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Machine-learning Based Prediction Model in Primary Immune Thrombocytopenia

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ClinicalTrials.gov Identifier: NCT05116423
Recruitment Status : Recruiting
First Posted : November 11, 2021
Last Update Posted : February 8, 2022
Sponsor:
Collaborators:
The Affiliated Zhongshan Hospital of Dalian University
Jiangsu Provincial People's Hospital
Qilu Hospital of Shandong University
Shanghai Zhongshan Hospital
Information provided by (Responsible Party):
Xiao Hui Zhang, Peking University People's Hospital

Brief 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. The objective of this study is to prospectively and externally validate the risk of critical ITP bleeds in newly admitted ITP patients.

Condition or disease
Immune Thrombocytopenia ITP

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.

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Study Type : Observational
Estimated Enrollment : 100 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Personalized Machine-Learning Based Prediction Model for Bleeding in Immune Thrombocytopenia: a Nationwide Representative Data Study
Actual Study Start Date : November 10, 2021
Estimated Primary Completion Date : March 1, 2022
Estimated Study Completion Date : June 30, 2022


Group/Cohort
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.



Primary Outcome Measures :
  1. 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.


Secondary Outcome Measures :
  1. Comparison between different machine learning algorithms used in the model [ Time Frame: 3 months ]
    Comparison of sensitivity and specificity of different machine learning algorithms used in the model.



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Probability Sample
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.
Criteria

Inclusion Criteria:

1. Confirmed ITP diagnosis;

Exclusion Criteria:

  1. Received chemotherapy or anticoagulants or other drugs affecting the platelet counts within 6 months before the screening visit;
  2. Current HIV infection or hepatitis B virus or hepatitis C virus infections;
  3. Maligancy;
  4. 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
  5. 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;
  6. Patients who are deemed unsuitable for the study by the investigator.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05116423


Contacts
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Contact: Xiao-Hui Zhang, MD +8615010638916 zhangxh100@sina.com
Contact: Zhuo-Yu An, MD +8615010638916 anzhuoyu@pku.edu.cn

Locations
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China, Beijing
Peking University Insititute of Hematology, Peking University People's Hospital Recruiting
Beijing, Beijing, China, 100010
Contact: Xiao-Hui Zhang, MD       zhangxh100@sina.com   
Contact: Zhuo-Yu An, MD    15010638916    anzhuoyu@pku.edu.cn   
Sponsors and Collaborators
Peking University People's Hospital
The Affiliated Zhongshan Hospital of Dalian University
Jiangsu Provincial People's Hospital
Qilu Hospital of Shandong University
Shanghai Zhongshan Hospital
Investigators
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Principal Investigator: Xiao-Hui Zhang, MD 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
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Responsible Party: Xiao Hui Zhang, Vice President of Peking University Institute of Hematology, Peking University People's Hospital
ClinicalTrials.gov Identifier: NCT05116423    
Other Study ID Numbers: PKU-ITP031
First Posted: November 11, 2021    Key Record Dates
Last Update Posted: February 8, 2022
Last Verified: January 2022

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Xiao Hui Zhang, Peking University People's Hospital:
Immune Thrombocytopenia
Prediction Model
Additional relevant MeSH terms:
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Thrombocytopenia
Immune System Diseases
Purpura, Thrombocytopenic, Idiopathic
Blood Platelet Disorders
Hematologic Diseases
Purpura, Thrombocytopenic
Purpura
Blood Coagulation Disorders
Thrombotic Microangiopathies
Hemorrhagic Disorders
Autoimmune Diseases
Hemorrhage
Pathologic Processes
Skin Manifestations