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

Study Overview
=================
Brief 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.

Detailed Description
-----------------
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.

Official Title
-----------------
Chart Review of Patients With COPD, Using Electronic Medical Records and Artificial Intelligence: BigCOPData

Conditions
-----------------
Chronic Obstructive Pulmonary Disease

Intervention / Treatment
-----------------
* Other: Factors associated with Hospital admission for an Acute Exacerbation Chronic Obstructive Pulmonary Disease (AECOPD)


Participation Criteria
=================
Eligibility Criteria
-----------------
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.

Ages Eligible for Study
-----------------
Minimum Age: 35 Years
Maximum Age: 99 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Sex<br>Male/Female | Other: Factors associated with Hospital admission for an Acute Exacerbation Chronic Obstructive Pulmonary Disease (AECOPD)<br>* Develop a descriptive predictive model fo factors that influence clinical characteristic of patients that require hospital admission<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of factors associated with hospital admission of patients with AECOPD | Number of factors associated with hospital admission of patients with AECOPD, using EMRs and artificial intelligence learning tool | 5 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of clinical characteristics of COPD hospitalized patients | Number of clinical characteristics of COPD patients that require hospital admission | 5 years | 
| Number of comorbidities of COPD patients hospitalized per sex | Number and description of comorbidities associated with hospitalized COPD patients, presented per sex, such as cardiovascular disease, anxiety, depression, gastroesophageal reflux. | 5 years | 
| Number of patients with increased eosinophil blood counts | Number of patients with COPD hospitalized with increased eosinophil blood counts. | 5 years | 
| Number of patients with COPD hospitalized with elevated inflammatory parameters | Number of COPD patients hospitalized with elevated inflammatory parameters such as white cell counts, neutrophil count and C-reactive protein, presented in a descriptive model | 5 years | 
| Number of clinical phenotypes identified in COPD patients hospitalized | Number of clinical phenotypes of patients with COPD that exacerbate and require hospital admissions | 5 years | 
| Number of COPD patients hospitalized do not follow treatment recommendations | Number of COPD patients hospitalized do not follow 100% treatment recommendations within the previous 6 weeks | 5 years | 
| Number of Risk on COPD patients hospitalized | Number of identified risks per COPD patient hospitalized, such as Spanish Guide COPD (GesEPOC), the Dyspnoea, Eosinopenia, Consolidation, Acidemia and Atrial Fibrillation [DECAF] Score, or another multicomponent index. | 5 years | 
| Number of biologic biomarkers different to eosinophil count | Number of biologic biomarkers (different to eosinophil count) associated to hospitalization and/or rehospitalizations due to COPD exacerbations | 5 years | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
COPD