SepsisNCT04771429
A Machine Learning Predictive Model for Sepsis
An observational study of Sepsis, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 1 site in China.
Trial summary
Timely and accurately predicting the occurrence of sepsis and actively intervening in treatment may effectively improve the survival and cure rate of patients with sepsis. Using machine learning and natural language processing, we want to develop models to 1) identify all children with sepsis admitted to hospital and… Read more about this study
- Who is it for?
- Ages 1 year to 18 yearsHealthy volunteers can take part
- Conditions the study is for
- Sepsis
- How many people
- 4,500 participants
- Where
- 1 siteChina
- Study dates
- 1 Apr 2019to 1 Apr 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 1 year to 18 years
- All sexes
- Healthy volunteers accepted
- Child, Adult
Study population: For data analysis in winter 2021, we have access to sepsis data up to February 2016 to July 2018.
Who may be able to join
- diagnosed with "infection", "septic shock" or "sepsis" or "septicemia"
Who cannot join
- Acute upper respiratory tract infection
- Newborns
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Shanghai, Yangpu, 200092
Xin Sun, PhD, 18902268716, Doctorsunxin@hotmail.com
About this study
Official title: A Machine Learning Model of Risk for Children With Sepsis
Timely and accurately predicting the occurrence of sepsis and actively intervening in treatment may effectively improve the survival and cure rate of patients with sepsis.
Using machine learning and natural language processing, we want to develop models to 1) identify all children with sepsis admitted to hospital and 2) stratify them to distinguish those who are at high risk of death b) How will you undertake your work? From Shanghai hospitals anf MIMIC III, we will develop a very large dataset of patient admissions for all medical conditions including sepsis 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 sepsis and identify those patients who will be at high risk of death. We will test the ability of these models to determine our predictive accuracies. We will then test these models at other institutions.
Read the detailed description
Introduction:Timely and accurately predicting the occurrence of sepsis and actively intervening in treatment may effectively improve the survival and cure rate of patients with sepsis. There have been a large number of research results on the prediction of sepsis produced by two methods: the sepsis detection and evaluation method based on clinical scoring mechanisms and the sepsis detection method based on machine learning model.
Objective: Reasonable and effective data pre-processing can significantly improve the timeliness and accuracy of early warning models of sepsis. Given the problems of high time dispersion, uneven distribution, and large differences of common sepsis prediction modeling indicators, the study proposed a method of hybrid interpolation based on time window-related sepsis indicators.
Methods:The study designed the traditional data interpolation method based on linear, MGP, average, nearest neighbour and the hybrid interpolation method (CTWH) based on correlation time window (CTW) proposed in the study for experimental comparison. Experiments were performed respectively in sample sets with no experimental data removal and sample sets with 90% missing values removal. By comparing with the performance of the existing sepsis indicator interpolation methods on the same baseline model, the effectiveness of the method was proven from the accuracy and timeliness of the prediction results. In the end, the results of the experimental method were analyzed and explained from a clinical perspective.
Significance:
In view of the characteristics of high dispersion, uneven distribution, and large differences between features of commonly used indicators in sepsis prediction models, this study proposed an efficient data interpolation strategy. After elimination of missing data of 90% and 0%, the interpolation method proposed in this study performed better than the existing methods like mean interpolation and linear interpolation, KNN, MGP on the static baseline and time series models. At the same time, this method also provided an idea to explore the length of the interpolation window, and supported the prospective study of missing value interpolation and data pre-processing.
- Conditions
- Sepsis
- Keywords
- sepsis prediction, machine learning, mix interpolation
- Medical subject headings
- Sepsis
What is being measured
The primary outcome is the main question the study is designed to answer.
Identification of sepsis
Sepsis patients were screened based on the Sepsis-III standard
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Feb 2021
- Study start1 Apr 2019
- Primary completion1 Mar 2021 (estimated)
- Study completion1 Apr 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for SepsisStudies 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
- Other
- Time perspective
- Retrospective
- Enrolment
- 4,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Xin Sun, MD, Principal investigator, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04771429
- Sponsor's protocol ID
- XHEC-C-2021-104-1
- Status last verified
- Feb 2021
- First submitted
- 24 Feb 2021
- Last updated
- 25 Feb 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: In the paper
Study at a glance
From the registry record, last updated by the sponsor on 25 Feb 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Machine Learning Model of Risk for Children With Sepsis
- ClinicalTrials.gov ID
- NCT04771429
- Acronym / sponsor ID
- XHEC-C-2021-104-1
- Conditions
- Sepsis
- Keywords
- sepsis prediction, machine learning, mix interpolation
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 4,500 participants (estimated)
- Lead sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of MedicineUniversity, hospital or other
- Study start
- 1 Apr 2019 (actual)
- Primary completion
- 1 Mar 2021 (estimated)
- Study completion
- 1 Apr 2021 (estimated)
- First posted
- 25 Feb 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.
Timely and accurately predicting the occurrence of sepsis and actively intervening in treatment may effectively improve the survival and cure rate of patients with sepsis.
Using machine learning and natural language processing, we want to develop models to 1) identify all children with sepsis admitted to hospital and 2) stratify them to distinguish those who are at high risk of death b) How will you undertake your work? From Shanghai hospitals anf MIMIC III, we will develop a very large dataset of patient admissions for all medical conditions including sepsis 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 sepsis and identify those patients who will be at high risk of death. We will test the ability of these models to determine our predictive accuracies. We will then test these models at other institutions.
Detailed description
Introduction:Timely and accurately predicting the occurrence of sepsis and actively intervening in treatment may effectively improve the survival and cure rate of patients with sepsis. There have been a large number of research results on the prediction of sepsis produced by two methods: the sepsis detection and evaluation method based on clinical scoring mechanisms and the sepsis detection method based on machine learning model.
Objective: Reasonable and effective data pre-processing can significantly improve the timeliness and accuracy of early warning models of sepsis. Given the problems of high time dispersion, uneven distribution, and large differences of common sepsis prediction modeling indicators, the study proposed a method of hybrid interpolation based on time window-related sepsis indicators.
Methods:The study designed the traditional data interpolation method based on linear, MGP, average, nearest neighbour and the hybrid interpolation method (CTWH) based on correlation time window (CTW) proposed in the study for experimental comparison. Experiments were performed respectively in sample sets with no experimental data removal and sample sets with 90% missing values removal. By comparing with the performance of the existing sepsis indicator interpolation methods on the same baseline model, the effectiveness of the method was proven from the accuracy and timeliness of the prediction results. In the end, the results of the experimental method were analyzed and explained from a clinical perspective.
Significance:
In view of the characteristics of high dispersion, uneven distribution, and large differences between features of commonly used indicators in sepsis prediction models, this study proposed an efficient data interpolation strategy. After elimination of missing data of 90% and 0%, the interpolation method proposed in this study performed better than the existing methods like mean interpolation and linear interpolation, KNN, MGP on the static baseline and time series models. At the same time, this method also provided an idea to explore the length of the interpolation window, and supported the prospective study of missing value interpolation and data pre-processing.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 1 year to 18 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study population
- For data analysis in winter 2021, we have access to sepsis data up to February 2016 to July 2018.
Key inclusion criteria (1 listed)
- diagnosed with "infection", "septic shock" or "sepsis" or "septicemia"
Key exclusion criteria (2 listed)
- Acute upper respiratory tract infection
- Newborns
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
Identification of sepsis
Time frame: Baseline
Sepsis patients were screened based on the Sepsis-III standard
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Feb 2021.
- Individual participant dataSharing planned: Undecided. In the paper
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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.
Xin Sun, MD principal investigator
Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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 Sepsis, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 1 site in China.
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 1 inclusion and 2 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: status not confirmed, as of the sponsor's last update on 25 Feb 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 China. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Qin Gao, MD (13761402225, 13761402225@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Identification of sepsis (time frame: Baseline). 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 25 Feb 2021.