Musculoskeletal infections (MSKI) remain the bane of orthopedic surgery, and result in grievous illness and inordinate costs that threaten healthcare systems. As prevention, diagnosis, and treatment has remained largely unchanged over the last 50 years, a 2nd International Consensus Meeting on Musculoskeletal Infection (ICM 2018, https://icmphilly.com) was completed. Questions pertaining to all areas of MSKI were extensively researched to prepare recommendations, which were discussed and voted on by the delegates using the Delphi methodology. The questions, including the General Assembly (GA) results, have been published (GA questions). However, as critical outcomes include: (i) incidence and cost data that substantiate the problems, and (ii) establishment of research priorities; an ICM 2018 research workgroup (RW) was assembled to accomplish these tasks. Here, we present the result of the RW consensus on the current and projected incidence of infection, and the costs per patient, for all orthopedic subspecialties, which range from 0.1% to 30%, and $17,000 to $150,000. The RW also identified the most important research questions. The Delphi methodology was utilized to initially derive four objective criteria to define a subset of the 164 GA questions that are high priority for future research. Thirty-eight questions (23% of all GA questions) achieved the requisite > 70% agreement vote, and are highlighted in this Consensus article within six thematic categories: acute versus chronic infection, host immunity, antibiotics, diagnosis, research caveats, and modifiable factors. Finally, the RW emphasizes that without appropriate funding to address these high priority research questions, a 3rd ICM on MSKI to address similar issues at greater cost is inevitable. ICM, International Consensus Meeting; MSKI, musculoskeletal infections; RW, research workgroup. * 2018 ICM RW consensus on the current and projected incidences of infection, and costs per patient, for all orthopedic subspecialties. These estimates, which were compiled from information were obtained from: (i) recent peer-reviewed publications, (ii) analysis of 2015 national administrative data using HCUPNet (https://hcupnet.ahrq.gov/#setup), and (iii) RW expertise. 998 SCHWARZ ET AL.

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Background: Little is known about how the geographic variation and disparities in use of elective primary total hip and knee replacements for Medicare beneficiaries have evolved in recent years. The study objectives are to determine these variations and disparities, whether Black Medicare beneficiaries have continued to undergo fewer total hip replacements and total knee replacements across regions, and whether disparities affected all Black beneficiaries or mainly affected socioeconomically disadvantaged Black beneficiaries. Methods: We used 2009 to 2017 Medicare enrollment and claims data to examine Hospital Referral Region (HRR)-level variation and disparities by race (non-Hispanic White and Black) and socioeconomic status (Medicare-only and dual eligibility for both Medicare and Medicaid). The outcomes were HRR-level age and sex-standardized total hip replacement and total knee replacement utilization rates for White Medicare-only beneficiaries, White dual-eligible beneficiaries, Black Medicare-only beneficiaries, and Black dual-eligible beneficiaries, and the differences in rates between these groups as a representation of disparities. The key exposure variables were race-socioeconomic group and year. We constructed multilevel mixed-effects linear regression models to estimate trends in total hip replacement and total knee replacement rates and to examine whether rates were lower in HRRs with high percentages of Black beneficiaries or dual-eligible beneficiaries. Results: The study included 924,844 total hip replacements and 2,075,968 total knee replacements. In 2017, the mean HRR-level total hip replacement rate was 4.64 surgical procedures per 1,000 beneficiaries, and the mean HRR-level total knee replacement rate was 9.66 surgical procedures per 1,000 beneficiaries, with a threefold variation across HRRs. In 2017, the total hip replacement rate was 32% higher for White Medicare-only beneficiaries and 48% higher for Black Medicare-only beneficiaries than in 2009 (p < 0.001). However, because the surgical rates for White and Black dual-eligible beneficiaries remained unchanged over the study period, the 2017 Medicare-only and dual-eligible disparity for White beneficiaries increased by 0.75 surgical procedures per 1,000 from 2009 (40.98% increase; p = 0.03), and the disparity for Black beneficiaries by 1.13 surgical procedures per 1,000 beneficiaries (297.37% increase; p < 0.001). The total knee replacement disparities remained unchanged. Notably, the rates for White dual-eligible beneficiaries were significantly lower than those for Black Medicare-only beneficiaries (p < 0.001 for both total hip replacements and total knee replacements), and fewer surgical procedures were conducted in HRRs with a higher density of Black or dual-eligible beneficiaries. Conclusions: Although the total hip replacement use for Medicare-only beneficiaries of both races increased, disparities for White and Black dual-eligible beneficiaries (compared with their Medicare-only counterparts) are increasing. Efforts to improve equity must identify and address both racial and socioeconomic barriers and focus on regions with high concentrations of disadvantaged beneficiaries. Clinical Relevance: Although total hip replacements and total knee replacements are highly successful surgical procedures for end-stage osteoarthritis, our findings show that, as recently as 2017, Black beneficiaries and those dual eligible for Medicaid (a proxy for socioeconomic status) are less likely to undergo these surgical procedures and that there is profound geographic variation in the use of these surgical procedures. This evidence is essential for the design and implementation of disparity-reduction strategies focused on patients, providers, and geographic areas that can potentially improve the equity in joint replacement care.

Background: The identification of surgical site infections for infection surveillance in hospitals depends on the manual abstraction of medical records and, for research purposes, depends mainly on the use of administrative or claims data. The objective of this study was to determine whether automating the abstraction process with natural language processing (NLP)-based models that analyze the free-text notes of the medical record can identify surgical site infections with predictive abilities that match the manual abstraction process and that surpass surgical site infection identification from administrative data. Methods:We used surgical site infection surveillance data compiled by the infection prevention team to identify surgical site infections among patients undergoing orthopaedic surgical procedures at a tertiary care academic medical center from 2011 to 2017. We compiled a list of keywords suggestive of surgical site infections, and we used NLP to identify occurrences of these keywords and their grammatical variants in the free-text notes of the medical record. The key outcome was a binary indicator of whether a surgical site infection occurred. We estimated 7 incremental multivariable logistic regression models using a combination of administrative and NLP-derived variables. We split the analytic cohort into training (80%) and testing data sets (20%), and we used a tenfold cross-validation approach. The main analytic cohort included 172 surgical site infection cases and 200 controls that were repeatedly and randomly selected from a pool of 1,407 controls.Results: For Model 1 (variables from administrative data only), the sensitivity was 68% and the positive predictive value was 70%; for Model 4 (with NLP 5-grams [distinct sequences of 5 contiguous words] from the medical record), the sensitivity was 97% and the positive predictive value was 97%; and for Model 7 (a combination of Models 1 and 4), the sensitivity was 97% and the positive predictive value was 97%. Thus, NLP-based models identified 97% of surgical site infections identified by manual abstraction with high precision and 43% more surgical site infections compared with models that used administrative data only.Conclusions: Models that used NLP keywords achieved predictive abilities that were comparable with the manual abstraction process and were superior to models that used administrative data only. NLP has the potential to automate and aid accurate surgical site infection identification and, thus, play an important role in their prevention.Clinical Relevance: This study examines NLP's potential to automate the identification of surgical site infections. This automation can potentially aid the prevention and early identification of these surgical complications, thereby reducing their adverse clinical and economic impact. Surgical site infections are frequently occurring and are the most expensive of all hospital-acquired infections 1-3 . In orthopaedics, the mean surgical site infection rates range from 0.5% to 3% following hip and knee replac...

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