Chart Review of Patients With COPD, Using Electronic Medical Records and Artificial Intelligence

Summary

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the World since 2003. Many people suffer from this disease or its complications for many years and die prematurely. In the European Union, the total direct costs of respiratory diseases are estimated to be around 6% of the total healthcare budget, with COPD accounting for 56% (38.6 billion Euros) of the costs of respiratory diseases.

In the natural history of COPD, many patients may experience acute exacerbations (AECOPD) that are described as episodes of sustained worsening of the respiratory symptoms that result in additional therapy. These episodes of exacerbation that often require been seen in the emergency department and/or a hospital admission are associated with significant morbidity and mortality; they are responsible for a significant portion of the economic burden of the disease too. The pharmacological approach used in the management of AECOPD (inhaled bronchodilators, corticosteroids, and antibiotics), has the objective to minimize the negative impact of the current exacerbation and to prevent subsequent events.

Despite the collaborative effort between the European Respiratory Society, the American Thoracic Society, and others to provide clinical recommendations for the prevention of AECOPD, there is still a considerable number of patients that are prone to suffer from recurrent exacerbations and to experience a more severe impairment in health status.

Based on all the above, the aim is to identify the factors potentially associated with hospital admission in patients with AECOPD in English, French, German, and Spanish, speaking countries, and to develop a predictive model that predicts the risk of hospitalization in this group of patients, by using artificial intelligence. In this study proposes to take advantage of SAVANA, a new clinical platform, created in the context of the era of electronic medical records (EMRs), to analyse the information included in the electronic medical files (i.e., big data). This clinical platform is a powerful free-text analysis engine, capable of meaningfully interpreting the contents of the EMRs, regardless of the management system in which they operate. In this context, this machine learning analytical method can be used to build a flexible, customized and automated predictive model using the information available in EMRs.

Conditions

• Chronic Obstructive Pulmonary Disease

Interventions

OTHER

Factors associated with Hospital admission for an Acute Exacerbation Chronic Obstructive Pulmonary Disease (AECOPD)

Develop a descriptive predictive model fo factors that influence clinical characteristic of patients that require hospital admission

Sponsors & Collaborators

• SAVANA

  collaborator UNKNOWN

• European Commission

  collaborator OTHER

• Sociedad Española de Neumología y Cirugía Torácica

  lead OTHER

Principal Investigators

• JB Soriano, MD · Sociedad Española de Neumología y Cirugía Torácica

Eligibility

Min Age

35 Years

Max Age

99 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2020-01-08

Primary Completion

2020-10-01

Completion

2020-12-30

Countries

• Austria
• Spain
• Switzerland
• United Kingdom

Study Locations