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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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Biomarker und Neuromonitoring zur Entwicklungsprognose nach perinataler Hirnschädigung. Monatsschr Kinderheilkd 2022; 170:688-703. [PMID: 35909417 PMCID: PMC9309449 DOI: 10.1007/s00112-022-01542-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/30/2022] [Indexed: 11/02/2022]
Abstract
Das sich entwickelnde Gehirn ist in der Perinatalperiode besonders empfindlich für eine Vielzahl von Insulten, wie z. B. Extremfrühgeburtlichkeit und perinatale Asphyxie. Ihre Komplikationen können zu lebenslangen neurokognitiven, sensorischen und psychosozialen Einschränkungen führen; deren Vorhersage bleibt eine Herausforderung. Eine Schlüsselfunktion kommt der möglichst exakten Identifikation von Hirnläsionen und funktionellen Störungen zu. Die Prädiktion stützt sich auf frühe diagnostische Verfahren und die klinische Erfassung der Meilensteine der Entwicklung. Zur klinischen Diagnostik und zum Neuromonitoring in der Neonatal- und frühen Säuglingsperiode stehen bildgebende Verfahren zur Verfügung. Hierzu zählen zerebrale Sonographie, MRT am errechneten Termin, amplitudenintegriertes (a)EEG und/oder klassisches EEG, Nah-Infrarot-Spektroskopie, General Movements Assessment und die frühe klinische Nachuntersuchung z. B. mithilfe der Hammersmith Neonatal/Infant Neurological Examination. Innovative Biomarker und -muster (Omics) sowie (epi)genetische Prädispositionen sind Gegenstand wissenschaftlicher Untersuchungen. Neben der Erfassung klinischer Risiken kommt psychosozialen Faktoren im Umfeld des Kindes eine entscheidende Rolle zu. Eine möglichst akkurate Prognose ist mit hohem Aufwand verbunden, jedoch zur gezielten Beratung der Familien und der Einleitung von frühen Interventionen, insbesondere vor dem Hintergrund der hohen Plastizität des sich entwickelnden Gehirns, von großer Bedeutung. Diese Übersichtsarbeit fokussiert die Charakterisierung der oben genannten Verfahren und ihrer Kombinationsmöglichkeiten. Zudem wird ein Ausblick gegeben, wie innovative Techniken in Zukunft die Prädiktion der Entwicklung und Nachsorge dieser Kinder vereinfachen können.
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Abstract
Clinical informatics can support quality improvement and patient safety in the pediatric intensive care unit (PICU) in several ways including data extraction, analysis, and decision support enabled by electronic health records (EHRs), and databases and registries. Clinical decision support (CDS), embedded in EHRs, now an integral part of the workflow in the PICU, includes several tools and is increasingly leveraging artificial intelligence (AI). Understanding the opportunities and challenges can improve the engagement of clinicians with the design, validation, and implementation of CDS, improve satisfaction with CDS, and improve patient safety, care quality, and value.
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Kelly CJ, Brown APY, Taylor JA. Artificial Intelligence in Pediatrics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Ehrmann D, Harish V, Morgado F, Rosella L, Johnson A, Mema B, Mazwi M. Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care. Front Pediatr 2022; 10:864755. [PMID: 35620143 PMCID: PMC9127438 DOI: 10.3389/fped.2022.864755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/18/2022] [Indexed: 12/02/2022] Open
Abstract
Pediatric intensivists are bombarded with more patient data than ever before. Integration and interpretation of data from patient monitors and the electronic health record (EHR) can be cognitively expensive in a manner that results in delayed or suboptimal medical decision making and patient harm. Machine learning (ML) can be used to facilitate insights from healthcare data and has been successfully applied to pediatric critical care data with that intent. However, many pediatric critical care medicine (PCCM) trainees and clinicians lack an understanding of foundational ML principles. This presents a major problem for the field. We outline the reasons why in this perspective and provide a roadmap for competency-based ML education for PCCM trainees and other stakeholders.
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Affiliation(s)
- Daniel Ehrmann
- Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada.,Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
| | - Vinyas Harish
- Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada.,MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Institute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Felipe Morgado
- Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada.,MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Laura Rosella
- MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Institute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Alistair Johnson
- MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.,Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada
| | - Briseida Mema
- Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada
| | - Mjaye Mazwi
- Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada.,MD/PhD Program, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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Tataranno ML, Vijlbrief DC, Dudink J, Benders MJNL. Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury. Front Pediatr 2021; 9:634092. [PMID: 34095022 PMCID: PMC8171663 DOI: 10.3389/fped.2021.634092] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 04/14/2021] [Indexed: 11/27/2022] Open
Abstract
Despite advances in neonatal care to prevent neonatal brain injury and neurodevelopmental impairment, predicting long-term outcome in neonates at risk for brain injury remains difficult. Early prognosis is currently based on cranial ultrasound (CUS), MRI, EEG, NIRS, and/or general movements assessed at specific ages, and predicting outcome in an individual (precision medicine) is not yet possible. New algorithms based on large databases and machine learning applied to clinical, neuromonitoring, and neuroimaging data and genetic analysis and assays measuring multiple biomarkers (omics) can fulfill the needs of modern neonatology. A synergy of all these techniques and the use of automatic quantitative analysis might give clinicians the possibility to provide patient-targeted decision-making for individualized diagnosis, therapy, and outcome prediction. This review will first focus on common neonatal neurological diseases, associated risk factors, and most common treatments. After that, we will discuss how precision medicine and machine learning (ML) approaches could change the future of prediction and prognosis in this field.
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Affiliation(s)
| | | | | | - Manon J. N. L. Benders
- Department of Neonatology, Wilhelmina Children's Hospital/University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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Artificial Intelligence in Pediatrics. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_316-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Chen K, Huang B, Yan S, Xu S, Li K, Zhang K, Wang Q, Zhuang Z, Wei L, Zhang Y, Liu M, Lian H, Zhong C. Two machine learning methods identify a metastasis-related prognostic model that predicts overall survival in medulloblastoma patients. Aging (Albany NY) 2020; 12:21481-21503. [PMID: 33159021 PMCID: PMC7695392 DOI: 10.18632/aging.103923] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 07/30/2020] [Indexed: 11/25/2022]
Abstract
Approximately 30% of medulloblastoma (MB) patients exhibit metastasis at initial diagnosis, which often leads to a poor prognosis. Here, by using univariate Cox regression analysis, two machine learning methods (Lasso-penalized Cox regression and random survival forest-variable hunting (RSF-VH)), and multivariate Cox regression analysis, we established two metastasis-related prognostic models, including the 47-mRNA-based model based on the Lasso method and the 21-mRNA-based model based on the RSF-VH method. In terms of the results of the receiver operating characteristic (ROC) curve analyses, we selected the 47-mRNA metastasis-associated model with the higher area under the curve (AUC). The 47-mRNA-based prognostic model could classify MB patients into two subgroups with different prognoses. The ROC analyses also suggested that the 47-mRNA metastasis-associated model may have a better predictive ability than MB subgroup. Multivariable Cox regression analysis demonstrated that the 47-mRNA-based model was independent of other clinical characteristics. In addition, a nomogram comprising the 47-mRNA-based model was built. The results of ROC analyses suggested that the nomogram had good discrimination ability. Our 47-mRNA metastasis-related prognostic model and nomogram might be an efficient and valuable tool for overall survival (OS) prediction and provide information for individualized treatment decisions in patients with MB.
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Affiliation(s)
- Kui Chen
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Bingsong Huang
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Shan Yan
- Huamu Community Health Service Center, Shanghai 201204, P.R. China
| | - Siyi Xu
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Keqin Li
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Kuiming Zhang
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Qi Wang
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Zhongwei Zhuang
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Liang Wei
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Yanfei Zhang
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Min Liu
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Hao Lian
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Chunlong Zhong
- Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
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Baldassano SN, Roberson SW, Balu R, Scheid B, Bernabei JM, Pathmanathan J, Oommen B, Leri D, Echauz J, Gelfand M, Bhalla PK, Hill CE, Christini A, Wagenaar JB, Litt B. IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit. IEEE J Biomed Health Inform 2020; 24:2389-2397. [PMID: 31940568 PMCID: PMC7485608 DOI: 10.1109/jbhi.2020.2965858] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU physiologic monitoring, clinical decision making, and caretaker notification. METHODS IRIS was implemented in the neurointensive care unit to stream multimodal time series data, including EEG, intracranial pressure (ICP), and brain tissue oxygenation (PbtO2), from ICU monitors to an analysis server. IRIS was applied for 364 patients undergoing continuous EEG, 26 patients undergoing burst suppression monitoring, and four patients undergoing intracranial pressure and brain tissue oxygen monitoring. Custom algorithms were used to identify periods of elevated ICP, compute burst suppression ratios (BSRs), and detect faulty or disconnected EEG electrodes. Hospital staff were notified of clinically relevant events using our secure API to route alerts through a password-protected smartphone application. RESULTS Sustained increases in ICP and concordant decreases in PbtO2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r2 0.633-0.781; p < 0.0001). The platform identified EEG electrodes with poor signal quality with 95% positive predictive value, and reduced latency of technician response by 93%. CONCLUSION This study validates a flexible real-time platform for monitoring and interpreting ICU data and notifying caretakers of actionable results, with potential to reduce the manual burden of continuous monitoring services on care providers. SIGNIFICANCE This work represents an important step toward facilitating translational medical data analytics to improve patient care and reduce health care costs.
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Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care. J Pediatr 2020; 221S:S3-S10. [PMID: 32482232 DOI: 10.1016/j.jpeds.2020.02.039] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 02/12/2020] [Accepted: 02/19/2020] [Indexed: 01/21/2023]
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