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 '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. The development of MELD-Plus was based on using unbiased approach toward discovery of biomarkers. In this approach, a feature selectionmachine 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. 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.
Calculators
A calculator capable of comparing MELD, MELD-Na, and MELD-Plus is available. Calculators capable of calculating MELD and MELD-Na are available.
Press coverage
Johnson HR. Developing a new score: how machine learning improves risk prediction. Livernois C. Harvard researchers develop predictive model for cirrhosis outcomes. Goedert J. IBM taps machine learning to predict cirrhosis mortality rates. Cohen JK. Harvard, IBM researchers develop prediction model for cirrhosis outcomes. Massachusetts General Hospital.
proposed that MELD-Na score may better rank candidates based on their risk of pre-transplant mortality and is projected to save 50-60 lives total per year. 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. 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." A review specifying alternatives to MELD, including MELD-Na, MELD-sarcopenia, UKELD, D-MELD, iMELD, and MELD-Plus, was published on June 2019 in Seminars in Liver Disease. The optimized prediction of mortality score is another tool that has been proposed to serve as an alternative to Model for End-Stage Liver Disease. A review published in Transplantation_ in Feb. 2020 highlighted the importance of incorporating machine-learning techniques into liver-related prediction tools, especially within the context of the limited accuracy of MELD-Na when applied to patients with low scores.
Criticism of machine learning in prediction modeling
Chen & 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 and fairly prioritizing patients for liver transplantation by means of MELD scores."
