GeoHealth: Geographic Information System for Health Management and Clinical, Epidemiological and Translational Research

NCT05637411 | Unknown

• 1 other identifier: PI19/01092
• Study Type: observational
• Target: 214
• Locations: 1 country
• Sites: 1

Brief Summary

The objective of the project is to develop a potentially marketable system for clinical, epidemiological and translational research capable of associating contextual variables and geospatial data with clinical patient information. The GeoHealth system will include a section to perform exploratory analysis that will help identify risk factors to optimize clinical decision making. The system will be used in a study of the influence of environmental factors in more than 800,000 asthmatic patients in the region of Andalucía, Spain. The results of this study will help to develop predictive models of risk of exacerbation in asthma patients. Currently, disease management guidelines state that asthma control has two components: current disease control and future risk of exacerbations. These two components are key when assessing disease management, following a stepwise model that seeks to improve current control and minimize future risk. However, although the current control of the disease is defined and has established methodologies for its measurement (Asthma Control Test -ACT- and Asthma Control Questionnaire -ACQ-), the risk of suffering exacerbations is not yet a measurable component. Therefore, these predictive models could help in professional decision making for asthmatic patients. The predictive models will be validated prospectively and implemented in a decision support system for the management of asthmatic patients.

Conditions

Asthma; Allergy; Heart Diseases; Obesity; Rheumatic Diseases

Outcome Measures

Primary Outcomes (1)

• Exacerbations

  Measured by the number of times the patient goes to the emergency room or uses rescue medication and/or cycles of oral corticosteroids.

  1 year

Secondary Outcomes (5)

• Asthma control

  1 year

• Quality-adjusted life year

  1 year

• Respiratory function

  1 year

• Immune response

  1 year

• Treatment adherence

  1 year

Study Arms (2)

Retrospective cohort

The clinical data, socioeconomical data and environmental exposure data from 800.000 asthmatic patients attended in our region from 2007 to 2021 will be analysed to train and validate a risk predictive model for exacerbation (defined as emergency visits)

Other: Predictive model

Prospective cohort

The same inclusion/exclusion criteria were applied prospectively. This is an external validation cohort.

Other: Predictive model

Interventions

Predictive model OTHER

We will define and validate predictive models of risk of exacerbations in asthmatic patients

Prospective cohort; Retrospective cohort

Eligibility Criteria

• Age: 6 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Our population is composed of patients with bornchial asthma.

You may qualify if:

• Patients diagnosed with bornchial asthma.

You may not qualify if:

• Women who are pregnant, breastfeeding or want to become pregnant during the duration of the study.
• Subjects with malignant disease, uncontrolled risk factors or any pathology that compromises the safety of the study.
• Subjects with problems that limit their ability to cooperate.

Sponsors & Collaborators

• Hospital Universitario Virgen Macarena: lead

Study Sites (1)

Alberto Moreno Conde

Seville, 41018, Spain

Location

MeSH Terms

Conditions

Asthma; Hypersensitivity; Heart Diseases; Obesity; Rheumatic Diseases

Interventions

Predictive Learning Models

Condition Hierarchy (Ancestors)

Bronchial Diseases; Respiratory Tract Diseases; Lung Diseases, Obstructive; Lung Diseases; Respiratory Hypersensitivity; Hypersensitivity, Immediate; Immune System Diseases; Cardiovascular Diseases; Overweight; Overnutrition; Nutrition Disorders; Nutritional and Metabolic Diseases; Body Weight; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Musculoskeletal Diseases; Connective Tissue Diseases; Skin and Connective Tissue Diseases

Intervention Hierarchy (Ancestors)

Prediction Methods, Machine; Artificial Intelligence; Algorithms; Mathematical Concepts

Study Officials

• Alberto Moreno

  Hospital Universitario Virgen Macarena

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: PHD Alberto Moreno Conde

Study Record Dates

• First Submitted: August 1, 2022
• First Posted: December 5, 2022
• Study Start: December 9, 2020
• Primary Completion: December 31, 2023
• Study Completion: December 31, 2023
• Last Updated: December 5, 2022

Record last verified: 2022-11

Data Sharing

• IPD Sharing: Will not share

Locations

ES (1)