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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

Background:

Many patients admitted for acute myocardial infarction (AMI) have chronic renal insufficiency. We studied the impact of chronic renal insufficiency on mortality and quality of inpatient care for AMI from the American Heart Association's Get With The Guidelines–Coronary Artery Disease Program.

Hypothesis:

We hypothesized that mortality and quality of inpatient care would not vary with renal function.

Methods:

We examined in-hospital AMI performance measures by renal function based on glomerular filtration rate (GFR). Severity of renal insufficiency was categorized as normal (GFR ≥ 90 mL/min/1.73 m2), mild (GFR 60–90 mL/min/1.73 m2), moderate (GFR 30–60 mL/min/1.73 m2), severe (GFR 15–30 mL/min/1.73 m2), and kidney failure (GFR ≤ 15 mL/min/1.73 m2 or dialysis). A total of 21721 patients from 291 sites were studied, with most data collected in 2008 to 2009. Multivariable regression analysis after adjusting for patient characteristics was performed and generalized estimating equations were used to account for within-hospital clustering. In-hospital mortality and quality of inpatient care were assessed.

Results:

Renal insufficiency was present in 82.0 percent of AMI patients. The adjusted odds ratio vs normal renal function for mortality increased with worsening renal function: 1.45 for mild renal insufficiency (95% confidence interval [CI]: 1.03–2.05, P = 0.03); 3.36 for moderate renal insufficiency (95% CI: 2.31–4.89, P < 0.0001); 5.43 for severe renal insufficiency (95% CI: 3.70–7.95, P < 0.0001); and 6.35 for kidney failure (95% CI: 4.48–9.01, P < 0.0001). Patients with renal insufficiency received less inpatient and discharge guideline-recommended therapy for AMI.

Conclusions:

Among AMI patients, mortality and guideline-recommended inpatient therapy correlated inversely with renal function. Adjusted mortality was equally poor among patients with severe renal dysfunction and on dialysis. Clin. Cardiol. 2012 doi: 10.1002/clc.22021

Christopher P. Cannon, MD, has received research grants/support from Accumetrics, AstraZeneca, GlaxoSmithKline, Intekrin Therapeutics, Merck, and Takeda; is a member of the advisory board (funds donated to charity) of Bristol-Myers Squibb/Sanofi, Novartis, and Alnylam; received an honorarium for development of independent educational symposia from Pfizer and AstraZeneca; and is a clinical advisor with equity in Automedics Medical Systems. Gregg C. Fonarow, MD, is a consultant for Novartis and Pfizer. W. Frank Peacock, MD, has received research grants (>$10000) from Abbott, Alere, Brahms, Corthera, EKR, Nanosphere, and The Medicines Company; is a consultant (<$10000) for Abbott, Alere, Beckman Coulter, Electrocore, and The Medicines Company; participates in the speakers' bureau (<$10000) with Abbott and Alere; and has an ownership interest (<$10000) in Comprehensive Research Associates LLC, Vital Sensors, and Emergencies in Medicine LLC. Lee H. Schwamm, MD, is a consultant for Stroke Systems and Medtronic. Deepak L. Bhatt, MD, MPH, discloses the following relationships - Advisory Board: Medscape Cardiology; Board of Directors: Boston VA Research Institute, Society of Chest Pain Centers; Chair: American Heart Association Get With The Guidelines Science Subcommittee; Honoraria: American College of Cardiology (Editor, Clinical Trials, Cardiosource), Duke Clinical Research Institute (clinical trial steering committees), Slack Publications (Chief Medical Editor, Cardiology Today Intervention), WebMD (CME steering committees); Research Grants: Amarin, AstraZeneca, Bristol-Myers Squibb, Eisai, Ethicon, Medtronic, Sanofi Aventis, The Medicines Company; Unfunded Research: FlowCo, PLx Pharma, Takeda. Sylvia E. Rosas has received a research grant from Abbott Laboratories and honorarium from Genzyme.

The Get With The Guidelines-Coronary Artery Disease (GWTG-CAD) program was provided by the American Heart Association. The GWTG-CAD program was supported in part through the American Heart Association Pharmaceutical Roundtable and an unrestricted educational grant from Merck. The authors have no other funding, financial relationships, or conflicts of interest to disclose.

Additional Supporting Information may be found in the online version of this article.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

Currently, 26 million adults in the United States have chronic kidney disease (CKD), and approximately 500000 have end-stage renal disease requiring hemodialysis.1 Chronic kidney disease is associated with increased risk of cardiovascular disease, and patients with concomitant CKD and coronary artery disease (CAD) are at a significantly higher risk for adverse cardiovascular events than those with either condition alone.2–4 This risk is even higher in patients presenting with acute myocardial infarction (AMI). Multiple studies have shown that CKD patients undergoing percutaneous coronary intervention (PCI) and coronary artery bypass graft surgery (CABG) have elevated mortality rates compared with patients without CKD.5–10 Therapeutic nihilism contributing to lower rates of evidence-based therapies possibly accounts for some of this increase.11–15 Despite these concerns, patients with renal dysfunction have been excluded from randomized acute coronary syndrome trials.16,17 As a result, physicians have turned to observational studies to guide MI treatment in these patients. We present the overall measures in quality of care (QOC) of patients with CKD presenting with MI from the American Heart Association (AHA) Get With The Guidelines-CAD Program from the years 2008 to 2009. Compared with prior analyses, this is a more contemporary database evaluating medical therapy for AMI following publication of recent guidelines.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

Study Sample and Data Source

Get With The Guidelines–CAD is a Web-based registry developed by the AHA with the goal of improving guideline adherence for treatment of CAD. Details of the program are described in a prior publication.18 For the current study, data were prospectively collected consecutively on CAD patients from 2008 to 2009 according to an electronic case record form Patient Management Tool (Outcome, Cambridge, MA). The starting population included 160940 patients from 410 fully participating sites. Trained individuals including nurse practitioners, case managers, house staff, and nursing staff performed data entry. Patients were excluded for missing creatinine or dialysis information (n = 137690), missing sex or race (n = 1261), and an out-of-range glomerular filtration rate (GFR; >150; n = 268). The analysis sample included 21721 patients from 291 sites. Given the large proportion of patients not contributing to the data due to a missing creatinine value, we first elected to compare the studied cohort with noncontributing patients to assess generalizability of our findings to the overall population for the key hospital and patient characteristics.

The primary endpoint was in-hospital mortality in the overall population. To compare the difference in QOC across the spectrum of renal function, the acute and discharge use of guideline-recommended therapy was measured.

Participating hospitals submitted the GWTG protocol for approval by their respective institutional review boards. Because the data were used for only quality improvement at each local site, a waiver of informed consent was granted under the common rule. The coordinating center for the registry was Outcome, and data were analyzed at the Duke Clinical Research Institute (Durham, NC).

Definition: Renal Function

Renal function was estimated by use of the general Modification of Diet in Renal Disease (MDRD) Study Equation: estimated GFR (mL/min/1.73 m2) = 186 × (serum creatinine [mg/dL])−1.154 × (age)−0.203 × (0.742 if female) × (1.21 if African American). The National Kidney Foundation definition for renal function was used to categorize patients according to their GFR: normal (GFR ≥90 mL/min/1.73 m2), mild renal insufficiency (GFR 60–90 mL/min/1.73 m2), moderate renal insufficiency (GFR 30–60 mL/min/1.73 m2), severe renal insufficiency (GFR 15–30 mL/min/1.73 m2), and kidney failure (GFR ≤15 mL/min/1.73 m2 or dialysis).

Statistical Analysis

Categorical variables are reported as percentages, continuous variables with nonskewed distribution as mean ± SD, and continuous variables with skewed distribution as median (25th, 75th percentiles). Comparisons across renal function groups were done with the Pearson χ2 test for categorical variables and Kruskal-Wallis test for continuous variables. Patients included in the studied cohort were qualitatively compared with those excluded for missing creatinine value or dialysis information using the χ2 test for categorical variables and Wilcoxon rank-sum test for continuous variables.

Multivariable regression analysis, using generalized estimating equations to account for within-hospital clustering, was performed to evaluate the effect of severity of renal dysfunction on the use of guideline-recommended therapies, in-hospital mortality and length of stay. Potential confounders including demographic and clinical characteristics, insurance information, and hospital characteristics were incorporated in the model. Length of stay was logarithm transformed prior to comparison across renal function groups. In order to test differences between patients in renal failure requiring dialysis and those with severe renal insufficiency, we performed a similar analysis with the renal failure/dialysis group as the referent.

All statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC). All P values were 2-tailed and with statistical significance set at 0.05.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

Baseline Patient and Hospital Characteristics

Of the 21721 patients presenting with AMI, >80% had renal insufficiency: 7658 (35.3%) mild, 5396 (24.8%) moderate, 1215 (5.6%) severe, and 3553 (16.4%) renal failure or dialysis. The mean age was 67 years, 40% of patients were female, and 74% were Caucasian. Patient characteristics across the renal function groups are described in Table 1.

Table 1. Baseline Patient and Hospital Characteristics by Kidney Function
VariableOverall (N = 21721)Normal (GFR ≥90) (n = 3899)Mild (GFR 60–90) (n = 7658)Moderate (GFR 30–60) (n = 5396)Severe (GFR 15–30) (n = 1215)Kidney Failure (GFR <15 or dialysis) (n = 3553)P Value
  1. Data from the GWTG-AHA CAD database.

  2. Abbreviations: AF, atrial fibrillation; AMI, acute myocardial infarction; BMI, body mass index; CABG, coronary artery bypass graft; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; CVA, cerebrovascular accident; DBP, diastolic blood pressure; DM, diabetes mellitus; LVEF, left ventricular ejection fraction; MI, myocardial infarction; NIDDM, non–insulin-dependent diabetes mellitus; NSTEMI, non–ST-elevation myocardial infarction; PCI, percutaneous coronary intervention; PVD, peripheral vascular disease; SBP, systolic blood pressure; SD, standard deviation; STEMI, ST-elevation myocardial infarction; TIA, transient ischemic attack.

Age, y, mean ± SD66.8 ± 14.257.1 ± 12.565.0 ± 13.873.8 ± 12.375.8 ± 12.867.3 ± 12.7<0.0001
Female sex, n (%)8633 (39.7)1060 (27.2)2583 (33.7)2712 (50.3)646 (53.2)1632 (45.9)<0.0001
Race, n (%)      <0.0001
 Caucasian16058 (73.9)2921 (74.9)6082 (79.4)4335 (80.3)891 (73.3)1829 (51.5) 
 African American1885 (8.7)364 (9.3)500 (6.5)316 (5.9)81 (6.7)624 (17.6) 
 Hispanic1882 (8.7)340 (8.7)567 (7.4)371 (6.9)115 (9.5)489 (13.8) 
 Asian945 (4.3)125 (3.2)241 (3.1)189 (3.5)66 (5.4)324 (9.1) 
 Other903 (4.2)149 (3.8)268 (3.5)185 (3.4)62 (5.1)239 (6.7) 
Admission data, mean ± SD       
 Weight at admission (kg)82.6 ± 21.986.7 ± 22.184.6 ± 21.579.5 ± 21.577.2 ± 21.477.1 ± 21.3<0.0001
 Weight at discharge (kg)78.3 ± 21.987.2 ± 23.884.7 ± 22.679.1 ± 23.875.6 ± 23.576.3 ± 20.9<0.0001
 BMI28.6 ± 6.929.4 ± 7.028.9 ± 6.628.3 ± 6.828.0 ± 7.227.7 ± 7.3<0.0001
 Heart rate83.1 ± 21.479.7 ± 18.280.8 ± 20.186.5 ± 23.888.4 ± 23.287.3 ± 21.6<0.0001
 SBP (mm Hg)138.2 ± 29.6138.4 ± 26.4139.4 ±28.0137.8 ± 31.3133.6 ± 33.2137.2 ± 34.5<0.0001
 DBP (mm Hg)77.3 ± 18.880.9 ± 17.479.3 ± 17.974.8 ± 19.270.6 ± 20.372.0 ± 20.4<0.0001
LVEF, mean ± SD (%)46.7 ± 13.949.0 ± 12.547.9 ± 13.245.9 ± 14.643.7 ± 14.943.5 ± 14.8<0.0001
LVEF< 40%, n (%)5345 (24.6)684 (17.5)1598 (20.9)1502 (27.8)422 (34.7)1139 (32.1)<0.0001
Medical history, n (%)       
AF1980 (9.2)134 (3.5)527 (6.9)653 (12.2)191 (15.8)475 (13.4)<0.0001
 COPD/Asthma3630 (16.8)539 (14.0)1101 (14.4)1093 (20.4)300 (24.8)597 (16.8)<0.0001
 Insulin-treated DM2532 (11.7)232 (6.0)583 (7.7)783 (14.6)341 (28.2)593 (16.7)<0.0001
 NIDDM4192 (19.4)768 (19.9)1440 (18.9)1282 (23.9)288 (23.8)414 (11.7)<0.0001
 Hyperlipidemia10557 (48.8)1752 (45.4)3826 (50.2)2805 (52.2)640 (52.9)1534 (43.2)<0.0001
 Hypertension15443 (71.4)2298 (59.5)5020 (65.8)4203 (78.3)1006 (83.2)2916 (82.1)<0.0001
 PVD2619 (12.1)196 (5.1)615 (8.1)697 (13.0)224 (18.5)887 (25.0)<0.0001
 CAD7510 (34.7)1123 (29.1)2595 (34.0)2304 (42.9)616 (51.0)872 (24.6)<0.0001
 Prior MI4786 (22.1)661 (17.1)1515 (19.9)1247 (23.2)344 (28.5)1019 (28.7)<0.0001
 CVA/TIA2514 (11.6)224 (5.8)672 (8.8)777 (14.5)253 (20.9)588 (16.6)<0.0001
 Heart failure3928 (18.2)224 (5.8)732 (9.6)1192 (22.2)477 (39.5)1303 (36.7)<0.0001
 Anemia1531 (7.1)88 (2.3)305 (4.0)511 (9.5)267 (22.1)360 (10.1)<0.0001
 Depression1626 (7.5)286 (7.4)555 (7.3)514 (9.6)132 (10.9)139 (3.9)<0.0001
 Prior PCI3062 (14.2)557 (14.4)1271 (16.7)859 (16.0)189 (15.6)186 (5.2)<0.0001
 Prior CABG2223 (10.3)246 (6.4)797 (10.5)823 (15.3)206 (17.0)151 (4.3)<0.0001
 Smoking6332 (29.1)2024 (51.9)2536 (33.1)1059 (19.6)168 (13.8)545 (15.3)<0.0001
 STEMI5618 (25.9)1416 (36.3)2549 (33.3)1264 (23.4)175 (14.4)214 (6.0) 
 NSTEMI13018 (59.9)2233 (57.3)4674 (61.0)3758 (69.6)947 (77.9)1406 (39.6) 
 Any AMI, unspecified3085 (14.2)250 (6.4)435 (5.7)374 (6.9)93 (7.7)1933 (54.4) 
Hospital characteristics       
 No. of beds, mean ± SD404.4 ± 208.4423.9 ± 194.0409.8 ± 184.3382.5 ± 190.2368.7 ± 193.9416.0 ± 280.8<0.0001
Hospital type, academic, n (%)11774 (54.2)2091 (53.6)4071 (53.2)2723 (50.5)649 (53.4)2240 (63.1)<0.0001
Primary PCI for AMI, n (%)18244 (84.0)3421 (87.7)6447 (84.2)4380 (81.2)942 (77.5)3054 (86.0)<0.0001
Region, n (%)      <0.0001
 West4708 (21.7)779 (20.0)1557 (20.3)1090 (20.2)299 (24.6)983 (27.7) 
 South7541 (34.7)1478 (37.9)2652 (34.6)1879 (34.8)428 (35.2)1104 (31.1) 
 Midwest5424 (25.0)1021 (26.2)2104 (27.5)1287 (23.9)212 (17.5)800 (22.5) 
 Northeast4048 (18.6)621 (15.9)1345 (17.6)1140 (21.1)276 (22.7)666 (18.7) 

Two-thirds of patients underwent coronary angiography and roughly 10% subsequently underwent cardiac surgery for either CABG or valve repair/replacement, and nearly a half of the patients underwent PCI.

Participating hospitals had the following characteristics: median bed size 400 (interquartile range 261, 553), >80% performed primary PCI for AMI, 70% had cardiac surgery on-site, and >50% were academic institutions. Regional distribution was diverse: 21.7% West, 34.7% South, 25.0% Midwest, and 18.6% Northeast.

Among the 410 total sites, contributing sites (n = 291) were more likely to be larger, academic centers that perform primary PCI for AMI and have on-site cardiac surgery. Contributing patients tended to be sicker with higher rates of diabetes, CAD, prior PCI, prior CABG, heart failure, and anemia (Supplementary Table).

In-Hospital Mortality

Overall in-hospital mortality was 6.5%. In-hospital mortality was highest among patients with severe renal insufficiency and renal failure (16.0% and 13.4%, respectively; Figure 1A). After multivariable adjustment, patients with kidney failure or on dialysis had the highest odds ratio (OR) for in-hospital mortality compared with those with normal renal function (OR: 6.35, 95% confidence interval [CI]: 4.48–9.01, P < 0.0001; figure 1B). No mortality difference was detected between patients on dialysis and those with severe renal dysfunction (OR: 0.85 for severe vs dialysis, 95% CI: 0.67–1.09, P = 0.21).

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Figure 1. Unadjusted rates of in-hospital mortality (A) increased with worsening kidney function. Adjusted odds ratio (B) compared with normal kidney function after multivariable adjustment. Variables in the model: age, female sex, Caucasian race, body mass index, insurance (Medicare, Medicaid, other, none), AF, COPD, diabetes, hyperlipidemia, hypertension, PVD, MI, CVA/TIA, heart failure, ischemic etiology of heart failure, smoking, SBP at admission, heart rate at admission. Hospital characteristics: region (Northeast, West, Midwest, South), bed size, academic institution. Horizontal line represents normal as reference (OR: 1.0). P-values for mild vs. normal is 0.03 and <0.001 for all other comparisons. Abbreviations: AA, aldosterone antagonist; ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker; AF, atrial fibrillation; ASA, aspirin; BB, β-blocker; CI, confidence interval; COPD, chronic obstructive pulmonary disease; CVA, cerebrovascular accident; D2B, door-to-balloon time; D/C, discharge; LVSD, left ventricular systolic dysfunction; MI, myocardial infarction; OR, odds ratio; PVD, peripheral vascular disease; rec, recommendation; SBP, systolic blood pressure; TIA, transient ischemic attack.

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Quality of Care Measures

Overall rates for both acute (within 24 h of admission) and discharge therapies for eligible AMI patients were better than 90% in most cases; however, patients with renal dysfunction were increasingly less likely to receive guideline-recommended therapy with worsening renal function (Figure 2A,B). Rates of acute therapy with aspirin and β-blockers for AMI patients were high overall, at 96.6% and 92.6%, respectively. However, these rates declined to 90.7% and 86.0% as renal function worsened (P < 0.0001 for both treatments). A similar trend was observed for the more chronic discharge treatments, including eligible patients with documented left ventricular systolic dysfunction receiving angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs; 95.5% vs 72.0%, P < 0.0001), patients discharged on aspirin (97.3%), β-blockers (97.2%), and eligible patients with low-density lipoprotein cholesterol >100 mg/dL receiving lipid-lowering therapy (92.8%). Adherence to guideline recommendations was particularly low in regard to discharge clopidogrel for patients undergoing PCI or those with AMI (75.4%), door-to-balloon time <90 minutes (69.9%), recorded low-density lipoprotein cholesterol (72.1%), and patients with post-AMI left ventricular systolic dysfunction receiving aldosterone antagonists (AA; 11.2%). In all these categories, patients with severe renal insufficiency or kidney failure/dialysis were consistently less likely to receive guideline-recommended therapy (Ptrend < 0.0001 in all categories). Patients with severe renal dysfunction were less likely to receive an ACEI than patients on dialysis (OR: 0.78, 95% CI: 0.63-0.98, P = 0.03).

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Figure 2. (A) Observed rates of quality of care measures per renal function group. (B) Adjusted odds ratios of deliver of quality of care measures relative to normal renal function. Horizontal line represents normal as reference (OR: 1.0). Abbreviations: AA, aldosterone antagonist; ACEI/ARB, angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker; ASA, aspirin; BB, β-blocker; D2B, door-to-balloon time; D/C, discharge; LDL, low-density lipoprotein cholesterol; LVSD, left ventricular systolic dysfunction; QOC, quality of care; rec, recommendation; SBP; systolic blood pressure.

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Nondrug therapy and recommendations were also progressively lower in patients with worsening renal failure. For instance, patients with renal insufficiency were less likely to receive rehabilitation or physical-activity recommendations (overall 90.3%; 94.8% for normal renal function vs 85.1%–79.2% for severe renal dysfunction and kidney failure) and smoking-cessation counseling for smokers (overall 97.8%; 98.9% for normal renal function vs 85.6% for kidney failure/dialysis). Overweight patients were less likely to receive weight-management and physical-activity recommendations (90.6% overall; 93.3% for normal renal function vs 80.9% for kidney failure/dialysis). The trend in the reduction of quality measures with worsening renal function was significant for all these variables (P < 0.0001).

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References

Our analysis of contemporary data from the GWTG program on 21721 patients presenting with AMI indicates that renal insufficiency is very common among patients presenting with AMI. These patients are less likely to receive guideline-recommended therapy for AMI with increasing likelihood of inadequate care with worsening renal function. Patients presenting for treatment for AMI with renal failure were less likely to receive standard therapies such as aspirin, β-blockers, ACEI/ARBs, AAs, and lipid-lowering agents despite a wealth of data showing benefit when such treatment is rendered. They were also less likely to receive nonpharmacologic measures such as smoking-cessation counseling for smokers, rehabilitation referrals, and weight-management and exercise recommendations for obese patients. Most importantly, worsening renal function was found to be associated with increased in-hospital mortality, the highest rate of which was among patients with severe renal dysfunction (16%, unadjusted), even greater than in patients with renal failure requiring dialysis (13.4%, unadjusted). However, the adjusted ORs for mortality comparing the severe renal dysfunction and dialysis groups with the normal function group were equally poor.

Our results extend those of prior analyses comparing quality measures and outcomes among patients with renal failure as a dichotomous predictor and echo those presented by Fox et alfrom the Acute Coronary Treatment and Intervention Outcomes Network (ACTION) Registry.14 In those studies, similar trends of worsening QOC measures and outcomes were reported. The mechanism explaining this discrepancy in care remains unexplained; however, 2 explanations are likely. First, guideline recommendations are derived from clinical trials, which for the most part excluded patients with renal failure. Lack of such randomized data showing benefit may be the cause of the conservative approach seen in managing this high-risk cohort. Second, patients with renal dysfunction may be expected to experience more adverse events from pharmacotherapy. However, an analysis from the Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes with Early Implementation of American College of Cardiology and AHA Guidelines (CRUSADE) Registry reported a lower adjusted morality rate among renal failure patients presenting with non–ST-elevation acute coronary syndromes when treated with early invasive strategy.11

To this point, patients in our study were less likely to meet the American College of Cardiology/AHA recommended door-to-balloon time of <90 minutes for primary PCI. The door-to-needle time of <30 minutes was also less frequently achieved among patients with renal dysfunction.

The trend of worsening QOC measures with increasing renal dysfunction is not linear for all variables. A unique aspect of our analysis involved comparisons vs the renal failure/dialysis group as a referent. We wished to examine differences in quality measures and outcomes among patients with severe renal failure and those on dialysis. We found that patients with severe renal insufficiency were less likely to receive an ACEI than those undergoing dialysis/renal failure. Whether patients with severe manifestations of AMI presenting with acute renal failure but not requiring dialysis were categorized as chronic severe renal dysfunction patients cannot be determined from this dataset. In such a scenario, it would not be unreasonable for physicians to avoid ACEIs to prevent further deterioration of renal function. On the other hand, it is also likely that patients with severe renal dysfunction are simply not treated with ACEIs due to the possibility of augmenting this problem further. Of course, patients who are already on dialysis are protected from electrolyte derangements that could result from the introduction of renin-angiotensin-aldosterone system antagonists (hyperkalemia), thereby allowing providers to prescribe ACEIs more leniently. This particular observation of dialysis patients receiving better care than patients with severe renal dysfunction did not extend to other QOC measures. From this dataset, we cannot explain the trend in worsening quality metrics for nonpharmacologic measures such as weight-reduction and smoking-cessation counseling and rehab placement. It is very likely that inadequacy in these measures augments mortality from renal disease postdischarge. Our dataset does not capture these events.

The results of this study must be interpreted within the context of its overall design. This was an observational analysis conducted with data acquired from review of medical records, and as a result it relies heavily on the accuracy of such records. Although contraindications to guideline-recommended therapies were evaluated and patients with contraindications were excluded from this analysis, we do not have the ability to distinguish those patients who may have had contraindications or intolerance to recommended therapies when such information was not included in the medical records. This is of particular concern at the higher end of the renal failure spectrum, where patients are more likely to not receive ACEIs, ARBs, and AAs secondary to potential electrolyte disturbances. Renal function was calculated using the MDRD equation, which, although reliable in stable disease, may not provide an accurate measure of renal function in a setting of acute kidney injury or more severe manifestations of AMI, such as cardiogenic shock. We used GFR cutoffs to categorize patients into 5 groups according to the severity of renal function. The National Kidney Foundation incorporates kidney damage as determined by the amount of urine protein into its definitions of renal failure stages. The GWTG program does not collect data on urine protein content; as a result, we were not able to classify renal failure according to the National Kidney Foundation definitions. Moreover, creatinine values were not available for all patients. Qualitatively, patients with recorded creatinine values were more likely to present with comorbid conditions such as diabetes and ischemic heart failure. A greater proportion of this population received care at academic medical centers and was more likely to receive guideline-recommended therapy for AMI. Whether the discrepancy in quality measures among contributing and noncontributing cohorts can be ascribed to disparity in comorbidities between the groups vs a reflection of care provided at academic vs nonacademic medical centers, or a combination of both, cannot be ascertained from this dataset. All outcomes and QOC measures were assessed in an in-hospital setting. Our primary outcome was limited to in-hospital mortality; the long-term impact of guideline adherence in both acute and discharge settings was not assessed. Although mortality increased with worsening renal function, it would be implausible to attribute this to discharge recommendations.

In conclusion, within an unrestricted contemporary population of patients with renal dysfunction treated for AMI, discrepancies in QOC and outcomes were detected across groups of patients with worsening renal function. Such inconsistencies were detected in hospitals participating in a guideline-implementation program such as GWTG. Whether these inconsistencies are more pronounced in nonparticipating hospitals is a matter of great concern. How implementation of guideline measures in this manner impacts long-term mortality and QOC measures in an outpatient setting are areas for further investigation.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. References
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