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R. Pivec, K. Issa, Qais Naziri, B. Kapadia, P. Bonutti, Michael Mont (2014)
Opioid use prior to total hip arthroplasty leads to worse clinical outcomesInternational Orthopaedics, 38
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ZywielMG, StrohDA, LeeSY, BonuttiPM, MontMA Chronic opioid use prior to total knee arthroplasty. J Bone Joint Surg Am. 2011;93(21):1988-1993. doi:10.2106/JBJS.J.01473 22048093ZywielMG, StrohDA, LeeSY, BonuttiPM, MontMA Chronic opioid use prior to total knee arthroplasty. J Bone Joint Surg Am. 2011;93(21):1988-1993. doi:10.2106/JBJS.J.01473 22048093, ZywielMG, StrohDA, LeeSY, BonuttiPM, MontMA Chronic opioid use prior to total knee arthroplasty. J Bone Joint Surg Am. 2011;93(21):1988-1993. doi:10.2106/JBJS.J.01473 22048093
K. Ladha, J. Gagne, Elisabetta Patorno, K. Huybrechts, J. Rathmell, Shirley Wang, B. Bateman (2018)
Opioid Overdose After Surgical DischargeJAMA, 320
Steven Frenk, K. Porter, L. Paulozzi (2015)
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FrenkS, PorterK, PaulozziL. Prescription opioid analgesic use among adults: United States, 1999–2012. NCHS Data Brief. 2015;189:-.25714043FrenkS, PorterK, PaulozziL. Prescription opioid analgesic use among adults: United States, 1999–2012. NCHS Data Brief. 2015;189:-.25714043, FrenkS, PorterK, PaulozziL. Prescription opioid analgesic use among adults: United States, 1999–2012. NCHS Data Brief. 2015;189:-.25714043
S. Schneeweiss, A. Robicsek, R. Scranton, D. Zuckerman, D. Solomon (2007)
Veteran's affairs hospital discharge databases coded serious bacterial infections accurately.Journal of clinical epidemiology, 60 4
E. Wright, J. Katz, S. Abrams, D. Solomon, E. Losina (2014)
Trends in Prescription of Opioids From 2003–2009 in Persons With Knee OsteoarthritisArthritis Care & Research, 66
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SolomonDH, RassenJA, GlynnRJ, LeeJ, LevinR, SchneeweissS The comparative safety of analgesics in older adults with arthritis. Arch Intern Med. 2010;170(22):1968-1976. doi:10.1001/archinternmed.2010.391 21149752SolomonDH, RassenJA, GlynnRJ, LeeJ, LevinR, SchneeweissS The comparative safety of analgesics in older adults with arthritis. Arch Intern Med. 2010;170(22):1968-1976. doi:10.1001/archinternmed.2010.391 21149752, SolomonDH, RassenJA, GlynnRJ, LeeJ, LevinR, SchneeweissS The comparative safety of analgesics in older adults with arthritis. Arch Intern Med. 2010;170(22):1968-1976. doi:10.1001/archinternmed.2010.391 21149752
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KiyotaY, SchneeweissS, GlynnRJ, CannuscioCC, AvornJ, SolomonDH Accuracy of Medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value on the basis of review of hospital records. Am Heart J. 2004;148(1):99-104. doi:10.1016/j.ahj.2004.02.013 15215798KiyotaY, SchneeweissS, GlynnRJ, CannuscioCC, AvornJ, SolomonDH Accuracy of Medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value on the basis of review of hospital records. Am Heart J. 2004;148(1):99-104. doi:10.1016/j.ahj.2004.02.013 15215798, KiyotaY, SchneeweissS, GlynnRJ, CannuscioCC, AvornJ, SolomonDH Accuracy of Medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value on the basis of review of hospital records. Am Heart J. 2004;148(1):99-104. doi:10.1016/j.ahj.2004.02.013 15215798
(World Health Organization International Classification of Diseases, Ninth Revision (ICD-9). Geneva, Switzerland: World Health Organization; 1977.)
World Health Organization International Classification of Diseases, Ninth Revision (ICD-9). Geneva, Switzerland: World Health Organization; 1977.World Health Organization International Classification of Diseases, Ninth Revision (ICD-9). Geneva, Switzerland: World Health Organization; 1977., World Health Organization International Classification of Diseases, Ninth Revision (ICD-9). Geneva, Switzerland: World Health Organization; 1977.
Model 1 is adjusted for age, sex, race/ethnicity and region of residence. Model 2 is adjusted for comorbidity index, frailty, and number of unique prescription drugs
K. Hadlandsmyth, M. Weg, Kimberly McCoy, H. Mosher, M. Vaughan-Sarrazin, B. Lund (2018)
Risk for Prolonged Opioid Use Following Total Knee Arthroplasty in Veterans.The Journal of arthroplasty, 33 1
Yuka Kiyota, S. Schneeweiss, R. Glynn, C. Cannuscio, J. Avorn, D. Solomon (2004)
Accuracy of Medicare claims-based diagnosis of acute myocardial infarction: estimating positive predictive value on the basis of review of hospital records.American heart journal, 148 1
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RayWA, GriffinMR, FoughtRL, AdamsML Identification of fractures from computerized Medicare files. J Clin Epidemiol. 1992;45(7):703-714. doi:10.1016/0895-4356(92)90047-Q 1619449RayWA, GriffinMR, FoughtRL, AdamsML Identification of fractures from computerized Medicare files. J Clin Epidemiol. 1992;45(7):703-714. doi:10.1016/0895-4356(92)90047-Q 1619449, RayWA, GriffinMR, FoughtRL, AdamsML Identification of fractures from computerized Medicare files. J Clin Epidemiol. 1992;45(7):703-714. doi:10.1016/0895-4356(92)90047-Q 1619449
Dae Kim, S. Schneeweiss, R. Glynn, L. Lipsitz, K. Rockwood, J. Avorn (2018)
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DesaiRJ, JinY, FranklinPD, Association of geography and access to healthcare providers with long-term prescription opioid use in Medicare patients with severe osteoarthritis: a cohort study. Arthritis Rheumatol. 2019;71(5):712-721. doi:10.1002/art.4083430688044DesaiRJ, JinY, FranklinPD, Association of geography and access to healthcare providers with long-term prescription opioid use in Medicare patients with severe osteoarthritis: a cohort study. Arthritis Rheumatol. 2019;71(5):712-721. doi:10.1002/art.4083430688044, DesaiRJ, JinY, FranklinPD, Association of geography and access to healthcare providers with long-term prescription opioid use in Medicare patients with severe osteoarthritis: a cohort study. Arthritis Rheumatol. 2019;71(5):712-721. doi:10.1002/art.4083430688044
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KimDH, SchneeweissS, GlynnRJ, LipsitzLA, RockwoodK, AvornJ Measuring frailty in Medicare data: development and validation of a claims-based frailty index. J Gerontol A Biol Sci Med Sci. 2018;73(7):980-987. doi:10.1093/gerona/glx229 29244057KimDH, SchneeweissS, GlynnRJ, LipsitzLA, RockwoodK, AvornJ Measuring frailty in Medicare data: development and validation of a claims-based frailty index. J Gerontol A Biol Sci Med Sci. 2018;73(7):980-987. doi:10.1093/gerona/glx229 29244057, KimDH, SchneeweissS, GlynnRJ, LipsitzLA, RockwoodK, AvornJ Measuring frailty in Medicare data: development and validation of a claims-based frailty index. J Gerontol A Biol Sci Med Sci. 2018;73(7):980-987. doi:10.1093/gerona/glx229 29244057
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(HadlandsmythK, Vander WegMW, McCoyKD, MosherHJ, Vaughan-SarrazinMS, LundBC Risk for prolonged opioid use following total knee arthroplasty in veterans. J Arthroplasty. 2018;33(1):119-123. doi:10.1016/j.arth.2017.08.022 28927564)
HadlandsmythK, Vander WegMW, McCoyKD, MosherHJ, Vaughan-SarrazinMS, LundBC Risk for prolonged opioid use following total knee arthroplasty in veterans. J Arthroplasty. 2018;33(1):119-123. doi:10.1016/j.arth.2017.08.022 28927564HadlandsmythK, Vander WegMW, McCoyKD, MosherHJ, Vaughan-SarrazinMS, LundBC Risk for prolonged opioid use following total knee arthroplasty in veterans. J Arthroplasty. 2018;33(1):119-123. doi:10.1016/j.arth.2017.08.022 28927564, HadlandsmythK, Vander WegMW, McCoyKD, MosherHJ, Vaughan-SarrazinMS, LundBC Risk for prolonged opioid use following total knee arthroplasty in veterans. J Arthroplasty. 2018;33(1):119-123. doi:10.1016/j.arth.2017.08.022 28927564
(FranklinPD, KarbassiJA, LiW, YangW, AyersDC Reduction in narcotic use after primary total knee arthroplasty and association with patient pain relief and satisfaction. J Arthroplasty. 2010;25(6)(suppl):12-16. doi:10.1016/j.arth.2010.05.003 20580191)
FranklinPD, KarbassiJA, LiW, YangW, AyersDC Reduction in narcotic use after primary total knee arthroplasty and association with patient pain relief and satisfaction. J Arthroplasty. 2010;25(6)(suppl):12-16. doi:10.1016/j.arth.2010.05.003 20580191FranklinPD, KarbassiJA, LiW, YangW, AyersDC Reduction in narcotic use after primary total knee arthroplasty and association with patient pain relief and satisfaction. J Arthroplasty. 2010;25(6)(suppl):12-16. doi:10.1016/j.arth.2010.05.003 20580191, FranklinPD, KarbassiJA, LiW, YangW, AyersDC Reduction in narcotic use after primary total knee arthroplasty and association with patient pain relief and satisfaction. J Arthroplasty. 2010;25(6)(suppl):12-16. doi:10.1016/j.arth.2010.05.003 20580191
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Seoyoung Kim, N. Choudhry, J. Franklin, Katsiaryna Bykov, M. Eikermann, J. Lii, M. Fischer, B. Bateman, B. Bateman (2017)
Patterns and predictors of persistent opioid use following hip or knee arthroplasty.Osteoarthritis and cartilage, 25 9
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PivecR, IssaK, NaziriQ, KapadiaBH, BonuttiPM, MontMA Opioid use prior to total hip arthroplasty leads to worse clinical outcomes. Int Orthop. 2014;38(6):1159-1165. doi:10.1007/s00264-014-2298-x 24573819PivecR, IssaK, NaziriQ, KapadiaBH, BonuttiPM, MontMA Opioid use prior to total hip arthroplasty leads to worse clinical outcomes. Int Orthop. 2014;38(6):1159-1165. doi:10.1007/s00264-014-2298-x 24573819, PivecR, IssaK, NaziriQ, KapadiaBH, BonuttiPM, MontMA Opioid use prior to total hip arthroplasty leads to worse clinical outcomes. Int Orthop. 2014;38(6):1159-1165. doi:10.1007/s00264-014-2298-x 24573819
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(KumamaruH, JuddSE, CurtisJR, Validity of claims-based stroke algorithms in contemporary Medicare data: reasons for geographic and racial differences in stroke (REGARDS) study linked with Medicare claims. Circ Cardiovasc Qual Outcomes. 2014;7(4):611-619. doi:10.1161/CIRCOUTCOMES.113.000743 24963021)
KumamaruH, JuddSE, CurtisJR, Validity of claims-based stroke algorithms in contemporary Medicare data: reasons for geographic and racial differences in stroke (REGARDS) study linked with Medicare claims. Circ Cardiovasc Qual Outcomes. 2014;7(4):611-619. doi:10.1161/CIRCOUTCOMES.113.000743 24963021KumamaruH, JuddSE, CurtisJR, Validity of claims-based stroke algorithms in contemporary Medicare data: reasons for geographic and racial differences in stroke (REGARDS) study linked with Medicare claims. Circ Cardiovasc Qual Outcomes. 2014;7(4):611-619. doi:10.1161/CIRCOUTCOMES.113.000743 24963021, KumamaruH, JuddSE, CurtisJR, Validity of claims-based stroke algorithms in contemporary Medicare data: reasons for geographic and racial differences in stroke (REGARDS) study linked with Medicare claims. Circ Cardiovasc Qual Outcomes. 2014;7(4):611-619. doi:10.1161/CIRCOUTCOMES.113.000743 24963021
Key Points Question Is preoperative opioid use IMPORTANCE Prescription opioid use is common among patients with moderate to severe knee associated with mortality and short- osteoarthritis before undergoing total knee replacement (TKR). Preoperative opioid use may be term safety outcomes after total knee associated with worse clinical and safety outcomes after TKR. replacement (TKR) surgical procedures? Findings In this cohort study of 316 593 OBJECTIVE To determine the association of preoperative opioid use among patients 65 years and patients 65 years and older who older with mortality and other complications at 30 days post-TKR. underwent a TKR, 22 895 patients (7.2%) had a history of continuous DESIGN, SETTING, AND PARTICIPANTS This cohort study used claims data from January 1, 2010, opioids use in 360 days prior to surgery. to December 31, 2014, from a random sample of US Medicare enrollees 65 years and older who After adjusting for the baseline risk underwent TKR. Based on opioid dispensing in 360 days prior to TKR, patients were classified as profile, continuous opioid users had continuous (1 opioid dispensing in each of the past 12 months) or intermittent (any dispensing of higher risks of revision operations, opioids in the past 12 months but not continuous use) opioid users or as opioid-naive patients (no vertebral fractures, and opioid overdose opioids dispensed in the past 12 months). Data analyses were conducted from October 3, 2017, to at 30 days post-TKR but not of November 8, 2018. in-hospital or 30-day mortality, compared with opioid-naive patients. MAIN OUTCOMES AND MEASURES Primary outcomes included in-hospital mortality and 30-day post-TKR mortality, hospital readmission, and revision operation. Secondary safety outcomes at 30 Meaning Better understanding of days post-TKR included opioid overdose and vertebral and nonvertebral fracture. Multivariable Cox patient characteristics associated with proportional hazards models estimated hazard ratios (HRs) and 95% CIs. chronic opioid use is needed to optimize preoperative assessment of overall risk RESULTS Of 316 593 patients (mean [SD] age, 73.9 [5.8] years; 214 677 [67.8%] women) who after TKR. underwent TKR, 22 895 (7.2%) were continuous opioid users, 161 511 (51.0%) were intermittent opioid users, and 132 187 (41.7%) were opioid naive. In-hospital mortality occurred in 276 patients Supplemental content (0.09%). At 30 days post-TKR, 828 patients (0.26%) died, 16 786 patients (5.30%) had hospital readmission, and 921 patients (0.29%) had a revision operation. All primary and secondary outcomes Author affiliations and article information are listed at the end of this article. occurred more frequently among continuous opioid users compared with opioid-naive patients. Compared with opioid-naive patients and after adjusting for demographic characteristics, combined comorbidity score, number of different prescription medications, and frailty, continuous opioid users had greater risk of revision operations (HR, 1.63; 95% CI, 1.15-2.32), vertebral fractures (HR, 2.37; 95% CI, 1.37-4.09), and opioid overdose (HR, 4.82; 95% CI, 1.36-17.07) at 30 days post-TKR. However, after adjusting covariates, there were no statistically significant differences in in-hospital (HR, 1.18; 95% CI, 0.73-1.90) or 30-day (HR, 1.05; 95% CI, 0.73-1.51) mortality between continuous opioid users and opioid-naive patients. CONCLUSIONS AND RELEVANCE After adjusting for baseline risk profiles, including comorbidities and frailty, continuous opioid users had a higher risk of revision operations, vertebral fractures, and opioid overdose at 30 days post-TKR but not of in-hospital or 30-day mortality, compared with (continued) Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 (Reprinted) July 31, 2019 1/13 JAMA Network Open | Orthopedics Association of Preoperative Opioid Use With Mortality and Safety Outcomes After Total Knee Replacement Abstract (continued) opioid-naive patients. These results highlight the need for better understanding of patient characteristics associated with chronic opioid use to optimize preoperative assessment of overall risk after TKR. JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 Introduction Overuse of prescription opioids in the United States has been a threat to public health during the past decade as opioid analgesic sales increased 4-fold from 1999 to 2010. While the use of opioids is prevalent across all adult age groups, adults older than 60 years use prescription opioids at a rate almost 2-fold more than younger adults aged 20 to 39 years. In older patients, given the known cardiovascular risks of nonsteroidal anti-inflammatory drugs (NSAIDs), the threshold for using opioids has decreased; opioids are used increasingly among elderly individuals and people with cardiovascular risk factors. Given increasing concern about opioid overuse and subsequent restrictions on opioid prescribing, management of chronic painful conditions, such as osteoarthritis (OA), has become particularly challenging. Opioid analgesics are often prescribed to relieve pain in patients with moderate to severe symptomatic OA not responsive to NSAIDs or acetaminophen. Based on the data from the US Medicare Current Beneficiary Survey, more than 40% of patients with OA with a mean age of 77 years received an opioid prescription in 2009. A 2017 study among a US commercially insured population of patients undergoing hip or knee arthroplasty found that 87.1% had received at least 1 dispensing for opioids in the year prior to the surgical procedure. A Medicare-based cohort study using data from 2010 through 2014 found that 42.3% of older patients with OA used prescription opioids for less than 90 days and 16.5% of older patients used prescription opioids for longer than 90 days in the year prior to total joint replacement. Several studies have raised concerns about potential associations of opioid use prior to total joint replacement with postsurgical adverse outcomes, including persistent pain, stiffness, patient 5-8 satisfaction, and requirement of additional surgical procedures. Furthermore, in 2 studies of patients with a mean age of 80 years with arthritis, compared with nonselective NSAIDs, patients who used opioids had a 5-fold increased risk of fracture and a 1.9-fold increased risk of cardiovascular events and death. However, to our knowledge, limited information is available on the association of preoperative opioid use with a broad range of post–total knee replacement (TKR) outcomes after accounting for patients’ preoperative risk profile among a nationally representative cohort of patients. Therefore, we sought to determine the association of preoperative opioid use with short-term safety outcomes after TKR, including in-hospital mortality and mortality, TKR complications, and safety events at 30 days post-TKR among Medicare enrollees in the United States. We also assessed these outcomes at 60 and 90 days after TKR. Methods Data Source We used claims data from Medicare Parts A (inpatient services), B (outpatient services), and D (pharmacy claims) from January 1, 2010, to December 31, 2014. Medicare is a federally funded program that provides health care coverage for nearly all legal residents of the United States older than 65 years and some individuals with disabilities younger than 65 years. This database contains longitudinal information on Medicare enrollees’ medical diagnoses recorded with the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, medical procedures recorded as Current Procedural Terminology or ICD-9-CM procedure codes, and JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 (Reprinted) July 31, 2019 2/13 JAMA Network Open | Orthopedics Association of Preoperative Opioid Use With Mortality and Safety Outcomes After Total Knee Replacement medication dispensing recorded using National Drug Codes. The protocol was reviewed and approved by the Institutional Review Board of the Brigham and Women’s Hospital, which granted a waiver of informed consent, as this study exclusively used deidentified patient data. The data use agreement was in place with the US Centers for Medicare & Medicaid Services. The reporting of this study is in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Study Cohort We obtained a random sample of 1 million patients who underwent a total knee or hip replacement from January 1, 2010, to December 31, 2014. We then selected patients with continuous enrollment in Medicare Parts A, B, and D for at least 360 days prior to TKR. All patients were required to have a diagnosis of OA or rheumatoid arthritis and be 65 years or older at the time of the index TKR (ie, index date). We excluded patients who had no claims during the 360-day baseline period (ie, those who were Medicare eligible but may have been receiving care through alternate health insurance coverage) or those who had both TKR and total hip replacement performed on the same date. Patients were included in the cohort once at the time of their first TKR, even if they had multiple eligible TKR dates identified during the study. Preoperative Opioid Use Pattern We identified opioids based on 16 different generic names, including buprenorphine, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, levorphanol, meperidine, methadone, morphine, oxycodone, oxymorphone, pentazocine, propoxyphene, tapentadol, and tramadol. Based on dispensing of opioids during the 360-day baseline period prior to TKR, patients were classified as (1) continuous opioid users (ie, 1 dispensing in each of twelve 30-day blocks prior to TKR), (2) intermittent opioid users (ie, any dispensing of opioids but not continuous use), or (3) opioid naive (ie, no opioid dispensing in the past 12 months). Outcomes of Interest The primary outcomes of interest were (1) in-hospital mortality (ie, death during the hospitalization for TKR), (2) 30-day mortality, (3) 30-day hospital readmission of any kind, and (4) 30-day TKR revision operations. Based on previously published algorithms using diagnosis or procedure codes, 12,13 we assessed the following secondary safety outcomes at 30 days post-TKR: (1) opioid overdose ; 14,15 (2) a composite cardiovascular endpoint, including myocardial infarction and stroke ; (3) 16 17 18 19 nonvertebral fracture ; (4) vertebral fracture ; (5) respiratory distress ; (6) pneumonia ; and (7) bowel obstruction. In addition, we examined the rate of primary and secondary outcomes at 60 and 90 days post-TKR as sensitivity analyses. Covariates During the 360-day baseline period prior to TKR, we assessed patient demographic characteristics (ie, age, sex, race/ethnicity [self-reported in the Medicare enrollment database], and region of residence), comorbidities (eg, falls, migraine, neuropathic pain, back pain, fractures, hyperlipidemia, hypertension, atrial fibrillation, heart failure, coronary heart disease, stroke, chronic kidney disease, diabetes, obesity, malignant tumors, smoking, substance use disorder, osteoporosis, psychosis, depression, sleep disorder, and anxiety), medication use (ie, NSAIDs, selective cyclooxygenase 2 inhibitors, corticosteroids, anticonvulsants, antidepressants, antipsychotics, benzodiazepines, other anxiolytics, and total number of unique prescriptions by generic name), and health care utilization patterns. These covariates were defined using ICD-9-CM diagnosis or procedure codes, Current Procedural Terminology codes, or National Drug Codes. In addition, to better assess older patients’ health status and physical function, we estimated a combined comorbidity score that incorporated 20 different medical conditions, including heart failure, renal failure, respiratory disease, cirrhosis, and malignant tumors, and a claims-based frailty index. Based on the frailty index score, patients JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 (Reprinted) July 31, 2019 3/13 JAMA Network Open | Orthopedics Association of Preoperative Opioid Use With Mortality and Safety Outcomes After Total Knee Replacement were categorized into 4 groups: robust (<0.15), prefrail (0.15-0.24), mildly frail (0.25-0.34), and moderately to severely frail (>0.34). Statistical Analysis We cross-tabulated baseline characteristics of patients by preoperative opioid use patterns. We calculated the proportion of patients who experienced primary or secondary outcomes of interest during 30 days post-TKR. Separate crude Cox proportional hazards models estimated hazard ratios (HRs) and 95% CIs for primary and secondary outcomes. To adjust for confounding, we performed partial adjustment for demographic factors only (model 1) and full adjustment for demographic characteristics, combined comorbidity score, frailty, and number of prescription drugs (model 2). We also repeated these steps for 60 and 90 days of follow-up after the surgical procedure. In addition, we performed a sensitivity analysis after excluding patients with malignant tumors to focus exclusively on patients who received opioids for chronic noncancer pain. All analyses were conducted in SAS statistical software version 9.4 (SAS Institute). Results Study Patients After applying the inclusion and exclusion criteria, the final study cohort included 316 593 patients who underwent TKR (mean [SD] age, 73.9 [5.8] years; 214 677 [67.8%] women) (Figure). Of these patients, 184 406 (58.2%) had any use of opioids in the 360 days prior to TKR, including 22 895 continuous opioid users (7.2%) and 161 511 intermittent opioid users (51.0%); 132 187 patients (41.7%) were opioid naive prior to the surgical procedure. The mean (SD) ages were 72.7 (5.7) years among continuous opioid users, 73.7 (5.7) years among intermittent opioid users, and 74.3 (5.8) years among opioid-naive patients. Continuous opioid users were more likely to be women and black and to live in the South. Continuous opioid users had more comorbidities, including diabetes, obesity, back pain, malignant tumors, cardiovascular disease, sleep disorder, psychiatric disorders, and substance use disorder. Furthermore, continuous opioid users were more frail than opioid-naive patients. Use of other analgesic medications, benzodiazepines, and anticonvulsants was more frequently seen among continuous opioid users than opioid-naive patients. Table 1 summarizes preoperative characteristics of the study population. A total of 60 040 patients (19.0%) had a history of malignant tumors. In the subgroup of 256 553 patients with no baseline malignant tumors, 148 926 (58.0%) had any use of opioids in 360 days pre-TKR and 190 241 (7.5%) were continuous opioid users (Table 1). Figure. Cohort Selection Flow 1 046 658 Medicare patient records with total knee or hip replacement surgical procedures from January 1, 2010, to December 21, 2014 730 065 Excluded 506 687 Without continuous enrollment in Medicare Parts A, B, and D at baseline 66 094 Younger than 65 y 26 Without any claims at baseline 155 586 Underwent hip replacement on or before the index date 1672 Without osteoarthritis or rheumatoid arthritis diagnosis at baseline 316 593 Eligible patients who underwent total knee replacement JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 (Reprinted) July 31, 2019 4/13 JAMA Network Open | Orthopedics Association of Preoperative Opioid Use With Mortality and Safety Outcomes After Total Knee Replacement Table 1. Patient Characteristics in 360 Days Prior to Total Knee Replacement No. (%) Opioid Users Continuous Intermittent Opioid-Naive Patients Characteristic (n = 22 895) (n = 161 511) (n = 132 187) Age, mean (SD), y 72.7 (5.7) 73.7 (5.7) 74.3 (5.8) Women 17 432 (76.1) 112 574 (69.7) 84 671 (64.1) Race/ethnicity White 20 227 (88.3) 144 455 (89.4) 121 605 (92.0) Black 1863 (8.1) 8967 (5.6) 4536 (3.4) Hispanic 307 (1.3) 3336 (2.1) 1730 (1.3) Other 309 (1.3) 2664 (1.6) 2280 (1.7) Region Northeast 2401 (10.5) 22 443 (13.9) 24 633 (18.6) Midwest 5905 (25.8) 43 364 (26.8) 41 153 (31.1) South 10 350 (45.2) 65 244 (40.4) 44 550 (33.7) West 4231 (18.5) 30 158 (18.7) 21 591 (16.3) Comorbidities Hypertension 20 537 (89.7) 139 063 (86.1) 107 306 (81.2) Diabetes 8737 (38.2) 54 213 (33.6) 37 498 (28.4) Obesity 5199 (22.7) 30 791 (19.1) 18 210 (13.8) Back pain 16 303 (71.2) 85 228 (52.8) 46 908 (35.5) Neuropathic pain 10 295 (45.0) 52 270 (32.4) 24 768 (18.7) Malignant tumor 3654 (16.0) 31 826 (19.7) 24 560 (18.6) Coronary heart disease 2579 (11.3) 14 478 (9.0) 8499 (6.4) Chronic kidney disease 3675 (16.1) 19 636 (12.2) 10 883 (8.2) Heart failure 3511 (15.3) 16 247 (10.1) 8389 (6.3) Hip fracture 122 (0.5) 682 (0.4) 232 (0.2) Migraine 3604 (15.7) 17 773 (11.0) 8354 (6.3) Sleep disorder 6219 (27.2) 32 891 (20.4) 17 777 (13.4) Depression 7167 (31.3) 28 744 (17.8) 13 204 (10.0) Anxiety disorder 5414 (23.6) 20 973 (13.0) 9958 (7.5) Bipolar disorder 594 (2.6) 2058 (1.3) 930 (0.7) Substance use disorder 291 (1.3) 343 (0.2) 57 (0) Alcohol use disorder 365 (1.6) 1615 (1.0) 771 (0.6) Tobacco use 4900 (21.4) 23 465 (14.5) 12 283 (9.3) Rheumatoid arthritis 2442 (10.7) 9831 (6.1) 4318 (3.3) Frailty Robust 3502 (15.3) 52 549 (32.5) 65 442 (49.5) Prefrail 14 969 (65.4) 96 700 (59.9) 63 689 (48.2) Mild frailty 4049 (17.7) 11 338 (7.0) 2924 (2.2) Moderate to severe frailty 375 (1.6) 924 (0.6) 132 (0.1) Combined comorbidity score, 1.9 (2.6) 1.3 (2.3) 0.8 (1.8) mean (SD) Medication use NSAIDs 10 584 (46.2) 71 998 (44.6) 39 018 (29.5) Cyclooxygenase 2 inhibitors 2773 (12.1) 18 651 (11.5) 9902 (7.5) Oral corticosteroids 10 527 (46.0) 64 672 (40.0) 37 443 (28.3) Antidepressants 12 371 (54.0) 53 669 (33.2) 26 321 (19.9) Benzodiazepines 5117 (22.3) 18 913 (11.7) 8240 (6.2) Bisphosphonates 1823 (8.0) 12 256 (7.6) 8787 (6.6) Anticonvulsants 9127 (39.9) 32 130 (19.9) 11 733 (8.9) (continued) JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 (Reprinted) July 31, 2019 5/13 JAMA Network Open | Orthopedics Association of Preoperative Opioid Use With Mortality and Safety Outcomes After Total Knee Replacement Table 1. Patient Characteristics in 360 Days Prior to Total Knee Replacement (continued) No. (%) Opioid Users Continuous Intermittent Opioid-Naive Patients Characteristic (n = 22 895) (n = 161 511) (n = 132 187) Health care utilization, mean (SD) Abbreviation: NSAIDs, nonsteroidal anti- No. of unique prescription drugs 15.5 (6.7) 12.2 (5.6) 7.8 (4.5) inflammatory drugs. Based on a frailty index score, patients were No. of emergency department visits 0.8 (1.7) 0.5 (1.1) 0.2 (0.6) categorized into 4 groups, robust (<0.15), prefrail No. of visits to any physician 16.2 (9.6) 13.9 (8.0) 10.7 (6.5) (0.15-0.24), mildly frail (0.25-0.34), and moderately No. of acute hospitalizations 0.4 (0.8) 0.3 (0.6) 0.1 (0.4 to severely frail (>0.34). Table 2. All-Cause Mortality and Short-term Complications After Total Knee Replacement Stratified by Preoperative Opioid Use Patterns Events, No. (%) Opioid Users Continuous Intermittent Opioid-Naive Patients Outcome (n = 22 895) (n = 161 511) (n = 132 187) All-cause mortality In hospital 27 (0.12) 165 (0.10) 84 (0.06) 30 d 75 (0.33) 451 (0.28) 302 (0.23) 60 d 123 (0.54) 628 (0.39) 412 (0.31) 90 d 156 (0.68) 760 (0.47) 499 (0.38) Hospital readmission 30 d 1672 (7.30) 9027 (5.59) 6087 (4.60) 60 d 2545 (11.12) 13 305 (8.24) 8891 (6.73) 90 d 3296 (14.40) 16 915 (10.47) 11 228 (8.49) Revision operation 30 d 112 (0.49) 524 (0.32) 285 (0.22) 60 d 162 (0.71) 778 (0.48) 424 (0.32) 90 d 198 (0.86) 912 (0.56) 503 (0.38) Primary Outcomes Among the full cohort, in-hospital mortality occurred in 282 patients (0.09%). At 30 days post-TKR, 828 patients (0.26%) died, 16 786 patients (5.30%) had hospital readmission, and 921 patients (0.29%) had a revision operation. In-hospital mortality occurred in 27 continuous opioid users (0.12%), 165 intermittent opioid users (0.10%), and 84 opioid-naive patients (0.06%) (Table 2). The all-cause mortality rate was higher among continuous opioid users compared with intermittent opioid users or opioid-naive patients at 30 days (75 continuous opioid users [0.33%]; 451 intermittent opioid users [0.28%]; 302 opioid-naive patients [0.23%]), 60 days (123 continuous opioid users [0.54%]; 628 intermittent opioid users [0.39%]; 412 opioid-naive patients [0.31%]), and 90 days (156 continuous opioid users [0.68%]; 760 intermittent opioid users [0.47%]; 499 opioid-naive patients [0.38%]) after TKR. Hospital readmission at 30 days post-TKR occurred in 1672 continuous opioid users (7.30%), 9027 intermittent opioid users (5.59%), and 6087 opioid-naive patients (4.60%). Revision operations within 30 days post-TKR were generally infrequent but noted in 112 continuous opioid users (0.49%), 524 intermittent opioid users (0.32%), and 285 opioid- naive patients (0.22%). Additionally, at 60 and 90 days post-TKR, all primary outcomes occurred more frequently in continuous opioid users vs opioid naive patients and in intermittent opioid users vs opioid-naive patients (Table 2). As summarized in Table 3, the unadjusted HR among continuous opioid users vs opioid-naive patients was greater for in-hospital mortality (HR, 1.95; 95% CI, 1.25-3.03), 30-day mortality (HR, 1.52; 95% CI, 1.09-2.11), 30-day hospital readmission (HR, 1.47; 95% CI, 1.36-1.60), and 30-day revision operation (HR, 2.55; 95% CI; 1.86-3.48). In the partially adjusted model 1, the HR remained greater for continuous opioid uses for these primary outcomes compared JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 (Reprinted) July 31, 2019 6/13 JAMA Network Open | Orthopedics Association of Preoperative Opioid Use With Mortality and Safety Outcomes After Total Knee Replacement with opioid-naive patients (Table 3). In the fully adjusted model 2, continuous opioid users vs opioid- naive patients were no longer associated with in-hospital mortality (HR, 1.18; 95% CI, 0.73-1.90), 30-day mortality (HR, 1.05; 95% CI, 0.73-1.51), or 30-day hospital readmission (HR, 1.06; 95% CI, 0.97-1.16) after TKR (Table 3). However, continuous opioid use was associated with a greater risk of a revision operation (HR, 1.63; 95% CI, 1.15-2.32) at 30 days post-TKR. Table 3. All-Cause Mortality, Short-term Complications, and Safety Outcomes After Total Knee Replacement Among Continuous Opioid Users vs Opioid-Naive Patients Hazard Ratio (95% CI) a b Outcome Unadjusted Model 1 Model 2 All-cause mortality In hospital 1.95 (1.25-3.03) 2.36 (1.51-3.69) 1.18 (0.73-1.90) 30 d 1.52 (1.09-2.11) 1.88 (1.34-2.63) 1.05 (0.73-1.51) 60 d 1.67 (1.24-2.23) 2.07 (1.55-2.79) 1.12 (0.82-1.54) 90 d 1.50 (1.14-1.99) 1.87 (1.41-2.49) 0.99 (0.73-1.35) Hospital readmission 30 d 1.47 (1.36-1.60) 1.57 (1.45-1.71) 1.06 (0.97-1.16) 60 d 1.57 (1.47-1.67) 1.67 (1.56-1.78) 1.12 (1.04-1.20) 90 d 1.63 (1.54-1.73) 1.74 (1.64-1.84) 1.19 (1.12-1.27) Revision operation 30 d 2.55 (1.86-3.48) 2.59 (1.88-3.57) 1.63 (1.15-2.32) 60 d 2.21 (1.70-2.87) 2.31 (1.77-3.01) 1.40 (1.05-1.88) 90 d 2.37 (1.87-2.99) 2.47 (1.94-3.14) 1.58 (1.21-2.05) Opioid overdose 30 d 8.89 (2.82-28.00) 8.50 (2.67-27.12) 4.82 (1.36-17.07) 60 d 15.41 (5.43-43.76) 15.1 (5.26-43.34) 7.91 (2.50-25.02) 90 d 25.99 (9.75-69.25) 26.6 (9.89-71.57) 13.64 (4.70-39.55) Nonvertebral fracture 30 d 3.08 (1.26-7.56) 3.00 (1.21-7.42) 1.89 (0.69-5.13) 60 d 2.24 (1.25-4.02) 2.23 (1.24-4.02) 1.50 (0.78-2.86) 90 d 2.44 (1.62-3.68) 2.57 (1.70-3.90) 1.75 (1.11-2.77) Vertebral fracture 30 d 4.40 (2.70-7.14) 4.69 (2.85-7.73) 2.37 (1.37-4.09) 60 d 4.22 (3.10-5.75) 4.51 (3.29-6.19) 2.42 (1.70-3.44) 90 d 4.18 (3.26-5.35) 4.41 (3.43-5.68) 2.36 (1.79-3.13) Myocardial infarction or stroke 30 d 1.02 (0.63-1.65) 1.21 (0.74-1.97) 0.76 (0.45-1.27) 60 d 1.11 (0.75-1.66) 1.34 (0.89-2.00) 0.85 (0.55-1.31) 90 d 1.25 (0.88-1.77) 1.50 (1.05-2.14) 0.90 (0.62-1.32) Respiratory distress 30 d 2.13 (0.77-5.85) 2.17 (0.78-6.01) 1.04 (0.35-3.14) 60 d 2.66 (1.22-5.78) 2.72 (1.24-5.97) 1.21 (0.51-2.85) 90 d 2.58 (1.36-4.91) 2.62 (1.37-5.01) 1.21 (0.59-2.50) Pneumonia 30 d 2.04 (1.32-3.14) 2.31 (1.49-3.57) 1.10 (0.68-1.80) 60 d 2.02 (1.39-2.93) 2.26 (1.55-3.30) 1.06 (0.69-1.61) 90 d 2.29 (1.63-3.20) 2.55 (1.82-3.59) 1.15 (0.79-1.68) Adjusted for age, sex, race/ethnicity, and region of Bowel obstruction residence. 30 d 1.84 (1.04-3.27) 2.04 (1.14-3.65) 1.40 (0.74-2.63) Adjusted for age, sex, race/ethnicity, region of 60 d 1.87 (1.15-3.04) 2.06 (1.26-3.36) 1.38 (0.81-2.35) residence, combined comorbidity score, frailty score, 90 d 1.86 (1.20-2.87) 2.02 (1.30-3.13) 1.36 (0.84-2.19) and number of unique prescription drugs. JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 (Reprinted) July 31, 2019 7/13 JAMA Network Open | Orthopedics Association of Preoperative Opioid Use With Mortality and Safety Outcomes After Total Knee Replacement Secondary Safety Outcomes Table 4 presents the results from the secondary safety outcome analysis. Opioid overdose occurred infrequently after TKR across the 3 groups. At 30 days post-TKR, 11 continuous opioid users (0.05%) experienced an opioid overdose, compared with 41 intermittent opioid users (0.03%) and fewer than 11 opioid-naive patients (<0.01%) (as required by the data use agreement with the Centers for Medicare & Medicaid Services, actual numbers for counts less than 11 are suppressed). The secondary outcomes at 30, 60, and 90 days post-TKR were generally more common among continuous opioid users than opioid-naive patients. Similarly, the unadjusted HR among continuous opioid users was greater for opioid overdose (HR, 8.89; 95% CI, 2.82-28.00), nonvertebral fractures (HR, 3.08; 95% CI, 1.26-7.56), vertebral fractures (HR, 4.40; 95% CI, 2.70-7.14), pneumonia (HR, 2.04; 95% CI, 1.32-3.14), and bowel obstruction (HR, 1.84; 95% CI, 1.04-3.27). After partial adjustment (model 1) for demographic factors, the HR remained higher for continuous opioid users compared with opioid- naive patients for opioid overdose (HR, 8.50; 95% CI, 2.67-27.12), nonvertebral fractures (HR, 3.00; 95% CI, 1.21-7.42), vertebral fractures (HR, 4.69; 95% CI, 2.85-7.73), pneumonia (HR, 2.31; 95% CI, 1.49-3.57), and bowel obstruction (HR, 2.04; 95% CI, 1.14-3.65) at 30 days post-TKR (Table 3). In the fully adjusted model 2 (Table 3), continuous opioid use was only associated with a greater risk of opioid overdose (HR, 4.82; 95% CI, 1.36-17.07) and vertebral fractures (HR, 2.37; 95% CI, 1.37-4.09) Table 4. Short-term Safety Outcomes After Total Knee Replacement Stratified by Preoperative Opioid Use Patterns Events, No. (%) Opioid Users Continuous Intermittent Opioid-Naive Patients Outcome (n = 22 895) (n = 161 511) (n = 132 187) Opioid overdose 30 d 11 (0.05) 41 (0.03) <11 (<0.01) 60 d 23 (0.10) 49 (0.03) 11 (0.01) 90 d 34 (0.15) 53 (0.03) 11 (0.01) Nonvertebral fracture 30 d 17 (0.07) 87 (0.05) 45 (0.03) 60 d 48 (0.21) 210 (0.13) 110 (0.08) 90 d 87 (0.38) 353 (0.22) 195 (0.15) Vertebral fracture 30 d 57 (0.25) 235 (0.15) 95 (0.07) 60 d 129 (0.56) 504 (0.31) 225 (0.17) 90 d 205 (0.90) 767 (0.47) 347 (0.26) Myocardial infarction or stroke 30 d 43 (0.19) 288 (0.18) 237 (0.18) 60 d 65 (0.28) 407 (0.25) 321 (0.24) 90 d 86 (0.38) 518 (0.32) 396 (0.30) Respiratory distress 30 d 15 (0.07) 65 (0.04) 34 (0.03) 60 d 29 (0.13) 91 (0.06) 53 (0.04) 90 d 41 (0.18) 106 (0.07) 65 (0.05) Pneumonia 30 d 59 (0.26) 258 (0.16) 156 (0.12) 60 d 89 (0.39) 368 (0.23) 218 (0.16) 90 d 124 (0.54) 469 (0.29) 258 (0.20) Bowel obstruction 30 d 35 (0.15) 159 (0.10) 118 (0.09) As required by the data use agreement with the 60 d 49 (0.21) 224 (0.14) 157 (0.12) Centers for Medicare & Medicaid Services, actual 90 d 58 (0.25) 281 (0.17) 188 (0.14) numbers for counts less than 11 are suppressed. JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 (Reprinted) July 31, 2019 8/13 JAMA Network Open | Orthopedics Association of Preoperative Opioid Use With Mortality and Safety Outcomes After Total Knee Replacement at 30 days post-TKR. Similar patterns were seen in the analyses for the outcomes at 60 and 90 days post-TKR. Using the fully adjusted model 2, compared with opioid-naive patients, intermittent opioid users were not associated with increased in-hospital mortality (HR, 1.20; 95% CI, 0.90-1.59), 30-day mortality (HR, 1.10; 95% CI, 0.90-1.34), or 30-day hospital readmission (HR, 0.99; 95% CI, 0.94-1.04). The fully adjusted HR associated with intermittent opioid users compared with opioid- naive patients was 1.29 (95% CI, 1.04-1.61) for revision operations, 3.07 (95% CI, 1.12-8.40) for opioid overdose, and 1.54 (95% CI, 1.04-2.28) for vertebral fractures at 30 days post-TKR (eTable 1 in the Supplement). In the sensitivity analysis excluding 256 553 patients with malignant tumors (eTable 2 in the Supplement), we also found consistent results. In the fully adjusted model 2, continuous opioid users compared with opioid-naive patients were associated with a greater risk of revision operations (HR, 1.66; 95% CI, 1.15-2.40), opioid overdose (HR, 3.65; 95% CI, 0.98-13.66), and vertebral fracture (HR, 2.32; 95% CI, 1.28-4.21) at 30 days but not with risk of in-hospital mortality (HR, 0.98; 95% CI, 0.56-1.69), 30-day mortality (HR, 0.95; 95% CI, 0.63-1.43), or 30-day hospital readmission (HR, 1.05; 95% CI, 0.96-1.16) (eTable 3 in the Supplement). Discussion In this large cohort of older Medicare enrollees with OA (mean age, >73 years), 58.3% had used opioids at least once in the year prior to TKR, and 7.2% had continuous opioid use, defined by a dispensing for opioid at least once every month for 12 months before the surgical procedure. Compared with opioid-naive patients, continuous opioid users had greater in-hospital mortality, all-cause mortality, revision operations, hospital readmission, and other safety events after TKR. After adjusting for differences in patient characteristics, we found no association of continuous preoperative opioid use with in-hospital mortality or with all-cause mortality, hospital readmission, myocardial infarction or stroke, or pneumonia at 30 days post-TKR (Table 3). However, in our fully adjusted analyses, continuous opioid use was associated with a higher risk of early (ie, 30-day) revision operation and vertebral fracture and of opioid overdose at 30, 60, and 90 days after TKR. Multivariable model 2 HRs for continuous opioid use vs no use were elevated for nonvertebral fractures, respiratory distress, and bowel obstruction after TKR, but the differences were not statistically significant. In the model 2 adjusted analyses, we found no association of continuous opioid use with in-hospital mortality, all-cause mortality, 30-day hospital readmission, myocardial infarction or stroke, or pneumonia post-TKR. Intermittent use of opioids vs no opioid use was also associated with an increased risk of revision operations, vertebral fractures, and opioid overdose at 30 days post-TKR. We found consistent results in a sensitivity analysis excluding patients with malignant tumors at baseline. The key clinical question is whether long-term use of opioids itself is a risk factor for worse outcomes after a surgical procedure or if patients’ conditions that lead to long-term use of opioids are 3,24,25 a risk factor. As seen in previous studies, the use of prescription opioids in these older patients was preoperatively prevalent in our study, regardless of baseline history of malignant tumors. Furthermore, a considerable number of patients had continuous use of opioids in the year prior to TKR, and these continuous opioid users had a higher rate of short-term complications after TKR compared with opioid-naive patients. In a small study by Zywiel et al of 98 patients who received TKR, patients who had preoperative long-term use of opioids had worse clinical outcomes and higher complication rates. Zywiel et al suggested alternative pain management with nonopioids. A 2018 study of more than 300 000 total joint replacements using claims data from a US commercial insurance database reported a greater risk of early revision operation and 30-day readmission among patients with longer than 60 days of preoperative opioid use vs those with no opioid use after 8,24 adjusting for age, sex, and combined comorbidity score. Similar to these studies, we also noted a higher rate of revision operations and other safety events among continuous opioid users vs JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 (Reprinted) July 31, 2019 9/13 JAMA Network Open | Orthopedics Association of Preoperative Opioid Use With Mortality and Safety Outcomes After Total Knee Replacement opioid-naive patients. It is uncommon to perform a revision operation during a postoperative period of 30, 60, or 90 days. Although, to our knowledge, the underlying mechanism associated with preoperative opioid use with early revision operation is not fully understood, it is important to note that continuous opioid users were more frail and had more comorbidities and other prescription drug use compared with opioid-naive patients. These patient characteristics might have been associated with more infection, persistent pain, or other unusual conditions, ultimately needing a revision operation. The HR of 1.95 (95% CI, 1.25-3.03) for in-hospital mortality associated with continuous opioid use in the unadjusted analysis was attenuated to 1.18 (95% CI, 0.73-1.90) in the multivariable model 2, which was adjusted for demographic factors, region, combined comorbidity score, frailty, and number of unique prescription drugs. Similarly, the unadjusted HR for pneumonia was 2.04 (95% CI, 1.32-3.14) for continuous opioid users vs opioid-naive patients, and it attenuated to 1.10 (95% CI, 0.68-1.80) in the adjusted model 2 analysis. Unlike a 2010 study that found an increased risk of cardiovascular events associated with opioid vs NSAID use, we found no cardiovascular risk associated with continuous opioid users vs opioid-naive patients. These findings suggest that differences in the baseline risk profile between continuous opioid users and opioid-naive patients may contribute more to the observed higher rate of mortality and some of the short-term safety events than the pattern of preoperative opioid use itself. In other words, it may be not possible to reduce the rate of some of the short-term complications after TKR 8,24 even if use of opioids is minimized. Nonetheless, observation from our study and previous studies suggest that, even if it is not a truly independent risk factor, preoperative long-term use of opioids may be a marker with an unfavorable risk profile leading to poor postoperative outcome. As such, evaluation of patients’ preoperative opioid use patterns may be helpful in planning a more rigorous monitoring strategy after a common elective surgical procedure, such as TKR. Strengths and Limitations Strengths of this study include the large size of the study cohort and high generalizability, as Medicare covers all legal residents 65 years and older in the United States. We also conducted a comprehensive assessment of short-term surgical complications as well as various safety events directly or indirectly associated with opioid use. Furthermore, we conducted a thorough evaluation of patient characteristics prior to their surgical procedures and accounted for many important variables, including comorbidities and frailty, in the analyses. Lastly, we examined the complication and safety event rates at 30, 60, and 90 days post-TKR for a complete postoperative outcome evaluation. This study has limitations. First, because we relied on diagnosis codes and pharmacy dispensing in Medicare data, there is a potential for misclassification of comorbidities or opioid use. We also do not have information on the reasons for opioid prescriptions. Second, because we evaluated short- term safety outcomes among patients who underwent an elective surgery (ie, TKR), rates of the outcomes were generally low, leading to imprecise estimates for some of the secondary outcomes, such as respiratory distress and bowel obstruction. Third, we did not have data on in-hospital opioid use or types of anesthesia during the index hospitalization, which may have had an important role in in-hospital mortality or some of the 30-day safety events, such as opioid overdose. Fourth, this observational study is subject to residual confounding among the groups. Conclusions Among 316 593 older patients with knee arthritis enrolled in Medicare, preoperative use of prescription opioids was common: 58.3% of patients had at least 1 dispensing for opioids in 360 days prior to TKR. Compared with opioid-naive individuals, after adjusting for a baseline risk profile, including comorbidities and frailty, continuous preoperative opioid use was associated with a higher risk of revision operations, vertebral fractures, and opioid overdose at 30 days post-TKR but was no longer associated with in-hospital or 30-day mortality. Similarly, intermittent opioid use vs no opioid JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 (Reprinted) July 31, 2019 10/13 JAMA Network Open | Orthopedics Association of Preoperative Opioid Use With Mortality and Safety Outcomes After Total Knee Replacement use was associated with a greater risk of revision operations, vertebral fractures, and opioid overdose at 30 days post-TKR, although to a lesser degree. It is important to recognize the harms of prescription opioids and minimize the doses or duration of opioids whenever possible. Nevertheless, our results suggest that differences in the baseline risk profile between opioid users and opioid- naive patients were likely more important contributing factors for in-hospital or short-term mortality, as well as some of the short-term safety events after TKR, than preoperative opioid use itself. Our study also highlights the need for better understanding of patient characteristics associated with chronic opioid use to optimize preoperative assessment of overall risk after TKR among older patients with arthritis. ARTICLE INFORMATION Accepted for Publication: June 9, 2019. Published: July 31, 2019. doi:10.1001/jamanetworkopen.2019.8061 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Kim SC et al. JAMA Network Open. Corresponding Author: Seoyoung C. Kim, MD, ScD, MSCE, Brigham and Women’s Hospital, 1620 Tremont St, Ste 3030, Boston, MA 02120 (sykim@bwh.harvard.edu). Author Affiliations: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts (Kim, Jin, Lii, J. M. Franklin, Desai); Division of Rheumatology, Immunology and Allergy, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts (Kim, Solomon, Katz); Division of Rheumatology, Northwestern University, Chicago, Illinois (Lee); Department of Medical Social Sciences, Northwestern University, Chicago, Illinois (P. D. Franklin); Department of Orthopedic Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts (Katz). Author Contributions: Dr Kim had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Kim, Jin, Solomon, Katz, Desai. Acquisition, analysis, or interpretation of data: Kim, Jin, Lee, Lii, P. D. Franklin, Solomon, J. M. Franklin, Katz. Drafting of the manuscript: Kim, Lii. Critical revision of the manuscript for important intellectual content: Kim, Jin, Lee, P. D. Franklin, Solomon, J. M. Franklin, Katz, Desai. Statistical analysis: Kim, Jin, Lii, Desai. Obtained funding: Kim, P. D. Franklin. Administrative, technical, or material support: Kim, P. D. Franklin, Solomon, J. M. Franklin. Supervision: Kim, Solomon, J. M. Franklin. Conflict of Interest Disclosures: Dr Kim reported receiving grants from the US National Institutes of Health (NIH) during the conduct of the study and grants from AbbVie, Bristol-Myers Squibb, Pfizer, and Roche Holding (paid to Brigham and Women’s Hospital) outside the submitted work. Dr Lee reported receiving grants from Pfizer outside the submitted work, owning stock in Cigna-Express Scripts, and serving as an advisory board member for Eli Lilly. Dr P. D. Franklin reported grants from the National Institute of Arthritis and Musculoskeletal and Skin Diseases and the Agency for Healthcare Research and Quality during the conduct of the study and grants from the Patient- Centered Outcomes Research Institute outside the submitted work. Dr J. M. Franklin reported receiving grants from NIH during the conduct of the study. Dr Katz reported receiving grants from NIH during the conduct of the study and grants from Samumed and Flexion Therapeutics outside the submitted work. Dr Desai reported receiving grants from Bayer, Novartis, and Vertex Pharmaceuticals outside the submitted work. No other disclosures were reported. Funding/Support: This study was supported by a grant from the National Institutes of Health and National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR069557-01A1). Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 (Reprinted) July 31, 2019 11/13 JAMA Network Open | Orthopedics Association of Preoperative Opioid Use With Mortality and Safety Outcomes After Total Knee Replacement REFERENCES 1. Frenk S, Porter K, Paulozzi L. Prescription opioid analgesic use among adults: United States, 1999–2012. NCHS Data Brief. 2015;189:1-8. 2. Wright EA, Katz JN, Abrams S, Solomon DH, Losina E. Trends in prescription of opioids from 2003-2009 in persons with knee osteoarthritis. Arthritis Care Res (Hoboken). 2014;66(10):1489-1495. doi:10.1002/acr.22360 3. Kim SC, Choudhry N, Franklin JM, et al. 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J Gerontol A Biol Sci Med Sci. 2018. doi:10.1093/ gerona/gly197 24. Weick J, Bawa H, Dirschl DR, Luu HH. Preoperative opioid use is associated with higher readmission and revision rates in total knee and total hip arthroplasty. J Bone Joint Surg Am. 2018;100(14):1171-1176. doi:10.2106/ JBJS.17.01414 25. Hadlandsmyth K, Vander Weg MW, McCoy KD, Mosher HJ, Vaughan-Sarrazin MS, Lund BC. Risk for prolonged opioid use following total knee arthroplasty in veterans. J Arthroplasty. 2018;33(1):119-123. doi:10.1016/j.arth.2017. 08.022 SUPPLEMENT. eTable 1. All-cause Mortality and Short-term Safety Outcomes After Total Knee Replacement Among Intermittent Opioid Users vs Opioid-Naive Patients eTable 2. Patient Characteristics Prior to Total Knee Replacement Excluding Patients With Malignant Tumors eTable 3. All-cause Mortality and Short-term Safety Outcomes After Total Knee Replacement Among Continuous Opioid Users vs Opioid-Naive Patients Excluding Patients With Malignant Tumors JAMA Network Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 (Reprinted) July 31, 2019 13/13 Supplementary Online Content Kim SC, Jin Y, Lee YC, et al. Association of preoperative opioid use with mortality and short- term safety outcomes after total knee replacement. JAMA Netw Open. 2019;2(7):e198061. doi:10.1001/jamanetworkopen.2019.8061 eTable 1. All-cause Mortality and Short-term Safety Outcomes After Total Knee Replacement Among Intermittent Opioid Users vs Opioid-Naive Patients eTable 2. Patient Characteristics Prior to Total Knee Replacement Excluding Patients With Malignant Tumors eTable 3. All-cause Mortality and Short-term Safety Outcomes After Total Knee Replacement Among Continuous Opioid Users vs Opioid-Naive Patients Excluding Patients With Malignant Tumors This supplementary material has been provided by the authors to give readers additional information about their work. © 2019 Kim SC et al. JAMA Network Open. eTable 1. All-cause Mortality and Short-term Safety Outcomes After Total Knee Replacement Among Intermittent Opioid Users vs Opioid-Naive Patients Unadjusted model Model 1 Model 2 HR (95%CI) HR (95%CI) HR (95%CI) All-cause mortality In hospital 1.65 (1.26-2.15) 1.76 (1.35-2.30) 1.20 (0.90-1.59) 30 days 1.39 (1.16-1.67) 1.49 (1.24-1.80) 1.10 (0.90-1.34) 60 days 1.41 (1.19-1.66) 1.52 (1.28-1.80) 1.10 (0.91-1.31) 90 days 1.32 (1.13-1.54) 1.43 (1.22-1.67) 1.02 (0.86-1.21) Hospital Readmission 30 days 1.18 (1.13-1.23) 1.21 (1.15-1.26) 0.99 (0.94-1.04) 60 days 1.19 (1.15-1.24) 1.22 (1.18-1.27) 1.00 (0.96-1.04) 90 days 1.20 (1.16-1.24) 1.23 (1.19-1.27) 1.02 (0.99-1.06) Revision surgery 30 days 1.60 (1.31-1.96) 1.63 (1.33-2.00) 1.29 (1.04-1.61) 60 days 1.53 (1.30-1.81) 1.56 (1.32-1.84) 1.23 (1.03-1.46) 90 days 1.50 (1.29-1.75) 1.53 (1.31-1.78) 1.23 (1.05-1.45) Opioid overdose 30 days 3.88 (1.47-10.20) 3.89 (1.47-10.26) 3.07 (1.12-8.40) 60 days 4.23 (1.62-11.05) 4.30 (1.64-11.26) 3.30 (1.22-8.92) 90 days 4.58 (1.76-11.90) 4.70 (1.80-12.22) 3.48 (1.31-9.26) Non-vertebral fracture 30 days 1.82 (0.99-3.36) 1.79 (0.96-3.31) 1.46 (0.76-2.81) 60 days 1.43 (0.99-2.07) 1.43 (0.98-2.07) 1.20 (0.81-1.78) 90 days 1.34 (1.02-1.75) 1.37 (1.04-1.80) 1.16 (0.86-1.55) Vertebral fracture 30 days 2.07 (1.44-2.98) 2.15 (1.48-3.14) 1.54 (1.04-2.28) 60 days 1.74 (1.38-2.20) 1.78 (1.40-2.27) 1.33 (1.03-1.71) 90 days 1.80 (1.49-2.16) 1.83 (1.51-2.21) 1.36 (1.11-1.66) Myocardial infarction or stroke 30 days 1.14 (0.85-1.51) 1.23 (0.92-1.64) 0.98 (0.72-1.33) 60 days 1.21 (0.94-1.57) 1.32 (1.02-1.71) 1.09 (0.83-1.44) 90 days 1.25 (0.99-1.59) 1.37 (1.08-1.74) 1.10 (0.85-1.42) Respiratory distress 30 days 1.86 (1.01-3.42) 1.84 (1.00-3.41) 1.26 (0.66-2.42) 60 days 1.70 (1.02-2.83) 1.70 (1.02-2.85) 1.12 (0.65-1.94) 90 days 1.19 (0.76-1.85) 1.18 (0.75-1.85) 0.79 (0.49-1.28) © 2019 Kim SC et al. JAMA Network Open. Pneumonia 30 days 1.13 (0.86-1.50) 1.18 (0.89-1.56) 0.81 (0.60-1.10) 60 days 1.08 (0.85-1.37) 1.12 (0.88-1.43) 0.77 (0.59-1.00) 90 days 1.22 (0.98-1.53) 1.28 (1.02-1.60) 0.86 (0.68-1.09) Bowel obstruction 30 days 1.27 (0.89-1.79) 1.32 (0.93-1.87) 1.10 (0.76-1.61) 60 days 1.28 (0.95-1.72) 1.32 (0.98-1.78) 1.10 (0.80-1.51) 90 days 1.32 (1.02-1.72) 1.37 (1.05-1.78) 1.14 (0.86-1.51) Model 1 is adjusted for age, sex, race/ethnicity and region of residence. Model 2 is adjusted for comorbidity index, frailty, and number of unique prescription drugs in addition to the variables in the Model 1. © 2019 Kim SC et al. JAMA Network Open. eTable 2. Patient Characteristics Prior to Total Knee Replacement Excluding Patients With Malignant Tumors Continuous users Intermittent users Opioid-naïve (n=19,241) (n=129,685) (n=107,627) Presented as percentage or mean ± SD Demographics Age, years 72.5 ± 5.6 73.5 ± 5.7 74.0 ± 5.8 Female 14,984 (77.9) 94,130 (72.6) 71,688 (66.6) Race/ethnicity White 16,875 (87.7) 114,625 (88.4) 98,262 (91.3) Black 1,648 (8.6) 7,880 (6.1) 3,948 (3.7) Hispanic 277 (1.4) 3,007 (2.3) 1,588 (1.5) Other 271 (1.4) 2,390 (1.8) 2,052 (1.9) Region Northeast 1,978 (10.3) 17,488 (13.5) 19,495 (18.1) Midwest 4,986 (25.9) 35,562 (27.4) 34,343 (31.9) South 8,732 (45.4) 52,282 (40.3) 36,021 (33.5) West 3,539 (18.4) 24,107 (18.6) 17,544 (16.3) Comorbidities Hypertension 17,274 (89.8) 111,792 (86.2) 87,327 (81.1) Diabetes 7,388 (38.4) 44,010 (33.9) 30,775 (28.6) Obesity 4,441 (23.1) 25,136 (19.4) 15,088 (14.0) Back pain 13,680 (71.1) 68,409 (52.8) 37,990 (35.3) Neuropathic pain 8,591 (44.6) 41,798 (32.2) 19,859 (18.5) Coronary heart disease 2,155 (11.2) 11,376 (8.8) 6,723 (6.2) Chronic kidney disease 2,966 (15.4) 15,071 (11.6) 8,395 (7.8) Heart failure 2,922 (15.2) 12,865 (9.9) 6,629 (6.2) Hip fracture 89 (0.5) 533 (0.4) 201 (0.2) Migraine 3,009 (15.6) 14,416 (11.1) 6,825 (6.3) Sleep disorder 5,227 (27.2) 26,101 (20.1) 14,390 (13.4) Depression 6,020 (31.3) 23,301 (18.0) 10,912 (10.1) Anxiety disorder 4,562 (23.7) 17,134 (13.2) 8,217 (7.6) Bipolar disorder 509 (2.6) 1,714 (1.3) 749 (0.7) Drug abuse 249 (1.3) 279 (0.2) 50 (0.0) Alcohol abuse 304 (1.6) 1,228 (0.9) 614 (0.6) Tobacco use 4,020 (20.9) 17,997 (13.9) 9,611 (8.9) Rheumatoid arthritis 2,032 (10.6) 7,992 (6.2) 3,558 (3.3) Frailty Robust 2,951 (15.3) 42,041 (32.4) 53,625 (49.8) Prefrail 12,603 (65.5) 77,858 (60.0) 51,616 (48.0) © 2019 Kim SC et al. JAMA Network Open. Mild frailty 3,367 (17.5) 9,040 (7.0) 2,272 (2.1) Moderate-to-severe frailty 320 (1.7) 746 (0.6) 114 (0.1) Comorbidity index 1.6 ± 2.4 1.1 ± 2.1 0.6 ± 1.7 Medication use NSAIDs 9,034 (47.0) 59,123 (45.6) 32,341 (30.0) Coxibs 2,340 (12.2) 15,054 (11.6) 8,042 (7.5) Oral corticosteroids 8,816 (45.8) 51,399 (39.6) 29,935 (27.8) Antidepressants 10,443 (54.3) 43,783 (33.8) 21,700 (20.2) Benzodiazepines 4,230 (22.0) 15,080 (11.6) 6,600 (6.1) Bisphosphonates 1,529 (7.9) 9,965 (7.7) 7,222 (6.7) Anticonvulsants 7,639 (39.7) 25,843 (19.9) 9,460 (8.8) Health care utilization No. of unique 15.4 ± 6.7 12.1 ± 5.6 7.7 ± 4.5 prescription drugs No. of emergency 0.8 ± 1.8 0.5 ± 1.1 0.2 ± 0.6 department visit No. of visits to any 15.7 ± 9.4 13.3 ± 7.6 10.1 ± 6.2 physicians No. of acute 0.4 ± 0.2 0.3 ± 0.1 0.1 ± 0.1 hospitalization © 2019 Kim SC et al. JAMA Network Open. eTable 3. All-cause Mortality and Short-term Safety Outcomes After Total Knee Replacement Among Continuous Opioid Users vs Opioid-Naive Patients Excluding Patients With Malignant Tumors Unadjusted Model 1 Model 2 HR (95% CI) HR (95% CI) HR (95%CI) All-cause mortality In hospital 1.61 (0.97, 2.68) 1.95 (1.17, 3.25) 0.98 (0.56, 1.69) 30 days 1.33 (0.91, 1.95) 1.65 (1.12, 2.42) 0.95 (0.63, 1.43) 60 days 1.56 (1.12, 2.16) 1.95 (1.40, 2.71) 1.08 (0.76, 1.54) 90 days 1.45 (1.06, 1.98) 1.81 (1.32, 2.48) 1.00 (0.71, 1.40) Hospital Readmission 30 days 1.48 (1.36, 1.62) 1.57 (1.44, 1.72) 1.05 (0.96, 1.16) 60 days 1.58 (1.47, 1.69) 1.67 (1.55, 1.79) 1.12 (1.03, 1.21) 90 days 1.62 (1.52, 1.73) 1.71 (1.61, 1.83) 1.17 (1.10, 1.26) Revision surgery 30 days 2.72 (1.96, 3.77) 2.74 (1.96, 3.83) 1.66 (1.15, 2.40) 60 days 2.24 (1.69, 2.95) 2.32 (1.75, 3.07) 1.38 (1.01, 1.89) 90 days 2.31 (1.79, 2.98) 2.38 (1.84, 3.09) 1.49 (1.12, 1.98) Opioid overdose 30 days 7.36 (2.25, 24.13) 7.04 (2.12, 23.39) 3.65 (0.98, 13.66) 60 days 11.16 (3.74, 33.31) 10.99 (3.64, 33.23) 5.66 (1.68, 19.05) 90 days 18.83 (6.84, 51.82) 19.41 (6.98, 54.01) 9.60 (3.16, 29.15) Non-vertebral fracture 30 days 3.21 (1.20, 8.55) 3.36 (1.25, 9.03) 2.25 (0.75, 6.70) 60 days 1.67 (0.84, 3.35) 1.67 (0.83, 3.36) 1.13 (0.53, 2.42) 90 days 2.03 (1.26, 3.28) 2.15 (1.33, 3.50) 1.49 (0.88, 2.53) Vertebral fracture 30 days 4.25 (2.51, 7.20) 4.54 (2.65, 7.78) 2.32 (1.28, 4.21) 60 days 4.29 (3.05, 6.02) 4.58 (3.24, 6.48) 2.44 (1.66, 3.60) 90 days 4.53 (3.45, 5.94) 4.77 (3.62, 6.29) 2.47 (1.82, 3.36) Myocardial infarction or stroke 30 days 1.04 (0.52, 2.10) 0.72 (0.34, 1.50) 0.84 (0.42, 1.69) 60 days 0.94 (0.51, 1.72) 1.17 (0.63, 2.15) 0.85 (0.45, 1.62) © 2019 Kim SC et al. JAMA Network Open. 90 days 1.14 (0.68, 1.91) 1.43 (0.85, 2.40) 0.93 (0.54, 1.62) Respiratory distress 30 days 2.06 (0.66, 6.38) 2.13 (0.68, 6.68) 1.30 (0.38, 4.46) 60 days 2.59 (1.07, 6.24) 2.61 (1.07, 6.37) 1.23 (0.46, 3.27) 90 days 2.44 (1.18, 5.07) 2.48 (1.18, 5.20) 1.20 (0.52, 2.72) Pneumonia 30 days 1.80 (1.09, 2.95) 1.99 (1.21, 3.29) 0.98 (0.56, 1.71) 60 days 1.74 (1.13, 2.69) 1.90 (1.23, 2.96) 0.87 (0.54, 1.43) 90 days 2.10 (1.43, 3.07) 2.30 (1.56, 3.38) 1.01 (0.65, 1.55) Bowel obstruction 30 days 2.56 (1.40, 4.66) 2.86 (1.56, 5.27) 1.97 (1.01, 3.85) 60 days 2.58 (1.55, 4.28) 2.87 (1.71, 4.80) 2.01 (1.14, 3.53) 90 days 2.30 (1.45, 3.67) 2.54 (1.58, 4.08) 1.79 (1.07, 3.01) Model 1 is adjusted for age, sex, race/ethnicity and region of residence. Model 2 is adjusted for comorbidity index, frailty, and number of unique prescription drugs in addition to the variables in the Model 1. © 2019 Kim SC et al. JAMA Network Open.
JAMA Network Open – American Medical Association
Published: Jul 31, 2019
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