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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development of a Nomogram for Clinical Risk Prediction of Preterm Neonate Death in Ethiopia
Habtamu Shimels Hailemeskel1, Sofonyas Abebaw Tiruneh2
1Department of Pediatrics and Neonatal Nursing, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Insights
This study developed a nomogram to predict preterm neonate mortality in Ethiopia, achieving 92.7% accuracy. This tool aids in identifying high-risk infants for targeted interventions in low-resource settings.
Area of Science:
- Neonatal Health
- Public Health
- Medical Informatics
Background:
- Millions of newborn deaths occur annually, with preterm neonates facing the highest risk.
- Ethiopia faces challenges in reducing neonatal mortality to meet the 2030 Sustainable Development Goals.
- Accurate prediction models are crucial for resource-limited settings to manage preterm infant mortality.
Purpose of the Study:
- To develop a clinical risk prediction nomogram for preterm neonate mortality in Ethiopia.
- To provide a tool for individualized risk assessment in low-resource healthcare environments.
- To support efforts in reducing preventable newborn deaths.
Main Methods:
- A prospective follow-up study design was utilized.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed for variable selection.
- A nomogram was created for individualized risk prediction, with model performance assessed using ROC (AUROC) curves and decision curve analysis (DCA).
Main Results:
- The prediction model demonstrated strong discriminatory power with an Area Under the ROC Curve (AUROC) of 92.7% (95% CI: 89.9-95.4%).
- The model achieved high specificity (95%) and sensitivity (77%) in predicting preterm neonate death.
- Key prognostic determinants included gestational age, respiratory distress syndrome, multiple neonates, low birth weight, and kangaroo mother care.
Conclusions:
- The developed nomogram serves as a practical tool for predicting preterm neonate mortality.
- Implementing this model can guide clinical decision-making and resource allocation for improved neonatal care.
- The risk prediction model offers a high cost-benefit ratio for critically monitoring preterm infants.
Introduction:
In 2020, over 6,500 newborn deaths occured every day, resulting in 2.4 million children dying in their 1st month of life. Ethiopia is one of the countries that will need to step up their efforts and expedite progress to meet the 2030 sustainable development goal. Developing prediction models to forecast the mortality of preterm neonates could be valuable in low-resource settings with limited amenities, such as Ethiopia. Therefore, the study aims to develop a nomogram for clinical risk prediction of preterm neonate death in Ethiopia in 2021.
Methods:
A prospective follow-up study design was employed. The data were used to analyze using R-programming version 4.0.3 software. The least absolute shrinkage and selection operator (LASSO) regression is used for variable selection to be retained in the multivariable model. The model discrimination probability was checked using the ROC (AUROC) curve area. The model's clinical and public health impact was assessed using decision curve analysis (DCA). A nomogram graphical presentation created an individualized prediction of preterm neonate risk of mortality.
Results:
The area under the receiver operating curve (AUROC) discerning power for five sets of prognostic determinants (gestational age, respiratory distress syndrome, multiple neonates, low birth weight, and kangaroo mother care) is 92.7% (95% CI: 89.9-95.4%). This prediction model was particular (specificity = 95%) in predicting preterm death, with a true positive rate (sensitivity) of 77%. The best cut point value for predicting a high or low risk of preterm death (Youden index) was 0.3 (30%). Positive and negative predictive values at the Youden index threshold value were 85.4 percent and 93.3 percent, respectively.
Conclusion:
This risk prediction model provides a straightforward nomogram tool for predicting the death of preterm newborns. Following the preterm neonates critically based on the model has the highest cost-benefit ratio.
