Printer Friendly

Cardiovascular disease prevalence in patients with osteoarthritis, gout, or both.

Gout and osteoarthritis (OA) are the most common arthritides, affecting approximately 4% and 10% of the adult population, respectively. (1,2) Both gout and OA have been associated with increased risk of cardiovascular disease (CVD). (1,3) For example, van Durme and coworkers found increased risk of coronary heart disease in gout patients. (4) Similarly, Rahman and associates reported a higher risk of ischemic heart disease and congestive heart failure (CHF) in patients with OA, including a possible association with OA severity. (5) In a recent meta-analysis, Hall and colleagues reported that the relative risk of ischemic heart disease in OA patients compared with matched non-OA cohorts was 1.78. (6)

The observation that CVD risk is increased in gout patients has multiple potential explanations. Traditional risk factors associated with increased CVD--including obesity, chronic kidney disease (CKD), diabetes mellitus (DM), and hypertension (HTN)--are more prevalent among gout patients compared with the general population. (7) In addition, multiple studies show a persistent association between gout and CVD even after adjustment for traditional risk factors, suggesting that gout may convey its own independent risk, possibly as a result of intermittent and chronic inflammation. (3) Indeed, other rheumatic diseases, such as lupus, rheumatoid arthritis, and psoriatic arthritis, are also associated with increased cardiovascular risk through mechanisms that are presumed to include chronic inflammation. (8,9) Studies also suggest that hyperuricemia per se--the sine qua non of gout--may have independent adverse vascular effects. (10) Of course, roles for hyperuricemia and gout in the pathogenesis of CVD need not be mutually exclusive. (11)

As in the case of gout, the association between OA and CVD has been ascribed by some investigators to comorbid conditions that are commonly found among OA patients, including obesity, physical inactivity, and chronic nonsteroidal anti-inflammatory drug use. (5) The observation that obesity confers higher risk of OA and that weight loss reduces inflammatory cytokine levels suggests that both mechanical and systemic biochemical factors may play a role in the association of obesity with both OA and CVD development. (12) The presence of adiposity has been associated with inflammation, particularly through the elucidation of mediators, such as adipokines, by the adipose tissue itself. Moreover, it is increasingly recognized that subsets of OA patients have low-level chronic inflammation, which can be detected in the tissues of the affected joints, as elevations in circulating plasma inflammatory biomarkers, and in effects on activation of circulating leukocytes. (13) Thus, OA may promote CVD through multiple mechanisms that include changes in physical activity as well as biochemical and inflammatory alterations.

Despite the fact that multiple rheumatic diseases have been associated with CVD, the relative risk of CVD between various rheumatic diseases has rarely been assessed. Whatever the mechanism(s) behind increased CVD risk, such knowledge would be valuable in counseling and treating patients. Therefore, in this study, we assessed the relative prevalence of CVD in patients with gout compared with OA. In addition, we evaluated the possibility of an additive effect of gout and OA together on CVD rates, beyond that of each condition alone.


Patient Selection

Using the VA NY Harbor Healthcare System electronic medical record, we identified all patients with an active medical record between August 2007 and August 2008 (N = 32,888) who were male and had an ICD-9 code for gout (274.XX) (N = 1,280). The cohort from which these patients were selected has been previously described. (14) Within this group, we further distinguished subjects with assigned ICD-9 codes for gout but without ICD-9 codes for OA (715.XX; the "gout-only" cohort) (N = 983) versus those having ICD-9 codes for both gout and OA ("gout+OA" cohort) (N = 297). In a separate search, we identified 3,208 patients with an active record in the same time period, an ICD-9 code for OA and no ICD-9 code for gout ("OA-only" cohort). Of these, 1,440 additionally had a physician-entered diagnosis of OA and no diagnosis of gout in the physician-entered problem list on the patient's EMR cover sheet. From this group, we consecutively selected 1,280 patients (first 1,280 alphabetically) to match our gout cohort. Subsequent exclusion of women and patients with incomplete records resulted in a cohort of 1,231 OA patients.

Data Collection

For each group, we collected demographic data, diagnoses of CVD risk factors, and diagnoses of CV events. The primary outcome was a composite index ("CV4") consisting of any diagnosis of myocardial infarction (MI), angina, coronary artery bypass graft (CABG), or coronary artery disease (CAD). Secondary outcomes included the individual diagnoses within the CV4, as well as CHF and death from any cause during the study year.

Statistical Analysis

For comparisons between the OA-only and gout group overall, Fisher's exact test and Student's t-test were used to compare categorical and continuous data, respectively. For multi-group comparisons between OA-only, gout-only, and gout+OA subgroups, one-way ANOVA with Tukey post-hoc comparisons were used to test for differences between baseline characteristics among the different groups. Chi-square statistic with eight degrees of freedom was used to test for differences among groups with respect to race or ethnicity. Logistic regression was used to compare the associations of gout-only, OA-only, and gout+OA with CV outcomes both not adjusting and adjusting for traditional CV risk factors.


OA-Only Versus Gout Subjects Overall

In comparison to OA-only subjects, gout subjects overall had higher rates of CV risk factors, including hypertension (HTN), diabetes mellitus (DM), hyperlipidemia (HLD), and chronic kidney disease (CKD) (Table 1). OA-only and gout subjects were similar but non-identical in terms of racial or ethnic identification, and OA-only subjects had slightly higher smoking rates than gout subjects. Compared to OA-only subjects, gout subjects overall had a 35.9% higher rate of CV4 and higher rates of all individual components of the CV4 except MI (for CAD, 31.1%; for angina, 466.7%; for CABG, 181.3%; for MI, 5.2% increase; p < 0.0001 for all comparisons except MI, p = 0.9). Gout subjects also had higher rates of CHF and death in 2007 to 2008 than subjects with OA-only (for CHF, 52% increase, p < 0.0001; for death, 92% increase, p = 0.004) (Fig. 2). In a small, consecutively selected, validation cohort of 20 patients, each of whom had been categorized as gout-only or gout+OA, respectively, detailed chart and x-ray review confirmed that physician diagnostic coding was correct at a rate of 95% (data not shown).

OA-Only Versus Gout Subsets

Because our overall gout cohort included patients who carried concurrent diagnoses of OA, we next compared the OA-only group with gout subsets who did not (gout-only; N = 983) and did (gout+OA; N = 297) carry a diagnosis of OA (Table 1). Using post-hoc Tukey analysis with significance at the 0.05 level, analysis of baseline characteristics showed significant differences in age between the cohorts. Gout+OA subjects were significantly older than OA-only and gout-only subjects. Similar analyses showed significantly higher prevalences of the CVD risk factors HTN, hyperlipidemia, and chronic kidney disease in the gout-only and gout+OA cohorts compared with the OA-only group. Overall, we also observed significant differences among the groups with respect to race and ethnicity. Subjects with gout-only had significantly higher prevalence of DM than patients with OA-only, and gout+OA subjects demonstrated a non-significant trend towards higher DM rates than OA-only subjects. Conversely, we found a lower prevalence of smoking in the gout+OA group compared with the OA-only group. Aside from age, there were no significant differences in any measured baseline characteristic between the gout-only and gout+OA groups.

In an unadjusted three-way analysis (Table 2), logistic regression modeling revealed significantly higher prevalence of the primary outcome (CV4) in both the gout-only and gout+OA groups compared with the OA-only group. In addition, the gout-only and gout+OA groups had increased risk of the secondary outcomes CAD, angina, CHF, and death, compared with the OA-only group. Risk of MI was not increased in either gout group compared with the OA-only group; however, we noted that the number of MI events was relatively small (N = 22 for OA-only, 22 for gout-only, and 6 for gout+OA), and the study may have lacked power to determine differences. Similarly, only a small number of CABG procedures were noted (2.8% of all subjects); nonetheless, patients with gout-only were significantly more likely to have undergone CABG than patients with OA-only.

To determine the extent to which increased CVD risk among gout patients was due to traditional versus nontraditional CVD risk factors, we next examined adjusted rates of CVD outcomes (Table 3). After adjusting for traditional risk factors (HTN, DM, CKD, HL, and smoking), as well as age and race or ethnicity, the three groups continued to demonstrate overall significant differences. However, several differences between the OA-only and gout groups were not statistically significant after adjustment. Specifically, while the gout-only group continued to demonstrate significantly higher risk for CV4 (primary outcome), the gout+OA showed a non-significant trend towards increase in CV4 risk, which we speculate may be a result of the much smaller number of gout+OA, as compared to gout-only patients. The gout-only group additionally had higher adjusted risk of CAD, angina, CABG, and CHF compared with the OA-only group but no significant increase in risk of MI or death. In contrast, patients with gout+OA continued to have significantly higher adjusted risk of angina, CHF, and death compared to the OA-only group but not CAD, MI, or CABG. These data suggest that greater prevalence of traditional CV risk factors may account for some but not all of the risk for CVD in patients with gout compared to those with OA only.

Gout-Only Versus Gout+OA Groups

We observed almost no differences in primary and secondary outcomes between patients with gout only as compared with subjects with gout+OA. Patients with gout+OA had a lower risk of CABG than patients in the gout only group, in the adjusted but not in the unadjusted analysis.


Many rheumatic diseases have been associated with increased risk of CVD. (8,9) Among these, osteoarthritis (the most common arthritis (5,6)) and gout (the most common inflammatory arthritis (2)) have received relatively little attention. Moreover, investigators have rarely compared the relative CV risk of different rheumatic diseases, an issue of potential importance to rheumatologists who may need to prioritize the management of comorbid conditions among their various patients.

To our knowledge, our study is the first to directly compare cardiovascular outcomes between gout and OA subjects. Our data suggest that gout patients are at higher risk of CVD--defined in this study as the presence of CAD, angina, MI or CABG, as well as CHF--compared with OA subjects. Although gout patients had a higher prevalence of comorbidities that represent cardiovascular risk factors than patients with OA, risk of CVD among gout patients was seen to persist even after adjustment for these risk factors, suggesting that other influences, intrinsic to the diagnosis of gout, are also active. Although patients with concurrent gout and OA had higher rates of CVD than patients with OA alone, they did not have higher CVD rates than patients with gout alone; thus, the presence of OA in gout patients did not convey any additional CVD risk beyond that of gout only.

We speculate that a diagnosis of OA may not add CVD risk to patients with gout for several possible reasons. First, while a number of studies support an increased incidence of cardiovascular disease in patients with OA, the extent of this impact is not fully established, and some studies have failed to reproduce this assertion. For example, Hoeven and coworkers found that neither radiographic nor clinical OA was related to future CVD. (15) The possibility that the CVD risk associated with OA may be slight could account for its relatively small impact in subjects who also had gout, such that the extent of CV risk conveyed by gout may simply be too large for any additional OA risk to be appreciated. Alternatively, to the extent that the risk conveyed by OA may be mechanical (e.g., loss of mobility) rather than inflammatory in nature, and to the extent that gout is associated with both inflammatory activation and mechanical limitation, any CVD risk from OA mechanical limitation may already be conveyed by the gouty condition.

In our study, gout patients were also at higher risk for death during the study year compared with OA patients. These results appear to be inconsistent with a prior report suggesting that standardized mortality ratios may be higher for patients with OA compared with gout. (16) However, that comparison was based on two independent studies, one of OA and one of gout, whose methodologies differed. (3,17) Moreover, the OA mortality rate that we observed was consistent with the 1-year probability of death among 71 to 72 year olds (the mean age in our study) in the general population, according to the United States Centers for Disease Control, (18) suggesting that the impact of OA on mortality may be limited. Certainly, the impact of OA on mortality needs further clarification.

Our study had both strengths and limitations. Our ability to hand-review charts for comorbidities and CVD outcomes, after first identifying patients with gout or OA ICD-9 codes, ensured that our diagnoses were likely to be fairly accurate. On the other hand, we did not evaluate the potential roles of inflammatory markers (ESR, CRP), body mass index (BMI), NSAID-use, or serum urate levels on the cardiovascular outcomes among the groups. Our study population was slightly older than the typical gout population, (19) consistent with the epidemiology of VA patients in general. (20) While the older age of our population facilitated a direct comparison with our OA patients (since the two groups were virtually identical in age), it may limit the generalizability of our study to younger gout populations. Importantly, we did not have access to data on the education levels of our subjects, a major contributor to cardiovascular risk discrepancy. (21) Since much of the impact of education on CVD may be due to conventional risk factors and access to care, (22) any possible impact of educational differences was likely mitigated by the fact that the patients in the gout and OA group were otherwise demographically similar, with similar CVD risk factors and with equal access to care. Our study did not include a non-gout, non-OA control group that would have allowed us to assess the absolute impact of either of the two diagnoses. In particular, our study cannot address the extent to which OA conveys independent cardiovascular risk, a matter of ongoing investigation. We also did not apply a formal set of classification criteria for diagnosing gout (e.g., American College of Rheumatology/EULAR 2015 gout classification criteria (23)), but prior studies suggest that ICD-9 codes in the VA are likely to be accurate to diagnose gout, including in the same VA regional system that was studied here. (24,25) Finally, the cross-sectional nature of our data ascertainment makes any discussion of causality (i.e., between OA, gout, and CVD) purely speculative.

In sum, we observed that patients with gout were more likely to have CVD than patients with OA, and that the combination of gout plus OA conveyed no additive CVD risk. Practitioners should be mindful of the potential impact of these diseases on their patients' cardiovascular health. Whether better management of these diseases might lead to reduced adverse CVD outcomes is a matter deserving additional study.

Daisy H. Bang, M.D., Virginia C. Pike, B.A., Robert A. Lehmann, M.D., and Daria B. Crittenden, M.D., Division of Rheumatology, Department of Medicine, VA NY Harbor Healthcare System, New York, New York. Jinfeng Xu, Ph.D., Department of Biostatistics, NYU School of Medicine, New York, New York. Robert T Keenan, M.D., Division of Rheumatology, Department of Medicine, VA NY Harbor Healthcare System, New York, New York; and Division of Rheumatology, Duke University School of Medicine, Durham, North Carolina. Craig Tenner, M.D., Division of General Internal Medicine, Department of Medicine, VA NY Harbor Healthcare System, New York, New York. Michael H. Pillinger, M.D., and Svetlana Krasnokutsky, M.D., M.S., Divisions of Rheumatology, Department of Medicine, VA NY Harbor Healthcare System, New York, New York.

Michael H. Pillinger, M.D., and Svetlana Krasnokutsky, M.D., M.S., contributed equally to this manuscript.

Correspondence: Michael H. Pillinger, M.D., Division of Rheumatology, NYU Hospital for Joint Diseases, 301 East 17th Street, Room 1410, New York, NY 10003;

Disclosure Statement

No author received direct support for this manuscript. Michael H. Pillinger, M.D., is partially supported by NIH Clinical and Translational Science Award UL1 TR000038, has served as a consultant to AstraZeneca and Crealta, and currently serves as a site investigator for, and also holds an investigator-initiated grant from Takeda. Robert T. Keenan, M.D., has served as a consultant for AstraZeneca. Daria B. Crittenden, M.D., is currently an employee of Amgen.


(1.) Hawker GA, Croxford R, Bierman AS, et al. All-cause mortality and serious cardiovascular events in people with hip and knee osteoarthritis: a population based cohort study. PLoS One. 2014 Mar 7;9(3):e91286.1-12.

(2.) Zhu Y, Pandya BJ, Choi HK. Prevalence of gout and hyperuricemia in the U.S. general population: the National Health and Nutrition Examination Survey 2007-2008. Arthritis Rheum. 2011 Oct;63(10):3136-41.

(3.) Choi HK, Curhan G. Independent impact of gout on mortality and risk for coronary heart disease. Circulation 2007 Aug 21;116(8):894-900.

(4.) van Durme C, van Echteld IA, Falzon L, et al. Cardiovascular risk factors and comorbidities in patients with hyperuricemia and/or gout: a systematic review of the literature [review]. J Rheumatol Suppl. 2014 Sep;92:9-14.

(5.) Rahman MM, Kopec JA, Anis AH, et al. Risk of cardiovascular disease in patients with osteoarthritis: a prospective longitudinal study. Arthritis Care Res (Hoboken). 2013 Dec;65(12):1951-8.

(6.) Hall AJ, Stubbs B, Mamas MA, et al. Association between osteoarthritis and cardiovascular disease: System review and meta-analysis. Eur J Prev Cardiol. 2015 Oct 13. pii: 2047487315610663. [Epub ahead of print].

(7.) Zhu Y, Pandya BJ, Choi HK. Comorbidities of gout and hyperuricemia in the U.S. general population: NHANES 2007-2008. Am J Med. 2012 Jul;125(7):679-87.

(8.) Ogdie A, Yu Y, Haynes K, et al. Risk of major cardiovascular events in patients with psoriatic arthritis, psoriasis and rheumatoid arthritis: a population-based cohort study. Ann Rheum Dis. 2015 Feb;74(2):326-32.

(9.) Skaggs BJ, Hahn BH, McMahon M. Accelerated atherosclerosis in patients with SLE--mechanisms and management. Nat Rev Rheumatol. 2012 Feb 14;8(4):214-23.

(10.) Jin M, Yang F, Yang I, et al. Uric acid, hyperuricemia and vascular diseases. Front Biosci(Landmark Ed). 2012 Jan 1;17:656-69.

(11.) Lin JC, Lin CL, Chen MC, et al. Gout, not hyperuricemia alone, impairs left ventricular diastolic function. Arthritis Res Ther. 2015 Nov 14;17(1):323-30.

(12.) Messier SP, Mihalko SL, Legault C, et al. Effects of intensive diet and exercise on knee joint loads, inflammation, and clinical outcomes among overweight and obese adults with knee osteoarthritis: the IDEA randomized clinical trial. JAMA. 2013 Sep 25;310(12):1263-73.

(13.) Attur M, Belitskaya-Levy I, Oh C, et al. Increased interleukin-1[beta] gene expression in peripheral blood leukocytes is associated with increased pain and predicts risk for progression of symptomatic knee osteoarthritis. Arthritis Rheum. 2011 Jul;63(7):1908-17.

(14.) Crittenden DB, Lehmann RA, Schneck L, et al. Colchicine use is associated with decreased prevalence of myocardial infarction in patients with gout. J Rheumatol. 2012 Jul;39(7):1458-64.

(15.) Hoeven TA, Leening MJ, Bindels PJ, et al. Disability and not osteoarthritis predicts cardiovascular disease: a prospective population-based cohort study. Ann Rheum Dis. 2015 Apr;74(4):752-6.

(16.) Pincus T, Gibson KA, Block JA. Premature mortality: a neglected outcome in rheumatic diseases? Arthritis Care Res (Hoboken). 2015 Aug;67(8):1043-46.

(17.) Nuesch E, Dieppe P, Reichenbach S, et al. All cause and disease specific mortality in patients with knee or hip osteoarthritis: population based cohort study. BMJ. 2011 Mar 8;342:d1165.

(18.) Arias E. United States Life Tables, 2009. Natl Vital Stat Rep. 2014 Jan 6;62(7):1-63.

(19.) Singh JA, Reddy SG. Quality of care in gout. In: Terkeltaub R (ed): Gout and Other Crystal Arthropathies. Philadelphia: Saunders Elsevier, 2012, pp. 209-223.

(20.) National Center for Veterans Analysis and Statistics. Profile of Veterans: 2013: Data from the American Community Survey. United States Department of Veterans Affairs, 2015: vetdata/docs/SpecialReports/Profile_of_Veterans_2013.pdf. Accessed December 27, 2015.

(21.) Mackenbach, JP, Stirbu I, Roskam A-JR, et al. Socioeconomic inequalities in health in 22 European countries. N Engl J Med. 2008 Jun 5;358(23):2468-81.

(22.) Ariansen I, Greaff-Iversen S, Stigum H, et al. Do repeated risk factor measurements influence the impact of education on cardiovascular mortality? Heart. 2015 Dec; 101(23): 1889-94.

(23.) Neogi T, Jansen TL, Dalbeth N, et al. 2015 gout classification criteria: an American College of Rheumatology/European Leagues Against Rheumatism collaborative initiative. Arthritis Rheumatol. 2015 Oct;67(10):2557-68.

(24.) Singh JA. Veterans Affairs databases are accurate for gout-related health care utilization: a validation study. Arthritis Res Ther. 2013;15(6):R224.

(25.) Keenan RT, O'Brien WR, Lee KH, et al. Prevalence of contraindications and prescription of pharmacologic therapies for gout. Am J Med. 2011 Feb;124(2):155-63.

Caption: Figure 1: Relative prevalence of cardiovascular outcomes and death among cohorts of OA (N = 1,230) versus gout patients (N = 1,288). CV4 = primary outcome, any diagnosis of CAD, angina, MI, or CABG. Data are unadjusted; death diagnosis is for study year (2007 to 2008) only; * p = 0.004; ** p < 0.0001; ns = not significant.

Table 1 Baseline Characteristics

Category            OA-only              All Gout
                    (N = 1231)           (N = 1280)

Age (years)         71.1 [+ or -] 13.4   71.5 [+ or -] 11.9
Race or ethnicity
White               43.5                 49.2
Black               30.2                 35.1
Hispanic            12                   6.4
Asian               0.6                  1.2
Other               13.6                 8.0
HTN, %              67.7                 84.4
DM, %               24.1                 29.3
HLD, %              53.4                 64.1
CKD, %              7.2                  33.1
Smoker, %           14.5                 11.1

Category            Gout-only          Gout+OA
                    (N = 983)          (N = 297)

Age (years)         70.3 [+ or -] 12   75.6 [+ or -] 10.2
Race or ethnicity
White               50.2               46.1
Black               32.9               39.4
Hispanic            6.4                6.4
Asian               1.5                0.3
Other               9.0                7.8
HTN, %              83.6               87.2
DM, %               29.6               29.3
HLD, %              62.7               68.7
CKD, %              32.5               35.7
Smoker, %           11.7               9.7

Data are as percentage except for age, which is in
mean [+ or -] SD. HTN = hypertension; DM = diabetes
mellitus; HLD = hyperlipidemia; CKD = chronic kidney
disease; Smoker = current smoker.

Table 2 Cardiovascular Disease Prevalence of Specific Outcomes,
Unadjusted Results

                        Gout-only               Gout+OA
                        versus                  versus
                        OA-only                 OA-only

CV4 (Primary outcome)   1.262 (1.042, 1.528)    1.398 (1.053, 1.846)
                        p = 0.017               p = 0.019
CAD                     1.377 (1.137, 1.668)    1.427 (1.072, 1.888)
                        p = 0.001               p = 0.014
Angina                  5.459 (2.646, 12.734)   4.762 (1.806, 12.785)
                        p < 0.001               p = 0.001
MI                      1.137 (0.612, 2.098)    1.128 (0.412, 2.642)
                        p = 0.681               p = 0.795
CABG                    3.169 (1.900, 5.485)    1.887 (0.810, 4.072)
                        p < 0.001               p = 0.118
CHF                     2.481 (1.785, 3.483)    2.831 (1.813, 4.369)
                        p < 0.001               p < 0.001
Death                   1.650 (1.020, 2.694)    2.729 (1.491, 4.881)
                        p = 0.042               p = 0.001


CV4 (Primary outcome)   1.108 (0.832, 1.468)
                        p = 0.478
CAD                     1.036 (0.777, 1.374)
                        p = 0.806
Angina                  0.872 (0.389, 1.764)
                        p = 0.720
MI                      0.993 (0.360, 2.356)
                        p = 0.988
CABG                    0.596 (0.271, 1.170)
                        p = 0.160
CHF                     1.141 (0.754, 1.691)
                        p = 0.520
Death                   1.654 (0.922, 2.871)
                        p = 0.080

Data are Odds Ratios with 95% Confidence Intervals and P-Values.

Table 3 Cardiovascular Disease Prevalence of Specific
Outcomes, Adjusted Results

               Gout-only versus        Gout+OA versus
               OA-only                 OA-only

CV4 (Primary   1.271 (1.025, 1.574)    1.263 (0.937, 1.692)
  outcome)     p = 0.029               p = 0.121
CAD            1.261 (1.010, 1.574)    1.084 (0.796, 1.467)
               p = 0.040               p = 0.604
Angina         4.367 (2.005, 10.609)   3.374 (1.233, 9.383)
               p < 0.001               p = 0.017
MI             0.972, (0.487, 1.921)   1.010, (0.356, 2.481)
               p = 0.936               p = 0.984
CABG           3.951 (2.249, 7.176)    1.782 ( 0.746, 3.961)
               p < 0.001               p = 0.170
CHF            2.379 (1.638, 3.483)    2.209 (1.378, 3.505)
               p < 0.001               p < 0.001
Death          1.30 (0.826, 2.487)     1.870 (0.973, 3.514)
               p = 0.203               p = 0.055

               Gout+OA versus

CV4 (Primary   0.994 (0.739, 1.329)
  outcome)     p = 0.967
CAD            0.860 (0.634, 1.159)
               p = 0.325
Angina         0.773 (0.337, 1.608)
               p = 0.513
MI             1.039, (0.371, 2.529)
               p = 0.937
CABG           0.451 (0.202, 0.905)
               p = 0.036
CHF            0.929 (0.605, 1.399)
               p = 0.729
Death          1.308 (0.716, 2.317)
               p = 0.367

Data are Odds Ratios with 95% Confidence Intervals
and P-Values.


Please note: Illustration(s) are not available due to copyright restrictions.
COPYRIGHT 2016 J. Michael Ryan Publishing Co.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2016 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Bang, Daisy H.; Xu, Jinfeng; Keenan, Robert T.; Pike, Virginia C.; Lehmann, Robert A.; Tenner, Craig
Publication:Bulletin of the NYU Hospital for Joint Diseases
Article Type:Report
Date:Apr 1, 2016
Previous Article:Pediatric thumb flexion deformities.
Next Article:Combined palmer type 1A and 1B traumatic lesions of the triangular fibrocartilage complex: a new category.

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters