Opioid prescriptions after total shoulder arthroplasty are associated with new persistent opioid use: a retrospective cohort study

Eve R. Glenn; Alexander R. Zhu; Henry Maxwell Fox; James H. Padley; Laurence Okeke; Edward G. McFarland

doi:10.1016/j.jor.2026.02.009

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74 (); 389-394

doi:

10.1016/j.jor.2026.02.009

Opioid prescriptions after total shoulder arthroplasty are associated with new persistent opioid use: a retrospective cohort study

Eve R. Glenn^a, Alexander R. Zhu^a, Henry Maxwell Fox^b, James H. Padley^b, Laurence Okeke^b, Edward G. McFarland^b,

a School of Medicine, The Johns Hopkins University, 733 N Broadway, Baltimore, MD, 21205, USA

b Department of Orthopaedic Surgery, The Johns Hopkins University, 2360 W Joppa Road, Lutherville, MD, 21093, USA

⁎Corresponding author: Edward G. McFarland. emcfarl1@jhmi.edu

Received: 2026-1-6, Accepted: 2026-2-2, Published: 2026-2-3

Professor P K Surendran Memorial Education Foundation

Disclaimer:
This article was originally published by Reed Elsevier India Pvt. Ltd. and was migrated to Scientific Scholar after the change of Publisher.

Abstract

Pain management after total shoulder arthroplasty (TSA) can be challenging. We examined the association between early postoperative opioid prescribing and postoperative (up to 1 year) outcomes of new persistent opioid use (NPOU), nonopioid substance use disorders, depressive episodes, and all-cause mortality among previously opioid-naive TSA patients. We hypothesized that early opioid prescribing would be associated with higher NPOU and differences in follow-up duration.

This retrospective study of 22,684 patients used the TriNetX Research Network database. Patients underwent TSA between July 31, 2004, and July 31, 2024, and were categorized into 2 cohorts: 1) those who received at least 1 opioid prescription within 30 days after TSA (opioid cohort, n = 7610) and 2) those who did not receive an opioid prescription during that period (nonopioid cohort, n = 15,074). Outcomes assessed between 90 days and 1 year after TSA were NPOU, nonopioid substance use disorders, depressive episodes, and all-cause mortality. Analyses were performed using propensity score matching and Kaplan-Meier survival curves. After matching, each cohort comprised 6977 patients. Alpha = .05.

Mean follow-up duration was 261 ± 141 days. After matching, the opioid cohort had a higher incidence of NPOU (10%) than the nonopioid cohort (7.7%) (P < .001), corresponding to a risk difference of 2.3% and a relative risk of 1.3. The opioid cohort had a lower incidence of nonopioid substance use disorders (1.2%) than the nonopioid cohort (1.6%) (P = .04), with no differences in depressive episodes (P = .82) or all-cause mortality (P = .74).

In previously opioid-naive patients undergoing TSA, early postoperative opioid prescribing was associated with greater risk of NPOU, whereas no differences were observed in depressive episodes or all-cause mortality. A lower incidence of nonopioid substance use disorders was seen in the opioid cohort. These findings underscore the need for tailored, multimodal pain management strategies after TSA.

III

Keywords

New persistent opioid use

Opioid prescribing

Perioperative pain management

Postoperative outcomes

Propensity score matching

Total shoulder arthroplasty

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1 Introduction

Total shoulder arthroplasty (TSA) is a common and highly effective procedure to alleviate pain and restore function to the shoulder joint.^1–5 Managing pain during the perioperative period is imperative. Modern multimodal analgesia approaches include cryotherapy, nonsteroidal anti-inflammatory drugs, acetaminophen, regional anesthesia or surgeon-applied blocks, opioids, gabapentinoids, and local anesthetics.⁶^,⁷ Opioids are effective in controlling acute pain; however, their associated risks, such as new persistent opioid use (NPOU) and opioid use disorder, are major public health concerns because of their associations with death and poor health outcomes.⁸

Although the acute phase of the opioid crisis has subsided, opioid-related overdose deaths continue to rise. In 2022, more than 80,000 people died of opioid-related overdose in the US, which was the highest number ever reported.⁹ In 2009, orthopaedic surgeons were identified as prescribing opioids at the fourth highest rate among physicians, but prescription volumes have been decreasing since.¹⁰^,¹¹ Recent evidence supports the efficacy of opioid-sparing analgesia after shoulder arthroplasty, which has been shown to be safe and effective without compromising patient satisfaction or pain control.^12–14 Avoidance of opioids may also improve orthopaedic outcomes, with one study finding that patients who are not prescribed opioids after TSA have shorter hospital stays and lower risks of infection and revision surgery.¹⁵ Such evidence supports wider adoption of postoperative protocols in which opioids are used as infrequently as possible.

Just as avoidance of opioids has improved outcomes, perioperative use of opioids has been shown to negatively affect outcomes. Initial overprescribing of oral morphine equivalents after either anatomic or reverse TSA has been associated with prolonged opioid dependency.¹⁶ Preoperative opioid use in patients who undergo TSA has been linked to higher healthcare utilization, revision surgery, and prolonged opioid use.¹⁷^,¹⁸ Factors that have been associated with prolonged opioid use after TSA include depression, anxiety, previous drug abuse, chronic pain, and age younger than 60 years.¹⁹^,²⁰ Opioids are commonly prescribed after TSA. Although opioid-naive patients typically receive lower doses during their hospital stays, the number of opioid pills prescribed at discharge is often similar between opioid-naive patients and those with a history of opioid use.¹⁶^,²¹^,²² There is a critical need to not only decrease or avoid the use of opioids, but when they are prescribed, to tailor the dose to individual patient needs, history, and inpatient opioid consumption.

To our knowledge, no study has examined the relationship between early opioid prescribing after TSA and subsequent NPOU among opioid-naive patients. Given the ongoing public health concerns surrounding opioid misuse, understanding how early opioid exposure after TSA may affect long-term opioid use is critical. Therefore, the present study examined the association of early postoperative opioid prescriptions (within 30 days after surgery) with the incidence of NPOU between 90 days and 1 year after TSA. Follow-up duration was also assessed—measured as time from surgery to last clinical visit—to consider potential differences in patient compliance and engagement with care. We hypothesized that patients who were prescribed opioids early after TSA would have a higher incidence of NPOU and shorter follow-up compared to those who were not prescribed opioids during this period.

2 Materials and methods

2.1

2.1 Data source

Data were obtained from the TriNetX platform, a global federated electronic medical record research network (TriNetX, Cambridge, MA) that uses real-time clinical data from more than 127 million patients from up to 20 years prior to the day of analysis.²³ Data elements used in this study included diagnosis codes, procedure codes, demographic variables, and prescription records as captured within participating organizations' electronic health record systems. Institution-level and country-level details are not accessible within the researcher interface. Data access and analyses were conducted in accordance with TriNetX policies and the platform's deidentification standards. Because the dataset is deidentified, this study was exempt from institutional review board approval.

2.2

2.2 Patient selection

We used Current Procedural Terminology (CPT) and International Classification of Diseases, Tenth Revision (ICD-10) codes to identify patients who underwent anatomic or reverse TSA (CPT code 23742) for osteoarthritis (ICD-10 code M15-M19) between July 31, 2004, and July 31, 2024. TSA was considered the index event. To ensure cohort homogeneity, we excluded patients undergoing reverse TSA for other diagnoses or procedures performed prior to ICD-10 implementation. Historical ICD-9 codes were mapped to ICD-10 when possible, but we focused on the ICD-10 era for consistency and coding accuracy.

To select for opioid-naive patients, we excluded patients who were prescribed opioids 1 year to 2 weeks before TSA (ICD-10 code CN101) and those with any diagnosis of opioid abuse or dependency (ICD-10 codes F11.1 and F11.2) before undergoing TSA. Patients prescribed opioids prior to surgery, even if intended for perioperative use, were excluded to avoid misclassification of preoperative exposure. Postoperative opioid prescriptions starting on or after the day of surgery were included in the opioid cohort. Patients were also excluded if they underwent any surgical procedures or anesthesia (CPT codes 1003143 and 1002796) within 30 days to 1 year after TSA. Two cohorts were created: patients who received prescriptions for opioids within 30 days after TSA (opioid cohort) and those who did not (nonopioid cohort).

2.3

2.3 Statistical analysis

Statistical analysis was conducted using the TriNetX platform and R software, version 4.4.1 (RStudio, Boston, MA). Baseline comparisons of demographic characteristics and comorbidities were assessed up to the day of the index event. Patients with missing data on sex, race, or ethnicity covariates were excluded from propensity score matching.

In the univariate analysis, age, sex, race, and ethnicity were considered. Matching variables were physiological conditions, psychiatric conditions, preoperative pain conditions, acute postprocedural pain, and potential health hazards related to socioeconomic and psychosocial circumstances (Appendix 1). Demographic data and comorbidities were analyzed using Student t-tests for continuous variables and chi-squared tests for categorical data. Statistical significance was set at an alpha level of .05. TriNetX's propensity score matching was then implemented, which uses 1:1 nearest neighbor greedy matching to build cohorts of equal size. All demographic variables and statistically significant comorbidities from the univariate analyses were balanced in this subsequent matching (i.e., physiological conditions, psychiatric conditions, preoperative pain conditions, acute postprocedural pain, and potential health hazards related to socioeconomic and psychosocial circumstances). Propensity score matching was then checked by assessing the standard mean difference, with any value greater than 0.1 indicating a residual imbalance in the matching process. Data are reported as means and standard differences.

After propensity score matching, time-specific outcomes were defined and compared between cohorts. NPOU was defined as the filling of an opioid prescription 90 days to 1 year after TSA; the platform does not provide morphine milligram equivalents, dispensing details, or confirmed patient consumption. The incidence of nonopioid substance use disorders, depressive episodes, and death was also analyzed between 90 days and 1 year after TSA. A Kaplan-Meier curve examining NPOU was developed using R software, version 4.4.1. This method was used to account for differences in follow-up duration and censoring, providing time-to-event estimates for the development of NPOU. Time to last follow-up, which is the time from TSA to death or the end of an individual's data collection within the first year after TSA, was calculated and analyzed for differences between subgroups.

2.4

2.4 Study population

We identified 22,684 patients with a history of osteoarthritis who underwent TSA during the study period. Of these patients, 7610 were in the opioid cohort, and 15,074 were in the nonopioid cohort. Baseline demographic variables and comorbidities before and after propensity score matching are shown in Tables I and II. After propensity score matching between opioid and nonopioid cohorts, there were 6977 patients in each group. Hypertensive disease was the only comorbidity that remained imbalanced between cohorts after matching (standard mean difference = 0.11; Table II).

Table 1 Comparison of demographic characteristics of patients who were prescribed opioids versus those who were not in the first 30 days after undergoing total shoulder arthroplasty, before and after matching, 2004–2024^a.

Characteristic	Opioid Cohort	Nonopioid Cohort	P-Value^c
Characteristic	N (%)	N (%)	P-Value^c
Before Matching
No. of patients	7610 (100)	15,074 (100)
Age at index surgery, yr	68.8 ± 9.3	68.5 ± 9.5	.04
Sex
Female	3801 (49.9)	7395 (49.3)	.39
Male	3515 (46.2)	6901 (46.0)	.83
Unknown	294 (3.9)	778 (5.2)	<.001
Race
American Indian or Alaska Native	18 (0.2)	30 (0.2)	.57
Asian	110 (1.4)	122 (0.8)	<.001
Black or African American	457 (6.0)	769 (5.1)	<.01
Native Hawaiian or Other Pacific Islander	13 (0.2)	16 (0.1)	.20
White	6131 (80.6)	12,220 (81.1)	.08
Other race	221 (2.9)	288 (1.9)	<.001
Unknown	660 (8.7)	1629 (10.8)	<.001
Ethnicity
Hispanic or Latino	244 (3.2)	650 (4.3)	<.001
Not Hispanic or Latino	5467 (71.8)	11,826 (78.9)	<.001
Unknown	1899 (25.0)	2598 (17.2)
After Matching
No. of patients	6977 (100)	6977 (100)
Age at index surgery, yr	69 ± 9.3^b	69 ± 9.3^b	.65
Sex
Female	3484 (49.9)	3518 (50.4)	.56
Male	3199 (45.9)	3173 (45.5)	.66
Unknown	294 (4.2)	286 (4.1)	.74
Race
American Indian or Alaska Native	17 (0.2)	17 (0.2)	>.99
Asian	86 (1.2)	87 (1.2)	.94
Black or African American	410 (5.9)	416 (6.0)	.83
Native Hawaiian or Other Pacific Islander	10 (0.1)	10 (0.1)	>.99
White	5611 (80.4)	5731 (82.1)	.01
Other race	167 (2.4)	157 (2.3)	.57
Unknown	676 (9.7)	559 (8.0)	<.01
Ethnicity
Hispanic or Latino	238 (3.4)	228 (3.3)	.64
Not Hispanic or Latino	5330 (76.4)	5333 (76.4)	.95
Unknown	1409 (20.2)	1416 (20.3)	.89

a We performed 1:1 propensity matching by age, sex, race, ethnicity, other acute postprocedural pain, unspecified abdominal pain, pain in joint, headache, pelvic and perineal pain, other headache syndromes, back pain, pain not elsewhere classified, migraine, and potential health hazards related to socioeconomic and psychosocial circumstances.

b Expressed as mean ± standard deviation.

c Standard difference for all comparisons was 0.0.

Table 2 Comparison of comorbidities among patients who were prescribed opioids versus those who were not in the first 30 days after undergoing total shoulder arthroplasty, before and after matching, 2004–2024^a.

Comorbidity	N (%)		P-Value	Standard Difference
Comorbidity	Opioid Cohort	Nonopioid Cohort	P-Value	Standard Difference
Before Matching
No. of patients	7610 (100)	15,074 (100)
Diabetes mellitus	1479 (19.4)	2924 (19.4)	.90	0.0
Hyperlipidemia (unspecified)	2936 (38.6)	6267 (41.6)	<.001	0.07
Hypertensive disease	4862 (63.6)	9347 (62.0)	.02	0.0
Ischemic heart diseases	1255 (16.5)	2571 (17.1)	.21	0.02
Kidney disease (chronic)	604 (7.9)	1238 (8.2)	.40	0.0
Mental health disorder
Depressive episode	1246 (16.4)	2669 (17.7)	.33	0.0
Other anxiety disorder	1246 (16.4)	2584 (17.1)	.10	0.0
Pain type
Abdominal (unspecified)	409 (5.4)	1391 (9.3)	<.001	0.2
Acute postprocedural	2534 (33.3)	5851 (38.8)	<.001	0.1
Back	1624 (21.3)	4939 (32.8)	<.001	0.3
Head
Migraine	255 (3.4)	667 (4.4)	<.001	0.06
Headache	348 (4.6)	1295 (8.6)	<.001	0.2
Other headache syndromes	169 (2.2)	737 (4.9)	<.001	0.2
Joint	4955 (65.1)	12,889 (85.5)	<.001	0.5
Pelvic and perineal	159 (2.1)	737 (4.9)	<.001	0.1
Not elsewhere classified	3585 (47.1)	8512 (56.5)	<.001	0.2
Potential health hazards related to socioeconomic and psychosocial circumstances	76 (1.0)	284 (1.9)
Substance use–related disorders	1057 (13.9)	2028 (13.5)	.46	0.0
Temporomandibular joint disorder (unspecified)	22 (0.3)	55 (0.4)	.34	0.0
After Matching
No. of patients	6977 (100)	6977 (100)
Diabetes mellitus	1380 (19.8)	1269 (18.2)	.02	0.0
Hyperlipidemia (unspecified)	2726 (39.1)	2632 (37.7)	.10	0.0
Hypertensive disease	4483 (64.3)	4101 (58.8)	<.001	0.11
Ischemic heart diseases	1177 (16.9)	1024 (14.7)	<.001	0.06
Kidney disease (chronic)	575 (8.2)	487 (7.0)	<.01	0.05
Mental health disorder
Depressive episode	1239 (17.8)	1025 (14.7)	<.001	0.08
Other anxiety disorder	1167 (16.7)	1014 (14.5)	<.001	0.06
Pain type
Abdominal (unspecified)	407 (5.8)	425 (6.1)	.52	0.0
Acute postprocedural	2458 (35.2)	2410 (34.5)	.39	0.0
Back	1604 (23.0)	1611 (23.1)	.89	0.0
Head
Migraine	248 (3.6)	224 (3.2)	.26	0.0
Headache	345 (4.9)	341 (4.9)	.88	0.0
Other headache syndromes	169 (2.4)	163 (2.3)	.74	0.0
Joint	4955 (71.0)	4964 (71.1)	.87
Pelvic and perineal	158 (2.3)	158 (2.3)	>.99	0.0
Not elsewhere classified	3486 (50.0)	3400 (48.7)	.15	0.0
Potential health hazards related to socioeconomic and psychosocial circumstances	75 (1.1)	70 (1.0)	.68	0.0
Substance use–related disorders	1017 (14.6)	806 (11.6)	<.001	0.09
Temporomandibular joint disorder (unspecified)	22 (0.3)	15 (0.2)	.25	0.0

a We performed 1:1 propensity matching by age, sex, race, ethnicity, other acute postprocedural pain, unspecified abdominal pain, pain in joint, headache, pelvic and perineal pain, other headache syndromes, dorsalgia, pain not elsewhere classified, migraine, and potential health hazards related to socioeconomic and psychosocial circumstances.

3 Results

Time to last follow-up and patient outcomes are available before and after propensity scoring in Table III. After matching, the mean overall follow-up duration for both cohorts was 261 ± 141 days. The incidence of NPOU was higher in the opioid cohort (10%) than in the nonopioid cohort (7.7%) (P < .001). We found a lower probability of remaining free of NPOU in the opioid cohort compared to the nonopioid cohort (hazard ratio 1.4, 95% CI: 1.2–1.5; Table III). Kaplan-Meier analysis further demonstrated a lower 1-year survival rate among patients who received an opioid prescription within 30 days of total shoulder arthroplasty (85%) compared to those who did not (89%) (Fig. 1). Additionally, the incidence of nonopioid substance use disorders was lower in the opioid cohort (1.2%) than in the nonopioid cohort (1.6%) (P = .04). No difference between cohorts was found in the incidence of depressive episodes (P = .82) or all-cause mortality (P = .74).

Table 3 Time to last follow-up and outcomes of patients who were prescribed opioids versus those who were not in the first 30 days after undergoing total shoulder arthroplasty, after propensity score matching, 2004–2024^a.

Outcome^b	N (%)		P-Value	RR (95% CI)
Outcome^b	Opioid Cohort (n = 6977)	Nonopioid Cohort (n = 6977)	P-Value	RR (95% CI)
Duration of follow-up, d	252 ± 146^c	266 ± 138^c	<.001	NA
New persistent opioid use	720 (10)	538 (7.7)	<.001	1.4 (1.2–1.5)
Nonopioid substance use–related disorders	85 (1.2)	114 (1.6)	.04	0.8 (0.6–0.99)
Depressive episode	150 (2.1)	154 (2.2)	.82	1.0 (0.8–1.2)
All-cause mortality	42 (0.60)	39 (0.56)	.74	1.1 (0.7–1.7)

a We performed 1:1 propensity matching by age, sex, race, ethnicity, other acute postprocedural pain, unspecified abdominal pain, pain in joint, headache, pelvic and perineal pain, other headache syndromes, dorsalgia, pain not elsewhere classified, migraine, potential health hazards related to socioeconomic and psychosocial circumstances.

b Outcome observations set for 90 days to one year after total shoulder arthroplasty.

c Expressed as mean ± standard deviation.

Kaplan-Meier analysis of new persistent opioid use (NPOU) after propensity score matching for patients who were prescribed opioids after undergoing total shoulder arthroplasty (opioid cohort) and those who were not (nonopioid cohort). The x-axis represents time in days after surgery, and the y-axis shows the probability of remaining free of NPOU. The analysis shows a significantly lower survival rate at 1 year after total shoulder arthroplasty for patients who received an opioid prescription within the first 30 days (85%) compared to those who did not receive an opioid prescription (89%) after surgery. This finding indicates that patients prescribed opioids shortly after surgery have a higher likelihood of developing NPOU.

4 Discussion

This study demonstrates that prescribing opioids within the first 30 days after TSA is associated with a higher risk of NPOU within 90 days to 1 year postoperatively. Although the absolute increase in NPOU was modest, the population-level impact is meaningful, with a number-needed-to-harm of approximately 44. Importantly, no differences were observed in depressive episodes or all-cause mortality, and a slightly lower incidence of nonopioid substance use disorders was seen in the early-opioid cohort. These findings highlight that early opioid exposure may act as a catalyst for persistent use in susceptible patients, rather than simply reflecting continuation of prior behavior. Prior studies have identified patient-level factors, such as mental health disorders, that are associated with NPOU after surgery, regardless of the procedure's invasiveness.¹⁹^,²⁰^,²⁴ In nonsurgical populations, Edlund et al.²⁵ highlighted a strong relationship between preexisting mental health disorders and chronic opioid use in patients with non-cancer-related chronic pain. Additionally, Agarwal et al.²⁶ identified a history of opioid use as an independent risk factor for NPOU after elective surgery. Despite adjustment for these variables through propensity score matching, early postoperative opioid prescribing remained associated with NPOU, suggesting that the prescription itself, rather than baseline patient characteristics alone, contributes to prolonged use. Our findings underscore the importance of moving beyond “one-size-fits-all” prescribing. Tailoring prescriptions to individual patient needs, including providing small-quantity opioid prescriptions when nonopioid management does not fully control pain and exploring nonopioid alternatives as appropriate, may mitigate the risk of persistent opioid use while ensuring adequate pain control.

Interestingly, the lower incidence of nonopioid substance use disorders in the group prescribed opioids within 30 days after TSA compared with those who were not suggests that effective pain management with opioids may mitigate the risk of seeking alternative substances for pain relief. This finding is concordant with research showing that uncontrolled chronic pain is associated with an increased risk of opioid use disorder and opioid prescription–related death.²⁷^,²⁸

Lastly, we observed that early opioid prescribing after TSA was associated with a shorter duration of follow-up. This finding, and the lower incidence of nonopioid substance use disorders in the opioid cohort, may have several potential explanations. Effective pain management—particularly through early opioid use—may reduce the need for extended follow-up and ongoing monitoring. Adequate pain control correlates with less healthcare utilization, shorter hospital stays, and greater postoperative patient satisfaction.^29–31 Conversely, patients with opioid use disorder, including those with private insurance, experience higher rates of loss to follow-up before completing their full course of therapy.³²^,³³ Prior studies have found low utilization of healthcare and low follow-up rates after overdose or emergency department visits related to opioid use disorder, with minority and resource-scarce populations being particularly vulnerable.^34–36 Finally, given the modest magnitude of this difference relative to the overall follow-up and the large variability in individual patient follow-up, the difference may not be clinically meaningful. However, demographic data for the cohorts after propensity score matching did not have residual imbalance, which undermines the suggestion that the shorter follow-up we observed in the opioid cohort was attributable to demographic or socioeconomic factors.

This study has several limitations. First, the reliance on an electronic medical records database like TriNetX limits access to detailed clinical measures, such as preoperative and postoperative range of motion and shoulder-specific functional scores, which may be obtained through manual medical record review. Second, despite the use of propensity score matching to balance cohorts, residual confounding factors may persist and affect the robustness of the conclusions. Third, the definition of opioid use was based on coded entries rather than actual opioid consumption, which introduces the potential for misclassification bias. Moreover, we did not account for high-risk opioid prescribing practices, which Vargas et al.³⁷ have associated with NPOU. Finally, only patients who underwent TSA for osteoarthritis were analyzed, limiting the generalizability of our findings to patients with other indications for TSA, such as rotator cuff tear arthropathy.

Strengths of our study include the use of a large, multicenter database and broad definitions of both opioid-naive status and NPOU. Unlike prior studies that limited opioid-naive designation to 180 days to 1 year before surgery,⁷^,²⁶^,³⁸^,³⁹ we considered any prior opioid use. Similarly, while NPOU has been variably defined—from prescription fills within 90–180 days after surgery to within 1 year after surgery²⁴^,³⁸^,⁴⁰^,⁴¹—we assessed opioid use through 1 year postoperatively. Extending follow-up to 1 year enables more complete assessment of persistent opioid use, particularly given that the first 30 days after surgery are a critical period for developing prolonged use. ^42–44

5 Conclusion

Opioid prescriptions within 30 days after TSA for osteoarthritis, while providing effective pain relief and reducing nonopioid substance use disorders, are associated with increased risk of NPOU. Additionally, opioid prescribing was associated with shorter follow-up duration, suggesting implications for postoperative care trajectories. These findings underscore the complexity of balancing adequate analgesia with the need to minimize long-term opioid exposure. Our results may support a paradigm shift from routine, protocol-driven opioid prescribing toward personalized, evidence-based pain management approaches that integrate multimodal analgesia and stringent opioid stewardship. By tailoring strategies to individual patient risk profiles, clinicians can optimize recovery while mitigating opioid-related harms.

Author contributions

ERG: conceptualization, software, validation, formal analysis, investigation, data curation, writing (original draft and review/editing), supervision, and project administration. ARZ: conceptualization, methodology, writing (original draft and review/editing). HMF: writing (review/editing). JHP: writing (review/editing). LO: conceptualization. EGM: validation, writing (original draft and review/editing), supervision, and project administration.

Ethical statement

As the data are deidentified, institutional review board approval was not required for this study.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Khazi Z.M., Lu Y., Patel B.H., Cancienne J.M., Werner B., Forsythe B., . Risk factors for opioid use after total shoulder arthroplasty. J Shoulder Elb Surg. 2020;29(2):235-243.
[Google Scholar]
Wilson J.M., Farley K.X., Gottschalk M.B., Daly C.A., Wagner E.R., . Preoperative opioid use is an independent risk factor for complication, revision, and increased health care utilization following primary total shoulder arthroplasty. J Shoulder Elb Surg. 2021;30(5):1025-1033.
[Google Scholar]
Best M.J., Harris A.B., Bansal A., Huish E., Srikumaran U., . Predictors of long-term opioid use after elective primary total shoulder arthroplasty. Orthopedics. 2021;44(1):58-63.
[Google Scholar]
Rao A.G., Chan P.H., Prentice H.A., et al . Risk factors for postoperative opioid use after elective shoulder arthroplasty. J Shoulder Elb Surg. 2018;27(11):1960-1968.
[Google Scholar]
Grace T.R., Khanna K., Choo K.J., et al . The influence of preoperative opioid use on inpatient opioid requirements and discharge prescriptions after primary shoulder arthroplasty. J Shoulder Elb Surg. 2018;27(9):1572-1579.
[Google Scholar]
Martusiewicz A., Khan A.Z., Chamberlain A.M., Keener J.D., Aleem A.W., . Outpatient narcotic consumption following total shoulder arthroplasty. JSES Int. 2020;4(1):100-104.
[Google Scholar]
Palchuk M.B., London J.W., Perez-Rey D., et al . A global federated real-world data and analytics platform for research. JAMIA Open. 2023;6(2)
[Google Scholar]
Brummett C.M., Waljee J.F., Goesling J., et al . New persistent opioid use after minor and major surgical procedures in US adults. JAMA Surg. 2017;152(6)
[Google Scholar]
Edlund M.J., Martin B.C., Devries A., Fan M.Y., Braden J.B., Sullivan M.D., . Trends in use of opioids for chronic non-cancer pain among individuals with mental health and substance use disorders: the TROUP study. Clin J Pain. 2010;26(1):1-8.
[Google Scholar]
Agarwal S., Shah A., Gunaseelan V., et al . New persistent opioid use after surgery in patients with a history of remote opioid use. Surgery. 2022;171(6):1635-1641.
[Google Scholar]
Inoue K., Ritz B., Arah O.A., . Causal effect of chronic pain on mortality through opioid prescriptions: application of the front-door formula. Epidemiology. 2022;33(4):572-580.
[Google Scholar]
Reuben D.B., Alvanzo A.A.H., Ashikaga T., et al . National institutes of health pathways to prevention workshop: the role of opioids in the treatment of chronic pain. Ann Intern Med. 2015;162(4):295-300.
[Google Scholar]
Horn R., Hendrix J.M., Kramer J., . Postoperative pain control. 2025
[Google Scholar]
Nimmo S.M., Foo I.T.H., Paterson H.M., . Enhanced recovery after surgery: pain management. J Surg Oncol. 2017;116(5):583-591.
[Google Scholar]
Shang A.B., Gan T.J., . Optimising postoperative pain management in the ambulatory patient. Drugs. 2003;63(9):855-867.
[Google Scholar]
Ali M.M., Mutter R., . Patients who are privately insured receive limited Follow-Up services after opioid-related hospitalizations. 2013
[Google Scholar]
Kilaru A.S., Xiong A., Lowenstein M., et al . Incidence of treatment for opioid use disorder following nonfatal overdose in commercially insured patients. JAMA Netw Open. 2020;3(5)
[Google Scholar]
Cunningham P., Barnes A., Mohamoud S., et al . Follow-up after ED visits for opioid use disorder: do they reduce future overdoses? J Subst Abuse Treat. 2022;142
[Google Scholar]
Kim S.J., Medina M., Chang J., . Healthcare utilization of patients with opioid use disorder in US hospitals from 2016 to 2019: focusing on racial and regional variances. Clin Drug Invest. 2022;42(10):853-863.
[Google Scholar]
Schoenfeld E.M., Westafer L.M., Soares W.E., . Missed opportunities to save lives-treatments for opioid use disorder after overdose. JAMA Netw Open. 2020;3(5)
[Google Scholar]
Vargas G.M., Gunaseelan V., Upp L., et al . High-risk opioid prescribing associated with postoperative new persistent opioid use in adolescents and young adults. Ann Surg. 2023;277(5):761-766.
[Google Scholar]
Alam A., Gomes T., Zheng H., Mamdani M.M., Juurlink D.N., Bell C.M., . Long-term analgesic use after low-risk surgery: a retrospective cohort study. Arch Intern Med. 2012;172(5):425-430.
[Google Scholar]
Calcaterra S.L., Yamashita T.E., Min S.J., Keniston A., Frank J.W., Binswanger I.A., . Opioid prescribing at hospital discharge contributes to chronic opioid use. J Gen Intern Med. 2016;31(5):478-485.
[Google Scholar]
Lee J.S.J., Hu H.M., Edelman A.L., et al . New persistent opioid use among patients with cancer after curative-intent surgery. J Clin Oncol. 2017;35(36):4042-4049.
[Google Scholar]
Howard R., Brown C.S., Lai Y.L., et al . Postoperative opioid prescribing and new persistent opioid use: the risk of excessive prescribing. Ann Surg. 2023;277(6):e1225-e1231.
[Google Scholar]
Aggarwal G., Broughton K.J., Williams L.J., Peden C.J., Quiney N., . Early postoperative death in patients undergoing emergency high-risk surgery: towards a better understanding of patients for whom surgery May not be beneficial. J Clin Med. 2020;9(5):1288.
[Google Scholar]
Haahr-Raunkjaer C., Mølgaard J., Elvekjaer M., et al . Continuous monitoring of vital sign abnormalities; association to clinical complications in 500 postoperative patients. Acta Anaesthesiol Scand. 2022;66(5):552-562.
[Google Scholar]
Khuri S.F., Henderson W.G., DePalma R.G., et al . Determinants of long-term survival after major surgery and the adverse effect of postoperative complications. Ann Surg. 2005;242(3):326-341.
[Google Scholar]

Introduction

Materials and methods

Data source
Patient selection
Statistical analysis
Study population

Results

Discussion

Conclusion

Patient consent

Author contributions

Ethical statement

Funding statement

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[Google Scholar]

[17] Khazi Z.M., Lu Y., Patel B.H., Cancienne J.M., Werner B., Forsythe B., . Risk factors for opioid use after total shoulder arthroplasty. J Shoulder Elb Surg. 2020;29(2):235-243.
[Google Scholar]

[18] Wilson J.M., Farley K.X., Gottschalk M.B., Daly C.A., Wagner E.R., . Preoperative opioid use is an independent risk factor for complication, revision, and increased health care utilization following primary total shoulder arthroplasty. J Shoulder Elb Surg. 2021;30(5):1025-1033.
[Google Scholar]

[19] Best M.J., Harris A.B., Bansal A., Huish E., Srikumaran U., . Predictors of long-term opioid use after elective primary total shoulder arthroplasty. Orthopedics. 2021;44(1):58-63.
[Google Scholar]

[20] Rao A.G., Chan P.H., Prentice H.A., et al . Risk factors for postoperative opioid use after elective shoulder arthroplasty. J Shoulder Elb Surg. 2018;27(11):1960-1968.
[Google Scholar]

[21] Grace T.R., Khanna K., Choo K.J., et al . The influence of preoperative opioid use on inpatient opioid requirements and discharge prescriptions after primary shoulder arthroplasty. J Shoulder Elb Surg. 2018;27(9):1572-1579.
[Google Scholar]

[22] Martusiewicz A., Khan A.Z., Chamberlain A.M., Keener J.D., Aleem A.W., . Outpatient narcotic consumption following total shoulder arthroplasty. JSES Int. 2020;4(1):100-104.
[Google Scholar]

[23] Palchuk M.B., London J.W., Perez-Rey D., et al . A global federated real-world data and analytics platform for research. JAMIA Open. 2023;6(2)
[Google Scholar]

[24] Brummett C.M., Waljee J.F., Goesling J., et al . New persistent opioid use after minor and major surgical procedures in US adults. JAMA Surg. 2017;152(6)
[Google Scholar]

[25] Edlund M.J., Martin B.C., Devries A., Fan M.Y., Braden J.B., Sullivan M.D., . Trends in use of opioids for chronic non-cancer pain among individuals with mental health and substance use disorders: the TROUP study. Clin J Pain. 2010;26(1):1-8.
[Google Scholar]

[26] Agarwal S., Shah A., Gunaseelan V., et al . New persistent opioid use after surgery in patients with a history of remote opioid use. Surgery. 2022;171(6):1635-1641.
[Google Scholar]

[27] Inoue K., Ritz B., Arah O.A., . Causal effect of chronic pain on mortality through opioid prescriptions: application of the front-door formula. Epidemiology. 2022;33(4):572-580.
[Google Scholar]

[28] Reuben D.B., Alvanzo A.A.H., Ashikaga T., et al . National institutes of health pathways to prevention workshop: the role of opioids in the treatment of chronic pain. Ann Intern Med. 2015;162(4):295-300.
[Google Scholar]

[29] Horn R., Hendrix J.M., Kramer J., . Postoperative pain control. 2025
[Google Scholar]

[30] Nimmo S.M., Foo I.T.H., Paterson H.M., . Enhanced recovery after surgery: pain management. J Surg Oncol. 2017;116(5):583-591.
[Google Scholar]

[31] Shang A.B., Gan T.J., . Optimising postoperative pain management in the ambulatory patient. Drugs. 2003;63(9):855-867.
[Google Scholar]

[32] Ali M.M., Mutter R., . Patients who are privately insured receive limited Follow-Up services after opioid-related hospitalizations. 2013
[Google Scholar]

[33] Kilaru A.S., Xiong A., Lowenstein M., et al . Incidence of treatment for opioid use disorder following nonfatal overdose in commercially insured patients. JAMA Netw Open. 2020;3(5)
[Google Scholar]

[34] Cunningham P., Barnes A., Mohamoud S., et al . Follow-up after ED visits for opioid use disorder: do they reduce future overdoses? J Subst Abuse Treat. 2022;142
[Google Scholar]

[35] Kim S.J., Medina M., Chang J., . Healthcare utilization of patients with opioid use disorder in US hospitals from 2016 to 2019: focusing on racial and regional variances. Clin Drug Invest. 2022;42(10):853-863.
[Google Scholar]

[36] Schoenfeld E.M., Westafer L.M., Soares W.E., . Missed opportunities to save lives-treatments for opioid use disorder after overdose. JAMA Netw Open. 2020;3(5)
[Google Scholar]

[37] Vargas G.M., Gunaseelan V., Upp L., et al . High-risk opioid prescribing associated with postoperative new persistent opioid use in adolescents and young adults. Ann Surg. 2023;277(5):761-766.
[Google Scholar]

[38] Alam A., Gomes T., Zheng H., Mamdani M.M., Juurlink D.N., Bell C.M., . Long-term analgesic use after low-risk surgery: a retrospective cohort study. Arch Intern Med. 2012;172(5):425-430.
[Google Scholar]

[39] Calcaterra S.L., Yamashita T.E., Min S.J., Keniston A., Frank J.W., Binswanger I.A., . Opioid prescribing at hospital discharge contributes to chronic opioid use. J Gen Intern Med. 2016;31(5):478-485.
[Google Scholar]

[40] Lee J.S.J., Hu H.M., Edelman A.L., et al . New persistent opioid use among patients with cancer after curative-intent surgery. J Clin Oncol. 2017;35(36):4042-4049.
[Google Scholar]

[41] Howard R., Brown C.S., Lai Y.L., et al . Postoperative opioid prescribing and new persistent opioid use: the risk of excessive prescribing. Ann Surg. 2023;277(6):e1225-e1231.
[Google Scholar]

[42] Aggarwal G., Broughton K.J., Williams L.J., Peden C.J., Quiney N., . Early postoperative death in patients undergoing emergency high-risk surgery: towards a better understanding of patients for whom surgery May not be beneficial. J Clin Med. 2020;9(5):1288.
[Google Scholar]

[43] Haahr-Raunkjaer C., Mølgaard J., Elvekjaer M., et al . Continuous monitoring of vital sign abnormalities; association to clinical complications in 500 postoperative patients. Acta Anaesthesiol Scand. 2022;66(5):552-562.
[Google Scholar]

[44] Khuri S.F., Henderson W.G., DePalma R.G., et al . Determinants of long-term survival after major surgery and the adverse effect of postoperative complications. Ann Surg. 2005;242(3):326-341.
[Google Scholar]

Opioid prescriptions after total shoulder arthroplasty are associated with new persistent opioid use: a retrospective cohort study

Abstract