Abstract
Background
The association between preoperative opioid or glucocorticoid (GC) use and clinical outcomes, such as postoperative mortality after total joint arthroplasty (TJA), is unclear.
Methods
A population-based retrospective cohort study was conducted. Data were obtained from the National Health Insurance Service of South Korea. Patients who underwent TJA (total knee or total hip arthroplasty) between January 1, 2016, and December 31, 2021, were included. We examined whether the patients had been prescribed opioids or oral GC for > 90 days prior to TJA.
Results
In total, 664,598 patients who underwent TJA were included, among whom 245,260 (52.4%), 23,076 (3.5%), and 47,777 (7.2%) were classified into the opioid, GC, and opioid and GC groups, respectively. Compared to the non-user group, the opioid and GC user groups showed 53% (odds ratio [OR], 1.53; 95% confidence interval [CI], 1.12–2.30; P = 0.010) higher odds of in-hospital mortality. Compared to non-users, GC users (hazard ratio [HR], 1.24; 95% CI, 1.15–1.34; P < 0.001) and opioid and GC users (HR, 1.24; 95% CI, 1.14–1.35; P < 0.001) showed a higher risk of 1-year all-cause mortality. Compared to the non-user group, GC users (OR, 1.09; 95% CI, 1.04–1.15; P < 0.001) and opioid and GC users (OR, 1.06; 95% CI, 1.01–1.11; P = 0.014) showed higher odds of postoperative complications.
Conclusion
Preoperative GC use and concomitant use of opioid analgesics with GC were associated with increased postoperative mortality and morbidity after TJA. However, preoperative chronic opioid analgesic use alone did not affect postoperative mortality or morbidity.
Keywords: Arthroplasty, Total Knee Arthroplasty, Total Hip Arthroplasty, Opioid, Glucocorticoid, Mortality
Graphical Abstract
INTRODUCTION
As one of the most efficacious medical interventions for hip and knee osteoarthritis, total joint arthroplasty (TJA) substantially reduces pain, restores function, and enhances quality of life.1 Total knee arthroplasty (TKA) and hip arthroplasty (THA) are the most frequently performed surgeries for osteoarthritis treatment.2,3 The demand for TJA has increased consistently owing to the rising prevalence of severe arthritis with the aging of the global population.4 The frequency of TKA and THA procedures have been steadily increasing in South Korea owing to the aging population.5,6 Therefore, TJA has become an increasingly important public health concern.
Most patients requiring THA or TKA experience severe osteoarthritis-associated joint pain before surgery.7,8 The medications commonly used to control pain in osteoarthritis of the knee or hip are opioid analgesics, the most potent analgesics for the treatment of chronic non-cancer pain.9 Glucocorticoids (GCs) are another class of medications that comprise effective pain relievers that target arthritis-associated inflammatory response.10 The potential relationship between preoperative opioid or GC use and adverse outcomes after TJA could be an important issue because long-term use of opioids and GC could cause side effects owing to their immunosuppressive effects.11,12 In a single-center retrospective study, patients who used opioids and GC together or separately before surgery had higher 90-day fatalities following non-cardiac surgery than did those who had never used them.13 However, the association between preoperative opioid or GC use and clinical outcomes, such as postoperative mortality after TJA, is unclear.
Therefore, we aimed to examine whether preoperative opioid and/or GC use affects mortality or morbidity after TJA using a large population-based database in South Korea. We hypothesized that preoperative opioid and/or GC use is associated with increased mortality and morbidity after TJA.
METHODS
National Health Insurance Service (NHIS) database
The only public insurance program in South Korea, the NHIS, provided all data for this study. The NHIS database, required by law, tracks all disease diagnoses and prescription information for treatments, medications, or both. Registration is the first step toward eligibility for government-sponsored health insurance plans, and the International Classification of Diseases, Tenth Revision (ICD-10) was used to code all diagnoses. Foreign nationals living in South Korea for more than 6 months are required to register with the government-run NHIS. Additionally, the NHIS database contains detailed data on each person’s socioeconomic status and death date.14
Study population
Patients who underwent TKA or THA between January 1, 2016, and December 31, 2021, who were admitted to a South Korean hospital and were aged ≥ 18 years were included. To guarantee patient homogeneity, the study was limited to the first instances when TKA or THA was performed more than twice within the designated study period. The purpose of the inclusion criterion was to ensure that the participants had similar qualities.
Opioid and GC use
We investigated whether the patients had been prescribed opioids or oral GC for > 90 days prior to TKA or THA. We categorized patients who were using opioids before surgery as opioid users, those who were using only GC as GC users, and those who were using both opioids and GC as opioid and GC users. Patients who were not using opioids or GC prior to surgery were defined as non-users. In South Korea, all drug prescriptions by physicians are recorded and managed using the National Centralized Prescription Management System (NHIS). Therefore, there were no cases of missing prescriptions for opioids or GC.
Study endpoint
The 3 endpoints were in-hospital mortality, 1-year all-cause mortality, and postoperative complications. In-hospital mortality was defined as death during hospitalization after TKA or THA. One-year all-cause mortality was defined as death after surgery. Postoperative complications were defined as the occurrence of any of the following diseases during hospitalization after surgery: cerebral infarction or hemorrhage (I60–I64), acute coronary events (I21, I22, and I252), heart failure (I50), pulmonary embolism (I26), acute or subacute hepatic failure (K720), acute renal failure (N17), sepsis (A40 and A41), wound infection (T793 and T814), pneumonia (J12–J18 and J69), or urinary tract infection (N390, T835, and N30).
Covariates analyzed
The NHIS database was queried for information on socioeconomic status, including occupation, household income, place of residence, and patient demographics (age and sex) at hospital admission. After excluding individuals enlisted in the Medical Aid Program, patients were categorized according to quartile ratios. Individuals who were impoverished and unable to afford health insurance were classified into the Medical Aid Program group by the government. Residential regions were classified into 2 categories: urban (Seoul or other major cities) and pastoral (everywhere else). To evaluate whether the patients had multiple medical conditions, the Charlson Comorbidity Index (CCI) score was computed using the most recent ICD-10 codes that had been updated in the NHIS database (Supplementary Table 1). Individuals in South Korea are required to disclose all their disabilities in the NHIS database to qualify for social welfare benefits. Consequently, information regarding underlying disabilities was gathered. Disability evaluations were performed by specialists in pertinent fields in South Korea. Following a qualified physician’s evaluation of the extent to which the condition impedes daily functioning, underlying disabilities were categorized as mild-to-moderate and severe. A comprehensive categorization of disabilities is presented in Supplementary Table 2. Perioperative data on TKA and THA, type of anesthesia (regional or general), perioperative packed red blood cell transfusion, and postoperative intensive care unit (ICU) admission were collected. Data on other preoperative analgesics (nonsteroidal anti-inflammatory drugs or paracetamol) and gabapentin or pregabalin were also collected because these medications were commonly used for the treatment of pain-associated osteoarthritis.15,16 Hospitals were categorized into 4 levels according to their capacity.
Statistical analysis
One-way analysis of variance and χ2 test were used to compare clinicopathologic features between the 4 groups (non-users, opioid users, GC users, and opioid and GC users). We analyzed hospital-related variables using agglomerative clustering and performed hierarchical cluster analysis to categorize hospital levels among covariates. The variables included the type of hospital (tertiary general hospital, general hospital, and other types of hospital); numbers of physicians, specialist physicians, nurses, pharmacists, and hospital beds; and numbers of beds in adult ICUs, operating rooms, and emergency departments. Based on hierarchical clustering analysis, 4 hospital levels were established. Supplementary Table 3 provides detailed information on the specific characteristics of each hospital.
We implemented a multivariable logistic regression model to analyze in-hospital mortality and the occurrence of postoperative complications after THA or TKA. We developed a multivariable Cox regression model to analyze 1-year all-cause mortality following TKA or THA. The multivariable models were incorporated using all covariates, such as age, sex, employment status, household income level, residence, underlying disability, CCI, 17 individual underlying comorbidities, type of anesthesia, perioperative transfusion, ICU admission, other analgesic use, hospital level, type of arthroplasty, and year of surgery. Hosmer–Lemeshow test was conducted to validate the appropriateness of the goodness of fit in the multivariable logistic regression models. Furthermore, the fundamental assumptions of the Cox proportional hazards models were verified using log–log plots. Finally, subgroup analyses were conducted according to the type of arthroplasty performed. The outcomes of the Cox regressions are depicted as hazard ratios (HR) accompanied by 95% confidence intervals (CIs), whereas odds ratios (OR) are used to represent the results of the logistic regressions. R (version 4.0.3, R Utilities; R Foundation for Statistical Computing, Vienna, Austria) was used to perform all statistical analyses, and the threshold for significance was set at P < 0.05.
Ethics statement
This retrospective population-based cohort study was approved by the Institutional Review Board (IRB) of Seoul National University Bundang Hospital (IRB permission number: X-2303-819-902). Permission to share data was granted by the NHIS Big Data Center (NHIS-2023-1-526). Informed consent was not necessary for data analysis because of the anonymized nature of the data and the retrospective nature of the study.
RESULTS
Study population
Between January 1, 2016, and December 31, 2021, there were 721,963 TJA cases in South Korea. We eliminated 57,286 cases of revision procedures performed on the same hip (n = 21,256) and knee (n = 36,030). Subsequently, 79 patients aged < 18 years were excluded, leaving 664,598 patients who underwent TKA or THA. Fig. 1 illustrates the distribution of these groups: 245,260 (52.4%), 23,076 (3.5%), and 47,777 (7.2%) in the opioid, GC, and opioid and GC groups, respectively. Table 1 shows a comparison of the clinicopathological characteristics of the 4 groups.
Fig. 1. Flowchart illustrating the patient selection procedure.
TKA = total knee arthroplasty, THA = total hip arthroplasty, GC = glucocorticoid.
Table 1. Comparison of clinicopathological characteristics among the four groups.
| Variables | Non-users (n = 348,485) | Opioid users (n = 245,260) | GC users (n = 23,076) | Opioid and GC users (n = 47,777) | P value | |
|---|---|---|---|---|---|---|
| Age, yr | 72.4 ± 9.3 | 71.8 ± 8.9 | 71.0 ± 10.2 | 70.6 ± 9.3 | < 0.001 | |
| Sex, male | 68,945 (19.8) | 53,549 (21.8) | 5,868 (25.4) | 13,001 (27.2) | < 0.001 | |
| Having a job | 180,091 (51.7) | 125,032 (51.0) | 12,074 (52.3) | 24,219 (50.7) | < 0.001 | |
| Household income level | < 0.001 | |||||
| Q1 (lowest) | 44,626 (12.8) | 33,034 (13.5) | 3,093 (13.4) | 6,691 (14.0) | ||
| Q2 | 40,940 (11.7) | 30,227 (12.3) | 2,853 (12.4) | 6,442 (13.5) | ||
| Q3 | 60,949 (17.5) | 43,660 (17.8) | 4,283 (18.6) | 8,769 (18.4) | ||
| Q4 (highest) | 11,982 (32.1) | 73,653 (30.0) | 7,279 (31.5) | 13,749 (28.8) | ||
| Medical aid program | 18,053 (5.2) | 16,569 (6.8) | 1,293 (5.6) | 3,554 (7.4) | ||
| Unknown | 71,935 (20.6) | 48,117 (19.6) | 4,275 (18.5) | 8,572 (17.9) | ||
| Residence | 0.002 | |||||
| Urban area | 112,934 (32.4) | 79,072 (32.2) | 7,201 (31.2) | 15,415 (32.3) | ||
| Rural area | 235,551 (67.6) | 166,188 (67.8) | 15,875 (68.8) | 32,362 (67.7) | ||
| Underlying disability | < 0.001 | |||||
| Mild to moderate | 35,734 (10.3) | 29,512 (12.0) | 2,640 (11.4) | 6,567 (13.7) | ||
| Severe | 9,909 (2.8) | 6,241 (2.5) | 725 (3.1) | 1,409 (2.9) | ||
| CCI, point | 1.0 ± 1.3 | 1.0 ± 1.2 | 1.1 ± 1.3 | 1.1 ± 1.3 | < 0.001 | |
| Myocardial infarction | 5,711 (1.6) | 3,856 (1.6) | 368 (1.6) | 750 (1.6) | 0.208 | |
| Congestive heart failure | 17,764 (5.1) | 12,693 (5.2) | 1,328 (5.8) | 2,690 (5.6) | < 0.001 | |
| Peripheral vascular disease | 14,943 (4.3) | 11,171 (4.6) | 1,002 (4.3) | 2,178 (4.6) | < 0.001 | |
| Cerebrovascular disease | 21,216 (6.1) | 14,344 (5.8) | 1,253 (5.4) | 2,692 (5.6) | < 0.001 | |
| Dementia | 10,785 (3.1) | 6,187 (2.5) | 756 (3.3) | 1,214 (2.5) | < 0.001 | |
| Chronic pulmonary disease | 41,785 (12.0) | 29,479 (12.0) | 3,359 (14.6) | 6,598 (13.8) | < 0.001 | |
| Rheumatic disease | 19,929 (5.7) | 15,884 (6.5) | 2,736 (11.9) | 5,495 (11.5) | < 0.001 | |
| Peptic ulcer disease | 33,020 (9.5) | 22,749 (9.3) | 2,143 (9.3) | 4,300 (9.0) | 0.002 | |
| Mild liver disease | 55,014 (15.8) | 38,407 (15.7) | 3,717 (16.1) | 7,896 (16.5) | < 0.001 | |
| DM without chronic complication | 81,851 (23.5) | 57,353 (23.4) | 5,007 (21.7) | 10,350 (21.7) | < 0.001 | |
| DM with chronic complication | 8,437 (2.4) | 6,800 (2.8) | 503 (2.2) | 1,185 (2.5) | < 0.001 | |
| Hemiplegia or paraplegia | 650 (0.2) | 335 (0.1) | 39 (0.2) | 54 (0.1) | < 0.001 | |
| Renal disease | 7,335 (2.1) | 4,702 (1.9) | 573 (2.5) | 1,153 (2.4) | < 0.001 | |
| Cancer | 4,775 (1.4) | 3,111 (1.3) | 351 (1.5) | 694 (1.5) | < 0.001 | |
| Moderate or severe liver disease | 425 (0.1) | 231 (0.1) | 21 (0.1) | 50 (0.1) | 0.012 | |
| Metastatic solid tumor | 535 (0.2) | 434 (0.2) | 52 (0.2) | 117 (0.2) | < 0.001 | |
| AIDS/HIV | 72 (0.0) | 64 (0.0) | 3 (0.0) | 11 (0.0) | 0.415 | |
| Regional anesthesia | 250,750 (72.0) | 174,212 (71.0) | 16,321 (70.7) | 33,649 (70.4) | < 0.001 | |
| Postoperative ICU admission | 4,089 (1.2) | 1,898 (0.8) | 286 (1.2) | 451 (0.9) | < 0.001 | |
| Perioperative transfusion | 215,120 (61.7) | 140,958 (57.5) | 13,266 (57.5) | 26,212 (54.9) | < 0.001 | |
| LOS, day | 20.7 ± 10.0 | 20.6 ± 9.8 | 20.0 ± 9.9 | 20.1 ± 9.8 | < 0.001 | |
| NSAIDs or paracetamol use | 35,176 (10.1) | 240,111 (97.9) | 4,899 (21.2) | 46,926 (98.2) | < 0.001 | |
| Gabapentin or pregabalin use | 27,015 (7.8) | 60,288 (24.6) | 3,151 (13.7) | 15,888 (33.3) | < 0.001 | |
| Hospital level | < 0.001 | |||||
| Level A | 13,005 (3.7) | 10,298 (4.2) | 1,053 (4.6) | 2,092 (4.4) | ||
| Level B | 110,901 (31.8) | 79,660 (32.5) | 7,185 (31.1) | 15,617 (32.7) | ||
| Level C | 66,922 (19.2) | 46,890 (19.1) | 4,933 (21.4) | 9,506 (19.9) | ||
| Level D | 157,657 (45.2) | 108,412 (44.2) | 9,905 (42.9) | 20,562 (43.0) | ||
| Type of arthroplasty | < 0.001 | |||||
| TKA | 280,849 (80.6) | 204,357 (83.3) | 17,845 (77.3) | 38,583 (80.7) | ||
| THA | 67,636 (19.4) | 40,903 (16.7) | 5,231 (22.7) | 9,214 (19.3) | ||
| Year of surgery | < 0.001 | |||||
| 2016 | 74,834 (21.5) | 19,855 (8.1) | 2,032 (8.8) | 1,879 (3.9) | ||
| 2017 | 60,773 (17.4) | 32,203 (13.1) | 3,223 (14.0) | 4,323 (9.0) | ||
| 2018 | 55,620 (16.0) | 39,354 (16.0) | 3,829 (16.6) | 6,608 (13.8) | ||
| 2019 | 56,997 (16.4) | 49,580 (20.2) | 4,459 (19.3) | 9,374 (19.6) | ||
| 2020 | 50,726 (14.6) | 49,808 (20.3) | 4,657 (20.2) | 11,125 (23.3) | ||
| 2021 | 49,535 (14.2) | 54,460 (22.2) | 4,876 (21.1) | 14,468 (30.3) | ||
Values are presented as mean ± standard deviation or number (%).
GC = glucocorticoid, CCI = Charlson Comorbidity Index, DM = diabetes mellitus, AIDS = acquired immunodeficiency syndrome, HIV = human immunodeficiency virus, ICU = intensive care unit, LOS = length of hospital stays, NSAIDs = nonsteroidal anti-inflammatory drugs, TKA = total knee arthroplasty, THA = total hip arthroplasty.
Mortality and postoperative complication
Table 2 shows the comparisons of in-hospital mortality, 1-year all-cause mortality, and postoperative complication rates. Table 3 shows the multivariable models for in-hospital mortality, 1-year all-cause mortality, and postoperative complications. In multivariable logistic model 1, compared with the non-user group, the opioid and GC user groups showed 53% (OR, 1.53; 95% CI, 1.12–2.30; P = 0.010) higher odds of in-hospital mortality, whereas opioid use (P = 0.582) and GC use (P = 0.835) were not significantly associated with in-hospital mortality. In multivariable Cox regression model 2, compared to the non-user group, GC users (HR, 1.24; 95% CI, 1.15–1.34; P < 0.001) and opioid and GC users (HR, 1.24; 95% CI, 1.14–1.35; P < 0.001) showed a higher risk of 1-year all-cause mortality, whereas opioid use (P = 0.900) was not significantly associated with 1-year all-cause mortality. In multivariable logistic regression model 3, compared to non-users, GC users (OR, 1.09; 95% CI, 1.04–1.15; P < 0.001) and opioid and GC users (OR, 1.06; 95% CI, 1.01–1.14; P = 0.014) had higher odds of postoperative complications, whereas opioid use (P = 0.922) was not significantly associated with postoperative complications. All ORs or HRs with 95% CIs in multivariable models 1, 2, and 3 are presented in Supplementary Tables 4, 5, and 6.
Table 2. Comparisons of in-hospital mortality, 1-year all-cause mortality, and postoperative complication rates.
| Variables | Non-users (n = 348,485) | Opioid users (n = 245,260) | GC users (n = 23,076) | Opioid and GC users (n = 47,777) | P value | |
|---|---|---|---|---|---|---|
| In-hospital mortality | 508 (0.1) | 238 (0.1) | 34 (0.1) | 66 (0.1) | < 0.001 | |
| 1-year mortality | 8,258 (2.4) | 4,074 (1.7) | 689 (3.0) | 1,054 (2.2) | < 0.001 | |
| Postoperative complication | 55,074 (15.8) | 37,280 (15.2) | 3,861 (16.7) | 7,599 (15.9) | < 0.001 | |
| Cerebral infarction or hemorrhage | 6,595 (1.9) | 4,102 (1.7) | 389 (1.7) | 754 (1.6) | < 0.001 | |
| Acute coronary events | 5,711 (1.6) | 3,856 (1.6) | 368 (1.6) | 750 (1.6) | 0.208 | |
| Heart failure | 15,279 (4.4) | 11,157 (4.5) | 1,145 (5.0) | 2,339 (4.9) | < 0.001 | |
| Pulmonary embolism | 4,460 (1.3) | 3,224 (1.3) | 316 (1.4) | 689 (1.4) | 0.023 | |
| Acute and subacute hepatic failure | 140 (0.0) | 129 (0.1) | 13 (0.1) | 21 (0.0) | 0.139 | |
| Acute renal failure | 2,644 (0.8) | 1,605 (0.7) | 164 (0.7) | 263 (0.6) | < 0.001 | |
| Sepsis | 2,466 (0.7) | 1,902 (0.8) | 235 (1.0) | 450 (0.9) | < 0.001 | |
| Wound infection | 8,783 (2.5) | 6,058 (2.5) | 679 (2.9) | 1,242 (2.6) | < 0.001 | |
| Pneumonia | 7,187 (2.1) | 3,712 (1.5) | 595 (2.6) | 890 (1.9) | < 0.001 | |
| Urinary tract infection | 15,200 (4.4) | 9,928 (4.0) | 975 (4.2) | 1,978 (4.1) | < 0.001 | |
Values are presented as number (%).
GC = glucocorticoid.
Table 3. Multivariable models for in-hospital mortality, 1-year all-cause mortality, and postoperative complications.
| Variables | OR (95% CI) or HR (95% CI) | P value | |
|---|---|---|---|
| In-hospital mortality (model 1) | |||
| Non-users | 1 | ||
| Opioid users | 1.10 (0.85–1.45) | 0.582 | |
| GC users | 0.95 (0.64–1.38) | 0.835 | |
| Opioid and GC users | 1.53 (1.12–2.30) | 0.010 | |
| 1-year mortality (model 2) | |||
| Non-users | 1 | ||
| Opioid users | 1.00 (0.93–1.07) | 0.900 | |
| GC users | 1.24 (1.15–1.34) | < 0.001 | |
| Opioid and GC users | 1.24 (1.14–1.35) | < 0.001 | |
| Postoperative complication (model 3) | |||
| Non-users | 1 | ||
| Opioid users | 1.00 (0.97–1.04) | 0.922 | |
| GC users | 1.09 (1.04–1.15) | < 0.001 | |
| Opioid and GC users | 1.06 (1.01–1.11) | 0.014 | |
OR = odds ratio, CI = confidence interval, HR = hazard ratio, GC = glucocorticoid.
Subgroup analyses (TKA or THA)
Table 4 shows the results of subgroup analyses according to the type of surgery. In the TKA group, opioid and GC users showed higher in-hospital mortality rates than did non-users (OR, 2.56; 95% CI, 1.14–5.74; P = 0.023). GC users (HR, 1.51; 95% CI, 1.28–1.78; P < 0.001) and opioid and GC users (HR, 1.64; 95% CI, 1.40–1.92; P < 0.001) showed higher 1-year all-cause mortality than did non-users. GC users (OR, 1.10; 95% CI, 1.04–1.16; P = 0.001) and opioid and GC users (OR, 1.10; 95% CI, 1.05–1.16; P < 0.001) experienced more postoperative complications than did non-users. In the THA group, compared to non-users, GC users (HR, 1.18; 95% CI, 1.08–1.29; P < 0.001) and opioid and GC users (HR, 1.12; 95% CI, 1.08–1.15; P < 0.001) showed higher 1-year all-cause mortality.
Table 4. Subgroup analyses according to type of surgery.
| Variables | OR (95% CI) or HR (95% CI) | P value | ||
|---|---|---|---|---|
| TKA group | ||||
| In-hospital mortality | ||||
| Non-users | 1 | |||
| Opioid users | 1.25 (0.61–2.55) | 0.544 | ||
| GC users | 1.57 (0.72–3.46) | 0.260 | ||
| Opioid and GC users | 2.56 (1.14–5.74) | 0.023 | ||
| 1-year mortality | ||||
| Non-users | 1 | |||
| Opioid users | 1.06 (0.93–1.21) | 0.398 | ||
| GC users | 1.51 (1.28–1.78) | < 0.001 | ||
| Opioid and GC users | 1.64 (1.40–1.92) | < 0.001 | ||
| Postoperative complication | ||||
| Non-users | 1 | |||
| Opioid users | 1.05 (1.01–1.09) | 0.017 | ||
| GC users | 1.10 (1.04–1.16) | 0.001 | ||
| Opioid and GC users | 1.10 (1.05–1.16) | < 0.001 | ||
| THA group | ||||
| In-hospital mortality | ||||
| Non-users | 1 | |||
| Opioid users | 1.17 (0.86–1.59) | 0.321 | ||
| GC users | 0.87 (0.58–1.30) | 0.486 | ||
| Opioid and GC users | 1.37 (0.91–2.05) | 0.135 | ||
| 1-year mortality | ||||
| Non-users | 1 | |||
| Opioid users | 0.96 (0.89–1.04) | 0.345 | ||
| GC users | 1.18 (1.08–1.29) | < 0.001 | ||
| Opioid and GC users | 1.12 (1.08–1.15) | < 0.001 | ||
| Postoperative complication | ||||
| Non-users | 1 | |||
| Opioid users | 0.94 (0.86–1.03) | 0.941 | ||
| GC users | 1.08 (0.99–1.18) | 0.086 | ||
| Opioid and GC users | 0.96 (0.89–1.02) | 0.443 | ||
OR = odds ratio, CI = confidence interval, HR = hazard ratio, GC = glucocorticoid, TKA = total knee arthroplasty, THA = total hip arthroplasty.
DISCUSSION
According to this population-based cohort study conducted in South Korea, among the analgesics prescribed before TJA, opioid analgesics alone had no influence on postoperative mortality or morbidity. In contrast, preoperative GC use and concomitant use of opioid analgesics and GC were significantly associated with increased postoperative mortality and morbidity after TJA. Therefore, prolonged GC use prior to TJA may contribute to the worsening of post-arthroplasty clinical outcomes; hence, GC should be prescribed with caution.
Adrenocortical steroids, or oral GCs, have potent anti-inflammatory and immunosuppressive properties and are commonly used to treat several illnesses.17 Oral GCs are commonly used in patients undergoing joint replacement surgery because they reduce inflammation in osteoarthritis-associated severe joint pain.18 According to a recent meta-analysis of 17 studies,19 preoperative chronic GC use was associated with an increased rate of 30-day overall complications, readmission, reoperation, pulmonary embolism, and deep vein thrombosis after orthopedic surgery. Moreover, preoperative GC use was associated with increased 90-day mortality after non-cardiac surgery in a South Korean hospital.13
According to a meta-analysis, the effects of increased oxidative stress are most noticeable 3 weeks after GC administration, and this effect is amplified with long-term use of the drug.20 Oxidative stress is a known risk factor for postoperative complications, multiple organ dysfunction syndrome, and increased long-term mortality among patients in the ICU.21,22 Furthermore, pro-inflammatory cytokines, such as interleukin (IL)-1, IL-2, IL-6, and IL-8, are produced in greater amounts during surgery, evoking both local and systemic inflammatory responses and increasing oxidative stress.23 From the perspective of oxidative stress, prolonged use of GC before surgery may contribute to increased complications, including mortality, after TJA.
Second, the long-term use of GC has profound effects on several systems. Long-term systemic steroid users have compromised cell-mediated immunity, which makes them more susceptible to infection.24 Patients who experienced a postoperative infection within 30 days had a 3.2 times higher risk of 1-year infection and a 1.9 times higher risk of mortality than did those who did not have a 30-day infection.25 Additionally, TJA (TKA and THA) involves the use of periprosthetic joints, and infections in these joints are significant factors that can increase patient mortality.26 The administration of steroids in the perioperative period is associated with a higher incidence of infection in periprosthetic joints, which have a higher susceptibility to infection associated with steroid injections.27 Furthermore, chronic GC use was associated with a higher incidence of surgical site infections, deep incisional surgical site infections, organ space surgical site infections, general wound infections, pneumonia, and urinary tract infection.28
Notably, preoperative opioid analgesic use was not significantly associated with increased mortality or morbidity after TJA. Although the chronic use of opioid analgesics is also known to affect the immune system of patients29 and is associated with poorer long-term survival outcomes,30 it did not affect mortality and morbidity after TJA in our study. Similar to our findings, a recent South Korean cohort study reported that preoperative opioid use was not associated with long-term mortality after THA.31 This may be because adequate preoperative pain control is an important factor in improving the clinical outcomes of patients undergoing TJA. Long-term and chronic pain can suppress immunity, which can worsen a patient’s postoperative prognosis.32 The immune system contributes significantly to the emergence and maintenance of numerous chronic pain disorders. The immune system is involved in the release of autoantibodies, cytokines, chemokines, and other inflammatory mediators.33 From this standpoint, the use of opioid analgesics to appropriately treat preoperative pain may compensate for the issues caused by the long-term use of opioids.
It is also important to note that the four groups showed significant differences in the preoperative prevalence of several diseases, including cardiovascular disease, rheumatic disease, cancer, and metabolic disease, as shown in Table 1. These differences may have contributed significantly to the between-group differences in postoperative mortality.34
This study had certain limitations. First, the database lacked information on factors such as body mass index, exercise, behavioral patterns, heavy smoking, and alcohol consumption history. Second, additional variables, such as the duration of surgery, intraoperative blood loss, and intraoperative hemodynamics, which might be linked to worse results, were not considered because of the lack of information in the NHIS database. Third, the results may not be applicable to other groups because this study included only South Korean hospitals and patients. Lastly, we considered the patients’ medications using prescription data for GC and opioids. However, this may be a limitation of the study, as we could not accurately determine the adherence or compliance of the patients to their medications.
In conclusion, preoperative GC use and concurrent use of opioid analgesics and GC were associated with higher postoperative mortality and morbidity following TJA. However, preoperative chronic opioid analgesic use alone did not affect postoperative mortality or morbidity after TJA.
Footnotes
Disclosure: The authors have no potential conflicts of interest to disclose.
- Conceptualization: Oh TK, Song IA.
- Data curation: Oh TK.
- Formal analysis: Oh TK.
- Methodology: Song IA.
- Writing - original draft: Oh TK.
- Writing - review & editing: Song IA.
SUPPLEMENTARY MATERIALS
The ICD-10 codes used by comorbidity to compute the CCI
Classification of disabilities in South Korea
Detailed information regarding the specific characteristics of each hospital
Other covariables in multivariate model 1
Other covariables in multivariate model 2
Other covariables in multivariate model 3
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
The ICD-10 codes used by comorbidity to compute the CCI
Classification of disabilities in South Korea
Detailed information regarding the specific characteristics of each hospital
Other covariables in multivariate model 1
Other covariables in multivariate model 2
Other covariables in multivariate model 3