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      Incidence and development of validated mortality prediction model among asphyxiated neonates admitted to neonatal intensive care unit at Felege Hiwot Comprehensive Specialized Hospital, Bahir Dar, Northwest Ethiopia, 2021: retrospective follow-up study

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          Abstract

          Introduction

          Perinatal asphyxia is failure to maintain normal breathing at birth. World Health Organization indicates that perinatal asphyxia is the third major cause of neonatal mortality in developing countries accounting for 23% of neonatal deaths every year. At global and national level efforts have done to reduce neonatal mortality, however fatalities from asphyxia remains high in Ethiopia (24%). And there are no sufficient studies to show incidence and prediction of mortality among asphyxiated neonates. Developing validated risk prediction model is one of the crucial strategies to improve neonatal outcomes with asphyxia. Therefore, this study will help to screen asphyxiated neonate at high-risk for mortality during admission by easily accessible predictors. This study aimed to determine the incidence and develop validated Mortality Prediction model among asphyxiated neonates admitted to the Neonatal Intensive Care Unit at Felege-Hiwot Comprehensive Specialized Hospital, Bahir Dar, Ethiopia.

          Method

          Retrospective follow-up study was conducted at Felege-Hiwot Comprehensive Specialized Hospital from September 1, 2017, to March 31, 2021. Simple random sampling was used to select 774 neonates, and 738 were reviewed. Since was data Secondary, it was collected by checklist. After the description of the data by table and graph, Univariable with p-value < 0.25, and stepwise multivariable analysis with p-value < 0.05 were done to develop final reduced prediction model by likelihood ratio test. To improve clinical utility, we developed a simplified risk score to classify asphyxiated neonates at high or low-risk of mortality. The accuracy of the model was evaluated using area under curve, and calibration plot. To measures all accuracy internal validation using bootstrapping technique were assessed. We evaluated the clinical impact of the model using a decision curve analysis across various threshold probabilities.

          Result

          Incidence of neonatal mortality with asphyxia was 27.2% (95% CI: 24.1, 30.6). Rural residence, bad obstetric history, amniotic fluid status, multiple pregnancy, birth weight (< 2500 g), hypoxic-ischemic encephalopathy (stage II and III), and failure to suck were identified in the final risk prediction score. The area under the curve for mortality using 7 predictors was 0.78 (95% CI 0.74 to 0.82). With ≥ 7 cutoffs the sensitivity and specificity of risk prediction score were 0.64 and 0.82 respectively.

          Conclusion and recommendation

          Incidence of neonatal mortality with asphyxia was high. The risk prediction score had good discrimination power built by rural residence, bad obstetric history, stained amniotic fluid, multiple pregnancy, birth weight (< 2500 g), hypoxic-ischemic encephalopathy (stage II and III), and failure to suck. Thus, using this score chart and improve neonatal and maternal service reduce mortality among asphyxiated neonates.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12887-024-04696-0.

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          Most cited references40

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          Global, regional, and national causes of child mortality in 2000-13, with projections to inform post-2015 priorities: an updated systematic analysis.

          Trend data for causes of child death are crucial to inform priorities for improving child survival by and beyond 2015. We report child mortality by cause estimates in 2000-13, and cause-specific mortality scenarios to 2030 and 2035. We estimated the distributions of causes of child mortality separately for neonates and children aged 1-59 months. To generate cause-specific mortality fractions, we included new vital registration and verbal autopsy data. We used vital registration data in countries with adequate registration systems. We applied vital registration-based multicause models for countries with low under-5 mortality but inadequate vital registration, and updated verbal autopsy-based multicause models for high mortality countries. We used updated numbers of child deaths to derive numbers of deaths by causes. We applied two scenarios to derive cause-specific mortality in 2030 and 2035. Of the 6·3 million children who died before age 5 years in 2013, 51·8% (3·257 million) died of infectious causes and 44% (2·761 million) died in the neonatal period. The three leading causes are preterm birth complications (0·965 million [15·4%, uncertainty range (UR) 9·8-24·5]; UR 0·615-1·537 million), pneumonia (0·935 million [14·9%, 13·0-16·8]; 0·817-1·057 million), and intrapartum-related complications (0·662 million [10·5%, 6·7-16·8]; 0·421-1·054 million). Reductions in pneumonia, diarrhoea, and measles collectively were responsible for half of the 3·6 million fewer deaths recorded in 2013 versus 2000. Causes with the slowest progress were congenital, preterm, neonatal sepsis, injury, and other causes. If present trends continue, 4·4 million children younger than 5 years will still die in 2030. Furthermore, sub-Saharan Africa will have 33% of the births and 60% of the deaths in 2030, compared with 25% and 50% in 2013, respectively. Our projection results provide concrete examples of how the distribution of child causes of deaths could look in 15-20 years to inform priority setting in the post-2015 era. More evidence is needed about shifts in timing, causes, and places of under-5 deaths to inform child survival agendas by and beyond 2015, to end preventable child deaths in a generation, and to count and account for every newborn and every child. Bill & Melinda Gates Foundation. Copyright © 2015 Elsevier Ltd. All rights reserved.
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            Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes

            When designing a study to develop a new prediction model with binary or time‐to‐event outcomes, researchers should ensure their sample size is adequate in terms of the number of participants (n) and outcome events (E) relative to the number of predictor parameters (p) considered for inclusion. We propose that the minimum values of n and E (and subsequently the minimum number of events per predictor parameter, EPP) should be calculated to meet the following three criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of ≥0.9, (ii) small absolute difference of ≤ 0.05 in the model's apparent and adjusted Nagelkerke's R 2 , and (iii) precise estimation of the overall risk in the population. Criteria (i) and (ii) aim to reduce overfitting conditional on a chosen p, and require prespecification of the model's anticipated Cox‐Snell R 2 , which we show can be obtained from previous studies. The values of n and E that meet all three criteria provides the minimum sample size required for model development. Upon application of our approach, a new diagnostic model for Chagas disease requires an EPP of at least 4.8 and a new prognostic model for recurrent venous thromboembolism requires an EPP of at least 23. This reinforces why rules of thumb (eg, 10 EPP) should be avoided. Researchers might additionally ensure the sample size gives precise estimates of key predictor effects; this is especially important when key categorical predictors have few events in some categories, as this may substantially increase the numbers required.
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              Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement.

              Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed.
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                Author and article information

                Contributors
                yibeltalshitu@gmail.com
                Journal
                BMC Pediatr
                BMC Pediatr
                BMC Pediatrics
                BioMed Central (London )
                1471-2431
                28 March 2024
                28 March 2024
                2024
                : 24
                : 219
                Affiliations
                [1 ]Department of Epidemiology, Curative and Preventive Health Service, Amhara Regional Health Bureau, ( https://ror.org/00b2nf889) Bahir Dar, Ethiopia
                [2 ]Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, ( https://ror.org/0595gz585) Gondar, Ethiopia
                [3 ]Department of Public Health, College of Health Sciences, Debre Markos University, ( https://ror.org/04sbsx707) Debre Markos, Ethiopia
                Article
                4696
                10.1186/s12887-024-04696-0
                10976726
                38539138
                ede7fa26-ea7c-4cdc-a723-bc9c30b6ca77
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 30 April 2023
                : 7 March 2024
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Pediatrics
                asphyxia,neonate,mortality,risk score,ethiopia
                Pediatrics
                asphyxia, neonate, mortality, risk score, ethiopia

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