Patient-reported Outcomes Associated With Preoperative Opioid Use in Revision Total Hip Arthroplasty: A Propensity Score-Matched Cohort Study

Predictive Modeling of Medical and Orthopaedic-Related 90-Day-Readmissions Following Primary Total Knee Arthroplasty

BACKGROUND

As the demand for total knee arthroplasty (TKA) escalates, 90-day readmissions have emerged as a pressing clinical and economic concern for the current value-based healthcare system. Consequently, healthcare providers have focused on estimating the risk levels of readmitted patients; however, it is unknown if specific factors are associated with different types of complications (i.e., medical or orthopaedic-related) that lead to readmissions. Therefore, this study aimed to: (1) Determine the overall, medical-related, and orthopaedic-related 90-day readmission rate; and (2) Develop a predictive model for risk factors affecting overall, medical-related, and orthopaedic-related 90-day readmissions following TKA.

METHODS

A prospective cohort of primary unilateral TKAs performed at a large tertiary academic center in the United States from 2016 to 2020 was included (n = 10,521 patients). Unplanned readmissions were reviewed individually to determine their primary cause, either medical or orthopaedic-related. Orthopaedic-related readmissions were specific complications affecting the joint, prosthesis, or surgical wound. Medical readmissions were due to any other cause requiring medical management. Multivariable logistic regression models were used to investigate associations between pre-specified risk factors and 90-day readmissions, as well as medical/orthopaedic-related readmissions independently.

RESULTS

Overall, the rate of 90-day readmissions was 6.7% (n = 704). Over 82% of these readmissions were due to medical-related causes (n = 580), with the remaining 18% being orthopaedic-related (n = 124) readmissions. The AUC for the 90-day readmission model was 0.68 (95% CI [confidence interval]: 0.67 to 0.70). Sex, smoking, length of stay (LOS), and discharge disposition (DD) were associated with orthopaedic readmission, while age, sex, race, Charlson Comorbidity Index (CCI), insurance, surgery day, opioid overdose risk (NARX) score, LOS, and DD were associated with medical-related 90-day readmissions.

CONCLUSION

Medical-related readmissions after TKA are more prevalent than orthopaedic-related readmissions. Through successfully constructing and validating multiple 90-day readmission predictive models, we highlight the distinct risk profiles for medical and orthopaedic-related readmissions. This emphasizes the necessity for nuanced, patient-specific risk stratification and preventive measures.

Natural History of Opioid Use in Naive and Tolerant Patients in Revision Total Hip Arthroplasty

BACKGROUND

Opioid use after revision total hip arthroplasty (rTHA) has not been well characterized. The purpose of this study was to characterize preoperative, perioperative, and postoperative opioid use during rTHA.

METHODS

Patients undergoing revision THA from 2010 to 2018 were screened for opioid use 3 months before revision surgery and tracked 24 months postoperatively. Patients were categorized as naïve or tolerant. Opioid prescriptions and average morphine milligram equivalents (MME) were compared between the two groups.

RESULTS

One hundred twenty-four of 247 patients (50%) in the tolerant group averaged a preoperative MME of 23.7 mg/day. Postoperatively, tolerant patients received significantly higher daily MME at all time points, including at 3 months 31.4 versus 18.1 mg/day (P < 0.001), 6 months 19.9 versus 2.95 mg/day (P < 0.001), 12 months 14.3 versus 3.5 mg/day (P < 0.001), and 24 months 10.7 versus 2.17 mg/day (P < 0.001). Tolerant patients were more likely to have a prescription at 6 months (44% versus 22%), 12 months (41.4% versus 24%), and 24 months (38% versus 19.3%) (P < 0.001, P = 0.002, P < 0.001, respectively).

DISCUSSION

Opioid-tolerant patients had higher postoperative MME requirements for longer recovery duration. Both groups reduced opioid use at 3 months and plateaued at 6 months. These findings can help the revision surgeon counsel patients and expectations.

The Same Day Discharges following Primary Total Knee Arthroplasty: A Single Surgeon, Propensity Score-Matched Cohort Analysis

This is a retrospective study. As new surgical techniques and improved perioperative care approaches have become available, the same-day discharge in selected total knee arthroplasty (TKA) patients was introduced to decrease health care costs without compromising outcomes. This study aimed to compare clinical and functional outcomes between same-day discharge TKA patients and inpatient-discharge TKA patients. A retrospective review of 100 consecutive patients with same-day discharge matched to a cohort of 300 patients with inpatient discharge that underwent TKA by a single surgeon at a tertiary referral center was conducted. Propensity-score matching was performed to adjust for baseline differences in preoperative patient demographics, medical comorbidities, and patient-reported outcome measures (PROMs) between both cohorts. All patients had a minimum of 1-year follow-up (range: 1.2-2.8 years). In terms of clinical outcomes for the propensity score-matched cohorts, there was no significant difference in terms of revision rates (1.0 vs. 1.3%, p = 0.76), 90-day emergency department visits (3.0 vs. 3.3%, p = 0.35), 30-day readmission rates (1.0 vs. 1.3%, p = 0.45), and 90-day readmission rates (3.0 vs. 3.6%, p = 0.69). Patients with same-day discharge demonstrated significantly higher postoperative PROM scores, at both 3-month and 1-year follow-up, for PROMIS-10 Physical Score (50 vs. 46, p = 0.028), PROMIS-10 Mental Score (56 vs. 53, p = 0.039), and Physical SF10A (57 vs. 52, p = 0.013). This study showed that patients with same-day discharge had similar clinical outcomes and superior functional outcomes, when compared with patients that had a standard inpatient protocol. This suggests that same-day discharge following TKA may be a safe, viable option in selected total knee joint arthroplasty patients.

The utility of machine learning algorithms for the prediction of patient-reported outcome measures following primary hip and knee total joint arthroplasty

BACKGROUND

Patient-reported outcome measures (PROMs) are increasingly used as quality benchmark in total hip and knee arthroplasty (THA; TKA) due to bundled payment systems that aim to provide a patient-centered, value-based treatment approach. However, there is a paucity of predictive tools for postoperative PROMs. Therefore, this study aimed to develop and validate machine learning models for the prediction of numerous patient-reported outcome measures following primary hip and knee total joint arthroplasty.

METHODS

A total of 4526 consecutive patients (2137 THA; 2389 TKA) who underwent primary hip and knee total joint arthroplasty and completed both pre- and postoperative PROM scores was evaluated in this study. The following PROM scores were included for analysis: HOOS-PS, KOOS-PS, Physical Function SF10A, PROMIS SF Physical and PROMIS SF Mental. Patient charts were manually reviewed to identify patient demographics and surgical variables associated with postoperative PROM scores. Four machine learning algorithms were developed to predict postoperative PROMs following hip and knee total joint arthroplasty. Model assessment was performed through discrimination, calibration and decision curve analysis.

RESULTS

The factors most significantly associated with the prediction of postoperative PROMs include preoperative PROM scores, Charlson Comorbidity Index, American Society of Anaesthesiology score, insurance status, age, length of hospital stay, body mass index and ethnicity. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration and decision curve analysis.

CONCLUSION

This study developed machine learning models for the prediction of patient-reported outcome measures at 1-year following primary hip and knee total joint arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four machine learning models, highlighting the potential of these models in clinical practice to inform patients prior to surgery regarding their expectations of postoperative functional outcomes following primary hip and knee total joint arthroplasty.

LEVEL OF EVIDENCE

Level III, case control retrospective analysis.

Preoperative Opioid Use Is Associated With Inferior Patient-Reported Outcomes Measurement Information System Scores Following Rotator Cuff Repair

PURPOSE

To determine the influence of preoperative opioid use on Patient-Reported Outcomes Measurement Information System (PROMIS) scores pre- and postoperatively in patients undergoing arthroscopic rotator cuff repair (RCR).

METHODS

A retrospective review of all RCR patients aged >18 years old was performed. PROMIS pain interference ("PROMIS PI"), upper extremity function ("PROMIS UE"), and depression ("PROMIS D") scores, were reviewed. These measures were collected at preoperative, 6-month, and 1-year postoperative time points. A prescription drug-monitoring program was queried to track opioid prescriptions. Patients were categorized as chronic users, acute users, and nonusers based on prescriptions filled. Comparison of means were carried out using analysis of variance and least squares means. Effect sizes and 95% confidence intervals were calculated.

RESULTS

In total, 184 patients who underwent RCR were included. Preoperatively, nonusers (n = 92) had superior PROMIS UE (30.6 vs 28.9 vs 26.1; P < .05) and PI scores (61.5 vs 64.9 vs 65.3; P < .001) compared with acute users (n = 65) and chronic users (n = 27), respectively. At 6 months postoperatively; nonusers demonstrated significantly greater PROMIS UE (41.7 vs 35.6 vs. 33.5; P < .001), lower PROMIS D (41.6 vs 45.8 vs 51.1; P < .001), and lower PROMIS PI scores (50.7 vs 56.3 vs 58.1; P < .01) when compared with acute and chronic users, respectively. Nonusers had lower PROMIS PI (47.9 vs 54.3 vs 57.4; P < .0001) and PROMIS D (41.6 vs 48.3 vs 49.2; P = .0002) scores compared with acute and chronic users at 1-year postoperatively. Nonusers experienced a significantly greater magnitude of improvement in PROMIS D 6 months postoperatively compared with chronic opioid users (-5.9 vs 0.0; P < .01).

CONCLUSIONS

Patients undergoing RCR demonstrated superior PROMIS scores pre- and postoperatively if they did not use opioids within 3 months before surgery.

LEVEL OF EVIDENCE

III, retrospective comparative trial.

The utilization of artificial neural networks for the prediction of 90-day unplanned readmissions following total knee arthroplasty

BACKGROUND

A reliable predictive tool to predict unplanned readmissions has the potential to lower readmission rates through targeted pre-operative counseling and intervention with respect to modifiable risk factors. This study aimed to develop and internally validate machine learning models for the prediction of 90-day unplanned readmissions following total knee arthroplasty.

METHODS

A total of 10,021 consecutive patients underwent total knee arthroplasty. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with 90-day unplanned hospital readmissions. Four machine learning algorithms (artificial neural networks, support vector machine, k-nearest neighbor, and elastic-net penalized logistic regression) were developed to predict 90-day unplanned readmissions following total knee arthroplasty and these models were evaluated using ROC AUC statistics as well as calibration and decision curve analysis.

RESULTS

Within the study cohort, 644 patients (6.4%) were readmitted within 90 days. The factors most significantly associated with 90-day unplanned hospital readmissions included drug abuse, surgical operative time, and American Society of Anaesthesiologist Physical Status (ASA) score. The machine learning models all achieved excellent performance across discrimination (AUC > 0.82), calibration, and decision curve analysis.

CONCLUSION

This study developed four machine learning models for the prediction of 90-day unplanned hospital readmissions in patients following total knee arthroplasty. The strongest predictors for unplanned hospital readmissions were drug abuse, surgical operative time, and ASA score. The study findings show excellent model performance across all four models, highlighting the potential of these models for the identification of high-risk patients prior to surgery for whom coordinated care efforts may decrease the risk of subsequent hospital readmission.

LEVEL OF EVIDENCE

Level III, case-control retrospective analysis.

Machine learning algorithms predict extended postoperative opioid use in primary total knee arthroplasty

PURPOSE

Adequate postoperative pain control following total knee arthroplasty (TKA) is required to achieve optimal patient recovery. However, the postoperative recovery may lead to an unnaturally extended opioid use, which has been associated with adverse outcomes. This study hypothesizes that machine learning models can accurately predict extended opioid use following primary TKA.

METHODS

A total of 8873 consecutive patients that underwent primary TKA were evaluated, including 643 patients (7.2%) with extended postoperative opioid use (> 90 days). Electronic patient records were manually reviewed to identify patient demographics and surgical variables associated with prolonged postoperative opioid use. Five machine learning algorithms were developed, encompassing the breadth of state-of-the-art machine learning algorithms available in the literature, to predict extended opioid use following primary TKA, and these models were assessed by discrimination, calibration, and decision curve analysis.

RESULTS

The strongest predictors for prolonged opioid prescription following primary TKA were preoperative opioid duration (100% importance; p < 0.01), drug abuse (54% importance; p < 0.01), and depression (47% importance; p < 0.01). The five machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration, and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients.

CONCLUSION

The study findings show excellent model performance for the prediction of extended postoperative opioid use following primary total knee arthroplasty, highlighting the potential of these models to assist in preoperatively identifying at risk patients, and allowing the implementation of individualized peri-operative counselling and pain management strategies to mitigate complications associated with prolonged opioid use.

LEVEL OF EVIDENCE

IV.

Preoperative Opioids and the Dose-Dependent Effect on Outcomes After Total Hip Arthroplasty

BACKGROUND

The purpose of this study is to identify the preoperative daily opioid dose associated with increased complications after primary total hip arthroplasty (THA).

METHODS

Primary THA patients in the Humana claims database (2007-2020) with an opioid prescription within 3 months prior to surgery were identified. Patients were stratified based on daily opioid dose: Tier 1, <5 milligram morphine equivalents (MME); Tier 2, 5-10 MME; Tier 3, 11-25 MME; Tier 4, 26-50 MME; Tier 5, >50 MME. Each tier was matched 1:1 to opioid-naïve patients. Emergency department (ED) visits, readmissions, and postoperative complications were compared.

RESULTS

In total, 67,719 patients using preoperative opioids were identified and matched. 17.0% of patients using preoperative opioids visited the ED within 90 days, compared to 13.3% of opioid-naïve patients (P < .001). About 9.5% of patients using preoperative opioids were readmitted within 90 days, compared to 7.4% of opioid-naïve patients (P < .001). When stratified by tier, opioid users in all tiers had higher risk of ED visits and readmission. Rates of superficial infection, periprosthetic joint infection, and dislocation were increased in patients taking preoperative opioids in Tiers 2 through 5. Patients in Tiers 3 through 5 had an increased risk of revision surgery.

CONCLUSION

Preoperative opioid use is associated with a dose-dependent increase in complications after THA. Just one 5 mg hydrocodone tablet daily leads to a significant increase in ED visits and readmission, while higher doses are associated with dislocation, superficial infection, periprosthetic joint infection, and revision surgery. Continued education regarding the harmful effects of opioids prescribed for the nonoperative treatment of osteoarthritis is still needed.

LEVEL OF EVIDENCE

Level III.

The Effect of Preoperative Tramadol Use on Postoperative Opioid Prescriptions After Primary Total Hip and Knee Arthroplasty: An Institutional Experience of 11,000 Patients

BACKGROUND

Preoperative opioid use increases opioid consumption postoperatively, but the effect of tramadol is poorly understood.

METHODS

We retrospectively reviewed 11,667 patients undergoing primary unilateral THA and TKA at a single institution. Preoperatively, there were 8,201 opioid-naïve patients (70.3%), 1,315 on tramadol (11.3%), 1,408 on narcotics (12.1%) and 743 on narcotics and tramadol (6.3%). We compared morphine milligram equivalents (MMEs) used during hospitalization, prescribed at discharge, and refilled during the first 90 days. We used multivariate analysis to assess whether preoperative tramadol use was associated with increased number of refills and total refilled MMEs.

RESULTS

Total in-hospital MMEs and daily MMEs was lowest for the opioid naïve patients and significantly increased for the remaining three groups (total in-hospital use: 119, 152, 211, and 196 MMEs, respectively-P < .001) (daily in-hospital use: 66, 74, 100, and 86 MMEs, respectively-P < .001). Opioid refill rate was significantly higher for all patients who were not opioid naïve (32%, 42%, 41%, and 52%, respectively-P < .001). Total MMEs prescribed after discharge was lowest for opioid naïve patients (477, 528, 590 and 658, respectively-P < .001). Logistic and linear regression controlling for age, sex, history of anxiety/depression revealed that THA patients taking tramadol preoperatively were 2.5 times more likely to require post-discharge refills and refilled 80 additional MMEs than opioid naïve patients (P < .001).

CONCLUSION

Tramadol is not recommended for pain beforeTKA or THA, and surgeons and patients should be aware that it is associated with a substantial increase in postoperative opioid use.