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Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

Machine learning (ML) studies are becoming increasingly popular in orthopedics but lack a critically appraisal of their adherence to peer‐reviewed guidelines. The objective of this review was to (1) evaluate quality and transparent reporting of ML prediction models in orthopedic surgery based on the transparent reporting of multivariable prediction models for individual prognosis or diagnosis (TRIPOD), and (2) assess risk of bias with the Prediction model Risk Of Bias ASsessment Tool. A systematic review was performed to identify all ML prediction studies published in orthopedic surgery through June 18th, 2020. After screening 7138 studies, 59 studies met the study criteria and were included. Two reviewers independently extracted data and discrepancies were resolved by discussion with at least two additional reviewers present. Across all studies, the overall median completeness for the TRIPOD checklist was 53% (interquartile range 47%–60%). The overall risk of bias was low in 44% (n = 26), high in 41% (n = 24), and unclear in 15% (n = 9). High overall risk of bias was driven by incomplete reporting of performance measures, inadequate handling of missing data, and use of small datasets with inadequate outcome numbers. Although the number of ML studies in orthopedic surgery is increasing rapidly, over 40% of the existing models are at high risk of bias. Furthermore, over half incompletely reported their methods and/or performance measures. Until these issues are adequately addressed to give patients and providers trust in ML models, a considerable gap remains between the development of ML prediction models and their implementation in orthopedic practice.

Background: The outcome differences following surgery for an impending versus a completed pathological fracture have not been clearly defined. The purpose of the present study was to assess differences in outcomes following the surgical treatment of impending versus completed pathological fractures in patients with long-bone metastases in terms of (1) 90day and 1-year survival and (2) intraoperative blood loss, perioperative blood transfusion, anesthesia time, duration of hospitalization, 30-day postoperative systemic complications, and reoperations. Methods:We retrospectively performed a matched cohort study utilizing a database of 1,064 patients who had undergone operative treatment for 462 impending and 602 completed metastatic long-bone fractures. After matching on 22 variables, including primary tumor, visceral metastases, and surgical treatment, 270 impending pathological fractures were matched to 270 completed pathological fractures. The primary outcome was assessed with the Cox proportional hazard model. The secondary outcomes were assessed with the McNemar test and the Wilcoxon signed-rank test. Results:The 90-day survival rate did not differ between the groups (HR, 1.13 [95% CI, 0.81 to 1.56]; p = 0.48), but the 1year survival rate was worse for completed pathological fractures (46% versus 38%) (HR, 1.28 [95% CI, 1.02 to 1.61]; p = 0.03). With regard to secondary outcomes, completed pathological fractures were associated with higher intraoperative estimated blood loss (p = 0.03), a higher rate of perioperative blood transfusions (p = 0.01), longer anesthesia time (p = 0.04), and more reoperations (OR, 2.50 [95% CI, 1.92 to 7.86]; p = 0.03); no differences were found in terms of the rate of 30-day postoperative complications or the duration of hospitalization.Conclusions: Patients undergoing surgery for impending pathological fractures had lower 1-year mortality rates and Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJS/G816).

Background: Limited health literacy has been associated with adverse health outcomes. Undergoing orthopedic surgery often requires patients to make complex decisions and adhere to complicated instructions, suggesting that health literacy skills might have a profound impact on orthopedic surgery outcomes. Purpose: We sought to review the literature for studies investigating the level of health literacy in patients undergoing orthopedic surgery and also to assess how those studies report factors affecting health equity. Methods: We conducted a systematic search of PubMed, Embase, and Cochrane Library for all health literacy studies published in the orthopedic surgery literature up to February 8, 2022. Search terms included synonyms for health literacy and for all orthopedic surgery subspecialties. Two reviewers independently extracted study data in addition to indicators of equity reporting using the PROGRESS+ checklist (Place of Residence, Race/Ethnicity, Occupation, Gender/sex, Religion, Education, Social capital, Socioeconomic status, plus age, disability, and sexual orientation). Results: The search resulted in 616 studies; 9 studies remained after exclusion criteria were applied. Most studies were of arthroplasty (4/9; 44%) or trauma (3/9; 33%) patients. Validated health literacy assessments were used in 4 of the included studies, and only 3 studies reported the rate of limited health literacy in the patients studied, which ranged between 34% and 38.5%. At least one PROGRESS+ item was reported in 88% (8/9) of the studies. Conclusions: We found a paucity of appropriately designed studies that used validated measures of health literacy in the field of orthopedic surgery. The potential impact of health literacy on orthopedic patients and their outcomes has yet to be elucidated. Thoughtful, high-quality trials across diverse demographics and geographies are warranted.

Study Design. Cross-sectional survey study. Objective. The aim was to determine if health literacy level is associated with patient-reported outcomes and self-reported health status among patients presenting to an academic outpatient spine center. Summary of Background Data. Patient reports are critical to assessing symptom severity and treatment success in orthopedic spine patients. Patient-reported outcome measures (PROMs) are important instruments commonly used for this purpose. However, the influence of patient health literacy on PROMs has not yet been given much consideration in spine literature. Materials and Methods. Consecutive English-speaking patients over the age of 18 years and new to our clinic verbally completed the Newest Vital Sign health literacy assessment tool and a sociodemographic survey, including self-reported health status. In addition, seven Patient-Reported Outcomes Measurement Information System scores were extracted from patient records. Regression modeling was performed with PROMs considered as dependent variables, health literacy level as the primary predictor, and all other factors (age, sex, race, ethnicity, native English speaker, highest educational degree, grade-level reading, marital status, employment status, annual household income, and type of insurance) as covariates. Results. Among the 318 included patients, 33% had limited health literacy. Adjusted regression analysis demonstrated that patients with limited health literacy had worse PROM scores across all seven domains (Physical Function: P=0.028; Depression: P=0.035; Global Health—Physical: P=0.001; Global Health—Mental: P=0.007; Pain Interference: P=0.036; Pain Intensity: P=0.002; Anxiety: P=0.047). In addition, patients with limited health literacy reported worse self-reported health status (P<0.001). Conclusions. Spine patients with limited health literacy have worse baseline PROM scores confounders and report worse general health. Further investigations are necessary to elucidate if limited health literacy is a marker or the root cause of these disparities. Findings from this study urge the consideration of patient health literacy when interpreting PROMs as well as the implications for patient assessment and discussion of treatment options.

BackgroundIt is well documented that routinely collected patient sociodemographic characteristics (such as race and insurance type) and geography-based social determinants of health (SDoH) measures (for example, the Area Deprivation Index) are associated with health disparities, including symptom severity at presentation. However, the association of patient-level SDoH factors (such as housing status) on musculoskeletal health disparities is not as well documented. Such insight might help with the development of more-targeted interventions to help address health disparities in orthopaedic surgery.Questions/purposes(1) What percentage of patients presenting for new patient visits in an orthopaedic surgery clinic who were unemployed but seeking work reported transportation issues that could limit their ability to attend a medical appointment or acquire medications, reported trouble paying for medications, and/or had no current housing? (2) Accounting for traditional sociodemographic factors and patient-level SDoH measures, what factors are associated with poorer patient-reported outcome physical health scores at presentation? (3) Accounting for traditional sociodemographic factor patient-level SDoH measures, what factors are associated with poorer patient-reported outcome mental health scores at presentation?MethodsNew patient encounters at one Level 1 trauma center clinic visit from March 2018 to December 2020 were identified. Included patients had to meet two criteria: they had completed the Patient-Reported Outcome Measure Information System (PROMIS) Global-10 at their new orthopaedic surgery clinic encounter as part of routine clinical care, and they had visited their primary care physician and completed a series of specific SDoH questions. The SDoH questionnaire was developed in our institution to improve data that drive interventions to address health disparities as part of our accountable care organization work. Over the study period, the SDoH questionnaire was only distributed at primary care provider visits. The SDoH questions focused on transportation, housing, employment, and ability to pay for medications. Because we do not have a way to determine how many patients had both primary care provider office visits and new orthopaedic surgery clinic visits over the study period, we were unable to determine how many patients could have been included; however, 9057 patients were evaluated in this cross-sectional study. The mean age was 61 ± 15 years, and most patients self-reported being of White race (83% [7561 of 9057]). Approximately half the patient sample had commercial insurance (46% [4167 of 9057]). To get a better sense of how this study cohort compared with the overall patient population seen at the participating center during the time in question, we reviewed all new patient clinic encounters (n = 135,223). The demographic information between the full patient sample and our study subgroup appeared similar. Using our study cohort, two multivariable linear regression models were created to determi...