InfectionNCT04784351
Prediction of Expected Length of Hospital Stay 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 length of stay 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
- 0 participants
- Where
- 2 sitesUnited States
- Study dates
- 20 Mar 2021to 1 Dec 2026
- 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 length of stay 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.
Length of Stay
The time spent by each patient in Home Hospital from time of admission to time of discharge, measured in hours
Measured: From date of admission to date of discharge (1 to 24 days)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Mar 2021
- Study start20 Mar 2021
- Primary completion1 Aug 2026 (estimated)
- Study completion1 Dec 2026 (estimated)
Why it stopped: Insufficient resources.
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)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
- 0 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
- NCT04784351
- Sponsor's protocol ID
- 2017P002583b
- Status last verified
- Mar 2026
- First submitted
- 2 Mar 2021
- Last updated
- 17 Mar 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Lubelski D, Ehresman J, Feghali J, Tanenbaum J, Bydon A, Theodore N, Witham T, Sciubba DM. Prediction calculator for nonroutine discharge and length of stay after spine surgery. Spine J. 2020 Jul;20(7):1154-1158. doi: 10.1016/j.spinee.2020.02.022. Epub 2020 Mar 13.
PubMed 32179154Karnuta JM, Churchill JL, Haeberle HS, Nwachukwu BU, Taylor SA, Ricchetti ET, Ramkumar PN. The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty. J Shoulder Elbow Surg. 2020 Nov;29(11):2385-2394. doi: 10.1016/j.jse.2020.04.009. Epub 2020 Jun 9.
PubMed 32713541Ramkumar PN, Navarro SM, Haeberle HS, Karnuta JM, Mont MA, Iannotti JP, Patterson BM, Krebs VE. Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models. J Arthroplasty. 2019 Apr;34(4):632-637. doi: 10.1016/j.arth.2018.12.030. Epub 2018 Dec 27.
PubMed 30665831Ma X, Si Y, Wang Z, Wang Y. Length of stay prediction for ICU patients using individualized single classification algorithm. Comput Methods Programs Biomed. 2020 Apr;186:105224. doi: 10.1016/j.cmpb.2019.105224. Epub 2019 Nov 20.
PubMed 31765937Daghistani TA, Elshawi R, Sakr S, Ahmed AM, Al-Thwayee A, Al-Mallah MH. Predictors of in-hospital length of stay among cardiac patients: A machine learning approach. Int J Cardiol. 2019 Aug 1;288:140-147. doi: 10.1016/j.ijcard.2019.01.046. Epub 2019 Jan 19.
PubMed 30685103Bacchi S, Oakden-Rayner L, Menon DK, Jannes J, Kleinig T, Koblar S. Stroke prognostication for discharge planning with machine learning: A derivation study. J Clin Neurosci. 2020 Sep;79:100-103. doi: 10.1016/j.jocn.2020.07.046. Epub 2020 Aug 5.
PubMed 33070874Navarro SM, Wang EY, Haeberle HS, Mont MA, Krebs VE, Patterson BM, Ramkumar PN. Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model. J Arthroplasty. 2018 Dec;33(12):3617-3623. doi: 10.1016/j.arth.2018.08.028. Epub 2018 Sep 5.
PubMed 30243882Young AJ, Hare A, Subramanian M, Weaver JL, Kaufman E, Sims C. Using Machine Learning to Make Predictions in Patients Who Fall. J Surg Res. 2021 Jan;257:118-127. doi: 10.1016/j.jss.2020.07.047. Epub 2020 Aug 18.
PubMed 32823009Sinha I, Aluthge DP, Chen ES, Sarkar IN, Ahn SH. Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR. J Vasc Interv Radiol. 2020 Jun;31(6):1018-1024.e4. doi: 10.1016/j.jvir.2019.11.030. Epub 2020 May 4.
PubMed 32376173Merrill 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 30803914Nemati M, Ansary J, Nemati N. Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data. Patterns (N Y). 2020 Aug 14;1(5):100074. doi: 10.1016/j.patter.2020.100074. Epub 2020 Jul 4.
PubMed 32835314
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 Expected Length of Hospital Stay Using Machine Learning
- ClinicalTrials.gov ID
- NCT04784351
- Acronym / sponsor ID
- 2017P002583b
- Conditions
- Infection, Heart Failure, Chronic Obstructive Pulmonary Disease, Asthma, Gout Flare, Chronic Kidney Diseases
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: Insufficient resources.
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Brigham and Women's HospitalUniversity, hospital or other
- Collaborators
- Biofourmis Inc.
- Study start
- 20 Mar 2021 (actual)
- Primary completion
- 1 Aug 2026 (estimated)
- Study completion
- 1 Dec 2026 (estimated)
- First posted
- 5 Mar 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 length of stay 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
Length of Stay
Time frame: From date of admission to date of discharge (1 to 24 days)
The time spent by each patient in Home Hospital from time of admission to time of discharge, measured in hours
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Mar 2026.
- Background: PubMed 32179154Lubelski D, Ehresman J, Feghali J, Tanenbaum J, Bydon A, Theodore N, Witham T, Sciubba DM. Prediction calculator for nonroutine discharge and length of stay after spine surgery. Spine J. 2020 Jul;20(7):1154-1158. doi: 10.1016/j.spinee.2020.02.022. Epub 2020 Mar 13.
- Background: PubMed 32713541Karnuta JM, Churchill JL, Haeberle HS, Nwachukwu BU, Taylor SA, Ricchetti ET, Ramkumar PN. The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty. J Shoulder Elbow Surg. 2020 Nov;29(11):2385-2394. doi: 10.1016/j.jse.2020.04.009. Epub 2020 Jun 9.
- Background: PubMed 30665831Ramkumar PN, Navarro SM, Haeberle HS, Karnuta JM, Mont MA, Iannotti JP, Patterson BM, Krebs VE. Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models. J Arthroplasty. 2019 Apr;34(4):632-637. doi: 10.1016/j.arth.2018.12.030. Epub 2018 Dec 27.
- Background: PubMed 31765937Ma X, Si Y, Wang Z, Wang Y. Length of stay prediction for ICU patients using individualized single classification algorithm. Comput Methods Programs Biomed. 2020 Apr;186:105224. doi: 10.1016/j.cmpb.2019.105224. Epub 2019 Nov 20.
- Background: PubMed 30685103Daghistani TA, Elshawi R, Sakr S, Ahmed AM, Al-Thwayee A, Al-Mallah MH. Predictors of in-hospital length of stay among cardiac patients: A machine learning approach. Int J Cardiol. 2019 Aug 1;288:140-147. doi: 10.1016/j.ijcard.2019.01.046. Epub 2019 Jan 19.
- Background: PubMed 33070874Bacchi S, Oakden-Rayner L, Menon DK, Jannes J, Kleinig T, Koblar S. Stroke prognostication for discharge planning with machine learning: A derivation study. J Clin Neurosci. 2020 Sep;79:100-103. doi: 10.1016/j.jocn.2020.07.046. Epub 2020 Aug 5.
- Background: PubMed 30243882Navarro SM, Wang EY, Haeberle HS, Mont MA, Krebs VE, Patterson BM, Ramkumar PN. Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model. J Arthroplasty. 2018 Dec;33(12):3617-3623. doi: 10.1016/j.arth.2018.08.028. Epub 2018 Sep 5.
- Background: PubMed 32823009Young AJ, Hare A, Subramanian M, Weaver JL, Kaufman E, Sims C. Using Machine Learning to Make Predictions in Patients Who Fall. J Surg Res. 2021 Jan;257:118-127. doi: 10.1016/j.jss.2020.07.047. Epub 2020 Aug 18.
- Background: PubMed 32376173Sinha I, Aluthge DP, Chen ES, Sarkar IN, Ahn SH. Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR. J Vasc Interv Radiol. 2020 Jun;31(6):1018-1024.e4. doi: 10.1016/j.jvir.2019.11.030. Epub 2020 May 4.
- 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 32835314Nemati M, Ansary J, Nemati N. Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data. Patterns (N Y). 2020 Aug 14;1(5):100074. doi: 10.1016/j.patter.2020.100074. Epub 2020 Jul 4.
- Individual participant dataSharing planned: No
Related trials for Infection
Not yet recruitingPhase 1
Phase I Open-Label Safety Trial of Pembrolizumab for Neurological Post- Acute Sequelae of SARS-CoV-2 (PD1-PASC I)
Post-Acute COVID-19 Syndrome
RecruitingObservational study
Effectiveness and Clinical Outcomes of Long-term Bulevirtide Monotherapy in Patients With HDV-related Compensated Cirrhosis
Hepatitis D
RecruitingObservational study
VIRal Pulmonary Infections in Older AGE
Viral Pulmonary Infections
Not yet recruitingPhase 2/3
A Pivotal Safety and Immunogenicity GBS-NN/NN2 Vaccine Maternal Immunisation Trial
Streptococcus Agalactiae Infection, Neonatal Sepsis
Active, not recruitingPhase 1
A Safety and Immunogenicity Trial of OCU500, ChAd36 Vector Encoding SARS-CoV-2 Spike Vaccine Via Intranasal and Inhalational Routes in Previously Vaccinated Adults
COVID-19
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: withdrawn, 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 was withdrawn before enrolling participants, so there are no results.
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?
Length of Stay (time frame: From date of admission to date of discharge (1 to 24 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 NCT04784351, which also lists 11 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.