Data analysis
We evaluated the diagnostic accuracy for each index test for each
individual food. The data was synthetized by tabulating the index test’s
sensitivity, specificity, true positives, true negatives, false
positives, and false negatives. For allergens with variable allergenic
profiles resulting from extensive heating or cooking, separate analysis
was conducted for each allergenic configuration. For hen’s egg (HE)
protein, the analysis was divided into baked HE, cooked (extensively
heated) HE and raw HE. For cow’s milk (CM), it was separated into baked
milk and fresh milk.
Where three or more studies for a given combination of index test and
food were available, a meta-analyses was performed with a generalized
linear mixed model of the binomial family with a logit link. This
approach was chosen to perform a random effect estimate of both
sensitivity and specificity, accounting for their correlation, computing
the pooled sensitivity and specificity and performing the summary
receiver operating curves (ROC)[14]. Briefly, every study
contributed with its own contingency table for its specific cut-off
value (i.e. true positive, true negative, false positive and false
negative) were included in the model as a count. These analyses resulted
in a bivariate random effect estimation of sensitivity and specificity
along with heterogeneity assessed by I-squares defined according to Zhou
and Dendukuri, 2014 [15]. We defined tests with high accuracy as
those which had a sensitivity or specificity of ≥90% with I-squares
under 50%. Low sensitivity and specificity were considered for test
performing under 75%.
We performed sensitivity and specificity analysis using the optimal
cut-off reported by the individual studies using the optimal cut-off
reported by the individual studies, e.g. Youden’s Index or other
methods. To obtain the estimated cut-offs used for each meta-analysis,
we reported the median and interquartile range of all cut-offs
considered optimal by the different authors. Further analyses were
performed and focused on the maximum values for sensitivity and
specificity as reported by the authors of included studies.
Further analyses were undertaken with the pre-established 95% positive
predictive values (PPV) available in literature [16]. For skin prick
tests (SPT) we used values of 8 mm for peanut [17] and CM and 7 mm
for HE [18]. For sIgE, we used the following values: 15
kUA/L for peanut [17], CM and tree nuts, 7
kUA/L for HE and 20 kUA/L for fish
[19, 20]. We included only values which have been previously
validated thus this are not available for all foods. [18, 21-23].
As the PPV is dependent on the prevalence of allergic disease in a
specific population, we looked at the sensitivity and specificity of
pooled data for these cut-offs and defined them as highly accurate if
they reached a value ≥ 90%.
In supplementary analyses, studies were stratified by test-specific
threshold values, age of the participants (below 24 months, 24 months to
16 years and above 16 years) and by the country of origin. Where data on
at least three different tests on the same food were available, a
comparison was performed. To this end, the relative ratio of sensitivity
and specificity was computed using an intercept only model [24].
Data for differences in subgroups were considered significant if there
was a change in sensitivity or specificity over 7% (CI 95%) or they
reached high diagnostic accuracy (over 90% of sensitivity or
specificity for any given test).
To reduce heterogeneity in the meta-analyses, only index tests using the
same characteristics where combined. For SPT, results are shown for
studies using commercial extracts separate from those using skin prick
to prick tests (SPP) with fresh foods. For sIgE testing, results from
different platforms were used individually for meta-analyses (ImmunoCAP
Specific IgE, ImmunoCAP™ ISAC, etc). Throughout the manuscript when
talking about sIgE this refers to ImmunoCAP, if other methods are used
for analysis, it will be specified accordingly. The random effect
bivariate meta-analysis was performed using the metadata function of the
STATA software version 15.