COVID-19NCT04432961
Natural Language Processing (NLP) Analysis of Free Text Notes to Investigate Coronavirus (COVID-19)
An observational study of COVID-19, run by Cambridge University Hospitals NHS Foundation Trust at 1 site in United Kingdom.
Trial summary
A retrospective cohort study investigating clinical notes using Natural Language Processing in combination with structured data from the Electronic Health Record (EHR) to create a database for analytics to identify features associated with outcomes. Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- COVID-19
- How many people
- 200 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 Jul 2020to 1 Jul 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 100 years
- All sexes
- Adult, Older adult
Study population: Patients admitted to Cambridge University Hospitals NHS Foundation Trust with confirmed COVID-19
Who may be able to join
- Male and female
- Age range: 18 to 100 years
- Patients admitted to Cambridge University Hospitals with confirmed COVID-19 on lab testing
Who cannot join
- Children and patients with a negative COVID test.
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site
Cambridge University NHS Foundation Trust
Cambridge
About this study
Official title: A Database and Analytics Study of Free Text Clinical Notes and Structured Data to Investigate Phenotype Associations With Outcomes in Patients With COVID-19
A retrospective cohort study investigating clinical notes using Natural Language Processing in combination with structured data from the Electronic Health Record (EHR) to create a database for analytics to identify features associated with outcomes.
Read the detailed description
Patients admitted to Cambridge University Hospitals (CUH)with COVID-19 have undergone routine clinical documentation and specific investigation and testing for COVID-19. The pathway for these patients ranges from supportive measures on the ward to deterioration requiring Intensive therapy Unit (ITU) admission and ventilatory support. Patients are also at risk of developing complications such as Acute Kidney Injury and thromboembolism. Identification of the risk factors for these and other outcomes such as the requirement for ventilation remain a challenge and reviewing the clinical data for these patients is critical in the understanding of the relationship between patient characteristics and outcomes.
There is data available in structured fields in the EHR, however, this is sometimes incomplete and inaccurate. An assessment of the free text clinical notes provides an opportunity to fill in the gaps and provide a much richer dataset for evaluation. We plan to use Natural Language Processing (NLP) (a field of machine learning that allows computers to analyse human language) to review Discharge Summaries of patients admitted to hospital with COVID-19 and convert free text data into structured data for analysis.
The NLP techniques developed by Dr Collier's team include methods for coding of free texts to SNOMED CT and other biomedical ontologies. These methods, based on statistical machine learning from human annotated texts, have been benchmarked for scientific texts and social media. In this project we intend to adapt these techniques for patient records. The techniques will require a number of human annotated patient records in order to adapt. The NLP output will be combined with structured data from the EHR and undergo statistical analysis to identify the rates of complications in patients with COVID-19 and risk factors associated with these. This may help to guide management decisions by earlier intervention to prevent poor outcomes in these patients.
- Conditions
- COVID-19
- Keywords
- Natural Language Processing
- Medical subject headings
- COVID-19
What is being measured
The primary outcome is the main question the study is designed to answer.
research database of EHR records from COVID-19 patients processed using NLP tools for named entity recognition and linking adapted to CUH EMR data to identify variables of interest
Our overarching hypothesis is that the NLP-extracted data from the free-text discharge summary can be combined with structured data from the EMR to yield insights into the development of complications. Patient with severe disease requiring ITU admission and non severe disease managed on an inpatient ward will be included. The variables of interest will include patient characteristics and specific encounter related information including length of stay and baseline investigations (e.g., blood tests) and interventions received
Measured: 1 year
Secondary outcomes (2)
A set of annotation guidelines to produce human-expert (gold) labelled data for a subset of the EHR
Measured: 6 months
A comparison of the NLP output to terms in the structured problem list to identify missing terms in the structured problem list
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jun 2020
- Study start1 Jul 2020
- Primary completion1 Jul 2021
- Study completion1 Jul 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for COVID-19Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 200 participants (actual)
Sponsor and investigators
- Lead sponsor
- Cambridge University Hospitals NHS Foundation Trust
- Sponsor type
- University, hospital or other
- Collaborators
- University of Cambridge
- Responsible party
- Principal investigator, Dr Sapna Trivedi, Cambridge University Hospitals NHS Foundation Trust
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04432961
- Sponsor's protocol ID
- A095625
- Status last verified
- Jul 2021
- First submitted
- 15 Jun 2020
- Last updated
- 28 Jul 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Database and Analytics Study of Free Text Clinical Notes and Structured Data to Investigate Phenotype Associations With Outcomes in Patients With COVID-19
- ClinicalTrials.gov ID
- NCT04432961
- Acronym / sponsor ID
- A095625
- Conditions
- COVID-19
- Keywords
- Natural Language Processing
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 200 participants (actual)
- Lead sponsor
- Cambridge University Hospitals NHS Foundation TrustUniversity, hospital or other
- Collaborators
- University of Cambridge
- Study start
- 1 Jul 2020 (actual)
- Primary completion
- 1 Jul 2021 (actual)
- Study completion
- 1 Jul 2021 (actual)
- First posted
- 16 Jun 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
A retrospective cohort study investigating clinical notes using Natural Language Processing in combination with structured data from the Electronic Health Record (EHR) to create a database for analytics to identify features associated with outcomes.
Detailed description
Patients admitted to Cambridge University Hospitals (CUH)with COVID-19 have undergone routine clinical documentation and specific investigation and testing for COVID-19. The pathway for these patients ranges from supportive measures on the ward to deterioration requiring Intensive therapy Unit (ITU) admission and ventilatory support. Patients are also at risk of developing complications such as Acute Kidney Injury and thromboembolism. Identification of the risk factors for these and other outcomes such as the requirement for ventilation remain a challenge and reviewing the clinical data for these patients is critical in the understanding of the relationship between patient characteristics and outcomes.
There is data available in structured fields in the EHR, however, this is sometimes incomplete and inaccurate. An assessment of the free text clinical notes provides an opportunity to fill in the gaps and provide a much richer dataset for evaluation. We plan to use Natural Language Processing (NLP) (a field of machine learning that allows computers to analyse human language) to review Discharge Summaries of patients admitted to hospital with COVID-19 and convert free text data into structured data for analysis.
The NLP techniques developed by Dr Collier's team include methods for coding of free texts to SNOMED CT and other biomedical ontologies. These methods, based on statistical machine learning from human annotated texts, have been benchmarked for scientific texts and social media. In this project we intend to adapt these techniques for patient records. The techniques will require a number of human annotated patient records in order to adapt. The NLP output will be combined with structured data from the EHR and undergo statistical analysis to identify the rates of complications in patients with COVID-19 and risk factors associated with these. This may help to guide management decisions by earlier intervention to prevent poor outcomes in these patients.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients admitted to Cambridge University Hospitals NHS Foundation Trust with confirmed COVID-19
Key inclusion criteria (3 listed)
- Male and female
- Age range: 18 to 100 years
- Patients admitted to Cambridge University Hospitals with confirmed COVID-19 on lab testing
Key exclusion criteria (1 listed)
- Children and patients with a negative COVID test.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
research database of EHR records from COVID-19 patients processed using NLP tools for named entity recognition and linking adapted to CUH EMR data to identify variables of interest
Time frame: 1 year
Our overarching hypothesis is that the NLP-extracted data from the free-text discharge summary can be combined with structured data from the EMR to yield insights into the development of complications. Patient with severe disease requiring ITU admission and non severe disease managed on an inpatient ward will be included. The variables of interest will include patient characteristics and specific encounter related information including length of stay and baseline investigations (e.g., blood tests) and interventions received
2 secondary outcomes
A set of annotation guidelines to produce human-expert (gold) labelled data for a subset of the EHR
Time frame: 6 months
A comparison of the NLP output to terms in the structured problem list to identify missing terms in the structured problem list
Time frame: 1 year
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Jul 2021.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of COVID-19, run by Cambridge University Hospitals NHS Foundation Trust at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 28 Jul 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
research database of EHR records from COVID-19 patients processed using NLP tools for named entity recognition and linking adapted to CUH EMR data to identify variables of interest (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Jul 2021.