American Journal of Therapeutics 23, e350–e356 (2016)
Association Between Opioid Abuse/Dependence and Outcomes in Hospitalized Heart Failure Patients Tanush Gupta, MD, Marjan Mujib, MD, MPH, Pallak Agarwal, MD, Priya Prakash, MD, Anjali Garg, MD, Nisha Sharma, MD, Wilbert S. Aronow, MD, and Christopher Nabors, MD, PhD*
Opioid use is associated with unintentional and intentional overdose and is one of the leading causes of emergency room visits and accidental deaths. However, the association between opioid abuse/dependence and outcomes in hospitalized patients has not been well studied. Congestive heart failure (HF) is the fourth most common cause of hospitalization in the United States. The purpose of this study was to examine the effect of opioid abuse/dependence on outcomes in patients hospitalized with HF. We queried the 2002–2010 Nationwide Inpatient Sample databases to identify all patients aged 18 years and older admitted with the primary diagnosis of HF. Multivariate logistic regression analysis was used to compare the frequency of hospital-acquired conditions (HACs) and in-hospital mortality between patients with and without a history of opioid abuse/dependence. Of 9,993,240 patients with HF, 29,014 had a history of opioid abuse or dependence. Opioid abusers/dependents were likely to be younger men of poor socioeconomic background with self pay or Medicaid as their primary payer. They had a lower prevalence of dyslipidemia, diabetes mellitus, coronary artery disease, prior myocardial infarction, and peripheral vascular disease (P , 0.001 for all). They were more likely to be smokers and have chronic pulmonary disease, depression, liver disease, and obesity (P , 0.001 for all). Patients with a history of opioid abuse/dependence had lower incidence of HACs (14.8% vs. 16.5%, adjusted odds ratio: 0.71, P , 0.001) and lower in-hospital mortality (1.3% vs. 3.6%, adjusted odds ratio: 0.64, P , 0.001) as compared with patients without prior opioid abuse/dependence. In conclusion, among adult patients aged 18 years and older hospitalized with HF, opioid abuse/dependence was associated with lower frequency of HACs and lower in-hospital mortality. Keywords: opioid abuse, heart failure, in-hospital outcomes
INTRODUCTION In the past 2 decades, medical use of opioids has increased dramatically in the United States.1,2 A parallel rise in opioid-related adverse effects such as
emergency room visits and overdose mortality has also occurred.3 Between 1999 and 2007, the rate of unintentional overdose deaths in the United States increased by 124%.4 Chronic opioid use among hospitalized veterans has been associated with increased
Department of Medicine, Westchester Medical Center, New York Medical College, Valhalla, NY. Christopher Nabors has received funding for the project from the Foundation for Innovations in Medical Education. The remaining authors report no financial or other conflict of interest that might bias their work. Nor has any other author or any immediate family member, within the last three years, maintained any affiliations relevant and important with any organization that has a direct interest, particularly a financial interest, in the subject matter or materials discussed herein. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.americantherapeutics.com). T. Gupta and M. Mujib have contributed equally to this work. *Address for correspondence: Department of Medicine, Westchester Medical Center, New York Medical College, Valhalla, NY 10595. E-mail: [email protected] 1075–2765 Copyright Ó 2015 Wolters Kluwer Health, Inc. All rights reserved.
www.americantherapeutics.com
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
Opioid Abuse and Outcomes in Heart Failure
risk of hospital readmission and death.5 In hospitals, over half of the patients admitted for nonsurgical reasons are treated with opioids, and in 1 study, 0.6% of these patients experienced a severe opioid-related adverse event.6 Although such adverse events that are directly related to opioid ingestion during hospitalization may contribute to increased mortality, the degree to which opioid users experience other adverse hospitalacquired conditions (HACs) that might contribute to morbidity and mortality has not been well studied. The incidence and prevalence of heart failure (HF) has increased considerably in the past few decades, and HF has become a major public health concern, especially in the older adults.7,8 Approximately 5.2 million (2.5%) Americans are currently estimated to have HF.9 The rate of HF hospitalizations has also increased progressively, making it the most common condition for hospital admission in the older adults and the fourth most common overall.10–12 The purpose of this study was to examine the association between opioid abuse/dependence and inpatient adverse events, as defined by the Centers for Medicare and Medicaid Services’ (CMS) HACs,13 among hospitalized HF patients, using a large, multi-institutional, real-world cohort of patients included in the Nationwide Inpatient Sample (NIS) database. We also sought to examine the association between opioid abuse/ dependence and in-hospital mortality.
MATERIALS AND METHODS Data were obtained for the NIS databases from 2002 to 2010. The NIS is the largest publicly available all-payer inpatient care database in the United States. The NIS contains discharge-level data from around 8 million hospital stays from 1000 hospitals, approximating a 20% stratified sample of all community hospitals in the United States.14 Criteria used for stratified sampling of hospitals into the NIS include hospital ownership, patient volume, teaching status, urban or rural location, and geographic region. The NIS provides a discharge weight for each patient discharge record, which was used to obtain national estimates. We used the International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) codes 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, and 428.xx to identify all patients aged 18 years and older with the principal diagnosis of HF (n 5 9,993,240). These codes denoted the primary reason for hospital admission. Patients with a history of opioid abuse or dependence were then identified using ICD-9-CM codes 304.0x, 304.7x, and 305.5x (n 5 29,014). Patients who did not have any of these www.americantherapeutics.com
e351
ICD-9-CM codes were considered nonabusers/dependents (n 5 9,964,227). Our primary outcome of interest was the occurrence of nonsurgical HACs as defined by the CMS, that is, pressure ulcer, fracture, dislocation, intracranial injury, crushing injury, burns, other injuries, catheter-associated urinary tract infections, vascular catheter-associated infection, diabetic complications (diabetic ketoacidosis, nonketotic hyperosmolar coma, hypoglycemic coma, secondary diabetes with ketoacidosis, and secondary diabetes with hyperosmolarity), and iatrogenic pneumothorax. The ICD-9-CM or Clinical Classification System (CCS) codes used to identify these conditions are provided in the Supplemental Digital Content 1 (see Table, http://links.lww.com/AJT/A22). We also studied the all-cause in-hospital mortality, defined as “died” during the hospitalization encounter in the NIS database. Baseline patient characteristics used included demographics (age, sex, and race), primary expected payer, weekday versus weekend admission, median household income for patients’ zip code, 29 Elixhauser comorbidities as defined by the Agency for Health Care Research and Quality, and other clinically relevant comorbidities (smoking, dyslipidemia, known coronary artery disease, family history of coronary artery disease, and prior acute myocardial infarction).15,16 A list of ICD-9-CM codes and CCS codes used to identify comorbidities is provided in the Supplemental Digital Content 1 (see Table, http://links.lww.com/AJT/A22). Hospital characteristics such as hospital region (northeast, midwest, south, and west), bed size (small, medium, and large), and location (rural and urban) were also included. We initially compared baseline and hospital characteristics between the 2 groups using the Pearson x2 test for categorical variables and Student t test for continuous variables to identify significant univariate associations. Multivariate logistic regression analysis was used to compare the outcomes between the 2 groups. The regression model adjusted for demographics, primary expected payer, weekday versus weekend admission, median household income, all Elixhauser comorbidities, other clinically relevant comorbidities (smoking, dyslipidemia, known coronary artery disease, family history of coronary artery disease, and prior acute myocardial infarction), and hospital characteristics. Statistical analysis was performed using IBM SPSS Statistics 20.0 (IBM Corp., Armonk, NY). We used a 2-sided P value of ,0.05 to assess for statistical significance for all analyses. Categorical variables are expressed as percentage and continuous variables as mean 6 SD. Odds ratio (OR) and 95% confidence interval (CI) are used to report the results of logistic regression. American Journal of Therapeutics (2016) 23(2)
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
e352
Gupta et al
Table 1. Baseline demographics, comorbidities, and hospital characteristics of patients aged 18 years and older hospitalized with HF.
Variable Age, mean 6 SD (yr) Women, % Race, % White Black Hispanic Asian or Pacific islander Native American Other Primary expected payer, % Medicare Medicaid Private insurance Self pay No charge Other Weekend admission Median household income (percentile), % 0–25th 26th–50th 51st–75th 76th–100th Comorbidities*, % Dyslipidemia Coronary artery disease Family history of coronary artery disease Prior myocardial infarction Smoking Deficiency anemia Chronic pulmonary disease Coagulopathy Depression Diabetes mellitus (uncomplicated) Hypertension Hypothyroidism Liver disease Fluid and electrolyte disorder Neurologic disorders Obesity Paralysis Peripheral vascular disease Pulmonary circulation disorders Renal failure
Nonabusers/dependents (n 5 9,964,227)
Opioid abusers/dependents (n 5 29,014)
72.7 6 14.3 51.9
52.7 6 10.8 33.3
68.6 19.1 8.0 1.7 0.5 2.1
28.4 56.2 11.8 0.7 0.2 2.7
75.3 7.3 12.5 2.9 0.3 1.6 22.2
29.9 42.1 11.3 11.9 1.2 3.5 24.5
,0.001
30.3 26.5 22.7 20.6
49.4 22.0 16.4 12.2
,0.001
27.1 44.0 0.9 11.3 14.6 22.8 35.3 3.2 6.9 32.4 57.7 12.7 1.9 22.6 5.6 10.5 1.5 9.6 0.2 26.1
15.5 27.3 1.1 9.4 38.4 20.9 42.5 4.2 10.5 23.1 58.1 4.9 13.9 23.0 5.6 11.5 0.9 5.0 0.2 25.2
,0.001 ,0.001 0.007 ,0.001 ,0.001 ,0.001 ,0.001 ,0.001 ,0.001 ,0.001 0.168 ,0.001 ,0.001 0.081 0.644 ,0.001 ,0.001 ,0.001 0.46 0.001
P ,0.001 ,0.001 ,0.001
,0.001
(Continued on next page)
American Journal of Therapeutics (2016) 23(2)
www.americantherapeutics.com
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
Opioid Abuse and Outcomes in Heart Failure
e353
Table 1. (Continued) Baseline demographics, comorbidities, and hospital characteristics of patients aged 18 years and older hospitalized with HF.
Variable
Nonabusers/dependents (n 5 9,964,227)
Opioid abusers/dependents (n 5 29,014)
P
2.2 0.2 0.4 2.3
0.6 0.1 0.4 2.1
,0.001 0.016 0.532 0.03
13.9 24.9 61.1 82.9
12.6 28.3 59.1 96.8
20.3 23.9 41.0 14.7
31.4 22.0 26.0 20.6
Solid tumor without metastasis Peptic ulcer (nonbleeding) Valvular disease Weight loss Hospital characteristics Number of beds†, % Small Medium Large Urban location Region, % Northeast Midwest South West
,0.001
,0.001 ,0.001
*Comorbidities (including the 29 Elixhauser comorbidities) were extracted from the database using International Classification of Diseases, Ninth Edition, Clinical Modification Diagnosis or Clinical Classification Software codes. †Number of bed categories are specific to hospital location and teaching status, available at http://www.hcup-us.ahrq.gov/db/vars/ hosp_bedsize/nisnote.jsp.
RESULTS From 2002 to 2010, of the 9,993,240 patients hospitalized with HF, 29,014 patients (0.3%) had a history of opioid abuse/dependence. Opioid abusers/dependents were more likely to be younger men of African American or Hispanic descent. They were more likely to come from poorer socioeconomic background with self pay or Medicaid as their primary payer. They were more likely to be smokers and more likely to have chronic pulmonary disease, coagulopathy, depression, liver disease, and obesity. They were less likely to have dyslipidemia, coronary artery disease, prior myocardial infarction, deficiency anemia, diabetes mellitus, hypothyroidism, paralysis, peripheral vascular disease, renal failure, solid tumors, peptic ulcer, and weight loss. Patients with a history of opioid abuse/ dependence were more likely to be admitted to medium-sized urban hospitals in the northeast and the west and were more likely to be admitted on a weekend (Table 1). Among all the hospitalized HF patients, incident HACs occurred in 16.5% of the patients. The most common incident HACs were diabetic complications (11.7% of all hospitalized HF patients and 70% of all HACs). Other incident HACs were injuries related to fall and www.americantherapeutics.com
trauma (4.4%), fracture (0.6%), crushing injury (0.2%), pressure ulcer (0.1%), and catheter-associated urinary tract infections (0.1%). Among all patients, incident HACs related to diabetic complications were less common among opioid users/dependents compared with nonabusers/dependents (10.4% vs. 11.7%, P , 0.001) (Table 2). Overall, incident HACs were less likely in patients with a history of opioid abuse/dependence (14.8% vs. 16.5%, unadjusted OR: 0.88; 95% CI: 0.85– 0.91; P , 0.001). When adjusted for demographics, hospital characteristics, and all comorbidities, HACs remained significantly lower in opioid abusers/ dependents (adjusted OR: 0.71; 95% CI: 0.69–0.74; P , 0.001). In-hospital mortality was significantly lower in patients with a history of opioid abuse/dependence (1.3% vs. 3.6%, unadjusted OR: 0.35; 95% CI: 0.31–0.38; P , 0.001). The association between opioid abuse/ dependence and lower in-hospital mortality remained significant even after adjustment for demographics, hospital characteristics, and comorbidities (adjusted OR: 0.64; 95% CI: 0.58–0.72; P , 0.001) (Table 3).
DISCUSSION This report provides the first large-scale examination of the association between opioid abuse/dependence American Journal of Therapeutics (2016) 23(2)
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
e354
Gupta et al
Table 2. Frequency of hospital-acquired conditions in hospitalized HF patients. No. patients (%); n 5 9,993,240
HAC Diabetic complications Miscellaneous injuries Fracture Crushing injury Pressure ulcer Catheter-associated urinary tract infections Intracranial injury Vascular catheter–associated infection Dislocation Burns Iatrogenic pneumothorax Any nonsurgical hospital-acquired condition
1,164,480 440,648 64,826 24,863 12,295 11,810
Nonabusers/dependents, n (%)
(11.7) (4.4) (0.6) (0.25) (0.1) (0.1)
116,1466 439,436 64,691 24,810 12,256 11,791
4683 (0.04) 3733 (0.04) 3286 2693 2381 1,653,038
Opioid abusers/ dependents, n (%)
(11.7) (4.4) (0.6) (0.25) (0.1) (0.12)
3014 1212 135 53 39 19
4667 (0.05) 3703 (0.04)
(0.03) (0.02) (0.02) (16.5)
3280 2683 2371 1,648,743
(10.4)* (4.2) (0.5)* (0.18)* (0.1) (0.07)*
16 (0.06) 30 (0.1)*
(0.03) (0.03) (0.02) (16.5)
5 11 10 4295
(0.02) (0.04) (0.03) (14.8)*
*P , 0.05.
and common in-hospital adverse events in patients hospitalized with HF. We found that CMS-defined HACs considered in toto (14.8% vs. 16.5%, unadjusted OR: 0.88; 95% CI: 0.85–0.91) occurred less frequently in patients with a history of opioid abuse/dependence. Of note, only 0.3% of HF patients had a history of opioid abuse/dependence based on hospitalization associated ICD-9 codes. In this population, HACs related to diabetic complications were by far most prevalent (10.4%) with miscellaneous injuries constituting another 4.2% of HACs, whereas less than 1% of HACs were related to iatrogenic infections, procedural complications, and pressure ulcers. Our results document a lower prevalence of opioid abuse/dependence than what might be anticipated from other reports. Mosher et al5 found that among a cohort of hospitalized veterans, 25.9% received chronic opioid therapy in the 6 months before admission, whereas 19.6% had occasional opioid therapy. Previous studies
have reported the prevalence of any opioid use in outpatient cohorts between 18% and 30%.17,18 In comparison, the prevalence of opioid abuse/dependence among patients admitted with HF (0.3%), as noted in our study, was surprisingly low. Prior studies have generally found a positive association between HACs and in-hospital outcomes. In their study involving 122,794 hospitalized veterans, Mosher et al5 reported that prior chronic opioid therapy was associated with hospital readmission (adjusted OR: 1.15; 95% CI: 1.10–1.20) and death (adjusted OR: 1.19; 95% CI: 1.10–1.29). Contrary to earlier reports, we found an inverse association between opioid abuse/dependence and outcomes among hospitalized HF patients. A possible reason for this paradoxical association may be the lower prevalence of diabetes mellitus in opioid abusers/dependents (23.1% vs. 32.4%), as approximately 70% of HACs were a manifestation of
Table 3. Hospital-acquired conditions and in-hospital mortality among hospitalized heart failure patients. OR (95% CI) Outcome HACs In-hospital mortality
Nonabusers/ dependents, %
Opioid abusers/ dependents, %
P
Unadjusted
Adjusted*
16.5 3.6
14.8 1.3
,0.001 ,0.001
0.88 (0.85–0.91) 0.35 (0.31–0.38)
0.71 (0.69–0.74) 0.64 (0.58–0.72)
*Adjusted for age, sex, primary payer status, weekday versus weekend admission, median household income, comorbidities, and hospital characteristics (bed size, location, and region).
American Journal of Therapeutics (2016) 23(2)
www.americantherapeutics.com
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
Opioid Abuse and Outcomes in Heart Failure
poor glycemic control. However, after multivariate adjustment, the association between opioid abuse/ dependence and lesser HACs became even stronger (adjusted OR: 0.71; 95% CI: 0.69–0.74), suggesting an independent association between opioid abuse/ dependence and lesser HACs. A potential explanation for this finding could be the underdiagnosis of fracture, dislocation, and other miscellaneous injuries among opioid abuse/dependents as such patients may be more tolerant to pain leading to less frequent reporting of in-hospital falls and trauma. Also, opioidrelated sedation and delirium may potentially result in underdiagnosis of diabetic complications, such as diabetic ketoacidosis and hyperosmolar nonketotic coma, as these conditions commonly present as an alteration in patients’ mental status. Our findings may also have been related to the age disparity between opioid abusers/dependents and nonabusers/dependents. The mean age for opioid abusers/dependents was 52.7 years versus 72.2 years for nonabusers/dependents. Opioid abusers/dependents were also much less likely to have coronary artery disease (27.3% vs. 44.0%) and prior myocardial infarction (9.4% vs. 11.3%); therefore, less likely to have ischemic cardiomyopathy as a cause of HF. It is well established that patients with ischemic HF have worse outcomes than patients with nonischemic cardiomyopathy.19–22 Therefore, it is possible that lower in-hospital mortality in opioid abuse/dependents with HF may in part be driven by lower prevalence of ischemic cardiomyopathy. Diabetes mellitus has also been shown to be an independent predictor of worse cardiovascular mortality in patients with HF.23 The lower prevalence of diabetes in opioid abusers/dependents may also have been a factor contributing to lower in-hospital mortality. Although the association between opioid abuse/dependence and lower in-hospital mortality was attenuated after adjusting for baseline demographic and clinical characteristics (adjusted OR: 0.64; 95% CI: 0.58–0.72), there was a residual association even after risk adjustment. This may be a result of inadequate adjustment with residual measured or unmeasured confounding or because of the abovementioned or additional pathophysiological differences in opioid abusers/dependents versus nonabusers/ dependents with HF. Being a retrospective observational study, this analysis of the administrative NIS database has its limitations. It has a potential for selection bias and unrecognized miscoding of diagnostic and procedure codes, which may have led to underestimation or overestimation of HACs, opioid abuse/dependence, and comorbidities based on ICD-9-CM coding. However, these 2 limitations may be, at least partially, www.americantherapeutics.com
e355
compensated by the large size of the database and the ability to obtain nationwide estimates based on using the discharge weights provided. The amount of opioid exposure could not be determined because of unavailability of patient charts or medical records. Duration and severity of comorbidities (diabetes mellitus, coronary artery disease, etc.) is not collected in this database and could not be adjusted for. These may have contributed to residual confounding. The NIS does not include information on the medications used. Finally, the database includes only discharge-level information and important laboratory parameters could not be studied. In conclusion, this observational study demonstrates that HF patients with a history of opioid abuse/dependence have lower frequency of HACs and lower in-hospital mortality. Further prospective studies are needed to confirm our observations and to elucidate the mechanisms underlying this paradox in HF patients.
ACKNOWLEDGMENTS Dr Nabors thanks Dr Howard Kerpen, Director of the Lorber Center for the Advancement of Medical Education, Long Island Jewish Medical Center, Lorber Professor of Medicine, Albert Einstein College of Medicine, Clinical Professor of Medicine, Hofstra North Shore-Long Island Jewish Health System, and the Foundation for Innovation in Medical Education for supporting this initiative.
REFERENCES 1. Caudill-Slosberg MA, Schwartz LM, Woloshin S. Office visits and analgesic prescriptions for musculoskeletal pain in US: 1980 vs. 2000. Pain. 2004;109:514–519. 2. Okie S. A flood of opioids, a rising tide of deaths. N Engl J Med. 2010;363:1981–1985. 3. Bohnert AS, Valenstein M, Bair MJ, et al. Association between opioid prescribing patterns and opioid overdose-related deaths. JAMA. 2011;305:1315–1321. 4. Centers for Disease Control and Prevention. Web-based inquiry stastistics query and reporting system: leading causes of death reports. Available from: http://www. cdc.gov/injury/wisqars/index.html. Accessed October 16, 2014. 5. Mosher HJ, Jiang L, Vaughan Sarrazin MS, et al. Prevalence and characteristics of hospitalized adults on chronic opioid therapy. J Hosp Med. 2014;9:82–87. 6. Herzig SJ, Rothberg MB, Cheung M, et al. Opioid utilization and opioid-related adverse events in nonsurgical patients in US hospitals. J Hosp Med. 2014;9:73–81. American Journal of Therapeutics (2016) 23(2)
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
e356 7. Rich MW. Epidemiology, pathophysiology, and etiology of congestive heart failure in older adults. J Am Geriatr Soc. 1997;45:968–974. 8. Braunwald E. Shattuck lecture–cardiovascular medicine at the turn of the millennium: triumphs, concerns, and opportunities. N Engl J Med. 1997;337:1360–1369. 9. Rosamond W, Flegal K, Friday G, et al. Heart disease and stroke statistics–2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2007;115: e69–171. 10. Kozak LJ, DeFrances CJ, Hall MJ. National hospital discharge survey: 2004 annual summary with detailed diagnosis and procedure data. Vital Health Stat 13. 2006:1–209. 11. Pfuntner A, Wier LM, Stocks C. Most frequent conditions in U.S. Hospitals, 2010: statistical brief #148. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville, MD; 2006. Available at http://www.hcupus.ahrq.gov/reports/statbriefs/sb162.pdf. 12. Fang J, Mensah GA, Croft JB, et al. Heart failure-related hospitalization in the U.S., 1979 to 2004. J Am Coll Cardiol. 2008;52:428–434. 13. Centers for Medicare and Medicaid Services, HHS. Medicare program; changes to the hospital inpatient prospective payment systems and fiscal year 2008 rates. Fed Regist. 2007;72:47129–48175. 14. NIS introduction page. Available from: http://www. hcup-us.ahrq.gov/db/nation/nis/nisarchive.jsp. Accessed October 16, 2014. 15. Elixhauser A, Steiner C, Harris DR, et al. Comorbidity measures for use with administrative data. Med Care. 1998;36:8–27.
American Journal of Therapeutics (2016) 23(2)
Gupta et al 16. HCUP-US Tools and Software Page. Available from: http:// www.hcup-us.ahrq.gov/toolssofstware/comorbidity/ comorbidity.jsp. Accessed October 16, 2014. 17. Campbell CI, Weisner C, Leresche L, et al. Age and gender trends in long-term opioid analgesic use for noncancer pain. Am J Public Health. 2010;100:2541–2547. 18. Sullivan MD, Edlund MJ, Fan MY, et al. Trends in use of opioids for non-cancer pain conditions 2000-2005 in commercial and Medicaid insurance plans: the TROUP study. Pain. 2008;138:440–449. 19. Bart BA, Shaw LK, McCants CB Jr, et al. Clinical determinants of mortality in patients with angiographically diagnosed ischemic or nonischemic cardiomyopathy. J Am Coll Cardiol. 1997;30:1002–1008. 20. Adams KF Jr, Dunlap SH, Sueta CA, et al. Relation between gender, etiology and survival in patients with symptomatic heart failure. J Am Coll Cardiol. 1996;28: 1781–1788. 21. Likoff MJ, Chandler SL, Kay HR. Clinical determinants of mortality in chronic congestive heart failure secondary to idiopathic dilated or to ischemic cardiomyopathy. Am J Cardiol. 1987;59:634–638. 22. Felker GM, Thompson RE, Hare JM, et al. Underlying causes and long-term survival in patients with initially unexplained cardiomyopathy. N Engl J Med. 2000;342: 1077–1084. 23. MacDonald MR, Petrie MC, Varyani F, et al. Impact of diabetes on outcomes in patients with low and preserved ejection fraction heart failure: an analysis of the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM) programme. Eur Heart J. 2008;29:1377–1385.
www.americantherapeutics.com
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
Association Between Opioid Abuse/Dependence and Outcomes in Hospitalized Heart Failure Patients Tanush Gupta, MD, Marjan Mujib, MD, MPH, Pallak Agarwal, MD, Priya Prakash, MD, Anjali Garg, MD, Nisha Sharma, MD, Wilbert S. Aronow, MD, and Christopher Nabors, MD, PhD*
Opioid use is associated with unintentional and intentional overdose and is one of the leading causes of emergency room visits and accidental deaths. However, the association between opioid abuse/dependence and outcomes in hospitalized patients has not been well studied. Congestive heart failure (HF) is the fourth most common cause of hospitalization in the United States. The purpose of this study was to examine the effect of opioid abuse/dependence on outcomes in patients hospitalized with HF. We queried the 2002–2010 Nationwide Inpatient Sample databases to identify all patients aged 18 years and older admitted with the primary diagnosis of HF. Multivariate logistic regression analysis was used to compare the frequency of hospital-acquired conditions (HACs) and in-hospital mortality between patients with and without a history of opioid abuse/dependence. Of 9,993,240 patients with HF, 29,014 had a history of opioid abuse or dependence. Opioid abusers/dependents were likely to be younger men of poor socioeconomic background with self pay or Medicaid as their primary payer. They had a lower prevalence of dyslipidemia, diabetes mellitus, coronary artery disease, prior myocardial infarction, and peripheral vascular disease (P , 0.001 for all). They were more likely to be smokers and have chronic pulmonary disease, depression, liver disease, and obesity (P , 0.001 for all). Patients with a history of opioid abuse/dependence had lower incidence of HACs (14.8% vs. 16.5%, adjusted odds ratio: 0.71, P , 0.001) and lower in-hospital mortality (1.3% vs. 3.6%, adjusted odds ratio: 0.64, P , 0.001) as compared with patients without prior opioid abuse/dependence. In conclusion, among adult patients aged 18 years and older hospitalized with HF, opioid abuse/dependence was associated with lower frequency of HACs and lower in-hospital mortality. Keywords: opioid abuse, heart failure, in-hospital outcomes
INTRODUCTION In the past 2 decades, medical use of opioids has increased dramatically in the United States.1,2 A parallel rise in opioid-related adverse effects such as
emergency room visits and overdose mortality has also occurred.3 Between 1999 and 2007, the rate of unintentional overdose deaths in the United States increased by 124%.4 Chronic opioid use among hospitalized veterans has been associated with increased
Department of Medicine, Westchester Medical Center, New York Medical College, Valhalla, NY. Christopher Nabors has received funding for the project from the Foundation for Innovations in Medical Education. The remaining authors report no financial or other conflict of interest that might bias their work. Nor has any other author or any immediate family member, within the last three years, maintained any affiliations relevant and important with any organization that has a direct interest, particularly a financial interest, in the subject matter or materials discussed herein. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.americantherapeutics.com). T. Gupta and M. Mujib have contributed equally to this work. *Address for correspondence: Department of Medicine, Westchester Medical Center, New York Medical College, Valhalla, NY 10595. E-mail: [email protected] 1075–2765 Copyright Ó 2015 Wolters Kluwer Health, Inc. All rights reserved.
www.americantherapeutics.com
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
Opioid Abuse and Outcomes in Heart Failure
risk of hospital readmission and death.5 In hospitals, over half of the patients admitted for nonsurgical reasons are treated with opioids, and in 1 study, 0.6% of these patients experienced a severe opioid-related adverse event.6 Although such adverse events that are directly related to opioid ingestion during hospitalization may contribute to increased mortality, the degree to which opioid users experience other adverse hospitalacquired conditions (HACs) that might contribute to morbidity and mortality has not been well studied. The incidence and prevalence of heart failure (HF) has increased considerably in the past few decades, and HF has become a major public health concern, especially in the older adults.7,8 Approximately 5.2 million (2.5%) Americans are currently estimated to have HF.9 The rate of HF hospitalizations has also increased progressively, making it the most common condition for hospital admission in the older adults and the fourth most common overall.10–12 The purpose of this study was to examine the association between opioid abuse/dependence and inpatient adverse events, as defined by the Centers for Medicare and Medicaid Services’ (CMS) HACs,13 among hospitalized HF patients, using a large, multi-institutional, real-world cohort of patients included in the Nationwide Inpatient Sample (NIS) database. We also sought to examine the association between opioid abuse/ dependence and in-hospital mortality.
MATERIALS AND METHODS Data were obtained for the NIS databases from 2002 to 2010. The NIS is the largest publicly available all-payer inpatient care database in the United States. The NIS contains discharge-level data from around 8 million hospital stays from 1000 hospitals, approximating a 20% stratified sample of all community hospitals in the United States.14 Criteria used for stratified sampling of hospitals into the NIS include hospital ownership, patient volume, teaching status, urban or rural location, and geographic region. The NIS provides a discharge weight for each patient discharge record, which was used to obtain national estimates. We used the International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) codes 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, and 428.xx to identify all patients aged 18 years and older with the principal diagnosis of HF (n 5 9,993,240). These codes denoted the primary reason for hospital admission. Patients with a history of opioid abuse or dependence were then identified using ICD-9-CM codes 304.0x, 304.7x, and 305.5x (n 5 29,014). Patients who did not have any of these www.americantherapeutics.com
e351
ICD-9-CM codes were considered nonabusers/dependents (n 5 9,964,227). Our primary outcome of interest was the occurrence of nonsurgical HACs as defined by the CMS, that is, pressure ulcer, fracture, dislocation, intracranial injury, crushing injury, burns, other injuries, catheter-associated urinary tract infections, vascular catheter-associated infection, diabetic complications (diabetic ketoacidosis, nonketotic hyperosmolar coma, hypoglycemic coma, secondary diabetes with ketoacidosis, and secondary diabetes with hyperosmolarity), and iatrogenic pneumothorax. The ICD-9-CM or Clinical Classification System (CCS) codes used to identify these conditions are provided in the Supplemental Digital Content 1 (see Table, http://links.lww.com/AJT/A22). We also studied the all-cause in-hospital mortality, defined as “died” during the hospitalization encounter in the NIS database. Baseline patient characteristics used included demographics (age, sex, and race), primary expected payer, weekday versus weekend admission, median household income for patients’ zip code, 29 Elixhauser comorbidities as defined by the Agency for Health Care Research and Quality, and other clinically relevant comorbidities (smoking, dyslipidemia, known coronary artery disease, family history of coronary artery disease, and prior acute myocardial infarction).15,16 A list of ICD-9-CM codes and CCS codes used to identify comorbidities is provided in the Supplemental Digital Content 1 (see Table, http://links.lww.com/AJT/A22). Hospital characteristics such as hospital region (northeast, midwest, south, and west), bed size (small, medium, and large), and location (rural and urban) were also included. We initially compared baseline and hospital characteristics between the 2 groups using the Pearson x2 test for categorical variables and Student t test for continuous variables to identify significant univariate associations. Multivariate logistic regression analysis was used to compare the outcomes between the 2 groups. The regression model adjusted for demographics, primary expected payer, weekday versus weekend admission, median household income, all Elixhauser comorbidities, other clinically relevant comorbidities (smoking, dyslipidemia, known coronary artery disease, family history of coronary artery disease, and prior acute myocardial infarction), and hospital characteristics. Statistical analysis was performed using IBM SPSS Statistics 20.0 (IBM Corp., Armonk, NY). We used a 2-sided P value of ,0.05 to assess for statistical significance for all analyses. Categorical variables are expressed as percentage and continuous variables as mean 6 SD. Odds ratio (OR) and 95% confidence interval (CI) are used to report the results of logistic regression. American Journal of Therapeutics (2016) 23(2)
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
e352
Gupta et al
Table 1. Baseline demographics, comorbidities, and hospital characteristics of patients aged 18 years and older hospitalized with HF.
Variable Age, mean 6 SD (yr) Women, % Race, % White Black Hispanic Asian or Pacific islander Native American Other Primary expected payer, % Medicare Medicaid Private insurance Self pay No charge Other Weekend admission Median household income (percentile), % 0–25th 26th–50th 51st–75th 76th–100th Comorbidities*, % Dyslipidemia Coronary artery disease Family history of coronary artery disease Prior myocardial infarction Smoking Deficiency anemia Chronic pulmonary disease Coagulopathy Depression Diabetes mellitus (uncomplicated) Hypertension Hypothyroidism Liver disease Fluid and electrolyte disorder Neurologic disorders Obesity Paralysis Peripheral vascular disease Pulmonary circulation disorders Renal failure
Nonabusers/dependents (n 5 9,964,227)
Opioid abusers/dependents (n 5 29,014)
72.7 6 14.3 51.9
52.7 6 10.8 33.3
68.6 19.1 8.0 1.7 0.5 2.1
28.4 56.2 11.8 0.7 0.2 2.7
75.3 7.3 12.5 2.9 0.3 1.6 22.2
29.9 42.1 11.3 11.9 1.2 3.5 24.5
,0.001
30.3 26.5 22.7 20.6
49.4 22.0 16.4 12.2
,0.001
27.1 44.0 0.9 11.3 14.6 22.8 35.3 3.2 6.9 32.4 57.7 12.7 1.9 22.6 5.6 10.5 1.5 9.6 0.2 26.1
15.5 27.3 1.1 9.4 38.4 20.9 42.5 4.2 10.5 23.1 58.1 4.9 13.9 23.0 5.6 11.5 0.9 5.0 0.2 25.2
,0.001 ,0.001 0.007 ,0.001 ,0.001 ,0.001 ,0.001 ,0.001 ,0.001 ,0.001 0.168 ,0.001 ,0.001 0.081 0.644 ,0.001 ,0.001 ,0.001 0.46 0.001
P ,0.001 ,0.001 ,0.001
,0.001
(Continued on next page)
American Journal of Therapeutics (2016) 23(2)
www.americantherapeutics.com
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
Opioid Abuse and Outcomes in Heart Failure
e353
Table 1. (Continued) Baseline demographics, comorbidities, and hospital characteristics of patients aged 18 years and older hospitalized with HF.
Variable
Nonabusers/dependents (n 5 9,964,227)
Opioid abusers/dependents (n 5 29,014)
P
2.2 0.2 0.4 2.3
0.6 0.1 0.4 2.1
,0.001 0.016 0.532 0.03
13.9 24.9 61.1 82.9
12.6 28.3 59.1 96.8
20.3 23.9 41.0 14.7
31.4 22.0 26.0 20.6
Solid tumor without metastasis Peptic ulcer (nonbleeding) Valvular disease Weight loss Hospital characteristics Number of beds†, % Small Medium Large Urban location Region, % Northeast Midwest South West
,0.001
,0.001 ,0.001
*Comorbidities (including the 29 Elixhauser comorbidities) were extracted from the database using International Classification of Diseases, Ninth Edition, Clinical Modification Diagnosis or Clinical Classification Software codes. †Number of bed categories are specific to hospital location and teaching status, available at http://www.hcup-us.ahrq.gov/db/vars/ hosp_bedsize/nisnote.jsp.
RESULTS From 2002 to 2010, of the 9,993,240 patients hospitalized with HF, 29,014 patients (0.3%) had a history of opioid abuse/dependence. Opioid abusers/dependents were more likely to be younger men of African American or Hispanic descent. They were more likely to come from poorer socioeconomic background with self pay or Medicaid as their primary payer. They were more likely to be smokers and more likely to have chronic pulmonary disease, coagulopathy, depression, liver disease, and obesity. They were less likely to have dyslipidemia, coronary artery disease, prior myocardial infarction, deficiency anemia, diabetes mellitus, hypothyroidism, paralysis, peripheral vascular disease, renal failure, solid tumors, peptic ulcer, and weight loss. Patients with a history of opioid abuse/ dependence were more likely to be admitted to medium-sized urban hospitals in the northeast and the west and were more likely to be admitted on a weekend (Table 1). Among all the hospitalized HF patients, incident HACs occurred in 16.5% of the patients. The most common incident HACs were diabetic complications (11.7% of all hospitalized HF patients and 70% of all HACs). Other incident HACs were injuries related to fall and www.americantherapeutics.com
trauma (4.4%), fracture (0.6%), crushing injury (0.2%), pressure ulcer (0.1%), and catheter-associated urinary tract infections (0.1%). Among all patients, incident HACs related to diabetic complications were less common among opioid users/dependents compared with nonabusers/dependents (10.4% vs. 11.7%, P , 0.001) (Table 2). Overall, incident HACs were less likely in patients with a history of opioid abuse/dependence (14.8% vs. 16.5%, unadjusted OR: 0.88; 95% CI: 0.85– 0.91; P , 0.001). When adjusted for demographics, hospital characteristics, and all comorbidities, HACs remained significantly lower in opioid abusers/ dependents (adjusted OR: 0.71; 95% CI: 0.69–0.74; P , 0.001). In-hospital mortality was significantly lower in patients with a history of opioid abuse/dependence (1.3% vs. 3.6%, unadjusted OR: 0.35; 95% CI: 0.31–0.38; P , 0.001). The association between opioid abuse/ dependence and lower in-hospital mortality remained significant even after adjustment for demographics, hospital characteristics, and comorbidities (adjusted OR: 0.64; 95% CI: 0.58–0.72; P , 0.001) (Table 3).
DISCUSSION This report provides the first large-scale examination of the association between opioid abuse/dependence American Journal of Therapeutics (2016) 23(2)
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
e354
Gupta et al
Table 2. Frequency of hospital-acquired conditions in hospitalized HF patients. No. patients (%); n 5 9,993,240
HAC Diabetic complications Miscellaneous injuries Fracture Crushing injury Pressure ulcer Catheter-associated urinary tract infections Intracranial injury Vascular catheter–associated infection Dislocation Burns Iatrogenic pneumothorax Any nonsurgical hospital-acquired condition
1,164,480 440,648 64,826 24,863 12,295 11,810
Nonabusers/dependents, n (%)
(11.7) (4.4) (0.6) (0.25) (0.1) (0.1)
116,1466 439,436 64,691 24,810 12,256 11,791
4683 (0.04) 3733 (0.04) 3286 2693 2381 1,653,038
Opioid abusers/ dependents, n (%)
(11.7) (4.4) (0.6) (0.25) (0.1) (0.12)
3014 1212 135 53 39 19
4667 (0.05) 3703 (0.04)
(0.03) (0.02) (0.02) (16.5)
3280 2683 2371 1,648,743
(10.4)* (4.2) (0.5)* (0.18)* (0.1) (0.07)*
16 (0.06) 30 (0.1)*
(0.03) (0.03) (0.02) (16.5)
5 11 10 4295
(0.02) (0.04) (0.03) (14.8)*
*P , 0.05.
and common in-hospital adverse events in patients hospitalized with HF. We found that CMS-defined HACs considered in toto (14.8% vs. 16.5%, unadjusted OR: 0.88; 95% CI: 0.85–0.91) occurred less frequently in patients with a history of opioid abuse/dependence. Of note, only 0.3% of HF patients had a history of opioid abuse/dependence based on hospitalization associated ICD-9 codes. In this population, HACs related to diabetic complications were by far most prevalent (10.4%) with miscellaneous injuries constituting another 4.2% of HACs, whereas less than 1% of HACs were related to iatrogenic infections, procedural complications, and pressure ulcers. Our results document a lower prevalence of opioid abuse/dependence than what might be anticipated from other reports. Mosher et al5 found that among a cohort of hospitalized veterans, 25.9% received chronic opioid therapy in the 6 months before admission, whereas 19.6% had occasional opioid therapy. Previous studies
have reported the prevalence of any opioid use in outpatient cohorts between 18% and 30%.17,18 In comparison, the prevalence of opioid abuse/dependence among patients admitted with HF (0.3%), as noted in our study, was surprisingly low. Prior studies have generally found a positive association between HACs and in-hospital outcomes. In their study involving 122,794 hospitalized veterans, Mosher et al5 reported that prior chronic opioid therapy was associated with hospital readmission (adjusted OR: 1.15; 95% CI: 1.10–1.20) and death (adjusted OR: 1.19; 95% CI: 1.10–1.29). Contrary to earlier reports, we found an inverse association between opioid abuse/dependence and outcomes among hospitalized HF patients. A possible reason for this paradoxical association may be the lower prevalence of diabetes mellitus in opioid abusers/dependents (23.1% vs. 32.4%), as approximately 70% of HACs were a manifestation of
Table 3. Hospital-acquired conditions and in-hospital mortality among hospitalized heart failure patients. OR (95% CI) Outcome HACs In-hospital mortality
Nonabusers/ dependents, %
Opioid abusers/ dependents, %
P
Unadjusted
Adjusted*
16.5 3.6
14.8 1.3
,0.001 ,0.001
0.88 (0.85–0.91) 0.35 (0.31–0.38)
0.71 (0.69–0.74) 0.64 (0.58–0.72)
*Adjusted for age, sex, primary payer status, weekday versus weekend admission, median household income, comorbidities, and hospital characteristics (bed size, location, and region).
American Journal of Therapeutics (2016) 23(2)
www.americantherapeutics.com
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
Opioid Abuse and Outcomes in Heart Failure
poor glycemic control. However, after multivariate adjustment, the association between opioid abuse/ dependence and lesser HACs became even stronger (adjusted OR: 0.71; 95% CI: 0.69–0.74), suggesting an independent association between opioid abuse/ dependence and lesser HACs. A potential explanation for this finding could be the underdiagnosis of fracture, dislocation, and other miscellaneous injuries among opioid abuse/dependents as such patients may be more tolerant to pain leading to less frequent reporting of in-hospital falls and trauma. Also, opioidrelated sedation and delirium may potentially result in underdiagnosis of diabetic complications, such as diabetic ketoacidosis and hyperosmolar nonketotic coma, as these conditions commonly present as an alteration in patients’ mental status. Our findings may also have been related to the age disparity between opioid abusers/dependents and nonabusers/dependents. The mean age for opioid abusers/dependents was 52.7 years versus 72.2 years for nonabusers/dependents. Opioid abusers/dependents were also much less likely to have coronary artery disease (27.3% vs. 44.0%) and prior myocardial infarction (9.4% vs. 11.3%); therefore, less likely to have ischemic cardiomyopathy as a cause of HF. It is well established that patients with ischemic HF have worse outcomes than patients with nonischemic cardiomyopathy.19–22 Therefore, it is possible that lower in-hospital mortality in opioid abuse/dependents with HF may in part be driven by lower prevalence of ischemic cardiomyopathy. Diabetes mellitus has also been shown to be an independent predictor of worse cardiovascular mortality in patients with HF.23 The lower prevalence of diabetes in opioid abusers/dependents may also have been a factor contributing to lower in-hospital mortality. Although the association between opioid abuse/dependence and lower in-hospital mortality was attenuated after adjusting for baseline demographic and clinical characteristics (adjusted OR: 0.64; 95% CI: 0.58–0.72), there was a residual association even after risk adjustment. This may be a result of inadequate adjustment with residual measured or unmeasured confounding or because of the abovementioned or additional pathophysiological differences in opioid abusers/dependents versus nonabusers/ dependents with HF. Being a retrospective observational study, this analysis of the administrative NIS database has its limitations. It has a potential for selection bias and unrecognized miscoding of diagnostic and procedure codes, which may have led to underestimation or overestimation of HACs, opioid abuse/dependence, and comorbidities based on ICD-9-CM coding. However, these 2 limitations may be, at least partially, www.americantherapeutics.com
e355
compensated by the large size of the database and the ability to obtain nationwide estimates based on using the discharge weights provided. The amount of opioid exposure could not be determined because of unavailability of patient charts or medical records. Duration and severity of comorbidities (diabetes mellitus, coronary artery disease, etc.) is not collected in this database and could not be adjusted for. These may have contributed to residual confounding. The NIS does not include information on the medications used. Finally, the database includes only discharge-level information and important laboratory parameters could not be studied. In conclusion, this observational study demonstrates that HF patients with a history of opioid abuse/dependence have lower frequency of HACs and lower in-hospital mortality. Further prospective studies are needed to confirm our observations and to elucidate the mechanisms underlying this paradox in HF patients.
ACKNOWLEDGMENTS Dr Nabors thanks Dr Howard Kerpen, Director of the Lorber Center for the Advancement of Medical Education, Long Island Jewish Medical Center, Lorber Professor of Medicine, Albert Einstein College of Medicine, Clinical Professor of Medicine, Hofstra North Shore-Long Island Jewish Health System, and the Foundation for Innovation in Medical Education for supporting this initiative.
REFERENCES 1. Caudill-Slosberg MA, Schwartz LM, Woloshin S. Office visits and analgesic prescriptions for musculoskeletal pain in US: 1980 vs. 2000. Pain. 2004;109:514–519. 2. Okie S. A flood of opioids, a rising tide of deaths. N Engl J Med. 2010;363:1981–1985. 3. Bohnert AS, Valenstein M, Bair MJ, et al. Association between opioid prescribing patterns and opioid overdose-related deaths. JAMA. 2011;305:1315–1321. 4. Centers for Disease Control and Prevention. Web-based inquiry stastistics query and reporting system: leading causes of death reports. Available from: http://www. cdc.gov/injury/wisqars/index.html. Accessed October 16, 2014. 5. Mosher HJ, Jiang L, Vaughan Sarrazin MS, et al. Prevalence and characteristics of hospitalized adults on chronic opioid therapy. J Hosp Med. 2014;9:82–87. 6. Herzig SJ, Rothberg MB, Cheung M, et al. Opioid utilization and opioid-related adverse events in nonsurgical patients in US hospitals. J Hosp Med. 2014;9:73–81. American Journal of Therapeutics (2016) 23(2)
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
e356 7. Rich MW. Epidemiology, pathophysiology, and etiology of congestive heart failure in older adults. J Am Geriatr Soc. 1997;45:968–974. 8. Braunwald E. Shattuck lecture–cardiovascular medicine at the turn of the millennium: triumphs, concerns, and opportunities. N Engl J Med. 1997;337:1360–1369. 9. Rosamond W, Flegal K, Friday G, et al. Heart disease and stroke statistics–2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2007;115: e69–171. 10. Kozak LJ, DeFrances CJ, Hall MJ. National hospital discharge survey: 2004 annual summary with detailed diagnosis and procedure data. Vital Health Stat 13. 2006:1–209. 11. Pfuntner A, Wier LM, Stocks C. Most frequent conditions in U.S. Hospitals, 2010: statistical brief #148. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville, MD; 2006. Available at http://www.hcupus.ahrq.gov/reports/statbriefs/sb162.pdf. 12. Fang J, Mensah GA, Croft JB, et al. Heart failure-related hospitalization in the U.S., 1979 to 2004. J Am Coll Cardiol. 2008;52:428–434. 13. Centers for Medicare and Medicaid Services, HHS. Medicare program; changes to the hospital inpatient prospective payment systems and fiscal year 2008 rates. Fed Regist. 2007;72:47129–48175. 14. NIS introduction page. Available from: http://www. hcup-us.ahrq.gov/db/nation/nis/nisarchive.jsp. Accessed October 16, 2014. 15. Elixhauser A, Steiner C, Harris DR, et al. Comorbidity measures for use with administrative data. Med Care. 1998;36:8–27.
American Journal of Therapeutics (2016) 23(2)
Gupta et al 16. HCUP-US Tools and Software Page. Available from: http:// www.hcup-us.ahrq.gov/toolssofstware/comorbidity/ comorbidity.jsp. Accessed October 16, 2014. 17. Campbell CI, Weisner C, Leresche L, et al. Age and gender trends in long-term opioid analgesic use for noncancer pain. Am J Public Health. 2010;100:2541–2547. 18. Sullivan MD, Edlund MJ, Fan MY, et al. Trends in use of opioids for non-cancer pain conditions 2000-2005 in commercial and Medicaid insurance plans: the TROUP study. Pain. 2008;138:440–449. 19. Bart BA, Shaw LK, McCants CB Jr, et al. Clinical determinants of mortality in patients with angiographically diagnosed ischemic or nonischemic cardiomyopathy. J Am Coll Cardiol. 1997;30:1002–1008. 20. Adams KF Jr, Dunlap SH, Sueta CA, et al. Relation between gender, etiology and survival in patients with symptomatic heart failure. J Am Coll Cardiol. 1996;28: 1781–1788. 21. Likoff MJ, Chandler SL, Kay HR. Clinical determinants of mortality in chronic congestive heart failure secondary to idiopathic dilated or to ischemic cardiomyopathy. Am J Cardiol. 1987;59:634–638. 22. Felker GM, Thompson RE, Hare JM, et al. Underlying causes and long-term survival in patients with initially unexplained cardiomyopathy. N Engl J Med. 2000;342: 1077–1084. 23. MacDonald MR, Petrie MC, Varyani F, et al. Impact of diabetes on outcomes in patients with low and preserved ejection fraction heart failure: an analysis of the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM) programme. Eur Heart J. 2008;29:1377–1385.
www.americantherapeutics.com
Copyright © 2015 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.
