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Towards precision medicine in Chronic Obstructive Pulmonary Diseases: predicting individual treatment response and general outcomes in SUMMIT

Towards precision medicine in Chronic Obstructive Pulmonary Diseases: predicting individual treatment response and general outcomes in SUMMIT

Wim Janssens

University Hospitals Leuven Department of Respiratory Diseases Research Laboratory BREATHE, Department CHROMETA, KU Leuven Herestraat 49 3000 Leuven Belgium

10 June 2020

With the constant increase in computational power and the rise of big data, artificial intelligence (AI) is rapidly moving forward and is currently invading different domains of healthcare. The central idea of the project is to use machine learning approaches to detect patterns between several baseline patient characteristics, interventions and particular outcomes of Randomized Controlled Trials (RCT). The advantage of machine learning, compared to the classical statistical models, is the ability to learn more complex patterns in the data without many assumptions about underlying distributions. At this stage, RCT are providing the highest level of evidence on the efficacy of any pharmaceutical intervention. These trials are absolutely needed in product development and obligatory in different phases before any drug will be considered by health authorities for reimbursement and broad clinical use.In RCT, the current statistical approaches are comparing groups and subgroups in terms of different predefined outcomes. These approaches are highly important to demonstrate the efficacy or futility of an intervention on group level. For a clinician in daily practice, treatment decisions must be made on individual level. However, it is still hard to predict if an individual patient will be a responder or non-responder to an intervention based on group evidence of its RCT. Machine learning models may overcome this problem by looking at the likelihood an individual is going to respond on a studied intervention. By learning from relationships in a detailed training data set, intelligent algorithms will be developed to predict probabilities of outcomes on an individual patient level.This project will focus on the Study to Understand Mortality and Morbidity (SUMMIT) dataset in the domain of Chronic Obstructive Pulmonary Diseases (COPD). COPD is most often caused by smoking and is currently the 3th leading cause of mortality worldwide (after cardiovascular disease and cancer). Different treatments via inhalers or oral drug intakes have been validated by large randomized controlled trials and are currently being used in daily practice. Unfortunately, many of the patients taking these medications daily, are not responding to the drug that has been prescribed to them. This is caused by the high heterogeneity and the complexity of the disease that is not yet well-understood. Some subgroups may respond better to the treatment while other subgroups of the disease do not respond at all. Intelligent tools that can identify responders from non-responders in advance, are therefore of utmost importance, not only from the individual perspective but also from a health-economic perspective.The objective of our research is to develop models based on different AI approaches, e.g., support vector machines, random forests and deep learning, and compare their accuracy performance in outcome prediction. Different sets of baseline features will be used to augment prediction power at minimal redundancy. Training and validation data sets will be used to develop ready-for-use approaches to allow fast evaluation of big pharma trials. In parallel, cloud-based software tools will be developed to allow clinicians to predict therapeutic responses. As such, artificial intelligence may be at the doorstep of personalized medicine in chronic respiratory diseases.

[{ "PostingID": 4109, "Title": "GSK-HZC113782", "Description": "A Clinical Outcomes Study to compare the effect of Fluticasone Furoate/Vilanterol Inhalation Powder 100/25mcg with placebo on Survival in Subjects with moderate Chronic Obstructive Pulmonary Disease (COPD) and a history of or at increased risk for cardiovascular disease" }]

Statistical Analysis Plan