Diagnostic models predicting paediatric viral acute respiratory infections: a systematic review

Abstract

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.

Keywords: epidemiology; respiratory infections; virology.

Figure 1

PRISMA flow chart for final study inclusion. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Figure 2

Common predictors considered and included in the development of viral diagnostic models, by study (n=18).

Figure 3

Heatmap of individual signalling questions from PROBAST assessing risk of bias for each study (n=18). Full details on PROBAST signalling questions are provided in online supplemental table 2. Participants 1: What study design was used and was it appropriate? Participants 2: Were the inclusion and exclusion criteria appropriate? Predictors 1: Were predictors assessed the same way for all participants? Predictors 2: Were predictor assessments used without knowledge of the outcome? Predictors 3: Are all predictors available at the time the model was indented to be used? Outcome 1: Was the outcome determined appropriately? Outcome 2: Was the outcome specified or standard? Outcome 3: Were predictors excluded from the outcome definition? Outcome 4: Was the outcome defined and determined in a similar way for all participants? Outcome 5: Was the outcome determined without predictor information? Outcome 6: Was the time interval between predictor assessment and outcome determination appropriate? Analysis 1: Were there a reasonable number of participants with the outcome? Analysis 2: Were continuous and categorical variables handled appropriately? Analysis 3: Were all enrolled participants included in the analysis? Analysis 4: Were participants with missing data handled appropriately? Analysis 5: Was univariable analysis avoided in the selection of predictors? Analysis 6: Were complexities in the data (eg, censoring, sampling of controls) accounted for appropriately? Analysis 7: Were relevant model performance measures evaluated appropriately? Analysis 8: Were model overfitting, underfitting and optimism in the model performance accounted for? Analysis 9: Do predictors and their assigned weights in the final model correspond to the results from the reported multivariable analysis? PROBAST, Prediction model Risk Of Bias Assessment Tool.

Figure 4

Overall risk of bias and applicability for each study using PROBAST (n=18). PROBAST, Prediction model Risk Of Bias Assessment Tool.