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Chen X, Zhou J, Yuan Q, Huang C, Li Y. A conceptual framework on determinants of the integrated tuberculosis control model implementation in China. Front Med (Lausanne) 2024; 11:1407131. [PMID: 39234037 PMCID: PMC11371783 DOI: 10.3389/fmed.2024.1407131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 07/22/2024] [Indexed: 09/06/2024] Open
Abstract
Improving the provision of tuberculosis (TB) care is both urgent and imperative to achieve the goals outlined in the End TB Strategy. China has initiated the integrated TB control model to enhance the quality of TB care Since 2012. Despite these efforts, the integrated TB control health system encounters numerous challenges in delivering effective TB care. The factors influencing TB care provision are intricate, and a conceptual framework to comprehend these potential determinants is currently lacking. To bridge this gap, this article proposed a conceptual framework that was developed through insights from the fields of both public management and health services, adjustment of PRISM model and elements, reference to the blocks of health system and reference to the framework of outcome indicators in implementation research. This conceptual framework included 4 modules which can be coherently and logically deduced, offered a multi-perspective understanding of the determinants to TB care, and hypothesized that the TB control services provided by the integrated TB control model is a public service and must be "patient-centered"; determinants of the integrated TB control model implementation can be divided into seven domains; the evaluation of the integrated TB control model implementation covers implementation outcomes and service outcomes. This framework offers the potential to guide empirical investigations, aiding in the understanding and identification of determinants, including barriers and facilitators, associated with the implementation of the integrated TB control health model. Furthermore, it serves as a valuable tool for developing interventions that address system-level barriers, drawing insights from the realms of public management and health services.
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Affiliation(s)
- Xi Chen
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China
- Army Medical University (Third Military Medical University), Chongqing, China
| | - Jiani Zhou
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China
| | - Quan Yuan
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China
| | - Chunji Huang
- Army Medical University (Third Military Medical University), Chongqing, China
| | - Ying Li
- Department of Social Medicine and Health Service Management, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China
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K SP, Parivakkam Mani A, S G, Yadav S. Advancements in Artificial Intelligence for the Diagnosis of Multidrug Resistance and Extensively Drug-Resistant Tuberculosis: A Comprehensive Review. Cureus 2024; 16:e60280. [PMID: 38872656 PMCID: PMC11173349 DOI: 10.7759/cureus.60280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/11/2024] [Indexed: 06/15/2024] Open
Abstract
Tuberculosis (TB) remains a significant global health concern, particularly with the emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). Traditional methods for diagnosing drug resistance in TB are time-consuming and often lack accuracy, leading to delays in appropriate treatment initiation and exacerbating the spread of drug-resistant strains. In recent years, artificial intelligence (AI) techniques have shown promise in revolutionizing TB diagnosis, offering rapid and accurate identification of drug-resistant strains. This comprehensive review explores the latest advancements in AI applications for the diagnosis of MDR-TB and XDR-TB. We discuss the various AI algorithms and methodologies employed, including machine learning, deep learning, and ensemble techniques, and their comparative performances in TB diagnosis. Furthermore, we examine the integration of AI with novel diagnostic modalities such as whole-genome sequencing, molecular assays, and radiological imaging, enhancing the accuracy and efficiency of TB diagnosis. Challenges and limitations surrounding the implementation of AI in TB diagnosis, such as data availability, algorithm interpretability, and regulatory considerations, are also addressed. Finally, we highlight future directions and opportunities for the integration of AI into routine clinical practice for combating drug-resistant TB, ultimately contributing to improved patient outcomes and enhanced global TB control efforts.
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Affiliation(s)
- Shanmuga Priya K
- Department of Pulmonology, Faculty of Medicine, Sri Lalithambigai Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai, IND
| | - Anbumaran Parivakkam Mani
- Department of Respiratory Medicine, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
| | - Geethalakshmi S
- Department of Microbiology, Sri Lalithambigai Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai, IND
| | - Sankalp Yadav
- Department of Medicine, Shri Madan Lal Khurana Chest Clinic, New Delhi, IND
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Zhang F, Zhang F, Li L, Pang Y. Clinical utilization of artificial intelligence in predicting therapeutic efficacy in pulmonary tuberculosis. J Infect Public Health 2024; 17:632-641. [PMID: 38428275 DOI: 10.1016/j.jiph.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/17/2024] [Accepted: 02/20/2024] [Indexed: 03/03/2024] Open
Abstract
Traditional methods for monitoring pulmonary tuberculosis (PTB) treatment efficacy lack sensitivity, prompting the exploration of artificial intelligence (AI) to enhance monitoring. This review investigates the application of AI in monitoring anti-tuberculosis (ATTB) treatment, revealing its potential in predicting treatment duration, adverse reactions, outcomes, and drug resistance. It provides important insights into the potential of AI technology to enhance monitoring and management of ATTB treatment. Systematic search across six databases from 2013 to 2023 explored AI in forecasting PTB treatment efficacy. Support vector machine and convolutional neural network excel in treatment duration prediction, while random forest, artificial neural network, and classification and regression tree show promise in forecasting adverse reactions and outcomes. Neural networks and random forest are effective in predicting drug resistance. AI advancements offer improved monitoring strategies, better patient prognosis, and pave the way for future AI research in PTB treatment monitoring.
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Affiliation(s)
- Fuzhen Zhang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, PR China; Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, PR China
| | - Fan Zhang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, PR China
| | - Liang Li
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, PR China; Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, PR China.
| | - Yu Pang
- Department of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis & Thoracic Tumor Research Institute, Beijing 101149, PR China.
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Li D, Tang SY, Lei S, Xie HB, Li LQ. A nomogram for predicting mortality of patients initially diagnosed with primary pulmonary tuberculosis in Hunan province, China: a retrospective study. Front Cell Infect Microbiol 2023; 13:1179369. [PMID: 37333854 PMCID: PMC10272565 DOI: 10.3389/fcimb.2023.1179369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/05/2023] [Indexed: 06/20/2023] Open
Abstract
Objective According to the Global Tuberculosis Report for three consecutive years, tuberculosis (TB) is the second leading infectious killer. Primary pulmonary tuberculosis (PTB) leads to the highest mortality among TB diseases. Regretfully, no previous studies targeted the PTB of a specific type or in a specific course, so models established in previous studies cannot be accurately feasible for clinical treatments. This study aimed to construct a nomogram prognostic model to quickly recognize death-related risk factors in patients initially diagnosed with PTB to intervene and treat high-risk patients as early as possible in the clinic to reduce mortality. Methods We retrospectively analyzed the clinical data of 1,809 in-hospital patients initially diagnosed with primary PTB at Hunan Chest Hospital from January 1, 2019, to December 31, 2019. Binary logistic regression analysis was used to identify the risk factors. A nomogram prognostic model for mortality prediction was constructed using R software and was validated using a validation set. Results Univariate and multivariate logistic regression analyses revealed that drinking, hepatitis B virus (HBV), body mass index (BMI), age, albumin (ALB), and hemoglobin (Hb) were six independent predictors of death in in-hospital patients initially diagnosed with primary PTB. Based on these predictors, a nomogram prognostic model was established with high prediction accuracy, of which the area under the curve (AUC) was 0.881 (95% confidence interval [Cl]: 0.777-0.847), the sensitivity was 84.7%, and the specificity was 77.7%.Internal and external validations confirmed that the constructed model fit the real situation well. Conclusion The constructed nomogram prognostic model can recognize risk factors and accurately predict the mortality of patients initially diagnosed with primary PTB. This is expected to guide early clinical intervention and treatment for high-risk patients.
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Affiliation(s)
- Dan Li
- Xiangya School of Nursing, Central South University, Changsha, Hunan, China
- College of Applied Technology, Hunan Open University, Changsha, Hunan, China
| | - Si-Yuan Tang
- Xiangya School of Nursing, Central South University, Changsha, Hunan, China
| | - Sheng Lei
- Interventional Radiology Center, Hunan Chest Hospital, Changsha, Hunan, China
| | - He-Bin Xie
- Department of Drug Clinical Trial Institutions, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan, China
| | - Lin-Qi Li
- School of Public Health, University of South China, Hengyang, China
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Wu Z(E, Xu D, Hu PJH, Huang TS. A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients. J Am Med Inform Assoc 2023; 30:846-858. [PMID: 36794643 PMCID: PMC10114116 DOI: 10.1093/jamia/ocad008] [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: 10/23/2022] [Revised: 12/26/2022] [Accepted: 01/25/2023] [Indexed: 02/17/2023] Open
Abstract
OBJECTIVE Estimating the deterioration paths of chronic hepatitis B (CHB) patients is critical for physicians' decisions and patient management. A novel, hierarchical multilabel graph attention-based method aims to predict patient deterioration paths more effectively. Applied to a CHB patient data set, it offers strong predictive utilities and clinical value. MATERIALS AND METHODS The proposed method incorporates patients' responses to medications, diagnosis event sequences, and outcome dependencies to estimate deterioration paths. From the electronic health records maintained by a major healthcare organization in Taiwan, we collect clinical data about 177 959 patients diagnosed with hepatitis B virus infection. We use this sample to evaluate the proposed method's predictive efficacy relative to 9 existing methods, as measured by precision, recall, F-measure, and area under the curve (AUC). RESULTS We use 20% of the sample as holdouts to test each method's prediction performance. The results indicate that our method consistently and significantly outperforms all benchmark methods. It attains the highest AUC, with a 4.8% improvement over the best-performing benchmark, as well as 20.9% and 11.4% improvements in precision and F-measures, respectively. The comparative results demonstrate that our method is more effective for predicting CHB patients' deterioration paths than existing predictive methods. DISCUSSION AND CONCLUSION The proposed method underscores the value of patient-medication interactions, temporal sequential patterns of distinct diagnosis, and patient outcome dependencies for capturing dynamics that underpin patient deterioration over time. Its efficacious estimates grant physicians a more holistic view of patient progressions and can enhance their clinical decision-making and patient management.
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Affiliation(s)
- Zejian (Eric) Wu
- Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, Utah, USA
| | - Da Xu
- Department of Information Systems, College of Business, California State University Long Beach, Long Beach, California, USA
| | - Paul Jen-Hwa Hu
- Department of Operations and Information Systems, David Eccles School of Business, University of Utah, Salt Lake City, Utah, USA
| | - Ting-Shuo Huang
- Department of General Surgery, Keelung Chang Gung Memorial Hospital, Keelung City, Taiwan
- Department of Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
- Community Medicine Research Center, Keelung Chang Gung Memorial Hospital, Keelung City, Taiwan
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Liao KM, Liu CF, Chen CJ, Feng JY, Shu CC, Ma YS. Using an Artificial Intelligence Approach to Predict the Adverse Effects and Prognosis of Tuberculosis. Diagnostics (Basel) 2023; 13:diagnostics13061075. [PMID: 36980382 PMCID: PMC10047137 DOI: 10.3390/diagnostics13061075] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/01/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
Background: Tuberculosis (TB) is one of the leading causes of death worldwide and a major cause of ill health. Without treatment, the mortality rate of TB is approximately 50%; with treatment, most patients with TB can be cured. However, anti-TB drug treatments may result in many adverse effects. Therefore, it is important to detect and predict these adverse effects early. Our study aimed to build models using an artificial intelligence/machine learning approach to predict acute hepatitis, acute respiratory failure, and mortality after TB treatment. Materials and Methods: Adult patients (age ≥ 20 years) who had a TB diagnosis and received treatment from January 2004 to December 2021 were enrolled in the present study. Thirty-six feature variables were used to develop the predictive models with AI. The data were randomly stratified into a training dataset for model building (70%) and a testing dataset for model validation (30%). These algorithms included XGBoost, random forest, MLP, light GBM, logistic regression, and SVM. Results: A total of 2248 TB patients in Chi Mei Medical Center were included in the study; 71.7% were males, and the other 28.3% were females. The mean age was 67.7 ± 16.4 years. The results showed that our models using the six AI algorithms all had a high area under the receiver operating characteristic curve (AUC) in predicting acute hepatitis, respiratory failure, and mortality, and the AUCs ranged from 0.920 to 0.766, 0.884 to 0.797, and 0.834 to 0.737, respectively. Conclusions: Our AI models were good predictors and can provide clinicians with a valuable tool to detect the adverse prognosis in TB patients early.
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Affiliation(s)
- Kuang-Ming Liao
- Department of Internal Medicine, Chi Mei Medical Center, Chiali, Tainan 722013, Taiwan
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan 710402, Taiwan;
- Correspondence: (C.-F.L.); (C.-J.C.)
| | - Chia-Jung Chen
- Department of Information Systems, Chi Mei Medical Center, Tainan 710402, Taiwan
- Correspondence: (C.-F.L.); (C.-J.C.)
| | - Jia-Yih Feng
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan
- School of Medicine, National Yang-Ming University, Taipei 112304, Taiwan
| | - Chin-Chung Shu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei 100225, Taiwan
- College of Medicine, National Taiwan University, Taipei 100233, Taiwan
| | - Yu-Shan Ma
- Department of Medical Research, Chi Mei Medical Center, Tainan 710402, Taiwan;
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Dynamic Risk Prediction via a Joint Frailty-Copula Model and IPD Meta-Analysis: Building Web Applications. ENTROPY 2022; 24:e24050589. [PMID: 35626474 PMCID: PMC9140593 DOI: 10.3390/e24050589] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/11/2022] [Accepted: 04/21/2022] [Indexed: 12/17/2022]
Abstract
Clinical risk prediction formulas for cancer patients can be improved by dynamically updating the formulas by intermediate events, such as tumor progression. The increased accessibility of individual patient data (IPD) from multiple studies has motivated the development of dynamic prediction formulas accounting for between-study heterogeneity. A joint frailty-copula model for overall survival and time to tumor progression has the potential to develop a dynamic prediction formula of death from heterogenous studies. However, the process of developing, validating, and publishing the prediction formula is complex, which has not been sufficiently described in the literature. In this article, we provide a tutorial in order to build a web-based application for dynamic risk prediction for cancer patients on the basis of the R packages joint.Cox and Shiny. We demonstrate the proposed methods using a dataset of breast cancer patients from multiple clinical studies. Following this tutorial, we demonstrate how one can publish web applications available online, which can be manipulated by any user through a smartphone or personal computer. After learning this tutorial, developers acquire the ability to build an online web application using their own datasets.
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