Combined Effect of Genetic Variants on Long-Term Weight Response after Bariatric Surgery.

The pathophysiology of body weight control involves complex interactions between hormonal, environmental, behavioral and genetic factors. The purpose of this study was to analyze the association between single nucleotide polymorphisms (SNPs) of 13 genes encoding gastrointestinal peptides, their receptors or the proteins involved in their expression, with long-term weight response in a cohort of 375 patients undergoing bariatric surgery (BS). To evaluate weight response, we combined several variables to define specific response phenotypes six years after surgery. The study protocol was registered in ISRCTN (ID80961259). The analysis of the selected SNPs was performed via allelic discrimination using Taqman® probes (Applied Biosystems, Foster City, CA, USA). The genotype association study was performed using the SNPstat program, with comparisons adjusted for sex, age, initial body mass index, type 2 diabetes, hypertension diagnosis and the type of surgery. We identified eight genetic variants associated with the weight response to BS, independently of the presurgery patient profile and the type of surgical technique, from which we calculated the unweighted risk score (RS) for each phenotype. The highest scoring category in each RS was significantly associated with lower weight loss (p = 0.0001) and greater weight regain (p = 0.0012) at the end of the follow-up.

Promoting Obesity Prevention and Healthy Habits in Childhood: The OCARIoT Experience.

OBJECTIVE: Long term behavioural disturbances and interventions in healthy habits (mainly eating and physical activity) are the primary cause of childhood obesity. Current approaches for obesity prevention based on health information extraction lack the integration of multi-modal datasets and the provision of a dedicated Decision Support System (DSS) for health behaviour assessment and coaching of children. METHODS: Continuous co-creation process has been applied in the frame of the Design Thinking Methodology, involving children, educators and healthcare professional in the whole process. Such considerations were used to derive the user needs and the technical requirements needed for the conception of the Internet of Things (IoT) platform based on microservices. RESULTS: To promote the adoption of healthy habits and the prevention of the obesity onset for children (9-12 years old), the proposed solution empowers children -including families and educators- in taking control of their health by collecting and following-up real-time information about nutrition, physical activity data coming from IoT devices, and interconnecting healthcare professionals to provide a personalised coaching solution. The validation has two phases involving +400 children (control/intervention group), on four schools in three countries: Spain, Greece and Brazil. The prevalence of obesity decreased in 75.5% from baseline levels in the intervention group. The proposed solution created a positive impression and satisfaction from the technology acceptance perspective. CONCLUSIONS: Main findings confirm that this ecosystem can assess behaviours of children, motivating and guiding them towards achieving personal goals. Clinical and Translational Impact Statement-This study presents Early Research on the adoption of a smart childhood obesity caring solution adopting a multidisciplinary approach; it involves researchers from biomedical engineering, medicine, computer science, ethics and education. The solution has the potential to decrease the obesity rates in children aiming to impact to get a better global health.

Business intelligence for the visualization and data analysis of Telepharmacy activity indicators in a hospital pharmacy service scorecard.

OBJECTIVE: The consolidation of Telepharmacy during the COVID-19 pandemic  has raised the need for managing large volumes of real-time activity data  through data analysis. The aim of this project was to design a dynamic, user- friendly, customizable scorecard in a hospital pharmacy service for the  visualization and analysis of Telepharmacy activity indicators through the use  of advanced business intelligence technology. METHOD: The software tool was developed by a multidisciplinary team between  April and May 2021, driven from the hospital pharmacy service. Once the Telepharmacy indicators of interest were established,  datasets were extracted from raw databases (administrative databases,  Telepharmacy database, outpatient dispensing software, drug catalogues)  through data analysis. The different data sources were integrated in a  scorecard using PowerBI®. The criteria for processing missing and duplicated  data were defined, and data pre-processing, normalization and transformation were performed. Once the pilot scorecard was validated by  different profiles of users, the structure was designed for the panels to  automatically update as databases were updated. RESULTS: Design and implementation of a scorecard of Telepharmacy activity:  general descriptive panel (demographic profile of patients, count and delivery  conditions, program and medical service); geolocation of destination;  pharmacological profile; relative analysis of patients involved in the  Telepharmacy program with respect to the total of outpatients. In the last  updating as of January 2022, data from 16,000 dispensations to more than  4,000 patients had been collected. This means that 21.93% of outpatients had  benefited at some time point from the Telepharmacy service. Filters enable the  visualization of timeline progress and patient characterization, and  measure Telepharmacy activity by program. CONCLUSIONS: The processing of large Telemedicine datasets from various  sources through Business Intelligence in a hospital pharmacy service makes it  possible to synthesize information, generate customized reports, and visualize  information in a dynamic and attractive format. The application of this new  technology will help us improve strategic clinical and management decision  making.

OBJETIVO: La consolidación de la Telefarmacia en el contexto de la pandemia por la COVID-19 exige manejar a tiempo real un gran volumen de datos de actividad mediante análisis de datos. El objetivo de este trabajo fue diseñar un cuadro de mando ágil, personalizable y dinámico para la visualización y análisis de indicadores de actividad en Telefarmacia en un servicio de farmacia de hospital, mediante el empleo de herramientas  avanzadas de inteligencia empresarial (business intelligence).Método: Un equipo de trabajo multidisciplinar desarrolló una herramienta de  software entre abril y mayo de 2021 impulsado desde el servicio de farmacia  de hospital. Una vez consensuados los indicadores de interés en Telefarmacia,  se extrajeron los datos a partir de bases de datos brutas (base de datos de  Telefarmacia, programa de dispensación de pacientes externos, bases de datos  administrativas, catálogos de fármacos) mediante análisis de datos. La  integración de las diferentes fuentes de datos en el cuadro de mando se realizó mediante PowerBI®. Se definió el manejo de los datos perdidos y duplicados y  se aplicó preprocesamiento, normalización y transformación de los datos. Una  vez  validado el piloto por diferentes tipos de usuarios, se diseñó la estructura para actualización automática de los paneles con las sucesivas actualizaciones de las fuentes de datos. RESULTADOS: Diseño e implementación de un cuadro de mando de la actividad  en Telefarmacia: panel descriptivo general (perfil demográfico de pacientes,  recuento y condiciones de envíos, programa y servicio médico); geolocalización  de destino; perfil farmacológico; análisis relativo de los  pacientes beneficiarios de Telefarmacia respecto del total de pacientes  externos. En el último corte, a enero de 2022, se habían incluido datos de  16.000 dispensaciones con entrega informada a más de 4.000 pacientes, lo  que supone que el 21,93% de los pacientes externos han estado en algún momento en el programa de Telefarmacia. La aplicación de filtros permite  visualizar la evolución temporal, caracterizar grupos de pacientes y  dimensionar la actividad por programas. CONCLUSIONES: El procesamiento de paquetes de datos de Telemedicina, de  gran volumen, difícil manejo y procedentes de diversas fuentes relativas a Telefarmacia mediante inteligencia empresarial, en un servicio de farmacia  de hospital, permite sintetizar la información y proporcionar informes  personalizados y visualizaciones dinámicas y atractivas. La aplicación de estas  nuevas tecnologías puede ayudarnos a mejorar la toma de decisiones  estratégicas, tanto clínicas como de gestión.

Inteligencia empresarial para la visualización y análisis de datos de indicadores de actividad de Telefarmacia en un cuadro de mando de un servicio de farmacia de hospital / Business intelligence for the visualization and data analysis of Telepharmacy activity indicators in a hospital pharmacy service scorecard

Objetivo: La consolidación de la Telefarmacia en el contexto de la pandemia por la COVID-19 exige manejar a tiempo real un gran volumen dedatos de actividad mediante análisis de datos. El objetivo de este trabajofue diseñar un cuadro de mando ágil, personalizable y dinámico para lavisualización y análisis de indicadores de actividad en Telefarmacia en unservicio de farmacia de hospital, mediante el empleo de herramientas avanzadas de inteligencia empresarial (business intelligence).Método: Un equipo de trabajo multidisciplinar desarrolló una herramienta de software entre abril y mayo de 2021 impulsado desde el servicio de farmacia de hospital. Una vez consensuados los indicadoresde interés en Telefarmacia, se extrajeron los datos a partir de bases dedatos brutas (base de datos de Telefarmacia, programa de dispensación de pacientes externos, bases de datos administrativas, catálogosde fármacos) mediante análisis de datos. La integración de las diferentesfuentes de datos en el cuadro de mando se realizó mediante PowerBI®.Se definió el manejo de los datos perdidos y duplicados y se aplicópreprocesamiento, normalización y transformación de los datos. Una vez validado el piloto por diferentes tipos de usuarios, se diseñó la estructurapara actualización automática de los paneles con las sucesivas actualizaciones de las fuentes de datos.Resultados: Diseño e implementación de un cuadro de mando de laactividad en Telefarmacia: panel descriptivo general (perfil demográficode pacientes, recuento y condiciones de envíos, programa y serviciomédico); geolocalización de destino; perfil farmacológico; análisis relativo de los pacientes beneficiarios de Telefarmacia respecto del totalde pacientes externos. En el último corte, a enero de 2022, se habíanincluido datos de 16.000 dispensaciones con entrega informada a másde 4.000 pacientes, lo que supone que el 21,93% de los pacientes externos han estado en algún momento en el programa de Telefarmacia. (AU)

Objective: The consolidation of Telepharmacy during the COVID-19pandemic has raised the need for managing large volumes of real-timeactivity data through data analysis. The aim of this project was to designa dynamic, user-friendly, customizable scorecard in a hospital pharmacyservice for the visualization and analysis of Telepharmacy activity indicators through the use of advanced business intelligence technology.Method: The software tool was developed by a multidisciplinary teambetween April and May 2021, driven from the hospital pharmacy service.Once the Telepharmacy indicators of interest were established, datasetswere extracted from raw databases (administrative databases, Telepharmacy database, outpatient dispensing software, drug catalogues) throughdata analysis. The different data sources were integrated in a scorecardusing PowerBI®. The criteria for processing missing and duplicated datawere defined, and data pre-processing, normalization and transformationwere performed. Once the pilot scorecard was validated by differentprofiles of users, the structure was designed for the panels to automaticallyupdate as databases were updated. Results: Design and implementation of a scorecard of Telepharmacyactivity: general descriptive panel (demographic profile of patients, countand delivery conditions, program and medical service); geolocation ofdestination; pharmacological profile; relative analysis of patients involvedin the Telepharmacy program with respect to the total of outpatients. Inthe last updating as of January 2022, data from 16,000 dispensations tomore than 4,000 patients had been collected. This means that 21.93%of outpatients had benefited at some time point from the Telepharmacyservice. (AU)

Influence of CLOCK Gene Variants on Weight Response after Bariatric Surgery.

The Circadian Locomotor Output Cycles Kaput (CLOCK) gene has been linked to metabolic dysfunction and obesity. The purpose of this study was to analyze the association between single nucleotide polymorphisms (SNPs) of CLOCK gene with obesity and with long-term weight response after different bariatric surgery (BS) techniques. The cohort includes 375 patients with morbid obesity (MO) and 230 controls. In the association study of SNPs with weight response we combined several variables as phenotype at 6 years after surgery. The study protocol was registered in ISRCTN (ID80961259). The analysis of the selected SNPs was performed by allelic discrimination using Taqman® probes. The genotype association study was performed using the SNPStats program, with comparisons adjusted for sex, age, initial Body Mass Index, type 2 diabetes and hypertension diagnosis, and type of surgery. In the case-control study two of three SNPs were significantly associated with MO. The variant rs1801260 had a protective effect for MO whereas the TT genotype of rs3749474 variant had the strongest association with MO (OR = 2.25 (1.39-3.66); p = 0.0006). In the linear regression analysis both variants showed significant association with long-term weight loss and weight regain after BS, independently of the pre-surgery patient profile.

Comorbidities of Primary Care patients with COVID-19 during the first wave of the SARS-CoV-2 pandemic in the Community of Madrid / Comorbilidades de los pacientes con COVID-19 atendidos en Atención Primaria durante la primera oleada de la pandemia de SARSCoV-2 en la Comunidad de Madrid

Objectives. Recent publications on inpatients with COVID-19 describing their comorbidities and demographic profile exists, but data from large populations requiring only primary care (PC) are scarce. This paper aims to fill this gap and report the prevalence of eight comorbidities (high blood pressure, diabetes mellitus, cancer, cardiovascular disease, asthma, chronic kidney disease, chronic obstructive pulmonary disease, and chronic heart failure) among patients attending PC during the onset of the SARS-CoV-2 pandemic in the Community of Madrid (CoM), Spain. Patients and methods. This is an observational retrospective study that collects data registered in the CoM between February 25th and May 31st, 2020. Data are divided in two groups: Group-1 (N=339,890) consist of all patients with suspected or proven SARS-CoV-2 infection; and Group-2 is the subgroup (N=48,556, 14.3% of Group-1) of individuals with COVID-19 confirmed by positive RT-PCR test. Results. Comparing Group-1 with Group-2, 339,890/48,556 patients, respectively, the main results were as follows: average age (60.9/69.9 years), presence of at least one comorbidity (33.51%/47.69%), high blood pressure (19.74%/32.74%), diabetes mellitus (7.13%/13.75%), cancer (6.56%/10.6%), cardiovascular disease (4.52%/9.26%), asthma (7.98%/6.56%), chronic kidney disease (1.84%/4.41%), chronic obstructive pulmonary disease (2%/4.03%), and chronic heart failure (1.14%/2.77%). High blood pressure and diabetes mellitus were seen to be the most frequent (6.56%/8.38%) association. Conclusions. Patients requiring PC attention during the first wave of the COVID-19 pandemic in the CoM presented with a very high rate of comorbidities, with marked differences among those with or without a confirmed SARS-CoV-2 infection. (AU)

Objetivos. Existen publicaciones sobre las comorbilidades y el perfil demográfico en pacientes hospitalizados por COVID-19, pero son escasas aquellas sobre grandes poblaciones atendidas en Atención Primaria (AP). El objetivo de este trabajo es llenar este vacío describiendo la prevalencia de ocho comorbilidades (hipertensión arterial, diabetes mellitus, cáncer, enfermedad cardiovascular, asma, enfermedad renal crónica, enfermedad pulmonar obstructiva crónica e insuficiencia cardíaca crónica) en los pacientes de AP durante el inicio de la pandemia por SARS-CoV-2 en la Comunidad de Madrid (CoM), España. Pacientes y métodos. Estudio observacional retrospectivo que recopila datos registrados en la CoM, entre el 25 de febrero y el 31 de mayo de 2020. Se diferencian dos cohortes de pacientes que acudieron a AP: Grupo-1 (N=339.890), que incluye todos los pacientes con sospecha de SARS-CoV-2 o infección confirmada; Grupo-2, que es el subgrupo (N= 48.556, 14,3% del Grupo-1) de casos confirmados de COVID-19 mediante prueba RT-PCR. Resultados. Comparando el Grupo-1 con el Grupo-2 (339.890/48.556 pacientes, respectivamente), los principales resultados fueron los siguientes: edad media (60,9/69,9 años), presencia de al menos una comorbilidad (33,51%/47,69%), hipertensión arterial (19,74%/32,74%), diabetes mellitus (7,13%/13,75%), cáncer (6,56%/10,6%), enfermedad cardiovascular (4,52%/9,26%), asma (7,98%/6,56%), enfermedad renal crónica (1,84%/4,41%), EPOC (2%/4,03%) e insuficiencia cardíaca crónica (1,14%/2,77%). La asociación más frecuente fue hipertensión arterial y diabetes (6,56%/8,38%). Conclusiones. Los pacientes atendidos en AP durante la primera ola de la actual pandemia de COVID-19 en la CoM presentaron una tasa muy alta de una o más comorbilidades comunes, con diferencias significativas según tuvieran una infección confirmada o no por SARS-CoV-2. (AU)

Adverse drug reactions to the three doses of the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) mRNA-1273 vaccine in a cohort of cancer patients under active treatment of a tertiary hospital in Madrid, Spain.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines efficacy and safety have been tested in phase 3 studies in which cancer patients were not included or were underrepresented. Methods: The objective of this study is to evaluate the safety profile of the mRNA-1273 vaccine across cancer patients and its relationship to patients' demographics. This retrospective cohort study included patients 18-years or older with solid malignancies receiving active treatment in our hospital who had received the three-dose schedule of the mRNA9 1273 vaccine and whose side effects after each dose were recorded. Patient electronic medical records were reviewed retrospectively to collect data between April 19, 2021, and December 31, 2021. Patients with documented previous infection by SARS-Cov-2 were excluded from the study. Results: A total of 93 patients met the inclusion criteria. Local adverse drug reactions (ADRs) were reported more frequently after the first and second dose than after the third (41.9%, 43% and 31.1% of the patients respectively), while systemic ADRs followed the opposite pattern (16.1%, 34.4% and 52.6% of the patients respectively). We found a statistically significant association between sex and systemic ADRs after the third dose. Cochran-Armitage test showed a statistically significant linear trend, p = 0.012, with a higher Eastern Cooperative Oncology Group (ECOG) score associated with a lower proportion of patients suffering from systemic side effects. A logistic regression showed that women had 5.79 times higher odds to exhibit systemic ADRs after the third dose (p=0.01) compared to males. Increasing age was associated with a decreased likelihood of exhibiting ADRs (p=0.016). Conclusion: The mRNA-1273 vaccine shows a tolerable safety profile. The likelihood of ADRs appears to be associated with gender and age. Its association with ECOG scores is less evident. Further studies are needed to elucidate this data in cancer patients.

Weight Regain Outcomes After Bariatric Surgery in the Long-term Follow-up: Role of Preoperative Factors.

PURPOSE: Weight regain (WR) compromises the effectiveness of bariatric surgery. The objective of this study was to determine differences in long-term WR prevalence using different definitions and analyze possible preoperative predictors involved. METHODS: Single-center retrospective cohort study including 445 adults who underwent 3 modalities of bariatric surgery between 2009 and 2014. EXPOSURE: age, gender, ethnicity, body mass index (BMI), type 2 diabetes (T2D), hypertension (HTN), and type of surgery. MAIN OUTCOMES: WR at year 6 assessed by 4 definitions and 6 multivariate models based on common thresholds. RESULTS: Our cohort (71.1% female) had a mean age of 44.78 ± 11.94 years, and mean presurgery BMI of 44.94 ± 6.88 kg/m2, with a median follow-up of 6 years (IQR=5-8). The prevalences of T2D and HTN were 36.0% and 46.7% respectively. WR rates over thresholds ranged from 25.4 to 68.1%, with significant differences between groups in the WR measured as the percentage of maximum weight loss (MWL) and the increase in excess weight loss (EWL). Presurgery BMI was a significant predictor in 3 models; restrictive techniques were associated with WR in all the models except for those considering WR over 10 kg and WR over 15% from nadir as dependent variables. CONCLUSIONS: In this long-term study, WR defined as percentage of MWL and increase in EWL from nadir had the greatest significance in logistic regression models with preoperative BMI and type of surgery as independent variables. These findings could serve to establish a standardized outcome reporting WR in other longitudinal studies. KEY POINTS: • Lack of standardized outcome to measure weight regain after bariatric surgery. • Lowest rates of weight regain in malabsorptive techniques in all definitions applied. • Weight regain measured as percentage of maximum weight lost.

Flexibility and Bed Margins of the Community of Madrid's Hospitals during the First Wave of the SARS-CoV-2 Pandemic.

Background: The COVID-19 pandemic has had global effects; cases have been counted in the tens of millions, and there have been over two million deaths throughout the world. Health systems have been stressed in trying to provide a response to the increasing demand for hospital beds during the different waves. This paper analyzes the dynamic response of the hospitals of the Community of Madrid (CoM) during the first wave of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in the period between 18 March and 31 May 2020. The aim was to model the response of the CoM's health system in terms of the number of available beds. Methods: A research design based on a case study of the CoM was developed. To model this response, we use two concepts: "bed margin" (available beds minus occupied beds, expressed as a percentage) and "flexibility" (which describes the ability to adapt to the growing demand for beds). The Linear Hinges Model allowed a robust estimation of the key performance indicators for capturing the flexibility of the available beds in hospitals. Three new flexibility indicators were defined: the Average Ramp Rate Until the Peak (ARRUP), the Ramp Duration Until the Peak (RDUP), and the Ramp Growth Until the Peak (RGUP). Results: The public and private hospitals of the CoM were able to increase the number of available beds from 18,692 on 18 March 2020 to 23,623 on 2 April 2020. At the peak of the wave, the number of available beds increased by 160 in 48 h, with an occupancy of 90.3%. Within that fifteen-day period, the number of COVID-19 inpatients increased by 200% in non-intensive care unit (non-ICU) wards and by 155% in intensive care unit (ICU) wards. The estimated ARRUP for non-ICU beds in the CoM hospital network during the first pandemic wave was 305.56 beds/day, the RDUP was 15 days, and the RGUP was 4598 beds. For the ICU beds, the ARRUP was 36.73 beds/day, the RDUP was 20 days, and the RGUP was 735 beds. This paper includes a further analysis of the response estimated for each hospital. Conclusions: This research provides insights not only for academia, but also for hospital management and practitioners. The results show that not all of the hospitals dealt with the sudden increase in bed demand in the same way, nor did they provide the same flexibility in order to increase their bed capabilities. The bed margin and the proposed indicators of flexibility summarize the dynamic response and can be included as part of a hospital's management dashboard for monitoring its behavior during pandemic waves or other health crises as a complement to other, more steady-state indicators.

Effects of Milk and Dairy Products on the Prevention of Osteoporosis and Osteoporotic Fractures in Europeans and Non-Hispanic Whites from North America: A Systematic Review and Updated Meta-Analysis.

Nutrition plays an important role in bone health. The aim of our study was to update the evidence regarding dairy intake, osteoporotic fracture (OF) risk, and prospective bone mass density (BMD) evolution assessed by dual-energy X-ray absorptiometry in Europeans and non-Hispanic whites from North America. A systematic search was conducted in MEDLINE, EMBASE, and Scopus for papers published from 1 January, 2000 to 30 April, 2018. The eligibility criteria were as follows: healthy adults; measurable dairy exposure; hip, vertebral, wrist or OF as outcomes; and cohort or case-control studies. Two independent investigators conducted the search and the data extraction. A pooled analysis was conducted with random-effects models. Publication bias and meta-regression were considered. Ten cohort studies relating to OF risk were selected for meta-analysis. Three papers reporting BMD changes associated with dairy intake could not be aggregated in the meta-analysis. The pooled HRs of the highest compared with the lowest levels of dairy intake were 0.95 (95% CI: 0.87, 1.03; I2 = 82.9%; P-heterogeneity < 0.001) for OF at any site; 0.87 (95% CI: 0.75, 1.01; I2 = 86.7%; P-heterogeneity < 0.001) for hip fractures; and 0.82 (95% CI: 0.68, 0.99; I2 = 0.0%; P-heterogeneity = 0.512) for vertebral fractures. Concerning BMD, the selected studies described a 1.7-3% lower hip BMD in young and postmenopausal women with poor intake of milk in their youth, a positive relationship between baseline milk ingestion and the percentage of trochanter BMD change in elderly people, and a positive correlation between milk consumption and BMD change at the radius in women aged >65 y. In conclusion, in the studied population, the highest consumption of dairy products did not show a clear association with the total OF or hip fracture risks; however, a diminished risk of vertebral fracture could be described. The results regarding BMD change were heterogeneous and did not allow for a definitive conclusion.