Adherence and persistence in allergen immunotherapy (APAIT): A reporting checklist for retrospective studies

Pfaar, Oliver; Devillier, Philippe; Schmitt, Jochen; Demoly, Pascal; Hilberg, Ole; DuBuske, L.M.; Hass, Nicole; Klok, Ted; Beutner, Caroline

doi:10.1111/all.15723

Cited by 5 publications

(2 citation statements)

References 34 publications

(91 reference statements)

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“…Poor adherence in the three to 5-year time span of AIT is an obstacle to reaching allergen tolerance and symptom remission. Recently “adherence and persistence in AIT (APAIT)” checklist was proposed to assists researchers in assessing adherence or persistence to AIT treatment ( Pfaar et al, 2023 ). The present study is the first research regarding the application of machine learning models in the adherence prediction of SCIT in AR patients.…”

Section: Discussionmentioning

confidence: 99%

Sequential model for predicting patient adherence in subcutaneous immunotherapy for allergic rhinitis

Li,

Xiong,

Fan

et al. 2024

Front. Pharmacol.

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ObjectiveSubcutaneous Immunotherapy (SCIT) is the long-lasting causal treatment of allergic rhinitis (AR). How to enhance the adherence of patients to maximize the benefit of allergen immunotherapy (AIT) plays a crucial role in the management of AIT. This study aims to leverage novel machine learning models to precisely predict the risk of non-adherence of AR patients and related local symptom scores in 3 years SCIT.MethodsThe research develops and analyzes two models, sequential latent-variable model (SLVM) of Stochastic Latent Actor-Critic (SLAC) and Long Short-Term Memory (LSTM). SLVM is a probabilistic model that captures the dynamics of patient adherence, while LSTM is a type of recurrent neural network designed to handle time-series data by maintaining long-term dependencies. These models were evaluated based on scoring and adherence prediction capabilities.ResultsExcluding the biased samples at the first time step, the predictive adherence accuracy of the SLAC models is from 60% to 72%, and for LSTM models, it is 66%–84%, varying according to the time steps. The range of Root Mean Square Error (RMSE) for SLAC models is between 0.93 and 2.22, while for LSTM models it is between 1.09 and 1.77. Notably, these RMSEs are significantly lower than the random prediction error of 4.55.ConclusionWe creatively apply sequential models in the long-term management of SCIT with promising accuracy in the prediction of SCIT nonadherence in AR patients. While LSTM outperforms SLAC in adherence prediction, SLAC excels in score prediction for patients undergoing SCIT for AR. The state-action-based SLAC adds flexibility, presenting a novel and effective approach for managing long-term AIT.

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Section: Discussionmentioning

confidence: 99%

Sequential model for predicting patient adherence in subcutaneous immunotherapy for allergic rhinitis

Li,

Xiong,

Fan

et al. 2024

Front. Pharmacol.

View full text Add to dashboard Cite

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“…Retrospective studies rely on existing data, which may not have been collected with the current research question in mind. This can introduce biases, such as selection bias or information bias, affecting the accuracy and reliability of the findings ( 2 ). Conducting prospective, multicenter studies with larger sample sizes would improve the generalizability of the findings and reduce selection bias.…”

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confidence: 99%