Opioid Use Disorder Among Cervical Fusion Inpatients: Trends,Complications,and Resource Use in a Retrospective National Inpatient Sample Analysis,2016-2022

Abstract

Study design

Retrospective cohort study.

Objectives

To describe temporal trends in opioid use disorder (OUD) among adults undergoing inpatient cervical fusion, identify factors independently associated with OUD, and compare resource use and complications by OUD status.

Methods

Adult elective cervical fusion hospitalizations were identified in the National Inpatient Sample (NIS) from 2016 to 2022. OUD was defined using ICD-10-CM code lists. Survey-weighted procedures estimated annual prevalence and group means. Multivariable logistic regression was used to evaluate risk factors associated with OUD as well as medical complications and discharge disposition. Significance was set at P < 0.05.

Results

Odds of OUD were higher at ages 30-39 (OR 2.38, P < 0.001), 40-49 (OR 2.25, P < 0.001), 50-59 (OR 2.11, P = 0.001), and 60-69 (OR 1.65, P = 0.031) vs <30, and increased with clinical severity (Severity 2 OR 1.29; Severity 3 OR 1.79; Severity 4 OR 1.58; P < 0.001 for all). OUD patients had higher odds of undergoing multilevel fusion (OR 1.12, P < 0.001) and having co-occurring mental illness (OR 1.23, P < 0.001), alcohol abuse (OR 1.41, P < 0.001), and cannabis use (OR 1.59, P < 0.001). OUD was associated with higher odds of cardiovascular (OR 1.31, P < 0.001) and mechanical complications (OR 1.76, P < 0.001). OUD was associated with longer inpatient stays (3.23 vs 2.47 days, P < 0.001) and higher costs ($31,900 vs $27,000, P < 0.001) than those without OUD.

Conclusions

Patients with OUD undergoing cervical fusion are a clinically complex, resource-intensive subgroup with higher risks of cardiovascular and mechanical complications and non-routine discharge. Targeted risk stratification and OUD-tailored perioperative pathways can be used to improve outcomes.

Keywords

opioid use disorder cervical fusion length of stay hospital costs comorbidity national inpatient sample

Introduction

Opioid use disorder (OUD) has risen sharply and is a major public health concern. In 2022, an estimated 2.1 million Americans aged 12 years or older met criteria for OUD, reflecting a growing healthcare burden.¹ Opioid use also affects surgical care. Nearly 1 in 4 patients present for surgery already using opioids, and the prevalence of diagnosed OUD among surgical inpatients more than doubled from 0.58% in 2010 to 1.41% in 2018.^2,3 Spine procedures, particularly cervical fusion operations such as anterior cervical discectomy and fusion (ACDF) and posterior cervical decompression and fusion (PCDF), involve substantial postoperative pain, so opioids remain common in perioperative management.⁴ As more opioid-dependent patients enter the cervical fusion pathway, concerns about management and outcomes continue to grow.

Preoperative opioid exposure predicts persistent postoperative use and adverse cervical fusion outcomes.⁵ Chronic opioid use is linked to lower fusion success, including a higher risk of pseudoarthrosis and, in some cases, revision after ACDF.⁶ Functional outcomes show similar patterns, with chronic users reporting worse pain and lower satisfaction at baseline.⁷ OUD also adds financial burden, with longer hospital stays, more intensive care unit (ICU) care, and markedly higher hospitalization costs — on the order of 5 extra days and more than $50,000 per admission.¹ These trends, together with evolving opioid epidemiology and changing cervical fusion volumes in the United States, support the need for updated, nationally representative analyses across single and multilevel ACDF and PCDF.

The aim of this study was to (1) characterize recent trends in OUD among patients undergoing cervical fusion, (2) quantify the association between OUD and perioperative resource use, and (3) evaluate OUD-associated differences in surgical and discharge outcomes. We hypothesized that patients with OUD would demonstrate higher complication rates, greater odds of non-routine discharge, and increased resource use when compared to those without OUD.

Methods

Data Source

We performed a retrospective observational study using the Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) from 2016 to 2022, a stratified sample of U.S. community hospital discharges that supports national estimates after applying survey weights. Given that the HCUP-NIS is a de-identified, publicly available database, this study was exempt from institutional review board (IRB) approval, and informed consent was not required. Because the NIS captures information only for individual hospitalizations and does not include longitudinal patient identifiers, outcomes in this study were limited to events occurring during the index hospitalization.

Cohort Identification and Exclusions

We included adults (≥18 years) hospitalized for cervical fusion procedures from 2016 to 2022. Encounters were excluded if the admission was non-elective; if age or essential hospital/payer/cost fields were missing or implausible; if the index fusion was not performed on admission day; or if recorded length of stay or costs were negative. Because indication-level clinical detail is limited in the NIS, revision procedures and specific underlying diagnoses such as pseudarthrosis, OPLL, or neoplasm were not separately excluded if they were captured within otherwise eligible elective cervical fusion hospitalizations.

Opioid Use Disorder

OUD was defined using the HCUP-published ICD-10-CM code list, which includes diagnostic codes for opioid use, abuse, dependence, poisonings/adverse effects, and long-term opioid therapy.⁸ Patients were assigned to the OUD cohort if any listed diagnosis during the index hospitalization matched the HCUP-published ICD-10-CM code list. Accordingly, this study examined diagnosis-based OUD identified during the index hospitalization rather than preoperative opioid exposure characterized by prescription duration, chronicity, or morphine milligram equivalent thresholds.

Patient, Hospital, and Clinical Characteristics

We summarized demographics, socioeconomic indicators, primary payer, and hospital characteristics. Clinical factors included illness severity, fusion level (single vs multi-level), co-occurring mental illness (depression or psychoses), alcohol use disorder, and cannabis use. Comorbidities were derived via HCUP Elixhauser definitions, and a summary Elixhauser Comorbidity Index (ECI) was computed as the count of present conditions.

Outcomes

Primary outcomes were annual prevalence of OUD among cervical fusion hospitalizations and patient and hospital factors associated with OUD (adjusted odds by demographics, payer, comorbidity burden, illness severity, fusion level, and hospital characteristics). Secondary outcomes were resource-use trends by OUD status such as inpatient length of stay (LOS), inflation-adjusted hospital costs, and perioperative complications. Perioperative complications were identified during the index hospitalization using ICD-10-CM/PCS codes and grouped into cardiovascular, respiratory, and neurologic categories. Individual complications such as dysphagia, mechanical complications, and sepsis were also captured. A binary composite indicating 1 or more complications was created and labeled adverse events.

Cost Conversion and Inflation Adjustment

Total hospital charges were converted to costs using hospital and year-specific cost-to-charge ratios and then adjusted to 2022 U.S. dollars using Consumer Price Index multipliers.

Statistical Analysis

All analyses incorporated NIS stratification, clustering, and discharge weights per HCUP guidance. Because this was an exploratory retrospective analysis of a national administrative database, no a priori sample size calculation was performed. Instead, all eligible cervical fusion hospitalizations meeting study criteria during 2016-2022 were included. We estimated annual OUD prevalence and survey-weighted means with 95% CIs and summarized temporal change using compound annual growth rate. To identify factors associated with OUD, we fit a survey-weighted logistic regression including year and all covariates above, reporting odds ratios (ORs) with 95% CIs. To evaluate perioperative outcomes associated with OUD, we fit survey-weighted logistic models for the adverse-event composite, complication categories and selected individual complications, and non-routine discharge, adjusting for the same covariates. Statistical significance was set at P < 0.05. Analyses were conducted in R (version 4.4.0; R Project for Statistical Computing, Vienna, Austria).

Results

Temporal Trends in OUD Prevalence and Utilization

From 2016 to 2022, the prevalence of documented OUD among cervical fusion hospitalizations increased from 4.1% in 2016 to 5.8% in 2019, remained relatively stable through 2021 (5.6%–5.8%), and then declined to 4.8% in 2022 (P < 0.001). Cannabis use, mental illness, alcohol abuse, and the mean ECI also rose over time (Figure 1; Table 1).

Figure 1.

Annual prevalence of opioid use disorder (OUD) among cervical fusion hospitalizations from 2016 to 2022

Table 1.

Temporal Trends for Patients Undergoing Cervical Fusion Procedures

	2016 N (%)	2017 N (%)	2018 N (%)	2019 N (%)	2020 N (%)	2021 N (%)	2022 N (%)	P value
OUD	4985 (4.1)	5325 (5.0)	5540 (5.4)	5835 (5.8)	3275 (5.6)	4240 (5.8)	3770 (4.8)	<0.001
Cannabis use	1050 (0.9)	1145 (1.1)	1120 (1.1)	935 (0.9)	690 (1.2)	815 (1.1)	925 (1.2)	0.043
Mental illness	22,265 (18.1)	21,050 (19.6)	21,040 (20.5)	21,005 (21.0)	12,840 (22.1)	16,495 (22.5)	17,635 (22.4)	<0.001
Alcohol abuse	1565 (1.3)	1435 (1.3)	1570 (1.5)	1485 (1.5)	1105 (1.9)	1345 (1.8)	1215 (1.5)	<0.001
Mean ECI	1.68 (1.49)	1.80 (1.51)	1.93 (1.55)	1.98 (1.56)	2.10 (1.61)	2.17 (1.65)	2.21 (1.65)	<0.001

N = number; (%) = percentage; OUD = opioid use disorder; ECI = Elixhauser comorbidity Index. Bolded P values are statistically significant.

Patient Characteristics by OUD Status

Compared with patients without OUD, those with OUD more frequently underwent multilevel fusion (76.8% vs 73.4%, P < 0.001) and were concentrated in middle-age groups (eg, 50-59: 31.0% vs 28.6%, P < 0.001). Women comprised a slight majority among OUD cases (54.2% vs 50.7%, P < 0.001). OUD was associated with higher illness severity (Severity 3-4: 16.1% vs 8.6%, P < 0.001) and greater comorbidity burden (mean ECI 2.59 vs 1.91, P < 0.001). OUD cases were more often treated in large hospitals (57.5% vs 51.4%, P < 0.001) and in the West (23.1% vs 17.8%, P < 0.001), and less often in the Northeast (13.5% vs 19.4%, P < 0.001). Medicaid coverage and the presence of co-occurring mental illness, alcohol abuse, and cannabis use were all more prevalent among OUD patients (P < 0.001 for all; Table 2).

Table 2.

Temporal Trends in Opioid Use Disorder and Comorbidities Among Patients Undergoing Cervical Fusion From 2016 to 2022

Demographics	No prior opioid use	Opioid use disorder	P value
Weighted, N	610,125	32,970
Levels fused (%)			<0.001
Single-level	162,455 (26.6)	7,645 (23.2)
Multilevel	447,670 (73.4)	25,325 (76.8)
Age group (%)			<0.001
<30	4,580 (0.8)	95 (0.3)
30-39	28,050 (4.6)	1,440 (4.4)
40-49	92,940 (15.2)	5,080 (15.4)
50-59	174,555 (28.6)	10,230 (31.0)
60-69	182,730 (29.9)	10,365 (31.4)
70-79	106,880 (17.5)	4,930 (15.0)
80+	20,390 (3.3)	830 (2.5)
Sex (%)			<0.001
Male	300,920 (49.3)	15,085 (45.8)
Female	309,205 (50.7)	17,885 (54.2)
Race (%)			<0.001
White	474,455 (77.8)	26,675 (80.9)
Black	68,140 (11.2)	3,170 (9.6)
Hispanic	39,530 (6.5)	1,695 (5.1)
Asian or pacific	9,670 (1.6)	355 (1.1)
Native American	3,085 (0.5)	250 (0.8)
Other	15,245 (2.5)	825 (2.5)
Income quartile (%)			0.031
0-25th percentile	153,680 (25.2)	8,500 (25.8)
26th to 50th percentile	161,570 (26.5)	8,945 (27.1)
51st to 75th percentile	155,615 (25.5)	8,560 (26.0)
76th to 100th percentile	139,260 (22.8)	6,965 (21.1)
Severity (%)			<0.001
0,1 (lowest)	303,955 (49.8)	12,410 (37.6)
2	254,215 (41.7)	15,270 (46.3)
3	43,675 (7.2)	4,440 (13.5)
4 (highest)	8,280 (1.4)	850 (2.6)
Hospital teaching status (%)			0.006
Rural	20,075 (3.3)	1,280 (3.9)
Urban nonteaching	122,510 (20.1)	5,900 (17.9)
Urban teaching	467,540 (76.6)	25,790 (78.2)
Hospital size (%)			<0.001
Small	139,875 (22.9)	6,205 (18.8)
Medium	156,780 (25.7)	7,805 (23.7)
Large	313,470 (51.4)	18,960 (57.5)
Hospital region (%)			<0.001
Northeast	118,245 (19.4)	4,435 (13.5)
Midwest	126,945 (20.8)	7,805 (23.7)
South	256,290 (42.0)	13,120 (39.8)
West	108,645 (17.8)	7,610 (23.1)
Payer status (%)			<0.001
Medicare	249,925 (41.0)	15,265 (46.3)
Medicaid	58,315 (9.6)	3,875 (11.8)
Private insurance	245,935 (40.3)	11,280 (34.2)
Self-pay	6,370 (1.0)	410 (1.2)
Other	49,580 (8.1)	2,140 (6.5)
Cannabis use			<0.001
No	604,135 (99.0)	32,280 (97.9)
Yes	5,990 (1.0)	690 (2.1)
Alcohol abuse			<0.001
No	601,450 (98.6)	31,925 (96.8)
Yes	8,675 (1.4)	1,045 (3.2)
Mental illness			<0.001
No	488,190 (80.0)	22,575 (68.5)
Yes	121,935 (20.0)	10,395 (31.5)
Mean Elixhauser index	1.91 (1.56)	2.59 (1.74)	<0.001

N = number; (%) = percentage; OUD = opioid use disorder; ECI = Elixhauser comorbidity Index. Bolded P values are statistically significant.

Factors Independently Associated With OUD

In multivariable modeling, several factors were independently associated with higher odds of OUD: ages 30-39 (OR 2.38, 95% CI 1.50-3.79, P < 0.001), 40-49 (OR 2.25, 1.42-3.56, P < 0.001), 50-59 (OR 2.11, 1.34-3.31, P = 0.001), and 60-69 (OR 1.65, 1.05-2.61, P = 0.031) vs <30 years; female sex (OR 1.07, 1.01-1.12, P = 0.014); higher clinical severity (Severity 2: OR 1.29; Severity 3: OR 1.79; Severity 4: OR 1.58; all P < 0.001); treatment in the Midwest (OR 1.41), South (OR 1.38), or West (OR 1.88) vs Northeast (all P < 0.001); multilevel fusion (OR 1.12, 1.06-1.19, P < 0.001); large hospital size (OR 1.22, 1.09-1.36, P < 0.001); cannabis use (OR 1.59, 1.32-1.91, P < 0.001); alcohol abuse (OR 1.41, 1.21-1.64, P < 0.001); mental illness (OR 1.23, 1.15-1.31, P < 0.001); and each one-point increase in ECI (OR 1.19, 1.17-1.21, P < 0.001). Conversely, Black (OR 0.75, P < 0.001), Hispanic (OR 0.71, P < 0.001), and Asian/Pacific Islander (OR 0.62, P < 0.001) race/ethnicity had lower odds vs White, as did private insurance vs Medicare (OR 0.74, P < 0.001; Table 3).

Table 3.

Multivariable Analysis of Factors Associated With Opioid Use Disorder in Cervical Fusion Patients

Variable	Odds ratio (95% CI)	P value
Year	1.02 (1.00-1.04)	0.057
Age group
<30	Reference
30-39	2.38 (1.50-3.79)	<0.001
40-49	2.25 (1.42-3.56)	<0.001
50-59	2.11 (1.34-3.31)	0.001
60-69	1.65 (1.05-2.61)	0.031
70-79	1.14 (0.72-1.80)	0.584
80+	0.94 (0.58-1.52)	0.794
Sex
Male	Reference
Female	1.07 (1.01-1.12)	0.014
Race
White	Reference
Black	0.75 (0.68-0.82)	<0.001
Hispanic	0.71 (0.63-0.80)	<0.001
Asian or pacific	0.62 (0.49-0.79)	<0.001
Native American	1.19 (0.88-1.61)	0.267
Other	1.08 (0.91-1.29)	0.380
Payer status
Medicare	Reference
Medicaid	0.89 (0.81-0.98)	0.019
Private insurance	0.74 (0.69-0.80)	<0.001
Self-pay	1.05 (0.80-1.37)	0.740
Other	0.76 (0.67-0.85)	<0.001
Income quartile
0-25th percentile	Reference
26th to 50th percentile	1.00 (0.93-1.08)	0.956
51st to 75th percentile	1.03 (0.96-1.12)	0.397
76th to 100th percentile	1.00 (0.92-1.09)	0.994
Severity
0,1 (lowest)	Reference
2	1.29 (1.21-1.37)	<0.001
3	1.79 (1.62-1.97)	<0.001
4 (highest)	1.58 (1.32-1.90)	<0.001
Hospital region
Northeast	Reference
Midwest	1.41 (1.24-1.60)	<0.001
South	1.38 (1.23-1.55)	<0.001
West	1.88 (1.67-2.13)	<0.001
Levels fused
Single-level	Reference
Multilevel	1.12 (1.06-1.19)	<0.001
Hospital teaching status
Rural	Reference
Urban nonteaching	0.84 (0.68-1.05)	0.125
Urban teaching	0.93 (0.76-1.14)	0.491
Hospital size
Small	Reference
Medium	1.06 (0.95-1.19)	0.307
Large	1.22 (1.09-1.36)	<0.001
Cannabis use
No	Reference
Yes	1.59 (1.32-1.91)	<0.001
Alcohol abuse
No	Reference
Yes	1.41 (1.21-1.64)	<0.001
Mental illness
No	Reference
Yes	1.23 (1.15-1.31)	<0.001
ECI	1.19 (1.17-1.21)	<0.001

CI = confidence interval; ECI = Elixhauser comorbidity Index. Bolded P values are statistically significant.

Length of Stay and Hospital Costs by OUD Status

OUD was associated with longer inpatient stays (3.23 vs 2.47 days, P < 0.001) and higher costs ($31,900 vs $27,000, P < 0.001) than those without OUD. Both groups showed temporal shifts consistent with broader utilization patterns (Figures 2 and 3).

Figure 2.

Length of stay (LOS) by opioid use disorder (OUD) status among cervical fusion hospitalizations from 2016 to 2022

Figure 3.

Hospital costs by opioid use disorder (OUD) status among cervical fusion hospitalizations from 2016 to 2022

Perioperative Complications Associated With OUD

OUD was independently associated with higher odds of cardiovascular complications (OR 1.31, 1.18-1.46, P < 0.001), mechanical complications (OR 1.76, 1.56-2.00, P < 0.001), the presence of any adverse event (OR 1.28, 1.19-1.37, P < 0.001), and non-routine discharge (OR 1.19, 1.11-1.27, P < 0.001). Associations with respiratory complications (OR 1.09, P = 0.560), neurological complications (OR 0.91, P = 0.210), dysphagia (OR 1.07, P = 0.150), and sepsis (OR 0.79, P = 0.320) were not significant (Table 4).

Table 4.

Adjusted Odds Ratios for Perioperative Complications Associated With OUD

Complications	Odds ratio (95% CI)	P value
Cardiovascular	1.31 (1.18-1.46)	<0.001
Respiratory	1.09 (0.82-1.45)	0.560
Neurological	0.91 (0.78-1.06)	0.210
Dysphagia	1.07 (0.98-1.18)	0.150
Mechanical complications	1.76 (1.56-2.00)	<0.001
Sepsis	0.79 (0.49-1.26)	0.320
Adverse events	1.28 (1.19-1.37)	<0.001
Discharge disposition	1.19 (1.11-1.27)	<0.001

Cardiovascular complications = transfusion, venous thromboembolism, acute post-hemorrhagic anemia, myocardial infarction; Respiratory complications = pneumonia, pulmonary embolism; Neurological complications = cerebrovascular accident, nervous system complication, CSF leak/dural tear; Adverse events = 1 or more complications.CI = confidence intervalBolded P values are statistically significant.

Discussion

OUD remains a prevalent and consequential risk factor in modern cervical spine surgery. In this nationally representative cohort of inpatient cervical fusion hospitalizations from 2016 to 2022, OUD was common and clustered with higher clinical severity and multimorbidity. Patients with OUD were more frequently middle-aged, underwent more multilevel cervical fusion, and were treated disproportionately in regions outside the Northeast. They also experienced longer LOS, higher inflation-adjusted costs, and higher rates of select medical and mechanical complications and non-routine discharge.

We observed an early rise and sustained prevalence of OUD among cervical fusion inpatients across the study period, a pattern consistent with prior studies showing increasing opioid-related diagnoses among hospitalized patients with spinal conditions.⁹ Regional concentration of OUD outside the Northeast aligns with geographic variation in perioperative opioid exposure and prolonged postoperative use, with elevated risk in the South and West.¹⁰ Together, these trend and distributional signals suggest that prescribing culture, access to treatment, and regional practice patterns likely shape the OUD burden within cervical fusion inpatients, reinforcing the importance of routine psychosocial and substance-use screening and region-aware care planning.^11,12

OUD was more likely among middle-aged patients, those with greater clinical severity, multilevel constructs, and co-occurring mental illness or substance use. This pattern aligns with a meta-analysis by Mohan et al, which found that preoperative opioid exposure, depression/anxiety, substance dependence, and female sex are among the strongest predictors of prolonged postoperative use after spine surgery.¹³ One possible explanation is the presence of opioid tolerance or opioid-induced hyperalgesia among patients with greater opioid exposure, which may increase postoperative analgesic requirements without proportionally altering perceived functional improvement or PROM trajectories. In patients undergoing ACDF, opioid tolerance and longer preoperative exposure have been associated with higher risk of persistent use.^14,15 Moreover, a substantial subset either remains or becomes newly opioid-dependent after surgery, with dependence associated with higher healthcare payments and increased readmissions.^14,15 A prospective study by Uhrbrand et al aimed to identify why patients continue opioids after elective spine surgery, reporting that withdrawal symptoms were common and that non-surgical reasons often sustained use despite taper plans.¹⁶ A multicenter registry analysis by Kim et al sought to determine predictors of long-term opioid use after elective cervical and lumbar procedures, reporting that preoperative opioid use and use at 90 days were the strongest predictors of 1-2-year dependence, and failure to achieve meaningful functional improvement further increased risk. These findings support front-end optimization, early addiction-medicine and behavioral-health involvement, clear expectation setting, and structured tapering, for patients who screen positive for psychosocial or substance-use risk.

Across years, OUD admissions stayed about 1 extra hospital day and incurred $4900 higher inflation-adjusted costs per admission, paralleling cervical-specific evidence that OUD in 1-2 level ACDF is associated with longer in-hospital stays, higher 90-day readmissions and complications, and greater episode-of-care costs.¹⁷ Procedure intensity and complications appear to amplify downstream opioid exposure. This is also consistent with lumbar spine literature suggesting that fusion procedures, relative to lower-intensity decompression operations, are associated with greater postoperative opioid exposure, highlighting the likely contribution of procedural intensity to opioid-related risk across spine surgery. A prospective study by Rhon et al demonstrated that higher-intensity procedures like spinal fusion showed greater postoperative opioid fills, and after adjusting for complications, the odds of chronic use were significantly higher in the high-intensity group.¹⁸ Discharge prescribing is a modifiable factor, whereby greater discharge morphine milligram equivalents (MMEs) are independently associated with preoperative opioid use, procedure type, and prescribing provider in patients undergoing ACDF and cervical disc arthroplasty (CDA), supporting protocolized targets.¹⁹ Cervical-specific Enhanced Recovery After Surgery (ERAS) pathways that combine multimodal, opioid-sparing analgesia with standardized discharge prescribing and proactive follow-up are supported by contemporary reviews and clinical algorithms in spine surgery.¹² Other potentially modifiable perioperative factors, including body mass index and operative duration, may also influence postoperative analgesic requirements and represent important targets for future study in datasets with greater clinical granularity.

This study has several limitations. Diagnostic codes cannot distinguish active OUD from remission or delineate medication-assisted treatment, such as buprenorphine/methadone status, limiting exposure granularity. We lacked information on preoperative opioid dose/duration, implant choices, and surgeon or center experience, which may confound utilization and complications. Accordingly, we could not distinguish chronic preoperative opioid use by dosage or duration, including thresholds such as daily morphine milligram equivalents or opioid use extending beyond 3 months before surgery, and our findings should therefore be interpreted as pertaining to diagnosis-based OUD rather than prescription-defined opioid sensitization. In addition, the NIS does not provide granular perioperative variables such as body mass index or operative time, which may confound postoperative opioid-related outcomes but could not be accounted for in the present analysis. Because the NIS is hospitalization-based, we could not assess readmissions, long-term opioid persistence, or outpatient/short-stay pathways. Future work linking inpatient records to longitudinal pharmacy and outcomes data, and embedding perioperative OUD-tailored ERAS pathways, can clarify causality and identify the most effective, scalable interventions.

Conclusion

OUD remains prevalent among cervical fusion inpatients and predominantly affects those with higher clinical severity, greater multimorbidity, and patients undergoing multilevel fusion. From 2016 through 2022, OUD status was consistently linked to longer hospital stays, higher costs, and elevated odds of select medical and mechanical complications and non-routine discharge, underscoring measurable resource and safety impacts. These findings support targeted preoperative risk stratification and perioperative pathways tailored to OUD, whereby the integration of addiction medicine, behavioral health, and protocolized, opioid-sparing analgesia can be used to improve value and outcomes. Potential preoperative interventions include structured opioid tapering protocols when clinically appropriate, early involvement of multidisciplinary pain management teams, and individualized multimodal analgesia planning before surgery. Future work that links inpatient episodes to longitudinal pharmacy and outcomes data, and evaluates OUD-tailored ERAS pathways in trials, can clarify causality and identify scalable, high-value interventions.

Supplemental Material

Supplemental material - Opioid Use Disorder Among Cervical Fusion Inpatients: Trends, Complications, and Resource Use in a Retrospective National Inpatient Sample Analysis, 2016-2022

Supplemental material for Opioid Use Disorder Among Cervical Fusion Inpatients: Trends, Complications, and Resource Use in a Retrospective National Inpatient Sample Analysis, 2016-2022 by Mitchell K. Ng, MD, Leonidas E. Mastrokostas, BA, Paul G. Mastrokostas, BA, BBA, Anton J. Cristofoli, BS, Sean Inzerillo, BS, Gregorio Baek, BS, Khaled Elmenawi, MD, Ameen Barghi, MD, Abigail Razi, BS, Afshin E. Razi, MD, Daniel R. Fassett, MD, MBA, Nicholas U. Ahn, MD, Thomas D. Cha, MD, Jose A. Canseco, MD, Alan S. Hilibrand, MD, Alexander R. Vaccaro, MD, PhD, MBA, Christopher K. Kepler, MD, Gregory D. Schroeder, MD, Andrew P. Alvarez, MD in Global Spine Journal

Footnotes

ORCID iDs

Mitchell K. Ng

Leonidas E. Mastrokostas

Paul G. Mastrokostas

Abigail Razi

Jose A. Canseco

Alan S. Hilibrand

Author Contributions

Mitchell K. Ng has the following disclosures: VB Spine Inc (paid consultant), Stryker Inc (paid consultant), Johnson & Johnson Ethicon Inc (paid consultant), Elevation Spine (paid consultant), CurvaFix Inc (paid consultant), Pacira BioSciences Inc (paid consultant), Sage Products Inc (paid consultant), Alafair Biosciences Inc (paid consultant), Next Science LLC (paid consultant), Bonutti Technologies Inc (paid consultant), Hippocrates Opportunities Fund LLC (paid consultant), FISSO USA (paid consultant), MM Labs (paid consultant), Ferghana Partners Inc (paid consultant). Jose A. Canseco has the following disclosures: Accelus (research support), Cervical Spine Research Society (board or committee member), PathKeeper Surgical (stock or stock options; unpaid consultant), and Wolters Kluwer Health – Lippincott Williams & Wilkins (editorial or governing board). Alan S. Hilibrand has the following disclosures: CTL Amedica (IP royalties), North American Spine Society (board or committee member), Paradigm Spine (stock or stock options), and ZimVie (IP royalties). Alexander R. Vaccaro has the following disclosures: Accelus (other financial or material support; stock or stock options), Advanced Spinal Intellectual Properties (stock or stock options), AO Spine (other financial or material support), Atlas Spine (stock or stock options), Avaz Surgical (stock or stock options), AVKN Patient Driven Care (stock or stock options), Cytonics (stock or stock options), Deep Health (stock or stock options), Dimension Orthotics LLC (stock or stock options), Electrocore (stock or stock options), Elsevier (publishing royalties, financial or material support), Flagship Surgical (stock or stock options), FlowPharma (stock or stock options), Globus Medical (stock or stock options), Harvard Medtech (stock or stock options), Innovative Surgical Design (stock or stock options), Jaypee (publishing royalties, financial or material support), Jushi (Haywood) (stock or stock options), National Spine Health Foundation (board or committee member), Nuvasive (stock or stock options), Orthobullets (stock or stock options), Parvizi Surgical Innovation (stock or stock options), Progressive Spinal Technologies (stock or stock options), Rothman Institute and Related Properties (stock or stock options), Sentryx (other financial or material support; stock or stock options), Stout Medical (stock or stock options), Taylor Francis/Hodder and Stoughton (publishing royalties, financial or material support), Thieme (publishing royalties, financial or material support), and ViewFi Health (stock or stock options). Gregory D. Schroeder has the following disclosures: Advance Medical (paid consultant), Bioventus (paid consultant), Surgalign (paid consultant), AO Spine (board or committee member; other financial or material support), Cervical Spine Research Society (board or committee member), Cerapedics (research support), DePuy, A Johnson & Johnson Company (research support), Medtronic Sofamor Danek (research support), and Wolters Kluwer Health – Lippincott Williams & Wilkins (editorial or governing board). Christopher K. Kepler has the following disclosures: Clinical Spine Surgery (editorial or governing board), Curetiva (IP royalties), and Regeneration Technologies, Inc (IP royalties). All other authors have no other relevant financial disclosures. The author(s) declare that there is no conflict of interest and received no financial support for the research, authorship, and/or publication of this article. No IRB approval was required for performance of this review.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental Material

Supplemental material is available online.

References

Maman

Nandakumar

Steinfeld

Berkovich

. Preoperative opioid use disorder predicts prolonged ventilation, central line placement, and major anesthesiology complications after ACDF surgery: A big data analysis of 180,000 cases. J Clin Med. 2025;14(18):6661. doi:10.3390/jcm14186661

Tran

Madrigal

Pan

, et al. Impact of opioid use disorder on resource utilization and readmissions after operative trauma. Surgery. 2022;171(2):541-548. doi:10.1016/j.surg.2021.06.025

Burgess

Heneghan

Barot

Stulberg

. Surgeons’ knowledge regarding perioperative pain management in patients with opioid use disorder: A survey among 260 members of the American college of surgeons. Patient Saf Surg. 2024;18(1):9. doi:10.1186/s13037-024-00392-1

Harris

Marrache

Jami

, et al. Chronic opioid use following anterior cervical discectomy and fusion surgery for degenerative cervical pathology. Spine J. 2020;20(1):78-86. doi:10.1016/j.spinee.2019.09.011

Kalakoti

Volkmar

Bedard

Eisenberg

Hendrickson

Pugely

. Preoperative chronic opioid therapy negatively impacts long-term outcomes following cervical fusion surgery. Spine. 2019;44(18):1279-1286. doi:10.1097/BRS.0000000000003064

Lambrechts

D’Antonio

Heard

, et al. Opioid use increases the rate of pseudarthrosis and revision surgery in patients undergoing anterior cervical discectomy and fusion. Glob Spine J. 2024;14(2):620-630. doi:10.1177/21925682221119132

Wang

Lozen

Laud

Nattinger

Krebs

. Factors associated with chronic opioid use after cervical spine surgery for degenerative conditions. J Neurosurg Spine. 2020;32(1):1-8. doi:10.3171/2019.7.SPINE19563

Table 4, ICD-9-CM and ICD-10-CM Diagnosis Codes Defining Opioid Use Disorder (OUD). Published online April 14, 2020.

Lee

Shen

Kim

, et al. US trends of opioid-use disorders and associated factors among hospitalized patients with spinal conditions and treatment from 2005 to 2014. Spine. 2020;45(2):124-133. doi:10.1097/BRS.0000000000003183

10.

Adogwa

Davison

Vuong

, et al. Regional variation in opioid use after lumbar spine surgery. World Neurosurg. 2019;121:e691-e699. doi:10.1016/j.wneu.2018.09.192

11.

Yerneni

Nichols

Abecassis

Karras

Tan

. Preoperative opioid use and clinical outcomes in spine surgery: a systematic review. Neurosurgery. 2020;86(6):E490-E507. doi:10.1093/neuros/nyaa050

12.

Corley

Charalambous

Mehta

, et al. Perioperative pain management for elective spine surgery: opioid use and multimodal strategies. World Neurosurg. 2022;162:118-125.e1. doi:10.1016/j.wneu.2022.03.084

13.

Mohan

Lynch

Cha

EDK

, et al. Baseline risk factors for prolonged opioid use following spine surgery: systematic review and meta-analysis. World Neurosurg. 2022;159:179-188.e2. doi:10.1016/j.wneu.2021.12.086

14.

Dietz

Sharma

Alhourani

, et al. Preoperative and postoperative opioid dependence in patients undergoing anterior cervical diskectomy and fusion for degenerative spinal disorders. J Neurol Surg Cent Eur Neurosurg. 2021;82(3):232-240. doi:10.1055/s-0040-1718759

15.

Reyes

Canseco

Mangan

, et al. Risk factors for prolonged opioid use and effects of opioid tolerance on clinical outcomes after anterior cervical discectomy and fusion surgery. Spine. 2020;45(14):968-975. doi:10.1097/BRS.0000000000003511

16.

Uhrbrand

Helmig

Haroutounian

Vistisen

Nikolajsen

. Persistent opioid use after spine surgery: a prospective cohort study. Spine. 2021;46(20):1428-1435. doi:10.1097/BRS.0000000000004039

17.

Kim

Lam

Golub

, et al. Opioid use disorder in patients undergoing primary 1- to 2-Level anterior cervical discectomy and fusion is associated with longer In-Hospital lengths of stay and higher rates of readmissions, complications, and costs of care. Glob Spine J. 2023;13(6):1467-1473. doi:10.1177/21925682211037265

18.

Rhon

Greenlee

Lawson

McCafferty

Gill

. Assessment of surgical complications strengthen the relationship between spine surgery procedure intensity and chronic opioid use after surgery. Spine. 2024;49(22):1607-1613. doi:10.1097/BRS.0000000000005069

19.

Shen

Wick

Patel

, et al. Factors associated with total discharge opioid prescription morphine milligram equivalent amounts following primary anterior cervical spine surgery. J Am Acad Orthop Surg. 2023;31(3):e157-e168. doi:10.5435/JAAOS-D-22-00513

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB