Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab

Chaparro, María; Baston‐Rey, Iria; Salgado, Estela Fernández; García, Javier González; Ramos, Laura; Palomares, M T Diz-Lois; Argüelles-Arias, Federico; Flores, Eva Iglesias; Cabello, Mercedes; Iturria, Saioa Rubio; Ortiz, Andrea Núñez; Charro, Mara; Ginard, Daniel; Sadornil, C Dueñas; Ochoa, Olga Merino; Busquets, David; Iyo, Eduardo; Casbas, Ana Gutiérrez; Piscina, Patricia Ramírez de la; Boscá-Watts, Marta Maia; Villarino, M.; García, Maria José; Hinojosa, Esther; Gordillo, Jordi; Montiel, P Martínez; Jiménez, Benito Velayos; Ivorra, Cristina Quílez; Morón, Juan María Vázquez; Huguet, José María; González-Lama, Yago; Santos, A I Muñagorri; Amo, Víctor Manuel; Arranz, María Dolores Martín; Bermejo, Fernando; Cadilla, J Martínez; Célix, Cristina Rubín de; Salazar, Paola Fradejas; Román, Antonio; Jiménez, Nuria; García-López, Santiago; Figuerola, Anna; Jiménez, I; Cerezo, Francisco; Taxonera, Carlos; Varela, Pilar; Francisco, Ruth de; Monfort, David; Arriero, G Molina; Camba, Alejandro Hernández; Alonso, Francisco; Domselaar, Manuel Van; Pajares-Villarroya, R.; Nunez, Alejandro Mena; Moranta, Francisco Rodríguez; Marín-Jiménez, Ignacio; Alonso, Virginia Robles; Rodríguez, María del Mar Martín; Camo-Monterde, Patricia; Tercero, Iván García; Navarro-Llavat, M; García, Lara Arias; Cruz, D. Hervías; Kloss, Sebastian; Passey, Alun; Novella, Cynthia; Vispo, Eugenia; Acosta, Manuel Barreiro‐de; Gisbert, Javier P.

doi:10.3390/jcm11154518

Cited by 3 publications

(2 citation statements)

References 15 publications

(19 reference statements)

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“…Machine learning belongs to the field of artificial intelligence and refers to the ability of computers to learn to make decisions or detect patterns from data, without explicitly being programmed. 277 Several machine learning predictive models have already been suggested such as (1) a machine learning algorithm to predict clinical remission with thiopurines 278 ; (2) a machine learning to predict non-durable response to anti-TNF therapy in CD patients using transcriptome imputed from genotypes 279 ; (3) a machine learning to identify, in CD patients, predictive factors of remission and drug durability with ustekinumab 280 ; or (4) a machine learning gene expression to predict response to ustekinumab in CD patients. 281 …”

Section: Choosing the Appropriate Treatmentmentioning

confidence: 99%

Precision medicine and drug optimization in adult inflammatory bowel disease patients

Vieujean

Louis

2023

Therap Adv Gastroenterol

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Inflammatory bowel diseases (IBD) encompass two main entities including ulcerative colitis and Crohn’s disease. Although having a common global pathophysiological mechanism, IBD patients are characterized by a significant interindividual heterogeneity and may differ by their disease type, disease locations, disease behaviours, disease manifestations, disease course as well as treatment needs. Indeed, although the therapeutic armamentarium for these diseases has expanded rapidly in recent years, a proportion of patients remains with a suboptimal response to medical treatment due to primary non-response, secondary loss of response or intolerance to currently available drugs. Identifying, prior to treatment initiation, which patients are likely to respond to a specific drug would improve the disease management, avoid unnecessary side effects and reduce the healthcare expenses. Precision medicine classifies individuals into subpopulations according to clinical and molecular characteristics with the objective to tailor preventative and therapeutic interventions to the characteristics of each patient. Interventions would thus be performed only on those who will benefit, sparing side effects and expense for those who will not. This review aims to summarize clinical factors, biomarkers (genetic, transcriptomic, proteomic, metabolic, radiomic or from the microbiota) and tools that could predict disease progression to guide towards a step-up or top-down strategy. Predictive factors of response or non-response to treatment will then be reviewed, followed by a discussion about the optimal dose of drug required for patients. The time at which these treatments should be administered (or rather can be stopped in case of a deep remission or in the aftermath of a surgery) will also be addressed. IBD remain biologically complex, with multifactorial etiopathology, clinical heterogeneity as well as temporal and therapeutic variabilities, which makes precision medicine especially challenging in this area. Although applied for many years in oncology, it remains an unmet medical need in IBD.

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Section: Choosing the Appropriate Treatmentmentioning

confidence: 99%

Precision medicine and drug optimization in adult inflammatory bowel disease patients

Vieujean

Louis

2023

Therap Adv Gastroenterol

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“…Overall, little is known about the effects of other serum inflammatory markers and antibodies such as ESR, ANCA and ASCA on the response rates of ustekinumab. Recently, the low baseline FC level was claimed to be a valuable predictor of good response to ustekinumab [ 171 ]. With respect to intestinal microbiota, the CERTIFI study suggested that baseline microbial signatures could predict disease remission with acceptable accuracy [ 169 ].…”

Section: Precision Treatment With Key Medicationsmentioning

confidence: 99%

Precision medicine in inflammatory bowel disease

Zeng,

Jiang,

et al. 2023

Precision Clinical Medicine

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Inflammatory bowel disease (IBD) is an incurable disease characterized by remission-relapse cycles throughout its course. Both Crohn's disease (CD) and ulcerative colitis (UC), the two main forms of IBD, exhibit tendency to develop complications and substantial heterogeneity in terms of frequency and severity of relapse, thus posing great challenges to the clinical management for IBD. Current treatment strategies are effective in different ways in induction and maintenance therapies for IBD. Recent advances in studies of genetics, pharmacogenetics, proteomics and microbiome provide a strong driving force for identifying molecular markers of prognosis and treatment response, which should help clinicians manage IBD patients more effectively, and then, improve clinical outcomes and reduce treatment costs of patients. In this review, we summarize and discuss precision medicine in IBD, focusing on predictive markers of disease course and treatment response, and monitoring indices during therapeutic drug monitoring.

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Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation

Rattsev,

Stearns,

Blackford

et al. 2024

JAMIA Open

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Objectives Early discontinuation is common among breast cancer patients taking aromatase inhibitors (AIs). Although several predictors have been identified, it is unclear how to simultaneously consider multiple risk factors for an individual. We sought to develop a tool for prediction of AI discontinuation and to explore how predictive value of risk factors changes with time. Materials and Methods Survival machine learning was used to predict time-to-discontinuation of AIs in 181 women who enrolled in a prospective cohort. Models were evaluated via time-dependent area under the curve (AUC), c-index, and integrated Brier score. Feature importance was analysis was conducted via Shapley Additive Explanations (SHAP) and time-dependence of their predictive value was analyzed by time-dependent AUC. Personalized survival curves were constructed for risk communication. Results The best-performing model incorporated genetic risk factors and changes in patient-reported outcomes, achieving mean time-dependent AUC of 0.66, and AUC of 0.72 and 0.67 at 6- and 12-month cutoffs, respectively. The most significant features included variants in ESR1 and emergent symptoms. Predictive value of genetic risk factors was highest in the first year of treatment. Decrease in physical function was the strongest independent predictor at follow-up. Discussion and Conclusion Incorporation of genomic and 3-month follow-up data improved the ability of the models to identify the individuals at risk of AI discontinuation. Genetic risk factors were particularly important for predicting early discontinuers. This study provides insight into the complex nature of AI discontinuation and highlights the importance of incorporating genetic risk factors and emergent symptoms into prediction models.

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Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab

Cited by 3 publications

References 15 publications

Precision medicine and drug optimization in adult inflammatory bowel disease patients

Precision medicine and drug optimization in adult inflammatory bowel disease patients

Precision medicine in inflammatory bowel disease

Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation

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