Clinical trial NCT04966637: Identifying Early Chronic Obstructive Pulmonary Disease (COPD) Using Health Administrative Data
Official title: Identifying Early Chronic Obstructive Pulmonary Disease (COPD) Using Health Administrative Data
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Chronic Obstructive Pulmonary Disease (COPD
Lead sponsor: University of Alberta
Sponsor class: OTHER
Collaborators: name: Boehringer Ingelheim
name: Alberta Health services
First posted: 2021-07-19
Enrollment: 54028
Sex: ALL
Minimum age (years): 35
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study population includes all individuals in the province of Alberta that meet the inclusion criteria, and have accessed the provincial health care system during the study period. All individuals that have an incident case of COPD within the 3 year time period for study inclusion that reside in Alberta will have their data included in the study. Based on our pre-existing knowledge of the administrative data available, we would expect over 50,000 individual patients in the cohort. Given our health care is provided at a provincial level, we would expect this would capture all individuals in the province that had their incident diagnosis of COPD in the time period. The exception may be individuals that were diagnosed with COPD while out of province.

Brief summary: Chronic Obstructive Pulmonary Disease (COPD) affects the airways that causes shortness of breath, cough. COPD gets worse over time, and often leads to emergency department visits, hospital visits, frequent doctor appointments and medications. This means COPD is expensive, and severely impacts patient quality of life. Unfortunately, patients are often not properly diagnosed until their disease is fairly advanced. We know a lot about the health care use of people with COPD once they have been diagnosed, but we do not know much about what happens to them leading up to their diagnosis. Through this project we want to better understand the time period prior to COPD diagnosis, so that we can learn more about what happens to people before they are diagnosed.

This project will use health data to find out if we can identify trends in health care use by individuals newly diagnosed with COPD. We will identify people that have COPD based on health records, and look back to find out about their health care use prior to their diagnosis. We will look at data related to doctors' visits, emergency department visits, hospital stays and medications. We want to use these markers to better understand what happens to people before they are diagnosed, and to find out if we can identify risk factors for a COPD diagnosis. We hope by doing this research we can better identify people at risk for COPD and ensure that they receive treatment early, which may improve their health outcomes.

Detailed description: Study Objectives

1. Identifying a cohort of individuals with a new onset of COPD in the between April 1, 2016 and March 31, 2019.
2. To determine factors associated with a new diagnosis of COPD through using traditional mixed-model regression.
3. To evaluate whether information collected within administrative data can be used to create a prediction model for a COPD diagnosis.
4. To determine whether machine learning methodology improves the prediction of a new COPD diagnosis from administrative data.

Measures:

1. The cohort of individuals with COPD in Alberta has already been defined, and this data exists within the Alberta Health Services, Respiratory Health Strategic Clinical Network (RHSCN) dataset. It will be used to further identify individuals with a new diagnosis of COPD within the three year study time period.
2. In order to conduct this study, a variety of data sets will be used including:

   * Inpatient Discharge Abstract Database;
   * Practitioner Claims Database;
   * Provincial Registry Database;
   * Population Health Database and
   * Pharmaceutical information Network

Project Hypothesis We anticipate individuals with a diagnosis of COPD in the last three years will have identifiable markers associated with lung disease in the five years prior to their diagnosis. These markers may include: diagnosis of acute respiratory disease (such as pneumonia, bronchitis, upper respiratory infections), increased health care utilization, and the use of medications such as antibiotics.

The project plan will address the specific project goals as follows:

1. Identifying a cohort of individuals with a new onset of COPD from April 1, 2016 to March 30, 2019. Through the RHSCN, a cohort of individuals with COPD has been identified of over 200,000 individuals with COPD in Alberta. This cohort will be refined to identify only those individuals that have been diagnosed within the specified time period. This time period was chosen due to data availability. Given our most recent data, we know that approximate 19,000 individuals have been diagnosed with COPD per year, over the last five years. Thus we can approximate that our dataset will include approximately 55,000 individuals with COPD diagnosed in a three year time period.
2. Retrospectively review the pattern of health care utilization for individuals with a new diagnosis of COPD in the five years prior to their diagnosis. The health care utilization (ED visits, hospitalization visits, physician visits) for each case in the cohort for the previous five years will be identified.
3. Explore the medication use for individuals with COPD for five years prior to their diagnosis. Lastly, the medication use for each case identified in the cohort during the time period five years prior to their diagnosis will be explored. The Pharmaceutical Information Network (PIN) database will be used to identify all medications (both respiratory and non-respiratory) for individuals in the cohort to assess medication use prior to diagnosis.
4. Working with the machine learning provider (AltaML) we will additionally conduct a machine learning based analysis to further explore this data set regarding the same variables.

Eligibility criteria: Inclusion Criteria:

1. Must have had at least one visit to a physician with a diagnosis of COPD (by ICD-9(-CM) 491-492, 496) or one hospital separation with a diagnosis of COPD (ICD-10-CA J41-44).
2. Incidence criteria included 5 year washout period in which they were not in Alberta, Canada but did not have any diagnosis of COPD
3. Individuals that meet this criteria with a new diagnosis in the last 3 years will be included in the cohort, thus individuals with their 'incident' diagnosis of COPD in the last three years will be included (01-APR-2016 to 31-MAR-2017, 01-APR-2017 to 31-MAR-2018; 01-APR-2018 to 31-MAR-2019)

Exclusion Criteria:

1. Individuals that were diagnosed with COPD prior to or after the enrollment period.
2. Individuals that did not live in Alberta (with access to Alberta health care) during the five year washout period

Primary outcomes:
measure: determine factors associated with a new diagnosis of COPD
timeFrame: each patient's COPD incidence diagnosis date and a COPD incidence diagnosis date (minus 5 years)
Secondary outcomes:
measure: prediction model for COPD
timeFrame: each patient's COPD incidence diagnosis date and a COPD incidence diagnosis date (minus 5 years)
measure: machine learning prediction model for COPD
timeFrame: each patient's COPD incidence diagnosis date and a COPD incidence diagnosis date (minus 5 years)
Other outcomes:
Locations:
city: Edmonton
country: Canada
facility: University of Alberta
state: Alberta
Study officials:
affiliation: University of Alberta
name: Michael Stickland, PhD
role: PRINCIPAL_INVESTIGATOR
References: