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Robin McLeod Lecture: Using Big Data to Solve Problems in Transplantation

Robin McLeod
Robin McLeod

Robin McLeod is celebrated by the lecture in Surgical Epidemiology given in her name each year in the Department of Surgery. The Lecture honours her distinguished career as a surgeon, teacher and clinical scientist. Robin has over 350 publications and has the highest teaching effectiveness and academic citizenship scores in the Department. Chairman Jim Rutka described her at the introduction of the Lecture, quoting from Marry Poppins - “she is practically perfect in every way”.

Dorry Segev, this year’s McLeod Lecturer, is Associate Vice - Chair of Surgery at Johns Hopkins. He is a transplant surgeon and an internationally recognized expert in organ allocation. Dorry has been instrumental in driving transplant policy at the local, regional and national level. He received his undergraduate degree in Computer Sciences from Rice University in Houston and his medical degree from Johns Hopkins. He completed general surgery residency and an abdominal organ transplant fellowship at Hopkins, where he also pursued and obtained a PhD in biostatistics and clinical investigation. He has been on the faculty since 2006, publishing more than 250 papers in leading journals, including the Journal of American Medical Association, the New England Journal of Medicine, and Lancet. He has received many prestigious awards, including the Jacobson Promising Investigator Award of the American College of Surgeons. His mentors include Marta Zeiger, an endocrine surgeon at Hopkins, paediatric surgeon Patricia K Donahoe at Massachusetts General Hospital, and Robert A. Montgomery, Chief of the Division of Transplantation at Johns Hopkins.


Dorry Segev

Dorry began by telling us that in the Big Data era transplantation has a particularly useful dataset because of the scarcity of organs and the long waiting lists. In addition, there is an abundance of payer and pharmaceutical claims data, giving hundreds of thousands to millions of data points. “Big Data of this type can be used very effectively through linkages. It is free of selection bias, such as occurs when an individual service or institution reports its experience. It is less granular than institutional reports.”

Dorry has been successful in changing the law against transplanting organs from patients with infectious diseases (the IRD- infectious risk donors). There was a federal law passed in 1984 against transplantation from IRD patients. Dorry went to the U.S. Congress, taking advantage of his proximity to Washington. He became well versed in the ways of Washington, and especially the effectiveness of knocking on doors, meeting intelligent young staff personnel of Congressmen. By telling them “how many people are affected by the problem, how many lives can be saved, and what the cost will be to benefit the constituents of the politicians who are influential in getting the Bill approved and eventually signed by the President.” He has had extraordinary success in this quest. He told us how he used the National Inpatient Sample, a large dataset, and found the number of donors that could be used for transplantation if the law were changed. He looked at an HIV dataset from 18 sites and linked it to the NIS (National Inpatient Sample). $500,000 per patient could be saved if transplant were substituted for dialysis. If the law against IRD transplantation were changed, there would be an enormous saving to the Medicare Program. On the basis of Dorry’s data, President Obama signed the “Hope Act”, reversing the 1984 law prohibiting IRD treatments.

Antibodies to the HLA antigens result in ineligibility for about 20,000 potential kidney transplant recipients. Dorry worked to resolve this problem by exchange transplants. At the time that he started, organ exchange (a swap among 2 pairs of incompatible individuals) was illegal by reason of the laws against exchange of “money, or anything of value” to purchase a transplant. A kidney from another person was viewed by the Courts as a valuable payment. Dorry approached this problem using the tongue- in- cheek axiom of computer geeks. “If we don’t have data, we make data”. This is shorthand for using simulation to develop a convincing numerical argument. There are between 1,500-3,000 incompatible pairs based on anti HLA antibodies.

Working with Sommer Gentry, the mathematician to whom he is married, Dorry and his colleagues got the Charlie W. Norwood Living Organ Donation Act passed and signed by President Bush. The Monte Carlo simulation revealed that the loss ofdkidneys based on high titres of antibody could be substantially reduced by desensitization. Matching a desensitized patient with a “counterfactual (i.e. a patient who has not been desensitized) showed that there was a substantial improvement in survival”. These data convinced Medicare to pay for the desensitization process.

He then worked on the data supporting the clinical maxim that black patients fared better on dialysis than whites, so they were erroneously being less favoured for transplantation. Nephrologists’ belief in this categorical misconception was based on their experience with predominantly older patients that they care for in dialysis clinics, but the maxim was untrue for younger patients.

Using big data, Dorry was able to calculate the risk of subsequent chronic kidney diseases in kidney donors. This figure of 37 per 10,000 donors was derived using social security data and National Health and Nutrition Examination Survey (NHANES) data. “We made a calculator from this data which will soon be published in the New England Journal of Medicine. “When you learn that there is a donor available, they are never perfect. It’s like house hunting - there is always a problem that has to be taken into consideration or modified”. For example, the IRD donors (infectious risk donors) may have a history of intravenous drug use or sex work. 20% of potential donors are IRD. In the past, they have been disqualified from transplantation, but with 100,000 patients on the waiting list for a kidney and 50% mortality of waiting patients, why not use IRD donors? For this problem Dorry used the Markov decision process model. This mathematical empirical study revealed a 10% difference in outcome between IRD and no-IRD donations. This difference, though significant, is still well-worth the risk to many patients facing a 50% risk of death on the waiting list.

In summary, Dorry’s big data studies helped him to re-write laws, to gain funding for the desensitization process, to clarify clinical misconceptions, to develop a donor risk calculator, and to introduce the use of infectious risk donors to help resolve the transplant organ shortage.

David Urbach asked about contrasts between Canada and America in terms of the Big Data studies. Dorry answered that the problems that he has been addressing are seen throughout the world, but we can model selection on regional data, including questions like “Is it reasonable to put kidneys from older patients into young patients? Simulators can help, but modelling is extremely helpful for policy decisions like this. John Marshall asked whether this very efficient use of available data could be helpful to less developed countries like Bangladesh. Dorry answered that calculators of the type he has developed are now trusted, they no longer meet the “garbage in, garbage out bias” that characterized an earlier age. The spread of the electronic medical record is not limited to more developed countries. Jim Rutka asked how Dorry managed to get a Bill to reverse a standing law “rocketed through the Congress, when we hear so much about gridlock in the US capital”. “Knocking on doors and persistence was the secret of success, but was not exactly rocket speed, as the paper was published in 2010, and news media picked the information up in 2011 and the Bill was signed in 2013.”

In closing, David Urbach reminded us that Robin McLeod had blazed the trail to changing practice through epidemiological studies, brilliantly exemplified by Dorry’s work.


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