Immune ThrombocytopeniaNCT05116423
Machine-learning Based Prediction Model in Primary Immune Thrombocytopenia
An observational study of Immune Thrombocytopenia and ITP, run by Peking University People's Hospital at 1 site in China.
Trial summary
This study developed the first prediction model for risk of critical ITP bleeds for ITP inpatients using a novel machine learning algorithm. This model has been implemented as a web-based model so that clinicians can obtain the estimated probability of critical ITP bleeds for ITP inpatients. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Immune Thrombocytopenia, ITP
- How many people
- 100 participants
- Where
- 1 siteChina
- Study dates
- 10 Nov 2021to 30 Jun 2022
- 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: The study population included nonsplenectomized primary ITP inpatients 18 years of age or older. Patients who had a diagnosis of connective tissue disease, cancer (solid tumor or leukemia), or primary immune deficiency were excluded.
Who may be able to join
- 1. Confirmed ITP diagnosis;
Who cannot join
- Received chemotherapy or anticoagulants or other drugs affecting the platelet counts within 6 months before the screening visit;
- Current HIV infection or hepatitis B virus or hepatitis C virus infections;
- Maligancy;
- Female patients who are nursing or pregnant, who may be pregnant, or who contemplate pregnancy during the study period; a history of clinically significant adverse reactions to previous corticosteroid therapy
- Have a known diagnosis of other autoimmune diseases, established in the medical history and laboratory findings with positive results for the determination of antinuclear antibodies, anti-cardiolipin antibodies, lupus anticoagulant or direct Coombs test;
- Patients who are deemed unsuitable for the study by the investigator.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Peking University Insititute of Hematology, Peking University People's Hospital
Beijing, Beijing Municipality, 100010
Xiao-Hui Zhang, MD, zhangxh100@sina.com
Zhuo-Yu An, MD, 15010638916, anzhuoyu@pku.edu.cn
About this study
Official title: Personalized Machine-Learning Based Prediction Model for Bleeding in Immune Thrombocytopenia: a Nationwide Representative Data Study
This study developed the first prediction model for risk of critical ITP bleeds for ITP inpatients using a novel machine learning algorithm. This model has been implemented as a web-based model so that clinicians can obtain the estimated probability of critical ITP bleeds for ITP inpatients. The objective of this study is to prospectively and externally validate the risk of critical ITP bleeds in newly admitted ITP patients.
Read the detailed description
Primary immune thrombocytopenia (ITP) is a common acquired autoimmune disease characterized by reduced platelet production and increased platelet destruction due to autoimmune disorders, as patients present with low platelet counts and a high risk of bleeding. Although most ITP patients present a good prognosis, the rare but important critical ITP bleeds events are the threatening-life complication to ITP patients, severely affecting their prognosis, quality of life and treatment decisions.
More recently, the development of clinical prediction models has provided powerful tools for precision diagnosis and early intervention of diseases, especially the application of machine learning methods. Machine learning approaches can overcome some of the limitations of current risk prediction analysis methods by applying computer algorithms to large data sets with numerous multidimensional variables, capturing the high-dimensional nonlinear relationships between clinical features to produce data, drive outcome prediction.
It suggests an unmet need for personalized patient management strategies and an urgent need for effective tools to predict the risk of critical ITP bleeds in hospitalized patients in medical practice.
Here, we aim to integrate clinical and laboratory data based on a nationwide multicenter study in China to build a clinical prediction model. In particular, we also perform external and prospective validation with large sample sizes to improve the robustness and utility of our models.
It is a simple and convenient tool to quickly assess newly admitted ITP patients and achieve early identification and intervention for those at high risk of life-threatening bleeding events, thus reducing disability and mortality rates in the future.
- Conditions
- Immune Thrombocytopenia, ITP
- Keywords
- Immune Thrombocytopenia, Prediction Model
- Medical subject headings
- Purpura, Thrombocytopenic, Idiopathic
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 |
|---|---|---|
| ITP inpatients | The study population included nonsplenectomized primary ITP inpatients 18 years of age or older. Patients who had a diagnosis of connective tissue disease, cancer (solid tumor or leukemia), or primary immune deficiency were excluded. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Performance of model
Area under receiver operating characteristic curve (AUC) of the model in predicting critical ITP bleeds in patients with ITP.
Measured: 3 months
Secondary outcomes (1)
Comparison between different machine learning algorithms used in the model
Comparison of sensitivity and specificity of different machine learning algorithms used in the model.
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Nov 2021
- Study start10 Nov 2021
- Primary completion1 Mar 2022 (estimated)
- Study completion30 Jun 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Immune ThrombocytopeniaStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Affiliated Zhongshan Hospital of Dalian University, Jiangsu Provincial People's Hospital, Qilu Hospital of Shandong University, Shanghai Zhongshan Hospital
- Responsible party
- Principal investigator, Xiao Hui Zhang, Peking University People's Hospital
- Investigators
- Xiao-Hui Zhang, MD, Principal investigator, Peking University People's Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Collaborative Innovation Center of Hematology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05116423
- Sponsor's protocol ID
- PKU-ITP031
- Status last verified
- Jan 2022
- First submitted
- 9 Nov 2021
- Last updated
- 8 Feb 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Feb 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Personalized Machine-Learning Based Prediction Model for Bleeding in Immune Thrombocytopenia: a Nationwide Representative Data Study
- ClinicalTrials.gov ID
- NCT05116423
- Acronym / sponsor ID
- PKU-ITP031
- Conditions
- Immune Thrombocytopenia, ITP
- Keywords
- Immune Thrombocytopenia, Prediction Model
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Peking University People's HospitalUniversity, hospital or other
- Collaborators
- Affiliated Zhongshan Hospital of Dalian University, Jiangsu Provincial People's Hospital, Qilu Hospital of Shandong University, Shanghai Zhongshan Hospital
- Study start
- 10 Nov 2021 (actual)
- Primary completion
- 1 Mar 2022 (estimated)
- Study completion
- 30 Jun 2022 (estimated)
- First posted
- 11 Nov 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 study developed the first prediction model for risk of critical ITP bleeds for ITP inpatients using a novel machine learning algorithm. This model has been implemented as a web-based model so that clinicians can obtain the estimated probability of critical ITP bleeds for ITP inpatients. The objective of this study is to prospectively and externally validate the risk of critical ITP bleeds in newly admitted ITP patients.
Detailed description
Primary immune thrombocytopenia (ITP) is a common acquired autoimmune disease characterized by reduced platelet production and increased platelet destruction due to autoimmune disorders, as patients present with low platelet counts and a high risk of bleeding. Although most ITP patients present a good prognosis, the rare but important critical ITP bleeds events are the threatening-life complication to ITP patients, severely affecting their prognosis, quality of life and treatment decisions.
More recently, the development of clinical prediction models has provided powerful tools for precision diagnosis and early intervention of diseases, especially the application of machine learning methods. Machine learning approaches can overcome some of the limitations of current risk prediction analysis methods by applying computer algorithms to large data sets with numerous multidimensional variables, capturing the high-dimensional nonlinear relationships between clinical features to produce data, drive outcome prediction.
It suggests an unmet need for personalized patient management strategies and an urgent need for effective tools to predict the risk of critical ITP bleeds in hospitalized patients in medical practice.
Here, we aim to integrate clinical and laboratory data based on a nationwide multicenter study in China to build a clinical prediction model. In particular, we also perform external and prospective validation with large sample sizes to improve the robustness and utility of our models.
It is a simple and convenient tool to quickly assess newly admitted ITP patients and achieve early identification and intervention for those at high risk of life-threatening bleeding events, thus reducing disability and mortality rates in the future.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| ITP inpatients | – | –The study population included nonsplenectomized primary ITP inpatients 18 years of age or older. Patients who had a diagnosis of connective tissue disease, cancer (solid tumor or leukemia), or primary immune deficiency were excluded. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population included nonsplenectomized primary ITP inpatients 18 years of age or older. Patients who had a diagnosis of connective tissue disease, cancer (solid tumor or leukemia), or primary immune deficiency were excluded.
Key inclusion criteria (1 listed)
- 1. Confirmed ITP diagnosis;
Key exclusion criteria (6 listed)
- Received chemotherapy or anticoagulants or other drugs affecting the platelet counts within 6 months before the screening visit;
- Current HIV infection or hepatitis B virus or hepatitis C virus infections;
- Maligancy;
- Female patients who are nursing or pregnant, who may be pregnant, or who contemplate pregnancy during the study period; a history of clinically significant adverse reactions to previous corticosteroid therapy
- Have a known diagnosis of other autoimmune diseases, established in the medical history and laboratory findings with positive results for the determination of antinuclear antibodies, anti-cardiolipin antibodies, lupus anticoagulant or direct Coombs 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
Performance of model
Time frame: 3 months
Area under receiver operating characteristic curve (AUC) of the model in predicting critical ITP bleeds in patients with ITP.
1 secondary outcome
Comparison between different machine learning algorithms used in the model
Time frame: 3 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Feb 2022.
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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.
Xiao-Hui Zhang, MD principal investigator
Peking University People's Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Collaborative Innovation Center of Hematology
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 Immune Thrombocytopenia and ITP, run by Peking University People's Hospital 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 6 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 8 Feb 2022. 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 Xiao-Hui Zhang, MD (+8615010638916, zhangxh100@sina.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance of model (time frame: 3 months). 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 8 Feb 2022.