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Published on: December 10, 2013
Prediction model of RSV-hospitalization in late preterm infants: An update and validation study
Koos Korsten1, Maarten O Blanken1, Elisabeth E Nibbelke1
1Division of Paediatric Immunology and Infectious Diseases, University Medical Center Utrecht, Utrecht, The Netherlands.
Insights
A new prediction model identifies infants at high risk for respiratory syncytial virus (RSV) hospitalization. This tool helps target new RSV therapeutics to infants who will benefit most, improving preventative care.
Area of Science:
- Pediatrics
- Infectious Diseases
- Epidemiology
Background:
- New respiratory syncytial virus (RSV) vaccines and therapeutics are emerging.
- Selecting appropriate target populations for these new treatments presents a challenge.
- An improved prediction model for RSV hospitalization in infants is needed.
Purpose of the Study:
- To update and validate a previously published prediction model for RSV hospitalization within the first year of life.
- To identify key predictors of RSV hospitalization in infants.
- To develop a user-friendly tool for risk stratification.
Main Methods:
- Two prospective multicenter birth cohort studies (RISK-I and RISK-II) were conducted.
- The RISK-I cohort (n=2524) was used to update the prediction model.
- The RISK-II cohort (n=1564) served for external validation of the updated model.
Main Results:
- 181 infants were hospitalized for RSV. Key predictors identified include daycare/siblings, birth date, neonatal respiratory support, limited breastfeeding, and maternal atopy.
- The updated model demonstrated superior discrimination in the validation cohort (AUROC 0.72).
- A nomogram was created for easy clinical application to distinguish high-risk infants.
Conclusions:
- A validated clinical prediction model for RSV hospitalization in preterm infants (32-35 weeks gestational age) was developed.
- The model and associated nomogram facilitate targeted use of RSV therapeutics.
- This approach aims to maximize the benefit of new RSV interventions.
Background:
New vaccines and RSV therapeutics have been developed in the past decade. With approval of these new pharmaceuticals on the horizon, new challenges lie ahead in selecting the appropriate target population. We aimed to improve a previously published prediction model for prediction of RSV-hospitalization within the first year of life.
Methods:
Two consecutive prospective multicenter birth cohort studies were performed from June 2008 until February 2015. The first cohort (RISK-I, n=2524, 2008-2011) was used to update the existing model. The updated model was subsequently validated in the RISK-II cohort (n=1564, 2011-2015). We used the TRIPOD criteria for transparent reporting.
Results:
181 infants (n=127 in RISK-I, n=54 in RISK-II) were hospitalized for RSV within their first year of life. The updated model included the following predictors; day care attendance and/or siblings (OR: 5.3; 95% CI 2.8-10.1), birth between Aug. 14th and Dec. 1st (OR: 2.4; 1.8-3.2), neonatal respiratory support (OR 2.2; 1.6-3.0), breastfeeding ≤4 months (OR 1.6; 1.2-2.2) and maternal atopic constitution (OR 1.5; 1.1-2.1). The updated models' discrimination was superior to the original model in the RISK-II cohort (AUROC 0.72 95% CI 0.65-0.78 versus AUROC 0.66, 95% CI 0.60-0.73, respectively). The updated model was translated into a simple nomogram to be able to distinguish infants with high versus low risk of RSV-hospitalization.
Conclusion:
We developed and validated a clinical prediction model to be able to predict RSV-hospitalization in preterm infants born within 32-35 weeks gestational age. A simple nomogram was developed to target RSV therapeutics to those children who will benefit the most.
