Abstract
Background
Alcohol use disorder has been associated with broad health consequences that may interfere with healing after total shoulder arthroplasty. The aim of this study was to explore the impact of alcohol use disorder on readmissions and complications following total shoulder arthroplasty.
Methods
We used data from the Healthcare Cost and Utilization Project National Readmissions Database (NRD) from 2016 to 2018. Patients were included based on International Classification of Diseases, 10th Revision (ICD-10) procedure codes for anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA). Patients with an alcohol use disorder (AUD) were identified using the ICD-10 diagnosis code F10.20. Demographics, complications, and 30-day and 90-day readmission were collected for all patients. A univariate logistic regression was performed to investigate AUD as a factor affecting readmission and complication rates. A multivariate logistic regression model was created to assess the impact of alcohol use disorder on complications and readmission while controlling for demographic factors.
Results
In total, 164,527 patients were included, and 503 (0.3%) patients had a prior diagnosis of AUD. Revision surgery was more common in patients with an alcohol use disorder (8.8% vs. 6.2%; p = 0.022). Postoperative infection (p = 0.026), dislocation (p = 0.025), liver complications (p < 0.01), and 90-day readmission (p < 0.01) were more common in patients with a diagnosed AUD. On multivariate analysis, patients with an AUD were found to be at increased odds for liver complications (OR: 46.8; 95% CI: [32.8, 66.8]; p < 0.01). Comparatively, mean age, length of stay, and over healthcare costs were also higher for patients with an AUD.
Conclusion
Patients with a diagnosis of AUD were more likely to suffer from shoulder dislocation, liver complications, and 90-day readmission, while also being younger and having longer hospital stays. Therefore, surgeons should take caution to anticipate and prevent complications and readmissions following total shoulder arthroplasty in patients with an AUD.
Keywords: Shoulder arthroplasty, Alcohol use disorder, Complications, Readmission, Infection
1. Introduction
As of 2016, it was estimated that 8.6% and 1.7% of the global male and female populations have some form of alcohol use disorder (AUD).1 These numbers include roughly 15 million Americans and 23 million Europeans.2,3 Additionally, over the last decade, the National Institutes of Health reported that AUD rates have been increasing.4 During this same time period, the annual case volume of anatomic (aTSA) and reverse (rTSA) total shoulder arthroplasty has seen a continued rise.5,6 With such a high case volume in conjunction with a high percentage of the population having AUD, it is necessary that we understand the impact AUD may have on the short-term complications and readmission rates following shoulder arthroplasty.
To this end, many modifiable risk factors have been shown to negatively impact short-term outcomes following aTSA and rTSA. Examples of these include tobacco usage, diabetes, (pulmonary) hypertension, and body mass index (BMI).7, 8, 9, 10, 11 Specific complications linked to some of these comorbidities include surgical site infection, wound complications, thromboembolism, and hospital readmissions.8,12 Moreover, excessive alcohol intake has been associated with decreases in bone mineral density, increased fracture risk, and decreased bone formation.13 In previous studies investigating the effects of AUD in knee and hip replacement, it was found that AUD patients had increased complication rates and increased overall length of hospital stays (LOS).14,15 Currently, there exists a discrepancy in the literature as to the impact of AUD on shoulder arthroplasty outcomes. Some studies concluded that AUD was associated with worse functional outcomes, increased healthcare utilization, and higher complication rates.16, 17, 18, 19 Other studies identified no impact of AUD on postoperative complication rates.20, 21, 22 However, only a single study to our knowledge has described these findings at a national level,17 but this did not include readmission data.
Complications and readmissions are costly to both patients and healthcare institutions in terms of monetary value.23 Therefore, it is in everyone's best interest to identify specific risk factors which make patients susceptible to these adverse outcomes. With the aforementioned discrepancies in mind, we sought to explore the impact of AUD on readmissions and complications following total shoulder arthroplasty. We hypothesize that AUD will be associated with increased rates of postoperative complications, prosthetic complications, and readmissions.
2. Methods
Patients who underwent aTSA and rTSA surgeries were queried from the National Readmissions Database (NRD), which is a publicly available dataset of inpatient readmissions from 30 different states maintained by the Healthcare Cost and Utilization Project (HCUP).24 Patients undergoing shoulder arthroplasty were identified through the International Classification of Disease Tenth Revision-Clinical Modification (ICD-10-CM) codes. Specifically, we included patients who underwent aTSA (right: 0RRJ0JZ, left: 0RRK0JZ) and rTSA (right: 0RRJ00Z, left: 0RRK00Z). Patients with the ICD-10 code F10.20, defined as uncomplicated alcohol dependence, were considered to have an AUD. Patients without code F10.20 were classified as non-AUD patients.
The present study analyzed NRD data from January 2016 to December 2018. Since NRD patient linkage numbers are only valid within each calendar year, patients whose initial admission occurred in the fourth quarter of each year (i.e., October 1st – December 31st) were excluded to provide adequate follow-up for 90-day readmission data. Non-adult patients (<18 years of age) and those who underwent a nonelective surgery were excluded from the study. Patients who died during their initial admission were unable to be readmitted and thus were also excluded.25
Patient demographic characteristics in our study included age, gender, insurance status, procedure type, discharge disposition, and comorbidities. Outcome variables were 30- and 90-day readmissions, LOS, cost, and complications. Patient complication data was collected using ICD-10 diagnostic and procedural codes. Intraoperative complications, peripheral vascular complications, respiratory complications, genitourinary complications, gastrointestinal complications, hardware-related complications, nonunion, postoperative infection, dehiscence, postoperative shock, deep vein thrombosis, and pulmonary embolism were collected.8 Additionally, complications specific to aTSA and rTSA were 90-day revision (indicating that this case was a revision rather than that the patient went on to have revision surgery), surgical site (SSI) or prosthetic joint infection (PJI), prosthetic complication (including mechanical complication, loosening, prosthetic fracture, and periprosthetic fracture), dislocation, and aseptic wound complications. SSI and PJI were grouped under “Infection” for analysis.
Elixhauser and Charlson comorbidities were identified from ICD-10 codes using the R Comorbidity package.26 Univariate logistic regression analyses were performed with AUD as the independent variable and readmissions and complications as dependent variables. Multivariate logistic regression analyses were then created for the dependent variables where alcohol use disorder was a significant predictor in the univariate analyses. The multivariate models allowed us to identify independent associations between AUD and outcomes. All analyses were performed using SciPy version 1.6.1.7,27,28
3. Results
3.1. Patient demographics
Using the inclusion criteria, a total of 164,527 patients were identified within the NRD that underwent shoulder replacement surgery between 2016 and 2018. Approximately 0.3% of these identified patients had a previously diagnosed AUD. Significantly less of the patients without AUD were male (43.9% vs. 65.3%, p < 0.01) and covered by Medicaid insurance (3.2% vs. 10.8%, p < 0.01). Patients without AUD were also older (69.4 ± 9.5 years vs. 65.4 ± 9.2 years, p < 0.01). There was no noted difference in the amount of Charlson (p = 0.54) or Elixhauser comorbidities (p = 0.90) between the two cohorts. Complete demographic data can be seen in Table 1.
Table 1.
Demographics.
| Demographics | Non-AUD, N (%) | AUD, N (%) | p-value |
|---|---|---|---|
| Total patients | 164,026 (99.7) | 501 (0.3) | N/A |
| Gender | <0.01 | ||
| Male | 71,959 (43.9) | 327 (65.3) | |
| Female | 92067 (56.1) | 174 (34.7) | |
| Age (yrs) | <0.01 | ||
| 18-49 | 4402 (2.7) | 15 (3.0) | |
| 50-59 | 18918 (11.5) | 120 (24.0) | |
| 60-69 | 55324 (33.7) | 209 (41.7) | |
| 70-79 | 63580 (38.8) | 124 (24.8) | |
| 80+ | 21802 (13.3) | 33 (6.6) | |
| Primary Payer | <0.01 | ||
| Medicaid | 5187 (3.2) | 54 (10.8) | |
| Medicare | 116679 (71.1) | 296 (59.1) | |
| No Charge | 71 (0.04) | a | |
| Other/UNK | 7748 (4.7) | 31 (6.2) | |
| Private Insurance | 33833 (20.6) | 117 (23.4) | |
| Self-Pay | 508 (0.3) | a | |
| Discharge Status | <0.01 | ||
| Against Medical Advice | 120 (0.1) | a | |
| Died in Hospital | 79 (0.1) | a | |
| Home Health Care | 35089 (21.4) | 120 (24.0) | |
| Other | 17 (0.01) | b | |
| Routine | 113527 (69.2) | 306 (61.1) | |
| Transfer Other (Skilled Nursing Facility, Intermediate Care Facility, and another type of facility) | 15033 (9.2) | 70 (14.0) | |
| Transfer to short-term hospital | 161 (0.1) | a | |
| Charlson Score | 0.54 | ||
| 0 | 84579 (51.6) | 256 (51.1) | |
| 1-2 | 70406 (42.9) | 218 (43.5) | |
| 3-4 | 8369 (5.1) | 27 (5.4) | |
| ≥5 | 667 (0.4) | b | |
| Elixhauser Score | 0.90 | ||
| 0 | 23616 (14.4) | 75 (15.0) | |
| 1-4 | 125353 (76.4) | 382 (76.2) | |
| ≥5 | 15052 (9.2) | 44 (8.8) |
Cell that contains less than 11 patients, with number not included as per database guidelines.
No patients with this characteristic.
3.2. Univariate analysis
While there was no difference noted between the cohorts for readmissions within 30 days of shoulder replacement (2.9% vs. 3.8%, p = 0.23), the non-AUD cohort had significantly less readmissions within 90 days (6.3% vs. 11.6%, p < 0.01). Patients without AUD also had significantly shorter initial LOS on average (1.8 ± 2.0 days vs. 2.4 ± 5.9 days, p < 0.01) and incurred lower average costs ($73,135.16 ± $47,487.08 vs. $84,129.00 ± $50,063.37, p < 0.01). Notably, the non-AUD cohort had lower rates of liver complications (0.2% vs. 7.4%, p < 0.01), dislocations (1.1% vs. 2.2%, p = 0.02), and revision surgeries (6.2% vs. 8.8%, p = 0.02). The non-AUD cohort also had lower rates of postoperative infections, but there was an insufficient number of patients to make a definitive conclusion per NRD criteria. Complete univariate analysis data can be seen in Table 2.
Table 2.
Univariate analysis.
| Complication | Non-AUD, N (%) | AUD, N (%) | p-value |
|---|---|---|---|
| Hardware Related Complications | 5955 (3.6) | 21 (4.2) | 0.58 |
| Intraoperative Complications | 1282 (0.8) | a | 0.83 |
| Peripheral Vascular Complications | 118 (0.1) | a | 0.82 |
| Respiratory Complications | 579 (0.4) | a | 0.84 |
| Genitourinary Complications | 263 (0.2) | a | 0.73 |
| Gastrointestinal Complications | 155 (0.1) | a | 0.97 |
| Postoperative Shock | 49 (0.03) | a | 0.37 |
| Postoperative Infection | 218 (0.1) | a | 0.026 |
| Dehiscence | 44 (0.03) | b | 0.32 |
| Deep Vein Thrombosis | 180 (0.1) | b | 0.95 |
| Pulmonary Embolism | 157 (0.1) | a | 0.98 |
| Nonunion | b | b | 1.00 |
| Dislocation | 1745 (1.1) | 11 (2.2) | 0.025 |
| Infection | 1105 (0.7) | a | 0.54 |
| Non-Infectious Wound Complications | 96 (0.1) | b | 0.70 |
| Prosthetic Complications | 4125 (2.5) | a | 0.55 |
| Revisions | 10177 (6.2) | 44 (8.8) | 0.022 |
| Liver Complications | 275 (0.2) | 37 (7.4) | <0.01 |
| Readmission within 30 Days | 4749 (2.9) | 19 (3.8) | 0.23 |
| Readmission within 90 Days | 10410 (6.3) | 58 (11.6) | <0.01 |
Cell that contains less than 11 patients, with number not included as per database guidelines.
No patients with this characteristic.
3.3. Multivariate analysis
A multivariate analysis was additionally performed on the demographic factors and comorbidities found to be significantly different in the univariate analysis between the non-AUD and AUD cohorts. Notably, patients in the AUD cohort were significantly more likely to have liver complications (Odds Ratio (OR) (95% Confidence Interval (CI)): 46.8 (32.8–66.8), p < 0.01). There was no difference noted between cohorts with regards to requiring revision surgery (OR (95% CI): 1.2 (0.80–1.66), p = 0.44), having a readmission within 90 days (OR (95% CI): 0.95 (0.66–1.37), p = 0.78), or having a dislocation (OR (95% CI): 0.52 (0.16–1.68), p = 0.27) (Table 3).
Table 3.
Multivariate analysis.
| Complication | Odds Ratio (95% Confidence Interval) | p-value |
|---|---|---|
| Dislocation | 0.52 (0.16–1.68) | 0.27 |
| Revisions | 1.2 (0.80–1.66) | 0.44 |
| Liver Complications | 46.8 (32.8–66.8) | <0.01 |
| Readmission within 90 Days | 0.95 (0.66–1.37) | 0.78 |
4. Discussion
The present study showed that patients with defined AUD were younger, had longer LOS, and were at increased risk of dislocations, revisions, liver complications, and 90-day readmissions compared to patients without AUD. However, AUD was only seen to be an independent predictor for postoperative liver complications following TSA. With simultaneous increases in both AUD and shoulder arthroplasty rates over the last decade, it is imperative that we fully understand the impact AUD has on operative outcomes. Prior literature only contains a single national-level analysis using data from 2002 to 2011, which showed that individuals with AUD had higher rates of short-term complications, intraoperative complications, mortality, and longer LOS.17 The present study provides an updated analysis as to the impact AUD has on TSA outcomes using 2016–2018 data.
Alcohol use has been associated with reductions in bone remodeling in the animal model, a vital process in the recovery process following arthroplasty.13 Pertinently, patients with AUD were found to require shoulder replacement upwards of seven years earlier than non-AUD patients in a previous study.17 To this end, the predicted survival (i.e. non-revision) for aTSA and rTSA implants is roughly 94% and 91% at 10-years respectively.29,30 Implant survival rates are further reduced in revised total shoulders.31 In this study, we showed that patients with AUD required TSA on average four years (p < 0.01) sooner, and were more likely to have had prior arthroplasties requiring revision surgery on the same shoulder at this age (non-AUD: 6.2% vs. AUD: 8.8%; p < 0.01), compared to non-AUD patients. By requiring replacements and or revision arthroplasty at a younger age, there is an increased risk of subsequent revision procedures later in life due to implant failure. Moreover, we saw that a higher percentage of our AUD patients were on Medicaid (3.2%) compared to their non-AUD (10.8%) counterparts. In previous reports, patients who were on Medicaid were found to have increased mortality, readmissions, and LOS following TSA compared to non-Medicaid patients.32,33 Ultimately, surgeons should be aware of these aforementioned demographic variables that can impact a patient's surgical outcomes and plan to address them in preoperative consultations.
Moreover, we showed that patients with AUD had a higher risk for 90-day readmissions compared to non-AUD patients on univariate analysis. Importantly, this association was lost at the multivariate level, a result likely attributable to the sample size of our cohorts. Nevertheless, readmissions are associated with higher healthcare costs for patients, as are increased LOS and non-home discharges. Overall, we showed that patients with AUD had total costs (average: $10,994) and LOS (average: 0.6 days) that were significantly higher comparatively. AUD patients were also discharged to skilled nursing/intermediate facilities or with home health care more often. Similarly, Ponce et al. identified AUD as being associated with increased LOS (non-AUD: 17% vs. AUD: 40%; p < 0.01) and nonroutine discharge (non-AUD: 36% vs. AUD: 42%; p < 0.01) following TSA using the National Inpatient Sample database.17 These findings, in combination with the aforementioned risk for revisions, can place a significant financial toll on patients and healthcare facilities alike.
Further, under the Hospital Readmissions Reduction Program (HRRP), hospitals risk incurring fiscal penalties when readmission rates are elevated for specific conditions (e.g.; hip arthroplasty). While TSA is not currently included under the HRRP guidelines, with the additional Estimate Readmission Ratio in place, it remains important that research continues to identify manageable risk factors that lead to adverse outcomes for both patients and hospitals alike. It was found that patients with AUD were predisposed to increased rates of liver complications compared to those without AUD in this study (non-AUD: 0.2% vs. AUD: 7.4%, p < 0.01). This finding held true after adjusting for possible confounders with multivariate analysis (OR: 46.8, p < 0.01). Liver complications include events such as steatosis, cirrhosis, hepatic failure, and fibrosis/sclerosis. A prior study by Ponce et al. investigated the effects of liver disease prior to surgery as a comorbidity but did not discuss liver complications post-operatively. As alcohol is one of the leading causes of cirrhosis and liver disease, it might be expected that these patients are more likely to experience liver complications postoperatively.34 However, this is worth mentioning as patients may not be aware of these risks when being primarily focused on their shoulder procedure; it is crucially important to consider all comorbidities that may lead to increased postoperative morbidity. Shoulder dislocations also occurred at greater rates for patients with AUD (1.1 vs. 2.2, p = 0.025). Prior research has shown that alcohol use is correlated with joint laxity, which fits with this finding of increased dislocations.35
Lastly, patients with AUD were found to require more revision surgeries following their index procedure (6.2 vs 8.8, p = 0.022). As discussed earlier, patients with AUD have diminished bone health. Studies of shoulder arthroplasty in patients with osteoporosis have shown that worse bone health complicates the procedure as it may hinder the surgeon's ability to achieve adequate fixation of the implant.35,36 This same concept may be contributing to the increased revisions in this patient population. As patients with AUD are undergoing more revisions following TSA, this also subjects them to repeat revisions in the future as revised procedures have worse implant survival compared to the index procedure.37
Some results from the univariate analysis were found to be not significant in the multivariate analysis which may be due to the strict definition of substance use disorders and AUD set by NRD, limiting our sample size. Thankfully, it appears that outcomes between these two groups are comparable regardless of AUD status; therefore, it is important to optimize short term outcomes by identifying the greatest risks for readmission.20,21Alcohol consumption is related to multiple chronic diseases and comorbidities such as liver disease, neuropathy, mental and behavioral disorders, gastritis, and various malignancies. These diseases can be attributed to AUD in certain patients, but it is worth noting that patients with AUD usually have other unhealthy lifestyle choices.38 For example, studies have shown that adults with AUD are more likely to smoke tobacco and have unhealthy diets.39,40 Further, while the NRD does not report on BMI, previous reports indicate that AUD is positively correlated with an individual's BMI.41 With these comorbidities heavily related to AUD it is important to holistically consider them in the interpretation of our analysis. To mitigate multiple confounding comorbidities, we included both the Elixhauser and Charlson Scores which showed that our groups of patients were comparable with no appreciable comorbidity differences.
The present study is not without inherent limitations. Given the NRD was used, we were unable to determine why a patient was readmitted within the 90-day window. Similarly, we were unable to characterize the full extent of alcohol usage, potential postoperative cessation, or prior AUD for each individual patient; this limited the sample size for our study group potentially impacting the subsequent multivariable analysis. The sample size also prevented us from stratifying by aTSA and rTSA. Additionally, differences in demographic variables (e.g., age, sex) were appreciated here, potentially acting as a study confounder. Lastly, although the multivariate analysis indicated that AUD was an independent predictor of postoperative liver complications after TSA, hepatic disease likely existed prior to the procedure and not a result of the procedure itself. Research into this topic would benefit from an additional matched cohort analysis and or a study design stratified by different levels of AUD and alcohol usage. We believe these suggestions should be a focus of future studies.
5. Conclusion
AUD is known to have deleterious effects on an individual's overall health. Here, AUD was found to be associated with a younger age at surgery and increased rates of revisions, liver complications, shoulder dislocations, and 90-day readmissions following TSA. However, AUD was only found to be an independent predictor of liver complications following shoulder replacement. These findings can be interpreted as either 1) all patients have similar outcomes regardless of AUD status or 2) a larger sample size needs to be analyzed to delineate independent impacts of AUD on TSA outcomes. Nevertheless, as AUD patients were found to have longer LOS and higher overall costs, it is imperative that surgeons work with patients both preoperatively and postoperatively to mitigate potential adverse outcomes following TSA.
Disclosures
The following individuals have no conflicts of interest or sources of support that require acknowledgement: Christopher A. White, Addison Quinones, Justin E. Tang, Liam R. Butler, and Akiro H. Duey.
Funding/sponsorship
This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
Informed consent
Informed consent was not required for this study as the data is publicly available and anonymized.
Institutional ethical committee approval
Ethics committee approval was not required as for this study as the data is publicly available and anonymized.
Authors contribution
CAW: Conceptualization, Methodology, Project administration, Roles/Writing – original draft, Writing – review & editing; AQ: Conceptualization, Methodology, Roles/Writing – original draft; JET: Data curation, Formal analysis, Roles/Writing – original draft; LRB: Conceptualization, Methodology, Roles/Writing – original draft; AHD: Methodology, Project administration, Writing – review & editing; JSK: Conceptualization, Supervision, Writing – review & editing; SKC: Conceptualization, Supervision, Writing – review & editing; PJC: Conceptualization, Supervision, Writing – review & editing.
Declaration of competing interest
None.
Acknowledgements
None.
References
- 1.Carvalho A.F., Heilig M., Perez A., Probst C., R J. Alcohol use disorders. Lancet. 2019;394(10200):781–792. doi: 10.1016/S0140-6736(19)31775-1. [DOI] [PubMed] [Google Scholar]
- 2.Rehm J., Anderson P., Barry J., et al. Prevalence of and potential influencing factors for alcohol dependence in Europe. Eur Addiction Res. 2015;21(1) doi: 10.1159/000365284. [DOI] [PubMed] [Google Scholar]
- 3.Website SAMHSA Center for behavioral health statistics and quality. 2019 national survey on drug use and health. Table 5.4A – alcohol use disorder in past year among persons aged 12 or older, by age group and demographic characteristics: numbers in thousands. 2018 and 2019. https://www.samhsa.gov/data/sites/default/files/reports/rpt29394/NSDUHD
- 4.Grant B.F., Goldstein R.B., Saha T.D., et al. Epidemiology of DSM-5 alcohol use disorder: results from the national epidemiologic survey on alcohol and related conditions III. JAMA Psychiatr. 2015;72(8) doi: 10.1001/jamapsychiatry.2015.0584. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Dillon M.T., Chan P.H., Inacio M.C.S., Singh A., Yian E.H., Navarro R.A. Yearly trends in elective shoulder arthroplasty, 2005-2013. Arthritis Care Res. 2017;69(10):1574–1581. doi: 10.1002/acr.23167. [DOI] [PubMed] [Google Scholar]
- 6.Best M.J., Aziz K.T., Wilckens J.H., McFarland E.G., Srikumaran U. Increasing incidence of primary reverse and anatomic total shoulder arthroplasty in the United States. J Shoulder Elbow Surg. 2021;30(5):1159–1166. doi: 10.1016/j.jse.2020.08.010. [DOI] [PubMed] [Google Scholar]
- 7.White CA, Dominy CL, Tang JE, et al. Impact of Tobacco Usage on Readmission and Complication Rates Following Shoulder Replacement Surgery: A Study of 164,527 Patients. Shoulder & Elbow. Published online 2022:175857322211023. doi:10.1177/17585732221102393. [DOI] [PMC free article] [PubMed]
- 8.Althoff A.D., Reeves R.A., Traven S.A., Wilson J.M., Woolf S.K., Slone H.S. Smoking is associated with increased surgical complications following total shoulder arthroplasty: an analysis of 14,465 patients. J Shoulder Elbow Surg. 2020;29(3):491–496. doi: 10.1016/j.jse.2019.07.012. [DOI] [PubMed] [Google Scholar]
- 9.Jain N.B., Guller U., Pietrobon R., Bond T.K., Higgins L.D. Comorbidities increase complication rates in patients having arthroplasty. Clin Orthop Relat Res. 2005;(435):232–238. doi: 10.1097/01.blo.0000156479.97488.a2. [DOI] [PubMed] [Google Scholar]
- 10.Wagner E.R., Houdek M.T., Schleck C., et al. Increasing body mass index is associated with worse outcomes after shoulder arthroplasty. J Bone Joint Surg Am. 2017;99(11):929–937. doi: 10.2106/JBJS.15.00255. [DOI] [PubMed] [Google Scholar]
- 11.Marigi E.M., Marigi I., Sperling J.W., Sanchez-Sotelo J. Primary and revision shoulder arthroplasty in patients with pulmonary hypertension: an underlying condition associated with high perioperative mortality and complications. Semin Arthroplasty: JSES. 2021;31(4):772–782. doi: 10.1053/j.sart.2021.05.005. [DOI] [Google Scholar]
- 12.Lyman S., Sherman S., Carter T.I., Bach P.B., Mandl L.A., Marx R.G. Prevalence and risk factors for symptomatic thromboembolic events after shoulder arthroplasty. Clin Orthop Relat Res. 2006;448:152–156. doi: 10.1097/01.blo.0000194679.87258.6e. [DOI] [PubMed] [Google Scholar]
- 13.Gaddini G.W., Turner R.T., Grant K.A., Iwaniec U.T. Alcohol: a simple nutrient with complex actions on bone in the adult skeleton. Alcohol Clin Exp Res. 2016;40(4):657–671. doi: 10.1111/acer.13000. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Luo T.D., Vakharia R.M., Gwam C.U., Zuskov A., Plate J.F., Roche M.W. A matched control analysis on the effects of alcohol use disorder after primary total knee arthroplasty in medicare patients. J Am Acad Orthop Surg. 2021;29(12):e593–e600. doi: 10.5435/JAAOS-D-20-00466. [DOI] [PubMed] [Google Scholar]
- 15.Best M.J., Buller L.T., Gosthe R.G., Klika A.K., Barsoum W.K. Alcohol misuse is an independent risk factor for poorer postoperative outcomes following primary total hip and total knee arthroplasty. J Arthroplasty. 2015;30(8):1293–1298. doi: 10.1016/j.arth.2015.02.028. [DOI] [PubMed] [Google Scholar]
- 16.Esteras-Serrano M.J., Ruiz-Hernández I.M., Gimferrer-Arriaga JO. Sánchez-Jimenez M., Far-Riera A., Rapariz J.M. Shoulder arthroplasty. Comorbidity as prognostic factor. J Orthop. 2018;15(3):889–893. doi: 10.1016/j.jor.2018.08.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ponce B.A., Oladeji L.O., Raley J.A., Menendez M.E. Analysis of perioperative morbidity and mortality in shoulder arthroplasty patients with preexisting alcohol use disorders. J Shoulder Elbow Surg. 2015;24(2):167–173. doi: 10.1016/j.jse.2014.05.019. [DOI] [PubMed] [Google Scholar]
- 18.Nassis E., Imas A.S., Roth E.S., et al. A matched-control analysis on the effects of alcohol use disorder following primary reverse shoulder arthroplasty. J Orthop. 2021;24:186–189. doi: 10.1016/j.jor.2021.02.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Leroux T.S., Saltzman B.M., Sumner S.A., et al. Elective shoulder surgery in the opioid naïve: rates of and risk factors for long-term postoperative opioid use. Am J Sports Med. 2019;47(5):1051–1056. doi: 10.1177/0363546519837516. [DOI] [PubMed] [Google Scholar]
- 20.Leschinger T., Raiss P., Loew M., Zeifang F. Total shoulder arthroplasty: risk factors for intraoperative and postoperative complications in patients with primary arthritis. J Shoulder Elbow Surg. 2017;26(3):e71–e77. doi: 10.1016/j.jse.2016.08.001. [DOI] [PubMed] [Google Scholar]
- 21.Donigan J.A., Frisella W.A., Haase D., Dolan L., Wolf B. Pre-operative and intra-operative factors related to shoulder arthroplasty outcomes. Iowa Orthop J. 2009;29:60–66. [PMC free article] [PubMed] [Google Scholar]
- 22.Torrens C., Martínez R., Santana F. Patients lost to follow-up in shoulder arthroplasty: descriptive characteristics and reasons. Clin Orthop Surg. 2022;14(1):112–118. doi: 10.4055/cios21034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Jencks S.F., Williams M.V., Coleman E.A. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418–1428. doi: 10.1056/NEJMsa0803563. [DOI] [PubMed] [Google Scholar]
- 24.Boslaugh S. 2008. Encyclopedia of Epidemiology. Published online. [DOI] [Google Scholar]
- 25.Rumalla K., Smith K.A., Arnold P.M. National rates, causes, risk factors, and outcomes associated with 30-day and 90-day readmissions following degenerative posterior cervical spine surgery utilizing the nationwide readmissions database. Neurosurgery. 2017;81(5):740–751. doi: 10.1093/neuros/nyx063. [DOI] [PubMed] [Google Scholar]
- 26.Gasparini A. comorbidity: an R package for computing comorbidity scores. J Open Source Software. 2018;3(23):648. doi: 10.21105/joss.00648. [DOI] [Google Scholar]
- 27.Virtanen P., Gommers R., Oliphant T.E., et al. Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):352. doi: 10.1038/s41592-020-0772-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Schwartz A.M., Farley K.X., Boden S.H., et al. The use of tobacco is a modifiable risk factor for poor outcomes and readmissions after shoulder arthroplasty. Bone Joint Lett J. 2020;(11):102-B. doi: 10.1302/0301-620X.102B11.BJJ-2020-0599.R1. [DOI] [PubMed] [Google Scholar]
- 29.Piper C., Neviaser A. Survivorship of anatomic total shoulder arthroplasty. J Am Acad Orthop Surg. 2022;30(10):457–465. doi: 10.5435/JAAOS-D-21-00302. [DOI] [PubMed] [Google Scholar]
- 30.Shields E., Koueiter D.M., Michael Wiater J. Rate of improvement in outcomes measures after reverse total shoulder arthroplasty: a longitudinal study with 2-year follow-up. J Should Elbow Arthropl. 2019;3 doi: 10.1177/2471549219861446. [DOI] [Google Scholar]
- 31.Chelli M., Boileau P., Domos P., et al. Survivorship of reverse shoulder arthroplasty according to indication, age and gender. J Clin Med Res. 2022;11(10) doi: 10.3390/jcm11102677. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Like B.J., White R.S., Tangel V., et al. Medicaid payer status is associated with increased mortality and morbidity after inpatient shoulder arthroplasty: a multistate analysis, 2007-2014. Reg Anesth Pain Med. 2019;44(2) doi: 10.1136/rapm-2018-000020. [DOI] [PubMed] [Google Scholar]
- 33.Issa K., Pierce C.M., Pierce T.P., et al. Total shoulder arthroplasty demographics, incidence, and complications-A nationwide inpatient sample database study. Surg Technol Int. 2016;29 https://pubmed.ncbi.nlm.nih.gov/27608744/ [PubMed] [Google Scholar]
- 34.Lefton H.B., Rosa A., Cohen M. Diagnosis and epidemiology of cirrhosis. Med Clin. 2009;93(4):787–799. doi: 10.1016/j.mcna.2009.03.002. vii. [DOI] [PubMed] [Google Scholar]
- 35.Al-Jarallah K.F., Shehab D.K., Watson Buchanan W. Rheumatic complications of alcohol abuse. Semin Arthritis Rheum. 1992;22(3):162–171. doi: 10.1016/0049-0172(92)90016-7. [DOI] [PubMed] [Google Scholar]
- 36.Casp A.J., Montgomery S.R., Jr., Cancienne J.M., Brockmeier S.F., Werner B.C. Osteoporosis and implant-related complications after anatomic and reverse total shoulder arthroplasty. J Am Acad Orthop Surg. 2020;28(3):121–127. doi: 10.5435/JAAOS-D-18-00537. [DOI] [PubMed] [Google Scholar]
- 37.Dillon M.T., Prentice H.A., Burfeind W.E., Singh A. Risk factors for Re-revision surgery in shoulder arthroplasty. J Am Acad Orthop Surg. 2020;28(23):e1049–e1058. doi: 10.5435/JAAOS-D-19-00635. [DOI] [PubMed] [Google Scholar]
- 38.Shield K.D., Parry C., Rehm J. Chronic diseases and conditions related to alcohol use. Alcohol Res. 2013;35(2):155–173. doi: 10.35946/arcr.v35.2.06. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Weinberger A.H., Funk A.P., Goodwin R.D. A review of epidemiologic research on smoking behavior among persons with alcohol and illicit substance use disorders. Prev Med. 2016;92:148–159. doi: 10.1016/j.ypmed.2016.05.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Traversy G., Chaput J.P. Alcohol consumption and obesity: an update. Curr Obes Rep. 2015;4(1):122–130. doi: 10.1007/s13679-014-0129-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Smarandescu L., Walker D., Wansink B. Mindless drinking: how gender and BMI relate to the consumption of alcohol. Int J Drug Pol. 2014;25(6):1131–1134. doi: 10.1016/j.drugpo.2014.08.018. [DOI] [PubMed] [Google Scholar]