Survival As a Quality Metric of Cancer Care: Use of the National Cancer Data Base to Assess Hospital Performance
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Dr. Pennell discusses survival as an important indicator of the quality of cancer care with author Lawrence Shulman.

Related Article: Survival As a Quality Metric of Cancer Care: Use of the National Cancer Data Base to Assess Hospital Performance)

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Hello, and welcome back to the ASCO Journal of Oncology Practice podcast. This is Dr. Nate Pennell, Medical Oncologist at the Cleveland Clinic and Consultant Editor for the journal. Over the last decade, there's been an important movement to try and improve the quality of medical care in the United States. But to do that, of course, we have to have reliable measures of quality.

But how do you really do that? Is it enough to measure compliance with guidelines or expert recommendations for high quality care? Ultimately, you might think that high quality care should lead to improvements in survival for cancer patients. And naturally, that leads to the question of whether survival could be used to compare the quality of care between different practices or different hospitals.

Joining me today to talk about this topic is Dr. Larry Shulman, Deputy Director for Clinical Services of the Abramson Cancer Center at the University of Pennsylvania and leader of the Cancer Quality Program for the University of Pennsylvania's health system. He's also the former chair of ASCO's Quality of Care Committee, former chair of the Commission on Cancer's Quality Integration Committee, and currently the chair of the Commission on Cancer.

Today we'll be discussing his recently published paper, Survival as a Quality Metric of Cancer Care, Use of the National Cancer Database to Assess Hospital Performance. Larry, thank you so much for joining me today.

Thanks for having me.

So can you give us a little bit of background? What are the some of the challenges in measuring quality of care, and what led you eventually to do this study?

Well, a decade ago, we were doing very little to measure the quality of cancer care in any respect. And then ASCO, in the early 2000s, started the QOPI program. And at around the same time, the Commission on Cancer began a quality program as well. And as you mentioned, most of the quality metrics that are included in those two programs are process measures, that the patient with a certain stage of disease get the appropriate treatment and so on. And those are very important metrics, and we've learned a lot from measuring those.

But at the same time, people have complained, including the public and the regulators, that we really need to know outcome quality measures. And the most important outcome measure for many people is survival. And survival is really, presumably, a culmination of all the aspects of care, not just whether you gave a particular treatment, but the other aspects of care that help patients to either do well or not do well. So that appeared to be an important measure.

I will say that there are a number of centers around the country that published their own survival metrics on their website with a variety of comparisons. And we were concerned that that was not really truth in advertising, and we wanted to understand measuring survival at the hospital level and also at the a hospital type, the class of hospitals. And that's what led us to do this study.

And that makes perfect sense. I mean, ultimately, when you're just measuring metrics, perhaps through ASCO's QOPI program, ultimately you're making an assumption that that's leading to better outcomes. But it would be nice to have some proof that that was true.

So could you please walk us through your paper a little bit? So what were you trying to accomplish with this particular study?

We queried the National Cancer Database. The National Cancer Database includes cancer registry data from 1,500 hospitals across the US that are accredited by the Commission on Cancer. And our estimate is that that covers about 70% of the cancer patients in the country. So this is a very robust database, and currently there are about 36 million patients in this database.

So we decided to look at patients with two different diseases, and we had very specific reasons for including them. We looked at patients who had Stage 3 breast cancer, and we did that because those patients ordinarily receive surgery, systemic therapies, and radiation. And we wanted to assess some disease where all the modalities were involved.

In addition, most of the technologies that we need to treat breast cancer patients are available at hospitals throughout the country, community hospitals as well as academic centers. The capability to do good breast surgery, give the types of systemic therapies we give for breast cancer and radiation, are widely available.

We also chose advanced non-small cell lung cancer, and we did that because that's a changing paradigm. The use of genomics to identify patients who have targetable mutations is not widely used throughout the country, and we wanted to see whether there were differences that we could assess in different hospitals and different hospital types.

And so what we did was we looked at both unadjusted survival, which is basically how many patients were cared for with a particular disease, and the death rates. And then we also looked at risk-adjusted survival because the patient populations are not the same at all hospitals. And so we risk adjusted for a number of variables, including age and gender, ethnic background, socioeconomic status, comorbidities, and insurance status.

So I won't ask you to delve into the details of how the analysis was done, but what did you find?

So we found three major points. One is that we looked at survival at the individual hospital level across the 1500 hospitals. And when we did risk adjustment, we found that very, very few of the hospitals had survivals of their patients that were either statistically better or statistically worse than the mean. And in fact, it turned out to be about 15 hospitals out of the 1500 hospitals that had survivals that were statistically better or worse.

And there are two reasons for that. One is that the survivals were in a pretty tight distribution, so that there wasn't a wide splay of survival differences among the different hospitals. And secondly, even large hospitals, comprehensive cancer centers, have relatively few patients with a particular stage of disease that can be assessed for survival.

So we felt that at the individual hospital level, this would not be a good quality metric to distinguish levels of care, and that some of the people who have argued for using survival to ultimately assess the quality of care of a hospital, this is probably not where we want to go.

The second thing that we found was that we looked at hospital types. So we aggregated hospitals into four groups. One was NCI-designated Comprehensive Cancer Centers. The second were academic cancer centers that were attached to a medical school and a training program. Third was large community hospitals with more than 500 new cases a year. And the fourth were small community hospitals with 500 or less cases a year.

And what we found was that when we aggregated those hospitals by these categories, there was a difference in survival. And the best survivals were seen at the NCI centers, followed by those in the academic cancer centers. Third were the large community hospitals, and fourth were the small community hospitals.

We spent a lot of time with the editors of JOP trying to assure ourselves that the statistical evaluations were valid. And in the end, they felt they were valid enough, obviously, to publish the manuscript. So I really think that the findings are real. And the question is why is there a difference in survival by hospital type?

And this study doesn't answer that question, but I think it gives us enough information to ask the question and start to delve deeper into what might be behind these survivals. I will say that Peter Bach and his colleagues from Memorial Sloan Kettering published a manuscript a year or so ago that had very similar findings. So I think this is a real finding, and we need to deal with it nationally.

Yeah, I think that's interesting. And I know that's not the first time that something like this has been shown, although I don't recall seeing such a linear breakdown from small center to larger center to academic center to NCI Conference Cancer Center. Do you have any speculation about what might be happening, or what could be done to try to further dig in to that?

You know, I'd like to make one point before we get to that. And that is that the answer to the question of how do we deal with these differences can't be that all patients should go to NCI Comprehensive Cancer Centers. The answer should be, we need to figure out what the differences in care are and try to figure out how to improve the care in the community hospitals because patients, most of them, should be able to stay in the hospital near their home, and the NCI centers and academic centers don't have the capacity to treat all the patients in the country.

So we need to understand what these differences are. We are in the process of doing a very deep dive into some of the quality metrics that the Commission on Cancer uses to accredit hospital programs and correlate those with survival outcomes, again, by hospital type to see whether there are correlations between compliance with those quality metrics and survival outcomes. And that work is underway, and I'm hoping that the early analysis will be available soon.

No, I think that's a good idea. I know that breast cancer programs have to go through a fairly rigorous accreditation. And I don't know if that's something that's included in the NCDB about whether they're accredited or not, but it might be worth looking to see if that makes a difference.

So we actually do have that information. And there's the accreditation program, what we call the NAPDC, the National Accreditation Program for Breast Centers. And they tend to do a little bit better in some of the quality metrics that we haven't looked at survival in those centers. But to some extent, it's a self-selected population. People who are vying for breast cancer accreditation have a special interest and focus on that disease.

I'm glad you pointed out that when you talk about large databases like this and looking at populations of people, that this is not something where, if you're getting your treatment at a small community center, you need to immediately leave and go to a big center somewhere in a big city. You may be getting perfectly appropriate care where you are, and you can't extrapolate from populations like this to your individual doctor's practice.

That's absolutely correct.

So one thing that jumps out at this, though, is I do have the privilege, as do you, of working at an NCI designated Comprehensive Cancer Center. And I know that many of our centers put out these publications where they're really marketing documents that show our benchmarks survivals compared to, say, the SEER Database or NCDB. And the implication is that you're going to get better care and potentially going to live longer if you come to one of these centers. So what do you think about that, given what you found in your study?

Well, frankly, I think it's a bad idea. And in fact the Commission on Cancer specifically prohibits-- though not everybody follows the prohibition-- prohibits the use of the survival data in public reporting. And the reason we do that is because we think that the comparisons are really not valid. And the cancer centers that I know that have used that in their publications or on their websites have generally used unadjusted survival, which is even further from being valid than risk-adjusted survival.

So we would discourage that. We don't think that it's really truth in advertising, quite frankly. And it's against the Commission on Cancer's formal policy to use NCDB data in that way.

So what would be your take home message from your study? Do you think that survival is not going to pan out as a comparator from practice to practice or hospital hospital? Or is this just not the right way to look at that?

No, I think that's correct. I think that we need to tell the payers and the government and other regulatory agencies which are thinking about ways to assess the quality of either practices or hospital programs that at least currently, we don't think that survival is the appropriate metric. But we do think that it raises a red flag for how care is being delivered across the country.

And we do think that it's our obligation as a profession-- and I think the oncology profession should take this initiative-- that we need to figure out where the opportunities are to assure patients who walk into any hospital in this country that they're getting top level care and have an equal chance of survival as if they walked into another hospital or a Comprehensive Cancer Center. I think, as a profession, we need to take these data seriously and act on them.

Is there anything we didn't cover that you wanted to make sure we highlighted from your paper?

The only other thing I would say-- and this was a little bit of a surprise-- again, we chose breast cancer because everything should be available everywhere, and lung cancer, maybe not. But the findings were identical for the two diseases. So we didn't see any difference in the breakdown of likelihood of survival by hospital type for breast cancer or advanced non-small cell lung cancer, for whatever that's worth.

So I think as we start to delve into what the factors are that drive survival, that we need to, again, take that into consideration. It's not just technology availability or not, but there must be other factors as well.

Yeah. The challenge of taking something like a pure clinical trial population, and then suddenly looking at an entire general real world population and trying to see if you're having the same levels of effects is something that I know everyone is interested in doing. We know in practice that it's a completely different scenario. But it's hard to delve into exactly what's happening to those patients in the real world.

Yeah. No, absolutely.

Well, Larry, thanks so much for talking with me today.

Thank you for your interest and for reaching out to me. And we look forward to the input from our colleagues as people start to read the manuscript, and also ideas from others about what next steps might be. So thank you very much, Nate.

And I also want to thank all our listeners out there who joined us for this podcast. You can read the full text of this paper at ascopubs.org backslash journal backslash jop, published online November 1, 2017. This is Dr. Nate Pennell for the Journal of Oncology Practice signing off.