“Beyond MELD” – Emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation

Journal of Hepatology - Tập 76 - Trang 1318-1329 - 2022
Jin Ge1, W. Ray Kim2, Jennifer C. Lai1, Allison J. Kwong2
1Division of Gastroenterology and Hepatology, Department of Medicine, University of California – San Francisco, San Francisco, CA, USA
2Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

Tài liệu tham khảo

1999 Jochmans, 2017, Adult liver allocation in eurotransplant, Transplantation, 101, 1542, 10.1097/TP.0000000000001631 Goudsmit, 2021, Refitting the model for end-stage liver disease for the eurotransplant region, Hepatology, 74, 351, 10.1002/hep.31677 Trotter, 2017, Liver transplantation around the world, Curr Opin Organ Transpl, 22, 123, 10.1097/MOT.0000000000000392 Wiesner, 2003, Model for end-stage liver disease (MELD) and allocation of donor livers, Gastroenterology, 124, 91, 10.1053/gast.2003.50016 Freeman, 2002, The new liver allocation system: moving toward evidence-based transplantation policy, Liver Transpl, 8, 851, 10.1053/jlts.2002.35927 Kamath, 2001, A model to predict survival in patients with end-stage liver disease, Hepatology, 33, 464, 10.1053/jhep.2001.22172 Quante, 2012, Experience since MELD implementation: how does the new system deliver?, Int J Hepatol, 2012, 264015, 10.1155/2012/264015 Kim, 2008, Hyponatremia and mortality among patients on the liver-transplant waiting list, N Engl J Med, 359, 1018, 10.1056/NEJMoa0801209 Kim, 2021, MELD 3.0: the model for end-stage liver disease updated for the modern era, Gastroenterology, 10.1053/j.gastro.2021.08.050 Kwong, 2021, OPTN/SRTR 2019 annual data report: liver, Am J Transpl, 21, 208, 10.1111/ajt.16494 Younossi, 2020, Epidemiology of chronic liver diseases in the USA in the past three decades, Gut, 69, 564, 10.1136/gutjnl-2019-318813 Leise, 2011, A revised model for end-stage liver disease optimizes prediction of mortality among patients awaiting liver transplantation, Gastroenterology, 140, 1952, 10.1053/j.gastro.2011.02.017 Godfrey, 2019, The decreasing predictive power of MELD in an era of changing etiology of liver disease, Am J Transpl, 19, 3299, 10.1111/ajt.15559 Kwong, 2020, Reply to: “The decreasing predictive power of MELD in an era of changing etiology of liver disease”, Am J Transpl, 20, 901, 10.1111/ajt.15733 Hernaez, 2020, Model for end-stage liver disease-sodium underestimates 90-day mortality risk in patients with acute-on-chronic liver failure, J Hepatol, 73, 1425, 10.1016/j.jhep.2020.06.005 Allen, 2018, Reduced access to liver transplantation in women: role of height, MELD exception scores, and renal function underestimation, Transplantation, 102, 1710, 10.1097/TP.0000000000002196 Myers, 2011, Gender, renal function, and outcomes on the liver transplant waiting list: assessment of revised MELD including estimated glomerular filtration rate, J Hepatol, 54, 462, 10.1016/j.jhep.2010.07.015 Mathur, 2011, Sex-based disparities in liver transplant rates in the United States, Am J Transpl, 11, 1435, 10.1111/j.1600-6143.2011.03498.x Verna, 2020, Time for action to address the persistent sex-based disparity in liver transplant access, JAMA Surg, 155, 545, 10.1001/jamasurg.2020.1126 Cholongitas, 2007, Female liver transplant recipients with the same GFR as male recipients have lower MELD scores--a systematic bias, Am J Transpl, 7, 685, 10.1111/j.1600-6143.2007.01666.x Leithead, 2011, Is estimated glomerular filtration rate superior to serum creatinine in predicting mortality on the waiting list for liver transplantation?, Transpl Int, 24, 482, 10.1111/j.1432-2277.2011.01231.x Asrani, 2020, MELD-GRAIL-Na: glomerular filtration rate and mortality on liver-transplant waiting list, Hepatology, 71, 1766, 10.1002/hep.30932 Asrani, 2019, A model for glomerular filtration rate assessment in liver disease (GRAIL) in the presence of renal dysfunction, Hepatology, 69, 1219, 10.1002/hep.30321 Finkenstedt, 2012, Cystatin C is a strong predictor of survival in patients with cirrhosis: is a cystatin C-based MELD better?, Liver Int, 32, 1211, 10.1111/j.1478-3231.2012.02766.x De Souza, 2014, Creatinine- versus cystatine C-based equations in assessing the renal function of candidates for liver transplantation with cirrhosis, Hepatology, 59, 1522, 10.1002/hep.26886 Nephew, 2017, Exception points and body size contribute to gender disparity in liver transplantation, Clin Gastroenterol Hepatol, 15, 1286, 10.1016/j.cgh.2017.02.033 Wood, 2021, Correcting the sex disparity in MELD-Na, Am J Transpl, 21, 3296, 10.1111/ajt.16731 Vyas, 2020, Hidden in plain sight - reconsidering the use of race correction in clinical algorithms, N Engl J Med, 383, 874, 10.1056/NEJMms2004740 Linecker, 2018, Potentially inappropriate liver transplantation in the era of the “sickest first” policy - a search for the upper limits, J Hepatol, 68, 798, 10.1016/j.jhep.2017.11.008 Rana, 2008, Survival outcomes following liver transplantation (SOFT) score: a novel method to predict patient survival following liver transplantation, Am J Transpl, 8, 2537, 10.1111/j.1600-6143.2008.02400.x Dutkowski, 2011, Are there better guidelines for allocation in liver transplantation? A novel score targeting justice and utility in the model for end-stage liver disease era, Ann Surg, 254, 745, 10.1097/SLA.0b013e3182365081 Goldberg, 2021, Development and validation of a model to predict long-term survival after liver transplantation, Liver Transpl, 27, 797, 10.1002/lt.26002 Luo, 2018, MELD as a metric for survival benefit of liver transplantation, Am J Transpl, 18, 1231, 10.1111/ajt.14660 Kwong, 2021, Predicting survival after liver transplantation: a noble pursuit or a fool’s errand?, Liver Transpl, 27, 789, 10.1002/lt.26057 Freeman, 2006, Liver Transpl, 12, S128, 10.1002/lt.20979 Cillo, 2015, A multistep, consensus-based approach to organ allocation in liver transplantation: toward a “blended principle model”, Am J Transpl, 15, 2552, 10.1111/ajt.13408 Kasiske, 2020, Continuous distribution as an organ allocation framework, Curr Opin Organ Transpl, 25, 115, 10.1097/MOT.0000000000000733 Continuous Distribution - OPTN n.d. https://optn.transplant.hrsa.gov/governance/key-initiatives/continuous-distribution/ (accessed October 3, 2021). Snyder, 2018, Organ distribution without geographic boundaries: a possible framework for organ allocation, Am J Transpl, 18, 2635, 10.1111/ajt.15115 Lai, 2021, Malnutrition, frailty, and sarcopenia in patients with cirrhosis: 2021 practice guidance by the american association for the study of liver diseases, Hepatology, 74, 1611, 10.1002/hep.32049 Lochs, 2006, Introductory to the ESPEN guidelines on enteral nutrition: terminology, definitions and general topics, Clin Nutr, 25, 180, 10.1016/j.clnu.2006.02.007 Morley, 2013, Frailty consensus: a call to action, J Am Med Dir Assoc, 14, 392, 10.1016/j.jamda.2013.03.022 Lai, 2017, Development of a novel frailty index to predict mortality in patients with end-stage liver disease, Hepatology, 66, 564, 10.1002/hep.29219 Lai, 2019, Frailty associated with waitlist mortality independent of ascites and hepatic encephalopathy in a multicenter study, Gastroenterology, 156, 1675, 10.1053/j.gastro.2019.01.028 Lai, 2014, Frailty predicts waitlist mortality in liver transplant candidates, Am J Transpl, 14, 1870, 10.1111/ajt.12762 Tandon, 2016, A rapid bedside screen to predict unplanned hospitalization and death in outpatients with cirrhosis: a prospective evaluation of the clinical frailty scale, Am J Gastroenterol, 111, 1759, 10.1038/ajg.2016.303 Tapper, 2018, Hepatic encephalopathy impacts the predictive value of the Fried Frailty Index, Am J Transpl, 18, 2566, 10.1111/ajt.15020 Lai, 2019, Frailty in liver transplantation: an expert opinion statement from the American Society of Transplantation Liver and Intestinal Community of Practice, Am J Transpl, 19, 1896, 10.1111/ajt.15392 Lai, 2018, Physical frailty after liver transplantation, Am J Transpl, 18, 1986, 10.1111/ajt.14675 Cruz-Jentoft, 2019, Sarcopenia: revised European consensus on definition and diagnosis, Age Ageing, 48, 16, 10.1093/ageing/afy169 Carey, 2017, A multicenter study to define sarcopenia in patients with end-stage liver disease, Liver Transpl, 23, 625, 10.1002/lt.24750 Mazurak, 2017, Nutrition and the transplant candidate, Liver Transpl, 23, 1451, 10.1002/lt.24848 Paris, 2020, Automated body composition analysis of clinically acquired computed tomography scans using neural networks, Clin Nutr, 39, 3049, 10.1016/j.clnu.2020.01.008 Carey, 2019, A north american expert opinion statement on sarcopenia in liver transplantation, Hepatology, 70, 1816, 10.1002/hep.30828 Englesbe, 2010, Sarcopenia and mortality after liver transplantation, J Am Coll Surg, 211, 271, 10.1016/j.jamcollsurg.2010.03.039 Kaido, 2013, Impact of sarcopenia on survival in patients undergoing living donor liver transplantation, Am J Transpl, 13, 1549, 10.1111/ajt.12221 Welch, 2020, Continued muscle loss increases mortality in cirrhosis: impact of aetiology of liver disease, Liver Int, 40, 1178, 10.1111/liv.14358 Leppke, 2013, Scientific Registry of Transplant Recipients: collecting, analyzing, and reporting data on transplantation in the United States, Transpl Rev, 27, 50, 10.1016/j.trre.2013.01.002 Langer, 2012, History of eurotransplant, Transpl Proc, 44, 2130, 10.1016/j.transproceed.2012.07.125 Mahmud, 2021, Best practices in large database clinical epidemiology research in hepatology: barriers and opportunities, Liver Transpl Okafor, 2016, Secondary analysis of large databases for hepatology research, J Hepatol, 64, 946, 10.1016/j.jhep.2015.12.019 Hirode, 2020, Trends in the burden of chronic liver disease among hospitalized US adults, JAMA Netw Open, 3, 10.1001/jamanetworkopen.2020.1997 OHDSI – Observational Health Data Sciences and Informatics n.d. https://ohdsi.org/ (accessed February 21, 2021). Haendel, 2021, The National COVID Cohort Collaborative (N3C): rationale, design, infrastructure, and deployment, J Am Med Inform Assoc, 28, 427, 10.1093/jamia/ocaa196 Peterson, 2021, Quantifying variation in treatment utilization for type 2 diabetes across five major university of California health systems, Diabetes Care, 10.2337/dc20-0344 Adoption of Electronic Health Record Systems among U.S. Non-Federal Acute Care Hospitals: 2008-2015 | HealthIT.gov n.d. https://www.healthit.gov/data/data-briefs/adoption-electronic-health-record-systems-among-us-non-federal-acute-care-1 (accessed October 3, 2021). Villanueva, 2018 Obermeyer, 2016, Predicting the future - big data, machine learning, and clinical medicine, N Engl J Med, 375, 1216, 10.1056/NEJMp1606181 Chua, 2021, Health care analytics with time-invariant and time-variant feature importance to predict hospital-acquired acute kidney injury: observational longitudinal study, J Med Internet Res, 23, 10.2196/30805 Weisenthal, 2018, Predicting acute kidney injury at hospital re-entry using high-dimensional electronic health record data, PLoS One, 13, 10.1371/journal.pone.0204920 Rajkomar, 2018, Scalable and accurate deep learning with electronic health records, Npj Digital Med, 1, 18, 10.1038/s41746-018-0029-1 Ge, 2021, A methodology to generate longitudinally updated acute-on-chronic liver failure prognostication scores from electronic health record data, Hepatol Commun, 5, 1069, 10.1002/hep4.1690 Haendel, 2018, Classification, ontology, and precision medicine, N Engl J Med, 379, 1452, 10.1056/NEJMra1615014 Atiemo, 2017, Mortality risk factors among patients with cirrhosis and a low model for End-Stage Liver Disease Sodium score (≤15): an analysis of liver transplant allocation policy using aggregated electronic health record data, Am J Transpl, 17, 2410, 10.1111/ajt.14239 Health Level Seven International - Homepage | HL7 International n.d. https://www.hl7.org/ (accessed October 3, 2021). European Health Data Evidence Network – ehden.eu n.d. https://www.ehden.eu/ (accessed November 20, 2021). Bennett, 2021, The national COVID cohort collaborative: clinical characterization and early severity prediction, medRxiv Ge, 2021, Outcomes of SARS-CoV-2 infection in patients with chronic liver disease and cirrhosis: a national COVID cohort collaborative study, Gastroenterology, 10.1053/j.gastro.2021.07.010 Rumsfeld, 2016, Big data analytics to improve cardiovascular care: promise and challenges, Nat Rev Cardiol, 13, 350, 10.1038/nrcardio.2016.42 Genta, 2014, Big data in gastroenterology research, Nat Rev Gastroenterol Hepatol, 11, 386, 10.1038/nrgastro.2014.18 Favaretto, 2020, What is your definition of Big Data? Researchers’ understanding of the phenomenon of the decade, PLoS One, 15, 10.1371/journal.pone.0228987 Asri, 2015, Big data in healthcare: Challenges and opportunities, 1 Wiens, 2018, Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology, Clin Infect Dis, 66, 149, 10.1093/cid/cix731 Obermeyer, 2020, Adoption of artificial intelligence and machine learning is increasing, but irrational exuberance remains, NEJM Catal, 1 Rajkomar, 2019, Machine learning in medicine, N Engl J Med, 380, 1347, 10.1056/NEJMra1814259 Spann, 2020, Applying machine learning in liver disease and transplantation: a comprehensive review, Hepatology, 71, 1093, 10.1002/hep.31103 Kanwal, 2020, Development, validation, and evaluation of a simple machine learning model to predict cirrhosis mortality, JAMA Netw Open, 3, 10.1001/jamanetworkopen.2020.23780 Jain, 1996, Artificial neural networks: a tutorial, Computer (Long Beach Calif), 29, 31 Schuster, 1997, Bidirectional recurrent neural networks, IEEE Trans Signal Process, 45, 2673, 10.1109/78.650093 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Guo, 2021, Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning, PLoS One, 16, 10.1371/journal.pone.0256428 Banerjee, 2003, Predicting mortality in patients with cirrhosis of liver with application of neural network technology, J Gastroenterol Hepatol, 18, 1054, 10.1046/j.1440-1746.2003.03123.x Cucchetti, 2007, Artificial neural network is superior to MELD in predicting mortality of patients with end-stage liver disease, Gut, 56, 253, 10.1136/gut.2005.084434 Ioannou, 2020, Assessment of a deep learning model to predict hepatocellular carcinoma in patients with hepatitis C cirrhosis, JAMA Netw Open, 3, 10.1001/jamanetworkopen.2020.15626 Ferrarese, 2021, Machine learning in liver transplantation: a tool for some unsolved questions?, Transpl Int, 34, 398, 10.1111/tri.13818 Bertsimas, 2019, Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantation, Am J Transpl, 19, 1109, 10.1111/ajt.15172 Kwong, 2018, Artificial neural networks and liver transplantation: are we ready for self-driving cars?, Liver Transpl, 24, 161, 10.1002/lt.24993 Miller, 2019, Predictive abilities of machine learning techniques may be limited by dataset characteristics: insights from the UNOS database, J Card Fail, 25, 479, 10.1016/j.cardfail.2019.01.018 Hu, 2020, Low predictability of readmissions and death using machine learning in cirrhosis, Am J Gastroenterol Raghupathi, 2014, Big data analytics in healthcare: promise and potential, Health Inf Sci Syst, 2, 3, 10.1186/2047-2501-2-3 Bates, 2020, Reporting and implementing interventions involving machine learning and artificial intelligence, Ann Intern Med, 172, S137, 10.7326/M19-0872 Finlayson, 2021, The clinician and dataset shift in artificial intelligence, N Engl J Med, 385, 283, 10.1056/NEJMc2104626 Wong, 2021, External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients, JAMA Intern Med, 181, 1065, 10.1001/jamainternmed.2021.2626 What Do We Do About the Biases in AI? n.d. https://hbr.org/2019/10/what-do-we-do-about-the-biases-in-ai (accessed October 3, 2021). Gianfrancesco, 2018, Potential biases in machine learning algorithms using electronic health record data, JAMA Intern Med, 178, 1544, 10.1001/jamainternmed.2018.3763 Kuppachi, 2021, Using race to estimate glomerular filtration and its impact in kidney transplantation, Clin Transpl, 35, 10.1111/ctr.14136 Kim, 2021, Big data in transplantation practice-the devil is in the detail-Fontan-associated liver disease, Transplantation, 105, 18, 10.1097/TP.0000000000003308 Adnan, 2020, Role and challenges of unstructured big data in healthcare, vol. 1042, 301 Kuo, 2021, Perspectives: a surgeon’s guide to machine learning, Int J Surg, 94, 106133, 10.1016/j.ijsu.2021.106133 Cauley, 2013, Deceased-donor split-liver transplantation in adult recipients: is the learning curve over?, J Am Coll Surg, 217, 672, 10.1016/j.jamcollsurg.2013.06.005 Feng, 2006, Characteristics associated with liver graft failure: the concept of a donor risk index, Am J Transpl, 6, 783, 10.1111/j.1600-6143.2006.01242.x Trapero-Marugán, 2018, Stretching the boundaries for liver transplant in the 21st century, Lancet Gastroenterol Hepatol, 3, 803, 10.1016/S2468-1253(18)30213-9 Bartoletti, 2019, AI in healthcare: ethical and privacy challenges, vol. 11526, 7 DeCamp, 2020, Latent bias and the implementation of artificial intelligence in medicine, J Am Med Inform Assoc, 27, 2020, 10.1093/jamia/ocaa094 Wang, 2020, Should health care demand interpretable artificial intelligence or accept “black box” medicine?, Ann Intern Med, 172, 59, 10.7326/M19-2548 Blease, 2018, Computerization and the future of primary care: a survey of general practitioners in the UK, PLoS One, 13, 10.1371/journal.pone.0207418 Pazzani, 2001, Acceptance of rules generated by machine learning among medical experts, Methods Inf Med, 40, 380, 10.1055/s-0038-1634196 Yakar, 2021, Do people favor artificial intelligence over physicians? A survey among the general population and their view on artificial intelligence in medicine, Value Health Holzinger, 2019, Causability and explainability of artificial intelligence in medicine, Wiley Interdiscip Rev Data Min Knowl Discov, 9, e1312, 10.1002/widm.1312 Gennatas, 2020, Expert-augmented machine learning, Proc Natl Acad Sci USA, 117, 4571, 10.1073/pnas.1906831117 Collins, 2021, Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence, BMJ Open, 11, 10.1136/bmjopen-2020-048008 Nadkarni, 2011, Natural language processing: an introduction, J Am Med Inform Assoc, 18, 544, 10.1136/amiajnl-2011-000464 Chowdhury, 2005, Natural language processing, Ann Rev Info Sci Tech, 37, 51, 10.1002/aris.1440370103 Van Vleck, 2019, Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression, Int J Med Inform, 129, 334, 10.1016/j.ijmedinf.2019.06.028 Redman, 2017, Accurate identification of fatty liver disease in data warehouse utilizing natural language processing, Dig Dis Sci, 62, 2713, 10.1007/s10620-017-4721-9 Tapper, 2016, Understanding and reducing ceruloplasmin overuse with a decision support intervention for liver disease evaluation, Am J Med, 129, 10.1016/j.amjmed.2015.07.019 Sidlow, 2015, Improving hepatitis C virus screening rates in primary care: a targeted intervention using the electronic health record, J Healthc Qual, 37, 319, 10.1097/JHQ.0000000000000010 Mudireddy, 2021, Impact of a clinical decision support intervention on albumin utilization and appropriateness of use in an academic healthcare system, medRxiv Mandel, 2016, SMART on FHIR: a standards-based, interoperable apps platform for electronic health records, J Am Med Inform Assoc, 23, 899, 10.1093/jamia/ocv189 Bloomfield, 2017, Opening the Duke electronic health record to apps: implementing SMART on FHIR, Int J Med Inform, 99, 1, 10.1016/j.ijmedinf.2016.12.005 Kawamoto, 2019, Association of an electronic health record add-on app for neonatal bilirubin management with physician efficiency and care quality, JAMA Netw Open, 2, 10.1001/jamanetworkopen.2019.15343 Debnath, 2020, Machine learning to assist clinical decision-making during the COVID-19 pandemic, Bioelectron Med, 6, 14, 10.1186/s42234-020-00050-8 Kipnis, 2016, Development and validation of an electronic medical record-based alert score for detection of inpatient deterioration outside the ICU, J Biomed Inform, 64, 10, 10.1016/j.jbi.2016.09.013 Jacobs, 2014, Assessment of readiness for clinical decision support to aid laboratory monitoring of immunosuppressive care at U.S. liver transplant centers, Appl Clin Inform, 5, 988, 10.4338/ACI-2014-08-RA-0060 Kao, 2020, Clinical decision support may link multiple domains to improve patient care: viewpoint, JMIR Med Inform, 8, 10.2196/20265 Pletcher, 2020, Randomized controlled trials of electronic health record interventions: design, conduct, and reporting considerations, Ann Intern Med, 172, S85, 10.7326/M19-0877 Horwitz, 2019, Creating a learning health system through rapid-cycle, randomized testing, N Engl J Med, 381, 1175, 10.1056/NEJMsb1900856 Pugh, 1973, Transection of the oesophagus for bleeding oesophageal varices, Br J Surg, 60, 646, 10.1002/bjs.1800600817 Kartoun, 2017, The MELD-Plus: a generalizable prediction risk score in cirrhosis, PLoS One, 12, 10.1371/journal.pone.0186301 Mahmud, 2021, The predictive role of model for end-stage liver disease-lactate and lactate clearance for in-hospital mortality among a national cirrhosis cohort, Liver Transpl, 27, 177, 10.1002/lt.25913 Kotronen, 2009, Prediction of non-alcoholic fatty liver disease and liver fat using metabolic and genetic factors, Gastroenterology, 137, 865, 10.1053/j.gastro.2009.06.005 Xin, 2021, Early prediction of acute kidney injury after liver transplantation by scoring system and decision tree, Ren Fail, 43, 1137, 10.1080/0886022X.2021.1945462 Audureau, 2020, Personalized surveillance for hepatocellular carcinoma in cirrhosis - using machine learning adapted to HCV status, J Hepatol, 73, 1434, 10.1016/j.jhep.2020.05.052 Kim, 2004, Cancer-associated molecular signature in the tissue samples of patients with cirrhosis, Hepatology, 39, 518, 10.1002/hep.20053 Cao, 2013, Two classifiers based on serum peptide pattern for prediction of HBV-induced liver cirrhosis using MALDI-TOF MS, Biomed Res Int, 2013, 814876, 10.1155/2013/814876 Lee, 2007, K-means clustering for classifying unlabelled MRI data, 92 Møller, 2011, Determinants of the hyperdynamic circulation and central hypovolaemia in cirrhosis, Gut, 60, 1254, 10.1136/gut.2010.235473 Das, 2018, Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques, Cogn Syst Res, 54, 165, 10.1016/j.cogsys.2018.12.009 Bartolomeo, 2011, Progression of liver cirrhosis to HCC: an application of hidden Markov model, BMC Med Res Methodol, 11, 38, 10.1186/1471-2288-11-38