EczemaPred: A computational framework for personalised prediction of eczema severity dynamics

Evaluation of measurement errors in the Patient-Oriented Eczema Measure (POEM) outcome

BACKGROUND

The Patient-Oriented Eczema Measure (POEM) is the recommended core outcome instrument for atopic dermatitis (AD) symptoms. POEM is reported by recalling the presence/absence of seven symptoms in the last 7 days.

OBJECTIVE

To evaluate measurement errors in POEM recordings due to imperfect recall.

METHODS

Using data from a clinical trial of 247 AD patients aged 12-65 years, we analysed the reported POEM score (r-POEM) and the POEM derived from the corresponding daily scores for the same seven symptoms without weekly recall (d-POEM). We quantified recall error by comparing the r-POEM and d-POEM for 777 patient-weeks collected from 207 patients, and estimated two components of recall error: (1) recall bias due to systematic errors in measurements and (2) recall noise due to random errors in measurements, using a bespoke statistical model.

RESULTS

POEM scores have a relatively low recall bias, but a high recall noise. Recall bias was estimated at 1.2 points lower for the r-POEM on average than the d-POEM, with a recall noise of 5.7 points. For example, a patient with a recall-free POEM of 11 (moderate) could report their POEM score anywhere from 5 to 14 (with 95% probability) because of recall error. Model estimates suggested that patients tend to recall itch and dryness more often than experienced (positive bias of less than 1 day), but less often for the other symptoms (bleeding, cracking, flaking, oozing/weeping and sleep disturbance; negative bias ranging 1-4 days).

CONCLUSIONS

In this clinical trial data set, we found that patients tended to slightly underestimate their symptoms when reporting POEM, with significant variation in how well they were able to recall the frequency of their symptoms every time they reported POEM. A large recall noise should be taken into consideration when interpreting POEM scores.

The Patient-Oriented Scoring of Atopic Dermatitis and SCORAD in young children: New data on interpretability and clinical usefulness

BACKGROUND

There is limited data about the clinical meaningfulness of the Scoring of Atopic Dermatitis (SCORAD) and Patient-Oriented SCORAD (PO-SCORAD), particularly in children with mild-to-moderate AD. Regular use of patient-reported outcomes, may deliver more accurate information about the overall health status of AD patients than routine but sparse physician assessments.

OBJECTIVE

To confirm the correlation between SCORAD, PO-SCORAD, Patient-Oriented Eczema Measure (POEM) and Investigator's Global Assessment (IGA). To evaluate the interpretability and clinical usefulness of the SCORAD and PO-SCORAD scores in children.

METHODS

Data were drawn from a 12-week randomized controlled trial in 335 children, aged 2-6 years, with mainly mild-to-moderate AD. Investigators captured SCORAD and IGA at each study visit. Parents used PO-SCORAD twice-weekly, and POEM once-weekly.

RESULTS

There were strong correlations between PO-SCORAD and SCORAD (r = 0.874), PO-SCORAD and POEM (0.734) and PO-SCORAD and IGA (0.613). The best fit ('k' statistic: 0.68) between SCORAD and IGA classes was noted for the following SCORAD categories: <12 (clear/almost clear); 12-25 (mild); and ≥25 (moderate/severe). PO-SCORAD area under the curve over 8 weeks was significantly greater than that of SCORAD (p = 0.0002), giving a better estimate of disease severity between visits. Patients with a flare within the next 7 days had significantly higher PO-SCORAD scores 7 days before the flare (p < 0.0001). Moderate erythema was the most significant flare predictor (p < 0.0001).

CONCLUSION

PO-SCORAD is robust and reliable and appears to warrant far greater utility in routine clinical practice than other scores. PO-SCORAD, used twice-weekly, may improve the management of patients with AD.

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

PURPOSE OF REVIEW

Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research.

RECENT FINDINGS

In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.