Hot Spotting as a Strategy to Identify High-Cost Surgical Populations

Machine learning prediction of hospitalization costs for coronary artery bypass grafting operations

BACKGROUND

With the steady rise in health care expenditures, the examination of factors that may influence the costs of care has garnered much attention. Although machine learning models have previously been applied in health economics, their application within cardiac surgery remains limited. We evaluated several machine learning algorithms to model hospitalization costs for coronary artery bypass grafting.

METHODS

All adult hospitalizations for isolated coronary artery bypass grafting were identified in the 2016 to 2020 Nationwide Readmissions Database. Machine learning models were trained to predict expenditures and compared with traditional linear regression. Given the significance of postoperative length of stay, we additionally developed models excluding postoperative length of stay to uncover other drivers of costs. To facilitate comparison, machine learning classification models were also trained to predict patients in the highest decile of costs. Significant factors associated with high cost were identified using SHapley Additive exPlanations beeswarm plots.

RESULTS

Among 444,740 hospitalizations included for analysis, the median cost of hospitalization in coronary artery bypass grafting patients was $43,103. eXtreme Gradient Boosting most accurately predicted hospitalization costs, with R2 = 0.519 over the validation set. The top predictive features in the eXtreme Gradient Boosting model included elective procedure status, prolonged mechanical ventilation, new-onset respiratory failure or myocardial infarction, and postoperative length of stay. After removing postoperative length of stay, eXtreme Gradient Boosting remained the most accurate model (R2 = 0.38). Prolonged ventilation, respiratory failure, and elective status remained important predictive parameters.

CONCLUSION

Machine learning models appear to accurately model total hospitalization costs for coronary artery bypass grafting. Future work is warranted to uncover other drivers of costs and improve the value of care in cardiac surgery.

Time-Driven Activity-Based Costing Analysis Identifies Use of Consumables and Operating Room Time as Factors Associated With Increased Cost of Outpatient Primary Hip Arthroscopic Labral Repair

PURPOSE

To use time-driven, activity-based costing (TDABC) methodology to investigate drivers of cost variation and to elucidate preoperative and intraoperative factors associated with increased cost of outpatient arthroscopic hip labral repair.

METHODS

A retrospective analysis of data from January 2020 to October 2021 was performed. Patients undergoing primary hip arthroscopy for labral repair in the outpatient setting were included. Indexed TDABC data from Avant-garde Health's analytics platform were used to represent cost-of-care breakdowns. Patients in the top decile of cost were defined as high cost, and cost category variance was determined as a percent increase between high and low cost. Analyses tested for associations between preoperative and perioperative factors with total cost. Surgical procedures performed concomitantly to labral repair were included in subanalyses.

RESULTS

Data from 151 patients were analyzed. Consumables made up 61% of total outpatient cost with surgical personnel costs (30%) being the second largest category. The average total cost was 19% higher for patients in the top decile of cost compared to the remainder of the cohort. Factors contributing to this difference were implants (36% higher), surgical personnel (20% higher), and operating room (OR) consumables (15% higher). Multivariate linear regression modeling indicated that OR time (Standardized β = 0.504; P < .001) and anchor quantity (standardized β = 0.443; P < .001) were significant predictors of increased cost. Femoroplasty (Unstandardized β = 15.274; P = .010), chondroplasty (Unstandardized β = 8.860; P = .009), excision of os acetabuli (unstandardized β = 13.619; P = .041), and trochanteric bursectomy (Unstandardized β = 21.176; P = .009) were also all independently associated with increasing operating time.

CONCLUSIONS

TDABC analysis showed that OR consumables and implants were the largest drivers of cost for the procedure. OR time was also shown to be a significant predictor of increased costs.

LEVEL OF EVIDENCE

Level IV, economic analysis.

High health care use prior to elective surgery for osteoarthritis is associated with poor postoperative outcomes: A Canadian population-based cohort study

BACKGROUND

The characterization and influence of preoperative health care use on quality-of-care indicators (e.g., readmissions) has received limited attention in populations with musculoskeletal disorders. The purpose of this study was to characterize preoperative health care use and examine its effect on quality-of-care indicators among patients undergoing elective surgery for osteoarthritis.

METHODS

Data on health care use for 124,750 patients with elective surgery for osteoarthritis in Ontario, Canada, from April 1, 2015 to March 31, 2018 were linked across health administrative databases. Using total health care use one-year previous to surgery, patients were grouped from low to very high users. We used Poisson regression models to estimate rate ratios, while examining the relationship between preoperative health care use and quality-of-care indicators (e.g., extended length of stay, complications, and 90-day hospital readmissions). We controlled for covariates (age, sex, neighborhood income, rural/urban residence, comorbidities, and surgical anatomical site).

RESULTS

We found a statistically significant trend of increasing worse outcomes by health care use gradients that persisted after controlling for patient demographics and comorbidities. Findings were consistent across surgical anatomical sites. Moreover, very high users have relatively large numbers of visits to non-musculoskeletal specialists.

CONCLUSIONS

Our findings highlight that information on patients' preoperative health care use, together with other risk factors (such as comorbidities), could help decision-making when benchmarking or reimbursing hospitals caring for complex patients undergoing surgery for osteoarthritis.

Comparing super-utilizers and lower-utilizers among commercial- and Medicare-insured adults with sickle cell disease

ABSTRACT

Sickle cell disease (SCD) is a rare but costly condition in the United States. Super-utilizers have been defined as a subset of the population with high health care encounters or expenditures. Although super-utilizers have been described in other disease states, little is known about super-utilizers among adults with SCD. This study aimed to characterize the differences in expenditures, overall health care encounters, and pain episode encounters between super-utilizers (top 10% expenditures) and lower-utilizers with SCD (high, top 10%-24.9%; moderate, 25%-49.9%; and low, bottom 50% expenditures). A retrospective longitudinal cohort of adults with SCD were identified using validated algorithms in MarketScan and Medicare claim databases from 2016 to 2020. Encounters and expenditures were analyzed from inpatient, outpatient, and emergency department settings. Differences in encounters and expenditures between lower-utilizers and super-utilizers were compared using logistic regression. Among super-utilizers, differences in encounters and expenditures were compared according to incidences of pain episode encounters. The study population included 5666 patients with commercial insurance and 8600 with Medicare. Adjusted total annual health care expenditure was 43.46 times higher for super-utilizers than for low-utilizers among commercial-insured and 13.37 times higher in Medicare-insured patients. Among super-utilizers, there were patients with few pain episode encounters who had higher outpatient expenditures than patients with a high number of pain episode encounters. Our findings demonstrate the contribution of expensive outpatient care among SCD super-utilizers, in which analyses of high expenditure have largely focused on short-term care. Future studies are needed to better understand super-utilizers in the SCD population to inform the effective use of preventive interventions and/or curative therapies.

Association of Minority-Serving Hospital Status with Post-Discharge Care Utilization and Expenditures in Gastrointestinal Cancer

BACKGROUND

Disparities in utilization of post-discharge care and overall expenditures may relate to site of care and race/ethnicity. We sought to define the impact of minority-serving hospitals (MSHs) on postoperative outcomes, discharge disposition, and overall expenditures associated with an episode of surgical care.

METHODS

Patients who underwent resection for esophageal, colon, rectal, pancreatic, and liver cancer were identified from Medicare Standard Analytic Files (2013-2017). A MSH was defined as the top decile of facilities treating minority patients (Black and/or Hispanic). The impact of MSH on outcomes of interest was analyzed using multivariable logistic regression and generalized linear regression models. Textbook outcome (TO) was defined as no postoperative complications, no prolonged length of stay, and no 90-day mortality or readmission.

RESULTS

Among 113,263 patients, only a small subset of patients underwent surgery at MSHs (n = 4404, 3.9%). While 52.3% of patients achieved TO, rates were lower at MSHs (MSH: 47.2% vs. non-MSH: 52.5%; p < 0.001). On multivariable analysis, receiving care at an MSH was associated with not achieving TO (odds ratio [OR] 0.81, 95% confidence interval [CI] 0.76-0.87) and concomitantly higher odds of additional post-discharge care (OR 1.10, 95% CI 1.01-1.20). Patients treated at an MSH also had higher median post-discharge expenditures (MSH: $8400, interquartile range [IQR] $2300-$22,100 vs. non-MSH: $7000, IQR $2200-$17,900; p = 0.002). In fact, MSHs remained associated with a 11.05% (9.78-12.33%) increase in index expenditures and a 16.68% (11.44-22.17%) increase in post-discharge expenditures.

CONCLUSIONS

Patients undergoing surgery at a MSH were less likely to achieve a TO. Additionally, MSH status was associated with a higher likelihood of requiring post-discharge care and higher expenditures.

Redefining Multimorbidity in Older Surgical Patients

BACKGROUND

Multimorbidity in surgery is common and associated with worse postoperative outcomes. However, conventional multimorbidity definitions (≥2 comorbidities) label the vast majority of older patients as multimorbid, limiting clinical usefulness. We sought to develop and validate better surgical specialty-specific multimorbidity definitions based on distinct comorbidity combinations.

STUDY DESIGN

We used Medicare claims for patients aged 66 to 90 years undergoing inpatient general, orthopaedic, or vascular surgery. Using 2016 to 2017 data, we identified all comorbidity combinations associated with at least 2-fold (general/orthopaedic) or 1.5-fold (vascular) greater risk of 30-day mortality compared with the overall population undergoing the same procedure; we called these combinations qualifying comorbidity sets. We applied them to 2018 to 2019 data (general = 230,410 patients, orthopaedic = 778,131 patients, vascular = 146,570 patients) to obtain 30-day mortality estimates. For further validation, we tested whether multimorbidity status was associated with differential outcomes for patients at better-resourced (based on nursing skill-mix, surgical volume, teaching status) hospitals vs all other hospitals using multivariate matching.

RESULTS

Compared with conventional multimorbidity definitions, the new definitions labeled far fewer patients as multimorbid: general = 85.0% (conventional) vs 55.9% (new) (p < 0.0001); orthopaedic = 66.6% vs 40.2% (p < 0.0001); and vascular = 96.2% vs 52.7% (p < 0.0001). Thirty-day mortality was higher by the new definitions: general = 3.96% (conventional) vs 5.64% (new) (p < 0.0001); orthopaedic = 0.13% vs 1.68% (p < 0.0001); and vascular = 4.43% vs 7.00% (p < 0.0001). Better-resourced hospitals offered significantly larger mortality benefits than all other hospitals for multimorbid vs nonmultimorbid general and orthopaedic, but not vascular, patients (general surgery difference-in-difference = -0.94% [-1.36%, -0.52%], p < 0.0001; orthopaedic = -0.20% [-0.34%, -0.05%], p = 0.0087; and vascular = -0.12% [-0.69%, 0.45%], p = 0.6795).

CONCLUSIONS

Our new multimorbidity definitions identified far more specific, higher-risk pools of patients than conventional definitions, potentially aiding clinical decision-making.

Novel Machine Learning Approach to Identify Preoperative Risk Factors Associated With Super-Utilization of Medicare Expenditure Following Surgery

Importance

Typically defined as the top 5% of health care users, super-utilizers are responsible for an estimated 40% to 55% of all health care costs. Little is known about which factors may be associated with increased risk of long-term postoperative super-utilization.

Objective

To identify clusters of patients with distinct constellations of clinical and comorbid patterns who may be associated with an elevated risk of super-utilization in the year following elective surgery.

Design, Setting, and Participants

A retrospective longitudinal cohort study of 1 049 160 patients who underwent abdominal aortic aneurysm repair, coronary artery bypass graft, colectomy, total hip arthroplasty, total knee arthroplasty, or lung resection were identified from the 100% Medicare inpatient and outpatient Standard Analytic Files at all inpatient facilities performing 1 or more of the evaluated surgical procedures from 2013 to 2015. Data from 2012 to 2016 were used to evaluate expenditures in the year preceding and following surgery. Using a machine learning approach known as Logic Forest, comorbidities and interactions of comorbidities that put patients at an increased chance of becoming a super-utilizer were identified. All comorbidities, as defined by the Charlson (range, 0-24) and Elixhauser (range, 0-29) comorbidity indices, were used in the analysis. Higher scores indicated higher comorbidity burden. Data analysis was completed on November 16, 2018.

Main Outcome and Measures

Super-utilization of health care in the year following surgery.

Results

In total, 1 049 160 patients met inclusion criteria and were included in the analytic cohort. Their median (interquartile range) age was 73 (69-78) years, and approximately 40% were male. Super-utilizers comprised 4.8% of the overall cohort (n = 79 746) yet incurred 31.7% of the expenditures. Although the difference in overall expenditures per person between super-utilizers ($4049) and low users ($2148) was relatively modest prior to surgery, the difference in expenditures between super-utilizers ($79 698) vs low users ($2977) was marked in the year following surgery. Risk factors associated with super-utilization of health care included hemiplegia/paraplegia (odds ratio, 5.2; 95% CI, 4.4-6.2), weight loss (odds ratio, 3.5; 95% CI, 2.9-4.2), and congestive heart failure with chronic kidney disease stages I to IV (odds ratio, 3.4; 95% CI, 3.0-3.9).

Conclusions and Relevance

Super-utilizers comprised only a small fraction of the surgical population yet were responsible for a disproportionate amount of Medicare expenditure. Certain subpopulations were associated with super-utilization of health care following surgical intervention despite having lower overall use in the preoperative period.

Local Referral of High-Risk Pancreatectomy Patients to Improve Surgical Outcomes and Minimize Travel Burden

BACKGROUND

Referring patients to high-quality hospitals for complex procedures may improve outcomes. This is most feasible within small geographic areas. However, access to specialized surgical procedures may be an implementation barrier. We sought to determine the availability of high-quality hospitals performing pancreatectomy and the potential benefit and travel burden of referral within small geographic areas.

METHODS

We identified elderly Medicare beneficiaries undergoing pancreatectomy between 2012 and 2014. Hospitals were stratified into quintiles of quality based on postoperative complication rates. Patient risk was assessed by modeling the predicted risk of developing a postoperative complication. The geographic unit of analysis was Metropolitan Statistical Area (MSA). Hospitals were categorized into MSA by zip code. Travel distance was calculated using patient and hospital zip code.

RESULTS

Among high-risk patients, 40.7% received care at the lowest-quality hospitals even though 80% had a high-quality hospital in the same MSA. Shifting these patients from low- to high-quality hospitals would decrease serious complications from 46.6 to 21.9% (P < 0.001) and mortality from 10.9 to 8.9% (P = 0.047). Three quarters of high-risk patients treated at low-quality hospitals could reach a high-quality hospital by extending their travel < 5 miles, and nearly 60% traveled farther to a low-quality hospital than was necessary to reach a high-quality hospital.

CONCLUSIONS

High-risk pancreatectomy patients often receive care at low-quality hospitals despite the availability of high-quality hospitals in the area or within an acceptable distance. Referral of high-risk patients to high-quality hospitals within small geographic areas may be an effective strategy to improve outcomes following pancreatic surgery.

Assessment of utilization efficiency using machine learning techniques: A study of heterogeneity in preoperative healthcare utilization among super-utilizers

INTRODUCTION

In the United States, 5% of patients represent up to 55% of all health care costs. This study sought to define healthcare utilization patterns among super-utilizers, as well as assess possible variation in patient outcomes.

METHODS

Medicare super-utilizers undergoing either a total hip or knee arthroplasty were identified and entered into a cluster analysis using annual preoperative charges to identify distinct patterns of utilization.

RESULTS

Among 19,522 super-utilizers who underwent THA or TKA, there was a marked heterogeneity in overall utilization with 5 distinct clusters of utilization patterns. Of note, comorbidity burden was similar among the 5 clusters. Patient outcomes also varied by Cluster type, ranging from 6.9% to 16.5% experiencing complications and 1.0%-3.2% experiencing 90-day mortality.

CONCLUSION

While previous studies have suggested that super-utilizers are a homogenous group of patients, the current study demonstrated a large degree of heterogeneity within super-utilizers. Variations in utilization patterns were associated with postoperative outcomes and subsequent health care costs.

Who Will be the Costliest Patients? Using Recent Claims to Predict Expensive Surgical Episodes

INTRODUCTION

Surgery accounts for almost half of inpatient spending, much of which is concentrated in a subset of high-cost patients. To study the effects of surgeon and hospital characteristics on surgical expenditures, a way to adjust for patient characteristics is essential.

DESIGN

Using 100% Medicare claims data, we identified patients aged 66-99 undergoing elective inpatient surgery (coronary artery bypass grafting, colectomy, and total hip/knee replacement) in 2014. We calculated price-standardized Medicare payments for the surgical episode from admission through 30 days after discharge (episode payments). On the basis of predictor variables from 2013, that is, Elixhauser comorbidities, hierarchical condition categories, Medicare's Chronic Conditions Warehouse (CCW), and total spending, we constructed models to predict the costs of surgical episodes in 2014.

RESULTS

All sources of comorbidity data performed well in predicting the costliest cases (Spearman correlation 0.86-0.98). Models on the basis of hierarchical condition categories had slightly superior performance. The costliest quintile of patients as predicted by the model captured 35%-45% of the patients in each procedure's actual costliest quintile. For example, in hip replacement, 44% of the costliest quintile was predicted by the model's costliest quintile.

CONCLUSIONS

A significant proportion of surgical spending can be predicted using patient factors on the basis of readily available claims data. By adjusting for patient factors, this will facilitate future research on unwarranted variation in episode payments driven by surgeons, hospitals, or other market forces.

Can We Improve Prediction of Adverse Surgical Outcomes? Development of a Surgical Complexity Score Using a Novel Machine Learning Technique

BACKGROUND

An optimal method to quantify surgical complexity using patient comorbidities derived from administrative billing data is lacking. We sought to develop a novel, easy-to-use surgical Complexity Score to accurately predict adverse outcomes among patients undergoing elective surgery.

STUDY DESIGN

A novel surgical Complexity Score was developed using 100% Medicare Inpatient and Outpatient Standard Analytic Files (SAFs) from years 2012 to 2016 (n = 1,049,160). Comorbid conditions were entered into a machine learning algorithm to assign weights to maximize the correlation with multiple postoperative outcomes including morbidity, readmission, mortality, and postoperative super-use. Predictive ability was compared against 3 of the most commonly used risk adjustment indices: the Charlson Comorbidity Index (CCI), Elixhauser Comorbidity Index (ECI), and the Centers for Medicare and Medicaid Service's Hierarchical Condition Category (CMS-HCC).

RESULTS

Patients underwent colectomy (12.6%), abdominal aortic aneurysm repair (4.4%), coronary artery bypass grafting (13.0%), total hip replacement (22.0%), total knee replacement (43.0%), or lung resection (5.0%). The Complexity Score had a good to very good predictive ability for all adverse outcomes. The Complexity Score had the highest accuracy in predicting perioperative morbidity (area under the curve [AUC]: 0.868, 95% CI 0.866 to 0.869); this performed better than the CCI (AUC: 0.717, 95% CI 0.715 to 0.719), ECI (AUC: 0.799, 95% CI 0.797 to 0.800), and similar to the CMS-HCC (AUC: 0.862, 95% CI 0.861 to 0.863). Similarly, the Complexity Score outperformed each of the 3 other comorbidity indices in predicting 90-day readmission (AUC: 0.707, 95% CI 0.705 to 0.709), 30-day readmission (AUC: 0.717, 95% CI 0.715 to 0.720), and postoperative super-use (AUC: 0.817, 95% CI 0.814 to 0.820).

CONCLUSIONS

Compared with the most commonly used comorbidity and surgical risk scores, the novel surgical Complexity Score outperformed the CCI, ECI, and CMS-HCC in predicting postoperative morbidity, 30-day readmission, 90-day readmission, and postoperative super-use.

Is Annual Preoperative Utilization an Indicator of Postoperative Surgical Outcomes? A Study in Medicare Expenditure

INTRODUCTION

Data on the association of high preoperative healthcare utilization and adverse clinical outcomes are scarce. We sought to evaluate the role of annual preoperative expenditure (APE) as a surrogate for latent variables of risk for adverse short-term postoperative outcomes.

METHODS

Low and super-utilizers who underwent abdominal aortic aneurysm repair, coronary artery bypass graft, colectomy, total hip arthroplasty, total knee arthroplasty, or lung resection between 2013 and 2015 were identified from 100% Medicare Inpatient Standard Analytic Files. To assess the association between APE and postoperative outcomes, multivariable logistic regression was utilized.

RESULTS

Among 1,049,160 patients, 788,488 (75.1%) and 21,700 (2.1%) patients were preoperative low- and super-utilizers, respectively. Median APE was more than 60 times higher among super-utilizers than low-utilizers ($57,160 vs. $932), as was the cost of the surgical episode ($21,141 vs. $13,179). The predictive ability of APE ranged from 0.683 (95% CI 0.678-0.687) for 90-day readmission to 0.882 (95% CI 0.879-0.886) for a complication at the index hospitalization. Among super-utilizers, the odds of a complication during the surgical episode was nearly double versus low-utilizers (OR = 1.96, 95% CI 1.89-2.04). Super-utilizers also had an increased odds of 30-day readmission (OR = 1.64, 95% CI 1.58-1.69) and mortality (OR = 2.22; 95% CI 2.04-2.42).

CONCLUSION

APE was able to predict adverse postsurgical outcomes including complications during the surgical episode, readmission, and 90-day mortality. APE should be considered in the assessment of patient populations when defining risk of adverse postoperative events.

Early Impact of Medicare Accountable Care Organizations on Inpatient Surgical Spending

OBJECTIVE

To evaluate whether hospital participation in accountable care organizations (ACOs) is associated with reduced Medicare spending for inpatient surgery.

BACKGROUND

ACOs have proliferated rapidly and now cover more than 32 million Americans. Medicare Shared Savings Program (MSSP) ACOs have shown modest success in reducing medical spending. Whether they have reduced surgical spending remains unknown.

METHODS

We used 100% Medicare claims from 2010 to 2014 for patients aged 65 to 99 years undergoing 6 common elective surgical procedures [abdominal aortic aneurysm (AAA) repair, colectomy, coronary artery bypass grafting (CABG), hip or knee replacement, or lung resection]. We compared total Medicare payments for 30-day surgical episodes, payments for individual components of care (index hospitalization, readmissions, physician services, and postacute care), and clinical outcomes for patients treated at MSSP ACO hospitals versus matched controls at non-ACO hospitals. We accounted for preexisting trends independent of ACO participation using a difference-in-differences approach.

RESULTS

Among 341,675 patients at 427 ACO hospitals and 1,024,090 matched controls at 1531 non-ACO hospitals, patient and hospital characteristics were well-balanced. Average baseline payments were similar at ACO versus non-ACO hospitals. ACO participation was not associated with reductions in total Medicare payments [difference-in-differences estimate=-$72, confidence interval (CI95%): -$228 to +$84] or individual components of payments. ACO participation was also not associated with clinical outcomes. Duration of ACO participation did not affect our estimates.

CONCLUSION

Although Medicare ACOs have had success reducing spending for medical care, they have not had similar success with surgical spending. Given that surgical care accounts for 30% of total health care costs, ACOs and policymakers must pay greater attention to reducing surgical expenditures.

Internal Validation of a Predictive Model for Complications After Total Hip Arthroplasty

BACKGROUND

Total hip arthroplasty (THA) is projected to increase in prevalence and associated complications will impose significant cost on the US healthcare system. The purpose of this study is to validate a predictive model for postoperative complications utilizing a novel 11-component hip-specific questionnaire encompassing preoperatively available clinical and radiographic data.

METHODS

Consecutive primary THA patients between January 2014 and January 2016 were included. Exclusion criteria included patients without questionnaire scoring variables and less than 1-year follow-up. Patients were stratified into 4 tiers based on their questionnaire score: low risk (>74), mild risk (57-73), moderate risk (41-56), and high risk (<40). A binary logistic regression was performed to determine if the questionnaire predicted complications. Receiver-operator curves were constructed to determine the threshold score below which there was a high likelihood of experiencing a complication.

RESULTS

Four hundred fifty patients were included in the final analysis with a mean (range) follow-up of 2.1 years (1.0-5.9), age of 63.1 years (25.7-9.17), and body mass index of 31.7 kg/m² (17.8-64.5). The complication rate was 13.6%. A hip questionnaire score of 73.8 conferred a 98.5% sensitivity and 98.5% negative predictive value for complications. The questionnaire score was the strongest predictor of a decreased complication likelihood (odds ratio 0.94, 95% confidence interval 0.90-0.97, P < .001). Risk tier was significantly associated with complications (low risk: 0; mild risk: 12; moderate risk: 25; and high risk: 24; P < .001).

CONCLUSION

This novel hip questionnaire demonstrated a high sensitivity and negative predictive value to identify patients at risk for postoperative complications. Future studies should attempt to prospectively validate the use of this questionnaire.

High pain intensity after total shoulder arthroplasty

BACKGROUND

As reimbursement becomes increasingly tied to quality and patient experience, there is growing interest in alleviation of postoperative pain combined with optimal opioid stewardship. We characterized predictors of severe inpatient pain after elective total shoulder arthroplasty and evaluated its association with opioid use, operative time, hospital length of stay, discharge disposition, and cost.

METHODS

We identified 415 patients undergoing elective primary total shoulder arthroplasty between 2016 and 2017 from our registry. Severe postoperative pain was defined as peak pain intensity ≥75th percentile. Multivariable logistic regression modeling was used to determine preoperative characteristics associated with severe pain, including demographics, emotional health, comorbidities, and American Shoulder and Elbow Surgeons score. Opioid consumption was expressed as oral morphine equivalents (OMEs). Costs were calculated using time-driven activity-based costing.

RESULTS

In decreasing order of magnitude, the predictors of severe postoperative pain were greater number of self-reported allergies, preoperative chronic opioid use, lower American Shoulder and Elbow Surgeons score, and depression. Patients reporting severe pain took more opioids (202 vs. 84 mg OMEs), stayed longer in the hospital (2.9 vs. 2.0 days), used postacute inpatient rehabilitation services more frequently (28% vs. 10%), and were more likely to be high-cost patients (23% vs. 5%; all P < .001), but they did not have longer operations (166 vs. 165 minutes, P = .86).

CONCLUSIONS

Efforts to address psychological and social determinants of health might do as much or more than technical improvements to alleviate pain, limit opioid use, and contain costs after shoulder arthroplasty. These findings are important in the redesign of care pathways and bundling initiatives.