Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT04192175 |
| Other study ID # |
19-5124 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
June 1, 2019 |
| Est. completion date |
December 31, 2023 |
Study information
| Verified date |
May 2023 |
| Source |
University Health Network, Toronto |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Patients with Chronic Obstructive Pulmonary Disease (COPD) who are admitted to hospital are
at high risk of readmission. While therapies have improved and there are evidence-based
guidelines to reduce readmissions, there are significant challenges to implementation
including 1) identifying all patients with COPD early in admission to ensure evidence-based,
high value care is provided and 2) identifying those who are at high risk of readmission in
order to effectively target resources.
Using machine learning and natural language processing, we want to develop models to 1)
identify all patients with COPD exacerbations admitted to hospital and 2) stratify them to
distinguish those who are at high risk of readmission b) How will you undertake your work?
From Toronto hospitals, we will develop a very large dataset of patient admissions for all
medical conditions including exacerbations of COPD from the electronic health record. This
data will include both structured data such as age, gender, medications, laboratory values,
co-morbidities as well as unstructured data such as discharge summaries and physician notes.
Using the dataset, we will train a model through natural language processing and machine
learning to be able to identify people admitted with COPD exacerbation and identify those
patients who will be at high risk of readmission within 30 days. We will test the ability of
these models to determine our predictive accuracies. We will then test these models at other
institutions.
Description:
One fifth of patients discharged from hospital for COPD exacerbations are readmitted within
30 days.(1, 3, 4) While therapies and care guidelines have improved, guideline implementation
remains poor.(5) Implementing appropriate standards through usual hospital workflow presents
significant challenges. One of the top challenges is ensuring all eligible patients with COPD
exacerbations are identified in a timely manner.(6) Another top challenge is that staff are
often too busy and do not have time to execute evidence-based practices that reduce
readmissions.(6) Furthermore, intensive case management can not be offered to everyone
because of limited resources. Therefore, it is important that we are able to identify both
people who are admitted with COPD early as well as those who are at high risk for
readmission.
COPD exacerbations may, at times, not be easily recognized at first and take days to become
apparent. Symptoms of exacerbations such as shortness of breath are not specific and signs
such as chest radiograph infiltrates can be due to one or more diagnoses. Furthermore, COPD
exacerbations can trigger or be triggered by other diseases. As a result, it is not uncommon
for admitting physicians to admit patients with multiple provisional diagnoses of heart
failure, pneumonia, COPD exacerbation and more. Distinguishing people with COPD exacerbations
is further confounded by Electronic Health Records (EHRs) that do not have diagnoses listed
as coded elements. The end result is that it is difficult for the rest of the
interprofessional team to find COPD patients early in admission. This has been addressed in
some U.S. hospitals by having non-health care providers review charts to identify patients
admitted with for COPD.(7) An alternate approach has been machine learning and natural
language processing. This has been implemented with some success for patients with heart
failure but little has been done for people with COPD.(13) In one pilot program, natural
language processing helped identify patients admitted with COPD.(7)
To target scarce resources for those who need it most, it would be helpful to further
identify patients at high risk of readmission. This would be the first step in determining
how to implement effective strategies to reduce readmission rates. There are readmission
prediction models developed for medical and surgical patients including the LACE score and
the HOSPITAL score.(8, 9) Unfortunately, those that have been studied do not appear to
perform well in the COPD population.(10) While factors have been identified that help predict
COPD readmission, the models have not been fully validated.(11, 12) The performance could be
improved through the use of unstructured data such as clinician progress notes and discharge
summaries.
Early identification of people with COPD and knowledge of those who are at risk of
readmission can improve health outcomes. Zafar et al. demonstrated that a comprehensive COPD
care bundle that consisted of 1. inhaler assessment, 2. appropriate inhaler regimen, 3. early
discharge follow up and 4. patient-centered discharge instructions reduced readmissions.(14)
Identification of those at high risk of readmission could facilitate enrollment into
intensive case management. Therefore, we will conduct the current study to identify patients
admitted with acute exacerbations of COPD and stratify patients according to risk of
readmission
Methods:
Using retrospective data from the University Health Network (UHN), we will create a data set
of admissions to General Internal Medicine for the past 5 years. We estimate this will
include approximately 40,000 admissions of which 2,000 will have a most responsible diagnoses
of a COPD exacerbation. The data set will contain both structured coded data as well as
unstructured text data. Coded data will include age, gender, medications ordered,
co-morbidities, laboratory values, and pulmonary function tests. Unstructured text data will
include notes in EHR: physician clinic notes, discharge summaries, admission diagnoses,
progress notes, and notes from our signover system.
Analysis: We will use several different methods to develop the model including logistic
regression, deep neural networks, and convolutional neural networks. Specifically, we will
also use statistical machine learning algorithms for event detection using bi-directional
long-short term memory neural networks across a variety of input types (e.g., Fourier filter
banks, Mel-frequency cepstral coefficients, wavelets, and raw audio). We will also use
traditional methods such as dynamic Bayes networks and conditional random fields. On the text
analytics side, we will identify key phrases that predict readmission. One approach will be
to use discourse analysis to single out "nucleus" phrases from background text. We will also
build "joint" predictive models that combine features from the unstructured text and features
from the structured coded data. We will use the standard Area under the ROC Curve to assess
model performance and use cross validation to minimize the impact of overfitting. Finally, we
will then validate our models using a dataset from different centres to determine whether
these results are valid and generalizable.
Anticipated results: The development of two validated models based on EHR data: one to
accurately identify patients with AECOPD and the second to accurately identify patients at
high risk of readmission within 30 days.
