Abstract
Importance:
Over 10 million people each year are prescribed opioids for chronic pain. Although surgical care is common, there is little evidence regarding coordination of opioid management and best practices for patients on long-term opioid therapy patients following surgery.
Objective:
We sought to describe the occurrence of high risk prescribing among chronic opioid users following surgery. We hypothesized that patients with a consistent usual prescriber (UP) who return to that prescriber within 30 days after surgery will be exposed to fewer high risk prescribing events.
Design:
Retrospective cohort study
Setting:
A large U.S. health insurer
Participants:
We identified 5749 commercially insured patients aged 18–64 with chronic opioid use who underwent elective surgery between January 2008 and March 2015.
Exposure(s):
The predictors were presence of a UP and early return (<30 days from surgery) to a UP.
Main Outcome(s):
The primary outcome was high-risk opioid prescribing in the 90 day post-operative period (multiple prescribers, overlapping opioid and/or benzodiazepine prescriptions, new long acting opioid prescriptions, or new dose escalations to >100mg OME).
Results:
In this cohort, 54.2% of patients were exposed to high risk prescribing postoperatively. Overall, 10% of patients did not have a UP preoperatively, and were more likely to have prescriptions from multiple prescribers (OR 2.89 95% CI 2.37–3.40) and new long acting opioid prescriptions (OR 1.87, 95% CI 1.37–2.54). Among patients with a UP, earlier return was associated with a decreased odds of receiving prescriptions from multiple prescribers (OR 0.83, 95% CI 0.71–0.96).
Conclusion:
Patients without a UP prior to surgery are more likely to be exposed to high risk opioid prescribing following surgery. Among patients who have a UP, early return visits may enhance care coordination with fewer prescribers. Increasing focus on identifying and communicating with prescribers can ensure safe transitions of care among chronic opioid users undergoing surgical care.
INTRODUCTION
There are currently over 10 million adults in the United States on long-term opioid therapy for chronic pain. (1, 2) Over a lifetime, the average individual will undergo approximately 9.2 procedures including inpatient and outpatient surgeries, presenting multiple opportunities for fragmentation of opioid prescribing for patients on chronic opioid therapy. (3) Recent studies have shown that pre-operative opioid use is a strong, independent risk factor for increased complications including hospital length of stay, discharge to rehabilitation facilities, readmissions, and overall greater expenditures. (4–6) Improved care coordination between surgeons and patients’ usual prescribers could potentially enhance care for chronic opioid users prior to procedural care. However, there are few standardized care pathways for long-term opioid users who present for procedural care, and best practices for transitions of care in this context are not well understood.
Current national guidelines on chronic pain management comprehensively detail opioid prescribing largely in the context of primary care, but do not address peri-procedural opioid management. (7) Recently published recommendations regarding perioperative prescribing often focus on inpatient care, and encourage appropriate reductions in opioid prescribing for opioid naïve patients. (8–14) Much less is known regarding the management of perioperative opioid prescribing for long-term opioid users. For example, a patient may have been taking long-term opioids for many years for chronic pain and receive monthly refills from his or her primary care physician. However, following surgical procedures, patients often receive additional prescriptions for acute post-operative pain management, which may or may not be coordinated with their existing regimens. To date, the extent to which the management of acute and chronic pain following elective surgical care has not been well described, but may represent an important opportunity to ensure that high-risk prescribing practices, such as multiple prescribers, overlapping prescriptions, and new long acting prescriptions do not occur. Prior studies have shown that high risk prescribing is correlated with increased risk of opioid misuse, overdose, and all-cause mortality.(15–17) Therefore, ensuring optimal care coordination between a patient’s usual opioid prescriber and the surgical care team may mitigate some of these risks.
Given the prevalence of long-term opioid use in the United States, we sought to examine the coordination of care with respect to opioid prescribing during procedural care among chronic opioid users. Specifically, we examined the role of the usual prescriber, defined as the physician prescribing the majority of their opioid prescriptions(18) during the year preceding surgery, in the postoperative period. We first aimed to describe the extent to which patients on long-term opioid therapy have a usual prescriber prior to elective surgery, and the specialty of the usual prescriber. We hypothesized that earlier return visits to the usual prescriber following surgery would mitigate the incidence of high risk prescribing (e.g. overlapping prescriptions, multiple prescribers, new long acting prescriptions) following surgery.
METHODS:
Data Source and Cohort:
We examined administrative health claims for members of a large national managed care company affiliated with OptumInsight between the times of January 1, 2008 and March, 31, 2015. We specifically examined data drawn from OptumInsight (Eden Prairie, MN), through which the Clinformatics™ Data Mart captures commercial health plan data from insurance claims for members of a large national managed care company spanning all 50 states, the District of Columbia, and Puerto Rico. Inpatient, outpatient, and pharmacy claims submitted for payment by prescribers and pharmacies are verified and de-identified prior to inclusion in ClinformaticsTM Data Mart. We limited our sample to adults aged 18–64 years old with continuous medical and prescription drug coverage during the year before and after surgery to evaluate the complete health care experience.
Patients were identified using the Current Procedural Terminology (CPT) codes or International Statistical Classification of Diseases and Related Health Problems (ICD9) procedure codes. Surgical procedures were categorized broadly as major (hysterectomy, colectomy, bariatric surgery, ventral incisional hernia repair, and reflux surgery) and minor (varicose vein removal, laparoscopic cholecystectomy, laparoscopic appendectomy, hemorrhoidectomy, thyroidectomy, transurethral prostate surgeries, parathyroidectomy, and carpal tunnel release). (19–21) We identified patients undergoing surgery using International Statistical Classification of Diseases and Related Health Problems (ICD-9) procedure codes. Data included claims from January 1, 2007 to September 30, 2015 to account for the 12-month preoperative through the 6-month postoperative study period. Patients also had to meet criteria for chronic opioid use which was defined as 180 day supply over one year + at least one prescription within 60 days of surgery.(4) We excluded patients greater than 64 years of age given the potential for dual enrollment in Medicare at this age, which could result in incomplete ascertainment of prescription data. Finally, we excluded patients who had subsequent anesthesia procedural codes for additional surgery in the 6-month postoperative follow up period to exclude patients who required further operations. The study was deemed exempt from human subjects review by the University of Michigan Institutional Review Board (Ann Arbor, MI).
Outcome Variables:
To capture opioid prescriptions filled before and after surgery, we used generic drug names matched with National Drug Codes to identify opioid prescriptions from pharmacy claims, which detail specific drug dose and type. For each prescription, we converted the unit of the opioid component to milligrams, and calculated oral morphine equivalents (OMEs) for each unit using standard published conversions for the morphine equivalent conversion factor per milligram.(22, 23) The OME dosage for each individual opioid prescription that was filled was calculated as the unit OME exposure multiplied by total quantity filled in this prescription.
Our primary outcome was high-risk prescribing defined as overlapping opioid prescriptions, overlapping benzodiazepine and opioid prescriptions, high daily doses, multiple prescribers, and long-acting opioid use in the 90 days post-operative period. (23–25) Overlapping prescriptions was defined as >7days of overlapping opioid prescriptions or opioid and benzodiazepine prescriptions within 90 days after surgical discharge. High post-operative daily dose was defined as at least one day of having >= 100 Oral Morphine Equivalents (OME) 91–120 days or after surgical admissions among the subgroup of patients maintained on <100 OMEs per day prior to surgery. Multiple prescribers was defined as 3 more clinicians attributed to the pharmacy claim for opioid prescriptions during the 90 days post-operative period. Prescribing clinician was determined by unique National Prescriber Identifier (NPI) codes for each pharmaceutical claim. Daily OME were calculated using standard conversion factors and divided by the day supply. (26) For overlapping prescriptions all available dosages were added for the day.
Primary Predictor:
Our primary predictor was early return visit to a usual prescriber (UP), which was defined as the prescriber who prescribed 50% or more of the opioid prescriptions in the year preceding surgery.(18) We identified the usual prescriber by unique National Prescriber Identifier (NPI) codes for each pharmaceutical claim. We defined early return to the usual prescriber if a return visit captured by Current Procedural Terminology codes (99211, 99212, 99213, 99214, 99215, 99204) were identified in the 30 days following the surgical procedures. Late return to a usual prescriber was defined as individuals for whom CPT codes were not identified until 30 to 90 days following the surgical procedure. Patients who did not have CPT codes for the usual prescriber following 90 days after surgery were classified as not returning to that prescriber.
Independent Variables:
We examined patient-level sociodemographic characteristics as covariates, including age, gender, race, geographic region, year of surgery, mean daily OME prior to surgery, and level of highest education achieved. In addition, we included comorbid conditions identified in the 12 months of claims prior to surgery and calculated the Charlson Comorbidity Index score.(27) Mental health conditions within the preceding 12 months were categorized according to the Clinical Classification System (CCS) from the Agency of Healthcare Research and Quality (AHRQ). (eTable 1) Mental health diagnoses were not mutually exclusive, and conditions were collapsed as mood disorders (adjustment, anxiety, and mood disorders), suicidality (suicide and intentional self-inflicted injury), disruptive behavior disorders (attention deficit, conduct, and disruptive behavior disorders; impulse control disorders), personality disorders, schizophrenia and other psychotic disorders, substance use disorders (alcohol and other substance-related disorders), and miscellaneous disorders. Preoperative pain-related conditions within the preceding 12 months were identified using ICD9 codes, and included back pain, neck pain, arthritis pain, and other pain disorders (eTable 2). Finally, we included separately an indicator of current or previous tobacco smoking identified using ICD-9 diagnosis codes (ICD9 305.1; V15.82) and smoking status in OptumInsight Health Risk Assessment dataset.
Prescriber Specialty
Provider specialty was derived using National Provider Identifier (NPI) codes assigned to healthcare providers. NPI codes were then used classify providers into five groups: (1) Surgery; (2) Primary Care; (3) Cardiology, Gastroenterology, Oncology, Neurology, & Other; (4) Physical Medicine & Rehabilitation (PM&R) / Pain Medicine (including Anesthesiology, Addiction Medicine, and Pain subspecialties); and (5) Emergency Medicine. PM&R and Pain subspecialties were grouped together as both care for patients with chronic pain, and opioid prescriptions encompass a large proportion of the prescriptions provided by these specialties. (28, 29) To prevent misclassification, we excluded clinicians for whom a specialty could not be identified using NPI codes.
Statistical Analysis:
All analyses were conducted using Stata version 14.2 (Stata-Corp). Descriptive statistics were used to describe demographic variables and comorbidities by presence of a preoperative UP. Univariate differences between patients with and without a UP were assessed using t tests and x2 tests. Further univariate analysis between those who had high risk prescribing were assessed with t tests and x2 tests. Multivariable logistic regression models were constructed to estimate differences in high risk prescribing by the presence of a UP and time to return to the UP while controlling for patient characteristics.
RESULTS:
Of the initial 151,784 patients undergoing elective surgery in the study cohort, 5749 patients were on long-term opioid therapy (3.8%) prior to undergoing major or minor surgery (Table 1). Among these patients, 90% (n=5149) had a UP identified prior to surgery, but 10% did not (n=600). Patients without a UP tended to be younger compared to patients who did have a UP prior to surgery (13% vs. 5%, p<0.001) (Table 1). In addition, patients without a UP had significantly higher mental health comorbidities including adjustment disorders, anxiety, mood, disruptive disorders, personality disorders, substance use disorders, and pain disorders.
Table 1.
Patients Characteristics by Having Usual Providers
| No Usual Provider (n=600) | Return to Usual Provider by within 30 days (n=1,597) | Not Return to Usual Provider within 30 days (n=3,552) | ||
|---|---|---|---|---|
| n (%) | n (%) | n (%) | p-value | |
| Major Surgery | 159 (26.5) | 382 (23.9) | 859 (24.2) | 0.423 |
| Daily OMEs (Mean, Minimum - Maximum) | ||||
| Preoperative 90 Days | 79.4 (1.0 – 1384.0) | 98.4 (2.3 – 4,240.0) | 79.9 (0.2 – 2,627.2) | <0.001 |
| Age: Mean (Std Dev) | 48.2 (10.6) | 51.4 (8.6) | 51.4 (8.8) | <0.001 |
| 18–34 | 78 (13.0) | 76 (4.8) | 176 (5.0) | <0.001 |
| 35–44 | 138 (23.0) | 255 (16.0) | 587 (16.5) | |
| 45–54 | 186 (31.0) | 593 (37.1) | 1,283 (36.1) | |
| 55–64 | 198 (33.0) | 673 (42.1) | 1,506 (42.4) | |
| Female | 414 (69.0) | 1,066 (66.8) | 2,398 (67.5) | 0.601 |
| Race | ||||
| White | 452 (75.3) | 1,227 (76.8) | 2,760 (77.7) | 0.425 |
| Black | 76 (12.7) | 189 (11.8) | 406 (11.4) | |
| Hispanic | 40 (6.7) | 108 (6.8) | 192 (5.4) | |
| Other | 32 (5.3) | 73 (4.6) | 194 (5.5) | |
| Net Worth | ||||
| Unknown | 81 (13.5) | 205 (12.8) | 455 (12.8) | <0.001 |
| <$25K | 133 (22.2) | 299 (18.7) | 541 (15.2) | |
| $25K-$149K | 157 (26.2) | 423 (26.5) | 906 (25.5) | |
| $150K-$249K | 82 (13.7) | 245 (15.3) | 586 (16.5) | |
| $250K-$499K | 85 (14.2) | 284 (17.8) | 714 (20.1) | |
| $500K+ | 62 (10.3) | 141 (8.8) | 350 (9.9) | |
| Charlson Index: Mean (SE) | 2.2 (2.8) | 2.2 (2.3) | 1.9 (2.2) | <0.001 |
| Tobacco Use | 395 (65.8) | 942 (59.0) | 1,995 (56.2) | <0.001 |
| Mental Health and Substance Use Disorder | ||||
| Adjustment Disorder | 49 (8.2) | 89 (5.6) | 189 (5.3) | 0.02 |
| Anxiety Disorder | 308 (51.3) | 652 (40.8) | 1,250 (35.2) | <0.001 |
| Mood Disorder | 330 (55.0) | 742 (46.5) | 1,556 (43.8) | <0.001 |
| Suicide and self-inflicted injury | 14 (2.3) | 24 (1.5) | 43 (1.2) | 0.091 |
| Disruptive Disorder | 34 (5.7) | 65 (4.1) | 126 (3.5) | 0.043 |
| Personality Disorder | 15 (2.5) | 15 (0.9) | 36 (1.0) | 0.004 |
| Schizophrenia and other psychosis | 23 (3.8) | 50 (3.1) | 111 (3.1) | 0.649 |
| Other Disorders | 67 (11.2) | 147 (9.2) | 285 (8.0) | 0.028 |
| Drug & Substance Use Disorders | 167 (27.8) | 305 (19.1) | 447 (12.6) | <0.001 |
| Pain Disorders | ||||
| Arthritis | 549 (91.5) | 1,387 (86.9) | 2,994 (84.3) | <0.001 |
| Back | 492 (82.0) | 1,208 (75.6) | 2,469 (69.5) | <0.001 |
| Neck | 291 (48.5) | 634 (39.7) | 1,244 (35.0) | <0.001 |
| Other Pain Disorders | 467 (77.8) | 1,173 (73.5) | 2,477 (69.7) | <0.001 |
In this cohort of elective surgery patients, primary care physicians were the most common preoperative UP (57.4%), followed by physical medicine and/or pain specialists (14.8%) (Figure 1). In contrast, only 4.5% of UP were prescribers within surgical disciplines.
Figure 1.
Percent Clinical Specialty of Usual Prescribers
Among patients with a UP, only 31% (1597/5149) returned to this prescriber within 30 days after surgery, and 69% (3552/5149) did not (Figure 2). In addition, 24.8% (1277/5149) of patients returned to the UP between 31–90 days and 44.2% (2276/5149) did not return within this 90-day period.
Figure 2:
Average Days to Return to Usual Provider
After adjusting for covariates, patients without a UP were more likely to be exposed to episodes of high risk prescribing compared to patients without a preoperative UP (OR 1.85, 95%CI 1.52–2.26) (Table 2). Specifically, patients were more likely to have multiple prescribers (OR 2.84 95% CI 2.37–3.40) and to be initiated on new long acting opioid prescriptions (OR 1.87, 95% CI 1.37–2.54). There were no significant differences between patients who had a UP and did not have a UP in overlapping opioid or benzodiazepine prescriptions in the 90 day post-operative period and in having a dose escalation to >100OME from 91–120 day period among patients who were maintained on doses of <100 OMEs per day prior to surgery.
Table 2:
Adjusted OR of High Risk Prescribing amongst those without a UP
| High Risk Prescribing | Multiple Prescribers (≥3) | Overlapping Prescriptions * | Overlapping w/ Benzodiazepine* | New Long-acting Prescription | High Daily Dose (≥100 OMEs)** | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Parameter | OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI |
| PreOp UP (Ref: Had UP) | ||||||||||||
| No UP | 1.853 | (1.522 2.256) | 2.838 | (2.367 3.402) | 1.079 | (0.885 1.314) | 0.813 | (0.417 1.586) | 1.867 | (1.374 2.537) | 1.34 | (0.87 2.08) |
Values adjusted for Preop Daily OME, Major/Minor Surgery, Age, Gender, Race/Ethnicity, Net worth, Charlson Comorbidity Index, Tobacco Use, Mental Health and Substance Use Disorders, and Pain Disorders
UP= Usual Prescriber
during Post-Op 90 days
w/i PostOp 91–120 Days
Amongst patients who had a UP (n=5,149), there was no difference in risk of any type of high risk prescribing (OR 1.014, 95% CI 0.89–1.16) when comparing patients who returned to their usual prescriber within 30 days versus those who saw this prescriber after 30 days (Table 3). However, patients that returned to the UP within 30 days did have a lower odds of having multiple prescribers in the post-operative period (OR 0.83, 95% CI 0.71–0.96). There was a non-significant trend (p=0.06) towards having overlapping opioid prescriptions with early follow-up (OR 1.15, 95% CI 1.00–1.32).
Table 3:
Association of Time to Return to UP with High Risk-Prescribing
| High Risk Prescribing | Multiple Prescribers (≥3) | Overlapping Prescriptions * | Overlapping w/ Benzodiazepine* | New Long-acting Prescription | High Daily Dose (≥100 OMEs)** | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Parameter | OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI |
| PreOp UP (Ref: Not return to UP w/i 30 days) | ||||||||||||
| Return to UP w/i 30 days | 1.014 | (0.889 1.156) | 0.827 | (0.709 0.964) | 1.145 | (0.997 1.316) | 1.105 | (0.728 1.675) | 0.910 | (0.683 1.212) | 1.22 | (0.85 1.74) |
Values adjusted for Preop Daily OME, Major/Minor Surgery, Age, Gender, Race/Ethnicity, Net worth, Charlson Comorbidity Index, Tobacco Use, Mental Health and Substance Use Disorders, and Pain Disorders
UP= Usual Prescriber
during Post-Op 90 days
w/i PostOp 91–120 Days
DISCUSSION:
In this cohort of adults with chronic opioid use undergoing elective surgery, the majority of individuals have a usual prescriber prior to surgery. Although most return to this prescriber in the postoperative period, only 31% do so within 1 month, and 44% do not return until well after 3 months. We observed that patients who do not have a usual prescriber are more likely to be exposed to episodes of high risk prescribing, particularly overlapping prescriptions, multiple prescribers and long-acting opioids. Amongst those with a usual prescriber, early return is associated with a lower likelihood of filling prescriptions from multiple prescribers.
These results highlight the potential for risk for chronic opioid users undergoing surgery, and an opportunity to improve care. Prior studies have shown that care fragmentation is associated with poor health outcomes following surgery. (30) Our results further indicate that for patients on chronic opioid therapy, having a consistent usual prescriber pre-operatively is associated with safer prescribing patterns. In this cohort, patients without a usual prescriber tended to be younger and have increased psychiatric comorbidities making them particularly vulnerable to poor health outcomes including overdose irrespective of the post-surgical period. Ideally, surgical teams could work to identify and refer the patient to more consistent primary care or specialty pain and behavioral health care when available. Specific to elective surgery, surgical teams have an opportunity to create a pain management plan that minimizes high-risk post-operative prescribing and emphasizes close surgical follow-up for this high-risk cohort. For patients with a usual prescriber pre-operatively, our study results also align with prior studies showing that an early return visits to a usual prescriber or primary care physicians following surgery may be associated with improved health outcomes.(31–33) Particularly, our results highlight that returning to a usual prescriber within 30 days places patients at lower odds of having multiple prescribers. Having multiple prescribers has been associated with overdose and opioid misuse in previous studies.(16) The results from this study highlight that early care coordination between the surgeon and usual prescriber could mitigate these adverse opioid outcomes and will need to be evaluated further in future studies.
Our results fill a critical gap and lay the foundation for future studies and interventions to optimize care coordination between surgeons and usual prescribers for patients on long-term opioid therapy. There is robust literature on how to optimize care transitions following hospital discharges for both medical and surgical indications including processes such as medication reconciliation and patient education on appropriate care utilization.(34) These care models include multidisciplinary pain management teams as well as enhanced use of centralized pharmacists to decrease adverse medication related events.(35) These types of models may similarly be applicable to patients with chronic pain or opioid use disorders for both routine medication management and following acute surgical episodes. (6, 36, 37) Compared to managing other medications, transitions of care models to manage opioids following surgery will additionally need to address how to coordinate opioid prescriptions between the usual prescriber, who are most often primary care physicians, and the surgeon. At the very least, surgical teams should have protocols in place to follow regulatory processes such as prescription drug monitoring program checks to minimize overlapping opioid prescriptions, co-prescriptions between opioids and other psychotropic medications, and appropriate post-surgical pain management. In addition, care models should proactively develop post-operative prescribing plans with patients who are at higher risk of poor outcomes, particularly those without a consistent opioid prescriber pre-operatively.
There are multiple limitations to this study. First, we examined retrospective observational data for associations between having a usual prescriber and adverse opioid related outcomes following surgery. However, by using a large nationally representative claims database with pharmacologic data, we captured representation of multiple prescribers that may be seen across health systems and settings. Second, we can only account for prescription opioid fills and not opioids consumed. Third, the observational nature of this study does not prove any causal relationships between usual prescribers and adverse opioid related outcomes. Future studies will need to better delineate this relationship. Finally, while we did control for confounders such as comorbidity and opioid dosage, we may have missed other confounders that would impact this association.
CONCLUSION:
In conclusion, our study signals that having a regular, consistent prescriber, such as a primary care physician, before and after surgery may decrease the risk of patients on chronic opioid therapy undergoing elective surgery, but close coordination of care is critical. Although a usual prescriber may mitigate some high risk prescribing patterns, they also may place patients at a higher risk of having overlapping prescriptions, particularly for patients without early follow-up. Future policy efforts and care models to promote safe opioid prescribing following surgery should not only focus on medication dosage and frequency, but should also incorporate recommendations on identifying a usual prescriber and improving care coordination between this prescriber and surgeons following procedures.
Supplementary Material
KEY POINTS.
Question:
How common is high risk opioid prescribing among patients with chronic opioid use following surgical procedures, and can care coordination mitigate these risks?
Findings:
In national cohort, 54.2% of chronic opioid users were exposed to episodes of high risk prescribing following surgery. Having a usual pre-operative opioid prescriber and returning to this prescriber early in the post-operative period was associated with decreased odds of high risk prescribing.
Meaning:
Coordinated prescribing between surgeons and pre-operative usual prescribers before and after surgery can mitigate high-risk opioid prescribing for patients on chronic opioids.
Acknowledgments
Financial Support: Drs. Waljee, Brummett, and Englesbe receive funding from the Substance Abuse and Mental Health Services Administration (E20180568-001), and the Michigan Department of Health and Human Services. The content is solely the responsibility of the authors and does not necessarily represent the official views of SAMSA and the Michigan Department of Health and Human Services.
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