Predicting RSV Hospitalization in Premature Infants
Author Information
Author(s): Eric AF Simoes, Xavier Carbonell-Estrany, John R Fullarton, Johannes G Liese, Jose Figueras-Aloy, Gunther Doering, Juana Guzman
Primary Institution: The University of Colorado School of Medicine
Hypothesis
Can a predictive model based on risk factors accurately forecast RSV-related hospitalizations in premature infants born at 33–35 weeks of gestational age?
Conclusion
A robust model based on seven risk factors was developed to predict which premature infants born between 33–35 weeks' GA are at highest risk of hospitalization from RSV.
Supporting Evidence
- The model correctly classified over 70% of cases.
- The area under the ROC curve was 0.791.
- The number needed to treat (NNT) to prevent hospitalization of 75% of at-risk infants was calculated to be 11.7.
- Bootstrapping validation showed a mean discriminant function of 72%.
Takeaway
Doctors can use a new model to figure out which premature babies are more likely to get really sick from a virus called RSV, so they can help them better.
Methodology
The model was developed using data from a case-control study of 183 hospitalized infants and 371 controls, analyzed through discriminant function analysis and validated with bootstrapping.
Potential Biases
Potential selection bias due to the case-control design and limited data on certain risk factors.
Limitations
The study was limited by being a case-control design, which may introduce selection bias, and variability in admission criteria across hospitals.
Participant Demographics
Premature infants born between 33–35 weeks of gestational age.
Statistical Information
P-Value
p<0.05
Confidence Interval
95% CI: 9.5–13.6
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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