Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis

Matabuena, Marcos; Salgado, Francisco; Nieto‐Fontarigo, Juan José; Alvarez-Puebla, M J; Arismendi, Ebymar; Paredes, Pilar; Bobolea, Irina; Caballero, María Luisa; Cañas, J. A.; Cárdaba, Blanca; Cruz, María Jesús; Curto, Elena; Domínguez‐Ortega, Javier; Luna, Juan Alberto; Martínez‐Rivera, Carlos; Mullol, Joaquim; Muñoz, Xavier; Rodrı́guez-Garcı́a, Javier; Olaguibel, José María; Picado, César; Plaza, Vicente; Quirce, Santiago; Rial, Manuel J; Romero‐Mesones, Christian; Sastre, Beatriz; Soto-Retes, Lorena; Valero, Antonio; Valverde‐Monge, Marcela; Pozo, Victoria del; Sastre, Joaquı́n; González‐Barcala, Francisco Javier

doi:10.1016/j.arbres.2023.01.007

Cited by 8 publications

(4 citation statements)

References 44 publications

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“…Otro aspecto relevante del asma es la heterogeneidad de la enfermedad, tanto en la presentación clínica, como en las comorbilidades asociadas o los mecanismos fisiopatológicos subyacentes, lo cual condiciona distintas respuestas al tratamiento en pacientes que pueden parecer inicialmente similares 67 , 68 , 69 . Para optimizar este aspecto, en lo que hemos denominado fase de caracterización del asma, se hará un estudio hospitalario, incluyendo únicamente los pacientes con diagnóstico de asma confirmado, para identificar los fenotipos de la enfermedad que presentan estos pacientes.…”

Section: Discussionunclassified

Estudio de prevalencia de asma en población general en España

Blanco-Aparicio¹,

García-Río²,

González-Barcala³

et al. 2023

Open Respiratory Archives

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Section: Discussionunclassified

Estudio de prevalencia de asma en población general en España

Blanco-Aparicio¹,

García-Río²,

González-Barcala³

et al. 2023

Open Respiratory Archives

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“…With a high accuracy of 87%-95% [36], machine learning models using EHR data have been used to profile patients in various areas, for example, to develop a phenotype for patients with Turner syndrome [61], identify low medication adherence profiles [62], find variable COVID-19 treatment response profiles [63], and predict hypertension treatment response [64]. Yet, while machine learning has helped find various asthma profiles [65][66][67][68][69][70][71][72], no prior study has predicted ICS response. Also, prior studies are mostly from single centers with small sample sizes and have not moved the needle of precision treatment for asthma [58,60].…”

Section: Introductionmentioning

confidence: 99%

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

Nkoy,

Stone,

Zhang

et al. 2024

JMIR Med Inform

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Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient’s characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient’s ICS response in the next year based on the patient’s characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.

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“…The clustering analysis method in unsupervised learning is being increasingly applied to various fields [ 19 , [20] , [21] , [22] , [23] , [24] , [25] ], which helps us make full use of a large number of unmarked samples and saves a lot of manual work and time. There are also many applications that combine clustering analysis with machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Research on fast marking method for indicator diagram of pumping well based on K-means clustering

Wang,

Shao,

Shen

et al. 2023

Heliyon

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Identification of Asthma Phenotypes in the Spanish MEGA Cohort Study Using Cluster Analysis

Cited by 8 publications

References 44 publications

Estudio de prevalencia de asma en población general en España

Estudio de prevalencia de asma en población general en España

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

Research on fast marking method for indicator diagram of pumping well based on K-means clustering

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