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Barbuto S, Hu L, Abenavoli C, Picotti M, Manna GL, Nicola LD, Genovesi S, Provenzano M. Coronary Artery Disease in Patients Undergoing Hemodialysis: A Problem that Sounds the Alarm. Rev Cardiovasc Med 2024; 25:200. [PMID: 39076335 PMCID: PMC11270123 DOI: 10.31083/j.rcm2506200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 11/25/2023] [Accepted: 12/08/2023] [Indexed: 07/31/2024] Open
Abstract
Chronic kidney disease (CKD) is affecting more and more individuals over time. The importance of the increased prevalence is enhanced by the close association with the increased risk of poor individual outcomes such as death, fatal and non-fatal cardiovascular (CV) events and progression to end stage kidney disease (ESKD). ESKD requires replacement treatment such as hemodialysis (HD), a particular and complex context that unfortunately has been rarely considered in observational studies in the last few decades. The current perspective of HD as a bridge to kidney transplant requires greater attention from observational and experimental research both in the prevention and treatment of CV events in ESKD patients. We present a narrative review by performing a literature review to extrapolate the most significant articles exploring the CV risk, in particular coronary artery disease (CAD), in ESKD and evaluating possible innovative diagnostic and therapeutic tools in these patients. The risk of CAD increases linearly when the estimated glomerular filtration rate (eGFR) declines and reached the most significant level in ESKD patients. Several diagnostic techniques have been evaluated to predict CAD in ESKD such as laboratory tests (Troponin-T, N-terminal pro b-type natriuretic peptide, alkaline phosphatase), echocardiography and imaging techniques for vascular calcifications evaluation. Similarly, treatment is based on lifestyle changes, medical therapy and invasive techniques such as coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI). Unfortunately in the literature there are no clear indications of the usefulness and validity of biomarkers and possible treatments in ESKD patients. Considering the ESKD weight in terms of prevalence and costs it is necessary to implement clinical research in order to develop prognostic reliable biomarkers for CV and CAD risk prediction, in patients with ESKD. It should be highlighted that HD is a peculiar setting that offers the opportunity to implement research and facilitates patient monitoring by favoring the design of clinical trials.
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Affiliation(s)
- Simona Barbuto
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
- Nephrology Unit, Department of Medical and Surgical Science (DIMEC), Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Lilio Hu
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
- Nephrology Unit, Department of Medical and Surgical Science (DIMEC), Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Chiara Abenavoli
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
- Nephrology Unit, Department of Medical and Surgical Science (DIMEC), Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Matilde Picotti
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
- Nephrology Unit, Department of Medical and Surgical Science (DIMEC), Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Gaetano La Manna
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
- Nephrology Unit, Department of Medical and Surgical Science (DIMEC), Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
| | - Luca De Nicola
- Division of Nephrology, University of Campania “Luigi Vanvitelli”, 80137 Naples, Italy
| | - Simonetta Genovesi
- School of Medicine and Surgery, Nephrology Clinic, University of Milano Bicocca, 20900 Monza, Italy
- Istituto Auxologico Italiano, IRCCS, 20095 Milan, Italy
| | - Michele Provenzano
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40126 Bologna, Italy
- Nephrology Unit, Department of Medical and Surgical Science (DIMEC), Alma Mater Studiorum University of Bologna, 40126 Bologna, Italy
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Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. J Nephrol 2023; 36:1101-1117. [PMID: 36786976 PMCID: PMC10227138 DOI: 10.1007/s40620-023-01573-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 01/01/2023] [Indexed: 02/15/2023]
Abstract
OBJECTIVES In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management. METHODS We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. RESULTS From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context. CONCLUSIONS Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.
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Lee W, Schwartz N, Bansal A, Khor S, Hammarlund N, Basu A, Devine B. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:2053-2061. [PMID: 35989154 DOI: 10.1016/j.jval.2022.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/10/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. METHODS We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics. RESULTS We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%). CONCLUSIONS The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.
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Affiliation(s)
- Woojung Lee
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
| | - Naomi Schwartz
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Aasthaa Bansal
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Sara Khor
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Noah Hammarlund
- Department of Health Services Research, Management & Policy, University of Florida, Gainesville, FL, USA
| | - Anirban Basu
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Beth Devine
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
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Magherini R, Mussi E, Volpe Y, Furferi R, Buonamici F, Servi M. Machine Learning for Renal Pathologies: An Updated Survey. SENSORS 2022; 22:s22134989. [PMID: 35808481 PMCID: PMC9269842 DOI: 10.3390/s22134989] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/22/2022] [Accepted: 06/29/2022] [Indexed: 12/04/2022]
Abstract
Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.
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Zhang F, Wu S, Qu M, Zhou L. Application of a Remotely Controlled Artificial Intelligence Analgesic Pump Device in Painless Treatment of Children. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:1013241. [PMID: 35585944 PMCID: PMC9007688 DOI: 10.1155/2022/1013241] [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: 10/08/2021] [Accepted: 03/18/2022] [Indexed: 11/23/2022]
Abstract
In order to effectively improve the application of analgesic pump devices in the treatment of children, a method based on remote control artificial intelligence is proposed. 100 children with dental pulpitis who were treated in a hospital from December 2018 to December 2020 were selected as the research subjects; they were randomly divided into control group and observation group by an equidistant sampling method, with 50 cases in each group. Children in the control group were given articaine and adrenaline anesthesia, and the observation group was treated with articaine and adrenaline combined with a computer-controlled anesthesia system, the anesthesia pain degree and satisfaction degree of the two groups of children were observed and compared. The results showed that the pain score in anesthesia and intraoperative pain score in the observation group was significantly lower than that in the control group, and the differences were statistically significant (P < 0.05). The total satisfaction of 96.6% patients in the observation group was significantly higher than that in the control group (84.7%) and the difference was statistically significant (P < 0.05). There were no serious complications in both groups. The application of the computer anesthesia system combined with articaine adrenaline in the painless treatment of children's dental pulp proved to have better effects, the treatment compliance is higher, and it is worthy of clinical promotion.
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Affiliation(s)
- Fengyang Zhang
- Department of Anesthesiology, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China
| | - Shihuan Wu
- Department of Anesthesiology, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China
| | - Meimin Qu
- Department of Anesthesiology, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China
| | - Li Zhou
- Department of Anesthesiology, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu 210000, China
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Wang Y, Liu W, Liu X. Explainable AI techniques with application to NBA gameplay prediction. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.01.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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AIM in Hemodialysis. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_254] [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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Yang Z, Tian Y, Zhou T, Zhu Y, Zhang P, Chen J, Li J. Time-series deep survival prediction for hemodialysis patients using an attention-based Bi-GRU network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 212:106458. [PMID: 34736175 DOI: 10.1016/j.cmpb.2021.106458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 10/03/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE The number of end-stage renal disease (ESRD) patients treated with hemodialysis (HD) has significantly increased, but the prognosis remains poor. Time-series features have been included in only a few studies to predict HD patient survival, and how to utilize such features effectively remains unclear. This article aims to develop a more accurate, interpretable, and clinically practical personalized survival prediction model for HD patients. METHODS This study proposed and evaluated an attention-based Bi-GRU network using time-series features for survival prediction. A distance-based loss function was proposed to improve performance. We used data from 1232 ESRD patients who received regular hemodialysis treatment for ≥ 3 months from 2007 to 2016 at the First Affiliated Hospital of Zhejiang University. The proposed model was compared with representative sequence modeling deep learning architectures and existing survival analysis methods in terms of the C-index and IBS value. Post hoc tests were used to test statistical significance. The attention map was used to assess feature importance over time. The impact of time-series changes on survival was investigated after controlling initial values (using BMI as an example). RESULTS The proposed method outperformed other sequence modeling architectures and the state-of-the-art survival analysis approaches in terms of the C-index and the integrated Brier score (IBS) value. Our method achieved a C-index of 0.7680 (95% confidence intervals [CI]: 0.7645, 0.7716) and an IBS of 0.1302 (95% confidence intervals [CI]: 0.1292, 0.1313), showing an improvement of up to 5.4% in terms of the C-index and a decrease of 3.2% in terms of the IBS value. The addition of the distance-based loss function improved the performance. The predicted risk and actual risk levels closely agreed. This study also found that even after controlling the initial body mass index (BMI) values, different 3-month BMI trends could produce different survival outcomes. CONCLUSIONS This study proposed a more effective and interpretable method to use time-series information in survival analysis. The proposed method may help promote personalized medicine and improve patient prognosis.
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Affiliation(s)
- Ziyue Yang
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, China
| | - Tianshu Zhou
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Yilin Zhu
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Ping Zhang
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jianghua Chen
- Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jingsong Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, No. 38 Zheda Road, Hangzhou 310027, China; Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.
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Chen W, Hou X, Hu Y, Huang G, Ye X, Nie S. A deep learning- and CT image-based prognostic model for the prediction of survival in non-small cell lung cancer. Med Phys 2021; 48:7946-7958. [PMID: 34661294 DOI: 10.1002/mp.15302] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 09/19/2021] [Accepted: 10/10/2021] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVE To assist clinicians in arranging personalized treatment, planning follow-up programs and extending survival times for non-small cell lung cancer (NSCLC) patients, a method of deep learning combined with computed tomography (CT) imaging for survival prediction was designed. METHODS Data were collected from 484 patients from four research centers. The data from 344 patients were utilized to build the A_CNN survival prognosis model to classify 2-year overall survival time ranges (730 days cut-off). Data from 140 patients, including independent internal and external test sets, were utilized for model testing. First, a series of preprocessing techniques were used to process the original CT images and generate training and test data sets from the axial, coronal, and sagittal planes. Second, the structure of the A_CNN model was designed based on asymmetric convolution, bottleneck blocks, the uniform cross-entropy (UC) loss function, and other advanced techniques. After that, the A_CNN model was trained, and numerous comparative experiments were designed to obtain the best prognostic survival model. Last, the model performance was evaluated, and the predicted survival curves were analyzed. RESULTS The A_CNN survival prognosis model yielded a high patient-level accuracy of 88.8%, a patch-level accuracy of 82.9%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.932. When tested on an external data set, the maximum patient-level accuracy was 80.0%. CONCLUSIONS The results suggest that using a deep learning method can improve prognosis in patients with NSCLC and has important application value in establishing individualized prognostic models.
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Affiliation(s)
- Wen Chen
- School of Medical Imaging, Shanghai University of Medicine & Health Science, Shanghai, China
| | - Xuewen Hou
- School of Medical Imaging, Shanghai University of Medicine & Health Science, Shanghai, China
| | - Ying Hu
- School of Medical Imaging, Shanghai University of Medicine & Health Science, Shanghai, China
| | - Gang Huang
- Department of Radiology, Shanghai Chest Hospital, Shanghai, China
| | - Xiaodan Ye
- Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Shengdong Nie
- School of Medical Imaging, Shanghai University of Medicine & Health Science, Shanghai, China
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Díez-Sanmartín C, Sarasa-Cabezuelo A, Andrés Belmonte A. The impact of artificial intelligence and big data on end-stage kidney disease treatments. EXPERT SYSTEMS WITH APPLICATIONS 2021; 180:115076. [DOI: 10.1016/j.eswa.2021.115076] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
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Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Phys Med 2021; 83:194-205. [DOI: 10.1016/j.ejmp.2021.03.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/07/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023] Open
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AIM in Hemodialysis. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_254-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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