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Rui F, Yeo YH, Xu L, Zheng Q, Xu X, Ni W, Tan Y, Zeng QL, He Z, Tian X, Xue Q, Qiu Y, Zhu C, Ding W, Wang J, Huang R, Xu Y, Chen Y, Fan J, Fan Z, Qi X, Huang DQ, Xie Q, Shi J, Wu C, Li J. Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort study. EClinicalMedicine 2024; 68:102419. [PMID: 38292041 PMCID: PMC10827491 DOI: 10.1016/j.eclinm.2023.102419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 02/01/2024] Open
Abstract
Background With increasingly prevalent coexistence of chronic hepatitis B (CHB) and hepatic steatosis (HS), simple, non-invasive diagnostic methods to accurately assess the severity of hepatic inflammation are needed. We aimed to build a machine learning (ML) based model to detect hepatic inflammation in patients with CHB and concurrent HS. Methods We conducted a multicenter, retrospective cohort study in China. Treatment-naive CHB patients with biopsy-proven HS between April 2004 and September 2022 were included. The optimal features for model development were selected by SHapley Additive explanations, and an ML algorithm with the best accuracy to diagnose moderate to severe hepatic inflammation (Scheuer's system ≥ G3) was determined and assessed by decision curve analysis (DCA) and calibration curve. This study is registered with ClinicalTrials.gov (NCT05766449). Findings From a pool of 1,787 treatment-naive patients with CHB and HS across eleven hospitals, 689 patients from nine of these hospitals were chosen for the development of the diagnostic model. The remaining two hospitals contributed to two independent external validation cohorts, comprising 509 patients in validation cohort 1 and 589 in validation cohort 2. Eleven features regarding inflammation, hepatic and metabolic functions were identified. The gradient boosting classifier (GBC) model showed the best performance in predicting moderate to severe hepatic inflammation, with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.83-0.88) in the training cohort, and 0.89 (95% CI 0.86-0.92), 0.76 (95% CI 0.73-0.80) in the first and second external validation cohorts, respectively. A publicly accessible web tool was generated for the model. Interpretation Using simple parameters, the GBC model predicted hepatic inflammation in CHB patients with concurrent HS. It holds promise for guiding clinical management and improving patient outcomes. Funding This research was supported by the National Natural Science Foundation of China (No. 82170609, 81970545), Natural Science Foundation of Shandong Province (Major Project) (No. ZR2020KH006), Natural Science Foundation of Jiangsu Province (No.BK20231118), Tianjin Key Medical Discipline (Specialty), Construction Project, TJYXZDXK-059B, Tianjin Health Science and Technology Project key discipline special, TJWJ2022XK034, and Research project of Chinese traditional medicine and Chinese traditional medicine combined with Western medicine of Tianjin municipal health and Family Planning Commission (2021022).
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Affiliation(s)
- Fajuan Rui
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
- Department of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
| | - Yee Hui Yeo
- Karsh Division of Gastroenterology and Hepatology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Liang Xu
- Clinical School of the Second People's Hospital, Tianjin Medical University, Tianjin, China
- Department of Hepatology, Tianjin Second People's Hospital, Tianjin, China
- Tianjin Research Institute of Liver Diseases, Tianjin, China
| | - Qi Zheng
- Department of Hepatology, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Xiaoming Xu
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
- Department of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
| | - Wenjing Ni
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
- Department of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
| | - Youwen Tan
- Department of Hepatology, The Third Hospital of Zhenjiang Affiliated Jiangsu University, Zhenjiang, Jiangsu, China
| | - Qing-Lei Zeng
- Department of Infectious Diseases and Hepatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zebao He
- Department of Infectious Diseases, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Xiaorong Tian
- School of Computer Science, China University of Geosciences, Wuhan, Hubei, China
- Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, Hubei, China
| | - Qi Xue
- Department of Infectious Disease, Shandong Provincial Hospital Affiliated to Shandong Frist Medical University, Ji'nan, Shandong, China
| | - Yuanwang Qiu
- Department of Infectious Diseases, The Fifth People's Hospital of Wuxi, Wuxi, Jiangsu, China
| | - Chuanwu Zhu
- Department of Infectious Diseases, The Affiliated Infectious Diseases Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Weimao Ding
- Department of Hepatology, Huai'an No.4 People's Hospital, Huai'an, Jiangsu, China
| | - Jian Wang
- Department of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
| | - Rui Huang
- Department of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
| | - Yayun Xu
- Department of Infectious Disease, Shandong Provincial Hospital, Shandong University, Ji'nan, Shandong, China
| | - Yunliang Chen
- School of Computer Science, China University of Geosciences, Wuhan, Hubei, China
- Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, Hubei, China
| | - Junqing Fan
- School of Computer Science, China University of Geosciences, Wuhan, Hubei, China
- Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, Hubei, China
| | - Zhiwen Fan
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| | - Xiaolong Qi
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical of School, Southeast University, Nanjing, Jiangsu, China
| | - Daniel Q. Huang
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Health System, Singapore
| | - Qing Xie
- Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China
| | - Junping Shi
- Department of Infectious & Hepatology Diseases, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Chao Wu
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
- Department of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
| | - Jie Li
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
- Department of Infectious Disease, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
- Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu, China
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Zheng TJ, Parra-Izquierdo I, Reitsma SE, Heinrich MC, Larson MK, Shatzel JJ, Aslan JE, McCarty OJT. Platelets and tyrosine kinase inhibitors: clinical features, mechanisms of action, and effects on physiology. Am J Physiol Cell Physiol 2022; 323:C1231-C1250. [PMID: 35938677 PMCID: PMC9576167 DOI: 10.1152/ajpcell.00040.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 08/01/2022] [Accepted: 08/01/2022] [Indexed: 11/22/2022]
Abstract
Tyrosine kinase inhibitors (TKIs) have emerged as a promising class of target-directed, small molecule inhibitors used to treat hematologic malignancies, inflammatory diseases, and autoimmune disorders. Recently, TKIs have also gained interest as potential antiplatelet-directed therapeutics that could be leveraged to reduce pathologic thrombus formation and atherothrombotic complications, while minimally affecting platelet hemostatic function. This review provides a mechanistic overview and summarizes the known effects of tyrosine kinase inhibitors on platelet signaling and function, detailing prominent platelet signaling pathways downstream of the glycoprotein VI (GPVI) receptor, integrin αIIbβ3, and G protein-coupled receptors (GPCRs). This review focuses on mechanistic as well as clinically relevant and emerging TKIs targeting major families of tyrosine kinases including but not limited to Bruton's tyrosine kinase (BTK), spleen tyrosine kinase (Syk), Src family kinases (SFKs), Janus kinases (JAK), and signal transducers and activators of transcription (STAT) and evaluates their effects on platelet aggregation and adhesion, granule secretion, receptor expression and activation, and protein phosphorylation events. In summation, this review highlights current advances and knowledge on the effects of select TKIs on platelet biology and furthers insight on signaling pathways that may represent novel druggable targets coupled to specific platelet functional responses.
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Affiliation(s)
- Tony J Zheng
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon
| | - Iván Parra-Izquierdo
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, Oregon
| | - Stéphanie E Reitsma
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon
| | - Michael C Heinrich
- Portland Veterans Affairs Health Care System and Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
- Department of Molecular and Cellular Biosciences, Oregon Health & Science University, Portland, Oregon
| | - Mark K Larson
- Department of Biology, Augustana University, Sioux Falls, South Dakota
| | - Joseph J Shatzel
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon
- Division of Hematology & Medical Oncology, Oregon Health & Science University, Portland, Oregon
| | - Joseph E Aslan
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, Oregon
- Department of Chemical Physiology & Biochemistry, Oregon Health & Science University, Portland, Oregon
| | - Owen J T McCarty
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon
- Division of Hematology & Medical Oncology, Oregon Health & Science University, Portland, Oregon
- Department of Cell, Developmental & Cancer Biology, School of Medicine, Oregon Health & Science University, Portland, Oregon
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Lindquist I, Olson SR, Li A, Al-Samkari H, Jou JH, McCarty OJT, Shatzel JJ. The efficacy and safety of thrombopoietin receptor agonists in patients with chronic liver disease undergoing elective procedures: a systematic review and meta-analysis. Platelets 2022; 33:66-72. [PMID: 33459573 PMCID: PMC8286270 DOI: 10.1080/09537104.2020.1859102] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/05/2020] [Accepted: 10/07/2020] [Indexed: 01/04/2023]
Abstract
Thrombopoietin receptor agonists (TPO-RAs) can mitigate preprocedural thrombocytopenia in patients with chronic liver disease (CLD) however their effects on procedural outcomes is unclear. In this meta-analysis, we aimed to better define the efficacy, thrombotic risk and bleeding mitigation associated with the use of preoperative TPO-RAs in patients with CLD. We performed a systematic review and meta-analysis of randomized placebo-controlled clinical trials to assess the use of preprocedural TPO-RAs in patients with CLD, searching MEDLINE, EMBASE and the Cochrane library database. Six publications comprising eight randomized trials (1229 patients; 717 received TPO-RAs, 512 received placebo) and three unique TPO-RAs were retrieved. The majority of the included procedures were endoscopic. TPO-RAs were significantly more likely to result in a preoperative platelet count greater than 50 x 109/L (72.1% vs 15.6%, RR 4.8, 95% CI 3.6-6.4 p < .00001. NNT 1.8) and reduced the incidence of platelet transfusions (22.5% vs 67.8%, RR 0.33, 95% CI 0.3-0.4 p < .00001. NNT 2.2). Total periprocedural bleeding was decreased in patients who received TPO-RAs (11.6% vs 15.6%, RR 0.64, 95% CI 0.5-0.9 p = .01. NNT 24.7) and there was no increase in the rate of thrombosis (2.2% vs 1.8% RR 1.25, 95% CI 0.6-2.9 p = .60. NNH 211.1). In patients with CLD the use of preprocedural TPO-RAs resulted in significant increased platelet counts, and decreased the incidence of platelet transfusions as compared to placebo. TPO use likewise decreased the incidence of total periprocedural bleeding without increasing the rate of thrombosis.
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Affiliation(s)
- Ingrid Lindquist
- Division of General Internal Medicine and Geriatrics, Oregon Health & Science University, Portland, OR, USA
| | - Sven R Olson
- Division of Hematology and Oncology, Oregon Health & Science University, Portland, OR, USA
| | - Ang Li
- Section of Hematology Oncology, Baylor College of Medicine, Houston, TX, USA
| | - Hanny Al-Samkari
- Division of Hematology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Janice H Jou
- Division of Gastroenterology and Hepatology, Oregon Health & Science University, Portland, OR, USA
| | - Owen J T McCarty
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Joseph J Shatzel
- Division of Hematology and Oncology, Oregon Health & Science University, Portland, OR, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
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Brown RS, Imawari M, Izumi N, Osaki Y, Bentley R, Ochiai T, Kano T, Peck-Radosavljevic M. Assessing the periprocedural magnitude of platelet count change in response to lusutrombopag. JHEP Rep 2021; 3:100228. [PMID: 33644726 PMCID: PMC7887643 DOI: 10.1016/j.jhepr.2021.100228] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/20/2020] [Accepted: 12/08/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND & AIMS Despite limitations, platelet transfusion has been used to minimise bleeding risk in patients with thrombocytopaenia. Lusutrombopag is an oral, thrombopoietin receptor agonist approved for treatment of thrombocytopaenia associated with chronic liver disease in patients undergoing planned invasive procedures. This post-hoc analysis assessed the magnitude of platelet count change based on the integrated per-protocol population from 2 similar phase III multicentre, randomised, double-blind, placebo-controlled trials. METHODS Adults with chronic liver disease-induced thrombocytopaenia and platelet count <50 (× 109/L) received lusutrombopag 3 mg or placebo ≤7 days before invasive procedure scheduled 9-14 days after randomisation. Platelet transfusion was required per protocol if the platelet count remained <50 no more than 2 days before the planned invasive procedure. Post-hoc analysis included: proportion of patients with platelet count ≥50, ≥1.5-fold increase, and a doubling of platelet count; maximum and maximum change in platelet count; and platelet count time course. RESULTS Platelet count ≥50, a platelet count increase ≥1.5-fold, and at least a doubling in platelet count were achieved in 88.3%, 86.9%, and 52.6% of patients in the lusutrombopag group (n = 137) vs. 58.6%, 32.3%, and 6.0% of patients in the placebo group (n = 133), respectively. In the lusutrombopag group, median maximum platelet count across baseline platelet counts of <30, ≥30 to <40, and ≥40 was 46, 76, and 87, respectively. Median maximum change in platelet count by baseline platelet count was +24, +42, and +40, respectively. Patients who received lusutrombopag without platelet transfusion achieved a median platelet count ≥50 for 3 weeks. CONCLUSIONS Patients treated with lusutrombopag experienced a clinically relevant response in platelet count for a substantial duration of time. LAY SUMMARY Patients with low platelet counts caused by chronic liver disease may not receive planned invasive procedures or surgeries because of an increased risk of bleeding. Lusutrombopag has previously demonstrated efficacy in raising platelet counts and is approved to treat chronic liver disease patients with low platelet counts in advance of a planned surgery. Physicians need to understand more clearly what to expect in terms of platelet count change when using lusutrombopag; this integrated analysis provides data to help guide its clinical application.
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Key Words
- AE, adverse event
- CLD, chronic liver disease
- CT, computerised tomography
- GCP, Good Clinical Practice
- HR, hazard ratio
- ICF, informed consent form
- ICH, International Conference on Harmonisation
- ITT, intention-to-treat
- LUSU, lusutrombopag
- Lusutrombopag
- MRI, magnetic resonance imaging
- Magnitude
- PBO, placebo
- PP, per protocol
- PT, platelet transfusion
- Platelet
- Procedural
- TCP, thrombocytopaenia
- TEAE, treatment-emergent adverse event
- Thrombocytopaenia
- US, ultrasonography
- WHO, World Health Organization
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Affiliation(s)
- Robert S. Brown
- Division of Gastroenterology and Hepatology, Weill Cornell Medical College, New York, NY, USA
| | - Michio Imawari
- Institute for Gastrointestinal and Liver Disease, Shin-Yurigaoka General Hospital, Kawasaki, Japan
| | - Namiki Izumi
- Department of Gastroenterology and Hepatology, Musashino Red Cross Hospital, Tokyo, Japan
| | | | - Roy Bentley
- Global Market Access, Shionogi Inc., Florham Park, NJ, USA
| | | | - Takeshi Kano
- Global Project Management Department, Shionogi & Co., Ltd., Osaka, Japan
| | - Markus Peck-Radosavljevic
- Abteilung Innere Medizin & Gastroenterologie (IMuG), mit Zentrale Aufnahme & Erstversorgung (ZAE), Klinikum Klagenfurt am Wörthersee, Klagenfurt, Austria
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