Socio-demographic factors impact disabilities caused by perinatal asphyxia among Chinese children

[Establishment of a predictive nomogram model for predicting the death of very preterm infants during hospitalization]

OBJECTIVES

To establish a nomogram model for predicting the risk of death of very preterm infants during hospitalization.

METHODS

A retrospective analysis was performed on the medical data of 1 714 very preterm infants who were admitted to the Department of Neonatology, the Third Affiliated Hospital of Zhengzhou University, from January 2015 to December 2019. These infants were randomly divided into a training cohort (1 179 infants) and a validation cohort (535 infants) at a ratio of 7∶3. The logistic regression analysis was used to screen out independent predictive factors and establish a nomogram model, and the feasibility of the nomogram model was assessed by the validation set. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to assess the discriminatory ability, accuracy, and clinical applicability of the model.

RESULTS

Among the 1 714 very preterm infants, 260 died and 1 454 survived during hospitalization. By the multivariate logistic regression analysis of the training set, 8 variables including gestational age <28 weeks, birth weight <1 000 g, severe asphyxia, severe intraventricular hemorrhage (IVH), grade III-IV respiratory distress syndrome (RDS), and sepsis, cesarean section, and use of prenatal glucocorticoids were selected and a nomogram model for predicting the risk of death during hospitalization was established. In the training cohort, the nomogram model had an AUC of 0.790 (95%CI: 0.751-0.828) in predicting the death of very preterm infants during hospitalization, while in the validation cohort, it had an AUC of 0.808 (95%CI: 0.754-0.861). The Hosmer-Lemeshow goodness-of-fit test showed a good fit (P>0.05). DCA results showed a high net benefit of clinical intervention in very preterm infants when the threshold probability was 10%-60% for the training cohort and 10%-70% for the validation cohort.

CONCLUSIONS

A nomogram model for predicting the risk of death during hospitalization has been established and validated in very preterm infants, which can help clinicians predict the probability of death during hospitalization in these infants.

On Gesell Evaluation System for Disabled Children in Minority Areas

This paper takes 105 disabled children from the Disabled Rehabilitation Research Center of the South China minority autonomous region as the survey objects. Based on the Gesell scale, it collects health data such as exercise ability and health status of disabled children, analyzes the data in accordance with SPSS software, and constructs comprehensive disease analysis model of 105 disabled children. Moreover, according to five indicator data collected and tested for disabled children (adaptability, big movements, fine movements, language, and personal-social) in Gesell scale, comprehensive disease indicators of the disabled children are calculated and statistically analyzed together with age and gender information. This study analyzed comprehensive diseases and the correlation among various indicators, concluding that most disabled children aged 3–7 are diagnosed with moderate and severe developmental retardation, and retardation level is in obvious normal distribution. At the same time, there is a significant correlation between indicators of ability test interval and measurement indicators with age. This study suggests that targeted treatment and rehabilitation plans should be implemented for disabled children of different ages according to different indicators of ability test interval, which has reference value and significance for improving the treatment level of disabled children and realizing targeted rehabilitation.