The Vitality algorithm: Classifying infant IgE-mediated food allergy in research studies when participant oral food challenges are not performed

The Vitality algorithm: Classifying infant IgE-mediated food allergy in research studies when participant oral food challenges are not performed | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 19 January 2026 V1 Latest version Share on The Vitality algorithm: Classifying infant IgE-mediated food allergy in research studies when participant oral food challenges are not performed Authors : Alexsandria Odoi 0000-0003-4811-5803 , Jennifer Koplin J , Victoria Soriano , Kayla Parker 0000-0003-1861-5663 , Katie Allen , Shyamali Dharmage 0000-0001-6063-1937 , Katherine Lee , … Show All … , Jana Eckert , Audrey M. Walsh , Angela Young , Tim Brettig 0000-0003-0558-153X , Rachel Peters 0000-0002-2411-6628 , and Kirsten Perrett [email protected] Show Fewer Authors Info & Affiliations https://doi.org/10.22541/au.176881524.48488830/v1 233 views 105 downloads Contents Abstract Abstract Key words Abbreviations Introduction Methods Oral Food Challenges Questionnaires Statistical methods and approach to validation Ethical Approval Results SPT positive: Peanut allergy validation Sensitivity analysis Discussion Supplementary Material References Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Food allergy algorithms provide a systematic approach to assigning food allergy outcomes in research studies when oral food challenges (OFC) are not performed. Currently no such algorithms, validated for infants, exist. We aimed to develop and validate a research algorithm for classifying infant food allergies. Methods: An algorithm incorporating skin prick test (SPT), allergen ingestion, and reaction data, was developed and validated by comparing peanut, cashew, egg and cow’s milk algorithm outcomes to OFC outcomes, in infants from the Vitality (n=285) (NCT02112734), EarlyNuts (n=111) (ACTRN12618001990213), and HealthNuts (n=191) studies. Results: Validation included 740 OFCs from 587 infants (191 SPT-negative [0mm] and 396 SPT-positive [≥ 1mm] infants; first-OFC median age: 14.4 months). The algorithm assigned all SPT-negative infants as tolerant, achieving 99.5% agreement with OFCs (peanut 100% [n=135/135]; egg 98.2% [n=55/56]). The algorithm assigned an allergic or tolerant outcome to 57% (82/144) of peanut SPT-positive, 28% (30/108) cashew SPT-positive, 76% (n=196/258) egg SPT-positive, and 88% (n=35/39) milk SPT-positive, infants. Agreement with OFC was 81% (n=66/82) for peanut, 83% (n=25/30) for cashew, 90% (n=176/196) for egg and 97% (n=34/35) for milk. Accuracy and areas under the curve (AUCs) were fair-to-excellent: peanut (AUC=0.79 [95% confidence interval [CI]: 0.70-0.90]; sensitivity=88% [95%CI: 75-96]; specificity=70% [95%CI: 51-84]), cashew (AUC=0.83 [95%CI: 0.69-0.97]; sensitivity=83% [95%CI: 59-96]; specificity=83% [95%CI: 52-98]); egg (AUC=0.85 [95%CI: 0.77-0.93]; sensitivity=92% [95%CI: 86-95]; specificity=79% [95%CI: 59-92]); milk (AUC=0.98 [95%CI: 0.94-1]; sensitivity=96% [95%CI: 80-100]; specificity=100% [95%CI: 69-100]). Conclusion: Using routinely collected data, this validated infant-focused food allergy algorithm performed well in classifying peanut, cashew, egg and cow’s milk allergies, offering applicability in research settings. Title: The Vitality algorithm: Classifying infant IgE-mediated food allergy in research studies when participant oral food challenges are not performed Authors Alexsandria Odoi, BBNSc 1,2 , [email protected] Jennifer J. Koplin, PhD 3,4,7 , [email protected] Victoria X. Soriano, PhD 2 , 9 , [email protected] Kayla M. Parker, MLabMed 1,2 , [email protected] Katrina J. Allen, MBBS, PhD 2,† Shyamali C. Dharmage, MBBS, PhD 3, 6 , [email protected] Katherine J. Lee, PhD 1,5 , [email protected] Jana K. Eckert, PhD 2 , [email protected] Audrey M. Walsh, MPH, 1,2,3 , [email protected] Angela Young, PhD 2, 3, 4 , [email protected] Timothy W Brettig, MBBS, PhD 2, 4 , [email protected] Rachel L Peters*, PhD 1, 2, 3, 4 , [email protected] Kirsten P Perrett*, MBBS, PhD 1,2,3,4,8 , [email protected] * Co-senior. †passed away 23 Dec 2025 Affiliations: 1, Department of Paediatrics, University of Melbourne, Parkville, VIC, Australia 2 Population Allergy Group, Murdoch Children\RL’s Research Institute, Parkville, VIC, Australia 3 Centre for Food and Allergy Research (CFAR), Parkville, VIC, Australia 4 National Allergy Centre of Excellence (NACE) Parkville, VIC, Australia 5 Clinical Epidemiology & Biostatistics (CEBU), Murdoch Children’s Research Institute, Parkville, VIC, Australia 6 School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia 7 Child Health Research Centre, University of Queensland, QLD, Australia 8 Department of Allergy and Immunology, Royal Children\RL’s Hospital, Parkville, VIC, Australia 9 National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK. Corresponding author: Professor Kirsten P. Perrett, MBBS (Hons), FRACP, PhD, Murdoch Children’s Research Institute, Royal Children’s Hospital, Flemington Rd, Parkville, VIC 3052, Australia. Email: [email protected] Phone: +61399366278 Funding: The Vitality study was funded by the National Health and Medical Research Council (NHMRC) Australia (APP1146913); the National Institute of Health (NIH) Immune Tolerance Network; the DHB foundation; the Rotary Club of Camberwell; the Isabel and John Gilberston Charitable Trust; the Ilhan Food Allergy Foundation; the Kimberly Foundation Australia; the Constantinou Foundation; and other Philanthropic donations. The EarlyNuts study was supported by funding from the NHMRC Australia (APP1146769) and the Murdoch Children’s Research Institute. The HealthNuts study was funded by the NHMRC Australia (APP491233), the Ilhan Food Allergy Foundation, AnaphylaxiStop, the and the Charles and Sylvia Viertel Medical Research Foundation. Research at MCRI is supported by the Victorian Government’s Operational Infrastructure Support Program. AO is supported by a Research Training Program PhD scholarship provided by the Australian Commonwealth Government and the University of Melbourne, and a PhD Top Up Scholarship provided by the Murdoch Children’s Research Institute. JJK is supported by an NHMRC fellowship, GNT1158699. KPP is supported by a NHMRC fellowship, GNT2008911 and a Melbourne Children’s Clinician-Scientist Fellowship. Audrey Walsh Australian Government Research Training Program Scholarship provided by the Australian Commonwealth Government and the University of Melbourne, and a PhD scholarship from the Australian Government funded National Allergy Centre of Excellence (NACE), hosted by the Murdoch Children’s Research Institute (MCRI), and their work was supported by the Victorian Government’s Operational Infrastructure Support Program. KJL is supported by an NHMRC investigator grant, GNT2017498. Conflicts of Interest: KPP’s institution has received research grants from Aravax, DBV Technologies, Novartis and Siolta and consultant fees from Aravax, Novartis, RAPT Therapeutics, outside the submitted work. KPP is Chair of the Scientific Advisory Board of AllergyPal and a non-executive director of OmnisOva. RLP’s institution has received research grants from NHMRC, Stallergenes Greer Foundation, the National Peanut Board, Allergy & immunology Foundation of Australia, paid to their institution, outside of the submitted work. RLP has received speaker fees from Stallergenes Greer. There are no conflicts of interest declared by the other authors. Author Contributions Conceptualisation and drafting the manuscript: A.O, K.P.P, R.L.P, J.J.K, S.C.D, K.J.L Data analyses: A.O, R.L.P, K.P.P, J.J.K, K.M.P, V.X.S, J.K.E, A.M.W, T.W.B, A.Y Data interpretation: A.O, K.P.P, R.L.P, J.J.K, K.J.A, S.C.D, K.J.L, T.W.B All authors substantially contributed to the generation and/or acquisition All authors critically reviewed the manuscript for important intellectual content Abstract Background: Food allergy algorithms provide a systematic approach to assigning food allergy outcomes in research studies when oral food challenges (OFC) are not performed. Currently no such algorithms, validated for infants, exist. We aimed to develop and validate a research algorithm for classifying infant food allergies. Methods: An algorithm incorporating skin prick test (SPT), allergen ingestion, and reaction data, was developed and validated by comparing peanut, cashew, egg and cow’s milk algorithm outcomes to OFC outcomes, in infants from the Vitality (n=285) (NCT02112734), EarlyNuts (n=111) (ACTRN12618001990213), and HealthNuts (n=191) studies. Results: Validation included 740 OFCs from 587 infants (191 SPT-negative [0mm] and 396 SPT-positive [≥ 1mm] infants; first-OFC median age: 14.4 months). The algorithm assigned all SPT-negative infants as tolerant, achieving 99.5% agreement with OFCs (peanut 100% [n=135/135]; egg 98.2% [n=55/56]). The algorithm assigned an allergic or tolerant outcome to 57% (82/144) of peanut SPT-positive, 28% (30/108) cashew SPT-positive, 76% (n=196/258) egg SPT-positive, and 88% (n=35/39) milk SPT-positive, infants. Agreement with OFC was 81% (n=66/82) for peanut, 83% (n=25/30) for cashew, 90% (n=176/196) for egg and 97% (n=34/35) for milk. Accuracy and areas under the curve (AUCs) were fair-to-excellent: peanut (AUC=0.79 [95% confidence interval [CI]: 0.70-0.90]; sensitivity=88% [95%CI: 75-96]; specificity=70% [95%CI: 51-84]), cashew (AUC=0.83 [95%CI: 0.69-0.97]; sensitivity=83% [95%CI: 59-96]; specificity=83% [95%CI: 52-98]); egg (AUC=0.85 [95%CI: 0.77-0.93]; sensitivity=92% [95%CI: 86-95]; specificity=79% [95%CI: 59-92]); milk (AUC=0.98 [95%CI: 0.94-1]; sensitivity=96% [95%CI: 80-100]; specificity=100% [95%CI: 69-100]). Conclusion: Using routinely collected data, this validated infant-focused food allergy algorithm performed well in classifying peanut, cashew, egg and cow’s milk allergies, offering applicability in research settings. Word count abstract: 259/250 Word count text: 3842/3500 Key messages • Accurate food allergy diagnoses are crucial. While oral food challenges (OFCs) provide diagnostic certainty, they are not always performed in research. In the absence of OFC, reliable algorithms are needed to classify food allergy outcomes. • We have validated an infant-focused food allergy algorithm which incorporates routine data, including SPT and clinical history. Validated with 740 infant OFCs, the research algorithm demonstrates good discriminative ability for infant food allergy classification. • The algorithm offers a resource-efficient, validated framework for accurately classifying infant food allergy in research settings, when gold-standard OFC are not performed. Key words Algorithm, Food Allergy, Infant, Paediatric, Diagnostic Abbreviations ASCIA: Australasian Society of Clinical Immunology and Allergy AUC: Area under the Curve BEEP: Barrier Enhancement for Eczema Prevention (study) CI: Confidence Interval ED50: Eliciting Dose 50 EAT: Enquiring about Tolerance (study) IU: International Unit IQR: Interquartile Range LEAP: Learning Early About Peanut allergy LTFU: Lost to follow-up MCRI: Murdoch Children’s Research Institute NPV: Negative Predictive Value OFC: Oral Food Challenge RCT: Randomised Controlled Trial RCH: The Royal Children’s Hospital PPV: Positive Predictive Value SPT: Skin Prick Test SMS: Short Message Service sIgE: Specific Immunoglobin E Introduction Food allergy is a significant health issue, affecting up to 10% of infants aged 12 months (1). In clinical and research settings precise food allergy diagnosis is required. Whilst oral food challenges (OFC) are the gold standard for diagnosis of food allergy, they are also labour, time, and cost intensive, and carry risk of serious allergic reactions. As such, in a research setting, resource constraints (2, 3) and participant/family preference mean this method is not always performed. When OFC are not completed, researchers can utilise a combination of routinely available clinical assessment methods, including caregiver-report of allergic reactions and tests of sensitisation such as skin prick test (SPT), and blood tests for allergen specific IgE (sIgE), to guide food allergy diagnosis. However, as such approaches are not always standardised between different settings, algorithms implementing a systematic approach to food allergy diagnosis are needed. Several diagnostic food allergy algorithms have been developed and validated for use, using data from both paediatric clinical and research study populations (1, 4-11) to predict food allergy where OFC is not performed. However, some rely on assessment techniques or data not readily available to all clinical or research settings (4-8, 10, 11), are onerous (11), or can yield high rates of inconclusive outcomes (11). Importantly, none have been developed and validated specifically for use in infants. The Vitality randomised controlled trial (RCT) (12) was designed to determine whether daily supplementation of Vitamin D in the first year of life reduces the prevalence of food allergy at age 1 year. Open OFCs were used in conjunction with other diagnostic measures to define participant food allergy status. Loss to follow-up (LTFU), partial participation, and impacts of stringent COVID-19 restrictions, prevented some infants from undergoing OFCs. Due to a lack of validated diagnostic food allergy algorithms for infants, it became imperative to develop a systematic approach, based on routinely collected allergy data, to aid infant food allergy classification for study participants who did not complete OFC. In this paper we describe the algorithm and validate it against the gold standard OFC outcomes performed in infants, using data from Vitality, plus two food allergy cohort studies, EarlyNuts (13) and HealthNuts (14). Methods Algorithm overview Our algorithm was developed by a panel consisting of paediatric allergists, allergy epidemiologists and data managers and clinical trial coordinators. The panel studied previous literature, including a comprehensive review of data collected in food allergy studies (12, 15), allergy profiles (16) reported in local clinical and research cohorts (17-19) (20), published paediatric food allergy algorithms (6-8, 10, 11), models (4, 5, 8), scoring questionnaires (9), and population cohort algorithms (1, 15, 21, 22). Using previously established allergy classification practices (1, 21) as guiding frameworks, the panel collated the literature and used an iterative, consensus building approach to refine the algorithm. Algorithm data and outcomes The panel defined four key algorithm data inputs: caregiver report of tolerance, caregiver report of allergic reaction or anaphylaxis, participant SPT and participant OFC outcomes. Thresholds for these inputs, such as the minimum ingestion quantity and frequency for allergen tolerance (23), are described in table 1, table e1 and table e2. Five algorithm outcomes were defined as definite food allergy, probable food allergy, definite tolerant, probable tolerant and inconclusive food allergy (table e3). Participants with incongruous data, such as a report of allergen tolerance following reaction, or an inconclusive algorithm outcome with caregiver-reported allergist diagnosis, underwent expert panel review (internal study allergist [K.P.P.] and external allergist [T.W.B]), to review participant data alongside additional clinical information, if available and appoint participants to one of the five algorithm outputs. A hypothetical alternative algorithm has been proposed (figure e1) for settings where serum sIgE is utilised. This alternative algorithm has not undergone validation. Study populations for algorithm validation The HealthNuts study The HealthNuts study is a population-based cohort study which recruited 5276, 11- to 15-month-old infants between August 2007 and September 2011 (14). The study aimed to assess food allergy prevalence and infant allergen introduction and recruited infants attending their 12-month immunisations at community run immunisation sessions (Melbourne, Australia). Infants underwent a community SPT (peanut, egg, sesame and/or shellfish) after the parent/guardian provided written informed consent. Those with a SPT wheal ≥ 1 mm were invited to undergo a repeat clinic SPT and OFC at the Royal Children’s Hospital (RCH). A random sample of infants with 0 mm community SPT at recruitment, were also invited to the RCH to undergo peanut and raw egg OFCs to test the assumption that infants with 0 mm SPT were truly food tolerant. The EarlyNuts Study The EarlyNuts study is a population-based cohort study of 11 to 15-month-olds (n=1933) recruited between November 2016 and December 2019 (15) (ACTRN12618001990213). The EarlyNuts study shares a similar study design and recruitment framework to the HealthNuts study and aims to measure infant food allergy prevalence and allergen introduction, following changes to national infant feeding guidelines. Like the HealthNuts study, infants were recruited at community-run immunisation sessions (Melbourne, Australia) and underwent a community screening SPT (peanut, egg, cashew, cow’s milk). Participants with a positive community SPT wheal (>=1 mm) were invited to undergo a repeat SPT and OFC at the RCH. The Vitality Trial The Vitality trial (NCT02112734) is a double-blind placebo-controlled RCT (12) which aims to determine if daily supplementation of 400 IU of vitamin D in the first year of life reduces the prevalence of food allergy at 12 months of age. Recruitment of 2739 infants from Melbourne (Australia), occurred between November 2014 and April 2022, with final follow up completed in August 2023. Written informed consent was obtained from the infant’s parent/guardian, and randomisation and study intervention commenced between 6 and 12 weeks of age. All infants were invited to a 12-month clinic assessment at the RCH to undergo a clinic SPT, irrespective of their allergy or food ingestion history. OFC was offered to all infants with a positive clinic SPT (wheal >=1mm above the negative control). Algorithm development and validation was performed without knowledge of the Vitality trial treatment assignment, and prior to unblinding the trial data. Allergy assessments and clinical history Clinical history and allergy assessment data was captured from 6 – 23 months of age. Clinic Skin Prick Test Clinic SPT outcomes were included in algorithm validation. Clinic SPTs for all 3 studies were performed by research nurses, according to previously published protocols (24) on the infant’s upper back. Allergens tested varied by study, however food allergens common to all included peanut, hen’s egg, cashew and cow’s milk, plus a positive (histamine 10 mg/mL) and negative control (saline). SPTs were interpreted after 15 minutes. An SPT wheal >= 1 mm above the negative control was considered positive (20). Algorithm SPT thresholds are defined in table e1. Oral Food Challenges OFCs for all three studies were performed at our allergy research clinic at the RCH. OFCs were performed and interpreted in accordance with previously published protocols (21) (table e4). Participants that were SPT-positive to multiple allergens, underwent multiple allergen OFCs, where possible. Due to safety protocols, SPT-positive infants with a recent allergic reaction (within 1 month of SPT for egg or milk, or 2 months for peanut or cashew), or a history of anaphylaxis to the allergen under investigation, did not undergo OFC. During the OFC, participants ingested pre-specified quantities of the food under investigation, in steadily increasing portions every 15-20 minutes. The OFC was stopped and considered positive if participants experienced at least one of the following symptoms within 2 hours of ingestion: 3 or more concurrent noncontact urticaria persisting for at least 5 minutes, perioral or periorbital angioedema, vomiting (excluding gag reflex), or anaphylaxis (25). OFCs were considered negative if all doses were successfully consumed without reaction symptoms. OFCs were considered inconclusive if the infant refused to initiate or complete the challenge. Questionnaires Vitality caregivers completed questionnaires when their infants were aged 3, 6, 9 and 12 months, and EarlyNuts and HealthNuts caregivers when their infants were 12 months. Questionnaires captured demographic data and information about common allergens, such as ingestion patterns (timing, frequency, quantity) (figure e2, figure e3) and reactions (timing and type of symptoms). Statistical methods and approach to validation Outcomes for each food allergen were considered individually and then combined into an outcome of ‘any allergy’. For ‘any allergy’, infants were deemed allergic if they were allergic to at least one food, and tolerant if they were tolerant to all foods tested. SPT-negative (0 mm) participants with an OFC outcome were included in algorithm validation to validate the use of 0 mm SPT for classifying tolerance. SPT-positive (>= 1 mm) participants with an OFC outcome, were included in algorithm validation to validate the use of positive SPT with caregiver-report of tolerance or allergy, to classify tolerance or allergy. Analysis OFC outcomes were used as the gold standard to validate algorithm performance for classifying IgE-mediated food allergy. Only participants with conclusive (positive or negative) OFC outcomes were included in the validation. OFC results remained blinded until algorithm outcomes were derived. Algorithm outcomes of definite tolerant and probable tolerant were aggregated into a single outcome (tolerant) for all analyses. Inconclusive food allergy outcomes were excluded from all analyses. The primary analysis combined definite and probable allergic algorithm outcomes into one single outcome of ‘allergic’. A sensitivity analysis was performed assessing the algorithm classification of definite allergic only (excluding probable allergic). Sensitivity, specificity, positive (PPV) and negative predictive values (NPV), and area under the curve (AUC) for predicting OFC outcomes were obtained for both the primary and sensitivity analysis using the ‘ diagt ’ package in Stata (26). Sensitivity and specificity were interpreted as: 90-100% very high; >80 - 90% high; moderate 65 - 80%; low <65% (27). AUC were interpreted as follows: 1.0 perfect; 0.9–0.99 excellent; 0.8–0.89 good; 0.7–0.79 fair; 0.51–0.69 poor; and 0.5, test of no value (28). We performed a complete case analysis . All analyses were performed using Stata version 18.0 (StataCorp, College Station, TX). Ethical Approval The RCH Human Research Ethics Committee (HREC) approved the Vitality trial on 9 September 2014 (#34168A) and EarlyNuts study on 9 September 2016 (#36160A). HREC approval for HealthNuts was obtained from the RCH HREC (ref. no. 27047A), the Office for Children, Government of Victoria HREC (ref. no. CDF/07/492), and the Department of Human Services, Government of Victoria (ref. no. 10/07). Results Participant demographics and OFC summary Table 2 and 3 summarise demographic and OFC characteristics of SPT-negative (0 mm) HealthNuts participants, and the SPT-positive (≥ 1 mm) EarlyNuts and Vitality and participants included in algorithm validation. Altogether, 191 HealthNuts participants completed 191 peanut or egg OFCs, 111 EarlyNuts participants completed 166 peanut, cashew, egg or milk OFCs, and 285 Vitality participants completed 383 peanut, cashew, egg or milk OFCs, and (table 3). Median age of first OFC was similar in the three studies (14.4 [13.5 – 15.4] months) (table 2). For the SPT-negative HealthNuts participants, the proportion of negative OFCs was 100% for peanut (n= 135/135) and 99.5% for egg (n= 55/56) (table 3). For the SPT-positive participants, the proportion of positive OFCs was highest for raw egg (82.9%), followed by milk (74.4%), peanut (54.9%), and cashew (39.8%); with 69.7% of these participants deemed allergic by OFC to at least one allergen (table 3). Algorithm validation SPT negative: Peanut and Egg validation OFCs from HealthNuts participants were used to validate the assumption of allergen tolerance across 191 negative peanut or egg SPTs (represented by pathway ‘A1’, Figure 1). A peanut 0mm SPT demonstrated very high agreement, specificity and NPV (all: 100% [95%CI, 97.3 – 100]) (table 4) in predicting negative OFC. An egg 0mm SPT showed specificity of 100% (95%CI, 93.5 – 100) and high agreement and NPV (98.2 % [95%CI, 90.4 – 100.0]) in predicting negative OFC (table 4), with one possible false negative out of 56 OFCs due to 1 infant report of a late reaction following egg OFC with SPT 0mm. SPT positive: Validation overview Peanut, cashew, egg and milk OFCs (n= 549) from 396 EarlyNuts and Vitality participants (table 3 and 5) were used to validate the algorithm’s ability to assign allergic or tolerant outcomes, in SPT-positive infants (represented by pathway ‘B3’, Figure 1). The algorithm classified tolerance or allergy in 343/549 (62.5%) OFCs (table 5). Of these, 301/343 (87.8% [95%CI 83.8 – 91.0]) algorithm outcomes agreed with the OFC outcome (table 5). Expert panel review outcomes were included in all analysis. Expert panel review was required for 64 of the 549 (11.7%) outcomes, largely due to high SPT (>=95% PPV) with caregiver-reported tolerance (table e5). Four outcomes were assigned as inconclusive by the review panel, with 88.7% (n= 47/53) of the remaining review panel outcomes correctly identifying tolerance or an allergy, compared to OFC outcome. SPT positive: Peanut allergy validation The algorithm assigned outcomes of allergy or tolerance in 56.9% (n= 82/144) of peanut SPT-positive infants (table 5). It demonstrated high sensitivity (EarlyNuts 84.6% [95%CI, 54.6 – 98.1] n= 18; Vitality 88.9% [95%CI, 73.9 – 96.6] n= 64) and moderate specificity with low precision, due to a small sample size in EarlyNuts (EarlyNuts: 80.0% [95%CI, 28.4 – 99.5]; Vitality (67.9% [95%CI, 47.7 – 84.2]) (table 6). The algorithm correctly classified 66 of 82 (accuracy 80.5% [95%CI, 70.3 – 88.4]) peanut outcomes across the EarlyNuts and Vitality groups with an AUC of 0.79 (95%CI, 0.70 - 0.90). Of the 10 infants with a negative peanut OFC that were misclassified as having a peanut allergy by the algorithm, 9 infants had caregiver-report of allergic reaction symptoms that met our threshold for IgE-mediated allergy; 6 of whom received a prior doctor diagnosis, external to the study, of peanut allergy. All 6 infants misclassified as peanut tolerant according to the algorithm had caregiver-report of sufficient peanut ingestion: 4 reporting sub-threshold reaction symptoms (rash) after ingestion, and 2 reporting no symptoms (data not shown). SPT positive: Cashew allergy validation The algorithm assigned outcomes of allergy or tolerance in 27.8% (n=30/108) of the cashew SPT-positive infants (table 5). Combined, the algorithm demonstrated high accuracy at 83.3% (95%CI, 65.3 – 94.4), 83.3% sensitivity (95%CI, 58.6 - 96.4), 83.3% specificity (95%CI, 51.6 - 97.9), 88.2% PPV (95%CI, 63.6 - 98.5), 76.9% NPV (95%CI, 46.2 - 95.0), and good AUC of 0.83 (95%CI, 0.69 - 0.97) (table 6). Of the 5 cashew misclassifications using the algorithm (1 from EarlyNuts and 4 from Vitality), 2 OFC-negative infants were misclassified as allergic by the algorithm due to high sensitisation (SPT ³8mm) and no cashew ingestion ever. Three OFC positive, moderately (2 - 7 mm) sensitised infants were misclassified by the algorithm as cashew tolerant due to parent reported ingestion history that met tolerance thresholds (data not shown). SPT positive: Egg allergy validation The algorithm assigned outcomes of allergy or tolerance in 76.0% (n= 196/258) of egg SPT-positive infants (table 5). It correctly classified 176 of 196 (90%) egg outcomes, resulting in very high algorithm sensitivity (91.7% [95%CI, 86.4 – 95.7]) and PPV (96.3% [95%CI, 92.0 – 98.6]), moderate specificity (78.6% [95%CI, 59.1 – 91.7]), low NPV (61.1% [95%CI, 43.5-76.9], and good AUC (0.85 [95%CI, 0.77 – 0.93]) (table 6). Fourteen low-to-moderately sensitised (1 - 3mm) egg OFC-positive infants were misclassified by the algorithm as tolerant; 13/14 met our egg ingestion threshold for tolerance, and 1/14 demonstrated a convincing recent tolerant ingestion history, after previous convincing report of allergic reaction (as reviewed by the expert panel). Six egg OFC-negative infants were misclassified by the algorithm as allergic. Two of these 6 reported receiving a doctor diagnosis of egg allergy during study participation, and 5 of these 6 were highly sensitised (SPT ³4mm). For the highly sensitised participants, 3 reported egg ingestion that met our threshold for soft-egg tolerance. However, due to difficulties translating soft-cooked egg ingestion history into raw egg OFC tolerance, in the context of egg SPT ≥95% PPV, the expert panel took a conservative approach to favour an egg allergy outcome in these 3 cases. This cautious expert panel approach was favourable for all other comparable egg cases (20/23 correctly deemed allergic with ³4mm SPT, data not shown). SPT positive: Cow’s milk allergy validation The algorithm assigned outcomes of allergy or tolerance to 89.7% (n= 35/39) of milk SPT-positive infants (table 5). The algorithm performed strongest for classifying cow’s milk, compared to all other allergens, with very high agreement (97.1% [95%CI, 85.1 -99.9]), sensitivity, specificity, PPV, and NPV, and an AUC of 0.98 (0.94 – 1) (table 6). SPT positive: Any allergy validation An ‘any allergy’ outcome via the algorithm was assigned to 264/396 (66.7%) of SPT-positive participants (table 5) with 88.3% (95%CI, 83.7 – 91.2) accuracy (table 6). The algorithm showed very high sensitivity (92.8 % [95%CI, 88.4 – 95.9]) and PPV (92.4%[95%CI, 87.9 – 95.6]), moderate specificity (70.9% [95%CI, 57.1 – 82.4]) and NPV (72.2% [95%CI, 58.4 – 83.5]) (table 6), and good AUC (0.82 [95%CI, 0.76 – 0.88]) (table 6). Sensitivity analysis A sensitivity analysis of SPT-positive infants including definite allergy outcomes (excluding probable allergy) (table e6) was performed. Algorithm classification of ‘any allergy’ was largely consistent with the primary analysis, however due to low numbers, estimates were wider for each allergen. Discussion In this paper we present the first validated research algorithm for classifying IgE-mediated food allergies in infants from clinical trial and observational research cohorts. Validated using 740 OFCs undertaken in 587 research participants, algorithm-derived outcomes demonstrated consistently high agreement with OFC outcomes, across peanut, cashew, egg and cow’s milk (80 – 97%). Minimal expert review and input was required, as the algorithm contained sufficient options to classify 88.3% of infants, based on readily accessible and routinely collected data. Our algorithm performance is particularly encouraging when compared to the only other research algorithm validated for younger children (29). Specifically, Kelleher and colleagues (2020) developed an algorithm to diagnose IgE-mediated food allergy in 2-3-year-olds, eligible for, but not undergoing, milk, egg or peanut OFC (29). The paediatric algorithm, validated for the 2-year-old Barrier Enhancement for Eczema Prevention (BEEP) (30) and 3-year-old Enquiring About Tolerance (EAT) study participants (31), focused on identifying ‘any allergy’ rather than specific allergies; likely due to the smaller participant numbers in their validation cohorts (n=124) compared to ours (n=587). For algorithm validation, a BEEP expert panel blinded to OFC-outcomes, used the algorithm to guide decision making and determine allergy outcomes for 124 participants: incorporating SPT outcomes (with a single ‘highly sensitised’ >= 7mm threshold for all allergens), and parent-reported participant ingestion and reaction data. In contrast, we incorporated allergen specific SPT thresholds (16, 17, 20) in our algorithm, and our comprehensive approach translated to minimal need for expert panel review in only 11.7% of cases. While the Kelleher et al (2020) algorithm reported similar performance in terms of AUC and sensitivity, their estimates were imprecise due to missing SPT and clinical data. As a result, 55% of algorithm outcomes were inconclusive in the BEEP 2-year-old cohort. Comparatively, the large cohort of infants included in our algorithm validation, with conclusive outcomes (67%) and minimal missing data, helped to strengthen the precision of our analysis. Although we had limited missing data, many infants had caregiver-reported allergen ingestion that fell below our threshold for tolerance. Few participants in our study had been introduced to cashew by age 1 year, were ingesting peanut in sufficient quantity and frequency, or were ingesting egg in a form (soft- or non-cooked) (data not shown), that met algorithm-defined thresholds for tolerance. This translated to 33% of SPT-positive infants, allocated an inconclusive ‘any allergy’ outcome. We had anticipated that a proportion of inconclusive algorithm outcomes would arise from inadequate allergen ingestion, as many parents remain hesitant to introduce allergens by 12-months, despite recommendations (24). However, given the recent trends towards earlier allergen introduction (13, 32) following updates to Australian feeding guidelines (2016) (33), we anticipate ongoing improvements to rates of conclusive algorithm outcomes, with time. Despite this, it is important to note that the inconclusive rate remains an inherent feature of the algorithm and may therefore affect its utility. There are several strengths to this study. We included a large number of participants and OFCs across a range of SPT wheal sizes and allergens. The inclusion of three population-based studies with uniform approaches to clinical allergy diagnosis, but diverse sampling frameworks, supports the generalisability of our algorithm across varied research settings. Further, in emphasising clinical history (with well-defined caregiver-reported criteria) alongside SPT, our algorithm offers immediacy of results. It is a viable option for research studies anticipating low OFC participation, or resource-limited settings, seeking to minimise logistical and economic burden, for participants, clinicians and research programs. Collectively, these strengths support the Vitality algorithm as an objective, resource-efficient tool that minimises need for expert clinician input, making it well suited large-scale studies. Our infant algorithm has some limitations. It is validated only for the four foods which represent the highest local prevalence (Melbourne, Australia, prevalence: 8.2% egg (21); 3.1% peanut (15); 1.3% cashew (24); 1.3% cow’s milk (34)) and greatest research-clinic demand (80% of the EarlyNuts and Vitality study OFCs performed). Due to the limited number of OFCs performed for other allergens, additional allergen validation was not feasible. Consequently, future research should include validating the algorithm in other common food allergens, such as sesame. Additionally, as caregiver-reported tolerance and reaction histories are susceptible to recall bias (33, 34), this may contribute to some misclassification. (35, 36). Specifically, inaccurate reporting of allergen ingestion or reaction symptoms could result in incorrect classification of participant tolerance or allergy. In this study, most infants misclassified as algorithm-tolerant, typically presented with convincing caregiver-reported ingestion histories and low-to-moderate SPT sensitisation (data not shown). Notably, while our ingestion thresholds for caregiver-reported tolerance surpass the cumulative ED50.0 (37), they do not meet the cumulative dose requirements for OFC tolerance criteria. The transient nature of food allergy in early childhood (22, 38), may also contribute to algorithm misclassification. Most false-positive outcomes for peanut and egg, arose from convincing caregiver report of prior allergic reaction or independent doctor diagnosis of allergy, consistent with the clinical observations reported by others (39, 40). Additionally, we utilised history of soft-cooked egg ingestion to estimate raw egg OFC tolerance, as caregiver-reported raw egg ingestion is uncommon in the infant diet (13), and was poorly captured in our study. Despite this difference in egg-preparations, and as the literature suggests (41), soft-cooked egg was suitable for predicting raw egg OFC tolerance in most low-to-moderately egg sensitised infants. For these reasons, this algorithm is not a replacement for the gold-standard OFC. However, quantifying its performance, enhances the rigour of the outcomes derived. This algorithm has potential to contribute to other allergy studies (3) or settings limited to SPT and clinical history, by assigning outcomes, with reasonable accuracy, for up to 70% of participants, who might otherwise have missing study outcome data. To establish broader relevance, it will be important to validate the algorithm in external populations beyond Australia, where allergy prevalence rates (42, 43) and early allergen feeding patterns vary (28, 44). In conclusion, our algorithm offers a useful framework for classifying of infant food allergy in settings where OFCs are not performed. To the best of our knowledge, we offer the first published, validated food allergy research algorithm specifically for infants, demonstrating strong performance for classifying peanut, cashew, egg and cow’s milk allergy. Acknowledgements We wish to thank the children and parents who participated in the EarlyNuts HealthNuts, and Vitality study, as well as the staff of Melbourne’s Local Government Areas, for welcoming us to the community immunisation clinics. We thank the additional EarlyNuts and Vitality staff members Andrew Know, Anita Hubbard, Ashleigh Rak, Beatriz Camesella Perez, Caitlin Louey, Carrie Service, Claire Buchanan, Clare Morrison, Deborah Anderson, Grace Gell, Helen Czech, Hannah Elborough, Judith Spotswood, Julie-Ann Quinn, Kate Wall, Kirsty Bowes, Michael Field, Natalie Schreurs, Natasha Burgess, Sarah Fowler. We would also like to thank the *EarlyNuts, HealthNuts and Vitality safety committee – Associate Professor Noel Cranswick (Australian Paediatric Pharmacology Research Unit/Murdoch Childrens Research Institute), Dr Jo Smart (Department of Allergy and Immunology, Royal Children’s Hospital, Melbourne, Australia) and Associate Professor Jo Douglass (Head of Allergy, Alfred Hospital, Melbourne, Australia. HealthNuts would also like to thank ALK Abello, S.A. Madrid, Spain for supplying the skin prick testing reagent. Supplementary Material File (2.figures_algorithm_pai.docx) Download 362.38 KB File (3.tables_algorithm_pai.docx) Download 100.12 KB File (5.graphical_abstract_algorithm_text_pai.docx) Download 23.48 KB References 23. https://afcd.foodstandards.gov.au/fooddetails.aspx?PFKID=F003729 Google Scholar 44. 1. Peters RL, Koplin JJ, Gurrin LC, Dharmage SC, Wake M, Ponsonby AL, et al. The prevalence of food allergy and other allergic diseases in early childhood in a population-based study: HealthNuts age 4-year follow-up. J Allergy Clin Immunol. 2017;140(1):145-53 e8.2. Patel N, Shreffler WG, Custovic A, Santos AF. Will Oral Food Challenges Still Be Part of Allergy Care in 10 Years’ Time? J Allergy Clin Immunol Pract. 2023;11(4):988-96.3. Sherenian MG, Chang WC, Almasri C, Biagini JM, Togias A, Khurana Hershey GK. Objective sensitization algorithm validates parental report of food allergy in children. J Allergy Clin Immunol Glob. 2025;4(4):100512.4. DunnGalvin A, Segal LM, Clarke A, Alizadehfar R, Hourihane JO. Validation of the Cork-Southampton Food Challenge Outcome Calculator in a Canadian sample. J Allergy Clin Immunol. 2013;131(1):230-2.5. DunnGalvin A, Daly D, Cullinane C, Stenke E, Keeton D, Erlewyn-Lajeunesse M, et al. Highly accurate prediction of food challenge outcome using routinely available clinical data. J Allergy Clin Immunol. 2011;127(3):633-9 e1-3.6. Goldberg MR, Appel MY, Tobi K, Levy MB, Epstein-Rigbi N, Holmqvist M, et al. Validation of the NUT CRACKER Diagnostic Algorithm and Prediction for Cashew and Pistachio Co-Allergy. J Allergy Clin Immunol Pract. 2024;12(5):1273-82 e5.7. Goldberg MR, Appel MY, Nega R, Levy MB, Epstein-Rigbi N, Nachshon L, et al. A Prospective Validation of the NUT CRACKER Diagnostic Algorithm for Walnut and Pecan Allergy with Prediction of Severity. J Allergy Clin Immunol Pract. 2021;9(1):265-74 e6.8. Sever ML, Calatroni A, Roberts G, du Toit G, Bahnson HT, Radulovic S, et al. Developing a Prediction Model for Determination of Peanut Allergy Status in the Learning Early About Peanut Allergy (LEAP) Studies. J Allergy Clin Immunol Pract. 2023;11(7):2217-27 e9.9. Erlewyn-Lajeunesse M, Weir T, Brown L, Howells H, Rowley J, Grainger-Allen E, et al. Fifteen-minute consultation: The EATERS method for the diagnosis of food allergies. Arch Dis Child Educ Pract Ed. 2019;104(6):286-91.10. Brettig T, Koplin JJ, Dang T, Lange L, McWilliam V, Sato S, et al. Cashew allergy diagnosis: A two-step algorithm leads to fewer oral food challenges. J Allergy Clin Immunol Pract. 2022;10(6):1652-4 e2.11. Kelleher MM, Jay N, Perkin MR, Haines RH, Batt R, Bradshaw LE, et al. An algorithm for diagnosing IgE-mediated food allergy in study participants who do not undergo food challenge. Clin Exp Allergy. 2020;50(3):334-42.12. Allen KJ, Panjari M, Koplin JJ, Ponsonby AL, Vuillermin P, Gurrin LC, et al. VITALITY trial: protocol for a randomised controlled trial to establish the role of postnatal vitamin D supplementation in infant immune health. BMJ Open. 2015;5(12):e009377.13. Soriano VX, Peters RL, Ponsonby AL, Dharmage SC, Perrett KP, Field MJ, et al. Earlier ingestion of peanut after changes to infant feeding guidelines: The EarlyNuts study. J Allergy Clin Immunol. 2019;144(5):1327-35 e5.14. Koplin JJ, Wake M, Dharmage SC, Matheson M, Tang ML, Gurrin LC, et al. Cohort Profile: The HealthNuts Study: Population prevalence and environmental/genetic predictors of food allergy. Int J Epidemiol. 2015;44(4):1161-71.15. Soriano VX, Peters RL, Moreno-Betancur M, Ponsonby AL, Gell G, Odoi A, et al. Association Between Earlier Introduction of Peanut and Prevalence of Peanut Allergy in Infants in Australia. JAMA. 2022;328(1):48-56.16. Ho MH, Heine RG, Wong W, Hill DJ. Diagnostic accuracy of skin prick testing in children with tree nut allergy. J Allergy Clin Immunol. 2006;117(6):1506-8.17. Peters RL, Allen KJ, Dharmage SC, Tang ML, Koplin JJ, Ponsonby AL, et al. Skin prick test responses and allergen-specific IgE levels as predictors of peanut, egg, and sesame allergy in infants. J Allergy Clin Immunol. 2013;132(4):874-80.18. Chan JCK, Peters RL, Koplin JJ, Dharmage SC, Gurrin LC, Wake M, et al. Food Challenge and Community-Reported Reaction Profiles in Food-Allergic Children Aged 1 and 4 Years: A Population-Based Study. J Allergy Clin Immunol Pract. 2017;5(2):398-409 e3.19. Koplin JJ, Tang ML, Martin PE, Osborne NJ, Lowe AJ, Ponsonby AL, et al. Predetermined challenge eligibility and cessation criteria for oral food challenges in the HealthNuts population-based study of infants. J Allergy Clin Immunol. 2012;129(4):1145-7.20. Hill DJ, Heine RG, Hosking CS. The diagnostic value of skin prick testing in children with food allergy. Pediatr Allergy Immunol. 2004;15(5):435-41.21. Osborne NJ, Koplin JJ, Martin PE, Gurrin LC, Lowe AJ, Matheson MC, et al. Prevalence of challenge-proven IgE-mediated food allergy using population-based sampling and predetermined challenge criteria in infants. J Allergy Clin Immunol. 2011;127(3):668-76 e1-2.22. Peters RL, Allen KJ, Dharmage SC, Koplin JJ, Dang T, Tilbrook KP, et al. Natural history of peanut allergy and predictors of resolution in the first 4 years of life: A population-based assessment. J Allergy Clin Immunol. 2015;135(5):1257-66 e1-2.23. FSANZ. Australian Food Composition Database - Release 2.0: Food Standards Australia New Zealand; [Available from: .24. Brettig T, Soriano VX, Dharmage SC, McWilliam V, Peters RL, Perrett K, et al. Cashew Allergy Prevalence and Sensitization in 1-Year-Old Infants. J Allergy Clin Immunol Pract. 2023.25. ASCIA. Anaphylaxis. Australasian Society of Clinical and Immunology. 2021;2022(08 May 2023).26. Seed P. DIAGT: Stata module to report summary statistics for diagnostic tests compared to true disease status. 2010.27. Evans HJ, Gibson NA, Bennett J, Chan SY, Gavlak J, Harman K, et al. British Thoracic Society guideline for diagnosing and monitoring paediatric sleep-disordered breathing. Thorax. 2023;78(Suppl 2):s1-s27.28. Venter C, Warren C, Samady W, Nimmagadda SR, Vincent E, Zaslavsky J, et al. Food allergen introduction patterns in the first year of life: A US nationwide survey. Pediatr Allergy Immunol. 2022;33(12):e13896.29. Perkin MR, Logan K, Bahnson HT, Marrs T, Radulovic S, Craven J, et al. Efficacy of the Enquiring About Tolerance (EAT) study among infants at high risk of developing food allergy. J Allergy Clin Immunol. 2019;144(6):1606-14 e2.30. Chalmers JR, Haines RH, Bradshaw LE, Montgomery AA, Thomas KS, Brown SJ, et al. Daily emollient during infancy for prevention of eczema: the BEEP randomised controlled trial. Lancet. 2020;395(10228):962-72.31. Perkin MR, Logan K, Marrs T, Radulovic S, Craven J, Flohr C, et al. Enquiring About Tolerance (EAT) study: Feasibility of an early allergenic food introduction regimen. J Allergy Clin Immunol. 2016;137(5):1477-86 e8.32. O’Sullivan M, Vale S, Loh RK, Metcalfe J, Orlemann K, Salter S, et al. SmartStartAllergy: a novel tool for monitoring food allergen introduction in infants. Med J Aust. 2020;212(6):271-5.33. ASCIA. Guidelines infant feeding and allergy prevention: Australasian Society of Clinical, Immunology; 2016 [34. Soriano VX, Allen KJ, Dharmage SC, Shifti DM, Perrett KP, Wijesuriya R, et al. Prevalence and Determinants of Food Allergy in the Era of Early Allergen Introduction: The EarlyNuts Population-Based Study. J Allergy Clin Immunol Pract. 2024;12(11):3068-78 e3.35. Pistiner M, Mendez-Reyes JE, Eftekhari S, Carver M, Lieberman J, Wang J, et al. Caregiver-Reported Presentation of Severe Food-Induced Allergic Reactions in Infants and Toddlers. J Allergy Clin Immunol Pract. 2021;9(1):311-20 e2.36. Gupta RS, Warren CM, Smith BM, Blumenstock JA, Jiang J, Davis MM, et al. The Public Health Impact of Parent-Reported Childhood Food Allergies in the United States. Pediatrics. 2018;142(6).37. Houben GF, Baumert JL, Blom WM, Kruizinga AG, Meima MY, Remington BC, et al. Full range of population Eliciting Dose values for 14 priority allergenic foods and recommendations for use in risk characterization. Food Chem Toxicol. 2020;146:111831.38. Peters RL, Guarnieri I, Tang MLK, Lowe AJ, Dharmage SC, Perrett KP, et al. The natural history of peanut and egg allergy in children up to age 6 years in the HealthNuts population-based longitudinal study. Journal of Allergy and Clinical Immunology. 2022;150(3):657-65.e13.39. Verhoeven DHJ, Benjamin-van Aalst O, Klok T, de Weger WW, Breukels M, Hendriks T, et al. Successful Introduction of Peanut in Sensitized Infants With Reported Reactions at Home. J Allergy Clin Immunol Pract. 2024;12(12):3363-9.40. Gard CN, Sanders GM, Slack IF, Schuler CFt, Freigeh GE, O’Shea KM. Peanut challenges prior to oral immunotherapy demonstrate high tolerance rates in selected patients. J Allergy Clin Immunol Glob. 2025;4(2):100442.41. Gantulga P, Lee J, Jeong K, Jeon SA, Lee S. Variation in the Allergenicity of Scrambled, Boiled, Short-Baked and Long-Baked Egg White Proteins. J Korean Med Sci. 2024;39(6):e54.42. Grimshaw KE, Bryant T, Oliver EM, Martin J, Maskell J, Kemp T, et al. Incidence and risk factors for food hypersensitivity in UK infants: results from a birth cohort study. Clin Transl Allergy. 2015;6:1.43. Hurley S, Franklin R, Murray D, Venter C, J OBH. Changes in food sensitization with changing allergy practice in Ireland. Clin Exp Allergy. 2023;53(3):372-5.44. Osterlund J, Granasen G, Boden S, Silfverdal SA, Domellof M, Winberg A, et al. Revised Swedish infant feeding guidelines are associated with earlier introduction of allergenic foods. J Allergy Clin Immunol. 2024;153(2):461-70. Google Scholar Information & Authors Information Version history V1 Version 1 19 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Alexsandria Odoi 0000-0003-4811-5803 Melbourne Medical School Department of Paediatrics View all articles by this author Jennifer Koplin J Centre for Food and Allergy Research View all articles by this author Victoria Soriano Murdoch Children's Research Institute Population Allergy Group View all articles by this author Kayla Parker 0000-0003-1861-5663 Melbourne Medical School Department of Paediatrics View all articles by this author Katie Allen Murdoch Children's Research Institute Population Allergy Group View all articles by this author Shyamali Dharmage 0000-0001-6063-1937 Centre for Food and Allergy Research View all articles by this author Katherine Lee Melbourne Medical School Department of Paediatrics View all articles by this author Jana Eckert Murdoch Children's Research Institute Population Allergy Group View all articles by this author Audrey M. Walsh Melbourne Medical School Department of Paediatrics View all articles by this author Angela Young Murdoch Children's Research Institute Population Allergy Group View all articles by this author Tim Brettig 0000-0003-0558-153X Murdoch Children's Research Institute Population Allergy Group View all articles by this author Rachel Peters 0000-0002-2411-6628 Melbourne Medical School Department of Paediatrics View all articles by this author Kirsten Perrett [email protected] Melbourne Medical School Department of Paediatrics View all articles by this author Metrics & Citations Metrics Article Usage 233 views 105 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Alexsandria Odoi, Jennifer Koplin J, Victoria Soriano, et al. The Vitality algorithm: Classifying infant IgE-mediated food allergy in research studies when participant oral food challenges are not performed. Authorea . 19 January 2026. DOI: https://doi.org/10.22541/au.176881524.48488830/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.176881524.48488830/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe3de4cfd7b1b23',t:'MTc3OTIwMTc0OA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();