ABSTRACT
OBJECTIVES@#To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia.@*METHODS@#A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively.@*RESULTS@#Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (P<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%CI: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia.@*CONCLUSIONS@#The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.
Subject(s)
Infant, Newborn , Humans , Male , Pregnancy , Female , Nomograms , Retrospective Studies , Cesarean Section , Risk Factors , Asphyxia Neonatorum/etiologyABSTRACT
Objective@#To Summarize mathematical and statistical models used in the area of infectious diseases modelling, to provide ideas and suggestions for the model-based analysis and decision-making of COVID-19.@*Methods@#By reviewing the commonly used mathematical and statistical models in the analysis of infectious diseases, with a focus on the mathematical models of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) that have been published and their practical effects.@*Results@#Infectious diseases modelling based on multi-source information has become the main research trend, and the published mathematical models of COVID-19 epidemic need to be improved in terms of accuracy and scalability.@*Conclusions@#It is recommended to build a more advanced mathematical/statistical model by allowing for the characteristics of new coronaviruses and to use more informative data to improve the analysis and decision-making of the new epidemic.
ABSTRACT
Objective:To Summarize mathematical and statistical models used in the area of infectious diseases modelling, to provide ideas and suggestions for the model-based analysis and decision-making of COVID-19.Methods:By reviewing the commonly used mathematical and statistical models in the analysis of infectious diseases, with a focus on the mathematical models of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) that have been published and their practical effects.Results:Infectious diseases modelling based on multi-source information has become the main research trend, and the published mathematical models of COVID-19 epidemic need to be improved in terms of accuracy and scalability.Conclusions:It is recommended to build a more advanced mathematical/statistical model by allowing for the characteristics of new coronaviruses and to use more informative data to improve the analysis and decision-making of the new epidemic.