El valor que aportan los servicios en la nube

The services widely known as “Cloud Services” are hardware and software services, with online access by Internet. The “value” they bring to organizations in general is a concept usually accepted and justified with basic arguments or without empirical data for supporting these arguments.The goal of this investigation is to know: The level of adoption of Cloud Services in companies and organizations in the local market; the key factors that are speeding up or slowing down such adoption; how are being perceived the potential benefits of Cloud Services; the type of impact Cloud Services have generated on the role of IT departments inside companies. A survey with 25 questions was sent to companies and organizations (potential consumers of Cloud Services) in the local market. One hundred participants answered this survey. Additionally, we interviewed several specialists currently working in four multinational Cloud Services providers. After analyzing the data gathered, we found that seventy percent of the surveyed organizations are currently using Cloud Services, although with different maturity levels for managing them.Only seven percent of the surveyed organizations are not using Cloud Services and reported that they don’t have plans for using such services in the future. About the key factors that are speeding up Cloud Services adoption, those related to business strategy and operation were considered as the most relevant. In second place of relevance is the organizational goal of lowering expenditures in IT infrastructure. “Security” is a factor that is still generating many worries, beside the practical problems in the integration of Cloud Services with internal IT applications.With regard to the perception of the potential benefits offered by Cloud Services to organizations,several factors from the business point of view were considered as the most relevant, followed by the possibility of lowering fixed costs and IT expenditures.In most organizations adopting Cloud Services, it has been perceived the impact on the role of the IT (for example, at the level of influence and control that IT departments have on users in organizations). However, the data gathered is insufficient for a more detailed analysis about the genesis and evolution of such changes and will be performed in future research.Los denominados “Servicios en la Nube” (Cloud Services) son servicios de hardware y/o de software, a los que se accede y utiliza en tiempo real, vía internet. El valor aportado por los Cloud Services es un concepto que suele aceptarse y justificarse sin mayor profundidad de argumentos o sin evidencia empírica. Esta investigación se propone relevar el nivel de adopción de los Cloud Services en compañías y organizaciones del ámbito local; identificar los factores que impulsen o demoren tal adopción;analizar cómo son percibidos los potenciales beneficios de tales servicios e identificar el tipo de impacto que los Cloud Services tienen sobre el rol de las áreas de IT (Information Technology) de las organizaciones.Se llevó a cabo una encuesta de 25 preguntas a empresas y organizaciones del mercado local, potenciales consumidoras de Cloud Services. La encuesta fue respondida por 100 participantes.También se realizaron entrevistas a varios especialistas, en cuatro empresas multinacionales proveedorasde Cloud Services.Tras analizar los datos obtenidos, hemos encontrado que los Cloud Services son utilizados actualmente por el 70% de las organizaciones, aunque con diferentes niveles de madurez de gestión.Solamente un 7% de las organizaciones reporta que no los adoptó ni tiene previsto adoptarlos. En relación con los factores que impulsan la adopción de Cloud Services, aquellos vinculados con la estrategia y operación del negocio fueron considerados como los más relevantes, seguidos por la necesidad de reducir desembolsos en infraestructuras de IT. El factor seguridad aún genera alta preocupación, junto con la problemática de la integración de tales servicios con las aplicaciones internas de las organizaciones.Respecto de la percepción de los potenciales beneficios que ofrecen los Cloud Services a las organizaciones, en el tope de relevancia se ubican factores propios de negocios, seguidos de la posibilidad de reducir gastos fijos y erogaciones en infraestructura de IT. En la mayoría de las organizaciones adoptantes de Cloud Services se ha percibido el impacto sobre el rol del área de IT (por ejemplo, en el grado con que ejerce control e influencia sobre los usuarios en el resto de la organización), pero los datos muestrales son insuficientes para profundizar en el análisis de cómo se gestaron tales cambios. o para diagnosticar su posible evolución, lo que se llevará a cabo en una segunda etapa

Corrigendum to Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression: [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180].

The authors regret an error in the pre-processing of the dataset that went unnoticed in the extensive code used for pre-processing of the study data. As a result of this error, the last EDSS overall for each patient was included as the last EDSS in the observation period. We corrected the error and repeated all analyses. [...

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression.

Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem. We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used. We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS

Comparison of fingolimod, dimethyl fumarate and teriflunomide for multiple sclerosis

Oral immunotherapies have become a standard treatment in relapsing-remitting multiple sclerosis. Direct comparison of their effect on relapse and disability is needed

Incidence of pregnancy and disease-modifying therapy exposure trends in women with multiple sclerosis: A contemporary cohort study

BACKGROUND: Exposure to disease-modifying therapy (DMT) during early pregnancy in women with relapsing-remitting MS (RRMS) may be increasing. OBJECTIVE: To retrospectively determine incidence of pregnancy, DMT exposure and pregnancy outcomes in women with RRMS. METHODS: We identified all women with RRMS aged 15-45 years in the MSBase Registry between 2005-2016. Annualised pregnancy incidence rates were calculated using Poisson regression models. DMT exposures and pregnancy outcomes were assessed. RESULTS: Of 9,098 women meeting inclusion criteria, 1,178 (13%) women recorded 1,521 pregnancies. The annualised incidence rate of pregnancy was 0.042 (95% CI 0.040, 0.045). A total of 635 (42%) reported pregnancies were conceived on DMT, increasing from 27% in 2006 to 62% in 2016. The median duration of DMT exposure during pregnancy was 30 days (IQR: 9, 50). There were a higher number of induced abortions on FDA pregnancy class C/D drugs compared with pregnancy class B and no DMT (p\u202f=\u202f0.010); but no differences in spontaneous abortions, term or preterm births. CONCLUSIONS: We report low pregnancy incidence rates, with increasing number of pregnancies conceived on DMT over the past 12-years. The median duration of DMT exposure in pregnancy was relatively short at one month

Effectiveness of multiple disease-modifying therapies in relapsing-remitting multiple sclerosis: causal inference to emulate a multiarm randomised trial

BACKGROUND: Simultaneous comparisons of multiple disease-modifying therapies for relapsing-remitting multiple sclerosis (RRMS) over an extended follow-up are lacking. Here we emulate a randomised trial simultaneously comparing the effectiveness of six commonly used therapies over 5 years. METHODS: Data from 74 centres in 35 countries were sourced from MSBase. For each patient, the first eligible intervention was analysed, censoring at change/discontinuation of treatment. The compared interventions included natalizumab, fingolimod, dimethyl fumarate, teriflunomide, interferon beta, glatiramer acetate and no treatment. Marginal structural Cox models (MSMs) were used to estimate the average treatment effects (ATEs) and the average treatment effects among the treated (ATT), rebalancing the compared groups at 6-monthly intervals on age, sex, birth-year, pregnancy status, treatment, relapses, disease duration, disability and disease course. The outcomes analysed were incidence of relapses, 12-month confirmed disability worsening and improvement. RESULTS: 23 236 eligible patients were diagnosed with RRMS or clinically isolated syndrome. Compared with glatiramer acetate (reference), several therapies showed a superior ATE in reducing relapses: natalizumab (HR=0.44, 95% CI=0.40 to 0.50), fingolimod (HR=0.60, 95% CI=0.54 to 0.66) and dimethyl fumarate (HR=0.78, 95% CI=0.66 to 0.92). Further, natalizumab (HR=0.43, 95% CI=0.32 to 0.56) showed a superior ATE in reducing disability worsening and in disability improvement (HR=1.32, 95% CI=1.08 to 1.60). The pairwise ATT comparisons also showed superior effects of natalizumab followed by fingolimod on relapses and disability. CONCLUSIONS: The effectiveness of natalizumab and fingolimod in active RRMS is superior to dimethyl fumarate, teriflunomide, glatiramer acetate and interferon beta. This study demonstrates the utility of MSM in emulating trials to compare clinical effectiveness among multiple interventions simultaneously

Risk of secondary progressive multiple sclerosis: A longitudinal study.

BACKGROUND: The risk factors for conversion from relapsing-remitting to secondary progressive multiple sclerosis remain highly contested. OBJECTIVE: The aim of this study was to determine the demographic, clinical and paraclinical features that influence the risk of conversion to secondary progressive multiple sclerosis. METHODS: Patients with adult-onset relapsing-remitting multiple sclerosis and at least four recorded disability scores were selected from MSBase, a global observational cohort. The risk of conversion to objectively defined secondary progressive multiple sclerosis was evaluated at multiple time points per patient using multivariable marginal Cox regression models. Sensitivity analyses were performed. RESULTS: A total of 15,717 patients were included in the primary analysis. Older age (hazard ratio (HR) = 1.02, p < 0.001), longer disease duration (HR = 1.01, p = 0.038), a higher Expanded Disability Status Scale score (HR = 1.30, p < 0.001), more rapid disability trajectory (HR = 2.82, p < 0.001) and greater number of relapses in the previous year (HR = 1.07, p = 0.010) were independently associated with an increased risk of secondary progressive multiple sclerosis. Improving disability (HR = 0.62, p = 0.039) and disease-modifying therapy exposure (HR = 0.71, p = 0.007) were associated with a lower risk. Recent cerebral magnetic resonance imaging activity, evidence of spinal cord lesions and oligoclonal bands in the cerebrospinal fluid were not associated with the risk of conversion. CONCLUSION: Risk of secondary progressive multiple sclerosis increases with age, duration of illness and worsening disability and decreases with improving disability. Therapy may delay the onset of secondary progression

The risk of secondary progressive multiple sclerosis is geographically determined but modifiable

Geographical variations in the incidence and prevalence of multiple sclerosis have been reported globally. Latitude as a surrogate for exposure to ultraviolet radiation but also other lifestyle and environmental factors are regarded as drivers of this variation. No previous studies evaluated geographical variation in the risk of secondary progressive multiple sclerosis, an advanced form of multiple sclerosis that is characterized by steady accrual of irreversible disability. We evaluated differences in the risk of secondary progressive multiple sclerosis in relation to latitude and country of residence, modified by high-to-moderate efficacy immunotherapy in a geographically diverse cohort of patients with relapsing-remitting multiple sclerosis. The study included relapsing-remitting multiple sclerosis patients from the global MSBase registry with at least one recorded assessment of disability. Secondary progressive multiple sclerosis was identified as per clinician diagnosis. Sensitivity analyses used the operationalized definition of secondary progressive multiple sclerosis and the Swedish decision tree algorithm. A proportional hazards model was used to estimate the cumulative risk of secondary progressive multiple sclerosis by country of residence (latitude), adjusted for sex, age at disease onset, time from onset to relapsing-remitting phase, disability (Multiple Sclerosis Severity Score) and relapse activity at study inclusion, national multiple sclerosis prevalence, government health expenditure, and proportion of time treated with high-to-moderate efficacy disease-modifying therapy. Geographical variation in time from relapsing-remitting phase to secondary progressive phase of multiple sclerosis was modelled through a proportional hazards model with spatially correlated frailties. We included 51 126 patients (72% female) from 27 countries. The median survival time from relapsing-remitting phase to secondary progressive multiple sclerosis among all patients was 39 (95% confidence interval: 37 to 43) years. Higher latitude [median hazard ratio = 1.21, 95% credible interval (1.16, 1.26)], higher national multiple sclerosis prevalence [1.07 (1.03, 1.11)], male sex [1.30 (1.22, 1.39)], older age at onset [1.35 (1.30, 1.39)], higher disability [2.40 (2.34, 2.47)] and frequent relapses [1.18 (1.15, 1.21)] at inclusion were associated with increased hazard of secondary progressive multiple sclerosis. Higher proportion of time on high-to-moderate efficacy therapy substantially reduced the hazard of secondary progressive multiple sclerosis [0.76 (0.73, 0.79)] and reduced the effect of latitude [interaction: 0.95 (0.92, 0.99)]. At the country-level, patients in Oman, Tunisia, Iran and Canada had higher risks of secondary progressive multiple sclerosis relative to the other studied regions. Higher latitude of residence is associated with a higher probability of developing secondary progressive multiple sclerosis. High-to-moderate efficacy immunotherapy can mitigate some of this geographically co-determined risk

Machine-learning-based prediction of disability progression in multiple sclerosis: an observational, international, multi-center study

AbstractBackgroundDisability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking.MethodsData of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. TRIPOD guidelines were followed.Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expended disability status scale, treatment, relapse information, and MS course.To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated on their area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error.FindingsA temporal attention model was the best model. It achieved a ROC-AUC of 0·71 ± 0·01, an AUC-PR of 0·26 ± 0·02, a Brier score of 0·1 ± 0·01 and an expected calibration error of 0·07 ± 0·04. The history of disability progression is more predictive for future disability progression than the treatment or relapses.InterpretationGood discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This makes these models ready for a clinical impact study. All our preprocessing and model code is available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS.</jats:sec