sebastiano-massaro

Dr Sebastiano Massaro

Associate Professor | Senior Lecturer in Organizational Neuroscience
PhD

Academic and research departments

Surrey Business School.

Biography

Areas of specialism

organizational neuroscience; behavioural science; affect and cognition; decision making; cooperation; morality

My qualifications

PhD Management Science
UCL School of Management

Previous roles

Assistant Professor of Behavioral Science
Warwick Business School
Deputy Academic Lead Global Research Priority in Behavioral Science
University of Warwick

Research

Research interests

Supervision

Postgraduate research supervision

My teaching

My publications

Publications

Vasilis Nikolaou, Sebastiano Massaro, Masoud Fakhimi, Lampros Stergioulas, David Price (2020)COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda, In: Journal of Respiratory Medicine106093 Elsevier
DOI: 10.1016/j.rmed.2020.106093

Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research

VASILEIOS NIKOLAOU, SEBASTIANO MASSARO, WOLFGANG GARN, MASOUD FAKHIMI, LAMPROS STERGIOULAS, David Price (2021)The Cardiovascular Phenotype of Chronic Obstructive Pulmonary Disease (COPD): Applying Machine Learning to the Prediction of Cardiovascular Comorbidities, In: Respiratory Medicine 186106528 Elsevier
DOI: 10.1016/j.rmed.2021.106528

Background: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous group of lung conditions that are challenging to diagnose and treat. As the presence of comorbidities often exacerbates this scenario, the characterization of patients with COPD and cardiovascular comorbidities may allow early intervention and improve disease management and care. Methods: We analysed a 4-year observational cohort of 6883 UK patients who were ultimately diagnosed with COPD and at least one cardiovascular comorbidity. The cohort was extracted from the UK Royal College of General Practitioners and Surveillance Centre database. The COPD phenotypes were identified prior to diagnosis and their reproducibility was assessed following COPD diagnosis. We then developed four classifiers for predicting cardiovascular comorbidities. Results: Three subtypes of the COPD cardiovascular phenotype were identified prior to diagnosis. Phenotype A was characterised by a higher prevalence of severe COPD, emphysema, hypertension. Phenotype B was char-acterised by a larger male majority, a lower prevalence of hypertension, the highest prevalence of the other cardiovascular comorbidities, and diabetes. Finally, phenotype C was characterised by universal hypertension, a higher prevalence of mild COPD and the low prevalence of COPD exacerbations. These phenotypes were reproduced after diagnosis with 92% accuracy. The random forest model was highly accurate for predicting hypertension while ruling out less prevalent comorbidities. Conclusions: This study identified three subtypes of the COPD cardiovascular phenotype that may generalize to other populations. Among the four models tested, the random forest classifier was the most accurate at predicting cardiovascular comorbidities in COPD patients with the cardiovascular phenotype.

Additional publications