 83 eventos de hipoglucemia. La exactitud numérica fue adecuada en proporciones similares con el sistema FreeStyle Libre en comparación con la aplicación xDrip+ (81,92% vs. 68,67%, p = 0,0630). La evaluación de la precisión clínica mostró que el 92,8% de las mediciones para xDrip+ y el 98,8% para FreeStyle libre cumplieron el criterio según la gradilla de Parkes (Consensus) (p = 0,0535), y el 79,5 y el 91,6% de las mediciones cumplieron el criterio según la gradilla de Clarke (p = 0,0273) siendo superior con Libre. Conclusiones El uso del transmisor NFC-Bluetooth (Miao-Miao) asociado a la aplicación xDrip+ no mejora la precisión numérica ni clínica para la detección de los eventos de hipoglucemia en los personas con diabetesQ3Q3Introduction There are data capture devices that attach to the FreeStyle Libre sensor and convert its communication from NFC (Near-field communication) to Bluetooth technology, generating real-time continuous glucose monitoring. The accuracy of hypoglycemia measurements displayed by smartphone apps using this device has not been established. Methods Study of diagnostic tests. Numerical accuracy was evaluated, utilizing the absolute difference with respect to capillary glucometry (ISO 15197:2015 standard) and clinical accuracy, using the Clarke and Parkes (Consensus) error grids, for glucose measurements less than 70 mg/dL performed with the FreeStyle Libre system and with the digital estimation xDrip+ app, in diabetic patients managed with insulin therapy. Results Twenty-seven patients were included (TIR 73.4%, TBR70 5.6%), who contributed 83 hypoglycemic events. Numerical accuracy was adequate in similar proportions with the FreeStyle Libre system compared to the xDrip+ app (81.92% vs. 68.67%, p = 0.0630). The clinical accuracy evaluation showed that 92.8% of the measurements for xDrip + and 98.8% for FreeStyle libre met the criteria according to the Parkes (Consensus) grid (p = 0.0535); and 79.5% and 91.6% of the measurements met the criteria according to the Clarke grid (p = 0.0273), being higher with FreeStyle libre. Conclusions The use of the NFC-Bluetooth transmitter (Miao-Miao) associated with the xDrip+ app does not improve numerical or clinical accuracy for detecting hypoglycemic events in diabetic patients managed with insulin therapy, compared to the FreeStyle Libre device.Revista Internacional - IndexadaS

Efficacy of the mHealth application in patients with type 2 diabetes transitioning from inpatient to outpatient care: A randomized controlled clinical trial

Introduction: No studies have assessed the efficacy of telemedicine using a platform for recording and adjusting insulin doses in patients with diabetes mellitus type 2 (DM2) transitioning from inpatient to outpatient care. This study aimed to assess, in a population of patients with DM2, discharged from a tertiary referral hospital, whether treatment based on the use of an mHealth application was associated with better glycemic control at the 3-month follow-up, than standard care. Methods: This open, randomized, controlled clinical trial included adult DM2 patients who were transitioning from inpatient to outpatient care. The efficacy and safety of patient management with and without mHealth was compared at the 3-month follow-up. The primary outcome was the change in the Glycosylated hemoglobin (HbA1c) levels. The secondary outcomes were the rates of hypoglycemic and hyperglycemic events and treatment satisfaction measured using the Insulin Treatment Satisfaction Questionnaire (ITSQ). Results: In total, 86 patients (41 using mHealth) were included in the clinical trial. HbA1c levels showed a significant decrease in both groups. The mean HbA1c level was significantly lower in the mHealth group. Patients using mHealth showed decreased incidence rate ratios of hypoglycemia 3.0 mmol/L [<54 mg/dl], hypoglycemia ranging from 3.0 to 3.8 mmol/L [54 to 70 mg/dl] and severe hypoglycemia. The level of satisfaction assessed using the ITSQ was higher in the mHealth group. Conclusion: Using mHealth in patients with DM2 transitioning from inpatient to outpatient care improves metabolic control and may reduce the hypoglycemia rates

Prediction of postprandial blood glucose under intra-patient variability and uncertainty and its use in the design of insulin dosing strategies for type 1 diabetic patients

In this thesis I propose a novel method to estimate the dose and injection-to-meal time for low-risk intensive insulin therapy. This dosage-aid system uses an optimization algorithm to determine the insulin dose and injection-to-meal time that minimizes the risk of postprandial hyper- and hypoglycaemia in type 1 diabetic patients. To this end, the algorithm applies a methodology that quantifies the risk of experiencing different grades of hypo- or hyperglycaemia in the postprandial state induced by insulin therapy according to an individual patient’s parameters. This methodology is based on modal interval analysis (MIA). Applying MIA, the postprandial glucose level is predicted with consideration of intra-patient variability and other sources of uncertainty. A worst-case approach is then used to calculate the risk index. In this way, a safer prediction of possible hyper- and hypoglycaemic episodes induced by the insulin therapy tested can be calculated in terms of these uncertainties.En esta tesis se propone un nuevo método para estimar la dosis y el instante de inyección que genere el menor riesgo para una terapia intensiva de insulina. El sistema de dosificación utiliza un algoritmo de optimización para determinar la dosis de insulina y el instante de inyección que reduzcan al máximo el riesgo de hiperglucemia e hipoglucemia posprandial en pacientes diabéticos tipo 1. Para ello, el algoritmo aplica una metodología que cuantifica el riesgo de sufrir diferentes grados de hipoglucemia e hiperglucemia en estado postprandial inducida por la terapia de insulina de acuerdo a los parámetros de cada paciente. Aplicando análisis intervalar modal se predice el nivel de glucosa postprandial considerando la variabilidad intrapaciente y otras fuentes de incertidumbre. Con un planteamiento del peor caso se calcula una predicción más segura de posibles episodios de hiperglucemia e hipoglucemia inducida por la terapia de insulina en términos de dichas incertidumbres

Prediction of postprandial blood glucose under uncertainty and intra-patient variability in type 1 diabetes: a comparative study of three interval models,

The behavior of three insulin action and glucose kinetics models was assessed for an insulin therapy regime in the presence of patient variability. For this purpose, postprandial glucose in patients with type 1 diabetes was predicted by considering intra- and inter-patient variability using modal interval analysis. Equations to achieve optimal prediction are presented for models 1, 2 and 3, which are of increasing complexity. The model parameters were adjusted to reflect the “same” patient in the presence of variability. The glucose response envelope for model 1, the simplest insulin–glucose model assessed, included the responses of the other two models when a good fit of the model parameters was achieved. Thus, under variability, simple glucose–insulin models may be sufficient to describe patient dynamics in most situations.This work was partially supported by the Spanish Ministry of Science and Innovation through Grant DPI-2010-20764-C02, and by the Autonomous Government of Catalonia through Grant SGR 523.García Jaramillo, MA.; Calm, R.; Bondía Company, J.; Vehí, J. (2012). Prediction of postprandial blood glucose under uncertainty and intra-patient variability in type 1 diabetes: a comparative study of three interval models,. Computer Methods and Programs in Biomedicine. 108(1):993-1001. doi:10.1016/j.cmpb.2012.04.003S9931001108

Insulin dosage optimization based on prediction of postprandial glucose excursions under uncertain parameters and food intake

Considering the difficulty in selecting correct insulin doses and the problem of hyper- and hypoglycemia episodes in type 1 diabetes, dosage-aid systems are very useful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as large intra-patient variability and food intake. In the present study, postprandial glucose is predicted considering this uncertain information using modal interval analysis. This approach calculates a safer prediction of possible hyper- and hypoglycemia episodes induced by insulin therapy for an individual patient's parameters and integrates this information into a dosage-aid system. Predictions of a patient's postprandial glucose at 5-h intervals are used to predict the risk for a given therapy. Then the insulin dose and injection-to-meal time with the lowest risk are calculated. The method has been validated for three different scenarios corresponding to preprandial glucose values of 100, 180 and 250 mg/dl.This work was partially supported by the Spanish Ministry of Science and Innovation and the European Union through grant DPI-2007-66728 and by the Autonomous Government of Catalonia through SGR00523.García Jaramillo, MA.; Calm, R.; Bondía Company, J.; Tarin, C.; Vehi, J. (2012). Insulin dosage optimization based on prediction of postprandial glucose excursions under uncertain parameters and food intake. Computer Methods and Programs in Biomedicine. 105(1):61-69. doi:10.1016/j.cmpb.2010.08.007S6169105

Software tool for glucose variability analysis from continuous glucose monitoring data

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Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus

Considering the difficulty in the insulin dosage selection and the problem of hyper- and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper- and hypoglycaemia episodes can be calculate

Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus

Considering the difficulty in the insulin dosage selection and the problem of hyper- and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper- and hypoglycaemia episodes can be calculate