Introduction

Cow’s milk allergy (CMA) is one of the most common food allergies in early childhood. The prevalence of CMA by one year of age is estimated to range between 1.8% and 7.5% [1]. Data from a UK birth cohort study revealed an adjusted incidence of CMA in children up to two years of age in the UK at approximately 2.4%, with non-IgE-mediated CMA accounting for half of diagnosed cases, around 1.7% [2]. However, there is a marked discrepancy between self-reported CMA and formally diagnosed cases, with diagnostic assessments indicating a much lower prevalence of just 0.6–1% [3, 4], compared to 16.1% based on parent reports and 11.3% from primary care records [4].

Accurate diagnosis of CMA relies on a high-quality allergy-focused clinical history (AFCH), which remains the cornerstone of food allergy assessment, particularly in primary care where access to diagnostic testing is limited [5]. Allergy tests are applied in a stepwise diagnostic approach based on the AFCH (Figure 1).

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Figure 1. Allergy diagnostic approach for food allergies. This figure illustrates how EATERS-X aligns with a stepwise diagnostic approach, reinforcing that structured history-taking underpins decisions regarding testing, elimination diets, and referral. The pyramid emphasises that accurate AFCH precedes investigation and escalation, particularly in primary care settings.

Distinguishing between the various types of CMA can be challenging. This article explores different clinical presentations of CMA applying the EATERS-X tool, to support AFCH and provide clarity (Table 1).

The EATERS framework is a structured, user-friendly tool for diagnosing food allergies recommended by international guidelines [6]. It can be applied by any healthcare professional, regardless of clinical experience, and maps well to the diagnosis of IgE-mediated food allergies.

EATERS emphasises six key elements of the history found in all allergic reactions: Environment, Allergen, Timing, Exposure, Reproducibility, and Symptoms [6]. While the EATERS framework is effective in structuring the temporal and symptomatic characteristics of allergic reactions, it does not explicitly capture how symptoms evolve following treatment or healthcare intervention. In routine clinical practice, response to interventions such as antihistamines, acid suppression, elimination diets, or escalation to emergency care often provides critical diagnostic information. The addition of “X” (Extra/Rx) in EATERS-X formalises this overlooked dimension of the AFCH. By systematically capturing treatment response and healthcare interaction, EATERS-X enhances diagnostic discrimination between allergic and non-allergic conditions, improves recognition of disease severity, supports triage decisions, and facilitates earlier identification of red flags such as treatment-resistant symptoms or evolving IgE-mediated disease. By incorporating both parental and healthcare professional responses, EATERS-X reduces reliance on symptom reporting alone and introduces an internal severity check: reported symptom severity should align with the level of treatment and observation provided. This represents a qualitative advancement beyond the original EATERS framework, strengthening causal inference and pre-test probability assessment rather than merely extending the mnemonic.

EATERS-X is a practical tool for conducting quick and efficient AFCH in busy clinic settings. It can be applied broadly across allergy types—including IgE- and non-IgE-mediated allergies, adverse drug reactions, venom allergies, and various food allergies. However, previous validation of the EATER method focused solely on IgE-mediated food allergies [5]. Therefore, interpreting the collected history for other allergy types requires appropriate clinical guidance. For example, histories related to CMA could be assessed using diagnostic frameworks such as the Allen et al. [7] algorithm to guide further investigation and management.

Application of EATERS-X in CMA

CMA is classified into IgE-mediated, non-IgE-mediated, and mixed IgE/non-IgE-mediated types [5, 8]. IgE-mediated CMA involves rapid Type I hypersensitivity with mast cell degranulation, while non-IgE-mediated CMA arises from delayed Type IVb hypersensitivity driven by Th2 cells. Mixed IgE/non-IgE CMA combines immediate and delayed reactions [8].

Discussion

CMA remains one of the most prevalent food allergies in early childhood, yet accurate diagnosis, particularly in primary care settings, remains a considerable challenge.

The BEEP trial highlighted significant overdiagnosis of CMA in the community: 16.1% had parental reports of milk hypersensitivity, 11.3% had hypersensitivity recorded in primary care, and 8.7% received prescriptions for hypoallergenic formula—yet only 1.4% were formally diagnosed with CMA [4]. Diagnosing CMA in primary care is challenging because of limited access to allergy testing [4] and symptoms that commonly overlap with those of unsettled infants [29].

The EATERS-X framework presents a structured, practical tool that supports clinicians—particularly non-specialists—in navigating the complex diagnostic landscape of CMA (Table 7). By emphasizing exposure, allergen identification, timing, environment, reproducibility, symptoms, and treatment (or healthcare response), this approach enables a comprehensive AFCH that aligns with the latest European Academy of Allergy and Clinical Immunology (EAACI) guidance. This is especially valuable in differentiating between IgE-mediated, non-IgE-mediated, and mixed-type CMA, each of which presents with distinct pathophysiology and timelines and requires a different management approach.

This table outlines the simplified clinical presentations of CMA, categorized using the EATERS-X allergy-focused history framework. A detailed table of explanation of each domain can be found in Table S1.

Non-IgE-mediated CMA, in particular, poses unique diagnostic difficulties due to its delayed symptom onset and presentation across a spectrum of gastrointestinal conditions, from FPIES to controversial entities like food protein-induced dysmotility disorders [16]. This article also draws attention to the phenomenon of “switched CMA”, where patients initially presenting with non-IgE-mediated reactions may later develop IgE-mediated responses, further underscoring the need for structured history-taking and ongoing reassessment [16].

Using specific IgE or SPT for cow’s milk protein alone provides limited sensitivity and specificity without a thorough AFCH [5]. Specific IgE food allergen panels have poor sensitivity, and multiplex IgE technologies should be requested cautiously and interpreted only by allergy specialists, as they require expert knowledge to avoid misdiagnosis and unnecessary dietary restrictions.

Importantly, many children with suspected CMA undergo dietary elimination without formal diagnostic confirmation, potentially leading to unnecessary dietary restrictions, nutritional deficiencies, and increased parental anxiety. The application of the EATERS-X framework in primary care could facilitate earlier recognition of red flags, promote timely management or referral to allergy services, and reduce unwarranted exclusion diets.

Once a diagnosis is established using this approach, management of each CMA subtype can be effectively guided by established national and international guidelines, including those from the British Society for Allergy & Clinical Immunology (BSACI) and EAACI. In cases where the allergy status remains uncertain, the EAACI recommendations for both IgE- and non-IgE-mediated food allergies provide valuable direction for further investigation and clinical decision-making [5, 16].

The EATERS-X framework offers several strengths, notably its structured and standardised approach to allergy-focused history-taking, which reduces subjective interpretation and supports more reproducible and clinically precise diagnosis of CMA across IgE-mediated, non-IgE-mediated, and mixed presentations. The inclusion of treatment and healthcare response (“X”) strengthens causal inference by linking symptom evolution to interventions, particularly in settings with limited access to objective testing. However, the framework has not yet undergone formal validation to determine its diagnostic accuracy, inter-observer reliability, or impact on clinical outcomes. Importantly, EATERS-X is intended to complement—not replace—objective testing and guideline-based diagnostic pathways.

Although the mnemonic is already a useful teaching aid, the future validation of EATERS-X should be an integral component of its proposed clinical utility rather than an adjunct consideration. Prospective, multi-centre studies across a range of healthcare settings are required to evaluate its diagnostic accuracy against reference standards such as oral food challenges and expert-confirmed diagnoses. Key outcomes should include sensitivity, specificity, positive and negative predictive values, and inter-observer reliability across clinicians with varying levels of allergy expertise. In addition, studies should assess whether inclusion of the “X” component improves discrimination between allergic and non-allergic conditions compared with the original EATERS framework alone. Secondary outcomes may include impact on referral rates, reduction in unnecessary elimination diets, nutritional outcomes, and parental anxiety. Such validation would clarify whether structured capture of treatment response meaningfully enhances diagnostic precision and clinical decision-making.

Conclusion

CMA is frequently over diagnosed in community settings, largely due to nonspecific symptoms and limited access to allergy diagnostics. The EATERS-X framework offers a practical and systematic method to improve diagnostic accuracy by guiding structured history-taking.

By reducing subjective interpretation and standardising key diagnostic variables, EATERS-X provides an unbiased and reproducible approach that enhances precision in differentiating IgE-mediated, non-IgE-mediated, and mixed forms of CMA.

When applied within guideline-based care pathways, EATERS-X has the potential to significantly reduce unnecessary elimination diets, optimise referral decisions, and improve patient-centred outcomes. While further validation is required, this framework represents a robust and scalable advance in the accurate diagnosis of CMA, particularly in primary care and non-specialist settings.