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Zou Y, Yue M, Jia L, Wang Y, Chen H, Zhang A, Xia X, Liu W, Yu R, Yang S, Huang P. Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data. BMC Cancer 2023; 23:1147. [PMID: 38007418 PMCID: PMC10676612 DOI: 10.1186/s12885-023-11628-1] [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: 05/28/2023] [Accepted: 11/09/2023] [Indexed: 11/27/2023] Open
Abstract
BACKGROUND Most existing predictive models of hepatocellular carcinoma (HCC) risk after sustained virologic response (SVR) are built on data collected at baseline and therefore have limited accuracy. The current study aimed to construct an accurate predictive model incorporating longitudinal data using a novel modeling strategy. The predictive performance of the longitudinal model was also compared with a baseline model. METHODS A total of 400 patients with HCV-related cirrhosis who achieved SVR with direct-acting antivirals (DAA) were enrolled in the study. Patients were randomly divided into a training set (70%) and a validation set (30%). Informative features were extracted from the longitudinal variables and then put into the random survival forest (RSF) to develop the longitudinal model. A baseline model including the same variables was built for comparison. RESULTS During a median follow-up time of approximately 5 years, 25 patients (8.9%) in the training set and 11 patients (9.2%) in the validation set developed HCC. The areas under the receiver-operating characteristics curves (AUROC) for the longitudinal model were 0.9507 (0.8838-0.9997), 0.8767 (0.6972,0.9918), and 0.8307 (0.6941,0.9993) for 1-, 2- and 3-year risk prediction, respectively. The brier scores of the longitudinal model were also relatively low for the 1-, 2- and 3-year risk prediction (0.0283, 0.0561, and 0.0501, respectively). In contrast, the baseline model only achieved mediocre AUROCs of around 0.6 (0.6113, 0.6213, and 0.6480, respectively). CONCLUSIONS Our longitudinal model yielded accurate predictions of HCC risk in patients with HCV-relate cirrhosis, outperforming the baseline model. Our model can provide patients with valuable prognosis information and guide the intensity of surveillance in clinical practice.
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Affiliation(s)
- Yanzheng Zou
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Ming Yue
- Department of Infectious Diseases, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Linna Jia
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yifan Wang
- Department of Infectious Disease, Jurong Hospital Affiliated to Jiangsu University, Jurong, China
| | - Hongbo Chen
- Department of Infectious Disease, Jurong Hospital Affiliated to Jiangsu University, Jurong, China
| | - Amei Zhang
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Yunnan, China
| | - Xueshan Xia
- Faculty of Life Science and Technology, Kunming University of Science and Technology, Yunnan, China
- Kunming Medical University, Kunming, China
| | - Wei Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
- Beijing Institute of Microbiology and Epidemiology, State Key Laboratory of Pathogen and Biosecurity, Beijing, China
| | - Rongbin Yu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| | - Sheng Yang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| | - Peng Huang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
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Ruffieux H, Hanson AL, Lodge S, Lawler NG, Whiley L, Gray N, Nolan TH, Bergamaschi L, Mescia F, Turner L, de Sa A, Pelly VS, Kotagiri P, Kingston N, Bradley JR, Holmes E, Wist J, Nicholson JK, Lyons PA, Smith KGC, Richardson S, Bantug GR, Hess C. A patient-centric modeling framework captures recovery from SARS-CoV-2 infection. Nat Immunol 2023; 24:349-358. [PMID: 36717723 PMCID: PMC9892000 DOI: 10.1038/s41590-022-01380-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 11/03/2022] [Indexed: 02/01/2023]
Abstract
The biology driving individual patient responses to severe acute respiratory syndrome coronavirus 2 infection remains ill understood. Here, we developed a patient-centric framework leveraging detailed longitudinal phenotyping data and covering a year after disease onset, from 215 infected individuals with differing disease severities. Our analyses revealed distinct 'systemic recovery' profiles, with specific progression and resolution of the inflammatory, immune cell, metabolic and clinical responses. In particular, we found a strong inter-patient and intra-patient temporal covariation of innate immune cell numbers, kynurenine metabolites and lipid metabolites, which highlighted candidate immunologic and metabolic pathways influencing the restoration of homeostasis, the risk of death and that of long COVID. Based on these data, we identified a composite signature predictive of systemic recovery, using a joint model on cellular and molecular parameters measured soon after disease onset. New predictions can be generated using the online tool http://shiny.mrc-bsu.cam.ac.uk/apps/covid-19-systemic-recovery-prediction-app , designed to test our findings prospectively.
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Affiliation(s)
- Hélène Ruffieux
- MRC Biostatistics Unit, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.
| | - Aimee L Hanson
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Samantha Lodge
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
| | - Nathan G Lawler
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
| | - Luke Whiley
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, Western Australia, Australia
| | - Nicola Gray
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
| | - Tui H Nolan
- MRC Biostatistics Unit, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Laura Bergamaschi
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Federica Mescia
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Lorinda Turner
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Aloka de Sa
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Victoria S Pelly
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Prasanti Kotagiri
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Nathalie Kingston
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
- NIHR BioResource, Cambridge University Hospitals NHS Foundation, Cambridge Biomedical Campus, Cambridge, UK
| | - John R Bradley
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
- NIHR BioResource, Cambridge University Hospitals NHS Foundation, Cambridge Biomedical Campus, Cambridge, UK
| | - Elaine Holmes
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Julien Wist
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Chemistry Department, Universidad del Valle, Cali, Colombia
| | - Jeremy K Nicholson
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Perth, Western Australia, Australia
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Paul A Lyons
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Kenneth G C Smith
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK
| | - Sylvia Richardson
- MRC Biostatistics Unit, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Glenn R Bantug
- Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland
- Botnar Research Centre for Child Health (BRCCH) University Basel & ETH Zurich, Basel, Switzerland
| | - Christoph Hess
- Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK.
- Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK.
- Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland.
- Botnar Research Centre for Child Health (BRCCH) University Basel & ETH Zurich, Basel, Switzerland.
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Ren X, Wang J, Luo S. Dynamic prediction using joint models of longitudinal and recurrent event data: A Bayesian perspective. BIOSTATISTICS & EPIDEMIOLOGY 2019; 5:250-266. [PMID: 34926969 PMCID: PMC8673593 DOI: 10.1080/24709360.2019.1693198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 10/30/2019] [Indexed: 06/14/2023]
Abstract
In cardiovascular disease (CVD) studies, the events of interest may be recurrent (multiple occurrences from the same individual). During the study follow-up, longitudinal measurements are often available and these measurements are highly predictive of event recurrences. It is of great clinical interest to make personalized prediction of the next occurrence of recurrent events using the available clinical information, because it enables clinicians to make more informed and personalized decisions and recommendations. To this end, we propose a joint model of longitudinal and recurrent event data. We develop a Bayesian approach for model inference and a dynamic prediction framework for predicting target subjects' future outcome trajectories and risk of next recurrent event, based on their data up to the prediction time point. To improve computation efficiency, embarrassingly parallel MCMC (EP-MCMC) method is utilized. It partitions the data into multiple subsets, runs MCMC sampler on each subset, and applies random partition trees to combine the posterior draws from all subsets. Our method development is motivated by and applied to the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT), one of the largest CVD studies to compare the effectiveness of medications to treat hypertension.
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Affiliation(s)
- Xuehan Ren
- Xuehan Ren, Ph.D. Candidate, Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jue Wang
- Jue Wang, Ph.D, Genentech Inc., South San Francisco, California, USA
| | - Sheng Luo
- Corresponding author: Sheng Luo, Ph.D, Associate Professor, Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Rd, Durham, NC 27705, USA (; Phone: 919-668-8038)
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