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: Simões, Eric AF, Carbonell-Estrany, Xavier, Fullarton, John R, Liese, Johannes G, Figueras-Aloy, Jose, Doering, Gunther, Guzman, Juana
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.
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
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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.
Copyright_xml – notice: 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.
– notice: COPYRIGHT 2008 BioMed Central Ltd.
– notice: Copyright © 2008 Simoes et al; licensee BioMed Central Ltd. 2008 Simoes et al; licensee BioMed Central Ltd.
CorporateAuthor European RSV Risk Factor Study Group
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Issue 1
Keywords Receiver Operator Characteristic Curve
Respiratory Syncytial Virus
Discriminant Function Analysis
Premature Infant
Respiratory Syncytial Virus Infection
Language English
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Snippet 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...
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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