¿Cuántos pacientes selecciono para mi estudio?

[Resumen] Introducción. Después de haber trabajado uno de los autores durante años en una unidad de apoyo a la investigación en un hospital grande, llegó al convencimiento de que esta pregunta, y sus variantes, es, probablemente, una delas causas más frecuentes de consulta entre los investigadores clínicos y, en ocasiones, motivo de ansiedad. Desarrollo. Vamos a dedicar unas breves reflexiones para ayudar a los investigadores clínicos a abordar el cálculo del tamaño muestral para un estudio. La orientación de las mismas intentará reproducir la línea argumental que le funciona al componente no matemático de los autores, e ir a través de las soluciones que le ha dado a los mismos problemas que afrontan otros compañeros para intentar que esto tenga sentido. Así, se huirá de fórmulas matemáticas en detalle, pero se abordarán los diferentes elementos que desempeñan un papel a la hora de calcular un tamaño muestral. La razón de esto es que existen muchos contextos diferentes en los que plantearse un tamaño muestral, en función del tipo de estudio, el interés u objetivo de la investigación, etc. En cada uno de estos contextos diferentes es de aplicación una fórmula específica, lo que hace muy difícil recordar las fórmulas. Por suerte, esto no es necesario, ya que existen múltiples programas disponibles en la red para ayudarnos en el proceso de estimar el tamaño muestral necesario. Conclusión. En el presente artículo se aborda la cuestión del cálculo del tamaño muestral a través de tres cuestiones: 1) intentar entender por qué tiene sentido y es necesario estimar un tamaño muestral; 2) en qué pensar cuando deseamos hacerlo; y 3) presentación de alguna herramienta disponible para ayudarnos en el proceso

Effect of an educational intervention in primary care physicians on the compliance of indicators of good clinical practice in the treatment of type 2 diabetes mellitus [OBTEDIGA project]

[Abstract] Aim. To evaluate the effect of an educational intervention among primary care physicians on several indicators of good clinical practice in diabetes care. Methods. Two groups of physicians were randomly assigned to the intervention or control group (IG and CG). Every physician randomly selected two samples of patients from all type 2 diabetic patients aged 40 years and above and diagnosed more than a year ago. Baseline and final information were collected cross-sectionally 12 months apart, in two independent samples of 30 patients per physician. The educational intervention comprised: distribution of educational materials and physicians' specific bench-marking information, an on-line course and three on-site educational workshops on diabetes. External observers collected information directly from the physicians and from the medical records of the patients on personal and family history of disease and on the evolution and treatment of their disease. Baseline information was collected retrospectively in the control group. Results. Intervention group comprised 53 physicians who included a total of 3018 patients in the baseline and final evaluations. CG comprised 50 physicians who included 2868 patients in the same evaluations. Measurement of micro-albuminuria in the last 12 months (OR = 1.6, 95% CI: 1.1–2.4) and foot examination in the last year (OR = 2.0, 95% CI: 1.1–3.6) were the indicators for which greater improvement was found in the IG. No other indicator considered showed statistically significant improvement between groups. Conclusions. The identification of indicators with very low level of compliance and the implementation of a simple intervention in physicians to correct them is effective in improving the quality of care of diabetic patients

Quality of care of patients with type-2 diabetes in Galicia (NW Spain) [OBTEDIGA project]

[Abstract] Aims:  The aim of this study was to describe the degree of compliance of agreed practices with reference to primary care patients with Type 2 diabetes of 40 years old and older in Galicia (NW Spain). Methods:  A total of 108 primary care physicians were selected at random from the totality of doctors. Each physician selected 30 patients at random from their patients suffering from diabetes of 40 years old or older. External observers gathered information from each patient’s medical record regarding their characteristics, condition and degree of compliance of selected indicators of good practice. Results:  Group of physicians participated in this study had a mean age of 50 years (standard deviation = 3.9); 48% of them were females; and 17.5% involved in medical residents training. A total of 3078 diabetic patients were included in the study: mean age = 69 years (SD = 10.9), 47.6% women, presence of high blood pressure (72%), hypercholesterolaemia (56%), and regular smokers (10.3%). Compliance with selected indicators such as foot examination (14%), ophthalmological examination (30.6%), abdominal circumference measurement (6.1%), measurement of total or LDL-cholesterol (78.1), blood pressure measurement (84.8), glycosylated haemoglobin measurement < 7% (54.3%) was observed. Adequate monitoring in cases of high blood pressure and hypercholesterolaemia were 34.2% and 27.4%, respectively. Variability between physicians differs according to the different indicators, with interquartile range for compliance of between 16.4 and 66%. Conclusions:  There is a wide margin for improvement in the adaptation of clinical practice to recommendations for diabetic patients. The large variation existing in certain indicators would suggest that certain control objectives are less demanding than advisable in those that comply least, while low compliance and low variability in other indicators point to structural problems or unsatisfactory training of doctors

Effect of a simple educational program for physicians on adherence to secondary prevention measures after discharge following acute coronary syndrome: the CAM project

[Abstract] Introduction and objectives. Adherence to established guidelines for patients discharged from the hospital after acute coronary syndrome is known to be suboptimal. The aim of this study was to assess the efficacy of a program for physicians centered on the treatment of acute coronary syndrome. Patients and method. 39 hospitals participated. Intervention: a set of measures was developed by consensus for the creation and distribution of educational materials. Outcomes of interest: proportion of patients in whom ejection fraction and residual ischemia were evaluated, treatment at discharge, and health and dietary recommendations to patients (smoking, diet, exercise, etc.) referred to all patients in whom these measures or treatments should have been used (“ideal patients”). Changes were assessed with four cross-sectional surveys. Results. A total of 1157, 1162, 1149, and 1158 patients were included. There were no relevant differences between these groups in baseline characteristics. In general, there was improvement in all variables between the first and the last survey. The proportion of patients who were weighed and measured increased (from 33.5% to 53.4%; P<.0001), as did the proportion of those in whom cholesterol was measured early (42.6 to 53.7%; P=.006). The proportion in whom residual ischemia was not measured despite indications for this test decreased (18.2% to 10.8%; P=.013), and the proportion increased for appropriate treatment with statins on discharge (68.6% to 81.4%; P<.0001), advice to quit smoking (60.1% to 72.2%; P<.0001) and advice to exercise (58.3% to 67.4%; P=.003). Conclusions. The educational intervention seems to have had a positive effect on improving the appropriateness of procedures and treatments for patients discharged after acute coronary syndrome.[Resumen] Introducción y objetivos. El cumplimiento de las recomendaciones establecidas como eficaces en el momento del alta en los pacientes hospitalizados por un síndrome coronario agudo es subóptimo. El objetivo de este estu-dio es evaluar la eficacia de un programa de intervención centrado en el abordaje y tratamiento del síndrome coronario agudo. Pacientes y método. Participaron en el proyecto 39 hospi-tales. La intervención realizada consistió en el desarrollo con-sensuado de acuerdos de mínimos y la elaboración y difusión de materiales educativos. Entre las medidas de interés cabe destacar la proporción de pacientes en la que se evaluaron la fracción de eyección, la isquemia residual y los tratamientos y recomendaciones higiénico-dietéticas en el momento del alta (tabaco, dieta, ejercicio, etc.) del total de pacientes en los que se deben determinar según el acuerdo de mínimos («pacientes ideales»). Asimismo, se valoraron los cambios en 4 cortes transversales. Resultados. Se incluyó a 1.157, 1.162, 1.149 y 1.158 pacientes, respectivamente. No había diferencias en las características basales de pacientes analizados en cada corte. En general, se aprecia una mejoría entre el primer y el último corte en todas las variables analizadas. Mejoró especialmente la proporción de pacientes en los que se determi-naron el peso y la talla (del 33,5 al 53,4%; p < 0,0001). También se observó una mejoría en la medición precoz del colesterol (del 42,6 al 53,7%; p = 0,006) y una reducción del porcentaje de pacientes en los que no se realizó un test de isquemia pese a estar indicado (del 18,2 al 10,8%; p = 0,013); asimismo, aumentó la propoción de pacientes con un tratamiento adecuado con estatinas en el momento del alta (del 68,6 al 81,4%; p < 0,0001) y el número de recomendaciones sobre tabaquismo (del 60,1 al 72,2%; p < 0,0001) y ejercicio (del 58,3 al 67,4%; p = 0,003). Conclusiones. La intervención educativa parece que tie-ne un efecto positivo en la mejora de la adecuación de los procedimientos realizados y en los tratamientos prescritos en el momento del alta tras un síndrome coronario agudo

Clinical implication of FMR1 intermediate alleles in a Spanish population

FMR1 premutation carriers (55-200 CGGs) are at risk of developing Fragile X-associated primary ovarian insufficiency as well as Fragile X-associated tremor/ataxia syndrome. FMR1 premutation alleles are also associated with a variety of disorders, including psychiatric, developmental, and neurological problems. However, there is a major concern regarding clinical implications of smaller CGG expansions known as intermediate alleles (IA) or gray zone alleles (45-54 CGG). Although several studies have hypothesized that IA may be involved in the etiology of FMR1 premutation associated phenotypes, this association still remains unclear. The aim of this study was to provide new data on the clinical implications of IA. We reviewed a total of 17 011 individuals: 1142 with primary ovarian insufficiency, 478 with movement disorders, 14 006 with neurodevelopmental disorders and 1385 controls. Similar IA frequencies were detected in all the cases and controls (cases 1.20% vs controls 1.39%, P =.427). When comparing the allelic frequencies of IA = 50CGGs, a greater, albeit not statistically significant, number of alleles were detected in all the cohorts of patients. Therefore, IA below 50 CGGs should not be considered as risk factors for FMR1 premutation-associated phenotypes, at least in our population. However, the clinical implication of IA = 50CGGs remains to be further elucidated

The atmospheric science of JEM-EUSO

An Atmospheric Monitoring System (AMS) is critical suite of instruments for JEM-EUSO whose aim is to detect Ultra-High Energy Cosmic Rays (UHECR) and (EHECR) from Space. The AMS comprises an advanced space qualified infrared camera and a LIDAR with cross checks provided by a ground-based and airborne Global Light System Stations. Moreover the Slow Data Mode of JEM-EUSO has been proven crucial for the UV background analysis by comparing the UV and IR images. It will also contribute to the investigation of atmospheric effects seen in the data from the GLS or even to our understanding of Space Weather

Modifiable risk factors associated with prediabetes in men and women: A cross-sectional analysis of the cohort study in primary health care on the evolution of patients with prediabetes

Background: Prediabetes is a high-risk state for diabetes development, but little is known about the factors associated with this state. The aim of the study was to identify modifiable risk factors associated with the presence of prediabetes in men and women. Methods: Cohort Study in Primary Health Care on the Evolution of Patients with Prediabetes (PREDAPS-Study) is a prospective study on a cohort of 1184 subjects with prediabetes and another cohort of 838 subjects without glucose metabolism disorders. It is being conducted by 125 general practitioners in Spain. Data for this analysis were collected during the baseline stage in 2012. The modifiable risk factors included were: smoking habit, alcohol consumption, low physical activity, inadequate diet, hypertension, dyslipidemia, and obesity. To assess independent association between each factor and prediabetes, odds ratios (ORs) were estimated using logistic regression models. Results: Abdominal obesity, low plasma levels of high-density lipoprotein cholesterol (HDL-cholesterol), and hypertension were independently associated with the presence of prediabetes in both men and women. After adjusting for all factors, the respective ORs (95% Confidence Intervals) were 1.98 (1.41-2.79), 1.88 (1.23-2.88) and 1.86 (1.39-2.51) for men, and 1.89 (1.36-2.62), 1.58 (1.12-2.23) and 1.44 (1.07-1.92) for women. Also, general obesity was a risk factor in both sexes but did not reach statistical significance among men, after adjusting for all factors. Risky alcohol consumption was a risk factor for prediabetes in men, OR 1.49 (1.00-2.24). Conclusions: Obesity, low HDL-cholesterol levels, and hypertension were modifiable risk factors independently related to the presence of prediabetes in both sexes. The magnitudes of the associations were stronger for men than women. Abdominal obesity in both men and women displayed the strongest association with prediabetes. The findings suggest that there are some differences between men and women, which should be taken into account when implementing specific recommendations to prevent or delay the onset of diabetes in adult population

Effectiveness of an mHealth intervention combining a smartphone app and smart band on body composition in an overweight and obese population: Randomized controlled trial (EVIDENT 3 study)

Background: Mobile health (mHealth) is currently among the supporting elements that may contribute to an improvement in health markers by helping people adopt healthier lifestyles. mHealth interventions have been widely reported to achieve greater weight loss than other approaches, but their effect on body composition remains unclear. Objective: This study aimed to assess the short-term (3 months) effectiveness of a mobile app and a smart band for losing weight and changing body composition in sedentary Spanish adults who are overweight or obese. Methods: A randomized controlled, multicenter clinical trial was conducted involving the participation of 440 subjects from primary care centers, with 231 subjects in the intervention group (IG; counselling with smartphone app and smart band) and 209 in the control group (CG; counselling only). Both groups were counselled about healthy diet and physical activity. For the 3-month intervention period, the IG was trained to use a smartphone app that involved self-monitoring and tailored feedback, as well as a smart band that recorded daily physical activity (Mi Band 2, Xiaomi). Body composition was measured using the InBody 230 bioimpedance device (InBody Co., Ltd), and physical activity was measured using the International Physical Activity Questionnaire. Results: The mHealth intervention produced a greater loss of body weight (–1.97 kg, 95% CI –2.39 to –1.54) relative to standard counselling at 3 months (–1.13 kg, 95% CI –1.56 to –0.69). Comparing groups, the IG achieved a weight loss of 0.84 kg more than the CG at 3 months. The IG showed a decrease in body fat mass (BFM; –1.84 kg, 95% CI –2.48 to –1.20), percentage of body fat (PBF; –1.22%, 95% CI –1.82% to 0.62%), and BMI (–0.77 kg/m2, 95% CI –0.96 to 0.57). No significant changes were observed in any of these parameters in men; among women, there was a significant decrease in BMI in the IG compared with the CG. When subjects were grouped according to baseline BMI, the overweight group experienced a change in BFM of –1.18 kg (95% CI –2.30 to –0.06) and BMI of –0.47 kg/m2 (95% CI –0.80 to –0.13), whereas the obese group only experienced a change in BMI of –0.53 kg/m2 (95% CI –0.86 to –0.19). When the data were analyzed according to physical activity, the moderate-vigorous physical activity group showed significant changes in BFM of –1.03 kg (95% CI –1.74 to –0.33), PBF of –0.76% (95% CI –1.32% to –0.20%), and BMI of –0.5 kg/m2 (95% CI –0.83 to –0.19). Conclusions: The results from this multicenter, randomized controlled clinical trial study show that compared with standard counselling alone, adding a self-reported app and a smart band obtained beneficial results in terms of weight loss and a reduction in BFM and PBF in female subjects with a BMI less than 30 kg/m2 and a moderate-vigorous physical activity level. Nevertheless, further studies are needed to ensure that this profile benefits more than others from this intervention and to investigate modifications of this intervention to achieve a global effect