14 research outputs found

    Neonatal deaths in infants born weighing ≥ 2500 g in low and middle-income countries

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    Background: Babies born weighing ≥ 2500 g account for more than 80% of the births in most resource-limited locations and for nearly 50% of the 28-day neonatal deaths. In contrast, in high-resource settings, 28-day neonatal mortality among this group represents only a small fraction of the neonatal deaths. Yet mortality risks for birth weight of ≥ 2500 g is limited. Knowledge regarding the factors associated with mortality in these babies will help in identifying interventions that can reduce mortality.Methods: The Global Network\u27s Maternal Newborn Health Registry (MNHR) is a prospective, population-based observational study that includes all pregnant women and their pregnancy outcomes in defined geographic communities that has been conducted in research sites in six low-middle income countries (India, Pakistan, Democratic Republic of Congo, Guatemala, Kenya and Zambia). Study staff enroll all pregnant women as early as possible during pregnancy and conduct follow-up visits to ascertain delivery and 28-day neonatal outcomes. We analyzed the neonatal mortality rates (NMR) and risk factors for deaths by 28 days among all live-born babies with a birthweight ≥ 2500 g from 2010 to 2018 across the Global Network sites.Results: Babies born in the Global Network sites from 2010 to 2018 with a birthweight ≥ 2500 g accounted for 84.8% of the births and 45.4% of the 28-day neonatal deaths. Among this group, the overall NMR was 13.1/1000 live births. The overall 28-day NMR for ongoing clusters was highest in Pakistan (29.7/1000 live births) and lowest in the Zambian/Kenyan sites (9.3/1000) for ≥ 2500 g infants. ≥ 2500 g NMRs declined for Zambia/Kenya and India. For Pakistan and Guatemala, the NMR remained almost unchanged over the period. The ≥ 2500 g risks related to maternal, delivery and newborn characteristics varied by site. Maternal factors that increased risk and were common for all sites included nulliparity, hypertensive disease, previous stillbirth, maternal death, obstructed labor, severe postpartum hemorrhage, and abnormal fetal presentation. Neonatal characteristics including resuscitation, hospitalization, congenital anomalies and male sex, as well as lower gestational ages and birthweights were also associated with increased mortality.Conclusions: Nearly half of neonatal deaths in the Global Network sites occurred in infants born weighing ≥ 2500 g. The NMR for those infants was 13.1 per 1000 live births, much higher than rates usually seen in high-income countries. The changes in NMR over time varied across the sites. Even among babies born ≥ 2500 g, lower gestational age and birthweight were largely associated with increased risk of mortality. Since many of these deaths should be preventable, attention to preventing mortality in these infants should have an important impact on overall NMR.Trial registration: https://ClinicalTrials.gov Identifier: NCT01073475

    Real-world experience of nintedanib for progressive fibrosing interstitial lung disease in the UK

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    Background Nintedanib slows progression of lung function decline in patients with progressive fibrosing (PF) interstitial lung disease (ILD) and was recommended for this indication within the United Kingdom (UK) National Health Service in Scotland in June 2021 and in England, Wales and Northern Ireland in November 2021. To date, there has been no national evaluation of the use of nintedanib for PF-ILD in a real-world setting.Methods 26 UK centres were invited to take part in a national service evaluation between 17 November 2021 and 30 September 2022. Summary data regarding underlying diagnosis, pulmonary function tests, diagnostic criteria, radiological appearance, concurrent immunosuppressive therapy and drug tolerability were collected via electronic survey.Results 24 UK prescribing centres responded to the service evaluation invitation. Between 17 November 2021 and 30 September 2022, 1120 patients received a multidisciplinary team recommendation to commence nintedanib for PF-ILD. The most common underlying diagnoses were hypersensitivity pneumonitis (298 out of 1120, 26.6%), connective tissue disease associated ILD (197 out of 1120, 17.6%), rheumatoid arthritis associated ILD (180 out of 1120, 16.0%), idiopathic nonspecific interstitial pneumonia (125 out of 1120, 11.1%) and unclassifiable ILD (100 out of 1120, 8.9%). Of these, 54.4% (609 out of 1120) were receiving concomitant corticosteroids, 355 (31.7%) out of 1120 were receiving concomitant mycophenolate mofetil and 340 (30.3%) out of 1120 were receiving another immunosuppressive/modulatory therapy. Radiological progression of ILD combined with worsening respiratory symptoms was the most common reason for the diagnosis of PF-ILD.Conclusion We have demonstrated the use of nintedanib for the treatment of PF-ILD across a broad range of underlying conditions. Nintedanib is frequently co-prescribed alongside immunosuppressive and immunomodulatory therapy. The use of nintedanib for the treatment of PF-ILD has demonstrated acceptable tolerability in a real-world setting

    Stochastic comparison of parallel systems with Pareto components

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    Pareto distribution is an important distribution in extreme value theory. In this paper, we consider parallel systems with Pareto components and study the effect of heterogeneity on skewness of such systems. It is shown that, when the lifetimes of components have different shape parameters, the parallel system with heterogeneous Pareto component lifetimes is more skewed than the system with independent and identically distributed Pareto components. However, for the case when the lifetimes of components have different scale parameters, the result gets reversed in the sense of star ordering. We also establish the relation between star ordering and dispersive ordering by extending the result of Deshpande and Kochar [(1983). Dispersive ordering is the same as tail ordering. Advances in Applied Probability 15(3): 686-687] from support to general supports, (a), 0. As a consequence, we obtain some new results on dispersion of order statistics from heterogeneous Pareto samples with respect to dispersive ordering. © 2021 The Author(s). Published by Cambridge University Press

    Machine Learning vs. Survival Analysis Models: a study on right censored heart failure data

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    Machine Learning Models are known to understand the intricacies of the data well, but native ML models cannot be used in time-to-event analysis due to censoring. In this paper, we explore the use of Machine Learning Models in the field of Survival Analysis using right censored Heart Failure Clinical Records Dataset. For this purpose, we first identify the top most important features responsible for death due to heart failure using Recursive Feature Elimination and then see how Machine Learning models can be adapted to improve the time-to-event analysis outcomes. To deal with this, Machine Learning Models are modified using the techniques Inverse Probability of Censoring Weighting (IPCW) and IPCW Bagging and are trained using the processed dataset alongside various survival analysis models. Area Under the time-dependent ROC (AUC) is used as a performance metric. The results reveal that the average AUC value for Survival Analysis Models is 0.51 while that of Machine Learning Models processed using IPCW increased to 0.80, and those processed using IPCW Bagging increased by 0.82. This reflects that Machine Learning models outperform Survival Analysis models in the case of time-to-event analysis of right censored dataset, and hence, are better indicators of risk of heart disease. © 2022 Taylor & Francis Group, LLC

    Some preservation properties of shifted stochastic orders

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    In this paper, we provide results on preservation of various shifted stochastic orders under the operations of mixtures. These results have been established with respect to up (down) shifted hazard rate order, up (down) shifted reversed hazard rate order and up (down) shifted likelihood ratio order. In addition, a discussion on preservation of shifted stochastic orders under the operations of convolutions is also presented. Some examples have been obtained to illustrate applications of these results. © Brazilian Statistical Association, 2021

    System signatures: A review and bibliometric analysis

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    System signatures have transitioned from an emerging topic to a growing research area. It has become essential to classify different research directions and examine the general trends of this promising research area. This would help in identifying potential research areas requiring future study. In this article, we review the literature on “system signatures,” dating back to 1985, and provide a thorough insight into this research field by using the techniques of bibliometric and network analyses. We proceed by identifying 539 published studies, and then scrutinize them to find works of proven influence, based on citation and PageRank analysis, and those authored by influential researchers. We also pinpoint the top contributing countries and key research topics. Finally, we identify and propose seven research clusters which might provide a robust roadmap for further investigation
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