InfectionNCT04849312
Prediction of 30-Day Readmission Using Machine Learning
An observational study of Infection and Heart Failure, run by Brigham and Women's Hospital at 2 sites in United States.
Trial summary
This is a retrospective observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict the likelihood of 30-day readmission throughout a patient's admission. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Infection, Heart Failure, Chronic Obstructive Pulmonary Disease
- How many people
- 372 participants
- Where
- 2 sitesUnited States
- Study dates
- 1 Jun 2017to 30 Nov 2019
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Subjects admitted at Brigham and Women's Hospital and Brigham and Women's Faulkner Hospital who meet primary diagnosis, age, and residence within 5 mile requirements and are enrolled in home hospital.
Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.
Trial locations and contacts
2 sites in 1 country.
United States2 sites
Brigham and Women's Hospital
Boston, Massachusetts, 02115
Brigham and Women's Faulkner Hospital
Boston, Massachusetts, 02130
About this study
This is a retrospective observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict the likelihood of 30-day readmission throughout a patient's admission. This algorithm was then validated in a validation cohort.
- Conditions
- Infection, Heart Failure, Chronic Obstructive Pulmonary Disease, Asthma, Gout Flare, Chronic Kidney Diseases
- Medical subject headings
- Infections, Heart Failure, Pulmonary Disease, Chronic Obstructive, Asthma, Renal Insufficiency, Chronic, Hypertensive Crisis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Training | A subset of patients that are used to train the machine learning algorithm. | |
| Validation | A subset of patients that are "held back" and used to validate the algorithm's accuracy. |
What is being measured
The primary outcome is the main question the study is designed to answer.
30-Day Readmission [ yes / no ]
Unplanned hospital admission within 30 days of having been discharged
Measured: From date of admission to 30-days post-discharge (31 to 54 days)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Apr 2021
- Study start1 Jun 2017
- Primary completion31 Oct 2019
- Study completion30 Nov 2019
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for InfectionStudies of the same condition, open and under way.
- More trials run by Brigham and Women's HospitalThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 372 participants (actual)
Sponsor and investigators
- Lead sponsor
- Brigham and Women's Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Biofourmis Inc.
- Responsible party
- Principal investigator, David Levine, Brigham and Women's Hospital
- Investigators
- David Levine, MD MPH MA, Principal investigator, Associate Physician
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04849312
- Sponsor's protocol ID
- 2017P002583a
- Status last verified
- Mar 2026
- First submitted
- 14 Apr 2021
- Last updated
- 17 Mar 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Merrill RK, Ferrandino RM, Hoffman R, Shaffer GW, Ndu A. Machine Learning Accurately Predicts Short-Term Outcomes Following Open Reduction and Internal Fixation of Ankle Fractures. J Foot Ankle Surg. 2019 May;58(3):410-416. doi: 10.1053/j.jfas.2018.09.004. Epub 2019 Feb 23.
PubMed 30803914Li Q, Yao X, Echevin D. How Good Is Machine Learning in Predicting All-Cause 30-Day Hospital Readmission? Evidence From Administrative Data. Value Health. 2020 Oct;23(10):1307-1315. doi: 10.1016/j.jval.2020.06.009. Epub 2020 Sep 7.
PubMed 33032774Xue Y, Klabjan D, Luo Y. Predicting ICU readmission using grouped physiological and medication trends. Artif Intell Med. 2019 Apr;95:27-37. doi: 10.1016/j.artmed.2018.08.004. Epub 2018 Sep 10.
PubMed 30213670Morel D, Yu KC, Liu-Ferrara A, Caceres-Suriel AJ, Kurtz SG, Tabak YP. Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach. Int J Med Inform. 2020 Jul;139:104136. doi: 10.1016/j.ijmedinf.2020.104136. Epub 2020 Apr 18.
PubMed 32353752Loreto M, Lisboa T, Moreira VP. Early prediction of ICU readmissions using classification algorithms. Comput Biol Med. 2020 Mar;118:103636. doi: 10.1016/j.compbiomed.2020.103636. Epub 2020 Feb 1.
PubMed 32174313Bolourani S, Tayebi MA, Diao L, Wang P, Patel V, Manetta F, Lee PC. Using machine learning to predict early readmission following esophagectomy. J Thorac Cardiovasc Surg. 2021 Jun;161(6):1926-1939.e8. doi: 10.1016/j.jtcvs.2020.04.172. Epub 2020 May 29.
PubMed 32711985Arvind V, London DA, Cirino C, Keswani A, Cagle PJ. Comparison of machine learning techniques to predict unplanned readmission following total shoulder arthroplasty. J Shoulder Elbow Surg. 2021 Feb;30(2):e50-e59. doi: 10.1016/j.jse.2020.05.013. Epub 2020 Jun 9.
PubMed 32868011
Study at a glance
From the registry record, last updated by the sponsor on 17 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of 30-Day Readmission Using Machine Learning
- ClinicalTrials.gov ID
- NCT04849312
- Acronym / sponsor ID
- 2017P002583a
- Conditions
- Infection, Heart Failure, Chronic Obstructive Pulmonary Disease, Asthma, Gout Flare, Chronic Kidney Diseases
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 372 participants (actual)
- Lead sponsor
- Brigham and Women's HospitalUniversity, hospital or other
- Collaborators
- Biofourmis Inc.
- Study start
- 1 Jun 2017 (actual)
- Primary completion
- 31 Oct 2019 (actual)
- Study completion
- 30 Nov 2019 (actual)
- First posted
- 19 Apr 2021 (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.
This is a retrospective observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict the likelihood of 30-day readmission throughout a patient's admission. This algorithm was then validated in a validation cohort.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Training | – | –A subset of patients that are used to train the machine learning algorithm. |
| Validation | – | –A subset of patients that are "held back" and used to validate the algorithm's accuracy. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Subjects admitted at Brigham and Women's Hospital and Brigham and Women's Faulkner Hospital who meet primary diagnosis, age, and residence within 5 mile requirements and are enrolled in home hospital.
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
30-Day Readmission [ yes / no ]
Time frame: From date of admission to 30-days post-discharge (31 to 54 days)
Unplanned hospital admission within 30 days of having been discharged
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 17 Mar 2026.
- Background: PubMed 30803914Merrill RK, Ferrandino RM, Hoffman R, Shaffer GW, Ndu A. Machine Learning Accurately Predicts Short-Term Outcomes Following Open Reduction and Internal Fixation of Ankle Fractures. J Foot Ankle Surg. 2019 May;58(3):410-416. doi: 10.1053/j.jfas.2018.09.004. Epub 2019 Feb 23.
- Background: PubMed 33032774Li Q, Yao X, Echevin D. How Good Is Machine Learning in Predicting All-Cause 30-Day Hospital Readmission? Evidence From Administrative Data. Value Health. 2020 Oct;23(10):1307-1315. doi: 10.1016/j.jval.2020.06.009. Epub 2020 Sep 7.
- Background: PubMed 30213670Xue Y, Klabjan D, Luo Y. Predicting ICU readmission using grouped physiological and medication trends. Artif Intell Med. 2019 Apr;95:27-37. doi: 10.1016/j.artmed.2018.08.004. Epub 2018 Sep 10.
- Background: PubMed 32353752Morel D, Yu KC, Liu-Ferrara A, Caceres-Suriel AJ, Kurtz SG, Tabak YP. Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach. Int J Med Inform. 2020 Jul;139:104136. doi: 10.1016/j.ijmedinf.2020.104136. Epub 2020 Apr 18.
- Background: PubMed 32174313Loreto M, Lisboa T, Moreira VP. Early prediction of ICU readmissions using classification algorithms. Comput Biol Med. 2020 Mar;118:103636. doi: 10.1016/j.compbiomed.2020.103636. Epub 2020 Feb 1.
- Background: PubMed 32711985Bolourani S, Tayebi MA, Diao L, Wang P, Patel V, Manetta F, Lee PC. Using machine learning to predict early readmission following esophagectomy. J Thorac Cardiovasc Surg. 2021 Jun;161(6):1926-1939.e8. doi: 10.1016/j.jtcvs.2020.04.172. Epub 2020 May 29.
- Background: PubMed 32868011Arvind V, London DA, Cirino C, Keswani A, Cagle PJ. Comparison of machine learning techniques to predict unplanned readmission following total shoulder arthroplasty. J Shoulder Elbow Surg. 2021 Feb;30(2):e50-e59. doi: 10.1016/j.jse.2020.05.013. Epub 2020 Jun 9.
- Individual participant dataSharing planned: No
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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.
David Levine, MD MPH MA principal investigator
Associate Physician
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Infection and Heart Failure, run by Brigham and Women's Hospital at 2 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
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 17 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. 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?
30-Day Readmission [ yes / no ] (time frame: From date of admission to 30-days post-discharge (31 to 54 days)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04849312, which also lists 7 linked publications.
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 17 Mar 2026; AzTrials last refreshed its copy on 26 Oct 2026.