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Published on: December 10, 2013
Diagnostic models predicting paediatric viral acute respiratory infections: a systematic review
Danielle A Rankin1,2, Lauren S Peetluk3, Stephen Deppen3,4
1Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA danielle.a.rankin@vanderbilt.edu.
Insights
This systematic review found that diagnostic models for predicting viral acute respiratory infections (ARIs) in children often have a high risk of bias in their analytical methods. External validation is crucial for these predictive models to improve clinical diagnosis.
Area of Science:
- Pediatrics
- Infectious Diseases
- Medical Diagnostics
Background:
- Viral acute respiratory infections (ARIs) are a common cause of childhood illness.
- Accurate diagnostic models are needed to aid clinicians in identifying the causes of ARIs.
Purpose of the Study:
- To systematically review and evaluate diagnostic models for predicting viral ARIs in children.
- To assess the quality and applicability of existing prediction models.
Main Methods:
- A systematic review of studies published between January 1975 and February 2022 was conducted.
- Studies were identified through searches of PubMed and Embase.
- Two independent reviewers screened studies, extracted data, and assessed quality using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and PROBAST.
Main Results:
- 18 studies were included in the review, with viral-specific influenza being the most common outcome.
- Internal validation was reported in 44% of studies, and discrimination measures in 56%.
- None of the studies performed external validation, and all exhibited a high risk of bias in analytic aspects, though other aspects had minimal bias concerns.
Conclusions:
- Diagnostic prediction models can assist clinicians in the etiological diagnosis of viral ARIs.
- Rigorous internal validation and subsequent external validation are recommended for these models.
- Future research should focus on developing and validating models with robust methodologies.
Objectives:
To systematically review and evaluate diagnostic models used to predict viral acute respiratory infections (ARIs) in children.
Design:
Systematic review.
Data Sources:
PubMed and Embase were searched from 1 January 1975 to 3 February 2022.
Eligibility Criteria:
We included diagnostic models predicting viral ARIs in children (<18 years) who sought medical attention from a healthcare setting and were written in English. Prediction model studies specific to SARS-CoV-2, COVID-19 or multisystem inflammatory syndrome in children were excluded.
Data Extraction And Synthesis:
Study screening, data extraction and quality assessment were performed by two independent reviewers. Study characteristics, including population, methods and results, were extracted and evaluated for bias and applicability using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and PROBAST (Prediction model Risk Of Bias Assessment Tool).
Results:
Of 7049 unique studies screened, 196 underwent full text review and 18 were included. The most common outcome was viral-specific influenza (n=7; 58%). Internal validation was performed in 8 studies (44%), 10 studies (56%) reported discrimination measures, 4 studies (22%) reported calibration measures and none performed external validation. According to PROBAST, a high risk of bias was identified in the analytic aspects in all studies. However, the existing studies had minimal bias concerns related to the study populations, inclusion and modelling of predictors, and outcome ascertainment.
Conclusions:
Diagnostic prediction can aid clinicians in aetiological diagnoses of viral ARIs. External validation should be performed on rigorously internally validated models with populations intended for model application.
Prospero Registration Number:
CRD42022308917.
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