| 词条 | MELD-Plus |
| 释义 |
| name = MELD-Plus | image = | alt = | caption = | pronounce = | synonyms = | purpose = Assess severity of chronic liver disease | reference_range = | calculator = | DiseasesDB = | ICD10 = | ICD9 = | ICDO = | MedlinePlus = | eMedicine = | MeshID = | OPS301 = | LOINC = }}MELD-Plus is a risk score to assess severity of chronic liver disease. The score includes nine variables as effective predictors for 90-day mortality after a discharge from a cirrhosis-related admission. The variables include all Model for End-Stage Liver Disease (MELD)'s components, as well as sodium, albumin, total cholesterol, white blood cell count, age, and length of stay. MELD-Plus was created as a result of a collaboration between Massachusetts General Hospital and IBM.[1] The development of MELD-Plus was based on using unbiased approach toward discovery of biomarkers. In this approach, a feature selection machine learning algorithm observes a large collection of health records and identifies a small set of variables that could serve as the most efficient predictors for a given medical outcome. An example for a notable feature selection method is lasso (least absolute shrinkage and selection operator).[2] Because total cholesterol and hospital length of stay are typically not uniform factors across different hospitals and may vary in different countries, an additional model that included only 7 of the 9 variables was evaluated. This yielded a performance close to the one of using all 9 variables and resulted in the following associations with increased mortality: INR, creatinine, total bilirubin, sodium, WBC, albumin, and age. CalculatorsA calculator capable of comparing MELD, MELD-Na, and MELD-Plus is available.[3] Calculators capable of calculating MELD and MELD-Na are available.[4][5][6][7] Press coverageJohnson HR. Developing a new score: how machine learning improves risk prediction.[8] Livernois C. Harvard researchers develop predictive model for cirrhosis outcomes.[9] Goedert J. IBM taps machine learning to predict cirrhosis mortality rates.[10] Cohen JK. Harvard, IBM researchers develop prediction model for cirrhosis outcomes.[11] Massachusetts General Hospital (Snapshot of Science).[12] External validationA study published in April 2018 in Surgery, Gastroenterology and Oncology reported on the increased accuracy of using MELD-Plus vs. MELD in predicting early acute kidney injury after liver transplantation.[13] Potential of alternative scores to extend life expectancyUnited Network for Organ Sharing proposed that MELD-Na score (an extension of MELD) may better rank candidates based on their risk of pre-transplant mortality and is projected to save 50-60 lives total per year.[14] Furthermore, a study published in the New England Journal of Medicine in 2008, estimated that using MELD-Na instead of MELD would save 90 lives for the period from 2005 to 2006.[15] In his viewpoint published in June 2018, co-creator of MELD-Plus, Uri Kartoun, suggested that " ...MELD-Plus, if incorporated into hospital systems, could save hundreds of patients every year in the United States alone."[16]Criticism of machine learning in prediction modelingChen & Asch 2017 wrote: "With machine learning situated at the peak of inflated expectations, we can soften a subsequent crash into a “trough of disillusionment” by fostering a stronger appreciation of the technology’s capabilities and limitations." However, the authors further added "Although predictive algorithms cannot eliminate medical uncertainty, they already improve allocation of scarce health care resources, helping to avert hospitalization for patients with low-risk pulmonary embolisms (PESI) and fairly prioritizing patients for liver transplantation by means of MELD scores."[17] Source codeA sample code for calculating MELD-Plus is available in GitHub.[18] References1. ^{{cite journal |doi=10.1371/journal.pone.0186301 |pmid=29069090 |pmc=5656314 |title=The MELD-Plus: A generalizable prediction risk score in cirrhosis |journal=PLoS ONE |volume=12 |issue=10 |pages=e0186301 |year=2017 |last1=Kartoun |first1=Uri |last2=Corey |first2=Kathleen E |last3=Simon |first3=Tracey G |last4=Zheng |first4=Hui |last5=Aggarwal |first5=Rahul |last6=Ng |first6=Kenney |last7=Shaw |first7=Stanley Y }} 2. ^Zou H. The adaptive lasso and its oracle properties. Journal of the American Statistical Association. Volume 101, 2006 - Issue 476. 2006. 3. ^https://github.com/kartoun/meld-plus/raw/master/MELD_Plus_Calculator.xlsx 4. ^https://www.mdcalc.com/meld-score-model-end-stage-liver-disease-12-older 5. ^https://optn.transplant.hrsa.gov/resources/allocation-calculators/meld-calculator/ 6. ^https://reference.medscape.com/calculator/meld-score-end-stage-liver-disease 7. ^http://gihep.com/calculators/hepatology/meld-na/ 8. ^{{Cite web | url=https://www.mddionline.com/developing-new-score-how-machine-learning-improves-risk-prediction | title=Developing a New Score: How Machine Learning Improves Risk Prediction| date=2017-11-17}} 9. ^{{Cite web | url=https://www.clinical-innovation.com/topics/clinical-practice/harvard-researchers-develop-predictive-model-cirrhosis-outcomes | title=Harvard researchers develop predictive model for cirrhosis outcomes}} 10. ^{{Cite web | url=https://www.healthdatamanagement.com/news/ibm-taps-machine-learning-to-predict-cirrhosis-mortality-rates | title=IBM taps machine learning to predict cirrhosis mortality rates}} 11. ^{{Cite web | url=https://www.beckershospitalreview.com/data-analytics-precision-medicine/harvard-ibm-researchers-develop-prediction-model-for-cirrhosis-outcomes.html | title=Harvard, IBM researchers develop prediction model for cirrhosis outcomes}} 12. ^{{Cite web | url=https://www.massgeneral.org/research/news/SnapshotScience/Snapshot-2017/snapshot-science-October-2017.aspx | title=Snapshot of Science for October 2017 - Massachusetts General Hospital, Boston, MA}} 13. ^{{cite journal |title=The Combination of Serum Cystatin C, Urinary Kidney Injury Molecule-1 and MELD plus Score Predicts Early Acute Kidney Injury after Liver Transplantation |journal=Surgery, Gastroenterology and Oncology |volume=23|issue=2 |pages=121–126 |year=2018 |last1=Marian-Irinel|first1=Marian-Tudoroiu|last2=Constantin|first2=Georgiana|last3=Pâslaru|first3=Liliana|last4=Iacob|first4=Speranţa |last5=Gheorghe|first5=Cristian |last6=Popescu|first6=Irinel |last7=Tomescu|first7=Dana |last8=Simona Gheorghe|first8=Liliana |url=https://www.sgo-iasgo.com/article/the-combination-of-serum-cystatin-c,-urinary-kidney-injury-molecule-1-and-meld-plus-score-predicts-early-acute-kidney-injury-after-liver-transplantation}} 14. ^{{cite web|url=https://optn.transplant.hrsa.gov/media/1834/liver_boardreport_20140702.pdf |title=Meeting agenda |date=2014 |website=optn.transplant.hrsa.gov |format=PDF}} 15. ^{{cite journal |title=Hyponatremia and mortality among patients on the liver-transplant waiting list |journal=N Engl J Med | volume=359 | issue=10 | pages=1018–6 | year=2008 | last1=Kim | first1=WR | last2=Biggins | first2=SW | last3=Kremers | first3=WK | last4=Wiesner | first4=RH | last5=Kamath | first5=PS | last6=Benson | first6=JT | last7=Edwards | first7=E | last8=Therneau | first8=TM | pmid=18768945|pmc=4374557 |doi=10.1056/NEJMoa0801209 }} 16. ^{{cite journal |title=Toward an accelerated adoption of data-driven findings in medicine |journal=Medicine, Health Care and Philosophy | year=2018 | last1=Kartoun| first1=Uri|doi=10.1007/s11019-018-9845-y |pmid=29882052 }} 17. ^{{cite journal |doi=10.1056/NEJMp1702071 |pmid=28657867 |pmc=5953825 |title=Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations |journal=New England Journal of Medicine |volume=376 |issue=26 |pages=2507–2509 |year=2017 |last1=Chen |first1=Jonathan H |last2=Asch |first2=Steven M }} 18. ^{{cite web|url=https://github.com/kartoun/meld-plus|title=kartoun/meld-plus|website=GitHub|date=2018-01-07}} 2 : Hepatology|Medical scoring system |
| 随便看 |
|
开放百科全书收录14589846条英语、德语、日语等多语种百科知识，基本涵盖了大多数领域的百科知识，是一部内容自由、开放的电子版国际百科全书。