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A Model to Simulate Clinically Relevant Hypoxia in Humans
Published on: December 22, 2016
Establishment and validation of apnea risk prediction models in preterm infants: a retrospective case control study
Xiaodan Xu1, Lin Li2, Daiquan Chen3
1Zhongshan Hospital Affiliated to Xiamen University, Xiamen, Fujian Province, 361000, China.
Insights
This study developed a risk prediction model for apnea in preterm infants. The model, using gestational age, birth length, Apgar score, and respiratory distress syndrome, accurately identifies high-risk infants for improved prognosis.
Area of Science:
- Neonatal Medicine
- Pediatric Pulmonology
- Clinical Prediction Modeling
Background:
- Apnea is a common and serious condition in preterm infants, often leading to hypoxic damage.
- Early identification of apnea risk is crucial for improving the prognosis of preterm infants.
Purpose of the Study:
- To construct and validate a prediction model for assessing apnea risk in premature infants.
- To identify high-risk groups of preterm infants for targeted interventions.
Main Methods:
- Retrospective analysis of 486 preterm infants (162 with apnea, 324 controls).
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression for variable selection and model building.
- Nomogram visualization and validation using ROC curves, calibration curves, and decision curves.
Main Results:
- Gestational age, birth length, Apgar score, and neonatal respiratory distress syndrome were identified as key predictors of apnea.
- The model demonstrated good fit (C-index=0.831) and predictive efficacy, validated by ROC and calibration curves.
- Decision curve analysis confirmed the clinical utility of the prediction model.
Conclusions:
- A validated risk prediction model incorporating gestational age, birth length, Apgar score, and neonatal respiratory distress syndrome effectively predicts apnea in preterm infants.
- The model exhibits good predictive efficacy and clinical utility, aiding in the identification of high-risk neonates.
Background:
Apnea is common in preterm infants and can be accompanied with severe hypoxic damage. Early assessment of apnea risk can impact the prognosis of preterm infants. We constructed a prediction model to assess apnea risk in premature infants for identifying high-risk groups.
Methods:
A total of 162 and 324 preterm infants with and without apnea who were admitted to the neonatal intensive care unit of Xiamen University between January 2018 and December 2021 were selected as the case and control groups, respectively. Demographic characteristics, laboratory indicators, complications of the patients, pregnancy-related factors, and perinatal risk factors of the mother were collected retrospectively. The participants were randomly divided into modeling (n = 388) and validation (n = 98) sets in an 8:2 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression analyses were used to independently filter variables from the modeling set and build a model. A nomogram was used to visualize models. The calibration and clinical utility of the model was evaluated using consistency index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve, and the model was verified using the validation set.
Results:
Results of LASSO combined with multivariate logistic regression analysis showed that gestational age at birth, birth length, Apgar score, and neonatal respiratory distress syndrome were predictors of apnea development in preterm infants. The model was presented as a nomogram and the Hosmer-Lemeshow goodness of fit test showed a good model fit (χ2=5.192, df=8, P=0.737), with Nagelkerke R2 of 0.410 and C-index of 0.831. The area under the ROC curve and 95% CI were 0.831 (0.787-0.874) and 0.829 (0.722-0.935), respectively. Delong's test comparing the AUC of the two data sets showed no significant difference (P=0.976). The calibration curve showed good agreement between the predicted and actual observations. The decision curve results showed that the threshold probability range of the model was 0.07-1.00, the net benefit was high, and the constructed clinical prediction model had clinical utility.
Conclusions:
Our risk prediction model based on gestational age, birth length, Apgar score 10 min post-birth, and neonatal respiratory distress syndrome was validated in many aspects and had good predictive efficacy and clinical utility.
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