Journal List > J Korean Med Sci > v.36(13) > 1146605

Cha, Jang, Yoo, Choi, Hwang, and Choy: Effect of Opioids on All-cause Mortality and Opioid Addiction in Total Hip Arthroplasty: a Korea Nationwide Cohort Study

Abstract

Background

The purpose of this study was to investigate the use of opioids before and after total hip arthroplasty (THA), to find out the effect of opioid use on mortality in patients with THA, and to analyze whether preoperative opioid use is a risk factor for sustained opioid use after surgery using Korean nationwide cohort data.

Methods

This retrospective nationwide study identified subjects from the Korean National Health Insurance Service-Sample cohort (NHIS-Sample) compiled by the Korean NHIS. The index date (time zero) was defined as 90 days after an admission to a hospital to fulfill the eligibility criteria of the THA.

Results

In the comparison of death risk according to current use and the defined daily dose of tramadol and strong opioids in each patient group according to past opioid use, there were no statistically significant differences in the adjusted hazard ratio for death compared to the current non-users in all groups (P > 0.05). Past tramadol and strong opioid use in current users increased the risk of the sustained use of tramadol and strong opioids 1.45-fold (adjusted rate ratio [aRR]; 95% confidence interval [CI], 1.12–1.87; P = 0.004) and 1.65-fold (aRR; 95% CI, 1.43–1.91; P < 0.001), respectively, compared to past non-users.

Conclusion

In THA patients, the use of opioids within 6 months before surgery and within 3 months after surgery does not affect postoperative mortality, but a past-use history of opioid is a risk factor for sustained opioid use. Even after THA, the use of strong opioids is observed to increase compared to before surgery.

Graphical Abstract

jkms-36-e87-abf001.jpg

INTRODUCTION

Total hip arthroplasty (THA) is the recommended treatment for patients with hip osteoarthritis who have failed both pharmacological and non-pharmacological treatments for severe pain.1 THA is the fourth most common surgery in the United States and was done for 460 million people in 2012.234 In 2013, 22,000 THAs were done, making it one of the 20 most common surgeries in Australia.5
However, despite these advantages, THA is associated with severe pain after surgery. Pain after THA can adversely affect early patient recovery postoperatively.6 In addition, pain can negatively affect mobility after surgery, increasing the risk of venous thromboembolism and interfering with rehabilitation. As a result, the consequences of such pain can prolong patient recovery and increase hospital stays and cost.7 In order to improve the patient's quality of life and improve postoperative results, it is necessary to emphasize proper postoperative pain management. Therefore, many pain management drugs and procedures are used to manage pain after surgery.8
As the cardiovascular risk of nonsteroidal anti-inflammatory drugs (NSAIDs), the ones most commonly used to control pain in these elderly patients, became known, the threshold for opioid use decreased.9 Because opioid painkiller purchases quadrupled between 1999 and 2010, the use of prescription drugs in the United States has been a danger to public safety over the last decade.10 Opioids can cause side effects, such as sedation, dizziness and balance problems, nausea, vomiting, constipation, and respiratory depression, and long-term use can lead to tolerance and physical dependence and sometimes, to addiction.11 Various studies on opioid use after THA reported prolonged opioid use before surgery as a representative risk factor for chronic use of opioid after THA.1213 Also, preoperative opioid use is associated with high complication rate and increased mortality after surgery.1214 And, it is reported that preoperative chronic users continue to use opioids up to 47%–62% at 1 year after surgery.15 However, studies on opioid use in patients with THA in East Asian countries are still lacking.
Therefore, the purpose of this study was to investigate the use of opioids before and after THA, to find out the effect of opioid use on mortality in patients with THA, and to analyze whether preoperative opioid use is a risk factor for sustained opioid use after surgery using Korean nationwide cohort data.

METHODS

Study subjects

The Korean National Health Insurance Service-Sample cohort (NHIS-Sample) was used to identify patients with THA. The NHIS-Sample was compiled and provided by the NHIS of South Korea for research purposes.16 To represent all people in South Korea, a total of 1,000,000 participants as of 2006 were selected into the NHIS Sample by simple 10% random sampling. Unless there was a disqualification, such as emigration or death, all the individuals in the NHIS Sample were followed until December 31, 2015. In addition to this data, data of all participants from 2002 to 2005 are provided. Under the single-insurer system of universal health coverage, the NHIS has all personal information, including demographics, medical use, or treatment information, for the Korean people.161718 The information in the data set included all inpatient and outpatient medical claims and prescription claims, including treatment and diagnostic procedures, and codes for generic prescription names.

Primary THA cohort

The eligibility criteria for patients with primary THA were as follows19: 1) first-time admission to a hospital with a surgery code of THA (International Classification of Diseases, 10th Revision [ICD-10] N0711, N2070); 2) at least a three-year THA-free period; 3) age 18–99 years at the time of THA. The exclusion criteria were as follows: To guarantee a minimal 1-year observation, patients with THA occurring less than 365 days before the end of the follow-up period (December 31, 2015) were excluded. 1) Patients with THA prior to December 31, 2004, were also excluded to ensure at least a 3-year THA-free period. 2) Patients with past history of malignancy. 3) Patients who had undergone THA twice were excluded. The last date of follow-up was defined as the date of death or December 31, 2015, whichever came first. To identify the use of opioids, the index date (time zero) was defined as 90 days after admission to a hospital to fulfill the eligibility criteria of patients with primary THA.

All-cause mortality

In the NHIS-Senior, each subject's unique de-identified number was linked to vital statistics, including dates and causes of death, from the Korean National Statistical Office.16 The dates of death from the mortality information were used to calculate the survival times.

Categorization by type of opioid and definition of opioid exposure

Opioids were defined as morphine, hydromorphone, oxycodone, dihydrocodeine, codeine, hydrocodone, pethidine, fentanyl, pentazocine, buprenorphine, butorphanol, nalbuphine, tapentadol, sufentanil, remifentanil, and tramadol.2021 The opioids were categorized into tramadol and strong opioids (other opioids except for tramadol). The patients who used opioids within six months before THA was defined as past users. If there was no history of opioid use within six months before THA, the patient was defined as a past non-user. Patients who received opioids within three months after THA were defined as current users, and those who did not were defined as current non-users. The cumulative dose of current tramadol and strong opioid use was calculated by adding the amount of dispensed defined daily dosages.22 To identify the sustained use of tramadol and strong opioids, their use was investigated during the period from three months to one year after THA (defined as sustained user).
Past and current medication history of antihypertensive, antidiabetic, lipid-lowering, and anti-rheumatoid agents; anti-platelet, anti-dementia, and anti-Parkinson drugs; antiepileptics, antipsychotics, NSAIDs, COX-2 inhibitors, steroids, and warfarin within six months before hip fracture and within three months after hip fracture were investigated.

Statistical analysis

The baseline characteristics were identified at the time of admission for THA. Survival time used in the survival analyses was defined by days from the index date (90 days after THA admission) to the date of death or December 31, 2015, whichever came first. Patients who died during the 90-day landmark period were truncated. We used a multivariable-adjusted Cox proportional hazards model to investigate the effects of tramadol and strong opioid use on all-cause mortality in patients with THA. The effect sizes are presented as hazard ratios (HRs) and 95% confidence intervals (CIs). We used a generalized estimating equation model with a Poisson distribution and logarithmic link function to estimate the adjusted rate ratios (aRRs) and 95% CIs to assess the association between past use and sustained use. The potential confounders included were age group, gender, household income level, Charlson Comorbidity Score (CCS), anesthesia, transfusions, calendar year of the THA, past medication history and current medication history, which included tramadol and strong opioids. We used Quan's ICD-10 coding algorithm of the CCS23 diagnostic codes during the three years before THA admission to assess each subject's comorbidities. The presence of CCS disease-constituting categories was defined by at least two outpatient visits or one admission upon the primary or first secondary diagnosis. We did the statistical analyses using SAS Enterprise Guide version 7.1 (SAS Institute, Cary, NC, USA); a P < 0.05 was considered to be statistically significant.

Ethic statement

The study design and protocol were approved by the Institutional Review Board (IRB) of our Hospital (IRB No. 2020-03-007). Written informed consent was waived for all patients involved in this study.

RESULTS

Between January 1, 2002, and December 31, 2015, a total of 1,766 patients were admitted to hospitals for THA. Of these patients, 645 were excluded because they did not meet the inclusion criteria (217 patients, 3-year free period; 149 patients, guarantee a 1-year observation; 1 patient, under 18 years; 205 patients, THA twice or more; 60 patients, histories of malignancy). Also, 13 patients who died within 90 days after hip fractures were excluded. A total of 1,121 patients were included in this study. The patient demographics and medication histories are presented in Tables 1 and 2.
Table 1

Demographics and medication histories according to current use of tramadol

jkms-36-e87-i001
Variables Tramadol current non-user Tramadol current user P value
Total 237 (21.14) 564 (78.86)
Age group 0.255
18–49 61 (25.74) 203 (22.96)
50–59 57 (24.05) 213 (24.10)
60–69 67 (28.27) 220 (24.59)
≥ 70 52 (21.94) 248 (28.05)
Sex 0.010
Male 135 (56.96) 420 (47.51)
Female 102 (43.04) 464 (52.49)
Income 0.212
Low 78 (32.91) 304 (34.39)
Mid 87 (36.71) 276 (31.22)
High 53 (22.36) 198 (22.40)
Missing 19 (8.02) 106 (11.99)
Charlson Comorbidity Score 0.085
0 105 (44.30) 337 (38.12)
1–2 107 (45.15) 404 (45.70)
3–5 21 (8.86) 130 (14.71)
≥ 6 4 (1.69) 13 (1.47)
Transfusion, mL 0.084
0 49 (20.68) 170 (19.23)
1–799 38 (16.03) 177 (20.02)
800–1,199 63 (26.58) 175 (19.80)
≥ 1,200 87 (36.71) 362 (40.95)
Anesthesia 0.028
General 114 (48.10) 355 (40.16)
Spinal 123 (51.90) 529 (59.84)
Hospital beds 0.050
< 200 56 (23.63) 199 (22.51)
200–499 50 (21.10) 248 (28.05)
500–799 68 (28.69) 190 (21.49)
≥ 800 63 (26.58) 247 (27.94)
Calendar year < 0.001
2006–2007 91 (38.40) 108 (12.22)
2008–2009 51 (21.52) 141 (15.95)
2010–2011 45 (18.99) 191 (21.61)
2012–2013 24 (10.13) 196 (22.17)
2014–2015 26 (10.97) 248 (28.05)
Past medication history
Tramadol past user 110 (46.41) 564 (63.80) < 0.001
Strong opioids past user 58 (24.47) 310 (35.07) 0.002
Antidepressant 51 (21.52) 280 (31.67) 0.002
Benzodiazepine 143 (60.34) 589 (66.63) 0.071
Anti-diabetic agent 39 (16.46) 138 (15.61) 0.751
Anti-hypertensive agent 114 (48.10) 477 (53.96) 0.109
Anti-rheumatoid drug 10 (4.22) 37 (4.19) 0.982
Lipid lowering agent 37 (15.61) 225 (25.45) 0.002
NSAIDs 211 (89.03) 824 (93.21) 0.032
COX-2 inhibitor 24 (10.13) 138 (15.61) 0.033
Steroid 177 (74.68) 708 (80.09) 0.070
Antiplatelet agent 56 (23.63) 263 (29.75) 0.064
Anti-dementia agent 4 (1.69) 20 (2.26) 0.587
Anti-Parkinson agent 4 (1.69) 20 (2.26) 0.584
Anti-epilepsy agent 18 (7.59) 97 (10.97) 0.128
Anti-psychotic agent 18 (7.59) 97 (10.97) 0.128
Warfarin 4 (1.69) 9 (1.02) 0.393
Current medication history
Strong opioid current user 164 (69.20) 689 (77.94) 0.005
Antidepressant 25 (10.55) 95 (10.75) 0.930
Benzodiazepine 88 (37.13) 321 (36.31) 0.816
Anti-diabetic agent 39 (16.46) 133 (15.05) 0.593
Anti-hypertensive agent 111 (46.84) 454 (51.36) 0.216
Anti-rheumatoid drug 5 (2.11) 27 (3.05) 0.438
Lipid lowering agent 21 (8.86) 133 (15.05) 0.014
NSAIDs 193 (81.43) 723 (81.79) 0.901
COX-2 inhibitors 37 (15.61) 152 (17.19) 0.563
Steroids 72 (30.38) 326 (36.88) 0.063
Antiplatelet agents 45 (18.99) 206 (23.30) 0.157
Anti-dementia agents 3 (1.27) 24 (2.71) 0.196
Anti-Parkinson agents 3 (1.27) 24 (2.71) 0.196
Anti-epilepsy agents 6 (2.53) 34 (3.85) 0.333
Anti-psychotic agents 6 (2.53) 34 (3.85) 0.333
Warfarin 3 (1.27) 9 (1.02) 0.742
Values are presented as number (%).
NSAID = nonsteroidal anti-inflammatory drug.
Table 2

Demographics and medication histories according to current use of strong opioid

jkms-36-e87-i002
Variables Strong opioid current non-user Strong opioid current user P value
Total 268 (23.91) 853 (76.09)
Age group 0.120
18–49 55 (20.52) 209 (24.50)
50–59 77 (28.73) 193 (22.63)
60–69 72 (26.87) 215 (25.21)
≥ 70 64 (23.88) 236 (26.67)
Sex 0.149
Male 143 (53.36) 412 (48.30)
Female 125 (46.64) 441 (51.70)
Income 0.900
Low 93 (34.70) 289 (33.88)
Mid 82 (30.60) 281 (32.94)
High 63 (23.51) 188 (22.04)
Missing 30 (11.19) 95 (11.14)
Charlson Comorbidity Score 0.772
0 113 (42.16) 329 (38.57)
1–2 119 (44.40) 392 (45.96)
3–5 34 (12.69) 117 (13.72)
≥ 6 2 (0.75) 15 (1.76)
Transfusion 0.687
0 53 (19.78) 166 (19.46)
1–799 45 (16.79) 170 (19.93)
800–1,199 61 (22.76) 177 (20.78)
≥ 1,200 109 (40.67) 340 (39.86)
Anesthesia < 0.001
General 77 (28.73) 392 (45.96)
Spinal 191 (71.27) 461 (54.04)
Hospital beds < 0.001
< 200 51 (19.03) 204 (23.92)
200–499 55 (20.52) 243 (28.49)
500–799 65 (24.25) 193 (22.63)
≥ 800 97 (36.19) 213 (24.97)
Calendar year < 0.001
2006–2007 71 (26.49) 128 (15.01)
2008–2009 48 (17.91) 144 (16.88)
2010–2011 63 (23.51) 173 (20.28)
2012–2013 50 (18.66) 170 (19.93)
2014–2015 36 (13.43) 238 (27.90)
Past medication history
Tramadol past user 153 (57.09) 521 (61.08) 0.245
Strong opioids past user 60 (22.39) 308 (36.11) < 0.001
Antidepressant 75 (27.99) 256 (30.01) 0.526
Benzodiazepine 172 (64.18) 560 (65.65) 0.659
Anti-diabetic agent 44 (16.42) 133 (15.59) 0.746
Anti-hypertensive agent 128 (47.76) 463 (54.28) 0.062
Anti-rheumatoid drug 9 (3.36) 38 (4.45) 0.435
Lipid lowering agent 51 (19.03) 211 (24.74) 0.054
NSAIDs 237 (88.43) 789 (93.55) 0.006
COX-2 inhibitor 30 (11.19) 132 (15.47) 0.082
Steroid 201 (75.00) 684 (80.19) 0.069
Antiplatelet agent 81 (30.22) 238 (27.90) 0.462
Anti-dementia agent 4 (1.49) 20 (2.34) 0.401
Anti-Parkinson agent 4 (1.49) 20 (2.34) 0.401
Anti-epilepsy agent 23 (8.58) 92 (10.79) 0.300
Anti-psychotic agent 23 (8.58) 92 (10.79) 0.300
Warfarin 3 (1.12) 10 (1.17) 0.944
Current medication history
Strong opioid current user 195 (72.76) 689 (80.77) 0.005
Antidepressant 21 (7.84) 99 (11.61) 0.082
Benzodiazepine 84 (31.34) 325 (38.10) 0.045
Anti-diabetic agent 45 (16.79) 127 (14.89) 0.451
Anti-hypertensive agent 124 (46.27) 441 (51.70) 0.121
Anti-rheumatoid drug 4 (1.49) 28 (3.28) 0.125
Lipid lowering agent 33 (12.31) 121 (14.19) 0.438
NSAIDs 226 (84.33) 690 (80.89) 0.204
COX-2 inhibitors 30 (11.19) 159 (18.64) 0.005
Steroids 77 (28.73) 321 (37.63) 0.008
Antiplatelet agents 62 (23.13) 189 (22.16) 0.738
Anti-dementia agents 5 (1.87) 22 (2.58) 0.506
Anti-Parkinson agents 5 (1.87) 22 (2.58) 0.506
Anti-epilepsy agents 8 (2.99) 32 (3.75) 0.555
Anti-psychotic agents 8 (2.99) 32 (3.75) 0.555
Warfarin 5 (1.87) 7 (0.82) 0.147
Values are presented as number (%).
NSAID = nonsteroidal anti-inflammatory drugs.
There were 774 (69.05%) patients who were past users of opioids (Fig. 1). There were 674 (60.12%) past users of tramadol and 368 (32.83%) past users of strong opioids. The number of current opioid users increased to 1,048 (93.49%). The numbers of current tramadol and strong opioid users were 884 (78.86%) and 853 (71.09%), respectively. The sustained users of opioids decreased to 702 (62.62%) patients. The trends in the use of tramadol were similar to that for opioids. However, the sustained use of strong opioids increased compared to past use of opioid.
Fig. 1

Trends in the percentage of opioid users before and after hip fracture in older patients with hip fractures.

jkms-36-e87-g001
In the comparison of death risk according to current use and the defined daily dose of tramadol in each patient group according to past opioid use, there were no statistically significant differences in the adjusted HR for death compared to the current non-users in all groups (P > 0.05) (Table 3). In the comparison of death risk according to current use and the defined daily dose of strong opioids in each patient group according to past opioid use, there were no statistically significant differences in the adjusted HR for death compared to the current non-users in all groups (P > 0.05) (Table 4).
Table 3

Comparison of hazard for death according to current use and DDD of tramadol in each patient groups according to the presence of past opioid use

jkms-36-e87-i003
Variables aHR 95% CI P value
Included all patients (n = 1,121)
Current non-user (n = 237) 1.00 (reference)
Tramadol current user (n = 884) 1.16 0.75–1.80 0.503
DDD ≤ 14 (n = 690) 1.04 0.66–1.63 0.881
14 < DDD (n = 194) 1.62 0.94–2.78 0.082
Past opioid non-user group (n = 272)
Current non-user (n = 83) 1.00 (reference)
Tramadol current user (n = 189) 1.71 0.29–10.14 0.553
DDD ≤ 14 (n = 156) 2.08 0.30–14.04 0.463
14 < DDD (n = 33) 1.13 0.11–12.15 0.920
Past opioid user group (n = 849)
Current non-user (n = 154) 1.00 (reference)
Tramadol current user (n = 695) 1.04 0.62–1.73 0.885
DDD ≤ 14 (n = 534) 0.95 0.57–1.91 0.854
14 < DDD (n = 161) 1.62 0.85–3.10 0.141
DDD = defined daily dosage, aHR = adjusted hazard ratio, CI = confidence interval.
Table 4

Comparison of hazard for death according to current use and DDD of strong opioid in each patient groups according to the presence of past opioid use

jkms-36-e87-i004
Variables aHR 95% CI P value
Included all patients (n = 1,121)
Current non-user (n = 268) 1.00 (reference)
Strong opioid current user (n = 853) 1.14 0.76–1.72 0.528
DDD ≤ 14 (n = 708) 1.06 0.69–1.63 0.802
14 < DDD (n = 145) 1.13 0.62–2.08 0.682
Past opioid non-user group (n = 272)
Current non-user (n = 84) 1.00 (reference)
Strong opioid current user (n = 188) 0.69 0.14–3.52 0.657
DDD ≤ 14 (n = 162) 0.81 0.16–4.17 0.805
14 < DDD (n = 26) 0.25 0.01–4.91 0.364
Past opioid user group (n = 849)
Current non-user (n = 184) 1.00 (reference)
Strong opioid current user (n = 665) 1.27 0.72–1.85 0.540
DDD ≤ 14 (n = 546) 1.28 0.77–2.13 0.333
14 < DDD (n = 119) 1.56 0.79–3.07 0.198
DDD = defined daily dosage, aHR = adjusted hazard ratio, CI = confidence interval.
Past tramadol and strong opioid use in current users increased the risk of sustained use of tramadol and strong opioids 1.45-fold (aRR; 95% CI, 1.12–1.87; P = 0.004) and 1.65-fold (aRR; 95% CI, 1.43–1.91; P < 0.001), respectively, compared to past non-users (Table 5). Also, the risks for sustained use of tramadol and strong opioids increased in both groups regardless of age.
Table 5

Among the current user of tramadol and strong opioid, the relationship between past user and sustained user of tramadol and strong opioid after 3 months of hip fracture in survivor until 1 year after hip fracture

jkms-36-e87-i005
Variables Sustained user aRR 95% CI P value
Tramadol current user (n = 866)
Past non-user (n = 241) 74 (30.71) 1.00 (reference)
Past user (n = 625) 379 (60.64) 1.45 1.23–1.72 < 0.001
18 ≤ age < 60 (n = 270) 142 (52.59) 1.45 1.12–1.87 0.004
60 ≤ age (n = 355) 237 (66.76) 1.48 1.19–1.89 < 0.001
Strong opioid current user (n = 837) (3 mon–1 yr)
Past non-user (n = 241) 75 (31.12) 1.00 (reference)
Past user (n = 596) 312 (52.35) 1.65 1.43–1.91 < 0.001
18 ≤ age < 60 (n = 256) 113 (44.14) 1.80 1.37–2.53 < 0.001
60 ≤ age (n = 340) 199 (58.53) 1.56 1.32–1.87 < 0.001
Values are presented as number (%).
aRR = adjusted rate ratio, CI = confidence interval.

DISCUSSION

The main finding of this study was that of the THA patients, 774 (69.05%) were past users of opioids, 674 (60.12%) were past users of tramadol, and 368 (32.83%) were past users of strong opioids. In addition, the number of current opioid users after THA was 1,048 (93.49%) with current tramadol and strong opioid users being 884 (78.86%) and 853 (71.09%), respectively. However, the sustained users of opioids decreased to 702 (62.62%) patients. In addition, the sustained use of strong opioids increased compared to past use of opioids. Past tramadol and strong opioid use in current users increased the risk of sustained use of tramadol and strong opioids 1.45-fold and 1.65-fold, respectively, compared to past non-users. Also, the risks for sustained use of tramadol and strong opioids increased in both groups regardless of age. When comparing the risk of death from current use and daily tramadol doses defined in each patient group from past opioid use, there was no statistically significant difference in deaths compared to current non-users in all groups.
Opioid has been reported as one of the common causes of medication related death, and Summer et al reported that mortality after hip fracture surgery was higher in opioid users compared to non-users.2425 However, previous studies on opioid use after total joint arthroplasty are limited to studies on the risk factors of chronic opioid use or whether opioid use is related to postoperative functional outcome, readmission, infection, and revision rate.122627 Also, there are few analyses of the effect of opioid use on death after THA. And, study designs that consider both preoperative and postoperative use after THA are also rare. Moreover, no studies have analyzed the effects of opioids in different age groups. Kim et al.28 investigated the association of preoperative opioid use among patients 65 years and older with mortality and other complications at 30 days after total knee arthroplasty. They reported that there were no statistically significant differences in in-hospital or 30-day mortality between continuous opioid users and opioid-naive patients. Fortunately, the results of our study are consistent with the above study. This is probably because the dosage and pattern of opioid prescriptions in Korea are different from those in Western countries.
Chen et al.29 reported that the proportion of chronic opioid users at 3 months postoperatively was 19% and decreased to 4% at 1 year in total joint arthroplasty patients 65 years of age or older. Pivec et al.30 reported that 19% of preoperative opioid users and 4% of preoperative non-opioid users were still using opioid at last follow-up. However, in our study, the proportion of opioid users reached 69% before THA, and even after 1 year or more after surgery, it was lower than before surgery, but it was 62%, indicating that opioids are still used in these patients at a high rate. This is thought to be due to differences in the definition of the types and user of opioids included in the study. In this study, if there was even one opioid prescription, it was classified as an opioid user, and the cause of opioid use was not identified. Nevertheless, the proportion of opioid users decreased at 1 year after surgery than before, and THA appears to be effective in reducing pain in these patients. However, there is a report that the use of opioid is high in patients with revision surgery, so it is considered that attention should be paid to follow-up of patients using opioid after surgery.13
One of the most important issues in the use of opioids is abuse and addiction. Goesling et al.31 did well-designed prospective studies for assessing trends and predictors of opioid use following total knee and THA. They estimated that 4.3% of patients who were opioid naïve on the day of surgery had opioid usage at postoperative 6 months. In contrast, 34.7% of THA patients who registered opioid usage on the day of surgery maintained opioid use at 6 months. Patients consuming > 60 mg oral morphine equivalents preoperatively had an 80% chance of continued postoperative use. In this study, current opioid users showed a high rate of continued use of opioids, and the use of strong opioids in this patient group was also high. Kim et al.32 used statistics on claims (2004–2013) from the US Private Health Program, recognizing individuals who received hip or knee arthroplasty and obtained ≥ 1 opioid prescription within 30 days of operation. They reported that overall, 7.6% persistently used opioids after the surgery. Among patients who used opioids 80% of the time for ≥ 4 months preoperatively (3,023 patients), 72.1% became persistent users. Preoperative opioid usage trends were good predictors of continued opioid use after surgery. In our study, as in previous studies, the proportion of sustained users after surgery was higher in preoperative opioid users. Moreover, this phenomenon has been observed not only in patients 65 years of age or older, but also in patients with younger age. Recently, it has been reported that opioid prescriptions are increasing in Korea. When using opioids for postoperative pain control after THA, taking history of preoperative medication use is thought to be important to prevent sustained use of opioids.33
This study has several limitations. First, we were unable to assess the severity of the specific disease or pain for which THA was done. In addition, we have not been able to confirm indications for opioids. In other words, some patients may have used opioids for reasons other than the health status of the disease for THA surgery. This is an obvious limitation of our observational cohort studies. However, we tried to investigate not only CCS, but also several medications to adjust for the various underlying diseases in these patients. Second, the patient's functional status, pain level, or other behavioral factors related to opioid use were not analyzed. Third, we were unable to assess whether the patient was actually taking the drug as prescribed or how often. Fourth, there is a possibility of misclassification, because we relied on diagnostic or procedural codes to define the type of operation and diagnosis, and the history of taking opioids.
In THA patients, the use of opioids within 6 months before surgery and within 3 months after surgery does not affect postoperative mortality, but past use of opioids is a risk factor for sustained opioid use. Even after THA, the use of strong opioids increases compared to use before surgery.

ACKNOWLEDGMENTS

This study was based on data from the Korean National Health Insurance Service (research administration number, NHIS-2020-2-063) and the results of the study are not related to the National Health Insurance Service.

Notes

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

  • Conceptualization: Yoo JI.

  • Data curation: Jang SY, Cha Y.

  • Formal analysis: Jang SY, Cha Y.

  • Investigation: Jang SY, Cha Y.

  • Methodology: Jang SY, Cha Y.

  • Validation: Jang SY, Cha Y.

  • Writing - original draft: Choy W, Cha Y, Choi HG.

  • Writing - review & editing: Jang SY, Cha Y, Hwang JW.

References

1. Wood AM, Brock TM, Heil K, Holmes R, Weusten A. A review on the management of hip and knee osteoarthritis. Int J Chronic Dis. 2013; 2013:845015. PMID: 26464847.
crossref
2. Kurtz S, Ong K, Lau E, Mowat F, Halpern M. Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030. J Bone Joint Surg Am. 2007; 89(4):780–785. PMID: 17403800.
crossref
3. Sloan M, Premkumar A, Sheth NP. Projected volume of primary total joint arthroplasty in the U.S., 2014 to 2030. J Bone Joint Surg Am. 2018; 100(17):1455–1460. PMID: 30180053.
crossref pmid
4. Trasolini NA, McKnight BM, Dorr LD. The opioid crisis and the orthopedic surgeon. J Arthroplasty. 2018; 33(11):3379–3382.e1. PMID: 30075877.
crossref pmid
5. Ackerman IN, Bohensky MA, Zomer E, Tacey M, Gorelik A, Brand CA, et al. The projected burden of primary total knee and hip replacement for osteoarthritis in Australia to the year 2030. BMC Musculoskelet Disord. 2019; 20(1):90. PMID: 30797228.
crossref pmid pmc
6. Min BW, Kim Y, Cho HM, Park KS, Yoon PW, Nho JH, et al. Perioperative pain management in total hip arthroplasty: Korean Hip Society guidelines. Hip Pelvis. 2016; 28(1):15–23. PMID: 27536639.
crossref pmid pmc
7. Gan TJ. Poorly controlled postoperative pain: prevalence, consequences, and prevention. J Pain Res. 2017; 10:2287–2298. PMID: 29026331.
crossref pmid pmc
8. Gray CF, Smith C, Zasimovich Y, Tighe PJ. Economic considerations of acute pain medicine programs. Tech Orthop. 2017; 32(4):217–225. PMID: 29403150.
crossref pmid pmc
9. Wright EA, Katz JN, Abrams S, Solomon DH, Losina E. Trends in prescription of opioids from 2003-2009 in persons with knee osteoarthritis. Arthritis Care Res (Hoboken). 2014; 66(10):1489–1495. PMID: 24782079.
crossref pmid pmc
10. Frenk SM, Porter KS, Paulozzi LJ. Prescription opioid analgesic use among adults: United States, 1999-2012. NCHS Data Brief. 2015; (189):1–8.
11. Fabi DW. Multimodal analgesia in the hip fracture patient. J Orthop Trauma. 2016; 30(Suppl 1):S6–S11. PMID: 27101321.
crossref
12. Prentice HA, Inacio MC, Singh A, Namba RS, Paxton EW. Preoperative risk factors for opioid utilization after total hip arthroplasty. J Bone Joint Surg Am. 2019; 101(18):1670–1678. PMID: 31567804.
crossref pmid
13. Inacio MC, Pratt NL, Roughead EE, Paxton EW, Graves SE. Opioid use after total hip arthroplasty surgery is associated with revision surgery. BMC Musculoskelet Disord. 2016; 17(1):122. PMID: 26965992.
crossref pmid pmc
14. 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(7):2402–2412. PMID: 25694266.
crossref
15. Cook DJ, Kaskovich SW, Pirkle SC, Mica MA, Shi LL, Lee MJ. Benchmarks of duration and magnitude of opioid consumption after total hip and knee arthroplasty: a database analysis of 69,368 patients. J Arthroplasty. 2019; 34(4):638–644.e1. PMID: 30642706.
crossref pmid
16. Kim YI, Kim YY, Yoon JL, Won CW, Ha S, Cho KD, et al. Cohort profile: National Health Insurance Service-Senior (NHIS-Senior) cohort in Korea. BMJ Open. 2019; 9(7):e024344.
crossref
17. Seong SC, Kim YY, Park SK, Khang YH, Kim HC, Park JH, et al. Cohort profile: the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) in Korea. BMJ Open. 2017; 7(9):e016640.
crossref
18. Lee J, Lee JS, Park SH, Shin SA, Kim K. Cohort profile: the National Health Insurance Service-National Sample Cohort (NHIS-NSC), South Korea. Int J Epidemiol. 2017; 46(2):e15. PMID: 26822938.
crossref
19. Suh YS, Lee JJ, Nho JH, Lee JJ, Won SH, Yang HJ. Transfusion trends in hip arthroplasty in Korea: a nationwide study by the Korean National Health Insurance Service. Transfusion. 2019; 59(7):2324–2333. PMID: 31022315.
crossref pmid
20. Cho SK, Jung SY, Choi S, Im SG, Kim H, Choi WS, et al. Factors related to the use of opioids as early treatment in patients with knee osteoarthritis. Arthritis Res Ther. 2019; 21(1):222. PMID: 31685008.
crossref pmid pmc
21. Simoni AH, Nikolajsen L, Olesen AE, Christiansen CF, Pedersen AB. Opioid use after hip fracture surgery: a Danish nationwide cohort study from 2005 to 2015. Eur J Pain. 2019; 23(7):1309–1317. PMID: 30848038.
crossref pmid
22. WHO Collaborating Center for drug Statistics Methodology. ATC/DDD Index 2020. Updated 2020. Assessed January 1, 2020. https://www.whocc.no/atc_ddd_index/.
23. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005; 43(11):1130–1139. PMID: 16224307.
crossref pmid
24. Gallagher R. New category of opioid-related death. Can Fam Physician. 2018; 64(2):95–96. PMID: 29449232.
pmid pmc
25. Summers S, Grau L, Massel D, Rosas S, Ong A, Hernandez VH. Opioid use disorders are associated with perioperative morbidity and mortality in the hip fracture population. J Orthop Trauma. 2018; 32(5):238–244. PMID: 29356800.
crossref pmid
26. Goplen CM, Verbeek W, Kang SH, Jones CA, Voaklander DC, Churchill TA, et al. Preoperative opioid use is associated with worse patient outcomes after total joint arthroplasty: a systematic review and meta-analysis. BMC Musculoskelet Disord. 2019; 20(1):234. PMID: 31103029.
crossref pmid pmc
27. Mudumbai SC, Chung P, Nguyen N, Harris B, Clark JD, Wagner TH, et al. Perioperative opioid prescribing patterns and readmissions after total knee arthroplasty in a national cohort of veterans health administration patients. Pain Med. 2020; 21(3):595–603. PMID: 31309970.
crossref pmid
28. Kim SC, Jin Y, Lee YC, Lii J, Franklin PD, Solomon DH, et al. Association of preoperative opioid use with mortality and short-term safety outcomes after total knee replacement. JAMA Netw Open. 2019; 2(7):e198061. PMID: 31365106.
crossref
29. Chen EY, Lasky R, Dotterweich WA, Niu R, Tybor DJ, Smith EL. Chronic prescription opioid use before and after total hip and knee arthroplasty in patients younger than 65 years. J Arthroplasty. 2019; 34(10):2319–2323. PMID: 31255407.
crossref pmid
30. Pivec R, Issa K, Naziri Q, Kapadia BH, Bonutti PM, Mont MA. Opioid use prior to total hip arthroplasty leads to worse clinical outcomes. Int Orthop. 2014; 38(6):1159–1165. PMID: 24573819.
crossref pmid pmc
31. Goesling J, Moser SE, Zaidi B, Hassett AL, Hilliard P, Hallstrom B, et al. Trends and predictors of opioid use after total knee and total hip arthroplasty. Pain. 2016; 157(6):1259–1265. PMID: 26871536.
crossref pmid pmc
32. Kim SC, Choudhry N, Franklin JM, Bykov K, Eikermann M, Lii J, et al. Patterns and predictors of persistent opioid use following hip or knee arthroplasty. Osteoarthritis Cartilage. 2017; 25(9):1399–1406. PMID: 28433815.
crossref pmid pmc
33. Oh TK, Jeon YT, Choi JW. Trends in chronic opioid use and association with five-year survival in South Korea: a population-based cohort study. Br J Anaesth. 2019; 123(5):655–663. PMID: 31558315.
crossref pmid
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