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Cruz EO, Sakowitz S, Mallick S, Le N, Chervu N, Bakhtiyar SS, Benharash P. Machine learning prediction of hospitalization costs for coronary artery bypass grafting operations. Surgery 2024; 176:282-288. [PMID: 38760232 DOI: 10.1016/j.surg.2024.03.051] [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: 12/06/2023] [Revised: 02/21/2024] [Accepted: 03/21/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND With the steady rise in health care expenditures, the examination of factors that may influence the costs of care has garnered much attention. Although machine learning models have previously been applied in health economics, their application within cardiac surgery remains limited. We evaluated several machine learning algorithms to model hospitalization costs for coronary artery bypass grafting. METHODS All adult hospitalizations for isolated coronary artery bypass grafting were identified in the 2016 to 2020 Nationwide Readmissions Database. Machine learning models were trained to predict expenditures and compared with traditional linear regression. Given the significance of postoperative length of stay, we additionally developed models excluding postoperative length of stay to uncover other drivers of costs. To facilitate comparison, machine learning classification models were also trained to predict patients in the highest decile of costs. Significant factors associated with high cost were identified using SHapley Additive exPlanations beeswarm plots. RESULTS Among 444,740 hospitalizations included for analysis, the median cost of hospitalization in coronary artery bypass grafting patients was $43,103. eXtreme Gradient Boosting most accurately predicted hospitalization costs, with R2 = 0.519 over the validation set. The top predictive features in the eXtreme Gradient Boosting model included elective procedure status, prolonged mechanical ventilation, new-onset respiratory failure or myocardial infarction, and postoperative length of stay. After removing postoperative length of stay, eXtreme Gradient Boosting remained the most accurate model (R2 = 0.38). Prolonged ventilation, respiratory failure, and elective status remained important predictive parameters. CONCLUSION Machine learning models appear to accurately model total hospitalization costs for coronary artery bypass grafting. Future work is warranted to uncover other drivers of costs and improve the value of care in cardiac surgery.
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Affiliation(s)
- Emma O Cruz
- Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA; Computer Science Department, Stanford University, Palo Alto, CA
| | - Sara Sakowitz
- Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA
| | - Saad Mallick
- Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA
| | - Nguyen Le
- Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA
| | - Nikhil Chervu
- Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA
| | - Syed Shahyan Bakhtiyar
- Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA; Department of Surgery, University of Colorado, Aurora, CO
| | - Peyman Benharash
- Cardiovascular Outcomes Research Laboratory, University of California, Los Angeles, CA; Division of Cardiac Surgery, Department of Surgery, University of California, Los Angeles, CA.
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Ramezani M, Takian A, Bakhtiari A, Rabiee HR, Fazaeli AA, Sazgarnejad S. The application of artificial intelligence in health financing: a scoping review. COST EFFECTIVENESS AND RESOURCE ALLOCATION 2023; 21:83. [PMID: 37932778 PMCID: PMC10626800 DOI: 10.1186/s12962-023-00492-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 10/25/2023] [Indexed: 11/08/2023] Open
Abstract
INTRODUCTION Artificial Intelligence (AI) represents a significant advancement in technology, and it is crucial for policymakers to incorporate AI thinking into policies and to fully explore, analyze and utilize massive data and conduct AI-related policies. AI has the potential to optimize healthcare financing systems. This study provides an overview of the AI application domains in healthcare financing. METHOD We conducted a scoping review in six steps: formulating research questions, identifying relevant studies by conducting a comprehensive literature search using appropriate keywords, screening titles and abstracts for relevance, reviewing full texts of relevant articles, charting extracted data, and compiling and summarizing findings. Specifically, the research question sought to identify the applications of artificial intelligence in health financing supported by the published literature and explore potential future applications. PubMed, Scopus, and Web of Science databases were searched between 2000 and 2023. RESULTS We discovered that AI has a significant impact on various aspects of health financing, such as governance, revenue raising, pooling, and strategic purchasing. We provide evidence-based recommendations for establishing and improving the health financing system based on AI. CONCLUSIONS To ensure that vulnerable groups face minimum challenges and benefit from improved health financing, we urge national and international institutions worldwide to use and adopt AI tools and applications.
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Affiliation(s)
- Maryam Ramezani
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | - Amirhossein Takian
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran.
- Department of Global Health and Public Policy, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
| | - Ahad Bakhtiari
- Department of Global Health and Public Policy, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid R Rabiee
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Ali Akbar Fazaeli
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Saharnaz Sazgarnejad
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Feng X, Hong T, Liu W, Xu C, Li W, Yang B, Song Y, Li T, Li W, Zhou H, Yin C. Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma. Front Endocrinol (Lausanne) 2022; 13:1054358. [PMID: 36465636 PMCID: PMC9716136 DOI: 10.3389/fendo.2022.1054358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 10/28/2022] [Indexed: 11/21/2022] Open
Abstract
SIMPLE SUMMARY Studies have shown that about 30% of kidney cancer patients will have metastasis, and lymph node metastasis (LNM) may be related to a poor prognosis. Our retrospective study aims to provide a reliable machine learning-based model to predict the occurrence of LNM in kidney cancer. We screened the pathological grade, liver metastasis, M staging, primary site, T staging, and tumor size from the training group (n=39016) formed by the SEER database and the validation group (n=771) formed by the medical center. Independent predictors of LNM in cancer patients. Using six different algorithms to build a prediction model, it is found that the prediction performance of the XGB model in the training group and the validation group is significantly better than any other machine learning model. The results show that prediction tools based on machine learning can accurately predict the probability of LNM in patients with kidney cancer and have satisfactory clinical application prospects. BACKGROUND Lymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer. METHODS Data on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds. RESULTS The training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients. CONCLUSIONS The predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.
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Affiliation(s)
- Xiaowei Feng
- Department of Neuro Rehabilitation, Shaanxi Provincial Rehabilitation Hospital, Xi ‘an, China
| | - Tao Hong
- Department of Cardiac Surgery, Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, Shenzhen, China
| | - Wencai Liu
- Department of Orthopaedic Surgery, the First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chan Xu
- Department of Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Wanying Li
- Department of Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China
| | - Bing Yang
- Life Science Department, Tianjin Prosel Biological Technology Co., Ltd, Tianjin, China
| | - Yang Song
- Department of Gastroenterology and Hepatology, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Ting Li
- Department of Cell Biology, College of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Wenle Li
- Department of Neuro Rehabilitation, Shaanxi Provincial Rehabilitation Hospital, Xi ‘an, China
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Fujian, China
- *Correspondence: Chengliang Yin, ; Hui Zhou, ; Wenle Li,
| | - Hui Zhou
- School of Pharmacy, Tianjin Medical University, Tianjin, China
- *Correspondence: Chengliang Yin, ; Hui Zhou, ; Wenle Li,
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, Macau SAR China
- *Correspondence: Chengliang Yin, ; Hui Zhou, ; Wenle Li,
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Hazardous Effect of Low-Dose Aspirin in Patients with Predialysis Advanced Chronic Kidney Disease Assessed by Machine Learning Method Feature Selection. Healthcare (Basel) 2021; 9:healthcare9111484. [PMID: 34828530 PMCID: PMC8625790 DOI: 10.3390/healthcare9111484] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 10/18/2021] [Accepted: 10/28/2021] [Indexed: 12/30/2022] Open
Abstract
Background: Low-dose aspirin (100 mg) is widely used in preventing cardiovascular disease in chronic kidney disease (CKD) because its benefits outweighs the harm, however, its effect on clinical outcomes in patients with predialysis advanced CKD is still unclear. This study aimed to assess the effect of aspirin use on clinical outcomes in such group. Methods: Patients were selected from a nationwide diabetes database from January 2009 to June 2017, and divided into two groups, a case group with aspirin use (n = 3021) and a control group without aspirin use (n = 9063), by propensity score matching with a 1:3 ratio. The Cox regression model was used to estimate the hazard ratio (HR). Moreover, machine learning method feature selection was used to assess the importance of parameters in the clinical outcomes. Results: In a mean follow-up of 1.54 years, aspirin use was associated with higher risk for entering dialysis (HR, 1.15 [95%CI, 1.10-1.21]) and death before entering dialysis (1.46 [1.25-1.71]), which were also supported by feature selection. The renal effect of aspirin use was consistent across patient subgroups. Nonusers and aspirin users did not show a significant difference, except for gastrointestinal bleeding (1.05 [0.96-1.15]), intracranial hemorrhage events (1.23 [0.98-1.55]), or ischemic stroke (1.15 [0.98-1.55]). Conclusions: Patients with predialysis advanced CKD and anemia who received aspirin exhibited higher risk of entering dialysis and death before entering dialysis by 15% and 46%, respectively.
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