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Koh HJW, Gašević D, Rankin D, Heritier S, Frydenberg M, Talic S. Variational Bayes machine learning for risk adjustment of general outcome indicators with examples in urology. NPJ Digit Med 2024; 7:249. [PMID: 39277683 PMCID: PMC11401950 DOI: 10.1038/s41746-024-01244-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 09/01/2024] [Indexed: 09/17/2024] Open
Abstract
Risk adjustment is often necessary for outcome quality indicators (QIs) to provide fair and accurate feedback to healthcare professionals. However, traditional risk adjustment models are generally oversimplified and not equipped to disentangle complex factors influencing outcomes that are out of a healthcare professional's control. We present VIRGO, a novel variational Bayes model trained on routinely collected, large administrative datasets to risk-adjust outcome QIs. VIRGO uses detailed demographics, diagnosis, and procedure codes to provide individualized risk adjustment and explanations on patient factors affecting outcomes. VIRGO achieves state-of-the-art on external datasets and features capabilities of uncertainty expression, explainable features, and counterfactual analysis capabilities. VIRGO facilitates risk adjustment by explaining how patient factors led to adverse outcomes and expresses the uncertainty of each prediction, allowing healthcare professionals to not only explore patient factors with unexplained variance that are associated with worse outcomes but also reflect on the quality of their clinical practice.
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Affiliation(s)
- Harvey Jia Wei Koh
- Centre for Learning Analytics, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
- Digital Health Cooperative Research Centre, Sydney, NSW, Australia
- School of Public Health and Preventative Medicine, Monash University, Melbourne, VIC, Australia
| | - Dragan Gašević
- Centre for Learning Analytics, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
- Digital Health Cooperative Research Centre, Sydney, NSW, Australia
| | - David Rankin
- Digital Health Cooperative Research Centre, Sydney, NSW, Australia
- School of Public Health and Preventative Medicine, Monash University, Melbourne, VIC, Australia
| | - Stephane Heritier
- School of Public Health and Preventative Medicine, Monash University, Melbourne, VIC, Australia
| | - Mark Frydenberg
- Cabrini Healthcare, Malvern, VIC, Australia
- Department of Surgery, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Stella Talic
- Centre for Learning Analytics, Faculty of Information Technology, Monash University, Clayton, VIC, Australia.
- Digital Health Cooperative Research Centre, Sydney, NSW, Australia.
- School of Public Health and Preventative Medicine, Monash University, Melbourne, VIC, Australia.
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Adida S, Legarreta AD, Hudson JS, McCarthy D, Andrews E, Shanahan R, Taori S, Lavadi RS, Buell TJ, Hamilton DK, Agarwal N, Gerszten PC. Machine Learning in Spine Surgery: A Narrative Review. Neurosurgery 2024; 94:53-64. [PMID: 37930259 DOI: 10.1227/neu.0000000000002660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/06/2023] [Indexed: 11/07/2023] Open
Abstract
Artificial intelligence and machine learning (ML) can offer revolutionary advances in their application to the field of spine surgery. Within the past 5 years, novel applications of ML have assisted in surgical decision-making, intraoperative imaging and navigation, and optimization of clinical outcomes. ML has the capacity to address many different clinical needs and improve diagnostic and surgical techniques. This review will discuss current applications of ML in the context of spine surgery by breaking down its implementation preoperatively, intraoperatively, and postoperatively. Ethical considerations to ML and challenges in ML implementation must be addressed to maximally benefit patients, spine surgeons, and the healthcare system. Areas for future research in augmented reality and mixed reality, along with limitations in generalizability and bias, will also be highlighted.
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Affiliation(s)
- Samuel Adida
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Andrew D Legarreta
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Joseph S Hudson
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - David McCarthy
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Edward Andrews
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Regan Shanahan
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Suchet Taori
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Raj Swaroop Lavadi
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Thomas J Buell
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - D Kojo Hamilton
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
| | - Nitin Agarwal
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh , Pennsylvania , USA
| | - Peter C Gerszten
- Department of Neurosurgery, University of Pittsburgh School of Medicine, Pittsburgh , Pennsylvania , USA
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Gokhale S, Taylor D, Gill J, Hu Y, Zeps N, Lequertier V, Prado L, Teede H, Enticott J. Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis. Front Med (Lausanne) 2023; 10:1192969. [PMID: 37663657 PMCID: PMC10469540 DOI: 10.3389/fmed.2023.1192969] [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: 03/24/2023] [Accepted: 07/19/2023] [Indexed: 09/05/2023] Open
Abstract
Background Unwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively. Objective This systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions. Method LOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist. Results Overall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting. Conclusion To the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021272198.
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Affiliation(s)
- Swapna Gokhale
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, VIC, Australia
- Eastern Health, Box Hill, VIC, Australia
| | - David Taylor
- Office of Research and Ethics, Eastern Health, Box Hill, VIC, Australia
| | - Jaskirath Gill
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, VIC, Australia
- Alfred Health, Melbourne, VIC, Australia
| | - Yanan Hu
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, VIC, Australia
| | - Nikolajs Zeps
- Monash Partners Academic Health Sciences Centre, Clayton, VIC, Australia
- Eastern Health Clinical School, Monash University Faculty of Medicine, Nursing and Health Sciences, Clayton, VIC, Australia
| | - Vincent Lequertier
- Univ. Lyon, INSA Lyon, Univ Lyon 2, Université Claude Bernard Lyon 1, Lyon, France
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
| | - Luis Prado
- Epworth Healthcare, Academic and Medical Services, Melbourne, VIC, Australia
| | - Helena Teede
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, VIC, Australia
- Monash Partners Academic Health Sciences Centre, Clayton, VIC, Australia
| | - Joanne Enticott
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, VIC, Australia
- Monash Partners Academic Health Sciences Centre, Clayton, VIC, Australia
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