Minimum 7-Year Follow-Up of Vitamin E-Diffused and Highly Cross-Linked Polyethylene Liners in Total Hip Arthroplasty: Findings From a Prospective, International, Multicenter Study of 977 Patients.

BACKGROUND: Vitamin E-diffused highly cross-linked polyethylene (VEPE) acetabular liners for total hip arthroplasty (THA) have shown favorable results in small cohort studies. However, larger studies are warranted to compare its performance to highly cross-linked polyethylene (XLPE) and demonstrate clinical significance in 10-year arthroplasty outcomes. This study compared acetabular liner wear and patient-reported outcome measures (PROMs) between patients treated with VEPE and XLPE liners in a prospective, international, multicenter study with minimum 7-year follow-up. METHODS: A total of 977 patients (17 centers; 8 countries) were enrolled from 2007 to 2012. The centers were randomly assigned to implants. At 1-year, 3-year, 5-year, and 7-year postoperative visits, radiographs, PROMs, and incidence of revision were collected. Acetabular liner wear was calculated using computer-assisted vector analysis of serial radiographs. General health, disease progression, and treatment satisfaction reported by patients were scored using 5 validated surveys and compared using Mann-Whitney U tests. At 7 years, 75.4% of eligible patients submitted data. RESULTS: The mean acetabular liner wear rate was -0.009 mm/y and 0.024 mm/y for the VEPE and XLPE group, respectively (P = .01). There were no statistically significant differences in PROMs. The overall revision incidence was 1.8% (n = 18). The revision incidence in VEPE and XLPE cohorts were 1.92% (n = 10) versus 1.75% (n = 8), respectively. CONCLUSION: We found that VEPE acetabular liners in total hip arthroplasty led to no significant clinical difference in 7-year outcomes as measured by acetabular liner wear rate, PROMs, and revision rate. While VEPE liners showed less wear, the wear rate for both the VEPE and XLPE liners was below the threshold for osteolysis. Therefore, the difference in liner wear may indicate comparative clinical performance at 7 years, as further indicated by the lack of difference in PROMs and the low revision incidence.

Can the Knee Outcome and Osteoarthritis Score (KOOS) Function Subscale Be Linked to the PROMIS Physical Function to Crosswalk Equivalent Scores?

BACKGROUND: An increased focus on patient-reported outcome measures (PROMs) has led to a proliferation of these measures in orthopaedic surgery. Mandating a single PROM in clinical and research orthopaedics is not feasible given the breadth of data already collected with older measures and the emergence of psychometrically superior measures. Creating crosswalk tables for scores between measures allows providers to maintain control of measure choice. Furthermore, crosswalk tables permit providers to compare scores collected with older outcome measures with newly collected ones. Given the widespread use of the newer Patient-reported Outcome Measure Information System Physical Function (PROMIS PF) and the established Knee Outcome and Osteoarthritis Score (KOOS), it would be clinically useful to link these two measures. QUESTION/PURPOSE: Can the KOOS Function in Activities of Daily Living (ADL) subscale be robustly linked to the PROMIS PF to create a crosswalk table of equivalent scores that accurately reflects a patient's reported physical function level on both scales? METHODS: We sought to establish a common standardized metric for collected responses to the PROMIS PF and the KOOS ADL to develop equations for converting a PROMIS PF score to a score for the KOOS-ADL subscale and vice versa. To do this, we performed a retrospective, observational study at two academic medical centers and two community hospitals in an urban and suburban healthcare system. Patients 18 years and older who underwent TKA were identified. Between January 2017 and July 2020, we treated 8165 patients with a TKA, 93% of whom had a diagnosis of primary osteoarthritis. Of those, we considered patients who had completed a full KOOS and PROMIS PF 10a on the same date as potentially eligible. Twenty-one percent (1708 of 8165) of patients were excluded because no PROMs were collected at any point, and another 67% (5454 of 8165) were excluded because they completed only one of the required PROMs, leaving 12% (1003 of 8165) for analysis here. PROMs were collected each time they visited the health system before and after their TKAs. Physical function was measured by the PROMIS PF version 1.0 SF 10a and KOOS ADL scale. Analyses to accurately create a crosswalk of equivalent scores between the measures were performed using the equipercentile linking method with both unsmoothed and log linear smoothed score distributions. RESULTS: Crosswalks were created, and adequate validation results supported their validity; we also created tables to allow clinicians and clinician scientists to convert individual patients' scores easily. The mean difference between the observed PROMIS PF scores and the scores converted by the crosswalk from the KOOS-ADL scores was -0.08 ± 4.82. A sensitivity analysis was conducted, confirming the effectiveness of these crosswalks to link the scores of two measures from patients both before and after surgery. CONCLUSION: The PROMIS PF 10a can be robustly linked to the KOOS ADL measure. The developed crosswalk table can be used to convert PROMIS PF scores from KOOS ADL and vice versa. CLINICAL RELEVANCE: The creation of a crosswalk table between the KOOS Function in ADL subscale and PROMIS PF allows clinicians and researchers to easily convert scores between the measures, thus permitting greater choice in PROM selection while preserving comparability between patient cohorts and PROM data collected from older outcome measures. Creating a crosswalk, or concordance table, between the two scales will facilitate this comparison, especially when pooling data for meta-analyses.

Low Body Mass Index Is a Predictor for Mortality and Increased Length of Stay Following Total Joint Arthroplasty.

BACKGROUND: Malnutrition is a devastating condition which disproportionally affects the elderly population. Malnutrition furthers the pre-existing elevated risk for osteoarthritis in this population, thus exacerbating joint damage in patients and furthering the need for total joint arthroplasty (TJA). A marker for malnutrition is a low body mass index (BMI). The purpose of this study is to investigate whether low BMI status increased the risk for 2-year mortality or reoperation, 90-day readmission, or extended length of stay (LOS) following TJA. METHODS: A retrospective study was performed using the Partners Arthroplasty Registry which contains data from 2016 to 2019. The registry was queried for primary total hip and primary total knee arthroplasty (TKA) patients that had a minimum of 2-years follow-up data. Demographic, surgical, and clinical outcome variables were obtained from these patients. The association between underweight BMI and objective outcomes of reoperation, 90-day readmission, mortality, and LOS was evaluated by univariate analysis followed by multiple logistic and linear regression analyses. RESULTS: The final cohort used for analysis consisted of 4802 TJA cases. After accounting for potential confounders, underweight BMI was found to be independently associated with increased risk of mortality within 2 years following TJA (odds ratio 8.77) (95% confidence interval 2.14-32.0) and increased LOS of 0.44 days (95% confidence interval 0.02-0.86). CONCLUSION: Our findings demonstrate that TJA patients with an underweight BMI experience an 8 times increased risk of 2-year mortality and an increased LOS of 0.44 days. Orthopedic surgeons should consider nutritional consultation and medical optimization in these high-risk patients prior to surgery.

Obesity Increases Risk of Failure to Achieve the 1-Year PROMIS PF-10a Minimal Clinically Important Difference Following Total Joint Arthroplasty.

INTRODUCTION: The aims of this study were to determine if increasing body mass index (BMI) is a risk factor for failure to attain the 1-year Patient Reported Outcomes Measurement Information System Physical Function (PROMIS PF-10a) minimal clinically important difference (MCID) following total joint arthroplasty (TJA) and to determine a possible BMI threshold beyond which this risk increases significantly. METHODS: This retrospective study was performed using 3506 TJAs sourced from a regional-based registry. An anchor-based MCID threshold of 7.9 was chosen. PROMIS PF-10a scores were collected at the preoperative and 1-year postoperative timepoints, and the change was used to determine failure to achieve the 1-year MCID. Demographic and surgical variables were also collected. The association between BMI and failure to achieve 1-year PROMIS PF-10 MCID was then evaluated using logistic regression. A BMI threshold was determined using receiver operating characteristic (ROC) curve analysis. RESULTS: Increasing BMI assessed continuously was a significant risk factor for failure to achieve the MCID (P < .001). "Obese Class I" (30-35 kg/m2), "Obese Class II" (35-40 kg/m2), and "Obese Class III" (>40 kg/m2) subgroups compared to "Normal BMI" (<25 kg/m2) were significantly associated (P < .05) with this adverse outcome as well. CONCLUSION: Our study showed that increasing BMI is a risk factor for failure to achieve the 1-year PROMIS PF-10a MCID following TJA. Among our patients, an increase in 1 kg/m2 increased the risk of failure to achieve the MCID by 2%. With these findings, surgeons will be better equipped to preoperatively advise patients with elevated BMIs considering TJA.

Availability and reporting quality of external validations of machine-learning prediction models with orthopedic surgical outcomes: a systematic review.

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.

Survival in Patients With Spinal Metastatic Disease Treated Nonoperatively With Radiotherapy: Are the SORG-ML Algorithms Relevant?

SUMMARY OF BACKGROUND DATA: The SORG-ML algorithms for survival in spinal metastatic disease were developed in patients who underwent surgery and were externally validated for patients managed operatively. OBJECTIVE: To externally validate the SORG-ML algorithms for survival in spinal metastatic disease in patients managed nonoperatively with radiation. STUDY DESIGN: Retrospective cohort. METHODS: The performance of the SORG-ML algorithms was assessed by discrimination [receiver operating curves and area under the receiver operating curve (AUC)], calibration (calibration plots), decision curve analysis, and overall performance (Brier score). The primary outcomes were 90-day and 1-year mortality. RESULTS: Overall, 2074 adult patients underwent radiation for spinal metastatic disease and 29% (n=521) and 59% (n=917) had 90-day and 1-year mortality, respectively. On complete case analysis (n=415), the AUC was 0.76 (95% CI: 0.71-0.80) and 0.78 (95% CI: 0.73-0.83) for 90-day and 1-year mortality with fair calibration and positive net benefit confirmed by the decision curve analysis. With multiple imputation (n=2074), the AUC was 0.85 (95% CI: 0.83-0.87) and 0.87 (95% CI: 0.85-0.89) for 90-day and 1-year mortality with fair calibration and positive net benefit confirmed by the decision curve analysis. CONCLUSION: The SORG-ML algorithms for survival in spinal metastatic disease generalize well to patients managed nonoperatively with radiation.

Natural language processing for prediction of readmission in posterior lumbar fusion patients: which free-text notes have the most utility?

BACKGROUND CONTEXT: The increasing volume of free-text notes available in electronic health records has created an opportunity for natural language processing (NLP) algorithms to mine this unstructured data in order to detect and predict adverse outcomes. Given the volume and diversity of documentation available in spine surgery, it remains unclear which types of documentation offer the greatest value for prediction of adverse outcomes. STUDY DESIGN/SETTING: Retrospective review of medical records at two academic and three community hospitals. PURPOSE: The purpose of this study was to conduct an exploratory analysis in order to examine the utility of free-text notes generated during the index hospitalization for lumbar spine fusion for prediction of 90-day unplanned readmission. PATIENT SAMPLE: Adult patients 18 years or older undergoing lumbar spine fusion for lumbar spondylolisthesis or lumbar spinal stenosis between January 1, 2016 and December 31, 2020. OUTCOME MEASURES: The primary outcome was inpatient admission within 90-days of discharge from the index hospitalization. METHODS: The predictive performance of NLP algorithms developed by using discharge summary notes, operative notes, nursing notes, physical therapy notes, case management notes, medical doctor (MD) (resident or attending), and allied practice professional (APP) (nurse practitioner or physician assistant) notes were assessed by discrimination, calibration, overall performance. RESULTS: Overall, 708 patients were included in the study and 83 (11.7%) had 90-day inpatient readmission. In the independent testing set of patients (n=141) not used for model development, the area under the receiver operating curve of NLP algorithms for prediction of 90-day readmission using discharge summary notes, operative notes, nursing notes, physical therapy notes, case management notes, MD/APP notes was 0.70, 0.57, 0.57, 0.60, 0.60, and 0.49 respectively. CONCLUSION: In this exploratory analysis, discharge summary, physical therapy, and case management notes had the most utility and daily MD/APP progress notes had the least utility for prediction of 90-day inpatient readmission in lumbar fusion patients among the free-text documentation generated during the index hospitalization.

Linking Hip Disability and Osteoarthritis Outcome Score-Physical Function Short Form and PROMIS Physical Function.

INTRODUCTION: Linking scores on patient-reported outcome measures can enable data aggregation for research, clinical care, and quality. We aimed to link scores on the Hip Disability and Osteoarthritis Outcome Score-Physical Function Short Form (HOOS-PS) and the Patient-reported Outcomes Measurement Information System Physical Function (PROMIS PF). METHODS: A retrospective study was conducted from 2017 to 2020 evaluating patients with hip osteoarthritis who received routine clinical care from an orthopaedic surgeon. Our sample included 3,382 unique patients with 7,369 pairs of HOOS-PS and PROMIS PF measures completed at a single nonsurgical, preoperative, or postoperative time point. We included one randomly selected time point of scores for each patient in our linking analysis sample. We compared the accuracy of linking using four methods, including equipercentile and item response theory-based approaches. RESULTS: PROMIS PF and HOOS-PS scores were strongly correlated ( r = -0.827 for raw HOOS-PS scores and r = 0.820 for summary HOOS-PS scores). The assumptions were met for equipercentile and item response theory approaches to linking. We selected the item response theory-based Stocking-Lord approach as the optimal crosswalk and estimated item parameters for the HOOS-PS items on the PROMIS metric. A sensitivity analysis demonstrated overall robustness of the crosswalk estimates in nonsurgical, preoperative, and postoperative patients. CONCLUSION: These crosswalks can be used to convert scores between HOOS-PS and PROMIS PF metric at the group level, which can be valuable for data aggregation. Conversion of individual patient-level data is not recommended secondary to increased risk of error.