Month: December 2019

Week 26 – ARISTOTLE

“Apixaban versus Warfarin in Patients with Atrial Fibrillation”

N Engl J Med. 2011 Sep 15;365(11):981-92. [free full text]
—

Prior to the development of the DOACs, warfarin was the standard of care for the reduction of risk of stroke in atrial fibrillation. Drawbacks of warfarin include a narrow therapeutic range, numerous drug and dietary interactions, the need for frequent monitoring, and elevated bleeding risk. Around 2010, the definitive RCTs for the oral direct thrombin inhibitor dabigatran (RE-LY) and the oral factor Xa inhibitor rivaroxaban (ROCKET AF) showed equivalence or superiority to warfarin. Shortly afterward, the ARISTOTLE trial demonstrated the superiority of the oral factor Xa inhibitor apixaban (Eliquis).

The trial enrolled patients with atrial fibrillation or flutter with at least one additional risk factor for stroke (age 75+, prior CVA/TIA, symptomatic CHF, or reduced LVEF). Notably, patients with Cr > 2.5 were excluded. Patients were randomized to treatment with either apixaban BID + placebo warfarin daily (reduced 2.5mg apixaban dose given in patients with 2 or more of the following: age 80+, weight < 60, Cr > 1.5) or to placebo apixaban BID + warfarin daily. The primary efficacy outcome was the incidence of stroke, and the primary safety outcome was “major bleeding” (clinically overt and accompanied by Hgb drop of ≥ 2, “occurring at a critical site,” or resulting in death). Secondary outcomes included all-cause mortality and a composite of major bleeding and “clinically-relevant non-major bleeding.”

9120 patients were assigned to the apixaban group, and 9081 were assigned to the warfarin group. Mean CHADS2 score was 2.1. Fewer patients in the apixaban group discontinued their assigned study drug. Median duration of follow-up was 1.8 years. The incidence of stroke was 1.27% per year in the apixaban group vs. 1.60% per year in the warfarin group (HR 0.79, 95% CI 0.66-0.95, p < 0.001). This reduction was consistent across all major subgroups (see Figure 2). Notably, the rate of hemorrhagic stroke was 49% lower in the apixaban group, and the rate of ischemic stroke was 8% lower in the apixaban group. All-cause mortality was 3.52% per year in the apixaban group vs. 3.94% per year in the warfarin group (HR 0.89, 95% CI 0.80-0.999, p = 0.047). The incidence of major bleeding was 2.13% per year in the apixaban group vs. 3.09% per year in the warfarin group (HR 0.69, 95% CI 0.60-0.80, p<0.001). The rate of intracranial hemorrhage was 0.33% per year in the apixaban group vs. 0.80% per year in the warfarin group (HR 0.42, 95% CI 0.30-0.58, p < 0.001). The rate of any bleeding was 18.1% per year in the apixaban group vs. 25.8% in the warfarin group (p <  0.001).

In patients with non-valvular atrial fibrillation and at least one other risk factor for stroke, anticoagulation with apixaban significantly reduced the risk of stroke, major bleeding, and all-cause mortality relative to anticoagulation with warfarin. This was a large RCT that was designed and powered to demonstrate non-inferiority but in fact was able to demonstrate the superiority of apixaban. Along with ROCKET AF and RE-LY, the ARISTOTLE trial ushered in the modern era of DOACs in atrial fibrillation. Apixaban was approved by the FDA for the treatment of non-valvular atrial fibrillation in 2012. Patient prescription cost is no longer a major barrier to prescription. These three major DOACs are all preferred in the DC Medicaid formulary (see page 13). To date, no trial has compared the various DOACs directly.

Further Reading/References:
1. ARISTOTLE @ Wiki Journal Club
2. ARISTOTLE @ 2 Minute Medicine
3. “Oral anticoagulants for prevention of stroke in atrial fibrillation: systematic review, network meta-analysis, and cost-effectiveness analysis,” BMJ 2017

Summary by Duncan F. Moore, MD

Week 25 – The Oregon Experiment

“The Oregon Experiment – Effects of Medicaid on Clinical Outcomes”

N Engl J Med. 2013 May 2;368(18):1713-22. [free full text]
—

Access to health insurance is not synonymous with access to healthcare. However, it has been generally assumed that increased access to insurance should improve healthcare outcomes among the newly insured. In 2008, Oregon expanded its Medicaid program by approximately 30,000 patients. These policies were lotteried among approximately 90,000 applicants. The authors of the Oregon Health Study Group sought to study the impact of this “randomized” intervention, and the results were hotly anticipated given the impending Medicaid expansion of the 2010 PPACA.

Population: Portland, Oregon residents who applied for the 2008 Medicaid expansion

Not all applicants were actually eligible.

Eligibility criteria: age 19-64, US citizen, Oregon resident, ineligible for other public insurance, uninsured for the previous 6 months, income below 100% of the federal poverty level, and assets < $2000.

Intervention: winning the Medicaid-expansion lottery

Comparison: The statistical analyses of clinical outcomes in this study do not actually compare winners to non-winners. Instead, they compare non-winners to winners who ultimately received Medicaid coverage. Winning the lottery increased the chance of being enrolled in Medicaid by about 25 percentage points. Given the assumption that “the lottery affected outcomes only by changing Medicaid enrollment, the effect of being enrolled in Medicaid was simply about 4 times…as high as the effect of being able to apply for Medicaid.” This allowed the authors to conclude causal inferences regarding the benefits of new Medicaid coverage.

Outcomes:
Values or point prevalence of the following at approximately 2 years post-lottery:

      1. blood pressure, diagnosis of hypertension
      2. cholesterol levels, diagnosis of hyperlipidemia
      3. HgbA1c, diagnosis of diabetes
      4. Framingham risk score for cardiovascular events
      5. positive depression screen, depression dx after lottery, antidepressant use
      6. health-related quality of life measures
      7. measures of financial hardship (e.g. catastrophic expenditures)
      8. measures of healthcare utilization (e.g. estimated total annual expenditure)

These outcomes were assessed via in-person interviews, assessment of blood pressure, and a blood draw for biomarkers.

Results:
The study population included 10,405 lottery winners and 10,340 non-winners. Interviews were performed ~25 months after the lottery. While there were no significant differences in baseline characteristics among winners and non-winners, “the subgroup of lottery winners who ultimately enrolled in Medicaid was not comparable to the overall group of persons ho did not win the lottery” (no demographic or other data provided).

At approximately 2 years following the lottery, there were no differences in blood pressure or prevalence of diagnosed hypertension between the lottery non-winners and those who enrolled in Medicaid. There were also no differences between the groups in cholesterol values, prevalence of diagnosis of hypercholesterolemia after the lottery, or use of medications for high cholesterol. While more Medicaid enrollees were diagnosed with diabetes after the lottery (absolute increase of 3.8 percentage points, 95% CI 1.93-5.73, p < 0.001; prevalence 1.1% in non-winners) and were more likely to be using medications for diabetes than the non-winners (absolute increase of 5.43 percentage points, 95% CI 1.39-9.48, p= 0.008), there was no statistically significant difference in HgbA1c values among the two groups. Medicaid coverage did not significantly alter 10-year Framingham cardiovascular event risk. At follow-up, fewer Medicaid-enrolled patients screened positive for depression (decrease of 9.15 percentage points, 95% CI -16.70 to -1.60,  p= 0.02), while more had formally been diagnosed with depression during the interval since the lottery (absolute increase of 3.81 percentage points, 95% CI 0.15-7.46, p= 0.04). There was no significant difference in prevalence of antidepressant use.

Medicaid-enrolled patients were more likely to report that their health was the same or better since 1 year prior (increase of 7.84 percentage points, 95% CI 1.45-14.23, p = 0.02). There were no significant differences in scores for quality of life related to physical health or in self-reported levels of pain or global happiness. As seen in Table 4, Medicaid enrollment was associated with decreased out-of-pocket spending (15% had a decrease, average decrease $215), decreased prevalence of medical debt, and a decreased prevalence of catastrophic expenditures (absolute decrease of 4.48 percentage points, 95% CI -8.26 to 0.69, p = 0.02).

Medicaid-enrolled patients were prescribed more drugs and had more office visits but no change in number of ED visits or hospital admissions. Medicaid coverage was estimated to increase total annual medical spending by $1,172 per person (an approximately 35% increase). Of note, patients enrolled in Medicaid were more likely to have received a pap smear or mammogram during the study period.

Implication/Discussion:
This study was the first major study to “randomize” health insurance coverage and study the health outcome effects of gaining insurance.

Overall, this study demonstrated that obtaining Medicaid coverage “increased overall health care utilization, improved self-reported health, and reduced financial strain.” However, its effects on patient-level health outcomes were much more muted. Medicaid coverage did not impact the prevalence or severity of hypertension or hyperlipidemia. Medicaid coverage appeared to aid in the detection of diabetes mellitus and use of antihyperglycemics but did not affect average A1c. Accordingly, there was no significant difference in Framingham risk score among the two groups.

The glaring limitation of this study was that its statistical analyses compared two groups with unequal baseline characteristics, despite the purported “randomization” of the lottery. Effectively, by comparing Medicaid enrollees (and not all lottery winners) to the lottery non-winners, the authors failed to perform an intention-to-treat analysis. This design engendered significant confounding, and it is remarkable that the authors did not even attempt to report baseline characteristics among the final two groups, let alone control for any such differences in their final analyses. Furthermore, the fact that not all reported analyses were pre-specified raises suspicion of post hoc data dredging for statistically significant results (“p-hacking”). Overall, power was limited in this study due to the low prevalence of the conditions studied.

Contemporary analysis of this study, both within medicine and within the political sphere, was widely divergent. Medicaid-expansion proponents noted that new access to Medicaid provided a critical financial buffer from potentially catastrophic medical expenditures and allowed increased access to care (as measured by clinic visits, medication use, etc.), while detractors noted that, despite this costly program expansion and fine-toothed analysis, little hard-outcome benefit was realized during the (admittedly limited) follow-up at two years.

Access to insurance is only the starting point in improving the health of the poor. The authors note that “the effects of Medicaid coverage may be limited by the multiple sources of slippage…[including] access to care, diagnosis of underlying conditions, prescription of appropriate medications, compliance with recommendations, and effectiveness of treatment in improving health.”

Further Reading/References:
1. Baicker et al. (2013), “The Impact of Medicaid on Labor Force Activity and Program Participation: Evidence from the Oregon Health Insurance Experiment”
2. Taubman et al. (2014), “Medicaid Increases Emergency-Department Use: Evidence from Oregon’s Health Insurance Experiment”
3. The Washington Post, “Here’s what the Oregon Medicaid study really said” (2013)
4. Michael Cannon, “Oregon Study Throws a Stop Sign in Front of ObamaCare’s Medicaid Expansion”
5. HealthAffairs Policy Brief, “The Oregon Health Insurance Experiment”
6. The Oregon Health Insurance Experiment

Summary by Duncan F. Moore, MD

Image Credit: Centers for Medicare and Medicaid Services, Public Domain, via Wikimedia Commons

Week 24 – CHOIR

“Correction of Anemia with Epoetin Alfa in Chronic Kidney Disease”

by the Investigators in the Correction of Hemoglobin and Outcomes in Renal Insufficiency (CHOIR)

N Engl J Med. 2006 Nov 16;355(20):2085-98. [free full text]
—

Anemia is a prevalent condition in CKD and ESRD. The anemia is largely attributable to the loss of erythropoietin production due to the damage of kidney parenchyma. Thus erythropoiesis-stimulating agents (ESAs) were introduced to improve this condition. Retrospective data and small interventional trials suggested that treatment to higher hemoglobin goals (such as > 12 g/dL) was associated with improved cardiovascular outcomes. However, in 1998, a prospective trial in ESRD patients on HD with a hematocrit treatment target of 42% versus 30% demonstrated a trend toward increased rates of non-fatal MI and death in the higher-target group. In an effort to clarify the hemoglobin goal in CKD patients, the 2006 CHOIR trial was designed. It was hypothesized that treatment of anemia in CKD to a target of 13.5 g/dL would lead to fewer cardiac events and reduced mortality when compared to a target of 11.3 g/dL.

The trial enrolled adults with CKD (eGFR 15-50 ml/min) and Hgb < 11.0 g/dL and notably excluded patients with active cancer. The patients were randomized to erythropoietin support regimens targeting a hemoglobin of either 13.5 g/dL or 11.3 g/dL. The primary outcome was a composite of death, MI, hospitalization for CHF, or stroke. Secondary outcomes included individual components of the primary outcome, need for renal replacement therapy, all-cause hospitalization, and various quality-of-life scores.

The study was terminated early due to an interim analysis revealing a < 5% chance that there would be a demonstrated benefit for the high-hemoglobin group by the scheduled end of the study. Results from 715 high-hemoglobin and 717 low-hemoglobin patients were analyzed. The mean change in hemoglobin was +2.5 g/dL in the high-hemoglobin group versus +1.2g/dL in the low-hemoglobin group (p < 0.001). The primary endpoint occurred in 125 of the high-hemoglobin patients (17.5%) versus 97 of the low-hemoglobin patients (13.5%) [HR 1.34, 95% CI 1.03-1.74, p = 0.03; number needed to harm = 25]. There were no significant group differences among the four components of the primary endpoint when analyzed as individual secondary outcomes, nor was there a difference in rates of renal replacement therapy. Any-cause hospitalization rates were 51.6% in the high-hemoglobin group versus 46.6% in the low-hemoglobin group (p = 0.03). Regarding quality-of-life scores, both groups demonstrated similar, statistically significant improvements from their respective baseline values.

In patients with anemia and CKD, treatment to a higher hemoglobin goal of 13.5 g/dL was associated with an increased incidence of a composite endpoint of death, MI, hospitalization for CHF, or stroke relative to a treatment goal of 11.3 g/dL. There were no differences between the two groups in hospitalization rates or progression to renal replacement therapy, and the improvement in quality of life was similar among the two treatment groups. Thus this study demonstrated no additional benefit and some harm with the higher treatment goal. The authors noted that “this study did not provide a mechanistic explanation for the poorer outcome with the use of a high target hemoglobin level.” Limitations of this trial included its non-blinded nature and relatively high patient withdrawal rates. Following this trial, the KDOQI clinical practice guidelines for the management of anemia in CKD were updated to recommend a Hgb target of 11.0-12.0 g/dL. However, this guideline was superseded by the 2012 KDIGO guidelines which, on the basis of further evidence, ultimately recommend initiating ESA therapy only in iron-replete CKD patients with Hgb < 10 g/dL with the goal of maintaining Hgb between 10 and 11.5 g/dL. Treatment should be individualized in patients with concurrent malignancy.

Further Reading/References:
1. Besarab et al. “The Effects of Normal as Compared with Low Hematocrit Values in Patients with Cardiac Disease Who Are Receiving Hemodialysis and Epoetin.” N Engl J Med. 1998 Aug 27;339(9):584-90.
2. CHOIR @ Wiki Journal Club
3. CHOIR @ 2 Minute Medicine
4. National Kidney Foundation Releases Anemia Guidelines Update (2007)
5. Pfeffer et al. “A trial of darbepoetin alfa in type 2 diabetes and chronic kidney disease.” N Engl J Med. 2009;361(21):2019.
6. KDOQI US Commentary on the 2012 KDIGO Clinical Practice Guideline for Anemia in CKD

Summary by Duncan F. Moore, MD

Week 23 – Effect of Early vs. Deferred Therapy for HIV (NA-ACCORD)

“Effect of Early versus Deferred Antiretroviral Therapy for HIV on Survival”

N Engl J Med. 2009 Apr 30;360(18):1815-26. [free full text]
—

Until recently, the optimal timing of initiation of antiretroviral therapy (ART) in asymptomatic patients with HIV had been a subject of investigation since the advent of antiretrovirals. Guidelines in 1996 recommended starting ART for all HIV-infected patients with CD4 count < 500, but over time provider concerns regarding resistance, medication nonadherence, and adverse effects of medications led to more restrictive prescribing. In the mid-2000s, guidelines recommended ART initiation in asymptomatic HIV patients with CD4 < 350. However, contemporary subgroup analysis of RCT data and other limited observational data suggested that deferring initiation of ART increased rates of progression to AIDS and mortality. Thus the NA-ACCORD authors sought to retrospectively analyze their large dataset to investigate the mortality effect of early vs. deferred ART initiation.

The study examined the cases of treatment-naïve patients with HIV and no hx of AIDS-defining illness evaluated during 1996-2005. Two subpopulations were analyzed retrospectively: CD4 count 351-500 and CD4 count 500+. No intervention was undertaken. The primary outcome was, within each CD4 sub-population, mortality in patients treated with ART within 6 months after the first CD4 count within the range of interest vs. mortality in patients for whom ART was deferred until the CD4 count fell below the range of interest.

8362 eligible patients had a CD4 count of 351-500, and of these, 2084 (25%) initiated ART within 6 months, whereas 6278 (75%) patients deferred therapy until CD4 < 351. 9155 eligible patients had a CD4 count of 500+, and of these, 2220 (24%) initiated ART within 6 months, whereas 6935 (76%) patients deferred therapy until CD4 < 500. In both CD4 subpopulations, patients in the early-ART group were older, more likely to be white, more likely to be male, less likely to have HCV, and less likely to have a history of injection drug use. Cause-of-death information was obtained in only 16% of all deceased patients. The majority of these deaths in both the early- and deferred-therapy groups were from non-AIDS-defining conditions.

In the subpopulation with CD4 351-500, there were 137 deaths in the early-therapy group vs. 238 deaths in the deferred-therapy group. Relative risk of death for deferred therapy was 1.69 (95% CI 1.26-2.26, p < 0.001) per Cox regression stratified by year. After adjustment for history of injection drug use, RR = 1.28 (95% CI 0.85-1.93, p = 0.23). In an unadjusted analysis, HCV infection was a risk factor for mortality (RR 1.85, p= 0.03). After exclusion of patients with HCV infection, RR for deferred therapy = 1.52 (95% CI 1.01-2.28, p = 0.04).

In the subpopulation with CD4 500+, there were 113 deaths in the early-therapy group vs. 198 in the deferred-therapy group. Relative risk of death for deferred therapy was 1.94 (95% CI 1.37-2.79, p < 0.001). After adjustment for history of injection drug use, RR = 1.73 (95% CI 1.08-2.78, p = 0.02). Again, HCV infection was a risk factor for mortality (RR = 2.03, p < 0.001). After exclusion of patients with HCV infection, RR for deferred therapy = 1.90 (95% CI 1.14-3.18, p = 0.01).

Thus, in a large retrospective study, the deferred initiation of antiretrovirals in asymptomatic HIV infection was associated with higher mortality.

This was the first retrospective study of early initiation of ART in HIV that was large enough to power mortality as an endpoint while controlling for covariates. However, it is limited significantly by its observational, non-randomized design that introduced substantial unmeasured confounders. A notable example is the absence of socioeconomic confounders (e.g. insurance status). Perhaps early-initiation patients were more well-off, and their economic advantage was what drove the mortality benefit rather than the early initiation of ART. This study also made no mention of the tolerability of ART or adverse reactions to it.

In the years that followed this trial, NIH and WHO consensus guidelines shifted the trend toward earlier treatment of HIV. In 2015, the INSIGHT START trial (the first large RCT of immediate vs. deferred ART) showed a definitive mortality benefit of immediate initiation of ART in patients with CD4 500+. Since that time, the standard of care has been to treat essentially all HIV-infected patients with ART (with some considerations for specific subpopulations, such as delaying initiation of therapy in patients with cryptococcal meningoencephalitis due to risk of IRIS). See further discussion at UpToDate.

Further Reading/References:
1. NA-ACCORD @ Wiki Journal Club
2. NA-ACCORD @ 2 Minute Medicine
3. INSIGHT START (2015), Pubmed, NEJM PDF
4. UpToDate, “When to initiate antiretroviral therapy in HIV-infected patients”

Summary by Duncan F. Moore, MD

Image Credit: Sigve, CC0 1.0, via WikiMedia Commons