Abstract
BACKGROUND:
Chronic opioid use is a significant public health concern. Surgery is a risk factor for developing chronic opioid use. Patients undergoing major spine surgery frequently are prescribed opioids preoperatively and may be at risk for chronic opioid use postoperatively. The aim of this study was to investigate the incidence of and perioperative risk factors associated with chronic opioid use after major spine surgery.
METHODS:
The records of patients who underwent elective major spine surgery at the University of Virginia between March 2011 and February 2016 were retrospectively reviewed. The primary outcome was chronic opioid use through 12 months postoperatively. Demographic data, medical comorbidities, preoperative pain scores, and medication use including daily morphine-equivalent (ME) dose, intraoperative use of lidocaine and ketamine, estimated blood loss, postoperative pain scores and medication use, and postoperative opioid use were collected. Logistic regression models were used to examine factors associated with chronic opioid use.
RESULTS:
Of 1477 patient records reviewed, 412 patients (27.9%) were opioid naive and 1065 patients (72.3%) used opioids before surgery. Opioid data were available for 1325 patients, while 152 patients were lost to 12-month follow-up and were excluded. Of 958 preoperative opioid users, 498 (52.0%) remained chronic users through 12 months. There was a decrease in opioid dosage (mg ME) from preoperative to 12 months postoperatively with a mean difference of –14.7 mg ME (standard deviation, 1.57; 95% confidence interval [CI], –17.8 to −11.7). Among 367 previously opioid-naive patients, 67 (18.3%) became chronic opioid users. Factors associated with chronic opioid use were examined using logistic regression models. Preoperative opioid users were nearly 4 times more likely to be chronic opioid users through 12 months than were opioid-naive patients (odds ratio, 3.95; 95% CI, 2.51–6.33; P < .001). Mean postoperative pain score (0–10) was associated with increased odds of chronic opioid use (odds ratio for a 1 unit increase in pain score 1.25, 95% CI, 1.13–1.38; P < .001). Use of intravenous ketamine or lidocaine was not associated with chronic opioid use through 12 months.
CONCLUSIONS:
Greater than 70% of patients presenting for major spine surgery used opioids preoperatively. Preoperative opioid use and higher postoperative pain scores were associated with chronic opioid use through 12 months. Use of ketamine and lidocaine did not decrease the risk for chronic opioid use. Surveillance of patients for these factors may identify those at highest risk for chronic opioid use and target them for intervention and reduction strategies. (Anesth Analg 2018;127:247–54)
Prescription opioid use and opioid-related deaths have reached epidemic proportions in the United States. Both the amount of prescription opioids sold and the number of deaths from prescription opioids have nearly quadrupled since the 1990s.1,2 Chronic opioid use is associated with numerous adverse systemic effects including constipation, respiratory depression, sleep-disordered breathing, cardiovascular events (myocardial infarction and heart failure), skeletal fractures, suppression of the hypothalamic-pituitary axis and immune system, as well as psychological risk for addiction and misuse.3 National attention has focused on opioid dependence and opioid-prescribing practices,4 with reform efforts aimed at identifying factors that contribute to opioid dependence to prevent dependency and improve the treatment of those affected.5,6
Surgery and the perioperative period are risk factors for developing chronic opioid dependence, according to several recent studies.7–9 Among surgical patients, female sex, younger age, preoperative history of tobacco, drug or alcohol abuse, benzodiazepine or antidepressant use, mood disorders, anxiety, and pain disorders are factors associated with increased risk for chronic opioid dependence.8–12 Procedures associated with an increased risk of chronic opioid use 1 year postoperatively include total knee arthroplasty (TKA), total hip arthroplasty (THA), laparoscopic cholecystectomy, open cholecystectomy, open appendectomy, cesarean delivery, and simple mastectomy.8 However, few studies have investigated the incidence of chronic opioid use after spine surgery.
Degenerative spine disease is common among the elderly.13 More than 488,000 spine surgeries were performed in the United States in 2011, a 70% increase from 2001.14 Patients undergoing multilevel spine surgery have a high incidence of preoperative opioid use, ranging from 20% to 55%.15,16 In a prospective study of 583 spine surgery patients, Armaghani et al17 demonstrated that preoperative opioid use is associated with increased perioperative and postoperative opioid requirements. The authors found that more invasive surgery, anxiety, revision surgery, and greater preoperative opioid use were associated with a decreased incidence of opioid independence at 12 months postoperatively (P < .01).
Preoperative opioid use may contribute to tolerance and opioid-induced hyperalgesia, making postoperative analgesia a challenge.18 Among orthopedic surgery patients, preoperative opioid use is associated with increased postoperative pain,19 higher postoperative opioid requirements,20 and continued use of opioids in the weeks to months after surgery.21 Preoperative opioid use has also been associated with worse postoperative outcomes, including increased complication rate,22,23 longer hospital stay,24 and need for early revision surgery.25 In a study of 574 patients undergoing THA and TKA, the incidence of chronic opioid use 6 months after surgery was 53.3% for TKA and 34.7% for THA patients who reported opioid use the day of surgery.21 For opioid-naive patients, the incidence of chronic opioid use 6 months postoperatively was 8.2% for TKA and 4.3% for THA. Greater overall body pain and greater affected joint pain and pain catastrophizing were risk factors for chronic opioid use. Interestingly, change in affected joint pain from baseline to 6 months was not predictive of opioid use.
The use of opioid-sparing multimodal analgesia may be beneficial to reduce postoperative pain and chronic opioid dependence.26–28 Nonopioid analgesics such as intravenous lidocaine29,30 and ketamine31,32 have been shown to reduce postoperative pain and opioid consumption in spine surgery patients. However, the effect on long-term opioid use has not been well studied. In their study of 102 opioid-dependent spine patients, Loftus et al31 reported that patients who received an infusion of intravenous ketamine intraoperatively consumed significantly less morphine equivalents (ME) at 24 hours, 48 hours, and 6 weeks postoperatively; however, longer term follow-up was not reported.
The aim of this study was to investigate the incidence and risk factors for chronic postoperative opioid use after major spine surgery in patients who were chronically using opioids before surgery compared to opioid-naive patients. We hypothesized that patients undergoing major spine surgery are at risk for chronic opioid use and that factors associated with chronic opioid use differ for preoperative opioid users compared to opioid-naive patients. A secondary hypothesis is that the use of lidocaine and ketamine perioperatively decreases the risk for chronic opioid use.
METHODS
The University of Virginia Institutional Review Board for Health Science Research (HSR-18786) approved this study and the requirement for written approved consent was waived.
Data
The records of all patients undergoing major spine surgery at the University of Virginia between March 2011 and February 2016 were retrospectively reviewed.
Sample
Patients ≥18 years of age who underwent elective spinal fusion of 2 or more level were included. All patients received relatively standardized perioperative management. Anesthesia was induced with intravenous lidocaine (1–1.5 mg/kg), propofol (1–2 mg/kg), and rocuronium (0.5–1 mg/kg), followed by administration of intravenous methadone (0.1–0.2 mg/kg) before surgical incision. Anesthesia was maintained with intravenous infusions of propofol (50–150 μg/kg/min), lidocaine (40 μg/kg/min), ketamine (0.3–0.5 mg/kg/h), with or without up to one half minimum alveolar concentration of volatile anesthetic to facilitate neuromonitoring. Based on the assessment of the anesthesiologist, intravenous hydromorphone was administered at the end of the procedure for additional analgesia. Patients were transferred to the intensive care unit for postoperative monitoring where they received opioid patient-controlled analgesia and oral analgesics for postoperative analgesia.
Outcome
The primary outcome was the incidence of chronic opioid use, defined as having a prescription for opioids documented in the medication administration record on each of postoperative days 1–3 and in the clinic medication reconciliation at each postoperative visit at the 1-, 6-, and 12-month time points. We chose this definition to focus on those patients with persistent opioid resulting from their spine surgery and to eliminate patients who discontinued opioid use but who were restarted on opioids during the 12-month period due to illness, injury, or another surgery. Patients who did not have a clinic note at one or more of these postoperative time points or for whom opioid prescription data or other data were unavailable were excluded from the logistic regression analysis.
Covariates
The following variables were collected from the electronic medical record for each patient. Patients lost to follow-up at 12 months were eliminated from the analysis.
Preoperative Variables.
Demographic data (age, body mass index [BMI], and American Society of Anesthesiologists [ASA] level), preoperative verbal response scale [VRS] pain scores, preoperative opioid use (defined as having a prescription for opioids in electronic medical record and use confirmed by preoperative nurse medication reconciliation on the day of surgery), dose in MEs (http://www.uptodate.com/contents/cancerpain-management-with-opioids-optimizing-analgesia),33 and preoperative use of gabapentin, acetaminophen, muscle relaxants (including baclofen, carisoprodol, cyclobenzaprine, metaxalone, methocarbamol, tizanidine), benzodiazepines/sedatives (diazepam, lorazepam, alpraxolam, clonazepam, oxazepam, trazodone, zolpidem, eszopicolone), antidepressants (citalopram, venlafaxine, escitalopram, amitriptyline, nortriptyline, sertraline, desvenlafaxine, fluoxetine, quetiapine, aripriprazole, paroxetine, mirtazapine), and lidocaine (transdermal patch) as documented in the medication reconciliation.
Intraoperative Variables.
Total opioid dose (mg ME), intraoperative use of ketamine and lidocaine and estimated blood loss (EBL; mL), an indicator of spine surgery invasiveness.
Postoperative Variables.
VRS pain scores, total daily postoperative opioid use in MEs, nonopioid analgesic use, opioid use at 1, 6, and 12 months after surgery. Medication use and dosages were obtained from the electronic medical record medication reconciliation on the day of surgery, during postoperative days 1–3 and per patient report as documented in the medication reconciliation at the postoperative clinic visits 1, 6, and 12 months after surgery. A sensitivity analysis was performed for the calculation of daily opioid dosage. Patients with a prescription for opioid with a dose frequency of pro re nata (PRN) were assumed to be taking either some (25%) or all (100%) the prescribed dose. For example, a patient with a prescription for “10 mg oxycodone every 4 hours PRN pain” was estimated to be taking between 25% of the prescribed amount (15 mg oxycodone) or 100% of the prescribed amount (60 mg oxycodone) daily.
Statistical Analysis
All variables, including opioid use, were treated as dichotomous variables with the exception of patient age, BMI, and opioid dose, which were treated as continuous variables. Analgesic medication use during postoperative days 1–3 were recoded into the number of days the drug was used (range, 0–3). Descriptive statistics are presented as number (n) and proportion (%) for dichotomous variables, and mean (M) and standard deviation (SD) for continuous variables. Medians are reported for highly skewed continuous variables (eg, intraoperative opioid dose). Differences in patient demographics and perioperative variables between opioidnaive patients and opioid users were compared using χ2 tests or Fisher exact tests (for dichotomous variables) and linear regression models (for continuous variables). Chronic opioid use was coded as a dichotomous variable (yes/no).
Logistic regression models were used to examine whether chronic opioid use through 12 months was associated with patients’ demographic characteristics (gender, age, BMI, ASA level), preoperative VRS pain and medication use (gabapentin, acetaminophen, muscle relaxants, benzodiazepine, antidepressants, lidocaine, intraoperative use of lidocaine, ketamine, opioid dosage), EBL (mL), pain scores, opioid dose, and use of acetaminophen, gabapentin, lidocaine, ketamine during the first 72 hours postoperatively. Of note, because chronic opioid users are defined as patients who consistently used opioid from postoperative, 1 month, 6 months, and 12 months postoperatively, the dosage of opioid (except intraoperative, because all patients received intraoperative opioid) was not included in the models. Odds ratios (ORs) are also presented for ease of interpretation. To control family-wise error, statistical significance was adjusted with Bonferroni correction at α of .05 divided by the number of comparisons (n = 30). Interactions between preoperative opioid use and each variable with a significance of P < .05 in the logistic regression model were tested to determine whether perioperative factors associated with chronic opioid use through 12 months differed between preoperative opioid users and opioid-naive patients. Given the limited power to detect interactions, a criterion for significance of P < .15 was used. All analyses were performed in R version 3.3.2.34 This manuscript adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
RESULTS
A total of 1477 patient records were reviewed, 412 patients (27.9%) were opioid naive before surgery and 1065 (72.1%) were prescribed opioids. A consort diagram showing opioid use preoperatively (1477 patients) and 12 months after surgery (1325 patients) is shown in the Figure. One hundred fifty-two patients were excluded from the analysis because they did not have a postoperative visit documented in the medical record at each of the 1-, 6-, and 12-month time points. Of the 958 patients who were using opioids before surgery, 498 patients (52.0%) continued to be prescribed opioids 12 months after surgery while 460 patients (48.0%) were no longer prescribed opioids. Of the 367 patients who were opioid naive before surgery, 67 patients (18.3%) continued to be prescribed opioids 12 months postoperatively and 300 patients (81.7%) were not prescribed opioid medications. Chi-square test showed that the odds of being a chronic opioid user 12 months after surgery was higher for patients who were using opioids before surgery than for opioid-naive patients (OR, 4.85; 95% confidence interval [CI], 3.63–6.54; P < .001).
Figure.
Consort diagram of study population showing the incidence of opioid use preoperatively and 12 mo after surgery.
Descriptive statistics of demographics and preoperative variables are presented in Table 1. Female gender, mean ASA level, incidence of depression, mean preoperative VRS pain score, and incidence of preoperative medication use for all medications was higher in those patients using opioids preoperatively compared to opioid-naive patients. Among preoperative opioid users, the mean daily opioid dose preoperatively was 34.8 ± 50.8 mg ME when it was assumed that patients were taking 100% of the PRN dosage, and 31.6 ± 50.1 mg ME when it was assumed that patients were taking only 25% of the PRN dosage. There was an overall decrease in opioid dosage (mg ME) from preoperative to 12 months postoperatively with a mean difference of –14.7 (SD, 1.6; 95% CI, –17.8 to –11.7), assuming 100% of the PRN opioid dose. The mean difference in opioid dosage was –14.0 (SD, 1.5; 95% CI, –17.0 to –11.0), assuming 25% of the PRN opioid dose.
Table 1.
Descriptive Statistics and Comparison Between Opioid-Naive Versus Non-Opioid-Native Patients on Demographics, Comorbidities, and Preoperative Variables
| Variable | All (n = 1477) |
Opioid-Naive Patients (n = 412) |
Preoperative Opioid Users (n = 1065) |
P Value |
|---|---|---|---|---|
| Sex (male) | 46.6% | 58.7% | 41.9% | <.001a |
| Age (y) | 59.8 ± 12.8 | 59.9 ± 13.6 | 59.8 ± 12.5 | .94 |
| Body mass index (kg/m2) | 30.3 ± 9.0 | 29.6 ± 6.0 | 30.6 ± 9.9 | .07 |
| American Society of Anesthesiologists physical status level (I–IV) | 2.5 ± 0.6 | 2.4 ± 0.6 | 2.6 ± 0.6 | <.001a |
| Anxiety (yes) | 21.9% | 19.7% | 22.7% | .23 |
| Asthma (yes) | 14.4% | 13.1% | 14.9% | .42 |
| Congestive heart failure (yes) | 7.5% | 6.6% | 7.9% | .45 |
| Coronary disease (yes) | 12.6% | 13.4% | 12.3% | .65 |
| Chronic obstructive pulmonary disease (yes) | 4.0% | 3.6% | 4.1% | .78 |
| Depression (yes) | 33.4% | 26.7% | 36.1% | <.001a |
| Diabetes (yes) | 10.4% | 10.9% | 10.2% | .77 |
| Hyperlipidemia (yes) | 39.9% | 37.6% | 40.8% | .30 |
| Hypertension (yes) | 63.3% | 62.1% | 63.9% | .58 |
| Preoperative verbal response scale pain score (0–10) | 4.7 ± 3.0 | 3.8 ± 3.1 | 5.1 ± 2.9 | <.001a |
| Preoperative opioid dose (mg morphine equivalent) | ||||
| 100% pro re nata dose | 34.8 ± 50.8 | |||
| 25% pro re nata dose | 31.6 ± 50.1 | |||
| Preoperative gabapentin use (yes) | 31.1% | 20.2% | 35.3% | <.001a |
| Preoperative acetaminophen use (yes) | 50.5% | 12.6% | 65.3% | <.001a |
| Preoperative muscle relaxant use (yes) | 30.0% | 18.2% | 34.7% | <.001a |
| Preoperative benzodiazepine use (yes) | 31.9% | 21.8% | 35.8% | <.001a |
| Preoperative antidepressant use (yes) | 38.0% | 25.0% | 43.1% | <.001a |
| Preoperative lidocaine use (yes) | 4.0% | 1.0% | 5.2% | <.001a |
Results reported as mean ± standard deviation or proportion of patients.
Significant result.
Intraoperative medication use, EBL (mL), postoperative medication use (mean number of postoperative days), total postoperative opioid dose (mg ME), and mean VRS pain scores (0–10) are shown in Table 2. The incidence of intraoperative ketamine use and mean intraoperative opioid dosage was greater for preoperative opioid users compared to opioid-naive patients (P < .001). Mean days of gabapentin, lidocaine, and opioid use were greater among preoperative opioid users compared to opioid-naive patients (P < .001). The proportion of patients using each type of medication during postoperative days 1, 2, and 3 is reported in Supplemental Digital Content 1, Table 1, http://links.lww.com/AA/C308. Total postoperative opioid dose (mg ME) and mean VRS pain scores (0–10) were also greater for preoperative opioid users (P < .001).
Table 2.
Comparision Between Opioid-Native Versus Non–Opioid-Native Patients on Intraoperative and Postoperative Variables
| Variable | All (n = 1477) |
Opioid-Naive Patients (n = 412) |
Preoperative Opioid Users (n = 1065) |
P Value |
|---|---|---|---|---|
| Intraoperative lidocaine use (yes) | 98.7% | 98.1% | 98.9% | .34 |
| Intraoperative ketamine use (yes) | 51.8% | 43.5% | 54.9% | <.001a |
| Total intraoperative opioid dose (mg morphine equivalent) | 34.2 ± 21.5 | 31.2 ± 14.1 | 35.3 ± 23.7 | <.001a |
| Estimated blood loss (mL) | 1220 ±1360 | 1040 ± 1170 | 1290 ± 1430 | .002 |
| Postoperative acetaminophen use (no. of days) | 2.4 ± 0.95 | 2.3 ± 1.0 | 2.4 ± 0.9 | .002 |
| Postoperative gabapentin use (no. of days) | 1.0 ± 1.3 | 0.7 ± 1.1 | 1.1 ± 1.3 | <.001a |
| Postoperative lidocaine use (no. of days) | 1.5 ± 1.4 | 1.3 ± 1.4 | 1.5 ± 1.3 | <.001a |
| Postoperative ketamine use (no. of days) | 0.1 ± 0.4 | 0.1 ± 0.4 | 0.1 ± 0.4 | .63 |
| Postoperative opioid use (no. of days) | 2.8 ± 0.5 | 2.8 ± 0.6 | 2.9 ± 0.5 | <.001a |
| Total postoperative opioid dose (mg ME) | 172.6 ± 170.6 | 116.8 ± 114.2 | 194.4 ± 183.6 | <.001a |
| Verbal response scale pain score (0–10) | 5.0 ± 2.0 | 4.3 ± 1.9 | 5.3 ± 1.9 | <.001a |
Results reported as mean ± standard deviation or proportion of patients.
Significant result.
Logistic regression models were used to assess the association between each baseline factor of interest and being a chronic opioid user through 12 months (Table 3). A total of 799 patients were included in the analysis. To simplify the model, postoperative drug use was recoded into the number of days the drug was used (range = 0–3). Patients who were using opioids preoperatively were at nearly 4 times more likely to be chronic opioid users than opioid-naive patients (OR for preoperative opioid use 3.95; 95% CI, 2.51–6.33; P < .001), after controlling for patients’ demographics, preoperative, intraoperative, and postoperative drug use, and EBL. Preoperative use of muscle relaxants was associated with increased odds of chronic opioid use through 12 months (OR, 1.80; 95% CI, 1.26–2.57; P < .001) at Bonferroni-adjusted significance criterion of 0.0025. Mean postoperative pain scores (0–10) were also significantly associated with the odds of chronic opioid use in this model (OR, 1.25; 95% CI, 1.13–1.38; P < .001). Use of intravenous ketamine or lidocaine intraoperatively or for more days postoperatively was not associated with chronic opioid use through 12 months at the Bonferroni-adjusted significance criterion.
Table 3.
Logistic Regression Model Estimating the Odds of Being a Chronic Opioid User at 12 mo Between Opioid-Native Patients and Chronic Preoperative Opioid User, Adjusting for Patients’ Demographic, Preoperative, Intraoperative, and Postoperative Variables
| 95% Confidence Interval |
||||
|---|---|---|---|---|
| Variable (Exposure) (n = 799) | Odds Ratio | Lower | Upper | P Value |
| Preoperative opioid use (yes) | 3.95 | 2.51 | 6.33 | <.001a |
| Sex (male) | 0.99 | 0.70 | 1.38 | .93 |
| Age (per 1 year increase) | 1.00 | 0.99 | 1.02 | .65 |
| Body mass index (per 1 kg/m2 increase) | 0.99 | 0.97 | 1.02 | .51 |
| American Society of Anesthesiologists physical status level | 1.04 | 0.76 | 1.44 | .80 |
| (per 1 level increase; I–IV) | ||||
| Anxiety (yes) | 0.74 | 0.49 | 1.12 | .15 |
| Asthma (yes) | 1.44 | 0.89 | 2.33 | .14 |
| Congestive heart failure (yes) | 1.08 | 0.53 | 2.16 | .84 |
| Coronary disease (yes) | 1.32 | 0.80 | 2.20 | .28 |
| Chronic obstructive pulmonary disease (yes) | 1.53 | 0.65 | 3.70 | .33 |
| Depression (yes) | 1.20 | 0.83 | 1.75 | .33 |
| Diabetes (yes) | 0.60 | 0.34 | 1.05 | .08 |
| Hyperlipidemia (yes) | 1.01 | 0.70 | 1.45 | .98 |
| Hypertension (yes) | 1.00 | 0.69 | 1.43 | .98 |
| Preoperative verbal response scale pain score | 0.99 | 0.94 | 1.05 | .84 |
| (per 1 unit increase; 0–10) | ||||
| Preoperative gabapentin use (yes) | 1.18 | 0.67 | 2.09 | .58 |
| Preoperative acetaminophen use (yes) | 0.89 | 0.62 | 1.27 | .51 |
| Preoperative muscle relaxant use (yes) | 1.80 | 1.26 | 2.57 | .001a |
| Preoperative benzodiazepine use (yes) | 1.42 | 1.00 | 2.03 | .05 |
| Preoperative antidepressant use (yes) | 0.97 | 0.68 | 1.39 | .87 |
| Preoperative lidocaine use (yes) | 1.28 | 0.54 | 3.13 | .58 |
| Intraoperative lidocaine use (yes) | 1.35 | 0.34 | 5.93 | .67 |
| Intraoperative ketamine use (yes) | 1.40 | 1.02 | 1.94 | .04 |
| Intraoperative opioid dosage (per 1 mg | 1.00 | 0.99 | 1.01 | .57 |
| morphine-equivalent increase) | ||||
| Estimated blood loss (per 1 mL increase) | 1.00 | 1.00 | 1.00 | .12 |
| Postoperative acetaminophen use (no. of days) | 1.02 | 0.85 | 1.24 | .81 |
| Postoperative gabapentin use (no. of days) | 0.91 | 0.75 | 1.11 | .37 |
| Postoperative lidocaine use (no. of days) | 1.09 | 0.97 | 1.24 | .17 |
| Postoperative ketamine use (no. of days) | 0.56 | 0.33 | 0.90 | .02 |
| Postoperative verbal response scale pain score | 1.25 | 1.13 | 1.38 | <.001a |
| (per 1 unit increase; 0–10) | ||||
Postoperative drug use recoded as number of days the drug was used (range, 0–3).
Significant result for Bonferroni-adjusted α = .05/30 = .0017.
To determine whether perioperative factors associated with chronic opioid use through 12 months differed between opioid-naive patients and preoperative opioid users, we tested interactions between preoperative opioid use and each variable with P < .05 in the previous model: preoperative muscle relaxant use, intraoperative ketamine use, EBL, postoperative ketamine use, and average postoperative pain score, controlling for age, BMI, ASA level, preoperative pain score, preoperative medication use, intraoperative use of lidocaine and ketamine, intraoperative opioid dosage, EBL, postoperative medication use and postoperative pain score. Results are shown in Supplemental Digital Content 2, Table 2, http://links.lww.com/AA/C309, with ORs reported for each interacting factor, stratified by gender. A criterion of P < .15 was considered significant. Compared with preoperative opioid users with lower postoperative pain scores, preoperative opioid users with higher postoperative pain scores were more likely to be chronic opioid at 12 months (P < .11). The interaction between preoperative opioid use and intraoperative ketamine use (P < .11) was not statistically significant based on the 95% CI. The interaction between preoperative opioids use and use of muscle relaxants or EBL were also not statistically significant.
DISCUSSION
In this retrospective study of 1477 patients presenting for major spine surgery, 1065 patients (72.1%) were chronically using opioids before surgery. Twelve months after surgery, 498 of the 958 patients (52.0%) who were using opioids chronically before surgery continued to be prescribed opioids. There was an overall decrease in opioid dosage from preoperative to 12 months postoperatively. In contrast, 412 patients (27.9%) were opioid naive before surgery and 67 of 367 previously opioid-naive patients (18.3%) continued to be prescribed opioids through 12 months. Our results are consistent with findings published by Armaghani et al17 in which 55% of spine surgery patients at their institution reported using opioids preoperatively. Fifty-nine percent of preoperative opioid users discontinued opioid use 12 months after surgery, while 26% of previously opioid-naive patients continued using opioids.
Previous studies have shown that psychological conditions, such as depression and anxiety, are associated with an increased risk for surgery-induced chronic opioid dependence.7,8,15 Armaghani et al17 showed that depression and anxiety were also risk factors for chronic opioid use. Preoperative history of depression or anxiety or the use of antidepressants or benzodiazepines preoperatively was not associated with chronic opioid use in our model. Previous studies have shown that younger age, anxiety, and depression are all risk factors for chronic opioid use. The mean age of our study population was 59.8 ± 12.8, slightly older than the mean age in Armaghani et al’s17 study (57.0 ± 13.2 years), which may account for this difference. Interestingly, the results here are consistent with a previous prospective study by our group which investigated the influence of pain catastrophizing, anxiety, and depression in 139 adult spine surgery patients.35 High levels of pain catastrophizing, but not anxiety or depression, was associated with worse postoperative pain. Similarly in thoracic surgery patients, Bayman et al36 showed that psychosocial factors, including anxiety and depression, were not predictive of chronic pain and opioid use. Anxiety and depression have been shown to mediate the relationship between pain catastrophizing and risk for opioid misuse in chronic pain patients when controlling for the effects of age and gender.37 The authors noted that the relationship between these factors and chronic opioid use is complex and requires further study.
In our model, preoperative opioid use was the factor most highly associated with chronic opioid use after spine surgery. This is consistent with Armaghani et al’s17 findings. Our study adds to the previous study by investigating the association of perioperative use of nonopioid analgesics, lidocaine, and ketamine with chronic opioid use after major spine surgery. Intravenous lidocaine has previously been shown to reduce postoperative pain scores and decrease opioid consumption in spine surgery patients29,30; however, this was not observed in our study. At our institution, intravenous lidocaine is routinely used for maintenance of anesthesia for spine procedures involving neuromonitoring, and nearly all patients in this study received intraoperative lidocaine. In contrast, intravenous lidocaine infusion may be added to standard analgesic regimen postoperatively if pain control proves challenging due to history of preoperative opioid use or a highly invasive surgery, and this may account for the results observed here.
Contrary to our hypothesis, use of ketamine intraoperatively or for more days postoperatively was not associated with decreased risk for chronic opioid use at Bonferroni-adjusted significance criterion of 0.0025. Interestingly, 54% of preoperative opioid users received intraoperative ketamine compared to 44% of preoperatively opioid-naive patients (P < .001). It seems logical that anesthesia providers recognize that patients using preoperative opioids are likely to have more postoperative pain and therefore use ketamine and other opioid-sparing analgesics. These results are consistent with a study by Subramaniam et al38 that showed no effect of intraoperative ketamine in patients using opioids before surgery, but are in contrast to other previous studies where intraoperative ketamine was shown to reduce postoperative pain and opioid consumption.31,32 A recent meta-analysis of 14 randomized control trials demonstrated an overall benefit of ketamine to reduce postoperative opioid consumption after spine surgery; however, many of the studies included did not control for preoperative opioid use.39
The international, multicenter Prevention of Delirium and Complications Associated with Surgical Treatments (PODCAST) study published in July 2017 investigated use of a single intraoperative dose of ketamine for prevention of postoperative delirium, with secondary outcomes being postoperative pain and opioid requirements.40 The study included 1360 patients undergoing cardiac and non-cardiac surgery and found no difference in pain scores or opioid consumption for patients receiving ketamine versus placebo. Given these results, future prospective studies to determine the effects of ketamine in opioid-naive versus opioid-tolerant patients, as well as appropriate dosage and timing for administration are necessary.
The use of intravenous ketamine and epidural local anesthetic infusions as part of a multimodal analgesic regimen for patients undergoing multilevel spine surgery has been shown to reduce opioid consumption on postoperative days 1 and 2 and decrease time to mobilization and ambulation.26 Future studies investigating the association of perioperative multimodal analgesia on long-term opioid dependence are indicated.
Finally, higher postoperative pain scores were also associated with increased risk for chronic opioid use, similar to the study by Bayman et al36, which showed that the severity of acute pain is predictive of chronic pain after thoracic surgery.
Our findings show a significant interaction between pre-operative opioid use and higher postoperative pain scores on chronic opioid use. This is not surprising given the findings above. It should be noted that the null findings for other factors may partly be due to the low power of the current statistical analyses.
There are several limitations to the present study. The first is that it is retrospective and the results suggest associations rather than causality. For example, we believe the association between intraoperative lidocaine and ketamine and chronic opioid use is not causal—lidocaine and ketamine are associated with lower opioid requirements in randomized trials. Second, preoperative opioid use was determined by medication reconciliation on the day of surgery. Postoperative opioid use was determined by medication reconciliation at 1-, 6-, and 12-month follow-up. Patients with a prescription for opioid use PRN were estimated to be taking either 25% or 100% of the amount of opioids prescribed. This represents a significant limitation of the present study as we do not have sufficient data to determine actual opioid use for this patient population. Prospective studies with opioid use verified by patient report or Prescription Monitoring Program records would provide valuable information and are indicated.
Preoperative opioid dependence is a significant concern in patients presenting for major spine surgery. Treatment of back pain is one of the major reasons that patients choose to undergo spine surgery. Some of these patients are able to discontinue opioid use after surgery. However, our study suggests that many patients continue to be prescribed opioids 1 year after surgery. A significant number of previously opioid-naive patients also continue to be prescribed opioids and this may contribute to surgery-induced opioid dependence. Functional improvement, as measured by the Oswestry Disability Index and quality-of-life metrics, should be considered when evaluating success of major spine surgery and the need for continued treatment with opioids. Surveillance of patients for the factors presented here may help to identify patients at risk for chronic opioid dependence. Given the significant risks associated with chronic opioid use, there is an urgent need to find perioperative solutions involving surgery, anesthesia, or perioperative analgesia to reduce the risk for opioid dependence.
Supplementary Material
KEY POINTS.
Question: What is the incidence of and risk factors for chronic opioid use after major spine surgery?
Findings: Patients undergoing major spine surgery are at risk for chronic opioid use. Use of opioids or muscle relaxants preoperatively or higher postoperative pain scores is associated with increased risk.
Meaning: Surveillance of patients for factors associated with chronic opioid use may identify those at high risk and target them for intervention strategies, including the use of nonopioid analgesics.
Footnotes
The authors declare no conflicts of interest.
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (www.anesthesia-analgesia.org).
DISCLOSURES
Name: Lauren K. Dunn, MD, PhD.
Contribution: This author helped design and conduct the study, analyze the data, and prepare the article.
Name: Sandeep Yerra, MBBS.
Contribution: This author helped collect the data.
Name: Shenghao Fang, MSc.
Contribution: This author helped collect the data.
Name: Mark F. Hanak, BS.
Contribution: This author helped collect the data.
Name: Maren K. Leibowitz, BS.
Contribution: This author helped collect the data.
Name: Siny Tsang, PhD.
Contribution: This author helped analyze the data and prepare the article.
Name: Marcel E. Durieux, MD, PhD.
Contribution: This author helped design the study and prepare the article.
Name: Edward C. Nemergut, MD.
Contribution: This author helped design the study and prepare the article.
Name: Bhiken I. Naik, MBBCh.
Contribution: This author helped design and conduct the study, analyze the data, and prepare the article.
This article was handled by: Honorio T. Benzon, MD.
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