Development of a Nomogram for Clinical Risk Prediction of Preterm Neonate Death in Ethiopia

Hailemeskel, Habtamu Shimels; Tiruneh, Sofonyas Abebaw

doi:10.3389/fped.2022.877200

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…Patel et al ( 44 ) reported that RDS was the leading cause of in-hospital death among EPIs with a GA of 24–27 weeks. Another predictive models also reported RDS as a critical indicator for the prognoses of preterm infants, especially in underdeveloped countries ( 45 ). Pulmonary hemorrhage and shock are prognostic determinants.…”

Section: Discussionmentioning

confidence: 94%

A nomogram for predicting death for infants born at a gestational age of <28 weeks: a population-based analysis in 18 neonatal intensive care units in northern China

Huang,

Li,

Feng

et al. 2023

Transl Pediatr

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Background In China, the number of preterm infants is the second largest globally. Compared with those in developed countries, the mortality rate and proportion of treatment abandonment for extremely preterm infants (EPIs) are higher in China. It would be valuable to conduct a multicenter study and develop predictive models for the mortality risk. This study aimed to identify a predictive model among EPIs who received complete care in northern China in recent years. Methods This study included EPIs admitted to eighteen neonatal intensive care units (NICUs) within 72 hours of birth for receiving complete care in northern China between January 1, 2015, and December 31, 2018. Infants were randomly assigned into a training dataset and validation dataset with a ratio of 7:3. Univariate Cox regression analysis and multiple regression analysis were used to select the predictive factors and to construct the best-fitting model for predicting in-hospital mortality. A nomogram was plotted and the discrimination ability was tested by an area under the receiver operating characteristic curve (AUROC). The calibration ability was tested by a calibration curve along with the Hosmer-Lemeshow (HL) test. In addition, the clinical effectiveness was examined by decision curve analysis (DCA). Results A total of 568 EPIs were included and divided into the training dataset and validation dataset. Seven variables [birth weight (BW), being inborn, chest compression in the delivery room (DR), severe respiratory distress syndrome, pulmonary hemorrhage, invasive mechanical ventilation, and shock] were selected to establish a predictive nomogram. The AUROC values for the training and validation datasets were 0.863 [95% confidence interval (CI): 0.813–0.914] and 0.886 (95% CI: 0.827–0.945), respectively. The calibration plots and HL test indicated satisfactory accuracy. The DCA demonstrated that positive net benefits were shown when the threshold was >0.6. Conclusions A nomogram based on seven risk factors is developed in this study and might help clinicians identify EPIs with risk of poor prognoses early.

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Section: Discussionmentioning

confidence: 94%

A nomogram for predicting death for infants born at a gestational age of <28 weeks: a population-based analysis in 18 neonatal intensive care units in northern China

Huang,

Li,

Feng

et al. 2023

Transl Pediatr

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HIF-1α promotes the expression of syndecan-1 and inhibits the NLRP3 inflammasome pathway in vascular endothelial cells under hemorrhagic shock

Feng

Xie

et al. 2022

Biochemical and Biophysical Research Communications

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Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer

Li,

Meng,

Xiao

et al. 2024

World J Surg Onc

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Background The tumor microenvironment (TME) plays a crucial role in tumorigenesis and tumor progression. This study aimed to identify novel TME-related biomarkers and develop a prognostic model for patients with non-small-cell lung cancer (NSCLC). Methods After downloading and preprocessing data from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) datasets, we classified the molecular subtypes using the “NMF” R package. We performed survival analysis and quantified immune scores between clusters. A Cox proportional hazards model was then constructed, and its formula was produced. We assessed model performance and clinical utility. A prediction nomogram was also constructed and validated. Additionally, we explored the potential regulatory mechanisms of our TME gene signature using Gene Set Enrichment Analysis (GSEA). Results From data processing and univariate Cox regression analysis, 57 TME-related prognostic genes were identified, and two significantly distinct clusters were established. Using Cox regression and Lasso regression, an 18-gene TME-related prognostic model was developed. Patients were stratified into high- and low-risk groups based on the risk score, with survival analysis showing that the low-risk group had significantly better outcomes than the high-risk group ( P < 0.01). ROC curve analysis demonstrated strong predictive performance, with 1-year, 3-year, and 5-year AUC values ranging from 0.654 to 0.702 across different cohorts. The model accurately predicted survival outcomes across subgroups with varying clinical features, and its predictive accuracy was validated through a nomogram. Conclusions We developed a prognostic model based on TME-related genes in NSCLC. Our 18-gene TME signature can effectively predict the prognosis of NSCLC with high accuracy. Supplementary Information The online version contains supplementary material available at 10.1186/s12957-024-03588-y.

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Development of a Nomogram for Clinical Risk Prediction of Preterm Neonate Death in Ethiopia

Cited by 4 publications

References 30 publications

A nomogram for predicting death for infants born at a gestational age of <28 weeks: a population-based analysis in 18 neonatal intensive care units in northern China

A nomogram for predicting death for infants born at a gestational age of <28 weeks: a population-based analysis in 18 neonatal intensive care units in northern China

HIF-1α promotes the expression of syndecan-1 and inhibits the NLRP3 inflammasome pathway in vascular endothelial cells under hemorrhagic shock

Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer

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