1
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Shrier I, Impellizzeri FM, Stovitz SD. Identifying and Minimizing Incentives for Competing Interests in Sports Medicine Publications. Sports Med 2024; 54:1991-2000. [PMID: 38714641 DOI: 10.1007/s40279-024-02037-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/17/2024] [Indexed: 05/10/2024]
Abstract
Academics in sports medicine as well as other medical fields are generally expected to publish research and opinions in peer-reviewed journals. The peer-review process is intended to protect against the publication of flawed research and unsubstantiated claims. However, both financial and non-financial competing interests may result in sub-optimal results by affecting investigators, editors, peer reviewers, academic institutions, and publishers. In this article, we focus on the non-financial competing interests created in our current academic system. Because these competing interests are embedded in our current scholastic framework, the potential biases are difficult to quantify. To minimize the effect of these competing interests, we review and highlight some underlying incentives for each stakeholder and some potential solutions to mitigate their effects.
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Affiliation(s)
- Ian Shrier
- Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, 3755 Cote Sainte Catherine Road, Montreal, QC, H3T 1E2, Canada.
| | - Franco M Impellizzeri
- School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney, Sydney, NSW, Australia
| | - Steven D Stovitz
- Department of Family Medicine and Community Health, University of Minnesota, Minnesota, USA
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2
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Clift AK, Mahon H, Khan G, Boardman-Pretty F, Worker A, Marchini E, Buendia O, Fish P, Khan MS. Identifying patients with undiagnosed small intestinal neuroendocrine tumours in primary care using statistical and machine learning: model development and validation study. Br J Cancer 2024; 131:305-311. [PMID: 38831012 PMCID: PMC11263687 DOI: 10.1038/s41416-024-02736-1] [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: 07/26/2023] [Revised: 05/10/2024] [Accepted: 05/23/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Neuroendocrine tumours (NETs) are increasing in incidence, often diagnosed at advanced stages, and individuals may experience years of diagnostic delay, particularly when arising from the small intestine (SI). Clinical prediction models could present novel opportunities for case finding in primary care. METHODS An open cohort of adults (18+ years) contributing data to the Optimum Patient Care Research Database between 1st Jan 2000 and 30th March 2023 was identified. This database collects de-identified data from general practices in the UK. Model development approaches comprised logistic regression, penalised regression, and XGBoost. Performance (discrimination and calibration) was assessed using internal-external cross-validation. Decision analysis curves compared clinical utility. RESULTS Of 11.7 million individuals, 382 had recorded SI NET diagnoses (0.003%). The XGBoost model had the highest AUC (0.869, 95% confidence interval [CI]: 0.841-0.898) but was mildly miscalibrated (slope 1.165, 95% CI: 1.088-1.243; calibration-in-the-large 0.010, 95% CI: -0.164 to 0.185). Clinical utility was similar across all models. DISCUSSION Multivariable prediction models may have clinical utility in identifying individuals with undiagnosed SI NETs using information in their primary care records. Further evaluation including external validation and health economics modelling may identify cost-effective strategies for case finding for this uncommon tumour.
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Affiliation(s)
| | | | | | | | | | | | | | - Peter Fish
- Mendelian, The Trampery Old Street, London, UK
| | - Mohid S Khan
- South Wales Neuroendocrine Cancer Service, University Hospital of Wales, Cardiff and Vale University Health Board, Heath Park, Cardiff, UK
- Cardiff University, School of Medicine, University Hospital of Wales, Cardiff, UK
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3
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Lyman GH, Kuderer NM. Artificial Intelligence in Cancer Clinical Research: II. Development and Validation of Clinical Prediction Models. Cancer Invest 2024; 42:447-451. [PMID: 38775011 DOI: 10.1080/07357907.2024.2354991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Affiliation(s)
- Gary H Lyman
- Editor-in-Chief, Cancer Investigation Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Nicole M Kuderer
- Deputy Editor, Cancer Investigation Advanced Cancer Research Group, Kirkland, WA, USA
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4
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Song J, Zhang J, Liu G, Guo Z, Liao H, Feng W, Lin W, Li L, Zhang Y, Yang Y, Liu B, Luo R, Chen H, Wang S, Liu JH. PET/CT deep learning prognosis for treatment decision support in esophageal squamous cell carcinoma. Insights Imaging 2024; 15:161. [PMID: 38913225 PMCID: PMC11196479 DOI: 10.1186/s13244-024-01737-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 06/02/2024] [Indexed: 06/25/2024] Open
Abstract
OBJECTIVES The clinical decision-making regarding choosing surgery alone (SA) or surgery followed by postoperative adjuvant chemotherapy (SPOCT) in esophageal squamous cell carcinoma (ESCC) remains controversial. We aim to propose a pre-therapy PET/CT image-based deep learning approach to improve the survival benefit and clinical management of ESCC patients. METHODS This retrospective multicenter study included 837 ESCC patients from three institutions. Prognostic biomarkers integrating six networks were developed to build an ESCC prognosis (ESCCPro) model and predict the survival probability of ESCC patients treated with SA and SPOCT. Patients who did not undergo surgical resection were in a control group. Overall survival (OS) was the primary end-point event. The expected improvement in survival prognosis with the application of ESCCPro to assign treatment protocols was estimated by comparing the survival of patients in each subgroup. Seven clinicians with varying experience evaluated how ESCCPro performed in assisting clinical decision-making. RESULTS In this retrospective multicenter study, patients receiving SA had a median OS 9.2 months longer than controls. No significant differences in survival were found between SA patients with predicted poor outcomes and the controls (p > 0.05). It was estimated that if ESCCPro was used to determine SA and SPOCT eligibility, the median OS in the ESCCPro-recommended SA group and SPOCT group would have been 15.3 months and 24.9 months longer, respectively. In addition, ESCCPro also significantly improved prognosis accuracy, certainty, and the efficiency of clinical experts. CONCLUSION ESCCPro assistance improved the survival benefit of ESCC patients and the clinical decision-making among the two treatment approaches. CRITICAL RELEVANCE STATEMENT The ESCCPro model for treatment decision-making is promising to improve overall survival in ESCC patients undergoing surgical resection and patients undergoing surgery followed by postoperative adjuvant chemotherapy. KEY POINTS ESCC is associated with a poor prognosis and unclear ideal treatments. ESCCPro predicts the survival of patients with ESCC and the expected benefit from SA. ESCCPro improves clinicians' stratification of patients' prognoses.
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Affiliation(s)
- Jiangdian Song
- School of Health Management, China Medical University, Shenyang, China.
| | - Jie Zhang
- Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China
| | - Guichao Liu
- The Fifth Affiliated Hospital of Sun Yat-Sen University, Guangdong, China
| | - Zhexu Guo
- Department of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors China Medical University, Ministry of Education, Shenyang, China
- Department of VIP In-Patient Ward, The First Hospital of China Medical University, Shenyang, China
| | - Hongxian Liao
- Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China
| | - Wenhui Feng
- Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China
| | - Wenxiang Lin
- Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China
| | - Lei Li
- Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China
| | - Yi Zhang
- Department of Medical Imaging, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, China
| | - Yuxiang Yang
- Department of Radiology, The Second People's Hospital of Xiangzhou, Zhuhai, China
| | - Bin Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Hong Kong, Hong Kong
| | - Hao Chen
- Department of Gastroenterology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, China.
| | - Siyun Wang
- Department of Oncology, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
| | - Jian-Hua Liu
- Department of Oncology, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
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Nordblom N, Büttner M, Schwendicke F. Artificial Intelligence in Orthodontics: Critical Review. J Dent Res 2024; 103:577-584. [PMID: 38682436 PMCID: PMC11118788 DOI: 10.1177/00220345241235606] [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] [Indexed: 05/01/2024] Open
Abstract
With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist's task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.
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Affiliation(s)
- N.F. Nordblom
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - M. Büttner
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - F. Schwendicke
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University of Munich, Munich, Germany
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Andaur Navarro CL, Damen JAA, Ghannad M, Dhiman P, van Smeden M, Reitsma JB, Collins GS, Riley RD, Moons KGM, Hooft L. SPIN-PM: a consensus framework to evaluate the presence of spin in studies on prediction models. J Clin Epidemiol 2024; 170:111364. [PMID: 38631529 DOI: 10.1016/j.jclinepi.2024.111364] [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: 10/24/2023] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVES To develop a framework to identify and evaluate spin practices and its facilitators in studies on clinical prediction model regardless of the modeling technique. STUDY DESIGN AND SETTING We followed a three-phase consensus process: (1) premeeting literature review to generate items to be included; (2) a series of structured meetings to provide comments discussed and exchanged viewpoints on items to be included with a panel of experienced researchers; and (3) postmeeting review on final list of items and examples to be included. Through this iterative consensus process, a framework was derived after all panel's researchers agreed. RESULTS This consensus process involved a panel of eight researchers and resulted in SPIN-Prediction Models which consists of two categories of spin (misleading interpretation and misleading transportability), and within these categories, two forms of spin (spin practices and facilitators of spin). We provide criteria and examples. CONCLUSION We proposed this guidance aiming to facilitate not only the accurate reporting but also an accurate interpretation and extrapolation of clinical prediction models which will likely improve the reporting quality of subsequent research, as well as reduce research waste.
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Affiliation(s)
- Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Mona Ghannad
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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7
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Jones C, Taylor M, Sperrin M, Grant SW. A systematic review of cardiac surgery clinical prediction models that include intra-operative variables. Perfusion 2024:2676591241237758. [PMID: 38649154 DOI: 10.1177/02676591241237758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
BACKGROUND Most cardiac surgery clinical prediction models (CPMs) are developed using pre-operative variables to predict post-operative outcomes. Some CPMs are developed with intra-operative variables, but none are widely used. The objective of this systematic review was to identify CPMs with intra-operative variables that predict short-term outcomes following adult cardiac surgery. METHODS Ovid MEDLINE and EMBASE databases were searched from inception to December 2022, for studies developing a CPM with at least one intra-operative variable. Data were extracted using a critical appraisal framework and bias assessment tool. Model performance was analysed using discrimination and calibration measures. RESULTS A total of 24 models were identified. Frequent predicted outcomes were acute kidney injury (9/24 studies) and peri-operative mortality (6/24 studies). Frequent pre-operative variables were age (18/24 studies) and creatinine/eGFR (18/24 studies). Common intra-operative variables were cardiopulmonary bypass time (16/24 studies) and transfusion (13/24 studies). Model discrimination was acceptable for all internally validated models (AUC 0.69-0.91). Calibration was poor (15/24 studies) or unreported (8/24 studies). Most CPMs were at a high or indeterminate risk of bias (23/24 models). The added value of intra-operative variables was assessed in six studies with statistically significantly improved discrimination demonstrated in two. CONCLUSION Weak reporting and methodological limitations may restrict wider applicability and adoption of existing CPMs that include intra-operative variables. There is some evidence that CPM discrimination is improved with the addition of intra-operative variables. Further work is required to understand the role of intra-operative CPMs in the management of cardiac surgery patients.
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Affiliation(s)
- Ceri Jones
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- Department of Clinical Perfusion, University Hospital Southampton NHS Foundation Trust, Southampton General Hospital, Southampton, UK
| | - Marcus Taylor
- Department of Cardiothoracic Surgery, Manchester University Hospital Foundation Trust, Wythenshawe Hospital, , Manchester, UK
| | - Matthew Sperrin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Stuart W Grant
- Division of Cardiovascular Sciences, ERC, Manchester University Hospitals Foundation Trust, University of Manchester, Manchester, UK
- South Tees Academic Cardiovascular Unit, South Tees Hospitals NHS Foundation Trust, Middlesbrough, UK
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8
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Collins GS. Making the black box more transparent: improving the reporting of artificial intelligence studies in healthcare. BMJ 2024; 385:q832. [PMID: 38626954 DOI: 10.1136/bmj.q832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Affiliation(s)
- Gary S Collins
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
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9
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Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 2024; 385:e078378. [PMID: 38626948 PMCID: PMC11019967 DOI: 10.1136/bmj-2023-078378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/17/2024] [Indexed: 04/19/2024]
Affiliation(s)
- Gary S Collins
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Karel G M Moons
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Paula Dhiman
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
| | - Andrew L Beam
- Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Science, Leiden University Medical Centre, Leiden, Netherlands
| | - Marzyeh Ghassemi
- Department of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xiaoxuan Liu
- Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Johannes B Reitsma
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Anne-Laure Boulesteix
- Institute for Medical Information Processing, Biometry and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-University of Munich and Munich Centre of Machine Learning, Germany
| | - Jennifer Catherine Camaradou
- Patient representative, Health Data Research UK patient and public involvement and engagement group
- Patient representative, University of East Anglia, Faculty of Health Sciences, Norwich Research Park, Norwich, UK
| | - Leo Anthony Celi
- Beth Israel Deaconess Medical Center, Boston, MA, USA
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | - Alastair K Denniston
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
- Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Robert M Golub
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | | | - Emily Lam
- Patient representative, Health Data Research UK patient and public involvement and engagement group
| | - Naomi Lee
- National Institute for Health and Care Excellence, London, UK
| | - Elizabeth W Loder
- The BMJ, London, UK
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lena Maier-Hein
- Department of Intelligent Medical Systems, German Cancer Research Centre, Heidelberg, Germany
| | - Bilal A Mateen
- Institute of Health Informatics, University College London, London, UK
- Wellcome Trust, London, UK
- Alan Turing Institute, London, UK
| | - Melissa D McCradden
- Department of Bioethics, Hospital for Sick Children Toronto, ON, Canada
- Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, Canada
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
| | - Johan Ordish
- Medicines and Healthcare products Regulatory Agency, London, UK
| | - Richard Parnell
- Patient representative, Health Data Research UK patient and public involvement and engagement group
| | - Sherri Rose
- Department of Health Policy and Center for Health Policy, Stanford University, Stanford, CA, USA
| | - Karandeep Singh
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
| | - Patricia Logullo
- Centre for Statistics in Medicine, UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
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10
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Hassan A, Critelli B, Lahooti I, Lahooti A, Matzko N, Adams JN, Liss L, Quion J, Restrepo D, Nikahd M, Culp S, Noh L, Tong K, Park JS, Akshintala V, Windsor JA, Mull NK, Papachristou GI, Celi LA, Lee PJ. Critical appraisal of machine learning prognostic models for acute pancreatitis: protocol for a systematic review. Diagn Progn Res 2024; 8:6. [PMID: 38561864 PMCID: PMC10986113 DOI: 10.1186/s41512-024-00169-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 02/15/2024] [Indexed: 04/04/2024] Open
Abstract
Acute pancreatitis (AP) is an acute inflammatory disorder that is common, costly, and is increasing in incidence worldwide with over 300,000 hospitalizations occurring yearly in the United States alone. As its course and outcomes vary widely, a critical knowledge gap in the field has been a lack of accurate prognostic tools to forecast AP patients' outcomes. Despite several published studies in the last three decades, the predictive performance of published prognostic models has been found to be suboptimal. Recently, non-regression machine learning models (ML) have garnered intense interest in medicine for their potential for better predictive performance. Each year, an increasing number of AP models are being published. However, their methodologic quality relating to transparent reporting and risk of bias in study design has never been systematically appraised. Therefore, through collaboration between a group of clinicians and data scientists with appropriate content expertise, we will perform a systematic review of papers published between January 2021 and December 2023 containing artificial intelligence prognostic models in AP. To systematically assess these studies, the authors will leverage the CHARMS checklist, PROBAST tool for risk of bias assessment, and the most current version of the TRIPOD-AI. (Research Registry ( http://www.reviewregistry1727 .).
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Affiliation(s)
- Amier Hassan
- Division of Gastroenterology and Hepatology, Weill Cornell Medical College, New York, USA
| | - Brian Critelli
- Division of Gastroenterology and Hepatology, Weill Cornell Medical College, New York, USA
| | - Ila Lahooti
- Division of Gastroenterology and Hepatology, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Ali Lahooti
- Division of Gastroenterology and Hepatology, Weill Cornell Medical College, New York, USA
| | - Nate Matzko
- Division of Gastroenterology and Hepatology, Weill Cornell Medical College, New York, USA
| | - Jan Niklas Adams
- Division of Process and Data Science, Rheinisch-Westfälische Technische Hochschule Aachen University, Aachen, Germany
| | - Lukas Liss
- Division of Process and Data Science, Rheinisch-Westfälische Technische Hochschule Aachen University, Aachen, Germany
| | - Justin Quion
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, USA
| | - David Restrepo
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, USA
| | - Melica Nikahd
- Division of Bioinformatics, Ohio State University Wexner Medical Center, Columbus, USA
| | - Stacey Culp
- Division of Bioinformatics, Ohio State University Wexner Medical Center, Columbus, USA
| | - Lydia Noh
- Northeast Ohio Medical School, Rootstown, USA
| | - Kathleen Tong
- Division of Gastroenterology and Hepatology, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Jun Sung Park
- Division of Gastroenterology and Hepatology, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Venkata Akshintala
- Division of Gastroenterology, Johns Hopkins Medical Center, Baltimore, USA
| | - John A Windsor
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - Nikhil K Mull
- Division of Hospital Medicine and Penn Medicine Center for Evidence-based Practice, University of Pennsylvania, Philadelphia, USA
| | - Georgios I Papachristou
- Division of Gastroenterology and Hepatology, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Leo Anthony Celi
- Department of Surgery, University of Auckland, Auckland, New Zealand
- Division of Critical Care, Beth Israel Medical Center, Boston, USA
| | - Peter J Lee
- Division of Gastroenterology and Hepatology, Ohio State University Wexner Medical Center, Columbus, OH, USA.
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11
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O'Connor S, Vercell A, Wong D, Yorke J, Fallatah FA, Cave L, Anny Chen LY. The application and use of artificial intelligence in cancer nursing: A systematic review. Eur J Oncol Nurs 2024; 68:102510. [PMID: 38310664 DOI: 10.1016/j.ejon.2024.102510] [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: 10/05/2023] [Revised: 01/07/2024] [Accepted: 01/10/2024] [Indexed: 02/06/2024]
Abstract
PURPOSE Artificial Intelligence is being applied in oncology to improve patient and service outcomes. Yet, there is a limited understanding of how these advanced computational techniques are employed in cancer nursing to inform clinical practice. This review aimed to identify and synthesise evidence on artificial intelligence in cancer nursing. METHODS CINAHL, MEDLINE, PsycINFO, and PubMed were searched using key terms between January 2010 and December 2022. Titles, abstracts, and then full texts were screened against eligibility criteria, resulting in twenty studies being included. Critical appraisal was undertaken, and relevant data extracted and analysed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. RESULTS Artificial intelligence was used in numerous areas including breast, colorectal, liver, and ovarian cancer care among others. Algorithms were trained and tested on primary and secondary datasets to build predictive models of health problems related to cancer. Studies reported this led to improvements in the accuracy of predicting health outcomes or identifying variables that improved outcome prediction. While nurses led most studies, few deployed an artificial intelligence based digital tool with cancer nurses in a real-world setting as studies largely focused on developing and validating predictive models. CONCLUSION Electronic cancer nursing datasets should be established to enable artificial intelligence techniques to be tested and if effective implemented in digital prediction and other AI-based tools. Cancer nurses need more education on machine learning and natural language processing, so they can lead and contribute to artificial intelligence developments in oncology.
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Affiliation(s)
- Siobhan O'Connor
- Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, London, United Kingdom.
| | - Amy Vercell
- Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, London, United Kingdom; The Christie NHS Foundation Trust, Wilmslow Rd, Manchester, M20 4BX, United Kingdom.
| | - David Wong
- Leeds Institute for Health Informatics, University of Leeds, Leeds, United Kingdom.
| | - Janelle Yorke
- Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, London, United Kingdom; The Christie NHS Foundation Trust, Wilmslow Rd, Manchester, M20 4BX, United Kingdom.
| | - Fatmah Abdulsamad Fallatah
- Department of Nursing Affairs, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
| | - Louise Cave
- NHS Transformation Directorate, NHS England, England, United Kingdom.
| | - Lu-Yen Anny Chen
- Institute of Clinical Nursing, College of Nursing, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Jin Z, Ma F, Chen H, Guo S. Leveraging machine learning to distinguish between bacterial and viral induced pharyngitis using hematological markers: a retrospective cohort study. Sci Rep 2023; 13:22899. [PMID: 38129529 PMCID: PMC10739959 DOI: 10.1038/s41598-023-49925-1] [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: 08/09/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
Accurate differentiation between bacterial and viral-induced pharyngitis is recognized as essential for personalized treatment and judicious antibiotic use. From a cohort of 693 patients with pharyngitis, data from 197 individuals clearly diagnosed with bacterial or viral infections were meticulously analyzed in this study. By integrating detailed hematological insights with several machine learning algorithms, including Random Forest, Neural Networks, Decision Trees, Support Vector Machine, Naive Bayes, and Lasso Regression, for potential biomarkers were identified, with an emphasis being placed on the diagnostic significance of the Monocyte-to-Lymphocyte Ratio. Distinct inflammatory signatures associated with bacterial infections were spotlighted in this study. An innovation introduced in this research was the adaptation of the high-accuracy Lasso Regression model for the TI-84 calculator, with an AUC (95% CI) of 0.94 (0.925-0.955) being achieved. Using this adaptation, pivotal laboratory parameters can be input on-the-spot and infection probabilities can be computed subsequently. This methodology embodies an improvement in diagnostics, facilitating more effective distinction between bacterial and viral infections while fostering judicious antibiotic use.
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Affiliation(s)
- Zhe Jin
- School of Medical Technology, Hebei Medical University, Shijiazhuang, 050017, People's Republic of China
| | - Fengmei Ma
- Department of Otorhinolaryngology, Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang, 050011, People's Republic of China
| | - Haoyang Chen
- Medicine-Education Coordination and Medical Education Research Center, Hebei Medical University, Shijiazhuang, 050017, People's Republic of China
| | - Shufan Guo
- Department of Otorhinolaryngology, Hebei Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang, 050011, People's Republic of China.
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Truchot A, Raynaud M, Loupy A. The authors reply. Kidney Int 2023; 104:1036. [PMID: 37863625 DOI: 10.1016/j.kint.2023.07.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 07/26/2023] [Indexed: 10/22/2023]
Affiliation(s)
- Agathe Truchot
- Paris Institute for Transplantation and Organ Regeneration, Université Paris Cité, Institut National de la Santé de Et de la Recherche Médicale (INSERM), U-970, AP-HP, Paris, France
| | - Marc Raynaud
- Paris Institute for Transplantation and Organ Regeneration, Université Paris Cité, Institut National de la Santé de Et de la Recherche Médicale (INSERM), U-970, AP-HP, Paris, France
| | - Alexandre Loupy
- Paris Institute for Transplantation and Organ Regeneration, Université Paris Cité, Institut National de la Santé de Et de la Recherche Médicale (INSERM), U-970, AP-HP, Paris, France.
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