Database studies: an increasing trend in the United States orthopaedic literature

Publication Rates of Poster and Podium Presentations at the American Association of Hip and Knee Surgeons Annual Meetings 2016 to 2019

BACKGROUND

The objective of this study was to provide the poster and podium publication rates from the 2016 to 2019 American Association of Hip and Knee Surgeons (AAHKS) annual meetings and assess the quality of presentations ultimately published during this time.

METHODS

Posters and podiums from 2016 to 2019 were located through the AAHKS e-poster and meeting archives. Titles were queried on Google, Google Scholar, and PubMed. Presentation title, topic type, number of authors, degrees held by first author, and award status were collected. For each identified full-text publication, journal name, study type, dates of online and print publication, and journal impact factor (JIF) were also obtained.

RESULTS

A total of 1,274 abstracts were presented at the 2016 to 2019 AAHKS annual meetings with an overall publication rate of 67.8% (864 of 1,274). Podiums (4.0 months, 95% confidence interval [CI]: 3.0 to 4.0) were more likely to be published earlier than posters (7.0 months, 95% CI: 6.0 to 7.0) (P < .001). The median JIF of published abstracts was 3.3 (95% CI: 3.3 to 3.7) with no significant difference in mean JIF of published posters or podiums (3.3 ± 1.0 versus 3.3 ± 1.0, P = .554). The likelihood of publication was significantly higher for podiums (odds ratio [OR]: 3.41, 95% CI: 2.29 to 5.07, P < .001), award-winning presentations (OR: 4.78, 95% CI: 1.69 to 13.55, P = .003), and with more authors (OR: 1.09, 95% CI: 1.02 to 1.16, P = .014).

DISCUSSION

This analysis demonstrates abstracts presented at the AAHKS annual meetings from 2016 to 2019 had an overall publication rate of 67.8%, with podiums (86.4%) 3 times as likely to be published as posters (63.6%). If selected to present at the AAHKS annual meeting, individuals can be confident that their work has a high chance of eventual publication.

Linkage Between Databases in Joint Arthroplasty and Orthopaedics: The Way Forward?

Each large observational database contains specific data elements. The number of data elements are chosen carefully to cover the essential needs of the database as well as to avoid excessive burden of collection. Frequently, an important study question cannot be answered because one database does not contain some essential data elements. This deficiency may be present because the proposed study is cross-disciplinary, because the study requires more granular information on a specific topic than is practical to collect in a broad-based registry, or because the relevant questions, and hence essential data elements, have changed over time. An obvious way to overcome some such challenges, when one database contains some of the information and another contains the further needed data, is to link different databases. While the prospect of linking databases is appealing, the practicalities of doing so often are daunting. Challenges may be practical (information-technology barriers to crosstalk between the registries), legal, and financial. In the first section of this paper, Dr. Nathanael Heckmann discusses linking large orthopaedic databases, focusing on linking databases with detailed, short-term data to those with longer-term longitudinal data. In the second part of this paper, Nathan Glusenkamp discusses efforts to link the American Joint Replacement Registry (AJRR) to other data sources, an ambition not yet fully realized but one that will bear fruit in the near future.

Investigating the International Normalized Ratio Thresholds for Complication in Shoulder Arthroplasty

BACKGROUND

In patients on warfarin anticoagulation therapy, elective shoulder arthroplasty surgeons must carefully balance bleeding and embolic risks. Currently, an international normalized ratio (INR) threshold of 1.5 is supported in the setting of elective surgery. However, no previous study has investigated the optimal preoperative INR target specifically in shoulder arthroplasty. The purpose of this study was to evaluate the association of preoperative INR with rates of transfusion, complication, and readmission/revision surgery in shoulder arthroplasty.

METHODS

Patients who underwent elective shoulder arthroplasty were identified in a national database. The primary outcome of interest was the risk for all-cause complication at 30 days postoperatively. Major and minor complication, revision surgery, and readmission rates were also investigated.

RESULTS

From 2006 to 2016, 1,014 procedures were identified who had undergone elective shoulder arthroplasty with a perioperative INR lab result within 24 hours of surgery. In our cohort, 550 patients (54.2%) were women, with an average age of 71.0 ± 9.8 years. After controlling for confounders, patients with a preoperative INR > 1.5 were 18.9 times as likely to have a major complication as those with a preoperative INR ≤ 1.0 (P = 0.003). Patients with an INR of 1.25 < INR ≤ 1.5 did not have a statistically significant risk of minor or major complication in comparison with those with an INR ≤ 1.0 (P = 0.23, P = 0.67).

DISCUSSION

Although recent hip and knee arthroplasty literature has found that an INR < 1.25 may be an optimal preoperative INR goal, our results did not find an increased risk for bleeding and complication with an INR ≤ 1.5 for shoulder arthroplasty. These results support current guidelines recommending a preoperative INR ≤ 1.5 for shoulder arthroplasty.

Assessing the level of evidence in the orthopaedic literature, 2013-2018: a review of 3449 articles in leading orthopaedic journals

Background

In biomedical research, level of evidence (LOE) indicates the quality of a study. Recent studies evaluating orthopaedic trauma literature between 1998 and 2013 have indicated that LOE in this field has improved. The objective of this study was to determine the validity of one such study by 1) comparing our results and how they relate to more recent years of publications; and 2) assessing how our findings may be used to estimate future changes.

Methods

A total of 3449 articles published from 2013 to 2018 in The Journal of Orthopaedic Trauma (JOT); Journal of Bone and Joint Surgery, American Volume (JBJS-Am); and Clinical Orthopaedics and Related Research (CORR) were evaluated for their LOE. Articles published in JBJS-Am or CORR were classified as trauma or nontrauma studies; articles published in JOT were considered trauma studies. Articles were assigned a LOE using guidance published by JBJS-Am in 2015.

Results

The percentage of total high-level (level I or II) trauma and nontrauma articles published in JOT, JBJS-Am, and CORR decreased from 2013 to 2018 (trauma 23.1 to 19.2%, p = 0.190; nontrauma 28.8 to 24.9%, p = 0.037). JBJS-Am published the highest percentage of level-I trauma studies, and CORR published the lowest percentage of level-IV studies. JBJS-Am and CORR published higher percentages of level-I trauma studies and lower percentages of level-IV nontrauma studies than all trauma studies.

Conclusions

Based on our results we cannot validate the findings of previous studies as we found the overall LOE of both trauma and nontrauma orthopaedic literature has decreased in recent years. JBJS-Am published a greater percentage of high-level studies than did JOT and CORR. Although the number and percentage of high-level studies published in JOT increased during the study period, it still lagged behind JBJS-Am and CORR.

Identifying a clinical decision tool to predict discharge disposition following operative treatment of hip fractures in the United States

BACKGROUND

Post-discharge management following operative treatment of hip fractures continues to be performed on a case-by-case basis, with no uniform guidelines dictating management. Predicting discharge to post-acute care (PAC) facilities (i.e. skilled nursing facilities and inpatient rehabilitation facilities) can assist preoperative planning and potentially decrease length of stay secondary to disposition issues. The goal of this study was to develop a nomogram using easily identified variables to preoperatively predict discharge disposition following operative treatment of hip fractures.

METHODS

Using the National Surgical Quality Improvement Program database, patients who underwent surgical intervention for hip fractures between 2012 and 2015 were identified. A multivariable logistic regression model was used to identify risk factors for discharge to a PAC facility, and a predictive nomogram was created based on these results.

RESULTS

From 2012 to 2015, 33,371 hip fractures were identified: 13,336 (40%) femoral neck fractures, and 20,035 (60%) intertrochanteric femur fractures. Of the patients identified, 26,082 (78.2%) were discharged to a PAC while the remainder were discharged home with or without home health. 70% of patients were female and 92.4% were Caucasian. When accounting for comorbidities, using the American Society of Anesthesiologists (ASA) classification system, 6,122 patients (18.4%) had 'Mild Systemic Disease' (ASA 2), 20,872 (62.6%) patients had 'Severe Systemic Disease' (ASA 3), and 6,006 (18.1%) had 'Life Threatening Disease' (ASA 4/5). The majority of patients were brought in from a 'Home' setting, while 10.4% of patients were admitted from a 'Long-Term Care' setting. After controlling for confounding variables, older age and increasing ASA class were predictive of an increased risk of discharge to a PAC. Diabetes, dyspnea, congestive heart failure, and chronic obstructive pulmonary disease were not associated with an increased risk of discharge to a PAC.

DISCUSSION

Discharge disposition following operative treatment of hip fractures can be reliably predicted using a nomogram with commonly identified preoperative variables.

LEVEL OF EVIDENCE

Level III, Retrospective Cohort Design, Observational Study.

Predicting Discharge Disposition After Shoulder Arthroplasty in the United States

INTRODUCTION

Postdischarge management for shoulder replacement continues to be performed on a case-by-case basis, with no uniform guidelines dictating management. The goal of this study was to develop a nomogram to preoperatively predict a patient's discharge disposition after elective shoulder arthroplasty.

METHODS

Patients who underwent elective shoulder arthroplasty between 2012 and 2015 were identified in the National Surgical Quality Improvement Program database. A multivariable logistic regression model was used to identify risk factors for discharge to a postacute care facility, and these results were used to create a predictive nomogram.

RESULTS

From 2012 to 2015, 8,363 procedures were identified. In our cohort, 962 patients (11.5%) were discharged to a postacute care facility, and 7,492 patients (88.5%) were discharged home. Preoperative functional status, followed by American Society of Anesthesiologists Class and age, had the strongest predictive value for discharge disposition after shoulder arthroplasty.

DISCUSSION

Discharge disposition can be predicted using a nomogram with commonly identified preoperative and intraoperative variables.

LEVEL OF EVIDENCE

Level III, retrospective cohort design, observational study.

Predicting adverse events, length of stay, and discharge disposition following shoulder arthroplasty: a comparison of the Elixhauser Comorbidity Measure and Charlson Comorbidity Index

BACKGROUND

Rising health care expenditures and the adoption of bundled-care initiatives require efficient resource allocation for shoulder arthroplasty. To determine a reliable and accessible metric for implementing perioperative care pathways, we compared the accuracy of the Elixhauser Comorbidity Measure (ECM) and Charlson Comorbidity Index (CCI) for predicting adverse events and postoperative discharge destination after shoulder arthroplasty.

MATERIALS AND METHODS

The National Inpatient Sample was queried for patients who underwent total shoulder arthroplasty or reverse total shoulder arthroplasty between 2002 and 2014. Logistic regression models were constructed with basic demographic variables and either the ECM or the CCI to predict inpatient deaths, complications, extended length of stay, and discharge disposition. The predictive discrimination of each model was evaluated using the concordance statistic (C-statistic).

RESULTS

We identified a total of 90,491 patients. The model incorporating both basic demographic variables and the complete set of ECM comorbidity variables provided the best predictive model, with a C-statistic of 0.867 for death, 0.752 for extended length of stay, and 0.81 for nonroutine discharge. The model's discrimination for postoperative complications was good, with C-statistics ranging from 0.641 to 0.879.

CONCLUSION

A predictive model using the ECM outperforms models using the CCI for anticipating resource utilization following shoulder arthroplasty. Our results may assist value-based reimbursement methods to promote quality of care and reduce health care expenditures.