A predictive model for respiratory syncytial virus (RSV) hospitalisation of premature infants born at 33–35 weeks of gestational age, based on data from the Spanish FLIP study
Background The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33–35 weeks' gestational age (GA). Methods The predictive model was developed using risk factors captured in the Spanish FLI...
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| Vydané v: | Respiratory research Ročník 9; číslo 1; s. 78 |
|---|---|
| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
London
BioMed Central
08.12.2008
BioMed Central Ltd BMC |
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| ISSN: | 1465-993X, 1465-9921, 1465-993X |
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| Abstract | Background
The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33–35 weeks' gestational age (GA).
Methods
The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33–35 weeks' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted.
Results
An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth ± 10 weeks of start of season, birth weight, breast feeding for ≤ 2 months, siblings ≥ 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768–0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5–13.6).
Conclusion
A robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33–35 weeks' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe. |
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| AbstractList | Background The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33-35 weeks' gestational age (GA). Methods The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33-35 weeks' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted. Results An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth [+ or -] 10 weeks of start of season, birth weight, breast feeding for [less than or equai to] 2 months, siblings [greater than or equai to] 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768-0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5-13.6). Conclusion A robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33-35 weeks' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe. The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33-35 weeks' gestational age (GA).BACKGROUNDThe aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33-35 weeks' gestational age (GA).The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33-35 weeks' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted.METHODSThe predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33-35 weeks' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted.An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth +/- 10 weeks of start of season, birth weight, breast feeding for < or = 2 months, siblings > or = 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768-0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5-13.6).RESULTSAn initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth +/- 10 weeks of start of season, birth weight, breast feeding for < or = 2 months, siblings > or = 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768-0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5-13.6).A robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33-35 weeks' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe.CONCLUSIONA robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33-35 weeks' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe. The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33-35 weeks' gestational age (GA). The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33-35 weeks' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted. An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth +/- 10 weeks of start of season, birth weight, breast feeding for < or = 2 months, siblings > or = 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768-0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5-13.6). A robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33-35 weeks' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe. Abstract Background The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33–35 weeks' gestational age (GA). Methods The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33–35 weeks' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted. Results An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth ± 10 weeks of start of season, birth weight, breast feeding for ≤ 2 months, siblings ≥ 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768–0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5–13.6). Conclusion A robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33–35 weeks' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe. Background The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33–35 weeks' gestational age (GA). Methods The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33–35 weeks' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted. Results An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth ± 10 weeks of start of season, birth weight, breast feeding for ≤ 2 months, siblings ≥ 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768–0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5–13.6). Conclusion A robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33–35 weeks' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe. |
| ArticleNumber | 78 |
| Audience | Academic |
| Author | Figueras-Aloy, Jose Liese, Johannes G Guzman, Juana Simões, Eric AF Carbonell-Estrany, Xavier Fullarton, John R Doering, Gunther |
| AuthorAffiliation | 3 Analyst, Strategen Limited, Basingstoke, Hampshire, UK 4 Dr. von Hauner Children's Hospital, Ludwig-Maximilians-University, Munich, Germany 6 Munich University of Technology, Department of Pediatrics, Munich, Germany 5 Neonatology Service, Hospital Clínic, Institut Clínic de Ginecologia Obstetricia i Neonatologia, Agrupació Sanitaria Hospital Clínic-Hospital SJ Deu, Universitat de Barcelona, Barcelona, Spain 1 Professor of Pediatrics, Department of Pediatrics, Section of Infectious Diseases, The University of Colorado School of Medicine and The Children's Hospital, Denver, Colorado, USA 2 Neonatology Service, Hospital Clínic, Institut Clínic de Ginecologia Obstetricia i Neonatologia, Agrupació Sanitaria Hospital Clínic-Hospital SJ Deu, Universitat de Barcelona, Barcelona,Spain 7 Hospital Reina Sofía, Córdoba, Spain |
| AuthorAffiliation_xml | – name: 6 Munich University of Technology, Department of Pediatrics, Munich, Germany – name: 4 Dr. von Hauner Children's Hospital, Ludwig-Maximilians-University, Munich, Germany – name: 1 Professor of Pediatrics, Department of Pediatrics, Section of Infectious Diseases, The University of Colorado School of Medicine and The Children's Hospital, Denver, Colorado, USA – name: 5 Neonatology Service, Hospital Clínic, Institut Clínic de Ginecologia Obstetricia i Neonatologia, Agrupació Sanitaria Hospital Clínic-Hospital SJ Deu, Universitat de Barcelona, Barcelona, Spain – name: 7 Hospital Reina Sofía, Córdoba, Spain – name: 2 Neonatology Service, Hospital Clínic, Institut Clínic de Ginecologia Obstetricia i Neonatologia, Agrupació Sanitaria Hospital Clínic-Hospital SJ Deu, Universitat de Barcelona, Barcelona,Spain – name: 3 Analyst, Strategen Limited, Basingstoke, Hampshire, UK |
| Author_xml | – sequence: 1 givenname: Eric AF surname: Simões fullname: Simões, Eric AF email: eric.simoes@uchsc.edu organization: Professor of Pediatrics, Department of Pediatrics, Section of Infectious Diseases, The University of Colorado School of Medicine and The Children's Hospital – sequence: 2 givenname: Xavier surname: Carbonell-Estrany fullname: Carbonell-Estrany, Xavier organization: Neonatology Service, Hospital Clínic, Institut Clínic de Ginecologia Obstetricia i Neonatologia, Agrupació Sanitaria Hospital Clínic-Hospital SJ Deu, Universitat de Barcelona – sequence: 3 givenname: John R surname: Fullarton fullname: Fullarton, John R organization: Analyst, Strategen Limited – sequence: 4 givenname: Johannes G surname: Liese fullname: Liese, Johannes G organization: Dr. von Hauner Children's Hospital, Ludwig-Maximilians-University – sequence: 5 givenname: Jose surname: Figueras-Aloy fullname: Figueras-Aloy, Jose organization: Neonatology Service, Hospital Clínic, Institut Clínic de Ginecologia Obstetricia i Neonatologia, Agrupació Sanitaria Hospital Clínic-Hospital SJ Deu, Universitat de Barcelona – sequence: 6 givenname: Gunther surname: Doering fullname: Doering, Gunther organization: Department of Pediatrics, Munich University of Technology – sequence: 7 givenname: Juana surname: Guzman fullname: Guzman, Juana organization: Hospital Reina Sofía |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19063742$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Contributor | Farri-Kostopoulou, E Morville, Patrice Guillois, Bernard Simoes, Eric A F Barberi, Ignazio Bottu, Jean Butler, Karina Resch, Bernhard Liese, Johannes Berger, Angelika Sauer, Kate Thwaites, Richard Lanari, Marcello Carbonell-Estrany, Xavier Bont, Louis Cossey, Veerle Doering, Gunther |
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| Copyright | Simoes et al. 2008 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. COPYRIGHT 2008 BioMed Central Ltd. Copyright © 2008 Simoes et al; licensee BioMed Central Ltd. 2008 Simoes et al; licensee BioMed Central Ltd. |
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| CorporateAuthor | European RSV Risk Factor Study Group |
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| Keywords | Receiver Operator Characteristic Curve Respiratory Syncytial Virus Discriminant Function Analysis Premature Infant Respiratory Syncytial Virus Infection |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature... The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born... Background The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature... Abstract Background The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in... |
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| SubjectTerms | Biological models Breast feeding Dosage and administration Drug therapy Female Gestational Age Health aspects Hospitalization - statistics & numerical data Humans Incidence Infant Nutrition Disorders - epidemiology Infant, Newborn Infant, Premature Infants (Premature) Male Medical research Medicine Medicine & Public Health Palivizumab Pneumology/Respiratory System Premature infants Proportional Hazards Models Reproducibility of Results Respiratory syncytial virus Respiratory Syncytial Virus Infections - epidemiology Respiratory tract infections Risk Assessment - methods Risk Factors Sensitivity and Specificity Spain - epidemiology |
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| Title | A predictive model for respiratory syncytial virus (RSV) hospitalisation of premature infants born at 33–35 weeks of gestational age, based on data from the Spanish FLIP study |
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