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Huang M, Guo Y, Zhou Z, Xu C, Liu K, Wang Y, Guo Z. Development and validation of a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China. Heliyon 2024; 10:e24526. [PMID: 38298731 PMCID: PMC10828688 DOI: 10.1016/j.heliyon.2024.e24526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/05/2024] [Accepted: 01/10/2024] [Indexed: 02/02/2024] Open
Abstract
Background Considering its high prevalence, estimating the risk of arthritis in middle-aged and older Chinese adults is of particular interest. This study was conducted to develop a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China. Methods Our study included a total of 9599 participants utilising data from the China Health and Retirement Longitudinal Study (CHARLS). Participants were randomly assigned to training and validation groups at a 7:3 ratio. Univariate and multivariate binary logistic regression analyses were used to identify the potential predictors of arthritis. Based on the results of the multivariate binary logistic regression, a nomogram was constructed, and its predictive performance was evaluated using the receiver operating characteristic (ROC) curve. The accuracy and discrimination ability were assessed using calibration curve analysis, while decision curve analysis (DCA) was performed to evaluate the net clinical benefit rate. Results A total of 9599 participants were included in the study, of which 6716 and 2883 were assigned to the training and validation groups, respectively. A nomogram was constructed to include age, hypertension, heart diseases, gender, sleep time, body mass index (BMI), residence address, the parts of joint pain, and trouble with body pains. The results of the ROC curve suggested that the prediction model had a moderate discrimination ability (AUC >0.7). The calibration curve of the prediction model demonstrated a good predictive accuracy. The DCA curves revealed a favourable net benefit for the prediction model. Conclusions The predictive model demonstrated good discrimination, calibration, and clinical validity, and can help community physicians and clinicians to preliminarily assess the risk of arthritis in middle-aged and older community-dwelling adults.
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Affiliation(s)
- Mina Huang
- Department of Orthopedics, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
- School of Nursing, Jinzhou Medical University, Jinzhou, China
| | - Yue Guo
- Department of Orthopedics, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Zipeng Zhou
- Department of Orthopedics, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Chang Xu
- Department of Orthopedics, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Kun Liu
- School of Medical College, Jinzhou Medical University, Jinzhou, China
| | - Yongzhu Wang
- Department of Orthopedics, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
| | - Zhanpeng Guo
- Department of Orthopedics, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China
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Tiulpin A, Saarakkala S, Mathiessen A, Hammer HB, Furnes O, Nordsletten L, Englund M, Magnusson K. Predicting total knee arthroplasty from ultrasonography using machine learning. OSTEOARTHRITIS AND CARTILAGE OPEN 2022; 4:100319. [DOI: 10.1016/j.ocarto.2022.100319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 10/15/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022] Open
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Appleyard T, Thomas MJ, Antcliff D, Peat G. Prediction Models to Estimate the Future Risk of Osteoarthritis in the General Population: A Systematic Review. Arthritis Care Res (Hoboken) 2022. [PMID: 36205228 DOI: 10.1002/acr.25035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 09/06/2022] [Accepted: 10/04/2022] [Indexed: 01/27/2023]
Abstract
OBJECTIVE To evaluate the performance and applicability of multivariable prediction models for osteoarthritis (OA). METHODS This was a systematic review and narrative synthesis using 3 databases (EMBASE, PubMed, and Web of Science) from inception to December 2021. We included general population longitudinal studies reporting derivation, comparison, or validation of multivariable models to predict individual risk of OA incidence, defined by recognized clinical or imaging criteria. We excluded studies reporting prevalent OA and joint arthroplasty outcome. Paired reviewers independently performed article selection, data extraction, and risk-of-bias assessment. Model performance, calibration, and retained predictors were summarized. RESULTS A total of 26 studies were included, reporting 31 final multivariable prediction models for incident knee (23), hip (4), hand (3) and any-site OA (1), with a median of 121.5 (range 27-12,803) outcome events, a median prediction horizon of 8 years (range 2-41), and a median of 6 predictors (range 3-24). Age, body mass index, previous injury, and occupational exposures were among the most commonly included predictors. Model discrimination after validation was generally acceptable to excellent (area under the curve = 0.70-0.85). Either internal or external validation processes were used in most models, although the risk of bias was often judged to be high with limited applicability to mass application in diverse populations. CONCLUSION Despite growing interest in multivariable prediction models for incident OA, focus remains predominantly on the knee, with reliance on data from a small pool of appropriate cohort data sets, and concerns over general population applicability.
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Affiliation(s)
| | - Martin J Thomas
- Keele University and Midlands Partnership NHS Foundation Trust, Staffordshire, and Haywood Hospital, Burslem, UK
| | - Deborah Antcliff
- Keele University, Staffordshire, Northern Care Alliance NHS Foundation Trust, Bury Care Organisation, Manchester, and University of Leeds, Leeds, UK
| | - George Peat
- Keele University, Staffordshire, and Sheffield Hallam University, Sheffield, UK
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McCabe PG, Lisboa P, Baltzopoulos B, Olier I. Externally validated models for first diagnosis and risk of progression of knee osteoarthritis. PLoS One 2022; 17:e0270652. [PMID: 35776714 PMCID: PMC9249202 DOI: 10.1371/journal.pone.0270652] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 06/14/2022] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE We develop and externally validate two models for use with radiological knee osteoarthritis. They consist of a diagnostic model for KOA and a prognostic model of time to onset of KOA. Model development and optimisation used data from the Osteoarthritis initiative (OAI) and external validation for both models was by application to data from the Multicenter Osteoarthritis Study (MOST). MATERIALS AND METHODS The diagnostic model at first presentation comprises subjects in the OAI with and without KOA (n = 2006), modelling with multivariate logistic regression. The prognostic sample involves 5-year follow-up of subjects presenting without clinical KOA (n = 1155), with modelling with Cox regression. In both instances the models used training data sets of n = 1353 and 1002 subjects and optimisation used test data sets of n = 1354 and 1003. The external validation data sets for the diagnostic and prognostic models comprised n = 2006 and n = 1155 subjects respectively. RESULTS The classification performance of the diagnostic model on the test data has an AUC of 0.748 (0.721-0.774) and 0.670 (0.631-0.708) in external validation. The survival model has concordance scores for the OAI test set of 0.74 (0.7325-0.7439) and in external validation 0.72 (0.7190-0.7373). The survival approach stratified the population into two risk cohorts. The separation between the cohorts remains when the model is applied to the validation data. DISCUSSION The models produced are interpretable with app interfaces that implement nomograms. The apps may be used for stratification and for patient education over the impact of modifiable risk factors. The externally validated results, by application to data from a substantial prospective observational study, show the robustness of models for likelihood of presenting with KOA at an initial assessment based on risk factors identified by the OAI protocol and stratification of risk for developing KOA in the next five years. CONCLUSION Modelling clinical KOA from OAI data validates well for the MOST data set. Both risk models identified key factors for differentiation of the target population from commonly available variables. With this analysis there is potential to improve clinical management of patients.
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Affiliation(s)
- Philippa Grace McCabe
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, England, United Kingdom
| | - Paulo Lisboa
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, England, United Kingdom
| | - Bill Baltzopoulos
- Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, England, United Kingdom
| | - Ivan Olier
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, England, United Kingdom
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Qiao J. The Application of Artificial Intelligence in Football Risk Prediction. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6996134. [PMID: 35733572 PMCID: PMC9208919 DOI: 10.1155/2022/6996134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 03/21/2022] [Indexed: 11/17/2022]
Abstract
Football is the most popular sports in the World, with an estimated global following of 4.0 billion fans worldwide. Football draws attention from people of various age groups. The result of the game only decides the performance of the team and individual players. The player has to train smarter to avoid a career-ending injury. Sports have also entered into the new era of artificial intelligence as any industry. Artificial intelligence (AI) in football acts like a teammate to the players and also plays the role of an assistant coach. The coach uses artificial intelligence and incorporates it into the traditional way of training. The Football Associations have already implemented sensors to collect data in the form of technologies such as Video Assistant Referee and Goal Line Technology. Additionally, the quality of the players and the coaches is improved with smart technological implementation. This technology itself incorporates the utilization of smart technologies for data acquisition using sensor networks and an intelligent data analysis. The proposed algorithm is compared with the fuzzy logic model (FLM) and found that it is 7.2% of higher risk predication by the proposed model than the existing.
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Affiliation(s)
- Jinyu Qiao
- Sports Department, Puyang Vocational & Technical College, Puyang 457000, Henan, China
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Yu Y, Ye Z. Healthcare Data-Based Prediction Algorithm for Potential Knee Joint Injury of Football Players. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:3461648. [PMID: 35915627 PMCID: PMC9338743 DOI: 10.1155/2021/3461648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/02/2021] [Accepted: 11/05/2021] [Indexed: 12/01/2022]
Abstract
It is important to predict the potential harm to the knee joint in order to prevent football players from inflicting numerous injuries to the knee during activity. Numerous professionals have been drawn to this subject, and many viable prediction systems have been developed. Prediction of potential knee joint injury is critical to effectively avoid knee joint injury during exercise. The current prediction algorithms are mainly implemented through expert interviews, medical reports, and historical documents. The algorithms have problems with low prediction accuracy or precision values. There is a need to understand more knee injury factors and improve the prediction accuracy; hence, the intelligent prediction algorithm for potential injury of knee joints of football players is proposed in this paper. Firstly, the characteristics of the knee joint injury and the injury factors of the football players are gathered and analyzed. Then, the damage is predicted by the similarity measurement. The experimental results show that the proposed algorithm has higher prediction accuracy and shorter time. According to the findings of a survey that collected healthcare data, several key factors contribute to football knee injuries. To a degree, this algorithm can predict the likelihood of a football player's knee injury.
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Affiliation(s)
- Yue Yu
- The Ministry of Public Basic Course, Wuhan Institute of Design and Sciences, Wuhan 430205, China
| | - Zi Ye
- The Ministry of Public Basic Course, Wuhan Institute of Design and Sciences, Wuhan 430205, China
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Johnsen MB, Magnusson K, Børte S, Gabrielsen ME, Winsvold BS, Skogholt AH, Thomas L, Storheim K, Hveem K, Zwart JA. Development and validation of a prediction model for incident hand osteoarthritis in the HUNT study. Osteoarthritis Cartilage 2020; 28:932-940. [PMID: 32360252 DOI: 10.1016/j.joca.2020.04.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 04/01/2020] [Accepted: 04/21/2020] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To develop and externally validate prediction models for incident hand osteoarthritis (OA) in a large population-based cohort of middle aged and older men and women. DESIGN We included 17,153 men and 18,682 women from a population-based cohort, aged 35-70 years at baseline (1995-1997). Incident hand OA were obtained from diagnostic codes in the Norwegian National Patient Register (1995-2018). We studied whether a range of self-reported and clinically measured predictors could predict hand OA, using the Area Under the receiver-operating Curve (AUC) from logistic regression. External validation of an existing prediction model for male hand OA was tested on discrimination in a sample of men. Bootstrapping was used to avoid overfitting. RESULTS The model for men showed modest discriminatory ability (AUC = 0.67, 95% CI 0.62-0.71). Adding a genetic risk score did not improve prediction. Similar discrimination was observed in the model for women (AUC = 0.62, 95% CI 0.59-0.64). Prediction was not improved by adding a genetic risk score or hormonal and reproductive factors. Applying external validation, similar results were observed among men in HUNT (The Nord-Trøndelag Health Study) as in the developmental sample (AUC = 0.62, 95% CI 0.57-0.65). CONCLUSION We developed prediction models for incident hand OA in men and women. For women, the model included body mass index (BMI), heavy physical work, high physical activity and perceived poor health. The model showed moderate discrimination. For men, we have shown that a prediction model including BMI, education and information on sleep can predict incident hand OA in several populations with moderate discriminative ability.
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Affiliation(s)
- M B Johnsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway; K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| | - K Magnusson
- Lund University, Faculty of Medicine, Department of Clinical Sciences, Clinical Epidemiology Unit, Lund, Orthopaedics, Lund, Sweden; National Advisory Unit on Rehabilitation in Rheumatology, Department of Rheumatology, Diakonhjemmet Hospital, Oslo, Norway.
| | - S Børte
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway; K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| | - M E Gabrielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| | - B S Winsvold
- Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway; K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| | - A H Skogholt
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| | - L Thomas
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
| | - K Storheim
- Research and Communication Unit for Musculoskeletal Health, Oslo University Hospital, Oslo, Norway.
| | - K Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
| | - J-A Zwart
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Research, Innovation and Education, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway; K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway.
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