- Email: [email protected]
Economic burden and clinical impact of preoperative opioid dependence for patients undergoing lower extremity bypass surgery
From the Society for Clinical Vascular Surgery
Economic burden and clinical impact of preoperative opioid dependence for patients undergoing lower extremity bypass surgery Matthew Aizpuru, BA,a Lindsay K. Gallo, BA,a Kevin X. Farley, BS,a Eric R. Wagner, MD,b Jaime Benarroch-Gampel, MD,c William D. Jordan Jr, MD,c and Robert S. Crawford, MD,c Atlanta, Ga
ABSTRACT Objective: Surgeons’ prescription practices and the opioid epidemic have received significant attention in the media. Limited data exist, however, on the impact of prior or coexistent opioid use on vascular surgery outcomes. This study aimed to quantify the incidence, economic burden, and clinical impact of pre-existing opioid dependency in patients undergoing lower extremity bypass (LEB) surgery. Methods: Data were collected from 1,132,645 weighted (230,858 unweighted) patient admissions for LEB in the National Inpatient Sample for the years 2002 to 2015. Patients with a concomitant diagnosis of opioid abuse or dependency were identified using International Classification of Diseases, Ninth Revision codes. Matched cohorts of patients with (n ¼ 606 unweighted) and without (n ¼ 32,343 unweighted) opioid dependence were created using coarsened exact matching to control for patient demographics. Linear regression was used to control for hospital-level factors and to identify differential outcomes for patients with opioid dependency. Our primary end points were hospital cost and length of stay. Our secondary end points were surgical complications and in-hospital mortality. Results: There were 1,132,645 (230,858 unweighted) patient admissions for LEB in the National Inpatient Sample during 2002 to 2015. There were 3190 (0.3%) patients (643 unweighted) who had a diagnosis of pre-existing opioid dependency. The incidence of opioid dependency rose over time (2002, 0.13%; 2015, 0.63%; R2 ¼ 0.90; P < .001). Before matching, opioid-dependent patients were younger (53.9 6 12.3 years vs 66.7 6 12.1 years; P < .001) and more likely to be male (65.2% vs 61.9%; P < .001), to be nonwhite (37.9% vs 24.1%; P < .001), to pay with Medicaid (29.6% vs 7.4%; P < .001), and to fall in the lowest income quartile based on ZIP code (39.6% vs 27.5%; P < .001). After matching, opioid-dependent patients (n ¼ 606 unweighted vs n ¼ 32,343 unweighted nonopioid-dependent patients) were at increased risk of surgical site infections (odds ratio [OR], 1.61; P ¼ .006), major bleeding (OR, 1.56; P ¼ .04), acute kidney injury (OR, 1.46; P ¼ .02), and deep venous thrombosis (OR, 2.53; P ¼ .005). Linear regression of matched cohorts revealed that opioid-dependent patients had an increased length of hospital stay (11.76 days vs 9.80 days; P < .001) and an increased mean inflationadjusted in-hospital cost of U.S. $7032 ($37,522 vs $30,490; P < .001). Conclusions: The incidence of pre-existing opioid dependency in patients undergoing LEB continues to rise. Patients with opioid use disorder undergoing LEB surgery have substantial increases in length of hospital stay and costs. These findings highlight the importance of early preoperative recognition of this disorder in vascular surgery patients and open the opportunity for early intervention in that cohort. (J Vasc Surg 2019;-:1-7.) Keywords: Opioid; Narcotic; Lower extremity bypass; Cost
The opioid epidemic has become a focus of media attention and is now recognized as a major health care crisis. In the United States, between 2000 and 2014, nearly 500,000 individuals died of drug overdoses, and in 2014, opioids were involved in 28,647 or 61% of all drug overdose deaths.1 Whereas many of these deaths are related to illicit opioid use, studies have also shown a surge in deaths related to prescription opioid use.1,2
As a result, the contribution of medical providers, including surgeons, has become a critical area of research and attention. The surgical literature has, to this point, focused primarily on opioid naive patients and postoperative opioid use3-5; however, there are limited data describing patients who are already opioid dependent and need to undergo surgery. Within the vascular surgery literature,
From the Emory University School of Medicinea; and the Department of Ortho-
Surgery, Emory University School of Medicine, 1364 Clifton Rd NE, Atlanta,
paedic Surgery,b and Division of Vascular Surgery and Endovascular Therapy, Department of Surgery,c Emory University School of Medicine. Author conflict of interest: none. Presented as an oral presentation at the Forty-seventh Annual Symposium of the Society for Clinical Vascular Surgery, Boca Raton, Fla, March 16-20, 2019. Correspondence: William D. Jordan Jr, MD, Professor of Surgery and Chief, Division of Vascular Surgery and Endovascular Therapy, Department of
GA 30322 (e-mail: [email protected]). The editors and reviewers of this article have no relevant financial relationships to disclose per the JVS policy that requires reviewers to decline review of any manuscript for which they may have a conflict of interest. 0741-5214 Copyright Ó 2019 by the Society for Vascular Surgery. Published by Elsevier Inc. https://doi.org/10.1016/j.jvs.2019.07.052
1
2
Journal of Vascular Surgery
Aizpuru et al
---
data are even more scant, with studies primarily exploring prescribing practices6 and opioid-sparing analgesia.7 With chronic opioid use approaching 4% nationwide, surgeons are more commonly operating on patients with chronic opioid dependency.8 Recently, a small number of studies in the orthopedic and general surgery literature have described the effects of preoperative opioid abuse on clinical and economic outcomes. In orthopedic patients, studies have shown an increase in the rate of preoperative opioid dependency, with a subsequent increase in postoperative morbidity, and have demonstrated that patients who used opioids before surgery had less pain relief after surgery.9-11 In abdominal surgery, one study has examined the clinical and health care utilization effects of concomitant opioid abuse, noting an increase in length of stay, readmission, and cost.12 These studies noted that significant interplay may exist between chronic pain and opioid abuse in these populations of patients. It is unclear whether there is a definite, causal relationship between chronic pain and opioid abuse in patients undergoing lower extremity bypass (LEB). The limited literature examining opioids in the context of LEB has focused on opioid-sparing or opioid-limiting techniques, such as continuous spinal anesthesia13 and catheterinfused local anesthesia.14 As a whole, little is known about the epidemiology, impact on clinical outcomes, or health care utilization associated with opioid abuse in LEB patients. Given the lack of data, we aimed to quantify the incidence, clinical impact, and economic burden of preexisting opioid dependency in patients undergoing LEB in the National Inpatient Sample (NIS). We hypothesized that opioid abuse is associated with prolonged length of hospital stay, higher costs, and higher incidence of postoperative complications.
METHODS Data source. The data were collected from the NIS, a publicly available administrative database consisting of hospital discharges in the United States. The NIS is maintained by the Agency for Healthcare Research and Quality for the Healthcare Cost and Utilization Project (HCUP). The NIS is an all-payer data set and contains patient demographics, length of stay, mortality, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis and procedure codes. It additionally provides information on hospitals, such as bed size, location, and teaching status.15 For this study, hospital costs were calculated using NIS-provided cost to charge ratios. Adjustments for inflation were performed using the Consumer Price Index provided by the Federal Reserve Bank.16 All investigators with access to NIS data completed the online training and certified Data Use Agreement with
2019
ARTICLE HIGHLIGHTS d
d
d
Type of Research: Retrospective review of administratively collected National Inpatient Sample data Key Findings: There were 1,132,645 weighted admissions for lower extremity bypass including 3190 opioid-dependent patients analyzed with matched cohorts (n ¼ 606 vs 32,343 unweighted cases). Opioid dependency increased 485% during 2002 to 2015. Opioid dependency was associated with increased complications, costs, and length of stay. Take Home Message: Opioid dependency is increasingly common among patients presenting for lower extremity bypass. Opioid dependency substantially increases health care utilization and surgical complications. Preoperative and perioperative interventions have enormous quality and cost-controlling implications for these patients.
HCUP. As a review of publicly available, deidentified data, this study was exempt from our Institutional Review Board. Data classification and statistical analysis. Data were collected from 1,132,645 (230,858 unweighted) patient admissions for LEB in the NIS during years 2002 to 2015Q3. Patients were identified with ICD-9 procedure codes for LEB (39.25, 39.29). To allow equal comparison, 2015Q3, the last period with ICD-9 coding (ICD-10 thereafter), was selected as the end date. Patients with a concomitant diagnosis of opioid abuse or dependency were identified using ICD-9 diagnosis codes specific for opioid dependence and abuse (304.00-304.03, 304.70304.73, 305.50-305.53). HCUP-provided weights were applied to create nationally representative sample estimates and for analyses before patient matching. Univariate analyses of weighted samples were performed with c2 test for categorial variables and t-tests for continuous variables to describe demographic differences between opioid-dependent and nonopioid-dependent patients. Linear regression analysis was used to describe trends in incidence rate over time. Because of significant differences in cohort size and demographics, matched cohorts of patients with and without opioid dependence were created using coarsened exact matching. Patients were matched according to age, race, sex, income quartile based on ZIP code, payer, comorbidities using the Elixhauser comorbidity measure, urgent vs elective admission, and year of admission; 606 unweighted opioid-dependent patient admissions were matched to 32,343 unweighted nonopioid-dependent patient admissions. Linear regression of matched cohorts including variables for hospital region (rural or urban), teaching status, and hospital
Journal of Vascular Surgery Volume
-,
Number
Aizpuru et al
3
-
Estimated Incidence of Opioid Dependency in Patients Undergoing Lower Extremity Bypass, U.S. 2002-2015 0.70% 0.60%
Incidence Rate
0.50% 0.40%
R² = 0.8988 P<.001
0.30% 0.20% 0.10% 0.00% 2000
2002
2004
2006
2008
2010
2012
2014
2016
Year Fig. Incidence of opioid dependency among patients undergoing lower extremity bypass (LEB) in the National Inpatient Sample (NIS) for years 2002 to 2015.
bed size was used to control for hospital-level factors and to describe the influence of opioid dependency on the primary outcomes for the study: hospital cost and length of stay. Binomial logistic regression was employed to control for confounders and to describe relationships between opioid dependency and the following postoperative complications: Clostridium difficile infection, urinary tract infection, surgical site infection, sepsis, major bleeding, acute kidney injury, myocardial infarction, deep venous thrombosis (DVT), pulmonary embolism, nonhome discharge, and mortality. Statistical significance was set at P < .05. All statistical analyses were performed using SPSS Statistics for Windows, version 25.0 (IBM Corp, Armonk, NY).
RESULTS There were 1,132,645 (230,858 unweighted) patient admissions for LEB in the NIS during 2002 to 2015. Of these, 3190 (0.3%) patients (643 unweighted) had a diagnosis of pre-existing opioid dependency. The incidence of preoperative opioid dependency increased 485% from 0.13% of patient admissions for LEB in 2002 to 0.63% of patients admitted for the procedure in 2015 (R2 ¼ 0.90; P < .001; Fig). Patient and hospital demographics were examined before cohort matching (Table I). Before matching, opioid-dependent patients were younger (53.9 6 12.3 years vs 66.7 6 12.1 years; P < .001) and more likely to be male (65.2% vs 61.9%; P < .001), to be nonwhite (37.9% vs 24.1%; P < .001), to pay with Medicaid (29.6% vs 7.4%; P < .001), and to fall in the lowest income quartile based on ZIP code (39.6% vs 27.5%; P < .001).
Logistic regression analyses of matched cohorts (606 unweighted opioid-dependent patients vs 32,343 unweighted nonopioid-dependent patients) examining postoperative complications are shown in Table II. Opioid-dependent patients were at increased risk of surgical site infection (odds ratio [OR], 1.61; P ¼ .006), major bleeding (OR, 1.56; P ¼ .04), acute kidney injury (OR, 1.46; P ¼ .02), and DVT (OR, 2.53; P ¼ .005). There was a trend toward increased risk of nonhome discharge (OR, 1.2; P ¼ .1) for opioid-dependent patients. There was no significant difference in in-hospital amputation (P ¼ .5) or mortality (P ¼ .4). Linear regression of the matched cohorts was used to control for hospital-level factors and to identify differences in cost and length of stay for patients with opioid dependency (Table III). Opioid-dependent patients had an increased length of hospital stay (11.76 days vs 9.80 days; P < .001) and an increased mean inflationadjusted cost of U.S. $7032 ($37,522 vs $30,490; P < .001).
DISCUSSION This review of the NIS demonstrates a clear and troubling rise in pre-existing opioid dependency for patients undergoing LEB, mirroring the national rise in opioid abuse.17 In our study, we found a 0.63% incidence rate of opioid abuse among LEB patients in 2015, which is consistent with but slightly lower than the 0.81% that has been reported in the general population.2,18,19 This difference is likely to be the result of ICD-9 coding in the NIS, which requires an inpatient diagnosis with associated ICD-9 code to be identified as an opioiddependent patient. It is likely that many patients who are truly opioid dependent were not assigned a
Journal of Vascular Surgery
Aizpuru et al
4
---
2019
Table I. Patient demographics and hospital characteristics among opioid-dependent (n ¼ 3190) and nonopioid-dependent (n ¼ 1,129,455) patients undergoing lower extremity bypass (LEB) in the National Inpatient Sample (NIS; 2002-2015) Characteristic
Nonopioid-dependent patients
Opioid-dependent patients
Male
61.9 (698,621)
65.2 (2078)
Female
38.1 (430,507)
34.8 (1111)
<.001
Sex
<.001
Race African American
14.1 (130,623)
24.7 (675)
75.8 (699,721)
62.1 (1695)
10.1 (93,033)
13.2 (360)
Medicare
63.3 (714,127)
41.6 (1326)
Private
White Hispanic Payer
.041 24.4 (274,297)
15.8 (502)
Medicaid
7.4 (83,789)
29.6 (942)
Other
4.9 (54,762)
13.0 (415) <.001
Income quartilea 0-25th percentile
27.5 (304,640)
26-50th percentile
27.1 (299,488)
23.9 (734)
51-75th percentile
24.2 (267,655)
20.2 (618)
76-100th percentile
21.2 (234,340)
16.3 (499)
39.6 (1214)
6.7 (75,076)
2.2 (69)
Urban nonteaching
39.3 (442,282)
26.2 (832)
Urban teaching
54.1 (608,869)
71.7 (2280)
<.001
Location/teaching status of hospital Rural
<.001
Region of hospital Northeast
P
20.3 (228,975)
20.5 (654)
Midwest
23.4 (264,184)
22.3 (710)
South
40.2 (453,725)
30.3 (965)
West
16.2 (182,571)
27.0 (861)
Values are presented as percentage (number). Data are not available for all patient admissions, and therefore patient numbers may not sum to total. a Based on median income in patient’s ZIP code.
diagnosis code for opioid dependency during their inpatient hospitalization for LEB. In addition, patients undergoing vascular surgery are likely to be older than the average American and therefore may have lower rates of substance abuse. Given that more than one in four patients appearing for surgery report opioid use,20 opioid abuse in LEB patients may be even more prevalent than this study’s data suggest. It is not clear from this administrative data set how LEB patients become addicted to opioids in the first place. Research has shown that patients with chronic pain are at particular risk for opioid abuse.21-23 A plausible hypothesis is that some of these patients’ claudication pain is being treated with opioids, leading to opioid abuse. Furthermore, the line between chronic use and abuse is not always clear. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) combines abuse and dependence into a single disorder spectrum, and a patient can be diagnosed with a substance use disorder when 2 or 3 of the 11 diagnostic criteria are met, including
symptoms such as cravings, using larger amounts than intended, using the substance longer than intended, tolerance to the drug, and interference with work and home life.24 Given that prolonged use is part of the diagnostic criteria for opioid use disorder, it may be difficult to say for certain whether a patient has transitioned from chronic use to dependence. Although the basis of addiction cannot be discerned from this data set, it is clear that if the opioid epidemic continues its current trend, opioid dependency among LEB patients will also rise.17 Increasingly, vascular surgeons are being asked to operate on opioid-dependent patients without evidence to guide preoperative decision-making about how to manage their addiction (eg, timing of surgery, doses of narcotics, additional perioperative risks). Apart from our study, which demonstrated significantly increased surgical complications among opioid-dependent patients, there are few data on the clinical impact of pre-existing opioid abuse in vascular surgery patients. Outside of
Journal of Vascular Surgery Volume
-,
Number
Aizpuru et al
5
-
Table II. Risk of surgical complications for matched cohorts of opioid-dependent patients (606 unweighted patient admissions) compared with nonopioid-dependent patients (32,343 unweighted patient admissions) undergoing lower extremity bypass (LEB) P
Complication
OR
95% CI
Clostridium difficile infection
0.24
(0.03-1.73)
.2
1.44
(0.89-2.33)
.1
Urinary tract infection Surgical site infection
1.61
(1.15-2.25)
.006
0.96
(0.58-1.60)
.9
Major bleed
1.56
(1.02-2.38)
.04
Acute kidney injury
1.46
(1.07-2.01)
.02
Myocardial infarction
0.74
(0.37-1.49)
.4
DVT
2.53
(1.33-4.81)
.005
Pulmonary embolism
1.98
(0.65-6.05)
.2
Amputation
1.21
(0.73-2.02)
.5
1.20
(0.95-1.51)
.1
(0.33-1.56)
.4
Sepsis
Nonhome discharge Mortality
0.71
CI, Confidence interval; DVT, deep venous thrombosis; OR, odds ratio. ORs are for opioid-dependent patients compared with nonopioid-dependent patients. Each complication was assessed with a logistic regression model, with matched patient cohorts further controlling for bed size, teaching status, urban vs rural hospital, and hospital region.
Table III. Mean and relative cost and length of stay associated with opioid dependency for matched cohorts of opioiddependent (606 unweighted patient admissions) compared with nonopioid-dependent (32,343 unweighted patient admissions) patients undergoing lower extremity bypass (LEB) Nonopioid-dependent patients
Opioid-dependent patients
Difference
Cost, USD
30,490
37,522
þ7032
1.23
<.001
9.80
11.76
þ1.96
1.20
<.001
Length of stay, days
Ratio
P
Outcome
USD, U.S. dollars. Linear regression with matched cohorts controlling for bed size, teaching status, urban vs rural hospital, and hospital region.
the vascular surgery literature, preoperative opioid abuse has, however, been associated with increased complications in the orthopedic literature25 and in abdominal surgery.26 In our study, patients with a concomitant diagnosis of opioid dependency or abuse were significantly more likely to have common postoperative complications. Opioid-dependent patients undergoing LEB were at significantly increased risk for development of surgical site infections. A similar effect has been shown in the kidney transplant literature27 and in abdominal surgery.26 One potential explanation is that chronic opioid use may have a negative impact on wound healing.28 The impact of chronic opioid abuse on perioperative infections is an area for future investigation. Patients in our study were at increased risk of both major bleeding and DVT. Although this seems paradoxical, both findings can be explained in the context of opioid dependency. Postoperative pain control is likely to be a significant issue for patients with pre-existing opioid dependency. This increased pain, in turn, may lead to immobility, a significant risk factor for DVT.29,30 In the general medical literature, opioid use has been associated with DVT
development. This association has been described in patients who inject opioids intravenously,31,32 although these findings may not be relevant to our population of hospitalized patients. The increased rate of bleeding among opioid-dependent patients in our study mirrors the findings in the study of Cron et al,26 in which opioid-dependent patients undergoing abdominal surgery were at increased risk of blood transfusion, suggesting increased bleeding in that cohort as well. Bleeding risk is intimately related to underlying coagulopathies, often due to liver disease and poor nutrition. Opioiddependent patients often have codependence on alcohol and may have underlying nutritional deficiencies. Further research is needed to determine whether chronic opioid abuse itself is associated with derangements in coagulation. Patients with opioid use disorder undergoing LEB surgery showed unequivocal increases in length of hospital stay and costs. Our findings echo the results of the existing literature examining health care utilization for opioid-dependent patients undergoing other types of surgery.10,12,26 The increases in length of stay are multifactorial, but we believe that the increased complications
6
Journal of Vascular Surgery
Aizpuru et al
---
and difficulty with postoperative pain control are significant contributors. There are data to suggest that preoperative opioid use is associated with increased postoperative opioid demand and decreased opioid independence after surgery.11 More research is needed to describe and to quantify postoperative pain control difficulties for opioid-dependent patients undergoing vascular surgery. Our data demonstrate significant economic and clinical costs for opioid-dependent patients undergoing LEB. Without meaningful intervention, however, these costs may continue to rise as opioid abuse remains a problem of epidemic proportions.17 We believe there is a role for early identification to avert increases in cost and length of stay. Currently, there are few data and few guidelines to help vascular surgeons address opioid dependency during preoperative evaluation of vascular surgery patients. Devin et al33 developed guidelines for evaluating and managing opioid-dependent patients undergoing orthopedic surgery. Whereas vascular patients are different, we believe many of their suggestions are applicable to LEB patients. More comprehensive guidelines could potentially help decrease costs and complications associated with what is now a common problem. Preoperative screening for opioid dependency offers the opportunity for intervention to improve perioperative outcomes. Devin et al33 suggested estimating opioid intake, discussing perioperative pain-related fear, and identifying other psychiatric comorbidities. Perhaps most important, they noted that early identification can allow use of referrals to pain specialists or psychiatrists to optimize patients before surgery. The Centers for Disease Control and Prevention offers prescribing guidelines for opioids in the setting of chronic pain,34 which are likely to be outside the scope of practice of a vascular surgeon but could prove useful in determining preoperative opioid prescriptions. In the perioperative period, the surgeon and anesthesiologist can aggressively pursue the use of adjunct pain control methods, such as regional blocks or catheter-infused local anesthesia, to assist with postoperative pain control. By identifying opioid-dependent patients early, multidisciplinary interventions and preoperative optimization can occur, potentially averting the increased length of stay and costs in these patients. Limitations. There are inherent limitations in this large administrative database study. The data for this study come primarily from billing codes and therefore may not represent the full scope of opioid dependency or surgical outcomes. Although it is possible to distinguish between patients with claudication and critical limb ischemia using ICD-9 diagnosis codes, the administrative nature of this data set limits the utility of such analysis. Complications were identified using ICD-9
2019
coding and theoretically may have occurred during the same admission but before surgery in some patients. Similarly, ICD-9 codes for opioid dependence and abuse could theoretically refer to opioid abuse or dependence for an inpatient rather than a chronic or pre-existing condition in some patients. As explained previously, our study probably underestimates the true incidence of opioid abuse. In addition, ICD-9 coding does not specify the drug used, the quantity used, or the method of administration. Surely patients who abuse prescription opioid narcotics will be different from intravenous heroin users, but ICD-9 groups these patients together. Last, the NIS provides no information on the specifics of patients’ operations, clinical course, and postoperative pain control or longer term outcomes, such as graft patency, reoperation, or thrombectomy, which could provide useful insights into how to best avert cost and length of stay increases for opioid-dependent patients.
CONCLUSIONS The incidence of pre-existing opioid dependence in patients undergoing LEB continues to rise. Opioid dependence is associated with increased perioperative complications, increased hospital length of stay, and increased cost. Our data highlight the importance of preoperative identification, optimization, and improved pain control strategies for this cohort.
AUTHOR CONTRIBUTIONS Conception and design: MA, KF, EW, JBG, WJ, RC Analysis and interpretation: MA, LG, KF, EW, JBG, WJ, RC Data collection: MA, KF, WJ, RC Writing the article: MA, LG, KF, EW, JBG, WJ, RC Critical revision of the article: MA, KF, EW, JBG, WJ, RC Final approval of the article: MA, LG, KF, EW, JBG, WJ, RC Statistical analysis: MA, KF, EW, RC Obtained funding: Not applicable Overall responsibility: RC MA and LG contributed equally to this article and share co-first authorship.
REFERENCES 1. Rudd RA, Aleshire N, Zibbell JE, Gladden RM. Increases in drug and opioid overdose deathsdUnited States, 20002014. MMWR Morb Mortal Wkly Rep 2016;64:1378-82. 2. American Society of Addiction Medicine. Opioid addiction 2016 facts and figures. Available at: https://www.asam.org/ docs/default-source/advocacy/opioid-addiction-diseasefacts-figures.pdf. Accessed February 1, 2019. 3. Hah JM, Bateman BT, Ratliff J, Curtin C, Sun E. Chronic opioid use after surgery: implications for perioperative management in the face of the opioid epidemic. Anesth Analg 2017;125:1733-40. 4. Vadivelu N, Mitra S, Kai AM, Kodumudi G, Gritsenko K. Review of perioperative pain management of opioiddependent patients. J Opioid Manag 2016;12:289-301. 5. Bicket MC, Long JJ, Pronovost PJ, Alexander GC, Wu CL. Prescription opioid analgesics commonly unused after surgery: a systematic review. JAMA Surg 2017;152:1066-71.
Journal of Vascular Surgery Volume
-,
Number
Aizpuru et al
7
-
6. Colton IB, Fujii MH, Ahern TP, MacLean CD, Lahiri JE, Alef M, et al. Postoperative opioid prescribing patterns and use after vascular surgery. Vasc Med 2019;24:63-9. 7. Guay J, Kopp S. Epidural pain relief versus systemic opioidbased pain relief for abdominal aortic surgery. Cochrane Database Syst Rev 2016;1:CD005059. 8. Volkow ND, McLellan AT. Opioid abuse in chronic paind misconceptions and mitigation strategies. N Engl J Med 2016;374:1253-63. 9. Menendez ME, Ring D, Bateman BT. Preoperative opioid misuse is associated with increased morbidity and mortality after elective orthopaedic surgery. Clin Orthop Relat Res 2015;473:2402-12. 10. Zywiel MG, Stroh DA, Lee SY, Bonutti PM, Mont MA. Chronic opioid use prior to total knee arthroplasty. J Bone Joint Surg Am 2011;93:1988-93. 11. Armaghani SJ, Lee DS, Bible JE, Archer KR, Shau DN, Kay H, et al. Preoperative opioid use and its association with perioperative opioid demand and postoperative opioid independence in patients undergoing spine surgery. Spine (Phila Pa 1976) 2014;39:E1524-30. 12. Waljee JF, Cron DC, Steiger RM, Zhong L, Englesbe MJ, Brummett CM. Effect of preoperative opioid exposure on healthcare utilization and expenditures following elective abdominal surgery. Ann Surg 2017;265:715-21. 13. Forster JG, Rosenberg PH, Niemi TT. Continuous spinal microcatheter (28 gauge) technique for arterial bypass surgery of the lower extremities and comparison of ropivacaine with or without morphine for postoperative analgesia. Br J Anaesth 2006;97:393-400. 14. Shutze W, Shutze WP Jr, Prajapati P, Ogola G, Schauer J, Biller E, et al. Postoperative continuous catheter-infused local anesthetic reduces pain scores and narcotic use after lower extremity revascularization. Vascular 2018;26:262-70. 15. HCUP databases. Healthcare Cost and Utilization Project (HCUP). NIS overview. Available at: https://www.hcup-us. ahrq.gov/nisoverview.jsp. Accessed February 1, 2019. 16. Federal Reserve Bank of St. Louis. Economic research: Consumer Price Indexes. Available at: https://fred.stlouisfed.org/ categories/9. Accessed February 1, 2019. 17. Chhatre S, Cook R, Mallik E, Jayadevappa R. Trends in substance use admissions among older adults. BMC Health Serv Res 2017;17:584. 18. Center for Behavioral Health Statistics and Quality. Results from the 2015 National Survey on Drug Use and Health: detailed tables. Key substance use and mental health indicators in the United States 2016. Available at: https://www. samhsa.gov/data/sites/default/files/NSDUH-DetTabs-2015/ NSDUH-DetTabs-2015/NSDUH-DetTabs-2015.pdf. Accessed February 1, 2019. 19. United States Census Bureau. U.S. and world population clock. Available at: https://www.census.gov/popclock/. Accessed February 1, 2019.
20. Hilliard PE, Waljee J, Moser S, Metz L, Mathis M, Goesling J, et al. Prevalence of preoperative opioid use and characteristics associated with opioid use among patients presenting for surgery. JAMA Surg 2018;153:929-37. 21. Salsitz EA. Chronic pain, chronic opioid addiction: a complex nexus. J Med Toxicol 2016;12:54-7. 22. Ballantyne JC. Opioids for the Treatment of chronic pain: mistakes made, lessons learned, and future directions. Anesth Analg 2017;125:1769-78. 23. Witkin LR, Zylberger D, Mehta N, Hindenlang M, Johnson C, Kean J, et al. Patient-reported outcomes and opioid use in outpatients with chronic pain. J Pain 2017;18:583-96. 24. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, Va: American Psychiatric Association; 2013. 25. Ben-Ari A, Chansky H, Rozet I. Preoperative opioid use is associated with early revision after total knee arthroplasty: a study of male patients treated in the Veterans Affairs system. J Bone Joint Surg Am 2017;99:1-9. 26. Cron DC, Englesbe MJ, Bolton CJ, Joseph MT, Carrier KL, Moser SE, et al. Preoperative opioid use is independently associated with increased costs and worse outcomes after major abdominal surgery. Ann Surg 2017;265:695-701. 27. Harris AD, Fleming B, Bromberg JS, Rock P, Nkonge G, Emerick M, et al. Surgical site infection after renal transplantation. Infect Control Hosp Epidemiol 2015;36:417-23. 28. Shanmugam VK, Couch KS, McNish S, Amdur RL. Relationship between opioid treatment and rate of healing in chronic wounds. Wound Repair Regen 2017;25:120-30. 29. Liebson CL, Petterson TM, Bailey KR, Melton LJ 3rd, Heit JA. Risk factors for venous thromboembolism in nursing home residents. Mayo Clin Proc 2008;83:151-7. 30. Muscedere JG, Heyland DK, Cook D. Venous thromboembolism in critical illness in a community intensive care unit. J Crit Care 2007;22:285-9. 31. Cornford CS, Mason JM, Inns F. Deep vein thromboses in users of opioid drugs: incidence, prevalence, and risk factors. Br J Gen Pract 2011;61:e781-6. 32. Wright NM, Allgar V, Tompkins CN. Associations between injecting illicit drugs into the femoral vein and deep vein thrombosis: a case control study. Drug Alcohol Rev 2016;35: 605-10. 33. Devin CJ, Lee DS, Armaghani SJ, Bible J, Shau DN, Martin PR, et al. Approach to pain management in chronic opioid users undergoing orthopaedic surgery. J Am Acad Orthop Surg 2014;22:614-22. 34. Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic paindUnited States, 2016. MMWR Recomm Rep 2016;65:1-49.
Submitted Apr 19, 2019; accepted Jul 3, 2019.
Economic burden and clinical impact of preoperative opioid dependence for patients undergoing lower extremity bypass surgery Matthew Aizpuru, BA,a Lindsay K. Gallo, BA,a Kevin X. Farley, BS,a Eric R. Wagner, MD,b Jaime Benarroch-Gampel, MD,c William D. Jordan Jr, MD,c and Robert S. Crawford, MD,c Atlanta, Ga
ABSTRACT Objective: Surgeons’ prescription practices and the opioid epidemic have received significant attention in the media. Limited data exist, however, on the impact of prior or coexistent opioid use on vascular surgery outcomes. This study aimed to quantify the incidence, economic burden, and clinical impact of pre-existing opioid dependency in patients undergoing lower extremity bypass (LEB) surgery. Methods: Data were collected from 1,132,645 weighted (230,858 unweighted) patient admissions for LEB in the National Inpatient Sample for the years 2002 to 2015. Patients with a concomitant diagnosis of opioid abuse or dependency were identified using International Classification of Diseases, Ninth Revision codes. Matched cohorts of patients with (n ¼ 606 unweighted) and without (n ¼ 32,343 unweighted) opioid dependence were created using coarsened exact matching to control for patient demographics. Linear regression was used to control for hospital-level factors and to identify differential outcomes for patients with opioid dependency. Our primary end points were hospital cost and length of stay. Our secondary end points were surgical complications and in-hospital mortality. Results: There were 1,132,645 (230,858 unweighted) patient admissions for LEB in the National Inpatient Sample during 2002 to 2015. There were 3190 (0.3%) patients (643 unweighted) who had a diagnosis of pre-existing opioid dependency. The incidence of opioid dependency rose over time (2002, 0.13%; 2015, 0.63%; R2 ¼ 0.90; P < .001). Before matching, opioid-dependent patients were younger (53.9 6 12.3 years vs 66.7 6 12.1 years; P < .001) and more likely to be male (65.2% vs 61.9%; P < .001), to be nonwhite (37.9% vs 24.1%; P < .001), to pay with Medicaid (29.6% vs 7.4%; P < .001), and to fall in the lowest income quartile based on ZIP code (39.6% vs 27.5%; P < .001). After matching, opioid-dependent patients (n ¼ 606 unweighted vs n ¼ 32,343 unweighted nonopioid-dependent patients) were at increased risk of surgical site infections (odds ratio [OR], 1.61; P ¼ .006), major bleeding (OR, 1.56; P ¼ .04), acute kidney injury (OR, 1.46; P ¼ .02), and deep venous thrombosis (OR, 2.53; P ¼ .005). Linear regression of matched cohorts revealed that opioid-dependent patients had an increased length of hospital stay (11.76 days vs 9.80 days; P < .001) and an increased mean inflationadjusted in-hospital cost of U.S. $7032 ($37,522 vs $30,490; P < .001). Conclusions: The incidence of pre-existing opioid dependency in patients undergoing LEB continues to rise. Patients with opioid use disorder undergoing LEB surgery have substantial increases in length of hospital stay and costs. These findings highlight the importance of early preoperative recognition of this disorder in vascular surgery patients and open the opportunity for early intervention in that cohort. (J Vasc Surg 2019;-:1-7.) Keywords: Opioid; Narcotic; Lower extremity bypass; Cost
The opioid epidemic has become a focus of media attention and is now recognized as a major health care crisis. In the United States, between 2000 and 2014, nearly 500,000 individuals died of drug overdoses, and in 2014, opioids were involved in 28,647 or 61% of all drug overdose deaths.1 Whereas many of these deaths are related to illicit opioid use, studies have also shown a surge in deaths related to prescription opioid use.1,2
As a result, the contribution of medical providers, including surgeons, has become a critical area of research and attention. The surgical literature has, to this point, focused primarily on opioid naive patients and postoperative opioid use3-5; however, there are limited data describing patients who are already opioid dependent and need to undergo surgery. Within the vascular surgery literature,
From the Emory University School of Medicinea; and the Department of Ortho-
Surgery, Emory University School of Medicine, 1364 Clifton Rd NE, Atlanta,
paedic Surgery,b and Division of Vascular Surgery and Endovascular Therapy, Department of Surgery,c Emory University School of Medicine. Author conflict of interest: none. Presented as an oral presentation at the Forty-seventh Annual Symposium of the Society for Clinical Vascular Surgery, Boca Raton, Fla, March 16-20, 2019. Correspondence: William D. Jordan Jr, MD, Professor of Surgery and Chief, Division of Vascular Surgery and Endovascular Therapy, Department of
GA 30322 (e-mail: [email protected]). The editors and reviewers of this article have no relevant financial relationships to disclose per the JVS policy that requires reviewers to decline review of any manuscript for which they may have a conflict of interest. 0741-5214 Copyright Ó 2019 by the Society for Vascular Surgery. Published by Elsevier Inc. https://doi.org/10.1016/j.jvs.2019.07.052
1
2
Journal of Vascular Surgery
Aizpuru et al
---
data are even more scant, with studies primarily exploring prescribing practices6 and opioid-sparing analgesia.7 With chronic opioid use approaching 4% nationwide, surgeons are more commonly operating on patients with chronic opioid dependency.8 Recently, a small number of studies in the orthopedic and general surgery literature have described the effects of preoperative opioid abuse on clinical and economic outcomes. In orthopedic patients, studies have shown an increase in the rate of preoperative opioid dependency, with a subsequent increase in postoperative morbidity, and have demonstrated that patients who used opioids before surgery had less pain relief after surgery.9-11 In abdominal surgery, one study has examined the clinical and health care utilization effects of concomitant opioid abuse, noting an increase in length of stay, readmission, and cost.12 These studies noted that significant interplay may exist between chronic pain and opioid abuse in these populations of patients. It is unclear whether there is a definite, causal relationship between chronic pain and opioid abuse in patients undergoing lower extremity bypass (LEB). The limited literature examining opioids in the context of LEB has focused on opioid-sparing or opioid-limiting techniques, such as continuous spinal anesthesia13 and catheterinfused local anesthesia.14 As a whole, little is known about the epidemiology, impact on clinical outcomes, or health care utilization associated with opioid abuse in LEB patients. Given the lack of data, we aimed to quantify the incidence, clinical impact, and economic burden of preexisting opioid dependency in patients undergoing LEB in the National Inpatient Sample (NIS). We hypothesized that opioid abuse is associated with prolonged length of hospital stay, higher costs, and higher incidence of postoperative complications.
METHODS Data source. The data were collected from the NIS, a publicly available administrative database consisting of hospital discharges in the United States. The NIS is maintained by the Agency for Healthcare Research and Quality for the Healthcare Cost and Utilization Project (HCUP). The NIS is an all-payer data set and contains patient demographics, length of stay, mortality, and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis and procedure codes. It additionally provides information on hospitals, such as bed size, location, and teaching status.15 For this study, hospital costs were calculated using NIS-provided cost to charge ratios. Adjustments for inflation were performed using the Consumer Price Index provided by the Federal Reserve Bank.16 All investigators with access to NIS data completed the online training and certified Data Use Agreement with
2019
ARTICLE HIGHLIGHTS d
d
d
Type of Research: Retrospective review of administratively collected National Inpatient Sample data Key Findings: There were 1,132,645 weighted admissions for lower extremity bypass including 3190 opioid-dependent patients analyzed with matched cohorts (n ¼ 606 vs 32,343 unweighted cases). Opioid dependency increased 485% during 2002 to 2015. Opioid dependency was associated with increased complications, costs, and length of stay. Take Home Message: Opioid dependency is increasingly common among patients presenting for lower extremity bypass. Opioid dependency substantially increases health care utilization and surgical complications. Preoperative and perioperative interventions have enormous quality and cost-controlling implications for these patients.
HCUP. As a review of publicly available, deidentified data, this study was exempt from our Institutional Review Board. Data classification and statistical analysis. Data were collected from 1,132,645 (230,858 unweighted) patient admissions for LEB in the NIS during years 2002 to 2015Q3. Patients were identified with ICD-9 procedure codes for LEB (39.25, 39.29). To allow equal comparison, 2015Q3, the last period with ICD-9 coding (ICD-10 thereafter), was selected as the end date. Patients with a concomitant diagnosis of opioid abuse or dependency were identified using ICD-9 diagnosis codes specific for opioid dependence and abuse (304.00-304.03, 304.70304.73, 305.50-305.53). HCUP-provided weights were applied to create nationally representative sample estimates and for analyses before patient matching. Univariate analyses of weighted samples were performed with c2 test for categorial variables and t-tests for continuous variables to describe demographic differences between opioid-dependent and nonopioid-dependent patients. Linear regression analysis was used to describe trends in incidence rate over time. Because of significant differences in cohort size and demographics, matched cohorts of patients with and without opioid dependence were created using coarsened exact matching. Patients were matched according to age, race, sex, income quartile based on ZIP code, payer, comorbidities using the Elixhauser comorbidity measure, urgent vs elective admission, and year of admission; 606 unweighted opioid-dependent patient admissions were matched to 32,343 unweighted nonopioid-dependent patient admissions. Linear regression of matched cohorts including variables for hospital region (rural or urban), teaching status, and hospital
Journal of Vascular Surgery Volume
-,
Number
Aizpuru et al
3
-
Estimated Incidence of Opioid Dependency in Patients Undergoing Lower Extremity Bypass, U.S. 2002-2015 0.70% 0.60%
Incidence Rate
0.50% 0.40%
R² = 0.8988 P<.001
0.30% 0.20% 0.10% 0.00% 2000
2002
2004
2006
2008
2010
2012
2014
2016
Year Fig. Incidence of opioid dependency among patients undergoing lower extremity bypass (LEB) in the National Inpatient Sample (NIS) for years 2002 to 2015.
bed size was used to control for hospital-level factors and to describe the influence of opioid dependency on the primary outcomes for the study: hospital cost and length of stay. Binomial logistic regression was employed to control for confounders and to describe relationships between opioid dependency and the following postoperative complications: Clostridium difficile infection, urinary tract infection, surgical site infection, sepsis, major bleeding, acute kidney injury, myocardial infarction, deep venous thrombosis (DVT), pulmonary embolism, nonhome discharge, and mortality. Statistical significance was set at P < .05. All statistical analyses were performed using SPSS Statistics for Windows, version 25.0 (IBM Corp, Armonk, NY).
RESULTS There were 1,132,645 (230,858 unweighted) patient admissions for LEB in the NIS during 2002 to 2015. Of these, 3190 (0.3%) patients (643 unweighted) had a diagnosis of pre-existing opioid dependency. The incidence of preoperative opioid dependency increased 485% from 0.13% of patient admissions for LEB in 2002 to 0.63% of patients admitted for the procedure in 2015 (R2 ¼ 0.90; P < .001; Fig). Patient and hospital demographics were examined before cohort matching (Table I). Before matching, opioid-dependent patients were younger (53.9 6 12.3 years vs 66.7 6 12.1 years; P < .001) and more likely to be male (65.2% vs 61.9%; P < .001), to be nonwhite (37.9% vs 24.1%; P < .001), to pay with Medicaid (29.6% vs 7.4%; P < .001), and to fall in the lowest income quartile based on ZIP code (39.6% vs 27.5%; P < .001).
Logistic regression analyses of matched cohorts (606 unweighted opioid-dependent patients vs 32,343 unweighted nonopioid-dependent patients) examining postoperative complications are shown in Table II. Opioid-dependent patients were at increased risk of surgical site infection (odds ratio [OR], 1.61; P ¼ .006), major bleeding (OR, 1.56; P ¼ .04), acute kidney injury (OR, 1.46; P ¼ .02), and DVT (OR, 2.53; P ¼ .005). There was a trend toward increased risk of nonhome discharge (OR, 1.2; P ¼ .1) for opioid-dependent patients. There was no significant difference in in-hospital amputation (P ¼ .5) or mortality (P ¼ .4). Linear regression of the matched cohorts was used to control for hospital-level factors and to identify differences in cost and length of stay for patients with opioid dependency (Table III). Opioid-dependent patients had an increased length of hospital stay (11.76 days vs 9.80 days; P < .001) and an increased mean inflationadjusted cost of U.S. $7032 ($37,522 vs $30,490; P < .001).
DISCUSSION This review of the NIS demonstrates a clear and troubling rise in pre-existing opioid dependency for patients undergoing LEB, mirroring the national rise in opioid abuse.17 In our study, we found a 0.63% incidence rate of opioid abuse among LEB patients in 2015, which is consistent with but slightly lower than the 0.81% that has been reported in the general population.2,18,19 This difference is likely to be the result of ICD-9 coding in the NIS, which requires an inpatient diagnosis with associated ICD-9 code to be identified as an opioiddependent patient. It is likely that many patients who are truly opioid dependent were not assigned a
Journal of Vascular Surgery
Aizpuru et al
4
---
2019
Table I. Patient demographics and hospital characteristics among opioid-dependent (n ¼ 3190) and nonopioid-dependent (n ¼ 1,129,455) patients undergoing lower extremity bypass (LEB) in the National Inpatient Sample (NIS; 2002-2015) Characteristic
Nonopioid-dependent patients
Opioid-dependent patients
Male
61.9 (698,621)
65.2 (2078)
Female
38.1 (430,507)
34.8 (1111)
<.001
Sex
<.001
Race African American
14.1 (130,623)
24.7 (675)
75.8 (699,721)
62.1 (1695)
10.1 (93,033)
13.2 (360)
Medicare
63.3 (714,127)
41.6 (1326)
Private
White Hispanic Payer
.041 24.4 (274,297)
15.8 (502)
Medicaid
7.4 (83,789)
29.6 (942)
Other
4.9 (54,762)
13.0 (415) <.001
Income quartilea 0-25th percentile
27.5 (304,640)
26-50th percentile
27.1 (299,488)
23.9 (734)
51-75th percentile
24.2 (267,655)
20.2 (618)
76-100th percentile
21.2 (234,340)
16.3 (499)
39.6 (1214)
6.7 (75,076)
2.2 (69)
Urban nonteaching
39.3 (442,282)
26.2 (832)
Urban teaching
54.1 (608,869)
71.7 (2280)
<.001
Location/teaching status of hospital Rural
<.001
Region of hospital Northeast
P
20.3 (228,975)
20.5 (654)
Midwest
23.4 (264,184)
22.3 (710)
South
40.2 (453,725)
30.3 (965)
West
16.2 (182,571)
27.0 (861)
Values are presented as percentage (number). Data are not available for all patient admissions, and therefore patient numbers may not sum to total. a Based on median income in patient’s ZIP code.
diagnosis code for opioid dependency during their inpatient hospitalization for LEB. In addition, patients undergoing vascular surgery are likely to be older than the average American and therefore may have lower rates of substance abuse. Given that more than one in four patients appearing for surgery report opioid use,20 opioid abuse in LEB patients may be even more prevalent than this study’s data suggest. It is not clear from this administrative data set how LEB patients become addicted to opioids in the first place. Research has shown that patients with chronic pain are at particular risk for opioid abuse.21-23 A plausible hypothesis is that some of these patients’ claudication pain is being treated with opioids, leading to opioid abuse. Furthermore, the line between chronic use and abuse is not always clear. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) combines abuse and dependence into a single disorder spectrum, and a patient can be diagnosed with a substance use disorder when 2 or 3 of the 11 diagnostic criteria are met, including
symptoms such as cravings, using larger amounts than intended, using the substance longer than intended, tolerance to the drug, and interference with work and home life.24 Given that prolonged use is part of the diagnostic criteria for opioid use disorder, it may be difficult to say for certain whether a patient has transitioned from chronic use to dependence. Although the basis of addiction cannot be discerned from this data set, it is clear that if the opioid epidemic continues its current trend, opioid dependency among LEB patients will also rise.17 Increasingly, vascular surgeons are being asked to operate on opioid-dependent patients without evidence to guide preoperative decision-making about how to manage their addiction (eg, timing of surgery, doses of narcotics, additional perioperative risks). Apart from our study, which demonstrated significantly increased surgical complications among opioid-dependent patients, there are few data on the clinical impact of pre-existing opioid abuse in vascular surgery patients. Outside of
Journal of Vascular Surgery Volume
-,
Number
Aizpuru et al
5
-
Table II. Risk of surgical complications for matched cohorts of opioid-dependent patients (606 unweighted patient admissions) compared with nonopioid-dependent patients (32,343 unweighted patient admissions) undergoing lower extremity bypass (LEB) P
Complication
OR
95% CI
Clostridium difficile infection
0.24
(0.03-1.73)
.2
1.44
(0.89-2.33)
.1
Urinary tract infection Surgical site infection
1.61
(1.15-2.25)
.006
0.96
(0.58-1.60)
.9
Major bleed
1.56
(1.02-2.38)
.04
Acute kidney injury
1.46
(1.07-2.01)
.02
Myocardial infarction
0.74
(0.37-1.49)
.4
DVT
2.53
(1.33-4.81)
.005
Pulmonary embolism
1.98
(0.65-6.05)
.2
Amputation
1.21
(0.73-2.02)
.5
1.20
(0.95-1.51)
.1
(0.33-1.56)
.4
Sepsis
Nonhome discharge Mortality
0.71
CI, Confidence interval; DVT, deep venous thrombosis; OR, odds ratio. ORs are for opioid-dependent patients compared with nonopioid-dependent patients. Each complication was assessed with a logistic regression model, with matched patient cohorts further controlling for bed size, teaching status, urban vs rural hospital, and hospital region.
Table III. Mean and relative cost and length of stay associated with opioid dependency for matched cohorts of opioiddependent (606 unweighted patient admissions) compared with nonopioid-dependent (32,343 unweighted patient admissions) patients undergoing lower extremity bypass (LEB) Nonopioid-dependent patients
Opioid-dependent patients
Difference
Cost, USD
30,490
37,522
þ7032
1.23
<.001
9.80
11.76
þ1.96
1.20
<.001
Length of stay, days
Ratio
P
Outcome
USD, U.S. dollars. Linear regression with matched cohorts controlling for bed size, teaching status, urban vs rural hospital, and hospital region.
the vascular surgery literature, preoperative opioid abuse has, however, been associated with increased complications in the orthopedic literature25 and in abdominal surgery.26 In our study, patients with a concomitant diagnosis of opioid dependency or abuse were significantly more likely to have common postoperative complications. Opioid-dependent patients undergoing LEB were at significantly increased risk for development of surgical site infections. A similar effect has been shown in the kidney transplant literature27 and in abdominal surgery.26 One potential explanation is that chronic opioid use may have a negative impact on wound healing.28 The impact of chronic opioid abuse on perioperative infections is an area for future investigation. Patients in our study were at increased risk of both major bleeding and DVT. Although this seems paradoxical, both findings can be explained in the context of opioid dependency. Postoperative pain control is likely to be a significant issue for patients with pre-existing opioid dependency. This increased pain, in turn, may lead to immobility, a significant risk factor for DVT.29,30 In the general medical literature, opioid use has been associated with DVT
development. This association has been described in patients who inject opioids intravenously,31,32 although these findings may not be relevant to our population of hospitalized patients. The increased rate of bleeding among opioid-dependent patients in our study mirrors the findings in the study of Cron et al,26 in which opioid-dependent patients undergoing abdominal surgery were at increased risk of blood transfusion, suggesting increased bleeding in that cohort as well. Bleeding risk is intimately related to underlying coagulopathies, often due to liver disease and poor nutrition. Opioiddependent patients often have codependence on alcohol and may have underlying nutritional deficiencies. Further research is needed to determine whether chronic opioid abuse itself is associated with derangements in coagulation. Patients with opioid use disorder undergoing LEB surgery showed unequivocal increases in length of hospital stay and costs. Our findings echo the results of the existing literature examining health care utilization for opioid-dependent patients undergoing other types of surgery.10,12,26 The increases in length of stay are multifactorial, but we believe that the increased complications
6
Journal of Vascular Surgery
Aizpuru et al
---
and difficulty with postoperative pain control are significant contributors. There are data to suggest that preoperative opioid use is associated with increased postoperative opioid demand and decreased opioid independence after surgery.11 More research is needed to describe and to quantify postoperative pain control difficulties for opioid-dependent patients undergoing vascular surgery. Our data demonstrate significant economic and clinical costs for opioid-dependent patients undergoing LEB. Without meaningful intervention, however, these costs may continue to rise as opioid abuse remains a problem of epidemic proportions.17 We believe there is a role for early identification to avert increases in cost and length of stay. Currently, there are few data and few guidelines to help vascular surgeons address opioid dependency during preoperative evaluation of vascular surgery patients. Devin et al33 developed guidelines for evaluating and managing opioid-dependent patients undergoing orthopedic surgery. Whereas vascular patients are different, we believe many of their suggestions are applicable to LEB patients. More comprehensive guidelines could potentially help decrease costs and complications associated with what is now a common problem. Preoperative screening for opioid dependency offers the opportunity for intervention to improve perioperative outcomes. Devin et al33 suggested estimating opioid intake, discussing perioperative pain-related fear, and identifying other psychiatric comorbidities. Perhaps most important, they noted that early identification can allow use of referrals to pain specialists or psychiatrists to optimize patients before surgery. The Centers for Disease Control and Prevention offers prescribing guidelines for opioids in the setting of chronic pain,34 which are likely to be outside the scope of practice of a vascular surgeon but could prove useful in determining preoperative opioid prescriptions. In the perioperative period, the surgeon and anesthesiologist can aggressively pursue the use of adjunct pain control methods, such as regional blocks or catheter-infused local anesthesia, to assist with postoperative pain control. By identifying opioid-dependent patients early, multidisciplinary interventions and preoperative optimization can occur, potentially averting the increased length of stay and costs in these patients. Limitations. There are inherent limitations in this large administrative database study. The data for this study come primarily from billing codes and therefore may not represent the full scope of opioid dependency or surgical outcomes. Although it is possible to distinguish between patients with claudication and critical limb ischemia using ICD-9 diagnosis codes, the administrative nature of this data set limits the utility of such analysis. Complications were identified using ICD-9
2019
coding and theoretically may have occurred during the same admission but before surgery in some patients. Similarly, ICD-9 codes for opioid dependence and abuse could theoretically refer to opioid abuse or dependence for an inpatient rather than a chronic or pre-existing condition in some patients. As explained previously, our study probably underestimates the true incidence of opioid abuse. In addition, ICD-9 coding does not specify the drug used, the quantity used, or the method of administration. Surely patients who abuse prescription opioid narcotics will be different from intravenous heroin users, but ICD-9 groups these patients together. Last, the NIS provides no information on the specifics of patients’ operations, clinical course, and postoperative pain control or longer term outcomes, such as graft patency, reoperation, or thrombectomy, which could provide useful insights into how to best avert cost and length of stay increases for opioid-dependent patients.
CONCLUSIONS The incidence of pre-existing opioid dependence in patients undergoing LEB continues to rise. Opioid dependence is associated with increased perioperative complications, increased hospital length of stay, and increased cost. Our data highlight the importance of preoperative identification, optimization, and improved pain control strategies for this cohort.
AUTHOR CONTRIBUTIONS Conception and design: MA, KF, EW, JBG, WJ, RC Analysis and interpretation: MA, LG, KF, EW, JBG, WJ, RC Data collection: MA, KF, WJ, RC Writing the article: MA, LG, KF, EW, JBG, WJ, RC Critical revision of the article: MA, KF, EW, JBG, WJ, RC Final approval of the article: MA, LG, KF, EW, JBG, WJ, RC Statistical analysis: MA, KF, EW, RC Obtained funding: Not applicable Overall responsibility: RC MA and LG contributed equally to this article and share co-first authorship.
REFERENCES 1. Rudd RA, Aleshire N, Zibbell JE, Gladden RM. Increases in drug and opioid overdose deathsdUnited States, 20002014. MMWR Morb Mortal Wkly Rep 2016;64:1378-82. 2. American Society of Addiction Medicine. Opioid addiction 2016 facts and figures. Available at: https://www.asam.org/ docs/default-source/advocacy/opioid-addiction-diseasefacts-figures.pdf. Accessed February 1, 2019. 3. Hah JM, Bateman BT, Ratliff J, Curtin C, Sun E. Chronic opioid use after surgery: implications for perioperative management in the face of the opioid epidemic. Anesth Analg 2017;125:1733-40. 4. Vadivelu N, Mitra S, Kai AM, Kodumudi G, Gritsenko K. Review of perioperative pain management of opioiddependent patients. J Opioid Manag 2016;12:289-301. 5. Bicket MC, Long JJ, Pronovost PJ, Alexander GC, Wu CL. Prescription opioid analgesics commonly unused after surgery: a systematic review. JAMA Surg 2017;152:1066-71.
Journal of Vascular Surgery Volume
-,
Number
Aizpuru et al
7
-
6. Colton IB, Fujii MH, Ahern TP, MacLean CD, Lahiri JE, Alef M, et al. Postoperative opioid prescribing patterns and use after vascular surgery. Vasc Med 2019;24:63-9. 7. Guay J, Kopp S. Epidural pain relief versus systemic opioidbased pain relief for abdominal aortic surgery. Cochrane Database Syst Rev 2016;1:CD005059. 8. Volkow ND, McLellan AT. Opioid abuse in chronic paind misconceptions and mitigation strategies. N Engl J Med 2016;374:1253-63. 9. Menendez ME, Ring D, Bateman BT. Preoperative opioid misuse is associated with increased morbidity and mortality after elective orthopaedic surgery. Clin Orthop Relat Res 2015;473:2402-12. 10. Zywiel MG, Stroh DA, Lee SY, Bonutti PM, Mont MA. Chronic opioid use prior to total knee arthroplasty. J Bone Joint Surg Am 2011;93:1988-93. 11. Armaghani SJ, Lee DS, Bible JE, Archer KR, Shau DN, Kay H, et al. Preoperative opioid use and its association with perioperative opioid demand and postoperative opioid independence in patients undergoing spine surgery. Spine (Phila Pa 1976) 2014;39:E1524-30. 12. Waljee JF, Cron DC, Steiger RM, Zhong L, Englesbe MJ, Brummett CM. Effect of preoperative opioid exposure on healthcare utilization and expenditures following elective abdominal surgery. Ann Surg 2017;265:715-21. 13. Forster JG, Rosenberg PH, Niemi TT. Continuous spinal microcatheter (28 gauge) technique for arterial bypass surgery of the lower extremities and comparison of ropivacaine with or without morphine for postoperative analgesia. Br J Anaesth 2006;97:393-400. 14. Shutze W, Shutze WP Jr, Prajapati P, Ogola G, Schauer J, Biller E, et al. Postoperative continuous catheter-infused local anesthetic reduces pain scores and narcotic use after lower extremity revascularization. Vascular 2018;26:262-70. 15. HCUP databases. Healthcare Cost and Utilization Project (HCUP). NIS overview. Available at: https://www.hcup-us. ahrq.gov/nisoverview.jsp. Accessed February 1, 2019. 16. Federal Reserve Bank of St. Louis. Economic research: Consumer Price Indexes. Available at: https://fred.stlouisfed.org/ categories/9. Accessed February 1, 2019. 17. Chhatre S, Cook R, Mallik E, Jayadevappa R. Trends in substance use admissions among older adults. BMC Health Serv Res 2017;17:584. 18. Center for Behavioral Health Statistics and Quality. Results from the 2015 National Survey on Drug Use and Health: detailed tables. Key substance use and mental health indicators in the United States 2016. Available at: https://www. samhsa.gov/data/sites/default/files/NSDUH-DetTabs-2015/ NSDUH-DetTabs-2015/NSDUH-DetTabs-2015.pdf. Accessed February 1, 2019. 19. United States Census Bureau. U.S. and world population clock. Available at: https://www.census.gov/popclock/. Accessed February 1, 2019.
20. Hilliard PE, Waljee J, Moser S, Metz L, Mathis M, Goesling J, et al. Prevalence of preoperative opioid use and characteristics associated with opioid use among patients presenting for surgery. JAMA Surg 2018;153:929-37. 21. Salsitz EA. Chronic pain, chronic opioid addiction: a complex nexus. J Med Toxicol 2016;12:54-7. 22. Ballantyne JC. Opioids for the Treatment of chronic pain: mistakes made, lessons learned, and future directions. Anesth Analg 2017;125:1769-78. 23. Witkin LR, Zylberger D, Mehta N, Hindenlang M, Johnson C, Kean J, et al. Patient-reported outcomes and opioid use in outpatients with chronic pain. J Pain 2017;18:583-96. 24. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, Va: American Psychiatric Association; 2013. 25. Ben-Ari A, Chansky H, Rozet I. Preoperative opioid use is associated with early revision after total knee arthroplasty: a study of male patients treated in the Veterans Affairs system. J Bone Joint Surg Am 2017;99:1-9. 26. Cron DC, Englesbe MJ, Bolton CJ, Joseph MT, Carrier KL, Moser SE, et al. Preoperative opioid use is independently associated with increased costs and worse outcomes after major abdominal surgery. Ann Surg 2017;265:695-701. 27. Harris AD, Fleming B, Bromberg JS, Rock P, Nkonge G, Emerick M, et al. Surgical site infection after renal transplantation. Infect Control Hosp Epidemiol 2015;36:417-23. 28. Shanmugam VK, Couch KS, McNish S, Amdur RL. Relationship between opioid treatment and rate of healing in chronic wounds. Wound Repair Regen 2017;25:120-30. 29. Liebson CL, Petterson TM, Bailey KR, Melton LJ 3rd, Heit JA. Risk factors for venous thromboembolism in nursing home residents. Mayo Clin Proc 2008;83:151-7. 30. Muscedere JG, Heyland DK, Cook D. Venous thromboembolism in critical illness in a community intensive care unit. J Crit Care 2007;22:285-9. 31. Cornford CS, Mason JM, Inns F. Deep vein thromboses in users of opioid drugs: incidence, prevalence, and risk factors. Br J Gen Pract 2011;61:e781-6. 32. Wright NM, Allgar V, Tompkins CN. Associations between injecting illicit drugs into the femoral vein and deep vein thrombosis: a case control study. Drug Alcohol Rev 2016;35: 605-10. 33. Devin CJ, Lee DS, Armaghani SJ, Bible J, Shau DN, Martin PR, et al. Approach to pain management in chronic opioid users undergoing orthopaedic surgery. J Am Acad Orthop Surg 2014;22:614-22. 34. Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic paindUnited States, 2016. MMWR Recomm Rep 2016;65:1-49.
Submitted Apr 19, 2019; accepted Jul 3, 2019.