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      Development and validation of a simplified score to predict neonatal mortality risk among neonates weighing 2000 g or less (NMR-2000): an analysis using data from the UK and The Gambia


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          78% of neonatal deaths occur in sub-Saharan Africa and southern Asia, among which, more than 80% are in low birthweight babies. Existing neonatal mortality risk scores have primarily been developed for high-resource settings. The aim of this study was to develop and validate a score that is practicable for low-income and middle-income countries to predict in-hospital mortality among neonates born weighing 2000 g or less using datasets from the UK and The Gambia.


          This analysis used retrospective data held in the UK National Neonatal Research Database from 187 neonatal units, and data from the Edward Francis Small Teaching Hospital (EFSTH), Banjul, The Gambia. In the UK dataset, neonates were excluded if birthweight was more than 2000 g; if the neonate was admitted aged more than 6 h or following discharge; if the neonate was stillborn; if the neonate died in delivery room; or if they were moribund on admission. The Gambian dataset included all neonates weighing less than 2000 g who were admitted between May 1, 2018, and Sept 30, 2019, who were screened for but not enrolled in the Early Kangaroo Mother Care Trial. 18 studies were reviewed to generate a list of 84 potential parameters. We derived a model to score in-hospital neonatal mortality risk using data from 55 029 admissions to a random sample of neonatal units in England and Wales from Jan 1, 2010, to Dec 31, 2016. All candidate variables were included in a complete multivariable model, which was progressively simplified using reverse stepwise selection. We validated the new score (NMR-2000) on 40 329 admissions to the remaining units between the same dates and 14 818 admissions to all units from Jan 1, to Dec 31, 2017. We also validated the score on 550 neonates admitted to the EFSTH in The Gambia.


          18 candidate variables were selected for inclusion in the modelling process. The final model included three parameters: birthweight, admission oxygen saturation, and highest level of respiratory support within 24 h of birth. NMR-2000 had very good discrimination and goodness-of-fit across the UK samples, with a c-index of 0·8859–0·8930 and a Brier score of 0·0232–0·0271. Among Gambian neonates, the model had a c-index of 0·8170 and a Brier score of 0·1688. Predictive ability of the simplified integer score was similar to the model using regression coefficients, with c-indices of 0·8903 in the UK full validation sample and 0·8082 in the Gambian validation sample.


          NMR-2000 is a validated mortality risk score for hospitalised neonates weighing 2000 g or less in settings where pulse oximetry is available. The score is accurate and simplified for bedside use. NMR-2000 requires further validation using a larger dataset from low-income and middle-income countries but has the potential to improve individual and population-level neonatal care resource allocation.


          Bill & Melinda Gates Foundation; Eunice Kennedy Shriver National Institute of Child Health & Human Development; Wellcome Trust; and Joint Global Health Trials scheme of Department of Health and Social Care, Department for International Development, Medical Research Council, and Wellcome Trust.

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          Most cited references40

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          Global, regional, and national causes of under-5 mortality in 2000–15: an updated systematic analysis with implications for the Sustainable Development Goals

          Summary Background Despite remarkable progress in the improvement of child survival between 1990 and 2015, the Millennium Development Goal (MDG) 4 target of a two-thirds reduction of under-5 mortality rate (U5MR) was not achieved globally. In this paper, we updated our annual estimates of child mortality by cause to 2000–15 to reflect on progress toward the MDG 4 and consider implications for the Sustainable Development Goals (SDG) target for child survival. Methods We increased the estimation input data for causes of deaths by 43% among neonates and 23% among 1–59-month-olds, respectively. We used adequate vital registration (VR) data where available, and modelled cause-specific mortality fractions applying multinomial logistic regressions using adequate VR for low U5MR countries and verbal autopsy data for high U5MR countries. We updated the estimation to use Plasmodium falciparum parasite rate in place of malaria index in the modelling of malaria deaths; to use adjusted empirical estimates instead of modelled estimates for China; and to consider the effects of pneumococcal conjugate vaccine and rotavirus vaccine in the estimation. Findings In 2015, among the 5·9 million under-5 deaths, 2·7 million occurred in the neonatal period. The leading under-5 causes were preterm birth complications (1·055 million [95% uncertainty range (UR) 0·935–1·179]), pneumonia (0·921 million [0·812 −1·117]), and intrapartum-related events (0·691 million [0·598 −0·778]). In the two MDG regions with the most under-5 deaths, the leading cause was pneumonia in sub-Saharan Africa and preterm birth complications in southern Asia. Reductions in mortality rates for pneumonia, diarrhoea, neonatal intrapartum-related events, malaria, and measles were responsible for 61% of the total reduction of 35 per 1000 livebirths in U5MR in 2000–15. Stratified by U5MR, pneumonia was the leading cause in countries with very high U5MR. Preterm birth complications and pneumonia were both important in high, medium high, and medium child mortality countries; whereas congenital abnormalities was the most important cause in countries with low and very low U5MR. Interpretation In the SDG era, countries are advised to prioritise child survival policy and programmes based on their child cause-of-death composition. Continued and enhanced efforts to scale up proven life-saving interventions are needed to achieve the SDG child survival target. Funding Bill & Melinda Gates Foundation, WHO.
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            Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis

            Summary Background Preterm birth is the leading cause of death in children younger than 5 years worldwide. Although preterm survival rates have increased in high-income countries, preterm newborns still die because of a lack of adequate newborn care in many low-income and middle-income countries. We estimated global, regional, and national rates of preterm birth in 2014, with trends over time for some selected countries. Methods We systematically searched for data on preterm birth for 194 WHO Member States from 1990 to 2014 in databases of national civil registration and vital statistics (CRVS). We also searched for population-representative surveys and research studies for countries with no or limited CRVS data. For 38 countries with high-quality data for preterm births in 2014, data are reported directly. For countries with at least three data points between 1990 and 2014, we used a linear mixed regression model to estimate preterm birth rates. We also calculated regional and global estimates of preterm birth for 2014. Findings We identified 1241 data points across 107 countries. The estimated global preterm birth rate for 2014 was 10·6% (uncertainty interval 9·0–12·0), equating to an estimated 14·84 million (12·65 million–16·73 million) live preterm births in 2014. 12· 0 million (81·1%) of these preterm births occurred in Asia and sub-Saharan Africa. Regional preterm birth rates for 2014 ranged from 13·4% (6·3–30·9) in North Africa to 8·7% (6·3–13·3) in Europe. India, China, Nigeria, Bangladesh, and Indonesia accounted for 57·9 million (41×4%) of 139·9 million livebirths and 6·6 million (44×6%) of preterm births globally in 2014. Of the 38 countries with high-quality data, preterm birth rates have increased since 2000 in 26 countries and decreased in 12 countries. Globally, we estimated that the preterm birth rate was 9×8% (8×3–10×9) in 2000, and 10×6% (9×0–12×0) in 2014. Interpretation Preterm birth remains a crucial issue in child mortality and improving quality of maternal and newborn care. To better understand the epidemiology of preterm birth, the quality and volume of data needs to be improved, including standardisation of definitions, measurement, and reporting. Funding WHO and the March of Dimes.
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              Towards better clinical prediction models: seven steps for development and an ABCD for validation.

              Clinical prediction models provide risk estimates for the presence of disease (diagnosis) or an event in the future course of disease (prognosis) for individual patients. Although publications that present and evaluate such models are becoming more frequent, the methodology is often suboptimal. We propose that seven steps should be considered in developing prediction models: (i) consideration of the research question and initial data inspection; (ii) coding of predictors; (iii) model specification; (iv) model estimation; (v) evaluation of model performance; (vi) internal validation; and (vii) model presentation. The validity of a prediction model is ideally assessed in fully independent data, where we propose four key measures to evaluate model performance: calibration-in-the-large, or the model intercept (A); calibration slope (B); discrimination, with a concordance statistic (C); and clinical usefulness, with decision-curve analysis (D). As an application, we develop and validate prediction models for 30-day mortality in patients with an acute myocardial infarction. This illustrates the usefulness of the proposed framework to strengthen the methodological rigour and quality for prediction models in cardiovascular research.

                Author and article information

                Lancet Child Adolesc Health
                Lancet Child Adolesc Health
                The Lancet. Child & Adolescent Health
                Elsevier Ltd
                1 April 2020
                April 2020
                : 4
                : 4
                : 299-311
                [a ]Department of Paediatrics, University of California San Francisco, San Francisco, CA, USA
                [b ]Maternal, Adolescent, Reproductive, and Child Health Centre, London School of Hygiene and Tropical Medicine, London, UK
                [c ]Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
                [d ]UK Medical Research Council Unit The Gambia at London School of Hygiene and Tropical Medicine, Fajara, The Gambia
                [e ]Department of Neonatal Medicine, University College London, London, UK
                [f ]Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe, Uganda
                [g ]Neonatal Medicine, School of Public Health, Faculty of Medicine, Chelsea and Westminster Hospital Campus, Imperial College London, London, UK
                [h ]Centre of Excellence for Maternal, Newborn, and Child Health, School of Public Health, Makerere University, Kampala, Uganda
                [i ]Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
                Author notes
                [* ]Correspondence to: Dr Melissa M Medvedev, Department of Paediatrics, University of California San Francisco, San Francisco, CA 94158, USA melissa.morgan@ 123456lshtm.ac.uk
                © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).



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