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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

2500000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-01-08

Study Completion Date

2020-12-30

Brief Summary

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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.

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Detailed Description

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The study will be conducted in accordance with legal and regulatory requirements, as well as with scientific purpose, value and rigor and follow generally accepted research practices described in the International Conference Harmonization (ICH) Guideline for Good Clinical Practice, the Helsinki Declaration in its latest edition, Good Pharmacoepidemiology Practices, and applicable local regulations.

To maintain patient confidentiality, demographic and personal identifying information (e.g., initials, date of birth, etc.) will not be collected; only age will be collected. In no case Savana staff will handle a correspondence table between the anonymized patient codes and their EMRs. Only the healthcare centre can identify patients. In any case, SEPAR, the study sponsor, or its partners, will not have gto EMRs. It will only access a report, which will contain aggregated information on the data obtained as described in this protocol. The final results will be published.

According to the European Data Protection Authority, an anonymous clinical record is released from its status as personal data, so that the General Data Protection Regulation no longer applies. The anonymization is performed at each site by the owner of the information (so that nobody else has access to that information and so that it is not possible to track it).

All actions will be taken in accordance with the Code of Good Data Protection Practices for Big Data Projects of the European Data Protection Authority, the European General Data Protection Regulation or another that may replace it.

In addition, clinical records will never be stored in a location other than the institution where it is implemented. Savana does not use EMRs from individual patients, but aggregate clinical information, which is also encrypted and secured. The aggregation of the data ensures the impossibility of identifying patients or individual centres. The system is based on the processing of a large amount of information (Big Data), so that the impact of random errors is minimized. The use of this software is possible nowadays since there has been a notable improvement in the implementation of EMRs, which may result in the use of this software in significant investments for the use and better knowledge of the health system.

In summary, this new technology allows a complete dissociation between the data obtained for the current study and the personal data of the patient, since this information is obtained in an aggregated and completely anonymous form. This clearly represents an advance in data protection in the context of classical observational epidemiological studies.

Therefore, only aggregated and completely anonymous data will be obtained, completely dissociated from the personal data of each patient and centre. The confidentiality of patient records will be maintained at all times. All study reports will contain only aggregated data and will not identify patients, doctors or individual centres. At no time during the study, the sponsor will receive information that may allow the identification of a patient or individual centre.

Conditions

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Chronic Obstructive Pulmonary Disease

Study Design

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Observational Model Type

OTHER

Study Time Perspective

OTHER

Study Groups

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Sex

Male/Female

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

Intervention Type OTHER

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

Interventions

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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

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Subjects aged ≥ 35-year-old, smokers or former smokers of more than 10 pack-years.
* Had a diagnosis of COPD (a post-bronchodilator ratio forced expiratory volume in the first second (FEV1) / forced vital capacity (FVC) \< 0.70, and the presence of respiratory symptoms such as cough, sputum, and dyspnoea).
* Admitted for ''respiratory disease'' \[respiratory infection or pleural effusion (OR) respiratory failure (OR) right/left heart failure (OR) chronic bronchitis (OR) bronchospasms (AND) \[historical diagnosis of COPD (OR) a documented FEV1/FVC \< 0.70 in the absence of other obstructive diseases, such as asthma or bronchiolitis\].

Exclusion Criteria

* Patients with a specific diagnosis upon admission of pulmonary oedema, pneumonia, radiological infiltration, pulmonary embolism, pneumothorax, rib fractures, aspiration, or any other associated respiratory or of non-respiratory condition, such as major cardiopathy with chronic heart failure, extended neoplasia, liver or kidney failure.

Minimum Eligible Age

35 Years

Maximum Eligible Age

99 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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JB Soriano, MD

Role: PRINCIPAL_INVESTIGATOR

Sociedad Española de Neumología y Cirugía Torácica

Locations

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Kepler Universitäts Klinikum

Linz, , Austria

Site Status NOT_YET_RECRUITING