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Primary Care Corner with Geoffrey Modest MD: Is Mammography Useful?

14 Feb, 17 | by EBM

By Dr. Geoffrey Modest

This blog will bring up 2 recent studies suggesting the lack of efficacy of mammography screening coupled with significant overdiagnosis.

  1. An article a couple of years ago looked at screening mammography in the US, with 10 year follow-up of breast cancer incidence and mortality (see Harding C. JAMA Intern Med 2015; 175: 1483).

Details:

  • This was an ecological study of 16,120,349 women 40 years of older who resided in 547 counties reporting in the Surveillance, Epidemiology, and End Results (SEER) cancer registries during the year 2000.
  • 53,207 had a diagnosis of breast cancer and were followed for 10 years.
  • The researchers looked at the extent of the screening in each county, and the results of both breast cancer incidence and mortality (the latter being defined as women diagnosed with breast cancer in the year 2000 who had died from the disease during the 10 year follow-up period). Overall, in the 547 counties, the overall 10-year incidence based mortality was 47.2 per 100,000 cases diagnosed in 2000.

Results:

  • There was a strong positive correlation between the extent of mammography screening and the breast cancer incidence (P<0.001)
  • But, there was no relationship between screening and breast cancer mortality.
  • Each increase of 10 percentage points in the extent of screening was accompanied by:
    • A 16% increase in breast cancer diagnoses, RR 1.16 (1.13- 1.19)
    • Not even a trend to a change in breast cancer deaths, RR 1.01 (0.96-1.06)
  • Analyzing by tumor size, screening led to a higher incidence of small breast cancers (<= 2 cm), but not with a decreased incidence of larger breast cancers (>2 cm )
  • Each increase of 10 percentage points in screening is associated with:
    • A 25% increase in the incidence of small breast cancers, RR 1.25 (1.18- 1.32)
    • A 7% increase in the incidence of larger breast cancers, RR 1.07 (1.02- 1.12)
  • The following figure shows that as the proportion of women had a mammogram in the past two years, the incidence of breast cancer diagnoses increased significantly yet the 10-year mortality did not budge

Commentary:

  • So, pretty powerful large-scale epidemiologic study, finding that mammography led to a large increase in the diagnosis of small cancers, but there was no decline in the detection of larger cancers. This may be the reason why there was no significant difference in the overall death rate from breast cancer by doing mammography screening.
  • What does this mean? It may mean that there are a subset of very aggressive small cancers which spread and cause clinical disease and mortality, and that screening is didn’t help for these. And that a very large number of small cancers that are picked up by mammography are in fact “overdiagnosed” (defined as: tumors that would not have become clinically evident in the remaining lifetime without screening).
  • One would have expected that if screening did pick up small tumors earlier, that over time the diagnosis of larger and less treatable cancers should decrease. It is quite concerning that the number of larger breast cancers in fact continued to increase over the study. And, of course, the goal of screening is to reduce mortality, which was not found in the study. One additional finding was that increased screening would lead to more breast conserving surgical procedures; however they found no evidence of a decrease in extensive mastectomies.
  • Without getting into a lot of detail, the authors present reasonable arguments that this is not just lead-time bias, or ecological bias (this latter happens when looking at group data and assuming that it applies to the individual who may or may not have had a mammogram). Also, there was no association between mortality rates even comparing those counties with much higher breast cancer incidence, reducing the potential bias of comparing counties with very different incidences of breast cancer. But, they also did not have data on women who had therapy or what the risk factors were for the women who developed breast cancer. Also, this was just a 10-year follow-up, and patients may well live more than 10 years with newer therapies, but I would have expected some evident benefit of screening by 10 years (and at least a trend to benefit…)
  • There have been several important changes in technology over the past several decades, some of which may make older studies less applicable now (these older studies are the ones on which current mammogram recommendations are based). On the one hand, the sensitivity of our screening methods is greater and we are picking up much smaller tumors; and, perhaps these smaller tumors are more likely to regress than the larger ones picked up previous, leading to increased overdiagnosis. On the other hand, treatments have improved a lot, and the risk/benefit equation may have changed some. Given the potential harms of overdiagnosis (including surgery, radiotherapy, and chemotherapy), we should be looking at the new balance. In addition, there are interesting advances in genomic profiling, which are helpful in determining how aggressive a tumor is likely to be as well as how intensive therapy should be

So, a large study like this offers interesting insights, especially when looking at likely overdiagnosis (which one cannot determine in an individual patient). As with all screening tests (e.g. PSA), it would be really useful to figure out how to risk stratify patients, with more aggressive screening in those at higher risk. That is much more likely to show benefit for screening then with screening the general population.

—————————————–

  1. A more recent article look specifically at breast cancer overdiagnosis by mammography screening in Denmark (see doi:10.7326/M16-0270)). The study looked at women aged 35 to 84, from 1980 to 2010.

Details:

  • Denmark had a perhaps unique opportunity to look at the results of mammography screening both because it has rigorous databases (the Danish Breast Cancer Group, DBCG, and the Danish Cancer Registry, DCR) as well as a 17-year screening program which involved 20% of the population aged 50 to 69. This differential access to mammography screening allowed for real-world comparisons to a large, essentially randomized non-screening populations. Clinical breast exams were not included.
  • The DBCG database included 90,665 women aged 35 to 94 who were diagnosed with invasive breast cancer, and 4267 diagnosed with DCIS
  • For the mammography group in DBCG, they divided tumors into two groups: large (>20 mm) and small (<20 mm), considering the large tumors as “advanced” because they are equivalent to T2 or greater in the TNM classification system
  • The screening routine, somewhat different from what we do in the US, was biennial screening with a 2-view mammography on the first round, with 1-view mammography at subsequent screens except for women with dense breasts who always received a 2-view mammogram.
  • The DCR provided individual data on tumor size in women with invasive breast cancer.

Results:

  • For women aged 35 to 69: in non-screening areas the incidence of advanced cancer increased throughout the observation period.
  • For women 70 to 84: in the non-screening areas the incidence of advanced cancer also increased throughout the observation period, and was most pronounced in the later years.
  • The incidence of non-advanced tumors increased in the screening versus prescreening periods, incidence ratio 1.49 (1.43- 1.54), i.e. a 49% increase
  • Looking just at nonadvanced tumors, there were 711 invasive tumors and 180 cases of DCIS that were overdiagnosed in 2010 (overdiagnosis rate of 48.3% including DCIS and 38.6% excluding DCIS
  • There was no reduction in the incidence of advanced cancers through mammography screening.
  • Their conclusion: “it is likely that one in every three invasive tumors in cases of DCIS diagnosed and women offered screening represents overdiagnosis (incidence increase of 48.3%)”

Commentary:

  • This study is quite remarkable since it is reasonably close to a really large-scale randomized controlled trial, in which for 17 years 20% of women got mammograms and the rest didn’t. And there was no difference in advanced breast cancers through consistent mammography screening
  • See below for other blogs on the poor utility of mammography screening, also showing almost no decrease in breast cancer mortality but large numbers of overdiagnosed mammogram-detected cancer.
  • A lot of the “overdiagnosis” is from DCIS (about 25% of all new breast cancer diagnoses), which the National Cancer Institute now classifies as a “noninvasive condition” (an observational study of 108,196 women with DCIS in the SEER registries found an overall breast cancer death rate of 3.3% over 20 years, similar to the general population: see Narod SA. JAMA Oncol 2015; 1(7):888
  • All of this reinforces the fact that early detection of breast cancer is fraught. For breast cancer, there are 230,815 diagnoses/year in women, 2109 in men; and 40,860 breast cancers deaths/year in women and 464 in men, and affects 1 in 8 women!!!; yet mammography screening has perhaps minimal benefit. Which really brings up the issue of prevention (which, it turns out, does not get much funding). As noted in prior blogs, one big unknown is the prevalence of industrial toxins (many of which are estrogenic, including pesticides, BPA, others used in plastics, etc etc) which are in our environment and may well be carcinogenic. Large numbers of new chemicals are being used and thousands of new ones are introduced each year with minimal attempts to look at potential toxicity. In addition, it is reasonably clear from the studies that healthy diet, weight control, and exercise are helpful. It seems to me that it would likely be much more useful to devote our national resources into preventing breast cancer by regulating environmental toxins and promoting healthy lifestyles than attempting early detection.

See http://blogs.bmj.com/ebm/2016/10/13/primary-care-corner-with-geoffrey-modest-md-radiologist-variability-in-mammography-readings/ which documents the quite remarkable discordance in radiologists’ reading of breast densities

See http://blogs.bmj.com/ebm/2014/02/13/primary-care-corner-with-geoffrey-modest-md-mammography-another-hit/​ for the 25-year results from the Canadian National Breast Screening Study finding NO benefit from mammography screening but that 22% of mammography-detected breast cancers were overdiagnosed.

See http://blogs.bmj.com/ebm/2014/04/22/primary-care-corner-with-geoffrey-modest-md-mammograms-again/ for a 2014 meta-analysis, finding that mammography yielded very small changes in breast cancer mortality (e.g. screening women in their 50s would lead to 3-32/10,000 decrease in breast cancer mortality, but have 6130 false positive and 30-137 overdiagnoses)​. As mentioned above, these studies were older ones.

Primary Care Corner with Geoffrey Modest MD: Increasing Deaths From Opioids

10 Feb, 17 | by EBM

By Dr. Geoffrey Modest

The CDC just published their report tracking drug and opioid-involved overdose deaths in the United States from 2010 to 2015 (see https://www.cdc.gov/mmwr/volumes/65/wr/mm655051e1.htm?s_cid=mm655051e1_x​ ).

Details:

  • Background: from 1999 to 2014 there was a tripling of drug overdose deaths, with 47,055 total drug deaths in 2014, and 60.9% involved an opioid. During 2013-2014, deaths from natural/semisynthetic prescribed opioids [this class includes natural opioids (morphine and codeine), and semisynthetic opioids (oxycodone, hydrocodone, hydromorphone, and oxymorphone)] increased slightly, but there was a rapid increase in deaths from heroin and “synthetic opioids other than methadone” (including tramadol, fentanyl).
  • In 2015, there were 52,404 drug overdose deaths including 33,091 (63.1%) involving an opioid. This death rate calculates to 16.3/100000 population (it was 12.3/100000 population in 2010, a 33% increase)
  • From 2014 to 2015 the death rate from “synthetic opioids other than methadone”, which includes fentanyl, increased by 72.2%, and heroin death rates increased by 20.6%. Natural/semisynthetic opioid death rates increased by 2.6%, but methadone death rates decreased by 9.1%
  • The rates of death involving heroin and “synthetic opioids other than methadone” increased among all demographic groups, regions, and most states (Florida and South Carolina had both decreasing and then increasing trends during this time: Florida decreased from 2010 to 2013 and then increased til 2015; South Carolina decreased from 2010 to 2012 and then increased til 2015)
  • The largest absolute rate increases in deaths from “synthetic opioids other than methadone” occurred in Massachusetts, New Hampshire, Ohio, Rhode Island, and West Virginia. The largest percent increases in rates occurred in New York (135.7%), Connecticut (125.9%) and Illinois (120%). The largest absolute rate increases in heroin deaths were in Connecticut, Massachusetts, Ohio, and West Virginia, and the largest percentage increases were in South Carolina (57.1%), North Carolina 46.4%, and Tennessee (43.5 percent)

Commentary:

  • The above includes 28 states with high quality reporting on death certificates, including specific drugs involved in overdoses. [Seems like all states should be tracking this. And this is why our national statistics and epidemiologic studies pale in comparison to western European countries, where they have large and inclusive national registries. Would be great to fix this…. much better data to act on]
  • Deaths from illicitly-manufactured fentanyl were probably largely responsible for the increase in deaths attributed to “synthetic opioids other than methadone” and were largely concentrated in eight of the 27 states examined. Actual fentanyl prescribing rates did not change during this time [confirming that this was likely illicitly-manufactured]
  • Factors likely involved in these changes include:
    • My guess is that the decrease in overdoses due to methadone is related to more vigorous efforts by the federal and state governments to limit its use in chronic pain. This shows that targeted strategies seem to work.
    • The smaller increase in natural/semisynthetic opioid deaths may also be related to changes in policies/education, use of the prescription drug monitoring program, and legislative changes in naloxone prescription and distribution.
    • The implementation of harm reduction strategies, including syringe exchange programs, increased access to naloxone, more available medication assisted treatments, strategies to reduce the transmission for hepatitis C and HIV, etc., are likely very important in decreasing the extremely important sequelae of drug use
    • But, ironically, it seems that as there is less availability of prescription opioids in opioid prescribing, many patients are taking some more dangerous but now much cheaper drugs such as heroin, which may well be laced with fentanyl (i.e., these opioid substitutes may be being used more, and these may even be more fatal, such as by the increase in fentanyl-related deaths).
  • So, the bottom line here for me is that single targeted measures (decreasing methadone prescribing) can lead to shifts in drug usage with potentially even worse clinical outcomes. Which really speaks to the need to deal with the underlying problems of poverty, lack of hope/avenues to advancement felt by many young (and old) people, lack of positive social supports, poor quality education for many, increasing income inequalities, etc. etc. etc.
  • As I have mentioned in several blogs, including one which evaluated/critiqued the CDC guideline for prescribing opioids for chronic pain (see past blogs below), the issue of chronic pain control is often one of the most difficult clinical issues we encounter in primary care. There are very clear examples (at least to me) of patients who need chronic opioids to function and lead reasonably normal lives. And several need very high doses (for a multitude of reasons, likely including genetic differences in mu receptors). There also are pretty clear examples of clinicians overprescribing opioids, from surgeons/emergency rooms as well as from primary care, and these can have profound social sequelae, such as drug diversion and opioid-related deaths. And, there are whole group of people in the middle, where it is not entirely clear and is a judgment call by the treating clinicians. The first two groups are easier to deal with; this middle group where there is more uncertainty about the need for opiates is quite challenging clinically and more socially concerning given the reality of the explosion of illicit drug use and deaths.
  • I would also like to mention again a prior blog finding that 12th graders prescribed opioids by clinicians (e.g. for surgery) but were considered beforehand to have a very low likelihood of using illicit drugs in the future per a standardized and validated questionnaire, had a much higher rate of opioid abuse at age 23 than a similar group who were not prescribed these opioids (see below).

Past blogs:

http://blogs.bmj.com/ebm/category/pain/ for the array of blogs on opiates and chronic pain

http://blogs.bmj.com/ebm/2016/03/25/primary-care-corner-with-geoffrey-modest-md-new-cdc-guidelines-for-opiate-prescribing/ for a critique of the CDC guidelines

http://blogs.bmj.com/ebm/2015/11/10/primary-care-corner-with-geoffrey-modest-md-prescribed-opioids-and-future-prescription-opioid-misuse-in-teens/ for the blog on 12th graders at low risk of addiction by a validated questionnaire, then getting prescribed opiates, finding that by age 23, there was a 33% increased risk of opioid misuse

Primary Care Corner with Geoffrey Modest MD: Metformin in those with CKD, CHF, CLD

6 Feb, 17 | by EBM

By Dr. Geoffrey Modest

A systematic review from the VA synthesized data on use of metformin in patients with chronic kidney disease (CKD), congestive heart failure (CHF), or chronic liver disease (CLD) with hepatic impairment (see doi:10.7326/M16-1901). The goal was to assess all-cause mortality, major adverse cardiac events (MACEs), and other outcomes in patients with these underlying diseases (patients with these diseases having been the ones in whom the FDA initially had warned against using metformin).

Details:

  • 17 observational studies that included patients with CKD, CHF, or CLD with hepatic impairment were analyzed. These studies compared patients on diabetes regimens that included Metformin vs those that did not.

Results:

  • CKD
    • 6 studies were included, with sample sizes ranging from 1246 to 11,481 patients, median age ranging from 65 to 76. Only one study reported median daily metformin dose (1100 to 1900 mg in the different subgroups)
    • All-cause mortality
      • 22% lower for patients on metformin, HR 0.78 (0.63-0.96)
      • 2 studies reported CKD severity subcategories:
        • eGFR of 30 to <45 had neither benefit nor harm
        • eGFR of 30 to 60 had clear benefit of around 38%
        • eGFR <30 (one study) had neither benefit nor harm
      • MACEs:
        • 2 studies were evaluated, finding no difference in outcomes with metformin in the subgroups of patients with eGFR <45
        • And much, much more hypoglycemia in those on non-metformin-based regimens (specifically, glyburide or insulin)
      • CHF
        • 11 observational studies were included, with sample sizes from 346 to 13,930 patients, median age 55 to 77 years old. No studies included median metformin dose
        • All-cause mortality:
          • 22% lower for patients on metformin, HR 0.78 (0. 71-0.87)
          • 2 studies reported CHF severity subcategories:
            • One study looked at LVEF, finding that both an LVEF of 30-39% and LVEF<30% had a nonsignificant 13% decreased mortality; another study looked at patients with LVEF < 40%, finding a nonsignificant 21% decrease
          • MACEs:
            • The relative chance of readmission for CHF during follow-up was 13% lower for patients on metformin: HR 0.87 (0.78-0.97)
            • The relative risk for cardiovascular mortality was 23% lower in those on metformin (their figure shows that the three studies that looked at this found statistically significant improvement with metformin, but their overall summary said it was nonsignificant?????)
          • CLD
            • 3 observational studies included, with sample sizes ranging from 82 to 250 patients, median age 60-61. No studies reported median metformin dose.
            • All cause mortality:
              • The one study with low risk of bias (n=250) found significantly longer survival: HR 0.43 (0.24-0.78), regardless of cirrhosis severity. Post hoc analysis found a positive association between metformin and survival only in those with nonalcoholic steatohepatitis, though the number of patients in the other subgroups was small.
            • The other studies in those with moderate-to-high risk of bias showed a trend to lower all-cause mortality with metformin

Commentary:

  • Metformin is accepted as the first line drug for diabetes in the US and other countries that I know of. It is such a good and appropriate drug, given both its positive effects on diabetes (including its being weight-neutral or leading to weight loss) as well as data suggesting decreased cardiovascular disease and all-cause mortality. As a result, many clinicians have been using it despite FDA precautions/contraindications, with estimates that 20-30% of patients have been prescribed metformin who have had these precautions/contraindications. The FDA itself has been progressively relaxing these restrictions. In 2006 they removed CHF as a contraindication (though acute or unstable CHF remains a precaution). In addition in 2016, the FDA changed the warning for CKD to be based on eGFR instead of creatinine, making approximately 1 million additional patients with moderate CKD eligible to receive metformin. See blogs noted below for other studies promoting the use of metformin.
  • Most of the above conclusions were based on studies which had low strength-of-evidence, moderate risk-of-bias. However there was consistency in their finding that metformin therapy was associated with reduced all-cause mortality among patients with moderate CKD, CHF, or CLD; fewer CHF admissions among those with moderate CKD or CHF; and a much lower hypoglycemia rate among those with moderate CKD
  • There are other concerns about a meta-analysis such as above, including the fact that they don’t have data on doses of metformin for most studies, what other medications were being used in addition to metformin (the studies did not have rigorous details about which patients were on which other hypoglycemic medications), whether there was “confounding by indication” (where people might have been selected to be on or off metformin based on unknown individual clinical considerations), or even more than baseline data on metformin use in most studies (i.e. patients may have started on metformin but somewhere during the study had stopped it; or alternatively patients may have started off metformin but then put on it during the course of the study)

But, bottom line, these studies reinforce not just the safety of metformin in what had previously been considered concerning underlying comorbidities, but strongly suggest a significant benefit of metformin-based regimens. I.e., there really is an imperative to use metformin as the first-line therapy. We know through our clinical practice that metformin’s major adverse reactions are GI. This is clearly less the case in those on lower doses or if metformin is taken with meals. The somewhat skimpy data suggest that much of the benefit of metformin is conferred by much less than full doses (one oft-repeated quote is that about 80% of the benefit of metformin is by giving 1000mg vs 2000mg). My personal experience is that many people get much better glucose control on just 500mg once a day (which is my starting dose, though I leave it there if there is good control, as happens pretty frequently), and I even have a person on 250mg (1/2 tablet) because of GI intolerance, who seems to get benefit…

Relevant past blogs:

http://blogs.bmj.com/ebm/2015/01/23/primary-care-corner-with-geoffrey-modest-md-metformin-in-renal-failure/ is a systematic review of studies in patients with chronic kidney disease, finding for example no cases (as in, zero) of lactic acidosis in 70,490 patient-years on metformin

http://blogs.bmj.com/ebm/2016/04/26/primary-care-corner-with-geoffrey-modest-md-fda-changes-metformin-guidelines/​  which gives the updated FDA changes for metformin prescribing in those with CKD, with reference to a study of 813 patients with creatinine >6 who did NOT have increased mortality on low dose metformin (<= 500 mg/d), as well as the study finding that metfomrin induces changes in the microbiome, which leads to decreased insulin resistance.

Primary Care Corner with Geoffrey Modest MD: 30-day hospital readmission rates, ?? an appropriate QI marker

30 Jan, 17 | by EBM

By Dr. Geoffrey Modest

A recent NIH study looked at the effect of the Medicare Hospital Readmissions Reduction Program (HRRP) on 30-day readmission rates after hospitalizations for acute myocardial infarction, congestive heart failure, or pneumonia, and in particular looking at whether the previously lowest performing hospitals improved more than the higher performing ones after the introduction of HRRP  (see doi:10.7326/M16-0185).

Details:

  • 15,170,008 Medicare patients discharged alive from US acute care hospitals between 2000 and 2013.
  • Mean age 79.5, 54% female, 85% white/10% black/4.7% other race, 19% admitted with acute MI/45% CHF/36% pneumonia, 52% discharged to home/18% to home with care/23% to nursing home, average length of stay 6 days, 25% rural hospitals/65% private nonprofit/9% major teaching hospitals, overall observed readmission rate 23%.
  • HRRP penalties for 30-day readmission rates were: 0% for the highest performing hospitals, 0-0.5% for average performing hospitals, 0.5-0.99% for low performing hospitals, and >1% for the lowest performing ones.
  • Of 2868 hospitals serving 1,109,530 Medicare discharges annually, 30.1% were highest performers, 44.0% were average performers, 16.8% were low performers, and 9.0% were lowest performers

Results:

  • Overall risk-standardized readmissions increased by an estimated 0.5 per 10,000 discharges per year prior to the passage of HRRP, then decreased by 76.6 per 10,000 discharges per year after passage.
    • For acute MI, risk-standardized readmissions decreased by 23.7 per 10,000 discharges per year before passage, then by 99.3 per 10,000 discharges per year after passage
    • For CHF, risk-standardized readmissions increased by 5.1 per 10,000 discharges per year before the passage and then decreased by 84.7 per 10,000 after.
    • For pneumonia, risk-standardized admissions increased by 3.1 per 10,000 discharges per year before passage and then decreased by 48.2 per 10,000 after.
  • After controlling for pre-HRRP trends, readmissions per 10,000 discharges that were averted and attributable to the law were:
    • 6 for the highest performing hospitals
    • 8 for the average performers
    • 4 for the low performers
    • 1 for the lowest performers

Commentary:

  • It does seem that after passage of the law, there was a pretty dramatic decrease in the 30-day readmission rate among all hospitals, but especially among those that had been the lowest performers.
  • It should be pointed out that the data overall on the utility of financial incentives in changing the “quality of care” metrics is pretty mixed. This study found that the lowest performing hospitals were able to change the most; however, other studies of financial incentives have not shown this to be true, often attributed to the fact that these hospitals had insufficient infrastructure to implement change. Also, this does raise the ideological concern that using financial incentives to make change in fact reinforces the conception and actuality in the US that health care is just a business and not a fundamental human right that should be managed as in most other industrialized (and may less resource-rich) countries: an essential governmental social program such as education.
  • However, and the reason I bring up the study, is that it really brings up to me some concerns about quality goals, especially when looking at surrogate markers.
    • For A1c: as mentioned in several blogs, using a target A1c goal is fraught with potential downsides
      • For patients who have really erratic blood sugars (often because of lack of dietary consistency), just increasing meds to lower the A1c (and get “credit” for better care) may well lead to significantly poorer real-world outcomes from hypoglycemia (e., over treating patients at times when their blood sugars are already low).
      • Some of the meds that decrease A1c may actually increase clinical morbidity (rosiglitazone increasing cardiac disease, —  see the many blogs on other new but concerning meds at: http://blogs.bmj.com/ebm/category/diabetes/ )
      • And those who use A1c as a metric do not include actual clinical outcomes as part of their assessment.
    • In terms of hospital 30-day readmission rates, there certainly should be a mechanism to make sure that hospitals don’t just discharge and readmit patients as a means to increase their earnings (e., getting paid for 2 admissions instead of 1), but there is also a real down-side to focusing on decreasing the readmission rate:
      • Hospitals are dangerous places to be because:
        • They are crawling with resistant bacteria
        • There is a tendency/imperative to do lots of testing for things that we in outpatient medicine might just observe and workup later as needed (this is due to several issues: specialists are often involved in the hospitalized patient’s care, and studies have shown that specialists order more tests than generalists; and, even if there is a lowish probability of a problem, it does in some ways make sense to get more tests in hospitalized patients to see what is going on, since putting off the tests might prolong the hospitalization. But the net result of more testing is the likelihood of more adverse events (either because of the test itself, or because of the downstream further testing/procedures for false positive findings)
        • And those who use hospital readmission rates as a metric do not include actual clinical outcomes as part of their assessment.​ interestingly, when there have been doctors’ strikes and dramatic decreases in hospital admissions, as in 1976 in California as well as others, there has been an attendant lower mortality (and, that is a clinical outcome…..)
      • So, I think it makes sense to avoid unnecessary hospitalizations, and, I would think, to keep the length-of-stay as short as possible
      • My practice until 2 years ago (when our health center was still doing in-patient rounding) was to discharge patients as soon as I felt they were stable (often during my rounding early the morning after their admission) when I felt they had a roughly 80% chance of doing well at home, but with aggressive outpatient follow-up (home visits, or seeing them in clinic the next day, ). And my experience was that it was really uncommon for patients to be readmitted. But with the incentives being strongly to avoid readmissions, I am afraid that might translate into longer in-hospital observation and lengths-of stay (at least it was clear that I discharged patients much sooner than the house staff would have, in large part because I could assure timely and appropriate outpatient follow-up). The point here is that we should be developing coherent integrated systems of care that would allow decreased hospitalizations overall, and lower lengths-of-stay if possible when hospitalizations happen. and, not simply using a single marker of “quality” for the complex and often highly individual decisions on how long to keep a patient in the hospital (for example, the same patient who is homeless or does not have adequate home supports may need to stay in the hospital longer appropriately.)
      • ​And, the other side of the issue: if a patient is really sick with end-stage heart failure, , they are likely to be readmitted within 30 days perhaps no matter what happens (though, of course, we should do as aggressive outpatient management as we can). And their being home as much as possible may have important value to them: being with family, in a friendly and supportive environment, etc., even if they are aware they might be back in the hospital soon

So, the real issue is how does one blend the need for some quality control issues (better care for diabetics or decreasing hospitalizations, in the above examples) but avoid using a blunt instrument (a1c levels, 30-day readmissions) which may well decrease real quality care????  This is certainly not easy to do by large-scale data-mining, looking just at numbers (a1c’s) or billing (readmission rates), but I think really requires looking at individual patients to see what an appropriate a1c might be for them, or whether they were really discharged too early and needed readmission because of poor clinical judgment. If you send me their emails, I can add them to the list

Primary Care Corner with Geoffrey Modest MD: Early Activity After Concussion?

26 Jan, 17 | by EBM

By Dr. Geoffrey Modest

A large Canadian study looked at outcomes in kids according to whether there was strict rest versus different levels of physical activity in the week after a concussion (see doi:10.1001/jama.2016.17396​ ).

Details at initial exam:

  • 2413 participants aged 5 to 18 with acute concussion completed the questionnaires in the emergency room, at day 7, and at day 28 post-injury. The researchers assessed persistent postconcussive symptoms (PPCS, defined as at least 3 new or worsening individual symptoms vs preconcussion status) to see how that varied according to the amount of physical activity begun within 7 days of the ED visit.
  • Mean age 11.8 years, 39% female, arrived at ED a median of 8.7 hours after injury, 24% lost consciousness (11% >3 minutes), 2% had seizure, 8% had prior concussions lasting more than a week.
  • 49% appeared dazed and confused, 41% answered questions slowly, 14% repeated the questions, 21% were forgetful.
  • Parental report of headache in 87%, nausea in 59%, balance problem in 44%, dizziness in 70%, drowsiness 73%, increased sleeping 35%, sensitivity to light or noise 37%, irritability 27%, sadness 40%, seemed mentally foggy 40%, increased fatigue 75%, poor concentration 37%, acts more emotional 40%

Results:

  • 1677 (69.5%) participated in early physical activity, 736 (30.5%) had no physical activity:
    • Light aerobic exercise (e.g. walking, swimming, or stationary cycling) in 795 (32.9%)
    • Sport-specific exercise (e.g. running drills in soccer or skating drills in ice hockey) in 214 (8.9%)
    • Noncontact drills (e.g. complex passing drills) in 143 (5.9%)
    • Full contact practice (e.g. normal training activities) in 106 (4.4%)
    • Full competition (e.g. normal game play) in 419 (17.4%)
  • PPCS at 28 days occurred in 733 people (30.4%)
  • The incidence of PPCS at 7:
    • Those who engaged in early physical activity: 523 (31.3%) were symptom-free and 803 (48%) had at least three persistent or worsening postconcussive symptoms.
    • Those not engaging in physical activity: 584 (79.5%) had at least three persistent or worsening postconcussive symptoms
  • The incidence of PPCS at 28 days, by propensity score matching:
    • Early physical activity: 28.7% versus 40.1% for no physical activity
    • Among those symptomatic at day 7, the incidence of PPCS:
      • Light aerobic activity: absolute risk benefit of 6.5% over no activity
      • Moderate activity: absolute risk benefit 14.3% over no activity
      • Full contact activity: absolute risk benefit 16.8% over no activity

Commentary:

  • Pediatric concussion guidelines uniformly recommend an initial period of cognitive as well as physical rest after a concussion. These recommendations include modification of school attendance and mental activities as well as avoidance of any physical activity until postconcussive symptoms have returned to baseline, and then a gradual resumption of activities. However, there is no actual evidence to support these recommendations: they reflect a concern for preventing harm.
  • It is, however, very clear from the literature, that re-injury and recurrent concussions are deleterious.
  • This study, though quite large, is an observational study. They did propensity score matching as a means to mathematically control for differences between the different groups of activity level, in an attempt to decrease the inherent bias in an observational study (by mathematically adjusting the groups for likely relevant variables). It was notable that of the 20 items that they asked parents initially (e.g. headache, balance problem, drowsiness, etc., as noted above), there really was not much difference between the groups that performed physical activity and those that did not. However, this study still does not rise to the same evidence quality as a randomized controlled trial (i.e., mathematically modeling is just not the same…). A further caveat is that they did not look at cognitive rest, and it is conceivable that those who did not do any physical activity had much more cognitive activity, and it was the cognitive activity actually caused an increase in PPCS (not so likely, but possible). Also, the cutpoint of beginning exercise within the first 7 days of injury is arbitrary. It would be useful to see data on when exercise was started, perhaps over the first 3 weeks post-concussion and stratified by the initial concussion scores, to see what was the optimal timing or degree of exercise postconcussion
  • It was also impressive that there was an apparent dose-response curve: those that did more activity seemed to benefit the most
  • There really are an array of reasons that might support the conclusions of the study: for some children having to avoid all activity creates significant dysphoria (being the parent of two kids who had concussions, I can attest that not participating in sports created a lot of unhappiness) which can account for some of the psychological symptoms such as fatigue, poor concentration, irritability, etc. As a contrary issue, it is quite clear in the literature that physical activity is important for skeletal health, cardiorespiratory fitness, improvement in symptoms of depression, anxiety, self-esteem, cognitive performance, and academic achievement. In addition, exercise may well lead to improved cerebral blood flow and promote neuro-plasticity,
  • The study is very important in challenging a long-held, though not rigorously demonstrated, view about dealing with injury, in this case concussion. Similarly, for a long time, we were all advised to limit any activity at all for patients with low back pain, for a minimum of two weeks. That also seemed prudent at the time, but turned out to be the antithesis of what we should have been doing. And in these cases, I think this conception that rest is the right prescription really undercuts the power of exercise in preserving and restoring health.

So, based on the study as well as some others, it seems to make sense to have a gradual resumption of physical activity as soon as tolerated after an acute concussion, but avoiding activities that might risk re-injury, given how much better kids did who resumed exercise within a week after a concussion. However, it certainly makes sense to have a real randomized controlled trial to assess the optimal degree of physical activity and its timing after concussion, as well as specific characteristics of the concussion which might dictate different exercise programs. And, also to look at the effect of cognitive rest (which, i think, may be nearly impossible in our technological era, given the intense cognitive stimulation of smartphones, electronic devices, etc.)

For prior blogs: http://blogs.bmj.com/ebm/2015/02/03/primary-care-corner-with-geoffrey-modest-md-concussion-a-less-aggressive-approach/ for another study suggesting more rapid introduction of physical activity; or http://blogs.bmj.com/ebm/2014/09/09/primary-care-corner-with-geoffrey-modest-md-need-for-safe-sustainable-sports/​ for a study looking at the time-course of postconcussive symptoms in kids seen in the Boston children’s hospital ED

Primary Care Corner with Geoffrey Modest MD: Thumb Sucking and Immunity

25 Jan, 17 | by EBM

By Dr. Geoffrey Modest

Another microbiome article (I realize this is the third in a series of two, but can’t help myself). This one looked at the “hygiene hypothesis”, which is basically that kids exposed to more microbes early in life have fewer allergies or asthma. This article looked at thumb-sucking, nail-biting and atopic sensitization, also finding that the more the fingers went into the mouth, the fewer had atopic sensitization (see DOI: 10.1542/peds.2016-0443).

Details:

  • The Dunedin Multidisciplinary Heath and Development Study, a population-based birth cohort study of 1037 people (52% male) born in Dunedin (the second largest city in the South Island of New Zealand with 120,000 inhabitants), with follow-up at ages 3,5,7,9,11,13,15,18,21,26,32, and 38
  • At age 5,7,9,11 the parents were asked about the kid’s thumb-sucking and nail-biting, along with an estimate of frequency
  • Skin-prick testing was done at age 13 on 724 of the 1031 kids (70%), including testing for house dust mites, grass, cat, dog, horse, aspergillus, penicillium, and a few others; a positive test was a wheal >2mm larger than the negative saline control
  • Detailed respiratory reviews were done since age 9
  • The researchers controlled for potential confounders of sex and parental history for asthma or hayfever; breastfeeding; exposure to cat or dog in childhood (a prior analysis of this cohort showed that this exposure led to lower risk of atopic sensitization); parental smoking history, household crowding (total number of kids divided by number of rooms), socioeconomic status

Results:

  • Overall 31% of children were frequent thumb-suckers or nail-biters at age >1yo
    • Nail-biting or thumb-sucking were each found in 20% of girls and 17% of boys
  • Incidence of atopy:
    • ​Atopic sensitization in 38% of girls/52% of boys age 13; 58% of girls/61% of boys at age 32
    • ​Asthma in 10% of girls/16% of boys age 13; 18% of girls/18% of boys at age 32
    • ​Hayfever in 28% of girls/32% of boys age 13; 42% of girls/37% of boys at age 32
  • For atopic sensitization, as compared to those without thumb-sucking or nail-biting:
    • At age 13:
      • There was an adjusted 36% lower likelihood of atopic sensitization: OR 0.64 (0.45-0.90) for either thumb-sucking or nail-biting
      • A 36% lower likelihood if only thumb-sucking, OR 0.64 (0.42-0.97)
      • A 30% lower likelihood if only nail-biting, OR 0.70 (0.47-1.10), nonsignificant
    • At age 32:
      • There was a 38% adjusted lower likelihood of atopic sensitization: OR 0.62 (0.45-0.86) for either thumb-sucking or nail-biting
      • A 31% lower likelihood if only thumb-sucking, OR 0.69 (0.47-1.00), borderline significant
      • A 29% lower likelihood if only nail-biting, OR 0.71 (0.49-1.02), nonsignificant
    • The only significant difference for specific allergens was for house dust mites in those aged 32, though all of the others had trends that were almost significant
  • For asthma or hayfever:
    • None were significantly associated, at either ages 13 or 32
  • A dose-response curve (doing both thumb-sucking and nail-biting vs either one of them) was only evident at age 13

Commentary:

  • This study further supports the “hygiene hypothesis”, though it was notable that the dramatic difference in atopy was only in the objective measurement of sensitization (but, one might argue that these clinical manifestations of atopy are what really matters….). Why not with asthma or hayfever?
  • Is it just that these were by report and therefore less “reliable” than the objective measure of atopic sensitization?
  • Asthma, also, is more complicated, given that atopy is only part of the issue playing into it
  • Or, my guess, is that they were looking at kids who were already too old (there were no data on thumb-sucking and nail-biting during the preschool years), that immune tolerance largely develops earlier in life, and other studies showing a relationship between “hygiene” and atopic conditions (e.g. hayfever or asthma) included much younger children (see blogs listed below)
  • The study does support the results of a prior study finding that in kids using pacifiers, there seemed to be fewer allergies later in life when the mothers sucked the pacifiers to clean them
  • The proposed mechanism here is that exposure to bacteria and other microorganisms early changes the gut microbiome (and, see blog below about the respiratory microbiome); and the microbiome can change the function of helper T cell (TH) subsets, increasing the helper T cell type 1 (TH-1, which produce interferon-g, IL-2, TNF-b and leads to cell-mediated immunity) and decreasing helper T cell type 2 (TH-2, which produces a slew of interleukins which lead to strong antibody responses), with these changes promoting the development of immune tolerance to allergen exposures.
  • But overall this study does support the concept that early exposure to some microbes leads to more immune tolerance. And thumb-sucking or nail-biting certainly increases exposure to a diverse variety of microbes.

See http://blogs.bmj.com/ebm/2016/09/19/primary-care-corner-with-geoffrey-modest-md-microbiome-and-type-1-diabetes-etc/​ which includes an article on the microbiome and type 1 diabetes, and two more on the “hygiene hypothesis”: one on the increased incidence of autoimmune disease in kids in those born in North Karelia Finland (more automated, advanced technologically) vs the Russian side (same gene pool but more natural environment/exposures); and the other being the recent NEJM article finding the same type of difference for asthma in the Hutterites (industrialized farming) vs the Amish (traditional farming)​

See http://blogs.bmj.com/ebm/2015/11/09/primary-care-corner-with-geoffrey-modest-md-gi-microbiome-in-little-kids-and-development-of-asthma/ which is a Canadian longitudinal study finding that early infancy microbiome changes increase the risk of childhood asthma; or http://blogs.bmj.com/ebm/2014/08/13/primary-care-corner-with-geoffrey-modest-md-asthma-and-early-exposure-to-allergens/ which looks at 4 US cities and similarly finding that early allergen exposure leads to more asthma

There was a blog I sent out 8/27/2014 (which did not make it into the BMJ blogs) which looked at the lung microbiome, showing that diet leads to changes in the TH1 and TH2 cells in the lung itself (i.e., there is more than one microbiome, not just the gut one). For the article, see doi:10.1038/nm.3444. With regards to asthma: there is evidence of increased prevalence of chlamydia and mycoplasma with asthma exacerbations. Also, the respiratory microbiome is different in asthmatic vs nonasthmatic patients, even in asymptomatic asthmatic patients, with abundance of Proteobacteria. There is also some evidence that airway hyperresponsiveness tracks with bacterial diversity and composition (esp. increase in Proteobacteria).

Primary Care Corner with Geoffrey Modest MD: Microbiome 2

24 Jan, 17 | by EBM

By Dr. Geoffrey Modest

This is the second of two blogs on the microbiome, inspired by a recent review that highlighted several other health-related data besides the non-caloric artificial sweeteners (see Lynch SV. N Engl J Med 2016;375:2369).

Details:

  • ​The microbiome is huge, with 9.9 million microbial genes represented, as found from studying 1200 people in the US, China, and Europe. And it has >1000 species of microbes
  • Although the microbiome was previously felt to develop after birth, bacteria are found in the placentas of healthy mothers, in the amniotic fluid of preterm infants, and in meconium. And, the mode of infant delivery does influence postnatal microbial exposure: intravaginal delivery does seem to confer an infant microbiome taxonomically similar to the maternal gut and vaginally microbiota. Also the infant microbiome does become more similar to the adult one with the cessation of breast-feeding, and over the years bacterial diversity and functional capacity expand. The microbiome becomes less diverse in elderly, which could reflect coexisting conditions and age-related declines in immunocompetence.
  • Things that affect the microbiome include sex, age, diet, exposure to antimicrobial agents, changes in stool consistency, PPIs and other meds, travel, malnutrition, exercise (the effect of exercise on the microbiome is pretty clear in mice, not so clear in humans, since it is hard to sort out the effect of exercise vs different diets in those who exercise more). Also, host genetic features, host immune response, xenobiotics (including antibiotics), other drugs, infections, diurnal rhythms (see below), and environmental microbial exposures.
  • Clostridium difficile infections
    • This is probably the most advanced and practicable microbiome application. See http://blogs.bmj.com/ebm/category/clostridium-difficile/ for many studies and analyses. However about 90% of patients affected with severe, recurrent antibiotic-resistant C. difficile infections respond to fecal microbial transplants
  • Effects on immunity:
    • There are data that the infant microbiota at one month of age is significantly related to allergy in two-year-old children and to asthma in four-year-old children. Several of the products of the higher risk microbiota are associated with subclinical inflammation, which precedes childhood disease. Also other studies have found that children born by cesarean section, who do have differences in their microbiota, are more likely to develop type I diabetes, celiac disease, asthma, hospitalizations for gastroenteritis, and allergic rhinitis.
  • Obesity/metabolic syndrome/insulin resistance/diabetes
    • There are several studies finding that there are significant differences in the microbiome between obese and lean human subjects, with a decrease in Bacteroidetes and an increase in Firmicutes species in obese individuals. Studies have shown that taking microbiome samples from pairs of identical human twins, one lean and one obese, and placing them into genetically identical baby mice, have found that the mice with the microbiota from the obese twin develops more weight gain and more body fat, along with a less diverse microbiome, than those from the lean twin. Also, interestingly, women in their third trimester of pregnancy have an altered microbiome, which, when transplanted into mice, leads to more obesity, and that pro-obesity microbiome is more efficient in extracting energy from food [one common clinical issue with overweight/obese patients is that they may often eat much less than others but still do not lose weight, which has been shown in several studies, and attributed to their being more efficient in metabolizing foods. But perhaps this is mediated through the microbiome???]
    • Some proteins elaborated by E. coli stimulate glucagon-like peptide-1 (GLP-1) secretion, which could augment glycemic control in diabetics, where this hormone is less active than in nondiabetics. In addition, E. coli can elaborate peptide YY (produced in the ileum in response to feeding), which can activate anoxeretic pathways in the brain, mediating satiety.
  • Atherosclerosis/cerebral artery occlusion
    • There are pretty convincing studies that eating red meat leads to changes in the gut microbiota, which leads to increase production of trimethylamine-N-oxide (TMAO), which is a very strong risk factor for human atherosclerotic disease. And feeding meat to vegetarians does not increase TMAO until there are these microbiota changes from recurrent red meat diets. See blogs listed below for more details. Also, experimental data on mice show that cerebral arterial occlusion leads to 60% less damage in those with microbiota which are sensitive to antibiotics; mice given probiotics have less impairment after spinal cord injury.
  • Cancer
    • In mice, specific gut bacteria (most clearly shown for Bifidobacterium) enhance the efficacy of cancer immunotherapy, delaying melanoma growth. Human data has shown that certain microbiota species (B. Thetaiotaomicron or B. fragilis) can improve the effects of anti-tumor therapy targeting cytotoxic T-lymphocytes-associated antigen 4.
  • Autism
    • There are even some suggestive data that the microbiome may play a role in autism spectrum disorders. MIA mice, a maternal immune activation mouse model, exhibits autistic-like behavior, gut microbiome dysbiosis, increased gut mucosal permeability, and an increase in 4-ethylphenylsulfate (4EPS, a metabolite of gut bacteria). Injection of 4EPS into healthy, normal mice results in anxiety. And, feeding the MIA strain of mice a strain of Bacteroides fragilis normalized these adverse gut changes and decreased behavioral abnormalities, associated with decreasing circulating 4EPS levels. There are other neuropsych issues potentially related to the microbiome: gut bacteria can produce several neurotransmitters (eg norepinephrine, serotonin, dopamine, GABA, acetylcholine), and can change emotional behavior of mice (which seems to be related to central GABA receptor expression).
  • Other diseases with suggestive data of a linkage to microbiome dysbiosis include inflammatory bowel disease, kwashiorkor, juvenile rheumatoid arthritis, and multiple sclerosis. Also, in mice, stress leads to altered microbiota (less Bacteroides and more Clostridia), and in humans undergoing bariatric surgery, there are huge differences in the microbiome by either the Roux-en-Y gastric bypass or vertical banded gastroplasty, and this microbiome transplanted into germ-free mice leads to reduced fat deposition, suggesting that these microbiome changes themselves might play a direct role in decreasing adiposity (see Tremaroli V. Cell Metabolism2015; 22:228)​. And perhaps the changes in the microbiome, through the gut-brain relationship is part of the reason for the documented improvement in memory noted after bariatric surgery.
  • Diurnal rhythms (see Thaiss CA. Cell. 2014; 159: 514): the gut microbiota has diurnal variations that reflect feeding rhythms; humans with jet lag have dysbiosis; this jet lag leads to microbiome changes promoting glucose intolerance and obesity and are transferable to germ-free mice.

Commentary:

  • We should approach these studies on the microbiome with caution: some of the most impressive studies were done in animals in highly controlled conditions, and predictions in humans based on the studies is always fraught. For example, in general the use of probiotics in human adults has not shown as dramatic a response as found in rodents. (Although an interesting study of human neonatal probiotic supplementation in the first month of life was associated with a 60% reduction in the risk of pancreatic islet cell autoimmunity, a precursor to type 1 diabetes, before school-age). In addition, a stool sample may not be an adequate proxy for the microbial content of the entire GI tract. And, most of these studies have focused primarily on bacterial species in the microbiota, not taking into account the many other types of microorganisms found or their complex interactions.
  • One concern I have in general is our tendency towards reductionism. The microbiome appears to be a quite complex organ, composed of many different varieties of organisms which undoubtedly interact with each other in complex ways, and which are influenced by many known and undoubtedly unknown external cues (diet, antibiotic use, etc., etc.). So, for example, simply attempting to manipulate that microbiome through the introduction of one species or another of probiotics (i.e., our usual medical fix) may not deal with the complexity of this situation.
  • There have been a slew of other blogs on the microbiome over the years. See http://blogs.bmj.com/ebm/category/microbiome/ . One particularly interesting finding in one of the blogs was that one of metformin’s major action might be in its effects on the microbiome (see http://blogs.bmj.com/ebm/2015/01/28/primary-care-corner-with-geoffrey-modest-md-heart-failure-microbiome/, which also reviews some of the TMAO data.
  • So, although I am pretty convinced of the importance of a healthy microbiome, it does seem to me that the major initiative should be around lifestyle changes overall: a healthy diet (and specifically one which is predominantly vegetarian), adequate exercise, perhaps adequate sleep (would be great to have more data on the effect of sleep patterns overall on the microbiome and if changing those patterns changes the microbiome), and minimizing exposure to unnecessary antibiotics (both in humans and in animals that make it into our food chain).

Primary Care Corner with Geoffrey Modest MD: Artificial Sweeteners Microbiome1

23 Jan, 17 | by EBM

By Dr. Geoffrey Modest

As mentioned in prior blogs, I think that the microbiome represents a very important mediator between the external environment and health/disease. A few recent articles supplement and strengthen this understanding. The first in a series of two is a study reinforcing the potentially deleterious effects of non-caloric sweeteners on the microbiome and health outcomes. The second (to be sent tomorrow) is a broader description of our understanding of the microbiome overall and its potential relationship to health.

​Non-caloric artificial sweeteners (NAS) were developed from the biological perspective that these potent sweeteners (more than 100 times sweeter than sucrose) are non-caloric and  are excreted unchanged; they should therefore be an important sugar alternative to help people lose weight and control glucose intolerance. Although a study done in the 1980s, prior to DNA sequencing capabilities, did show that saccharin could alter the rat microbiome, it is only relatively recently that we understand the fuller effects of NAS on both the microbiota as well as clinical outcomes. Many of the clearest studies were done on animals, since it is easier to control the environment completely and isolate the effects attributable to NAS. A recent study looked further into the relationship between NAS, the microbiome, and the clinical effects (see Suez J. Gut Microbes 2015; 6(2), 149). This is an update of a prior article in Nature (see prior blog: http://blogs.bmj.com/ebm/2014/12/04/primary-care-corner-with-geoffrey-modest-md-artificial-sweeteners-microbiome-and-glucose-intolerance-in-mice-and-men-and-women/​ )

Background:

  • The human weight control studies here are a bit mixed. However it should be noted that most of the comparisons were between individuals consuming NAS to those consuming caloric sweeteners, with very few comparing NAS consumption to avoiding all sweeteners.
  • Several studies have shown NAS leads to weight gain in rats (including saccharin, sucralose, aspartame and Stevia), and are associated with increased adiposity
  • NAS can also induce hyperinsulinemia, impaired insulin tolerance, impaired glucose homeostasis, and worsened atherosclerosis in genetically susceptible mice
  • It should be noted that there are some genetically-altered mice where there are some discordant defects: some with decreasing glucose and insulin levels but increasing adiposity, and in some cases hyperinsulinemia

Details of the current study:

  • Mice drinking water supplemented with high doses of commercial saccharin, sucralose, or aspartame, after 11 weeks had marked glucose intolerance, as compared to controls drinking water, sucrose, or glucose.
  • Further studies of saccharin showed that mice on different baseline diets (e.g. high-fat or other) and at different doses of saccharin had increased glucose intolerance
  • The glucose intolerance induced by NAS was ameliorated by prior dosing with antibiotics (ciprofloxacin and metronidazole, in an attempt to sterilize the gut)
  • There were specific changes in the microbiome associated with NAS, including enrichment of Bacteroides and some Clostridiales and decreases in Lactobacilli and some other members of Clostridiales, several of the microbiota changes previously associated with type II diabetes in humans
  • Fecal microbiomes from mice consuming either water or commercial saccharin were then transplanted into germ-free mice, finding that those germ-free mice receiving the saccharin-associated microbiome developed glucose intolerance
  • In 381 nondiabetic humans, NAS consumption was associated with increases in BMI, blood pressure, hemoglobin A1c, and fasting glucose levels. Also there were changes in microbial taxa in the microbiome: more Actinobacteria, Enterobacteriales, and certain Clostridiales.
  • A preliminary small-scale human study found that supplementing the regular diet with higher doses of saccharin led to elevated glycemic responses in four of the seven volunteers, those 4 had microbiome alterations. And when these microbiomes were transplanted into germ-free mice, these mice also developed the same abnormal glycemic responses. Of note, in two of these 4 volunteers, their microbiome changes reverted to normal within 2 to 8 weeks.

Commentary:

  • NAS is consumed by approximately 32% of adult Americans.
  • The microbiome can be rapidly altered by diet, as noted in diets rich in fat (for example, see http://blogs.bmj.com/ebm/2015/01/28/primary-care-corner-with-geoffrey-modest-md-heart-failure-microbiome/)
  • There are a remarkable number of largely unregulated food additives in the current food supply, many added for purely commercial ends, such as preservatives to extend the shelf life of some foods. I believe this NAS data challenges the concept that even those ingredients that are not absorbed and internalized could conceivably adversely affect the human microbiome. The main point here is not that all additives or chemicals are necessarily bad, but that we should be very circumspect about assuming that they are probably benign based on our often incomplete models (i.e. It did make intuitive sense at the time that a non-absorbed sweetener would lead to less obesity and diabetes; but as our understanding and models have expanded/become more complex, our “intuitive” sense has changed). But, I think all of this reinforces what Michael Pollan (author or many books, including The Omnivore’s Dilemma) has suggested: it really does make sense to eat natural foods, especially ones which our bodies have evolutionarily adapted to, and avoid foods with ingredients that your grandmother would not know.

In my practice, I have focused on trying to get patients to decrease their consumption of sodas, and with some reasonable success. I think this is often the low-hanging fruit (though less healthy than other fruits), and at least most of my patients say they have dramatically decreased or eliminated sodas by either substituting water (best) or water slightly flavored by fruit juice. For regular sodas, the attempt is to decrease the consumption of high-fructose corn syrup (a bad actor with multiple bad effects, including increasing uric acid levels), was well as “diet” sodas (commenting on the fact that they really are not benign, non-sugar alternatives, as above). I think my patients have been able to change this soda habit by our regularly and repeatedly targeting this issue (with motivational interviewing) over the past several years, especially with my patients who are overweight, glucose intolerant/diabetic or hyperuricemic.

Primary Care Corner with Geoffrey Modest MD: Cardiovascular Fitness — a new vital sign?

19 Jan, 17 | by EBM

By Dr. Geoffrey Modest

A recent scientific statement from the American Heart Association stresses the importance of assessing cardiorespiratory fitness (CRF) as part of the risk assessment for cardiovascular disease (see DOI: 10.1161/CIR.0000000000000461)​.

Details:

  • Studies since the 1950s have consistently found that CRF is a strong and independent marker of cardiovascular risk as well as all-cause mortality, adjusting for age and the other standard risk factors. This is been found in healthy men and women, those with known or suspected cardiovascular disease, and those with the co-morbidities of obesity, type 2 diabetes, hypertension, and hyperlipidemia. In many studies CRF is a more powerful predictor of mortality risk than traditional cardiovascular risk factors. It has even been shown to be a more powerful risk predictor than ST-segment depression, cardiovascular symptoms, or hemodynamic responses.
  • The survival benefit in 13 studies showed that each 1-MET (metabolic equivalent) higher CRF, a small increment, was associated with a marked 10-25% improvement in survival. And, one study found that men who improved from unfit to fit between two successive examinations had a reduction in mortality risk of 44% relative to those who remained unfit in both exams (i.e., those with higher CRF have dramatic clinical benefit)
  • As a quick guide to METs:
    • Light activity (<3 METs): includes walking 2.5 mph (2.9 METs)
    • Moderate activity (3-6 METS): includes walking 3.0 mph (3.3 METs), walking 3.4 mph (3.6 METs), stationary biking (light effort) 5.5 METs
    • Vigorous activity (>6 METs):  jogging (7.0 METs), calisthenics/pushups/situps (8.0 METs), rope jumping (10.0 METs)
  • Of note, even though the most dramatic differences in all-cause and cardiovascular mortality were found comparing the most fit to the least fit subjects (70% and 56% respectively), the greatest increase in mortality benefit was in comparing the least fit group to the next least fit category
  • A recommendation in the paper is that CRF should become an accepted “vital sign”, and should be part of the standard clinical encounter
  • CRF also is associated with heart failure exacerbations and mortality, with one study finding that for every 6% increase in CRF over three months there was a 4% lower risk of cardiovascular mortality or hospitalization, and an 8% decrease risk of cardiovascular mortality or heart failure hospitalization (for example, see http://blogs.bmj.com/ebm/2016/11/09/primary-care-corner-with-geoffrey-modest-md-vigorous-exercise-helps-those-with-heart-failure/ which shows the benefit of vigorous exercise in patients with heart failure and reduced ejection fraction), timing of cardiac transplantation, preoperative surgical risk prediction (including studies of abdominal aortic aneurysm repair, liver transplant, lung cancer resection, upper GI surgery, intra-abdominal surgery, bariatric surgery, coronary artery bypass grafting). And interventions seem to help: in patients waiting for CABG surgery, those randomized into an exercise training group had superior outcomes to the control group, with a reduced rate of perioperative complications and shorter hospital stays. And observational studies have also shown that men with higher CRF had 68% lower stroke mortality, controlling for standard risk factors.
  • There were a few studies showing that those with a higher level of CRF had a reduced risk of developing dementia or Alzheimer’s, one study showing a 36% lower risk of developing dementia in those with the highest quartile of CRF. Higher levels of CRF are also associated with lower measures of anxiety or depression symptoms
  • Many studies have shown decreased risk of development of prediabetes, metabolic syndrome, and type 2 diabetes in those with higher CRF, again with the biggest difference in those going from lower CRF to the moderate range.
  • Lower levels of CRF at a younger age are also associated with a higher risk of disability at an older age. For example one study of obese adults with type 2 diabetes found that after four years, improvement of CRF decrease the likelihood of developing disability
  • Added value of CRF to the traditional risk calculators:
    • Several analyses have looked at various ways of incorporating the additional value of CRF. In one 30-year study of patients with stage II hypertension, the 30 year risk of cardiovascular mortality was 18.4% in those with low CRF versus 10.1 present in those with high CRF (i.e., a huge difference)
    • Overall, adding CRF to the traditional risk stratification led to actual CVD and all-cause mortality outcomes being correctly reclassified through the risk predictor as being decreased 23.3% and 20.6% respectively through correctly reclassifying patients as higher risk, and 55.8% and 46.0% respectively for correctly reclassifying patients as lower risk. The overall changes reflected a 30.5% and 24.5% correct reclassification for all-cause mortality, with larger changes in correctly reclassifying patients as lower risk because of CRF. [e., those at apparently high risk by a traditional risk calculator, in reality have significantly lower risk if they are more fit; there are changes apparent in the other direction as well, but less emphatically so]. Also as a point of comparison, when looking at the nontraditional risk factors, such as coronary artery calcium scores  (which seems to be the best of the lot), the level of correct reclassification from the traditional risk calculators is much lower
  • So, how does one measure CRF?
    • The most accurate and standardized quantification of CRF is through CPX (cardiopulmonary exercise testing), a combination of conventional exercise testing with ventilatory expired gas analysis
    • A step below that is to look at achieved treadmill speed/grade and duration, making sure the patient does not hold the hand rails
    • Another approach is to look at submaximal exercise testing or the 6-minute walk test to assess distance walked (walking <350 meters is associated with high risk).
    • And the easiest overall/least time-consuming/cheapest/easiest to implement is to do nonexercise prediction calculations. These are not standardized at this point, and each study seems to have somewhat different calculators. Perhaps the best is to use the one by Nes BM. Med Sci Sports Exerc 2011; 43: 2024, which incorporated an assessment of patient reported physical activity, age, waist circumference, and resting heart rate, and this is one of the studies which included a lot of people (n= 2067 men and 2193 women) and looked at actual clinical outcomes, finding that 90.2% of women and 92.5% of men in the lowest two quartiles of fitness were correctly classified. Their questions for physical activity included: frequency of exercise (never, <1x/wk, 1x/wk, 2-3x/wk, >3x/wk), intensity (“no sweat/heavy breathing, “heavy breath and sweat”, “push myself to exhaustion”), and duration (<15 in, 15-30 min, 30-60 min, >60 min).
    • Overall exercise recommendations:
      • Type: exercise should involve major muscle groups (legs, arms, trunk) that is continuous and rhythmic (e.g. brisk walking, jogging, cycling, swimming, rowing)
      • Intensity: moderate and/or vigorous intensity relative to the persons capacity
      • Frequency: at least five days per week of moderate or three days per week of vigorous intensity exercise
      • Time: 30 to 60 minutes per day (150 minutes per week) of moderate and 20 to 60 minutes per day (75 minutes per week) of vigorous exercise. Of note between 10 and 20 minutes can be beneficial in previously inactive people
      • Amount: a target of 500 to 1000 MET-min/wk
      • Pattern: one continuous session per day or multiple sessions per day of greater than 10 minutes each. Less than 10 minutes may work in deconditioned individuals.

Commentary:

  • Incorporating CRF reflects a more individualized physiologic approach (assessing the constellation of how well the heart, lung, circulation, and oxygen extraction by muscles works). It is clear from epidemiologic data that on a community basis, as well as individual basis, the traditional risk factors of smoking, hypertension, hyperlipidemia, and diabetes confer an increased risk of cardiovascular disease. However, CRF is a truly specific individual physiologic risk factor, reflecting how these risk factors and more play out in the individual’s body. For example, hypertension itself confers different levels of individual risk dependent on CRF.
  • One note of caution: there is no uniformity in clinical practice as to which of the traditional risk calculators is the best: the Am Heart Association/Am College of Cardiology just published an updated tool, including a spreadsheet calculator (see org/10.1161/CIR.0000000000000467 for the article, and http://circ.ahajournals.org/highwire/filestream/234917/field_highwire_adjunct_files/0/Appendix_Users_Guide_Spreadsheet.xlsfor the spreadsheet to calculate risk. BUT, this tool also needs to be validated in different populations prior to being accepted (also, see http://blogs.bmj.com/ebm/2015/08/05/primary-care-corner-with-geoffrey-modest-md-comparison-of-the-2013-accaha-lipid-guidelines-to-atpiii/ for a critique of the 2013 ACC/AHA lipid guidelines.)
  • Interestingly, several studies suggest that CRF is a more potent predictor of cardiovascular disease than any of the individual risk factors we have incorporated into our predictive models
  • Why is CRF so important? There are several explanations: improved traditional cardiovascular risk profiles (though most of the studies did control for the major ones we know), changes in autonomic tone that may reduce arrhythmogenic risk, fewer thrombotic events (exercise decreases fibrinogen levels, for example), improved endothelial function, lower levels of visceral adiposity/improved insulin sensitivity, lower levels of inflammation, as well as perhaps improved mental health and sense of well-being. And, there might be important positive changes in the gut microbiome with exercise, which is clear in animal models, less clear in humans where those who exercise tend to eat differently from those who do not, so hard to control well).
  • I should add a couple of caveats here: it is important not to confound fitness with doing lots of exercise; a significant component of fitness (on the order of 30+%) is genetic and not related to regular exercise. And most of the studies above are observational, not intervention studies (i.e., only a few actually randomized patients to exercise programs vs none and looked at long-term outcomes. Though the one on pre-surgery exercise programs was pretty impressive. And, the overall data on the benefits of exercise overall are quite robust)
  • For ballpark figures, those with a CRF level less than 5 METs have a particularly high risk of mortality, whereas those with CRF levels of greater than 8 to 10 METS seem to have much more protection. And, more than half the reduction in all-cause mortality occurs between those who are least fit (e.g. CRF less than 5 METs) and those in the next least fit group (e.g. CRF 5-7METS); i.e., benefits for cardiorespiratory fitness are particularly strong in those people in the least fit as compared to the next higher category (i.e., one does not need to be an Olympic athlete to achieve the benefits)

So, the key points here are:

  • Cardiorespiratory fitness is an independent in additive risk assessor for total and cardiovascular mortality
  • Improving CRF dramatically decreases cardiovascular and all-cause mortality
  • This clinical improvement is especially profound in those who are the least fit, finding a greater than 50% risk reduction by moving one step up to the next least fit group. An increase in CRF of only one MET is associated with the 10 to 20% decrease in mortality rate
  • There is a reasonable argument based on studies that have been done to propose that a simple, non-exercise based calculator should be added as a vital sign. This could easily be measured by nonclinical staff and would provide clinicians important information to help encourage patient-specific exercise programs. This should to be evaluated more completely in different populations to assess its generalizability. However, even without those studies, given the documented benefits of exercise and the dramatic relationship in the above studies of CRF as a risk predictor, I personally will ask patients about CRF more and further reinforce the importance of exercise as part of a healthy lifestyle.

For other blogs on exercise, see http://blogs.bmj.com/ebm/category/exercise/

Primary Care Corner with Geoffrey Modest MD: Drug shenanigans come home to roost (finally, at least a beginning)

19 Dec, 16 | by EBM

By Dr. Geoffrey Modest

There was a timely article in STATnews (in particular their Pharmalot series by Ed Silverman which regularly exposes drug company malfeasance) about price-fixing among generic drug makers. And, 2 generic pharmaceutical executives have finally been accused by the feds (see https://www.statnews.com/pharmalot/2016/12/14/heritage-generics-antitrust-price-fixing/?s_campaign=trendmd ). Hopefully more to come…

Jeffrey Glazer and Jason Malek of Heritage Pharmaceuticals, the former chief executive officer and president respectively, were accused by federal authorities of conspiring with rivals to fix prices for doxycycline hyclate and glyburide. This is the first criminal charges of a two-year federal investigation into such price-fixing among generic pharmaceuticals. At this point no other companies have been named, though Mylan Pharmaceuticals, Teva Pharmaceuticals, Actavis, Lannett Co, Impax Laboratories, Sun Pharmaceuticals, Endo International’s Par subsidiary, and Taro Pharmaceuticals have disclosed receding subpoenas in this investigation. These two executives had been fired from Heritage in August 2016 for a seven-year long “running criminal conspiracy that severely damaged Heritage”, noting that they had “looted tens of millions of dollars from Heritage by misappropriating its business opportunities, fraudulently obtaining compensation for themselves, and embezzling its intellectual property”, per the court documents. Heritage Pharmaceuticals claimed that these two had redirected more than $9 million to a dummy company between 2012 and 2015.

Prior findings have found that half of all generics had become more expensive during the prior 12 months, with 11 at least doubling the price, and one medication increasing 17,000%. This led to an investigation by Sen. Bernie Sanders of Vermont and Representative Elijah Cummings from Maryland, but the manufacturers have refused to testify (including Heritage), stifling the investigation. At that point Sanders had his formidable quote: “pharmaceutical executives must be held accountable for ripping off the American people by charging them the highest prices in the world for prescription drugs… At a time when one out of five Americans cannot afford the medication they need, we must do everything we can to end the greed and illegal behavior of the drugmakers. Fraud can no longer be an acceptable business model for the pharmaceutical industry.”

Also as reported by a prior issue of STAT and on a PBS news-hour blog on November 4, 2016 (http://www.pbs.org/newshour/rundown/bernie-sanders-requests-federal-investigation-insulin-makers-price-collusion/ ) Sanders and Cummings want an FTC investigation into Eli Lilly, Sanofi, and Novo Nordisk, since their price for insulin has been going up in tandem over several years. The cost of insulin more than tripled from 2000 to 2013, from $231 to $736 a year per patient, despite the fact that the original insulin patent had expired 75 years ago.

There have been many blogs in the past on drug company shenanigans (see http://blogs.bmj.com/ebm/category/pharmacy/ ). One (http://blogs.bmj.com/ebm/2015/09/30/primary-care-corner-with-geoffrey-modest-md-and-the-drug-company-shenanigans-continue/ ) commented on pyrimethamine but also mentioned the above Heritage increase in price of doxycycline from $20 for 50 tablets in October 2013 to $1849 in April 2014, an increase of  >8000%, leading many of us (including myself) scouring around for substitutes….

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