1
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Evans MD, Helgeson ES, Rule AD, Vock DM, Matas AJ. Consequences of low estimated glomerular filtration rate either before or early after kidney donation. Am J Transplant 2024:S1600-6135(24)00374-5. [PMID: 38878866 DOI: 10.1016/j.ajt.2024.04.023] [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: 11/02/2023] [Revised: 03/20/2024] [Accepted: 04/20/2024] [Indexed: 07/11/2024]
Abstract
In the general population, decreases in glomerular filtration rate (GFR) are associated with subsequent development of chronic kidney disease (CKD), cardiovascular disease (CVD), and death. It is unknown if low estimated GFR (eGFR) before or early after kidney donation was also associated with these risks. One thousand six hundred ninety-nine living donors who had both predonation and early (4-10 weeks) postdonation eGFR were included. We studied the relationships between eGFR, age at donation, and the time to sustained eGFR<45 (CKD stage 3b) and <30 mL/min/1.73m2 (CKD stage 4), hypertension, diabetes mellitus (DM), CVD, and death. Median follow-up was 12 (interquartile range, 6-21) years. Twenty-year event rates were 5.8% eGFR<45 mL/min/1.73m2; 1.2% eGFR<30 mL/min/1.73m2; 29.0% hypertension; 7.8% DM; 8.0% CVD; and 5.2% death. The median time to eGFR<45 mL/min/1.73m2 (N = 79) was 17 years, and eGFR<30 mL/min/1.73m2 (N = 22) was 25 years. Both low predonation and early postdonation eGFR were associated with eGFR<45 mL/min/1.73m2 (P < .0001) and eGFR<30 mL/min/1.73m2 (P < .006); however, the primary driver of risk for all ages was low postdonation (rather than predonation) eGFR. Predonation and postdonation eGFR were not associated with hypertension, DM, CVD, or death. Low predonation and early postdonation eGFR are risk factors for developing eGFR<45 mL/min/1.73m2 (CKD stage 3b) and <30 mL/min/1.73m2 (CKD stage 4), but not CVD, hypertension, DM, or death.
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Affiliation(s)
- Michael D Evans
- Clinical and Translational Science Institute, University of Minnesota, Minneapolis, Minnesota, USA
| | - Erika S Helgeson
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Andrew D Rule
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - David M Vock
- Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Arthur J Matas
- Division of Transplantation, Department of Surgery, University of Minnesota, Minneapolis, Minnesota, USA.
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2
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Rabindranath M, Naghibzadeh M, Zhao X, Holdsworth S, Brudno M, Sidhu A, Bhat M. Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold? Transplantation 2023:00007890-990000000-00616. [PMID: 38059716 DOI: 10.1097/tp.0000000000004876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.
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Affiliation(s)
- Madhumitha Rabindranath
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Maryam Naghibzadeh
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Xun Zhao
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Sandra Holdsworth
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Michael Brudno
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Aman Sidhu
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
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3
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Nemati M, Zhang H, Sloma M, Bekbolsynov D, Wang H, Stepkowski S, Xu KS. Predicting kidney transplant survival using multiple feature representations for HLAs. Artif Intell Med 2023; 145:102675. [PMID: 37925205 DOI: 10.1016/j.artmed.2023.102675] [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: 02/07/2022] [Revised: 06/14/2023] [Accepted: 10/02/2023] [Indexed: 11/06/2023]
Abstract
Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease. A significant factor that affects graft survival time (the time until the transplant fails and the patient requires another transplant) for kidney transplantation is the compatibility of the Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper, we propose 4 new biologically-relevant feature representations for incorporating HLA information into machine learning-based survival analysis algorithms. We evaluate our proposed HLA feature representations on a database of over 100,000 transplants and find that they improve prediction accuracy by about 1%, modest at the patient level but potentially significant at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.
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Affiliation(s)
- Mohammadreza Nemati
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, 43606, OH, United States; Department of Computer and Data Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, 44106, OH, United States
| | - Haonan Zhang
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, 43606, OH, United States
| | - Michael Sloma
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, 43606, OH, United States
| | - Dulat Bekbolsynov
- Department of Medical Microbiology and Immunology, University of Toledo, United States
| | - Hong Wang
- Department of Engineering Technology, University of Toledo, United States
| | - Stanislaw Stepkowski
- Department of Medical Microbiology and Immunology, University of Toledo, United States
| | - Kevin S Xu
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, 43606, OH, United States; Department of Computer and Data Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, 44106, OH, United States.
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4
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Bisson A, Lemrini Y, Romiti GF, Proietti M, Angoulvant D, Bentounes S, El-Bouri W, Lip GYH, Fauchier L. Prediction of early death after atrial fibrillation diagnosis using a machine learning approach: A French nationwide cohort study. Am Heart J 2023; 265:191-202. [PMID: 37595659 DOI: 10.1016/j.ahj.2023.08.006] [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: 08/14/2023] [Accepted: 08/14/2023] [Indexed: 08/20/2023]
Abstract
AIMS Atrial fibrillation is associated with important mortality but the usual clinical risk factor based scores only modestly predict mortality. This study aimed to develop machine learning models for the prediction of death occurrence within the year following atrial fibrillation diagnosis and compare predictive ability against usual clinical risk scores. METHODS AND RESULTS We used a nationwide cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in French hospitals from 2011 to 2019. Three machine learning models were trained to predict mortality within the first year using a training set (70% of the cohort). The best model was selected to be evaluated and compared with previously published scores on the validation set (30% of the cohort). Discrimination of the best model was evaluated using the C index. Within the first year following atrial fibrillation diagnosis, 342,005 patients (14.4%) died after a period of 83 (SD 98) days (median 37 [10-129]). The best machine learning model selected was a deep neural network with a C index of 0.785 (95% CI, 0.781-0.789) on the validation set. Compared to clinical risk scores, the selected model was superior to the CHA2DS2-VASc and HAS-BLED risk scores and superior to dedicated scores such as Charlson Comorbidity Index and Hospital Frailty Risk Score to predict death within the year following atrial fibrillation diagnosis (C indexes: 0.597; 0.562; 0.643; 0.626 respectively. P < .0001). CONCLUSION Machine learning algorithms predict early death after atrial fibrillation diagnosis and may help clinicians to better risk stratify atrial fibrillation patients at high risk of mortality.
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Affiliation(s)
- Arnaud Bisson
- Service de Cardiologie, Centre Hospitalier Régional Universitaire et Faculté de Médecine de Tours, Tours, France; EA4245, Transplantation Immunité Inflammation, Université de Tours, Tours, France; Service de Cardiologie, Centre Hospitalier Régional Universitaire d'Orléans, Orléans, France; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom.
| | - Yassine Lemrini
- Service de Cardiologie, Centre Hospitalier Régional Universitaire et Faculté de Médecine de Tours, Tours, France
| | - Giulio Francesco Romiti
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom; Department of Translational and Precision Medicine, Sapienza - University of Rome, Italy
| | - Marco Proietti
- Department of Clinical Sciences and Community Health, University of Milan, Italy; Division of Subacute Care, IRCCS Istituti Clinici Scientifici Maugeri, Milano, Italy
| | - Denis Angoulvant
- Service de Cardiologie, Centre Hospitalier Régional Universitaire et Faculté de Médecine de Tours, Tours, France; EA4245, Transplantation Immunité Inflammation, Université de Tours, Tours, France
| | - Sidahmed Bentounes
- Service de Cardiologie, Centre Hospitalier Régional Universitaire et Faculté de Médecine de Tours, Tours, France
| | - Wahbi El-Bouri
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom; Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Laurent Fauchier
- Service de Cardiologie, Centre Hospitalier Régional Universitaire et Faculté de Médecine de Tours, Tours, France
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5
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Sauthier N, Bouchakri R, Carrier FM, Sauthier M, Mullie LA, Cardinal H, Fortin MC, Lahrichi N, Chassé M. Automated screening of potential organ donors using a temporal machine learning model. Sci Rep 2023; 13:8459. [PMID: 37231073 DOI: 10.1038/s41598-023-35270-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 05/15/2023] [Indexed: 05/27/2023] Open
Abstract
Organ donation is not meeting demand, and yet 30-60% of potential donors are potentially not identified. Current systems rely on manual identification and referral to an Organ Donation Organization (ODO). We hypothesized that developing an automated screening system based on machine learning could reduce the proportion of missed potentially eligible organ donors. Using routine clinical data and laboratory time-series, we retrospectively developed and tested a neural network model to automatically identify potential organ donors. We first trained a convolutive autoencoder that learned from the longitudinal changes of over 100 types of laboratory results. We then added a deep neural network classifier. This model was compared to a simpler logistic regression model. We observed an AUROC of 0.966 (CI 0.949-0.981) for the neural network and 0.940 (0.908-0.969) for the logistic regression model. At a prespecified cutoff, sensitivity and specificity were similar between both models at 84% and 93%. Accuracy of the neural network model was robust across donor subgroups and remained stable in a prospective simulation, while the logistic regression model performance declined when applied to rarer subgroups and in the prospective simulation. Our findings support using machine learning models to help with the identification of potential organ donors using routinely collected clinical and laboratory data.
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Affiliation(s)
- Nicolas Sauthier
- Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Rima Bouchakri
- Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | | | - Michaël Sauthier
- Centre Hospitalier Universitaire Sainte-Justine, Montreal, Canada
| | | | - Héloïse Cardinal
- Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | | | | | - Michaël Chassé
- Centre Hospitalier de l'Université de Montréal, Montreal, Canada.
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6
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Truchot A, Raynaud M, Kamar N, Naesens M, Legendre C, Delahousse M, Thaunat O, Buchler M, Crespo M, Linhares K, Orandi BJ, Akalin E, Pujol GS, Silva HT, Gupta G, Segev DL, Jouven X, Bentall AJ, Stegall MD, Lefaucheur C, Aubert O, Loupy A. Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction. Kidney Int 2023; 103:936-948. [PMID: 36572246 DOI: 10.1016/j.kint.2022.12.011] [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: 06/14/2022] [Revised: 11/04/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
Machine learning (ML) models have recently shown potential for predicting kidney allograft outcomes. However, their ability to outperform traditional approaches remains poorly investigated. Therefore, using large cohorts of kidney transplant recipients from 14 centers worldwide, we developed ML-based prediction models for kidney allograft survival and compared their prediction performances to those achieved by a validated Cox-Based Prognostication System (CBPS). In a French derivation cohort of 4000 patients, candidate determinants of allograft failure including donor, recipient and transplant-related parameters were used as predictors to develop tree-based models (RSF, RSF-ERT, CIF), Support Vector Machine models (LK-SVM, AK-SVM) and a gradient boosting model (XGBoost). Models were externally validated with cohorts of 2214 patients from Europe, 1537 from North America, and 671 from South America. Among these 8422 kidney transplant recipients, 1081 (12.84%) lost their grafts after a median post-transplant follow-up time of 6.25 years (Inter Quartile Range 4.33-8.73). At seven years post-risk evaluation, the ML models achieved a C-index of 0.788 (95% bootstrap percentile confidence interval 0.736-0.833), 0.779 (0.724-0.825), 0.786 (0.735-0.832), 0.527 (0.456-0.602), 0.704 (0.648-0.759) and 0.767 (0.711-0.815) for RSF, RSF-ERT, CIF, LK-SVM, AK-SVM and XGBoost respectively, compared with 0.808 (0.792-0.829) for the CBPS. In validation cohorts, ML models' discrimination performances were in a similar range of those of the CBPS. Calibrations of the ML models were similar or less accurate than those of the CBPS. Thus, when using a transparent methodological pipeline in validated international cohorts, ML models, despite overall good performances, do not outperform a traditional CBPS in predicting kidney allograft failure. Hence, our current study supports the continued use of traditional statistical approaches for kidney graft prognostication.
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Affiliation(s)
- Agathe Truchot
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France
| | - Marc Raynaud
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France
| | - Nassim Kamar
- Université Paul Sabatier, INSERM, Department of Nephrology and Organ Transplantation, CHU Rangueil and Purpan, Toulouse, France
| | - Maarten Naesens
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
| | - Christophe Legendre
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Michel Delahousse
- Department of Transplantation, Nephrology and Clinical Immunology, Foch Hospital, Suresnes, France
| | - Olivier Thaunat
- Department of Transplantation, Nephrology and Clinical Immunology, Hospices Civils de Lyon, Lyon, France
| | - Matthias Buchler
- Nephrology and Immunology Department, Bretonneau Hospital, Tours, France
| | - Marta Crespo
- Department of Nephrology, Hospital del Mar Barcelona, Barcelona, Spain
| | - Kamilla Linhares
- Hospital do Rim, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Babak J Orandi
- University of Alabama at Birmingham Heersink School of Medicine, Birmingham, Alabama, USA
| | - Enver Akalin
- Renal Division, Montefiore Medical Centre, Kidney Transplantation Program, Albert Einstein College of Medicine, New York, New York, USA
| | - Gervacio Soler Pujol
- Unidad de Trasplante Renopancreas, Centro de Educacion Medica e Investigaciones Clinicas Buenos Aires, Buenos Aires, Argentina
| | - Helio Tedesco Silva
- Hospital do Rim, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Gaurav Gupta
- Division of Nephrology, Department of Internal Medicine, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA
| | - Dorry L Segev
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Xavier Jouven
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Cardiology Department, European Georges Pompidou Hospital, Paris, France
| | - Andrew J Bentall
- William J von Liebig Centre for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark D Stegall
- William J von Liebig Centre for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota, USA
| | - Carmen Lefaucheur
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Olivier Aubert
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Alexandre Loupy
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.
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7
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Mark E, Goldsman D, Gurbaxani B, Keskinocak P, Sokol J. Predicting a kidney transplant patient's pre-transplant functional status based on information from waitlist registration. Sci Rep 2023; 13:6164. [PMID: 37061525 PMCID: PMC10105757 DOI: 10.1038/s41598-023-33117-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 04/07/2023] [Indexed: 04/17/2023] Open
Abstract
With over 100,000 patients on the kidney transplant waitlist in 2019, it is important to understand if and how the functional status of a patient may change while on the waitlist. Recorded both at registration and just prior to transplantation, the Karnofsky Performance Score measures a patient's functional status and takes on values ranging from 0 to 100 in increments of 10. Using machine learning techniques, we built a gradient boosting regression model to predict a patient's pre-transplant functional status based on information known at the time of waitlist registration. The model's predictions result in an average root mean squared error of 12.99 based on 5 rolling origin cross validations and 12.94 in a separate out-of-time test. In comparison, predicting that the pre-transplant functional status remains the same as the status at registration, results in average root mean squared errors of 14.50 and 14.11 respectively. The analysis is based on 118,401 transplant records from 2007 to 2019. To the best of our knowledge, there has been no previously published research on building a model to predict kidney pre-transplant functional status. We also find that functional status at registration and total serum albumin, have the most impact in predicting the pre-transplant functional status.
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Affiliation(s)
- Ethan Mark
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - David Goldsman
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Brian Gurbaxani
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Pinar Keskinocak
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Joel Sokol
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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8
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Martin P, Gupta D, Pruett T. Predicting older-donor kidneys' post-transplant renal function using pre-transplant data. NAVAL RESEARCH LOGISTICS 2023; 70:21-33. [PMID: 37082424 PMCID: PMC10108525 DOI: 10.1002/nav.22083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/29/2022] [Accepted: 09/15/2022] [Indexed: 05/03/2023]
Abstract
This paper provides a methodology for predicting post-transplant kidney function, that is, the 1-year post-transplant estimated Glomerular Filtration Rate (eGFR-1) for each donor-candidate pair. We apply customized machine-learning algorithms to pre-transplant donor and recipient data to determine the probability of achieving an eGFR-1 of at least 30 ml/min. This threshold was chosen because there is insufficient survival benefit if the kidney fails to generate an eGFR-1 ≥ 30 ml/min. For some donor-candidate pairs, the developed algorithm provides highly accurate predictions. For others, limitations of previous transplants' data results in noisier predictions. However, because the same kidney is offered to many candidates, we identify those pairs for whom the predictions are highly accurate. Out of 6977 discarded older-donor kidneys that were a match with at least one transplanted kidney, 5282 had one or more identified candidate, who were offered that kidney, did not accept any other offer, and would have had ≥80% chance of achieving eGFR-1 ≥ 30 ml/min, had the kidney been transplanted. We also show that transplants with ≥80% chance of achieving eGFR-1 ≥ 30 ml/min and that survive 1 year have higher 10-year death-censored graft survival probabilities than all older-donor transplants that survive 1 year (73.61% vs. 70.48%, respectively).
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Affiliation(s)
- Paola Martin
- Kelley School of BusinessIndiana UniversityBloomingtonIndianaUSA
| | - Diwakar Gupta
- McCombs School of BusinessUniversity of TexasAustinTexasUSA
| | - Timothy Pruett
- Department of SurgeryUniversity of MinnesotaMinneapolisMinnesotaUSA
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9
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Aslani N, Galehdar N, Garavand A. A systematic review of data mining applications in kidney transplantation. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
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10
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Estimation of Mycophenolic Acid Exposure in Chinese Renal Transplant Patients by a Joint Deep Learning Model. Ther Drug Monit 2022; 44:738-746. [PMID: 36070781 DOI: 10.1097/ftd.0000000000001020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 12/04/2021] [Indexed: 01/29/2023]
Abstract
BACKGROUND To predict mycophenolic acid (MPA) exposure in renal transplant recipients using a deep learning model based on a convolutional neural network with bilateral long short-term memory and attention methods. METHODS A total of 172 Chinese renal transplant patients were enrolled in this study. The patients were divided into a training group (n = 138, Ruijin Hospital) and a validation group (n = 34, Zhongshan Hospital). Fourteen days after renal transplantation, rich blood samples were collected 0-12 hours after MPA administration. The plasma concentration of total MPA was measured using an enzyme-multiplied immunoassay technique. A limited sampling strategy based on a convolutional neural network-long short-term memory with attention (CALS) model for the prediction of the area under the concentration curve (AUC) of MPA was established. The established model was verified using the data from the validation group. The model performance was compared with that obtained from multiple linear regression (MLR) and maximum a posteriori (MAP) methods. RESULTS The MPA AUC 0-12 of the training and validation groups was 54.28 ± 18.42 and 41.25 ± 14.53 µg·ml -1 ·h, respectively. MPA plasma concentration after 2 (C 2 ), 6 (C 6 ), and 8 (C 8 ) hours of administration was the most significant factor for MPA AUC 0-12 . The predictive performance of AUC 0-12 estimated using the CALS model of the validation group was better than the MLR and MAP methods in previous studies (r 2 = 0.71, mean prediction error = 4.79, and mean absolute prediction error = 14.60). CONCLUSIONS The CALS model established in this study was reliable for predicting MPA AUC 0-12 in Chinese renal transplant patients administered mycophenolate mofetil and enteric-coated mycophenolic acid sodium and may have good generalization ability for application in other data sets.
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11
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Carlson SF, Kamalia MA, Zimmerman MT, Urrutia RA, Joyce DL. The current and future role of artificial intelligence in optimizing donor organ utilization and recipient outcomes in heart transplantation. HEART, VESSELS AND TRANSPLANTATION 2022. [DOI: 10.24969/hvt.2022.350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Heart failure (HF) is a leading cause of morbidity and mortality in the United States. While medical management and mechanical circulatory support have undergone significant advancement in recent years, orthotopic heart transplantation (OHT) remains the most definitive therapy for refractory HF. OHT has seen steady improvement in patient survival and quality of life (QoL) since its inception, with one-year mortality now under 8%. However, a significant number of HF patients are unable to receive OHT due to scarcity of donor hearts. The United Network for Organ Sharing has recently revised its organ allocation criteria in an effort to provide more equitable access to OHT. Despite these changes, there are many potential donor hearts that are inevitably rejected. Arbitrary regulations from the centers for Medicare and Medicaid services and fear of repercussions if one-year mortality falls below established values has led to a current state of excessive risk aversion for which organs are accepted for OHT. Furthermore, non-standardized utilization of extended criteria donors and donation after circulatory death, exacerbate the organ shortage. Data-driven systems can improve donor-recipient matching, better predict patient QoL post-OHT, and decrease needless organ waste through more uniform application of acceptance criteria. Thus, we propose a data-driven future for OHT and a move to patient-centric and holistic transplantation care processes.
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12
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Pezel T, Sanguineti F, Garot P, Unterseeh T, Champagne S, Toupin S, Morisset S, Hovasse T, Faradji A, Ah-Sing T, Nicol M, Hamzi L, Dillinger JG, Henry P, Bousson V, Garot J. Machine-Learning Score Using Stress CMR for Death Prediction in Patients With Suspected or Known CAD. JACC Cardiovasc Imaging 2022; 15:1900-1913. [PMID: 35842360 DOI: 10.1016/j.jcmg.2022.05.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/27/2022] [Accepted: 05/20/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND In patients with suspected or known coronary artery disease, traditional prognostic risk assessment is based on a limited selection of clinical and imaging findings. Machine learning (ML) methods can take into account a greater number and complexity of variables. OBJECTIVES This study sought to investigate the feasibility and accuracy of ML using stress cardiac magnetic resonance (CMR) and clinical data to predict 10-year all-cause mortality in patients with suspected or known coronary artery disease, and compared its performance with existing clinical or CMR scores. METHODS Between 2008 and 2018, a retrospective cohort study with a median follow-up of 6.0 (IQR: 5.0-8.0) years included all consecutive patients referred for stress CMR. Twenty-three clinical and 11 stress CMR parameters were evaluated. ML involved automated feature selection by random survival forest, model building with a multiple fractional polynomial algorithm, and 5 repetitions of 10-fold stratified cross-validation. The primary outcome was all-cause death based on the electronic National Death Registry. The external validation cohort of the ML score was performed in another center. RESULTS Of 31,752 consecutive patients (mean age: 63.7 ± 12.1 years, and 65.7% male), 2,679 (8.4%) died with 206,453 patient-years of follow-up. The ML score (ranging from 0 to 10 points) exhibited a higher area under the curve compared with Clinical and Stress Cardiac Magnetic Resonance score, European Systematic Coronary Risk Estimation score, QRISK3 score, Framingham Risk Score, and stress CMR data alone for prediction of 10-year all-cause mortality (ML score: 0.76 vs Clinical and Stress Cardiac Magnetic Resonance score: 0.68, European Systematic Coronary Risk Estimation score: 0.66, QRISK3 score: 0.64, Framingham Risk Score: 0.63, extent of inducible ischemia: 0.66, extent of late gadolinium enhancement: 0.65; all P < 0.001). The ML score also exhibited a good area under the curve in the external cohort (0.75). CONCLUSIONS The ML score including clinical and stress CMR data exhibited a higher prognostic value to predict 10-year death compared with all traditional clinical or CMR scores.
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Affiliation(s)
- Théo Pezel
- Cardiovascular Magnetic Resonance Laboratory, Institut Cardiovasculaire Paris Sud, Hôpital Privé Jacques Cartier, Ramsay Santé, Massy, France; Inserm UMRS 942, Service de Cardiologie, Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France; Service de Radiologie, Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Francesca Sanguineti
- Cardiovascular Magnetic Resonance Laboratory, Institut Cardiovasculaire Paris Sud, Hôpital Privé Jacques Cartier, Ramsay Santé, Massy, France
| | - Philippe Garot
- Cardiovascular Magnetic Resonance Laboratory, Institut Cardiovasculaire Paris Sud, Hôpital Privé Jacques Cartier, Ramsay Santé, Massy, France
| | - Thierry Unterseeh
- Cardiovascular Magnetic Resonance Laboratory, Institut Cardiovasculaire Paris Sud, Hôpital Privé Jacques Cartier, Ramsay Santé, Massy, France
| | - Stéphane Champagne
- Cardiovascular Magnetic Resonance Laboratory, Institut Cardiovasculaire Paris Sud, Hôpital Privé Jacques Cartier, Ramsay Santé, Massy, France
| | - Solenn Toupin
- Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France
| | | | - Thomas Hovasse
- Cardiovascular Magnetic Resonance Laboratory, Institut Cardiovasculaire Paris Sud, Hôpital Privé Jacques Cartier, Ramsay Santé, Massy, France
| | - Alyssa Faradji
- Service de Radiologie, Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Tania Ah-Sing
- Service de Radiologie, Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Martin Nicol
- Inserm UMRS 942, Service de Cardiologie, Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Lounis Hamzi
- Cardiovascular Magnetic Resonance Laboratory, Institut Cardiovasculaire Paris Sud, Hôpital Privé Jacques Cartier, Ramsay Santé, Massy, France; Service de Radiologie, Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Jean Guillaume Dillinger
- Inserm UMRS 942, Service de Cardiologie, Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Patrick Henry
- Inserm UMRS 942, Service de Cardiologie, Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Valérie Bousson
- Service de Radiologie, Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, Université de Paris, Paris, France
| | - Jérôme Garot
- Cardiovascular Magnetic Resonance Laboratory, Institut Cardiovasculaire Paris Sud, Hôpital Privé Jacques Cartier, Ramsay Santé, Massy, France.
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13
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Su D, Zhang X, He K, Chen Y, Wu N. Individualized prediction of chronic kidney disease for the elderly in longevity areas in China: Machine learning approaches. Front Public Health 2022; 10:998549. [PMID: 36339144 PMCID: PMC9634246 DOI: 10.3389/fpubh.2022.998549] [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: 07/20/2022] [Accepted: 09/20/2022] [Indexed: 01/26/2023] Open
Abstract
Background Chronic kidney disease (CKD) has become a major public health problem worldwide and has caused a huge social and economic burden, especially in developing countries. No previous study has used machine learning (ML) methods combined with longitudinal data to predict the risk of CKD development in 2 years amongst the elderly in China. Methods This study was based on the panel data of 925 elderly individuals in the 2012 baseline survey and 2014 follow-up survey of the Healthy Aging and Biomarkers Cohort Study (HABCS) database. Six ML models, logistic regression (LR), lasso regression, random forests (RF), gradient-boosted decision tree (GBDT), support vector machine (SVM), and deep neural network (DNN), were developed to predict the probability of CKD amongst the elderly in 2 years (the year of 2014). The decision curve analysis (DCA) provided a range of threshold probability of the outcome and the net benefit of each ML model. Results Amongst the 925 elderly in the HABCS 2014 survey, 289 (18.8%) had CKD. Compared with the other models, LR, lasso regression, RF, GBDT, and DNN had no statistical significance of the area under the receiver operating curve (AUC) value (>0.7), and SVM exhibited the lowest predictive performance (AUC = 0.633, p-value = 0.057). DNN had the highest positive predictive value (PPV) (0.328), whereas LR had the lowest (0.287). DCA results indicated that within the threshold ranges of ~0-0.03 and 0.37-0.40, the net benefit of GBDT was the largest. Within the threshold ranges of ~0.03-0.10 and 0.26-0.30, the net benefit of RF was the largest. Age was the most important predictor variable in the RF and GBDT models. Blood urea nitrogen, serum albumin, uric acid, body mass index (BMI), marital status, activities of daily living (ADL)/instrumental activities of daily living (IADL) and gender were crucial in predicting CKD in the elderly. Conclusion The ML model could successfully capture the linear and nonlinear relationships of risk factors for CKD in the elderly. The decision support system based on the predictive model in this research can help medical staff detect and intervene in the health of the elderly early.
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Affiliation(s)
- Dai Su
- Department of Health Management and Policy, School of Public Health, Capital Medical University, Beijing, China
| | - Xingyu Zhang
- Department of Systems, Populations, and Leadership, University of Michigan School of Nursing, Ann Arbor, MI, United States,Thomas E. Starzl Transplantation Institute, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Kevin He
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - Yingchun Chen
- Department of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,Research Center for Rural Health Services, Hubei Province Key Research Institute of Humanities and Social Sciences, Wuhan, China
| | - Nina Wu
- Department of Health Management and Policy, School of Public Health, Capital Medical University, Beijing, China,*Correspondence: Nina Wu
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14
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Wong JYY, Imani P, Grigoryan H, Bassig BA, Dai Y, Hu W, Blechter B, Rahman ML, Ji BT, Duan H, Niu Y, Ye M, Jia X, Meng T, Bin P, Downward G, Meliefste K, Leng S, Fu W, Yang J, Ren D, Xu J, Zhou B, Hosgood HD, Vermeulen R, Zheng Y, Silverman DT, Rothman N, Rappaport SM, Lan Q. Exposure to diesel engine exhaust and alterations to the Cys34/Lys525 adductome of human serum albumin. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2022; 95:103966. [PMID: 36067935 PMCID: PMC9757949 DOI: 10.1016/j.etap.2022.103966] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/28/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
We investigated whether exposure to carcinogenic diesel engine exhaust (DEE) was associated with altered adduct levels in human serum albumin (HSA) residues. Nano-liquid chromatography-high resolution mass spectrometry (nLC-HRMS) was used to measure adducts of Cys34 and Lys525 residues in plasma samples from 54 diesel engine factory workers and 55 unexposed controls. An untargeted adductomics and bioinformatics pipeline was used to find signatures of Cys34/Lys525 adductome modifications. To identify adducts that were altered between DEE-exposed and unexposed participants, we used an ensemble feature selection approach that ranks and combines findings from linear regression and penalized logistic regression, then aggregates the important findings with those determined by random forest. We detected 40 Cys34 and 9 Lys525 adducts. Among these findings, we found evidence that 6 Cys34 adducts were altered between DEE-exposed and unexposed participants (i.e., 841.75, 851.76, 856.10, 860.77, 870.43, and 913.45). These adducts were biologically related to antioxidant activity.
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Affiliation(s)
- Jason Y Y Wong
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
| | - Partow Imani
- School of Public Health, University of California, Berkeley, CA, USA
| | - Hasmik Grigoryan
- School of Public Health, University of California, Berkeley, CA, USA
| | - Bryan A Bassig
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Yufei Dai
- National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Wei Hu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Batel Blechter
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Mohammad L Rahman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Bu-Tian Ji
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Huawei Duan
- National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yong Niu
- National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Meng Ye
- National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiaowei Jia
- National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Tao Meng
- National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Ping Bin
- National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, China
| | - George Downward
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Kees Meliefste
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Shuguang Leng
- Cancer Control and Population Sciences, University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA; Division of Epidemiology, Biostatistics, and Preventive Medicine, Department of Internal Medicine, University of New Mexico School of Medicine, University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA
| | - Wei Fu
- Chaoyang Center for Disease Control and Prevention, Chaoyang, Liaoning, China
| | - Jufang Yang
- Chaoyang Center for Disease Control and Prevention, Chaoyang, Liaoning, China
| | - Dianzhi Ren
- Chaoyang Center for Disease Control and Prevention, Chaoyang, Liaoning, China
| | - Jun Xu
- School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Baosen Zhou
- China Medical University, Shenyang, Liaoning, China
| | - H Dean Hosgood
- Division of Epidemiology, Albert Einstein College of Medicine, New York, NY, USA
| | - Roel Vermeulen
- Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands
| | - Yuxin Zheng
- School of Public Health, Qingdao University, Qingdao, China
| | - Debra T Silverman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | | | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
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15
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Lung Transplantation Advanced Prediction Tool: Determining Recipient's Outcome for a Certain Donor. Transplantation 2022; 106:2019-2030. [PMID: 35389371 PMCID: PMC9521589 DOI: 10.1097/tp.0000000000004131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Many risk-prediction models for lung transplantation are centered on recipient characteristics and do not account for impact of donor and transplant-related factors or only examine short-term outcomes (eg, predicted 1-y survival). We sought to develop a comprehensive model guiding recipient-donor matching. METHODS We identified double lung transplant recipients (≥12 y old) in the United Network for Organ Sharing Registry (2005-2020) to develop a risk scoring tool. Cohort was divided into derivation and validation sets. A total of 42 recipient, donor, and transplant factors were included in the analysis. Lasso method was used for variable selection. Survival was estimated using Cox-proportional hazard models. An interactive web-based tool was developed for clinical use. RESULTS A derivation cohort (n = 10 660) informed the model with 13-recipient, 4-donor, and 2-transplant variables. Adjusted risk scores were computed for every transplant and grouped into 3 clusters. Model-estimated survival probabilities were similar to the observed in the validation cohort (n = 4464) for all clusters. The mortality increases for medium- and high-risk groups was similar in both derivation and validation cohorts (C statistics for 1-, 5-, and 10-y survival were 0.67, 0.64, and 0.72, respectively). The web-based application estimated 1-, 5-, 10-y survival and half-life for low- (92%, 73%, 52%; 10.5 y), medium- (89%, 62%, 38%; 7.3 y), and high-risk clusters (85%, 52%, 26%; 5.2 y). CONCLUSIONS Advanced methods incorporating machine/deep learning led to a risk scoring model (including recipient, donor, and transplant factors) and a web-based clinical tool providing short- and long-term survival probabilities for recipient-donor matches. This will enable risk-based matching that could improve utilization of and benefit from a limited donor pool.
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16
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Magga L, Maturana S, Olivares M, Valdevenito M, Cabezas J, Chapochnick J, González F, Kompatzki A, Müller H, Pefaur J, Ulloa C, Valjalo R. Identifying Factors Predicting Kidney Graft Survival in Chile Using Elastic-Net-Regularized Cox’s Regression. Medicina (B Aires) 2022; 58:medicina58101348. [PMID: 36295509 PMCID: PMC9608564 DOI: 10.3390/medicina58101348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 09/15/2022] [Accepted: 09/19/2022] [Indexed: 11/25/2022] Open
Abstract
Background and Objectives: We developed a predictive statistical model to identify donor–recipient characteristics related to kidney graft survival in the Chilean population. Given the large number of potential predictors relative to the sample size, we implemented an automated variable selection mechanism that could be revised in future studies as more national data is collected. Materials and Methods: A retrospective multicenter study was conducted to analyze data from 822 adult kidney transplant recipients from adult donors between 1998 and 2018. To the best of our knowledge, this is the largest kidney transplant database to date in Chile. A procedure based on a cross-validated regularized Cox regression using the Elastic Net penalty was applied to objectively identify predictors of death-censored graft failure. Hazard ratios were estimated by adjusting a multivariate Cox regression with the selected predictors. Results: Seven variables were associated with the risk of death-censored graft failure; four from the donor: age (HR = 1.02, 95% CI: 1.00–1.03), male sex (HR = 0.64, 95% CI: 0.46–0.90), history of hypertension (HR = 1.49, 95% CI: 0.98–2.28), and history of diabetes (HR = 2.04, 95% CI: 0.97–4.29); two from the recipient: years on dialysis log-transformation (HR = 1.29, 95% CI: 0.99–1.67) and history of previous solid organ transplantation (HR = 2.02, 95% CI: 1.18–3.47); and one from the transplant: number of HLA mismatches (HR = 1.13, 95% CI: 0.99–1.28). Only the latter is considered for patient prioritization in deceased kidney allocation in Chile. Conclusions: A risk model for kidney graft failure was developed and trained for the Chilean population, providing objective criteria which can be used to improve efficiency in deceased kidney allocation.
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Affiliation(s)
- Leandro Magga
- Department of Industrial Engineering, University of Chile, Santiago 8370456, Chile
| | - Simón Maturana
- Department of Industrial Engineering, University of Chile, Santiago 8370456, Chile
- Instituto Sistemas Complejos de Ingeniería, Santiago 8370398, Chile
| | - Marcelo Olivares
- Department of Industrial Engineering, University of Chile, Santiago 8370456, Chile
- Instituto Sistemas Complejos de Ingeniería, Santiago 8370398, Chile
- Correspondence: ; Tel.: +56-(2)26894429 or +56-(2)26894403
| | - Martín Valdevenito
- Department of Industrial Engineering, University of Chile, Santiago 8370456, Chile
| | - Josefa Cabezas
- Department of Industrial Engineering, University of Chile, Santiago 8370456, Chile
| | | | | | | | - Hans Müller
- Hospital Las Higueras, Talcahuano 4270918, Chile
| | | | - Camilo Ulloa
- Clínica Alemana de Santiago, Santiago 8320000, Chile
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17
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Gotlieb N, Azhie A, Sharma D, Spann A, Suo NJ, Tran J, Orchanian-Cheff A, Wang B, Goldenberg A, Chassé M, Cardinal H, Cohen JP, Lodi A, Dieude M, Bhat M. The promise of machine learning applications in solid organ transplantation. NPJ Digit Med 2022; 5:89. [PMID: 35817953 PMCID: PMC9273640 DOI: 10.1038/s41746-022-00637-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor–recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.
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Affiliation(s)
- Neta Gotlieb
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.,Department of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Amirhossein Azhie
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Divya Sharma
- Department of Gastroenterology, Toronto General Hospital Research Institute, Toronto, ON, Canada
| | - Ashley Spann
- Division of Gastroenterology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nan-Ji Suo
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Jason Tran
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Ani Orchanian-Cheff
- Library and Information Services, University Health Network, Toronto, ON, Canada
| | - Bo Wang
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Anna Goldenberg
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Michael Chassé
- Department of Medicine (Critical Care), University of Montreal Hospital, Montréal, QC, Canada.,Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada
| | - Heloise Cardinal
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Centre hospitalier de l'Université de Montréal Research Center, Université de Montréal, Montréal, QC, Canada
| | - Joseph Paul Cohen
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA, USA.,Mila, Quebec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Andrea Lodi
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Canada Excellence Research Chair, Polytechnique Montréal, Montréal, QC, Canada
| | - Melanie Dieude
- Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada.,Centre hospitalier de l'Université de Montréal Research Center, Université de Montréal, Montréal, QC, Canada.,Department Microbiology, Infectiology and Immunology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada.,Héma-Québec, Montréal, QC, Canada
| | - Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada. .,Canadian Donation and Transplantation Research Program, Data and Innovation Expert Group, Toronto, ON, Canada. .,Division of Gastroenterology and Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
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18
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Dominant predictors of early post-transplant outcomes based on the Korean Organ Transplantation Registry (KOTRY). Sci Rep 2022; 12:8706. [PMID: 35610279 PMCID: PMC9130148 DOI: 10.1038/s41598-022-12302-5] [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] [Received: 07/30/2021] [Accepted: 05/09/2022] [Indexed: 11/20/2022] Open
Abstract
Data for Asian kidney transplants are very limited. We investigated the relative importance of prognostic markers in Asian kidney transplants by using Korean Organ Transplantation Registry (KOTRY) cohort. Prediction models were developed by data-driven variable selection approach. The relative importance of the selected predictors was measured by dominance analysis. A total of 4854 kidney transplant donor-recipient pairs were analyzed. Overall patient survival rates were 99.8%, 98.8%, and 91.8% at 1, 3, and 5 years, respectively. Death-censored graft survival rates were 98.4%, 97.0%, and 95.8% at 1, 3, and 5 years. Biopsy-proven acute rejection free survival rates were 90.1%, 87.4%, and 87.03% at 1, 3, and 5 years. The top 3 dominant predictors for recipient mortality within 1 year were recipient cardiovascular disease history, deceased donor, and recipient age. The dominant predictors for death-censored graft loss within 1 year were acute rejection, deceased donor, and desensitization. The dominant predictors to acute rejection within 1 year were donor age, HLA mismatched numbers, and desensitization. We presented clinical characteristics of patients enrolled in KOTRY during the last 5 years and investigated dominant predictors for early post-transplant outcomes, which would be useful for clinical decision-making based on quantitative measures.
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19
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Dani A, Heidel JS, Qiu T, Zhang Y, Ni Y, Hossain MM, Chin C, Morales DLS, Huang B, Zafar F. External validation and comparison of risk score models in pediatric heart transplants. Pediatr Transplant 2022; 26:e14204. [PMID: 34881481 PMCID: PMC9157612 DOI: 10.1111/petr.14204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 11/16/2021] [Accepted: 11/26/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Pediatric heart transplant (PHT) patients have the highest waitlist mortality of solid organ transplants, yet more than 40% of viable hearts are unutilized. A tool for risk prediction could impact these outcomes. This study aimed to compare and validate the PHT risk score models (RSMs) in the literature. METHODS The literature was reviewed to identify RSMs published. The United Network for Organ Sharing (UNOS) registry was used to validate the published models identified in a pediatric cohort (<18 years) transplanted between 2017 and 2019 and compared against the Scientific Registry of Transplant Recipients (SRTR) 2021 model. Primary outcome was post-transplant 1-year mortality. Odds ratios were obtained to evaluate the association between risk score groups and 1-year mortality. Area under the curve (AUC) was used to compare the RSM scores on their goodness-of-fit, using Delong's test. RESULTS Six recipient and one donor RSMs published between 2008 and 2021 were included in the analysis. The validation cohort included 1,003 PHT. Low-risk groups had a significantly better survival than high-risk groups as predicted by Choudhry (OR = 4.59, 95% CI [2.36-8.93]) and Fraser III (3.17 [1.43-7.05]) models. Choudhry's and SRTR models achieved the best overall performance (AUC = 0.69 and 0.68, respectively). When adjusted for CHD and ventricular assist device support, all models reported better predictability [AUC > 0.6]. Choudhry (AUC = 0.69) and SRTR (AUC = 0.71) remained the best predicting RSMs even after adjustment. CONCLUSION Although the RSMs by SRTR and Choudhry provided the best prediction for 1-year mortality, none demonstrated a strong (AUC ≥ 0.8) concordance statistic. All published studies lacked advanced analytical approaches and were derived from an inherently limited dataset.
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Affiliation(s)
- Alia Dani
- Cardiothoracic Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Justin S. Heidel
- Cardiothoracic Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Tingting Qiu
- Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Yin Zhang
- Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Yizhao Ni
- Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Md Monir Hossain
- Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Clifford Chin
- Cardiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - David L. S. Morales
- Cardiothoracic Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Bin Huang
- Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Farhan Zafar
- Cardiothoracic Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
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Fu R, Schwartz R, Mitsakakis N, Diemert LM, O’Connor S, Cohen JE. Predictors of perceived success in quitting smoking by vaping: A machine learning approach. PLoS One 2022; 17:e0262407. [PMID: 35030208 PMCID: PMC8759658 DOI: 10.1371/journal.pone.0262407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 12/25/2021] [Indexed: 11/18/2022] Open
Abstract
Prior research has suggested that a set of unique characteristics may be associated with adult cigarette smokers who are able to quit smoking using e-cigarettes (vaping). In this cross-sectional study, we aimed to identify and rank the importance of these characteristics using machine learning. During July and August 2019, an online survey was administered to a convenience sample of 889 adult smokers (age ≥ 20) in Ontario, Canada who tried vaping to quit smoking in the past 12 months. Fifty-one person-level characteristics, including a Vaping Experiences Score, were assessed in a gradient boosting machine model to classify the status of perceived success in vaping-assisted smoking cessation. This model was trained using cross-validation and tested using the receiver operating characteristic (ROC) curve. The top five most important predictors were identified using a score between 0% and 100% that represented the relative importance of each variable in model training. About 20% of participants (N = 174, 19.6%) reported success in vaping-assisted smoking cessation. The model achieved relatively high performance with an area under the ROC curve of 0.865 and classification accuracy of 0.831 (95% CI [confidence interval] 0.780 to 0.874). The top five most important predictors of perceived success in vaping-assisted smoking cessation were more positive experiences measured by the Vaping Experiences Score (100%), less previously failed quit attempts by vaping (39.0%), younger age (21.9%), having vaped 100 times (16.8%), and vaping shortly after waking up (15.8%). Our findings provide strong statistical evidence that shows better vaping experiences are associated with greater perceived success in smoking cessation by vaping. Furthermore, our study confirmed the strength of machine learning techniques in vaping-related outcomes research based on observational data.
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Affiliation(s)
- Rui Fu
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Robert Schwartz
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
| | - Nicholas Mitsakakis
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Lori M. Diemert
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Shawn O’Connor
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Joanna E. Cohen
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
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21
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Hanis TM, Islam MA, Musa KI. Top 100 Most-Cited Publications on Breast Cancer and Machine Learning Research: A Bibliometric Analysis. Curr Med Chem 2021; 29:1426-1435. [PMID: 34749608 DOI: 10.2174/0929867328666211108110731] [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: 03/09/2021] [Revised: 07/28/2021] [Accepted: 08/26/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND Rapid advancement in computing technology and digital information leads to the possible use of machine learning on breast cancer. OBJECTIVE This study aimed to evaluate the research output of the top 100 publications and further identify a research theme of breast cancer and machine-learning studies. METHODS Databases of Scopus and Web of Science were used to extract the top 100 publications. These publications were filtered based on the total citation of each paper. Additionally, a bibliometric analysis was applied to the top 100 publications. RESULTS The top 100 publications were published between 1993 and 2019. The most productive author was Giger ML, and the top two institutions were the University of Chicago and the National University of Singapore. The most active countries were the USA, Germany and China. Ten clusters were identified as both basic and specialised themes of breast cancer and machine learning. CONCLUSION Various countries demonstrated comparable interest in breast cancer and machine-learning research. A few Asian countries, such as China, India and Singapore, were listed in the top 10 countries based on the total citation. Additionally, the use of deep learning and breast imaging data was trending in the past 10 years in the field of breast cancer and machine-learning research.
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Affiliation(s)
- Tengku Muhammad Hanis
- Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian. Malaysia
| | - Md Asiful Islam
- Department of Haematology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian. Malaysia
| | - Kamarul Imran Musa
- Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian. Malaysia
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22
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Paquette FX, Ghassemi A, Bukhtiyarova O, Cisse M, Gagnon N, Della Vecchia A, Rabearivelo HA, Loudiyi Y. Machine learning support for decision making in kidney transplantation: step-by-step development of a technological solution (Preprint). JMIR Med Inform 2021; 10:e34554. [PMID: 35700006 PMCID: PMC9240927 DOI: 10.2196/34554] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 01/29/2022] [Accepted: 01/31/2022] [Indexed: 01/29/2023] Open
Abstract
Background Kidney transplantation is the preferred treatment option for patients with end-stage renal disease. To maximize patient and graft survival, the allocation of donor organs to potential recipients requires careful consideration. Objective This study aimed to develop an innovative technological solution to enable better prediction of kidney transplant survival for each potential donor-recipient pair. Methods We used deidentified data on past organ donors, recipients, and transplant outcomes in the United States from the Scientific Registry of Transplant Recipients. To predict transplant outcomes for potential donor-recipient pairs, we used several survival analysis models, including regression analysis (Cox proportional hazards), random survival forests, and several artificial neural networks (DeepSurv, DeepHit, and recurrent neural network [RNN]). We evaluated the performance of each model in terms of its ability to predict the probability of graft survival after kidney transplantation from deceased donors. Three metrics were used: the C-index, integrated Brier score, and integrated calibration index, along with calibration plots. Results On the basis of the C-index metrics, the neural network–based models (DeepSurv, DeepHit, and RNN) had better discriminative ability than the Cox model and random survival forest model (0.650, 0.661, and 0.659 vs 0.646 and 0.644, respectively). The proposed RNN model offered a compromise between the good discriminative ability and calibration and was implemented in a technological solution of technology readiness level 4. Conclusions Our technological solution based on the RNN model can effectively predict kidney transplant survival and provide support for medical professionals and candidate recipients in determining the most optimal donor-recipient pair.
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Affiliation(s)
| | | | | | | | | | - Alexia Della Vecchia
- BI Expertise, Quebec, QC, Canada
- Research Institute McGill University Heath Centre, Montreal, QC, Canada
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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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Taherkhani N, Sepehri MM, Khasha R, Shafaghi S. Determining the Level of Importance of Variables in Predicting Kidney Transplant Survival Based on a Novel Ranking Method. Transplantation 2021; 105:2307-2315. [PMID: 33534528 DOI: 10.1097/tp.0000000000003623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Kidney transplantation is the best alternative treatment for end-stage renal disease. To optimal use of donated kidneys, graft predicted survival can be used as a factor to allocate kidneys. The performance of prediction techniques is highly dependent on the correct selection of predictors. Hence, the main objective of this research is to propose a novel method for ranking the effective variables for predicting the kidney transplant survival. METHODS Five classification models were used to classify kidney recipients in long- and short-term survival classes. Synthetic minority oversampling and random undersampling were used to overcome the imbalanced class problem. In dealing with missing values, 2 approaches were used (eliminating and imputing them). All variables were categorized into 4 levels. The ranking was evaluated using the sensitivity analysis approach. RESULTS Thirty-four of the 41 variables were identified as important variables, of which, 5 variables were categorized in very important level ("Recipient creatinine at discharge," "Recipient dialysis time," "Donor history of diabetes," "Donor kidney biopsy," and "Donor cause of death"), 17 variables in important level, and 12 variables in the low important level. CONCLUSIONS In this study, we identify new variables that have not been addressed in any of the previous studies (eg, AGE_DIF and MATCH_GEN). On the other hand, in kidney allocation systems, 2 main criteria are considered: equity and utility. One of the utility subcriteria is the graft survival. Our study findings can be used in the design of systems to predict the graft survival.
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Affiliation(s)
- Nasrin Taherkhani
- Faculty Member of Computer Engineering, Payam-e-Noor University, Saveh, Iran
| | - Mohammad Mehdi Sepehri
- Department of Healthcare Systems Engineering, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Roghaye Khasha
- Center of Excellence in Healthcare Systems Engineering, Tarbiat Modares University, Tehran, Iran
| | - Shadi Shafaghi
- Lung Transplantation Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
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25
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Abstract
BACKGROUND Despite the kidney supply shortage, 18%-20% of deceased donor kidneys are discarded annually in the United States. In 2018, 3569 kidneys were discarded. METHODS We compared machine learning (ML) techniques to identify kidneys at risk of discard at the time of match run and after biopsy and machine perfusion results become available. The cohort consisted of adult deceased donor kidneys donated between December 4, 2014, and July 1, 2019. The studied ML models included Random Forests (RF), Adaptive Boosting (AdaBoost), Neural Networks (NNet), Support Vector Machines (SVM), and K-nearest Neighbors (KNN). In addition, a Logistic Regression (LR) model was fitted and used for comparison with the ML models' performance. RESULTS RF outperformed other ML models. Of 8036 discarded kidneys in the test dataset, LR correctly classified 3422 kidneys, whereas RF correctly classified 4762 kidneys (area under the receiver operative curve [AUC]: 0.85 versus 0.888, and balanced accuracy: 0.681 versus 0.759). For the kidneys with kidney donor profile index of >85% (6079 total), RF significantly outperformed LR in classifying discard and transplant prediction (AUC: 0.814 versus 0.717, and balanced accuracy: 0.732 versus 0.657). More than 388 kidneys were correctly classified using RF. Including biopsy and machine perfusion variables improved the performance of LR and RF (LR's AUC: 0.888 and balanced accuracy: 0.74 versus RF's AUC: 0.904 and balanced accuracy: 0.775). CONCLUSIONS Kidneys that are at risk of discard can be more accurately identified using ML techniques such as RF.
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Affiliation(s)
- Masoud Barah
- Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL
| | - Sanjay Mehrotra
- Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL
- Center for Engineering and Health, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
- Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Northwestern University Feinberg School of Medicine, Chicago, IL
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26
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Schwantes IR, Axelrod DA. Technology-Enabled Care and Artificial Intelligence in Kidney Transplantation. CURRENT TRANSPLANTATION REPORTS 2021; 8:235-240. [PMID: 34341714 PMCID: PMC8317681 DOI: 10.1007/s40472-021-00336-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/30/2021] [Indexed: 01/24/2023]
Abstract
Purpose of Review Artificial intelligence (AI), machine learning, and technology-enabled remote patient care have evolved rapidly and have now been incorporated into many aspects of medical care. Transplantation is fortunate to have large data sets upon which machine learning algorithms can be constructed. AI are now available to improve pretransplant management, donor selection, and post-operative management of transplant patients. Recent Findings Changes in patient and donor characteristics warrant new approaches to listing and organ acceptance practices. Machine learning has been employed to optimize donor selection to identify patients likely to benefit from transplantation of higher risk organs, increasing organ discard and reducing waitlist mortality. These models have greater precisions and predictive ability than currently employed metrics including the Kidney Donor Profile Index and the expected posttransplant survival models. After transplant, AI tools have been developed to optimize immunosuppression management, track patients adherence, and assess graft survival. Summary AI and technology-enabled management tools are now available throughout the transplant journey. Unfortunately, those are frequently not available at the point of decision (patient listing, organ acceptance, posttransplant clinic), limiting utilization. Incorporation of these tools into the EMR, the Donor Net® organ offer system, and mobile devices is vital to ensure widespread adoption.
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Affiliation(s)
- Issac R Schwantes
- Department of Surgery, Oregon Health & Science University, Portland, OR USA
| | - David A Axelrod
- Organ Transplant Center, University of Iowa, 200 Hawkins Dr, Iowa City, LA 52240 USA
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27
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Connor KL, O'Sullivan ED, Marson LP, Wigmore SJ, Harrison EM. The Future Role of Machine Learning in Clinical Transplantation. Transplantation 2021; 105:723-735. [PMID: 32826798 DOI: 10.1097/tp.0000000000003424] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive datasets, exponential increases in processing power, and key algorithmic developments that allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain. In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation in which ML is driving progress, explore the future potential roles of ML, and discuss the challenges and limitations of these powerful tools.
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Affiliation(s)
- Katie L Connor
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Eoin D O'Sullivan
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Lorna P Marson
- Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen J Wigmore
- Edinburgh Transplant Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom.,Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
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28
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Seyahi N, Ozcan SG. Artificial intelligence and kidney transplantation. World J Transplant 2021; 11:277-289. [PMID: 34316452 PMCID: PMC8290997 DOI: 10.5500/wjt.v11.i7.277] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 05/17/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.
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Affiliation(s)
- Nurhan Seyahi
- Department of Nephrology, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul 34098, Fatih, Turkey
| | - Seyda Gul Ozcan
- Department of Internal Medicine, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul 34098, Fatih, Turkey
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29
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Nemati M, Zhang H, Sloma M, Bekbolsynov D, Wang H, Stepkowski S, Xu KS. Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs. ARTIFICIAL INTELLIGENCE IN MEDICINE. CONFERENCE ON ARTIFICIAL INTELLIGENCE IN MEDICINE (2005- ) 2021; 12721:51-60. [PMID: 34179894 PMCID: PMC8224462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Kidney transplantation can significantly enhance living standards for people suffering from end-stage renal disease. A significant factor that affects graft survival time (the time until the transplant fails and the patient requires another transplant) for kidney transplantation is the compatibility of the Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper, we propose new biologically-relevant feature representations for incorporating HLA information into machine learning-based survival analysis algorithms. We evaluate our proposed HLA feature representations on a database of over 100,000 transplants and find that they improve prediction accuracy by about 1%, modest at the patient level but potentially significant at a societal level. Accurate prediction of survival times can improve transplant survival outcomes, enabling better allocation of donors to recipients and reducing the number of re-transplants due to graft failure with poorly matched donors.
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Affiliation(s)
- Mohammadreza Nemati
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, OH, USA 43606
| | - Haonan Zhang
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, OH, USA 43606
| | - Michael Sloma
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, OH, USA 43606
| | - Dulat Bekbolsynov
- Department of Medical Microbiology and Immunology, University of Toledo
| | - Hong Wang
- Department of Engineering Technology, University of Toledo
| | | | - Kevin S. Xu
- Department of Electrical Engineering and Computer Science, University of Toledo, 2801 W Bancroft St, Toledo, OH, USA 43606
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30
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A machine learning prediction model for waiting time to kidney transplant. PLoS One 2021; 16:e0252069. [PMID: 34015020 PMCID: PMC8136711 DOI: 10.1371/journal.pone.0252069] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/09/2021] [Indexed: 11/19/2022] Open
Abstract
Background Predicting waiting time for a deceased donor kidney transplant can help patients and clinicians to discuss management and contribute to a more efficient use of resources. This study aimed at developing a predictor model to estimate time on a kidney transplant waiting list using a machine learning approach. Methods A retrospective cohort study including data of patients registered, between January 1, 2000 and December 31, 2017, in the waiting list of São Paulo State Organ Allocation System (SP-OAS) /Brazil. Data were randomly divided into two groups: 75% for training and 25% for testing. A Cox regression model was fitted with deceased donor transplant as the outcome. Sensitivity analyses were performed using different Cox models. Cox hazard ratios were used to develop the risk-prediction equations. Results Of 54,055 records retrieved, 48,153 registries were included in the final analysis. During the study period, approximately 1/3 of the patients were transplanted with a deceased donor. The major characteristics associated with changes in the likelihood of transplantation were age, subregion, cPRA, and frequency of HLA-DR, -B and -A. The model developed was able to predict waiting time with good agreement in internal validation (c-index = 0.70). Conclusion The kidney transplant waiting time calculator developed shows good predictive performance and provides information that may be valuable in assisting candidates and their providers. Moreover, it can significantly improve the use of economic resources and the management of patient care before transplant.
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31
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Elfanagely O, Toyoda Y, Othman S, Mellia JA, Basta M, Liu T, Kording K, Ungar L, Fischer JP. Machine Learning and Surgical Outcomes Prediction: A Systematic Review. J Surg Res 2021; 264:346-361. [PMID: 33848833 DOI: 10.1016/j.jss.2021.02.045] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 02/13/2021] [Accepted: 02/27/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND Machine learning (ML) has garnered increasing attention as a means to quantitatively analyze the growing and complex medical data to improve individualized patient care. We herein aim to critically examine the current state of ML in predicting surgical outcomes, evaluate the quality of currently available research, and propose areas of improvement for future uses of ML in surgery. METHODS A systematic review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) checklist. PubMed, MEDLINE, and Embase databases were reviewed under search syntax "machine learning" and "surgery" for papers published between 2015 and 2020. RESULTS Of the initial 2677 studies, 45 papers met inclusion and exclusion criteria. Fourteen different subspecialties were represented with neurosurgery being most common. The most frequently used ML algorithms were random forest (n = 19), artificial neural network (n = 17), and logistic regression (n = 17). Common outcomes included postoperative mortality, complications, patient reported quality of life and pain improvement. All studies which compared ML algorithms to conventional studies which used area under the curve (AUC) to measure accuracy found improved outcome prediction with ML models. CONCLUSIONS While still in its early stages, ML models offer surgeons an opportunity to capitalize on the myriad of clinical data available and improve individualized patient care. Limitations included heterogeneous outcome and imperfect quality of some of the papers. We therefore urge future research to agree upon methods of outcome reporting and require basic quality standards.
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Affiliation(s)
- Omar Elfanagely
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Yoshiko Toyoda
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Sammy Othman
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Joseph A Mellia
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Marten Basta
- Department of Plastic and Reconstructive Surgery, Brown University, Providence, Rhode Island
| | - Tony Liu
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Konrad Kording
- Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Lyle Ungar
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania
| | - John P Fischer
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania
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32
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Toward Advancing Long-Term Outcomes of Kidney Transplantation with Artificial Intelligence. TRANSPLANTOLOGY 2021. [DOI: 10.3390/transplantology2020012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
After decades of pioneering advances and improvements, kidney transplantation is now the renal replacement therapy of choice for most patients with end-stage kidney disease (ESKD). Despite this success, the high risk of premature death and frequent occurrence of graft failure remain important clinical and research challenges. The current burst of studies and other innovative initiatives using artificial intelligence (AI) for a wide range of analytical and practical applications in biomedical areas seems to correlate with the same trend observed in publications in the kidney transplantation field, and points toward the potential of such novel approaches to address the aforementioned aim of improving long-term outcomes of kidney transplant recipients (KTR). However, at the same time, this trend underscores now more than ever the old methodological challenges and potential threats that the research and clinical community needs to be aware of and actively look after with regard to AI-driven evidence. The purpose of this narrative mini-review is to explore challenges for obtaining applicable and adequate kidney transplant data for analyses using AI techniques to develop prediction models, and to propose next steps in the field. We make a call to act toward establishing the strong collaborations needed to bring innovative synergies further augmented by AI, which have the potential to impact the long-term care of KTR. We encourage researchers and clinicians to submit their invaluable research, including original clinical and imaging studies, database studies from registries, meta-analyses, and AI research in the kidney transplantation field.
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33
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Nemati M, Zhang H, Sloma M, Bekbolsynov D, Wang H, Stepkowski S, Xu KS. Predicting Kidney Transplant Survival Using Multiple Feature Representations for HLAs. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-77211-6_6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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34
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Bae S, Massie AB, Caffo BS, Jackson KR, Segev DL. Machine learning to predict transplant outcomes: helpful or hype? A national cohort study. Transpl Int 2020; 33:1472-1480. [PMID: 32996170 PMCID: PMC8269970 DOI: 10.1111/tri.13695] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 11/30/2019] [Accepted: 06/29/2020] [Indexed: 12/13/2022]
Abstract
An increasing number of studies claim machine learning (ML) predicts transplant outcomes more accurately. However, these claims were possibly confounded by other factors, namely, supplying new variables to ML models. To better understand the prospects of ML in transplantation, we compared ML to conventional regression in a "common" analytic task: predicting kidney transplant outcomes using national registry data. We studied 133 431 adult deceased-donor kidney transplant recipients between 2005 and 2017. Transplant centers were randomly divided into 70% training set (190 centers/97 787 recipients) and 30% validation set (82 centers/35 644 recipients). Using the training set, we performed regression and ML procedures [gradient boosting (GB) and random forests (RF)] to predict delayed graft function, one-year acute rejection, death-censored graft failure C, all-cause graft failure, and death. Their performances were compared on the validation set using -statistics. In predicting rejection, regression (C = 0.601 0.6110.621 ) actually outperformed GB (C = 0.581 0.5910.601 ) and RF (C = 0.569 0.5790.589 ). For all other outcomes, the C-statistics were nearly identical across methods (delayed graft function, 0.717-0.723; death-censored graft failure, 0.637-0.642; all-cause graft failure, 0.633-0.635; and death, 0.705-0.708). Given its shortcomings in model interpretability and hypothesis testing, ML is advantageous only when it clearly outperforms conventional regression; in the case of transplant outcomes prediction, ML seems more hype than helpful.
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Affiliation(s)
- Sunjae Bae
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Allan B Massie
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Kyle R Jackson
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Dorry L Segev
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
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35
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Predictors of Survival After Liver Transplantation in Patients With the Highest Acuity (MELD ≥40). Ann Surg 2020; 272:458-466. [PMID: 32740239 DOI: 10.1097/sla.0000000000004211] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
OBJECTIVE To identify factors that accurately predict 1-year survival for liver transplant recipients with a MELD score ≥40. BACKGROUND Although transplant is beneficial for patients with the highest acuity (MELD ≥40), mortality in this group is high. Predicting which patients are likely to survive for >1 year would be medically and economically helpful. METHODS The Scientific Registry of Transplant Recipients database was reviewed to identify adult liver transplant recipients from 2002 through 2016 with MELD score ≥40 at transplant. The relationships between 44 recipient and donor factors and 1-year patient survival were examined using random survival forests methods. Variable importance measures were used to identify the factors with the strongest influence on survival, and partial dependence plots were used to determine the dependence of survival on the target variable while adjusting for all other variables. RESULTS We identified 5309 liver transplants that met our criteria. The overall 1-year survival of high-acuity patients improved from 69% in 2001 to 87% in 2016. The strongest predictors of death within 1 year of transplant were patient on mechanical ventilator before transplantation, prior liver transplant, older recipient age, older donor age, donation after cardiac death, and longer cold ischemia. CONCLUSIONS Liver transplant outcomes continue to improve even for patients with high medical acuity. Applying ensemble learning methods to recipient and donor factors available before transplant can predict survival probabilities for future transplant cases. This information can be used to facilitate donor/recipient matching and to improve informed consent.
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Machine learning for predicting long-term kidney allograft survival: a scoping review. Ir J Med Sci 2020; 190:807-817. [PMID: 32761550 DOI: 10.1007/s11845-020-02332-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 07/26/2020] [Indexed: 12/24/2022]
Abstract
Supervised machine learning (ML) is a class of algorithms that "learn" from existing input-output pairs, which is gaining popularity in pattern recognition for classification and prediction problems. In this scoping review, we examined the use of supervised ML algorithms for the prediction of long-term allograft survival in kidney transplant recipients. Data sources included PubMed, the Cumulative Index to Nursing and Allied Health Literature, and the Institute for Electrical and Electronics Engineers (IEEE) Xplore libraries from inception to November 2019. We screened titles and abstracts and potentially eligible full-text reports to select studies and subsequently abstracted the data. Eleven studies were identified. Decision trees were the most commonly used method (n = 8), followed by artificial neural networks (ANN) (n = 4) and Bayesian belief networks (n = 2). The area under receiver operating curve (AUC) was the most common measure of discrimination (n = 7), followed by sensitivity (n = 5) and specificity (n = 4). Model calibration examining the reliability in risk prediction was performed using either the Pearson r or the Hosmer-Lemeshow test in four studies. One study showed that logistic regression had comparable performance to ANN, while another study demonstrated that ANN performed better in terms of sensitivity, specificity, and accuracy, as compared with a Cox proportional hazards model. We synthesized the evidence related to the comparison of ML techniques with traditional statistical approaches for prediction of long-term allograft survival in patients with a kidney transplant. The methodological and reporting quality of included studies was poor. Our study also demonstrated mixed results in terms of the predictive potential of the models.
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Thongprayoon C, Hansrivijit P, Leeaphorn N, Acharya P, Torres-Ortiz A, Kaewput W, Kovvuru K, Kanduri SR, Bathini T, Cheungpasitporn W. Recent Advances and Clinical Outcomes of Kidney Transplantation. J Clin Med 2020; 9:jcm9041193. [PMID: 32331309 PMCID: PMC7230851 DOI: 10.3390/jcm9041193] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 04/20/2020] [Indexed: 02/07/2023] Open
Abstract
Recent advances in surgical, immunosuppressive and monitoring protocols have led to the significant improvement of overall one-year kidney allograft outcomes. Nonetheless, there has not been a significant change in long-term kidney allograft outcomes. In fact, chronic and acute antibody-mediated rejection (ABMR) and non-immunological complications following kidney transplantation, including multiple incidences of primary kidney disease, as well as complications such as cardiovascular diseases, infections, and malignancy are the major factors that have contributed to the failure of kidney allografts. The use of molecular techniques to enhance histological diagnostics and noninvasive surveillance are what the latest studies in the field of clinical kidney transplant seem to mainly focus upon. Increasingly innovative approaches are being used to discover immunosuppressive methods to overcome critical sensitization, prevent the development of anti-human leukocyte antigen (HLA) antibodies, treat chronic active ABMR, and reduce non-immunological complications following kidney transplantation, such as the recurrence of primary kidney disease and other complications, such as cardiovascular diseases, infections, and malignancy. In the present era of utilizing electronic health records (EHRs), it is strongly believed that big data and artificial intelligence will reshape the research done on kidney transplantation in the near future. In addition, the utilization of telemedicine is increasing, providing benefits such as reaching out to kidney transplant patients in remote areas and helping to make scarce healthcare resources more accessible for kidney transplantation. In this article, we discuss the recent research developments in kidney transplants that may affect long-term allografts, as well as the survival of the patient. The latest developments in living kidney donation are also explored.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | - Panupong Hansrivijit
- Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA;
| | - Napat Leeaphorn
- Department of Nephrology, Department of Medicine, Saint Luke’s Health System, Kansas City, MO 64111, USA;
| | - Prakrati Acharya
- Division of Nephrology, Department of Medicine, Texas Tech University Health Sciences Center, El Paso, TX 79905, USA;
| | - Aldo Torres-Ortiz
- Department of Medicine, Ochsner Medical Center, New Orleans, LA 70121, USA;
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Karthik Kovvuru
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.)
| | - Swetha R. Kanduri
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.)
| | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, Tucson, AZ 85724, USA;
| | - Wisit Cheungpasitporn
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.)
- Correspondence: ; Tel.: +1-601-984-5670; Fax: +1-601-984-5765
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Thongprayoon C, Kaewput W, Kovvuru K, Hansrivijit P, Kanduri SR, Bathini T, Chewcharat A, Leeaphorn N, Gonzalez-Suarez ML, Cheungpasitporn W. Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation. J Clin Med 2020; 9:jcm9041107. [PMID: 32294906 PMCID: PMC7230205 DOI: 10.3390/jcm9041107] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 04/09/2020] [Indexed: 02/07/2023] Open
Abstract
Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Karthik Kovvuru
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Panupong Hansrivijit
- Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA;
| | - Swetha R. Kanduri
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, Tucson, AZ 85721, USA;
| | - Api Chewcharat
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.T.); (A.C.)
| | - Napat Leeaphorn
- Department of Nephrology, Department of Medicine, Saint Luke’s Health System, Kansas City, MO 64111, USA;
| | - Maria L. Gonzalez-Suarez
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
| | - Wisit Cheungpasitporn
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA; (K.K.); (S.R.K.); (M.L.G.-S.)
- Correspondence: ; Tel.: +1-601-984-5670; Fax: +1-601-984-5765
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Sealfon RSG, Mariani LH, Kretzler M, Troyanskaya OG. Machine learning, the kidney, and genotype-phenotype analysis. Kidney Int 2020; 97:1141-1149. [PMID: 32359808 PMCID: PMC8048707 DOI: 10.1016/j.kint.2020.02.028] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 01/13/2020] [Accepted: 02/06/2020] [Indexed: 01/23/2023]
Abstract
With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcriptomics, proteomics, metabolomics, and genome sequencing), as well as other data modalities such as electronic health records, digital nephropathology repositories, and radiology renal images, makes machine learning approaches increasingly essential for analyzing human kidney data sets. Here, we discuss how machine learning approaches can be applied to the study of kidney disease, with a particular focus on how they can be used for understanding the relationship between genotype and phenotype.
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Affiliation(s)
- Rachel S G Sealfon
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA
| | - Laura H Mariani
- Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA
| | - Matthias Kretzler
- Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA.
| | - Olga G Troyanskaya
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA; Department of Computer Science, Princeton University, Princeton, New Jersey, USA.
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Di Zazzo A, Lee SM, Sung J, Niutta M, Coassin M, Mashaghi A, Inomata T. Variable Responses to Corneal Grafts: Insights from Immunology and Systems Biology. J Clin Med 2020; 9:E586. [PMID: 32098130 PMCID: PMC7074162 DOI: 10.3390/jcm9020586] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 02/18/2020] [Indexed: 12/13/2022] Open
Abstract
Corneal grafts interact with their hosts via complex immunobiological processes that sometimes lead to graft failure. Prediction of graft failure is often a tedious task due to the genetic and nongenetic heterogeneity of patients. As in other areas of medicine, a reliable prediction method would impact therapeutic decision-making in corneal transplantation. Valuable insights into the clinically observed heterogeneity of host responses to corneal grafts have emerged from multidisciplinary approaches, including genomics analyses, mechanical studies, immunobiology, and theoretical modeling. Here, we review the emerging concepts, tools, and new biomarkers that may allow for the prediction of graft survival.
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Affiliation(s)
- Antonio Di Zazzo
- Ophthalmology Complex Operative Unit, Campus Bio Medico University, 00128 Rome, Italy; (A.D.Z.); (M.N.); (M.C.)
| | - Sang-Mok Lee
- Department of Ophthalmology, Catholic Kwandong University College of Medicine, Gangneung-si, Gangwon-do 25601, Korea;
- Department of Cornea, External Disease & Refractive Surgery, HanGil Eye Hospital, Incheon 21388, Korea
| | - Jaemyoung Sung
- University of South Florida, Morsani College of Medicine, Tampa, FL 33612, USA;
- Department of Ophthalmology, Juntendo University Faculty of Medicine, Tokyo 1130033, Japan
| | - Matteo Niutta
- Ophthalmology Complex Operative Unit, Campus Bio Medico University, 00128 Rome, Italy; (A.D.Z.); (M.N.); (M.C.)
| | - Marco Coassin
- Ophthalmology Complex Operative Unit, Campus Bio Medico University, 00128 Rome, Italy; (A.D.Z.); (M.N.); (M.C.)
| | - Alireza Mashaghi
- Systems Biomedicine and Pharmacology Division, Leiden Academic Centre for Drug Research, Leiden University, 2333CC Leiden, The Netherlands
| | - Takenori Inomata
- Department of Ophthalmology, Juntendo University Faculty of Medicine, Tokyo 1130033, Japan
- Department of Strategic Operating Room Management and Improvement, Juntendo University Faculty of Medicine, Tokyo 1130033, Japan
- Department of Hospital Administration, Juntendo University Faculty of Medicine, Tokyo 1130033, Japan
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Díez-Sanmartín C, Sarasa Cabezuelo A. Application of Artificial Intelligence Techniques to Predict Survival in Kidney Transplantation: A Review. J Clin Med 2020; 9:jcm9020572. [PMID: 32093027 PMCID: PMC7074285 DOI: 10.3390/jcm9020572] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 02/17/2020] [Accepted: 02/18/2020] [Indexed: 12/20/2022] Open
Abstract
A key issue in the field of kidney transplants is the analysis of transplant recipients' survival. By means of the information obtained from transplant patients, it is possible to analyse in which cases a transplant has a higher likelihood of success and the factors on which it will depend. In general, these analyses have been conducted by applying traditional statistical techniques, as the amount and variety of data available about kidney transplant processes were limited. However, two main changes have taken place in this field in the last decade. Firstly, the digitalisation of medical information through the use of electronic health records (EHRs), which store patients' medical histories electronically. This facilitates automatic information processing through specialised software. Secondly, medical Big Data has provided access to vast amounts of data on medical processes. The information currently available on kidney transplants is huge and varied by comparison to that initially available for this kind of study. This new context has led to the use of other non-traditional techniques more suitable to conduct survival analyses in these new conditions. Specifically, this paper provides a review of the main machine learning methods and tools that are being used to conduct kidney transplant patient and graft survival analyses.
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Shigemura N. Revisiting the link between PGD and BOS in lung transplantation: highlighting the role of tregs. Transpl Int 2020; 33:497-499. [PMID: 32053220 DOI: 10.1111/tri.13595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 02/10/2020] [Indexed: 11/30/2022]
Affiliation(s)
- Norihisa Shigemura
- Division of Cardiovascular Surgery, Temple University Health System, Lewis Katz School of Medicine, Philadelphia, PA, USA
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Kamburova EG, Hoitsma A, Claas FH, Otten HG. Results and reflections from the PROfiling Consortium on Antibody Repertoire and Effector functions in kidney transplantation: A mini-review. HLA 2019; 94:129-140. [PMID: 31099989 PMCID: PMC6772180 DOI: 10.1111/tan.13581] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 04/05/2019] [Accepted: 05/14/2019] [Indexed: 12/15/2022]
Abstract
Kidney transplantation is the best treatment option for patients with end‐stage renal disease (ESRD). The waiting time for a deceased donor kidney in the Netherlands is approximately 3 years. Mortality among patients on the waiting list is high. The aim of the PROCARE consortium (PROfiling Consortium on Antibody Repertoire and Effector functions) was to decrease the waiting time by providing a matching algorithm yielding a prolonged graft survival and less HLA‐immunization compared with the currently used Eurotransplant Kidney allocation system. In this study, 6097 kidney transplants carried out between January 1995 and December 2005 were re‐examined with modern laboratory techniques and insights that were not available during that time period. In this way, we could identify potential new parameters that can be used to improve the matching algorithm and prolong graft survival. All eight University Medical Centers in the Netherlands participated in this multicenter study. To improve the matching algorithm, we used as central hypothesis that the combined presence of class‐I and ‐II single‐antigen bead (SAB)‐defined donor‐specific HLA antibodies (DSA) prior to transplantation, non‐HLA antibodies, the number of B‐ and/or T‐cell epitopes recognized on donor HLA, and specific polymorphisms in effector mechanisms of IgG were associated with an increased risk for graft failure. The purpose of this article is to relate the results obtained from the PROCARE consortium study to other studies published in recent years. The clinical relevance of SAB‐defined DSA, complement‐fixing DSA, non‐HLA antibodies, and the effector functions of (non)‐HLA‐antibodies will be discussed.
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Affiliation(s)
- Elena G Kamburova
- Laboratory of Translational Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Andries Hoitsma
- Dutch Organ Transplant Registry (NOTR), Dutch Transplant Foundation (NTS), Leiden, The Netherlands
| | - Frans H Claas
- Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands
| | - Henny G Otten
- Laboratory of Translational Immunology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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