Supportive care interventions in metastatic bone disease: scoping review.

BACKGROUND: Patients with secondary metastatic involvement of the musculoskeletal system due to primary cancers are a rapidly growing population with significant risks for health-related end-of-life morbidities. In particular, bone metastases or metastatic bone disease (MBD) imparts significant adversity to remaining quality of life. No rigorous review of clinical trials on the use of supportive care interventions for MBD has been conducted. The objective of this review was to examine the characteristics of supportive care interventions for MBD and critically appraise study designs, key findings, and quality of evidence of the research. METHODS: We searched for published clinical trials, systematic reviews and meta-analyses in PubMED, CINAHL and Google Scholar for articles published between September 2017 and September 2022. Some examples of Medical Subject Headings terms were: 'secondary neoplasm', 'metastatic bone disease', 'palliative care' and 'supportive care intervention'. Quality of published evidence was evaluated based on treatment types and study design. RESULTS: After reviewing 572 publications, 13 articles were included in the final review and evaluation including seven clinical trials, two trial protocols and four systematic reviews. Feasible interventions included enhanced palliative care consultation, palliative radiotherapy and alternative medicines. Interventions addressed primary endpoints of fatigue (N=4, 31%), pain (N=3, 23%) or cancer-related symptoms (N=3, 23%) with patient-reported outcome instruments. No interventions reported on fracture complications or endpoints, specifically. The quality of most studies was moderate to strong. CONCLUSION: Supportive care interventions for MBD are feasible and the impact is measurable via patient-reported outcome measures. While the evidence for interventions was moderate to strong, there are very few specific controlled trials for skeletal-related events and impacts of social determinants of health. Further clinical trials are needed to define supportive care interventions for MBD that demonstrate reduced risk of fracture and that mitigate the reduced quality of life when negative musculoskeletal outcomes arise.

Linking QuickDASH and PROMIS Upper-Extremity Computer-Adaptive Test Scores in Hand Surgery: A Crosswalk Study.

PURPOSE: Assessment of patient-reported outcome measures (PROMs) for hand and upper-extremity surgery patients using measures such as the Quick Disabilities of the Arm, Shoulder, and Hand (qDASH), as well as general measures including the Patient-Reported Outcomes Measurement Information System Upper Extremity Physical Function domain via a Computer-Adaptive Test (PROMIS UE CAT), has become commonplace. The aim of this study was to link, for crosswalking, the qDASH measure to both versions of the PROMIS UE CAT (v1.2 and v2.0). METHODS: We included 18,944 hand and upper-extremity patients who completed both versions of the PROMIS UE CAT and the qDASH at the same clinical encounter. Shoulder pathology was excluded. Score linkage was performed using the R package equate, and multiple equating models (linear regression, identity, mean, linear, equipercentile, and circle-arc models) were used to establish crosswalk tables. RESULTS: Mean qDASH and PROMIS UE CAT v1.2 scores were 38.2 (SD = 23.1) and 36.6 (SD = 9.8), respectively. Mean qDASH and PROMIS UE CAT v2.0 scores were 37.3 (SD = 21.8) and 38.3 (SD = 10.4), respectively. Pearson correlations had very strong linear relationships between the qDASH and the PROMIS UE CAT v1.2 and PROMIS UE CAT v2.0 (r = -0.83 [-0.84, -0.92] and r = -0.80 [-0.81, -0.80], respectively). For the equipercentile equating models, the intraclass correlation coefficient (ICC) had very strong positive relationships to linking measures with ICC = 0.85 (0.84, 0.86) for the qDASH-UE CAT v1.2 crosswalk and ICC = 0.83 (0.82, 0.84) for the qDASH-UE CAT v2.0 crosswalk. CONCLUSIONS: The linkages establish crosswalk tables using equipercentile equating models to convert the PROMIS UE CAT v1.2 and v2.0 scores to the qDASH and vice versa. CLINICAL RELEVANCE: This study provides crosswalk tables for commonly collected PROMs in hand surgery, increasing the comparability of results between centers using different PROMs to study the same conditions or treatments.

Measuring Value in Spine Care Using the PROMIS-Preference Scoring System.

BACKGROUND: A transition to value-based care requires a thorough understanding of the costs and impacts of various interventions on patients' overall health utility. The Patient-Reported Outcomes Measurement Information System (PROMIS) has gained popularity and is frequently used to assess physical, mental, and social health domains in clinical and research settings. To assess health utility, the PROMIS-Preference (PROPr) score, a societal preference-based measure, has been proposed to produce a single estimate of health utility. We determined the psychometric properties (validity and responsiveness) of the PROPr score as a health state utility measure in patients undergoing spine surgery. We hypothesized that PROPr score would be lower in the presence of comorbid conditions and lower socioeconomic status and in those with more severe pain-related disability and would be responsive to changes in health status following spine surgery. METHODS: In this prospective cohort study, 904 adults presented for cervical (n = 359) and/or lumbar (n = 622) conditions, and 624 underwent surgery, from August 2019 through January 2022. To assess concurrent validity, we correlated the PROPr score with Neck Disability Index (NDI)/Oswestry Disability Index (ODI) values. To assess known-groups validity, we regressed the PROPr score on participant age, sex, pain-related disability, and social determinants of health. To assess responsiveness, we used an anchor-based approach, evaluating change from preoperatively to 6 and 12 months postoperatively anchored by the Patient Global Impression of Change. A p level of <0.05 was considered significant. RESULTS: The median overall preoperative PROPr score was 0.20 (interquartile range [IQR], 0.10 to 0.32; range, -0.02 to 0.95). The PROPr score was associated with higher educational attainment (p = 0.01), higher household income (p < 0.001), and a greater number of comorbid conditions (p = 0.04). The median PROPr score decreased (worse health utility) with greater disability (NDI, 0.44 [none] to 0.09 [severe/complete], p < 0.001; ODI, 0.57 [none] to 0.08 [severe/complete], p < 0.001). The change in the median PROPr score differed in participants who rated their postoperative health as improved (0.17) compared with little or no change (0.04; p < 0.001) or worse (-0.06; p = 0.025) at 6 months and in those who rated their health as improved (0.15) compared with little or no change (0.02; p < 0.001) or worse (-0.05; p = 0.043) at 12 months. CONCLUSIONS: The PROPr score is a valid and responsive preference-based assessment of health utility for patients undergoing spine surgery. It can be calculated from PROMIS outcome data. LEVEL OF EVIDENCE: Prognostic Level II . See Instructions for Authors for a complete description of levels of evidence.

Determining the clinical significance of the PROMIS physical function score in the setting of femur fractures.

BACKGROUND: To adequately utilize patient reported outcome scores in the clinical setting, accurate determination of a cohort-specific minimal clinically important differences (MCID) is necessary. The purpose of this study was to assess MCID for Patient Reported Outcome Information System Physical Function Scores (PROMIS®) Physical Function (PF) in a sample of patients who have undergone operative fixation for femur fractures. METHODS: All patients at a single Level 1 trauma center who were treated for operative femur fractures were identified by Current Procedural Terminology (CPT) codes (27,244, 27,245, 27,506, 27,507). PROMIS PF was collected as part of routine clinical care via computer adaptive testing (CAT). MCID calculations were performed using both anchor-based and distribution-based methods. RESULTS: A total of 182 patients with 723 score observations were included in the overall distribution-based analysis and 131 patients with 309 score observations were included in the anchor-based analysis. In the overall cohort, the average age was 53.1 (SD 22.3), and 45% of participants were female. MCID for PROMIS PF scores was 5.43 in the distribution-based method and 5.18 in the anchor-based method. Overall scores in the distribution group improved from mean of 27.4 (SD 7.0) at the first postoperative visit to a mean of 36.7 (SD 10.0) at a subsequent follow up visit. Overall scores in the anchor group improved from mean of 26.7 (SD 7.3) at the first postoperative visit to a mean of 37.5 (SD 9.3) at a subsequent follow up visit. CONCLUSIONS: This study identifies two MCID values (5.18, 5.43) based on two calculation methods for PROMIS physical function scores in the operative femur fracture population. This data could be helpful in targeting postoperative patients who fall below expected norms or in allowing clinical correlation with changes in surgical practice.

Establishing the Patient Acceptable Symptom State in a Nonshoulder Hand and Upper Extremity Population for the Disabilities of the Arm, Shoulder, and Hand and Patient-Reported Outcomes Measurement Information System Upper Extremity Computer Adaptive Tests.

PURPOSE: It is unclear what score thresholds on patient-reported outcomes instruments reflect an acceptable level of upper extremity (UE) function from the perspective of patients undergoing hand surgery. The purpose of this study was to calculate the patient acceptable symptom state (PASS) for the Disabilities of the Arm, Shoulder, and Hand (QuickDASH) and Patient-Reported Outcomes Measurement Information System (PROMIS) UE Computer Adaptive Test (CAT), version 2.0, in a population who underwent hand surgery. METHODS: Adult patients who underwent hand surgery between February 2019 and December 2019 at a single academic tertiary institution were identified. QuickDASH and PROMIS UE CAT version 2.0 scores were collected 1 year after surgery, as were separate symptom- and function-specific anchor questions that queried the acceptability of patients' current state. Threshold values predictive of a patient reporting an acceptable symptom state (PASS[+]) were calculated for both instruments using the 75th percentile score for patients in the PASS(+) group and the Youden Index as determined by receiver operating curve (ROC) analysis. RESULTS: A total of 222 patients were included. QuickDASH and PROMIS UE CAT scores differed significantly between the PASS(+) and PASS(-) groups. The 75th percentile method yielded PASS values of <16 for the QuickDASH and >43 for the PROMIS UE CAT for both anchor questions. The ROC analysis yielded PASS estimates of <15.9 to <20.5 for the QuickDASH and >38.1 to >46.2 for the PROMIS UE CAT, with ranges calculated from differing threshold values for each of the 2 anchor questions. The ROC-based estimates demonstrated high levels of model discrimination (area under the curve ≥ 0.80). CONCLUSIONS: We propose PASS estimates obtained using the 75th percentile and ROC methods. CLINICAL RELEVANCE: Specifically, PASS values in the range of 15.9-20.5 for the QuickDASH and 38.1-46.2 for the PROMIS UE CAT version 2.0 should be used when interpreting outcomes at a population level.

The Lumbar Stenosis Prognostic Subgroups for Personalizing Care and Treatment (PROSPECTS) study: protocol for an inception cohort study.

BACKGROUND: Lumbar spinal stenosis (LSS) is a common degenerative condition that contributes to back and back-related leg pain in older adults. Most patients with symptomatic LSS initially receive non-operative care before surgical consultation. However, there is a scarcity of data regarding prognosis for patients seeking non-surgical care. The overall goal of this project is to develop and evaluate a clinically useful model to predict long-term physical function of patients initiating non-surgical care for symptomatic LSS. METHODS: This is a protocol for an inception cohort study of adults 50 years and older who are initiating non-surgical care for symptomatic LSS in a secondary care setting. We plan to recruit up to 625 patients at two study sites. We exclude patients with prior lumbar spine surgeries or those who are planning on lumbar spine surgery. We also exclude patients with serious medical conditions that have back pain as a symptom or limit walking. We are using weekly, automated data pulls from the electronic health records to identify potential participants. We then contact patients by email and telephone within 21 days of a new visit to determine eligibility, obtain consent, and enroll participants. We collect data using telephone interviews, web-based surveys, and queries of electronic health records. Participants are followed for 12 months, with surveys completed at baseline, 3, 6, and 12 months. The primary outcome measure is the 8-item PROMIS Physical Function (PF) Short Form. We will identify distinct phenotypes using PROMIS PF scores at baseline and 3, 6, and 12 months using group-based trajectory modeling. We will develop and evaluate the performance of a multivariable prognostic model to predict 12-month physical function using the least absolute shrinkage and selection operator and will compare performance to other machine learning methods. Internal validation will be conducted using k-folds cross-validation. DISCUSSION: This study will be one of the largest cohorts of individuals with symptomatic LSS initiating new episodes of non-surgical care. The successful completion of this project will produce a cross-validated prognostic model for LSS that can be used to tailor treatment approaches for patient care and clinical trials.

Significant Factors of High Performance Outcomes for Tibial Plafond Fractures.

BACKGROUND: Limited research exists about high performance postsurgical tibial plafond fractures. This study aimed to identify aspects of the plafond fracture injury and care associated with "high performance" based on Patient Reported Outcome Measurement Information System (PROMIS) Physical Function (PF) scores. METHODS: Tibial plafond fracture patients with minimum 12-month follow-up treated at a level 1 trauma center from 2006 to 2019 were categorized into high (top 25%) vs average-low (AL) (bottom 75%) performers based on PROMIS PF scores. Demographics and fracture characteristics of high and AL performers were compared. Variables with a P value less than .1 were used in stepwise logistic regressions. RESULTS: The final cohort of 198 patients was divided into high (n=51) and AL (n=147) performers based on PF scores. The mean PF scores for the high and AL groups were 58 (SD=5.3) and 41.9 (SD=6.5), respectively. The mean PROMIS Pain Interference scores for the high performers and AL were 43.3 (SD=4.9) and 56.8 (SD=8.6) (P < .001), respectively. FAAM activities of daily living (high 95.4 [SD=5.5] vs AL 70.4 [SD=19.8], P < .001) and FAAM sports (high 76.8 [SD=21.3] vs AL 27.3 [SD=28.9], P < .001) subscale scores were significantly greater in the high-performing group. Sex, age, marital status, and diabetes were nonsignificant factors in univariate analysis. Significant demographic factors associated with high performance from univariate analysis were Caucasian race, private insurance, no tobacco use, lower body mass index (BMI), and mechanism of injury. Patients with fracture characteristics of OTA 43C (complete articular) fractures, bone loss, open fractures, or nonexcellent reduction were significantly less likely to be high performers. Additionally, less than excellent reductions were more common in more complex fracture patterns. Multivariable regression modeling showed that higher BMI, OTA/AO 43-C (complete articular) fracture classification, and open fracture were significant independent risk factors for reducing likelihood of high performance. CONCLUSION: Significant independent factors associated with high performance after tibial plafond fracture are lower BMI, closed fracture, and OTA/AO 43-B (partial articular) fracture.

How Do PROMIS Scores Correspond to Common Physical Abilities?

BACKGROUND: The Patient-Report Outcomes Measurement Information System (PROMIS) is increasingly used as a general-purpose tool for measuring orthopaedic surgery outcomes. This set of questionnaires is efficient, precise, and correlates well with specialty-specific measures, but impactful implementation of patient-specific data, especially at the point of care, remains a challenge. Although clinicians may have substantial experience with established patient-reported outcome measures in their fields, PROMIS is relatively new, and the real-life meaning of PROMIS numerical summary scores may be unknown to many orthopaedic surgeons. QUESTIONS/PURPOSES: We aimed to (1) identify a small subset of important items in the PROMIS Physical Function (PF) item bank that are answered by many patients with orthopaedic conditions and (2) graphically display characteristic responses to these items across the physical function spectrum in order to translate PROMIS numerical scores into physical ability levels using clinically relevant, familiar terms. METHODS: In a cross-sectional study, 97,852 PROMIS PF assessments completed by 37,517 patients with orthopaedic conditions presenting to a tertiary-care academic institution were pooled and descriptively analyzed. Between 2017 and 2020, we evaluated 75,354 patients for outpatient orthopaedic care. Of these, 67% (50,578) were eligible for inclusion because they completed a PROMIS version 2.0 physical function assessment; 17% (12,720) were excluded because they lacked information in the database on individual item responses, and another < 1% (341) were excluded because the assessment standard error was greater than 0.32, leaving 50% of the patients (37,517) for analysis. The PROMIS PF is scored on a 0-point to 100-point scale, with a population mean of 50 and SD of 10. Anchor-based minimum clinically important differences have been found to be 8 to 10 points in a foot and ankle population, 7 to 8 points in a spine population, and approximately 4 points in a hand surgery population. The most efficient and precise means of administering the PROMIS PF is as a computerized adaptive test (CAT), whereby an algorithm intelligently tailors each follow-up question based on responses to previous questions, requiring only a few targeted questions to generate an accurate result. In this study, the mean PROMIS PF score was 41 ± 9. The questions most frequently used by the PROMIS CAT software were identified (defined in this study as any question administered to > 0.1% of the cohort). To understand the ability levels of patients based on their individual scores, patients were grouped into score categories: < 18, 20 ± 2, 25 ± 2, 30 ± 2, 35 ± 2, 40 ± 2, 45 ± 2, 50 ± 2, 55 ± 2, 60 ± 2, and > 62. For each score category, the relative frequency of each possible response (ranging from "cannot do" to "without any difficulty") was determined for each question. The distribution of responses given by each score group for each question was graphically displayed to generate an intuitive map linking PROMIS scores to patient ability levels (with ability levels represented by how patients responded to the PROMIS items). RESULTS: Twenty-eight items from the 165-question item bank were used frequently (that is, administered to more than 0.1% of the cohort) by the PROMIS CAT software. The top four items constituted 63% of all items. These top four items asked about the patient's ability to perform 2 hours of physical labor, yard work, household chores, and walking more than 1 mile. Graphical displays of responses to the top 28 and top four items revealed how PROMIS scores correspond to patient ability levels. Patients with a score of 40 most frequently responded that they experienced "some difficulty" with physical labor, yard work, household chores, and walking more than 1 mile, compared with "little" or "no" difficulty for patients with a score of 50 and "cannot do" for patients with a score of 30. CONCLUSION: We provided a visual key linking PROMIS numerical scores to physical ability levels using clinically relevant, familiar terms. Future studies might investigate whether using similar graphical displays as a patient education tool enhances patient-provider communication and improves the patient experience. CLINICAL RELEVANCE: The visual explanation of PROMIS scores provided by this study may help new users of the PROMIS understand the instrument, feel empowered to incorporate it into their practices, and use it as a tool for counseling patients about their scores.

Decision-making in Orthopaedic Oncology: Does Cognitive Bias Affect a Virtual Patient's Choice Between Limb Salvage and Amputation?

BACKGROUND: The local treatment of extremity sarcomas usually is predicated on a decision between limb salvage and amputation. The manner in which surgical options are presented in the context of shared decision-making may influence this decision. In a population of "simulated" patients-survey respondents presented with a mock clinical vignette and then asked to choose between treatments-we assessed cognitive bias by deliberate alteration of the subjective presentation of the same objective information. QUESTIONS/PURPOSES: (1) Will the manner in which information is presented to a simulated patient, in the setting of treatment for a bone sarcoma, bias their decision regarding pursuing amputation versus limb salvage? (2) At the time of decision-making, will a simulated patient's personal background, demographics, or mood affect their ultimate decision? METHODS: Survey respondents (Amazon MTurk platform) were presented with mock clinical vignettes simulating a sarcoma diagnosis and were asked to choose between amputation and limb salvage. Specific iterations were designed to assess several described types of cognitive bias. These scenarios were distributed, using anonymous online surveys, to potential participants aged 18 years or older. Recruitment was geographically restricted to individuals in the United States. Overall, 404 respondents completed the survey. The average age of respondents was 33 years (SD 1.2 years), 60% were male and 40% were female. In all, 12% of respondents worked in healthcare. Each respondent also completed questions regarding his or her demographics and his or her current mood. Associations between the type of bias presented and the respondent's choice of limb salvage versus amputation were examined. Independent sample t-tests were used to compare means. Statistical significance was defined as p < 0.05. RESULTS: When amputation was presented as an option to mitigate functional loss (framing bias), more patients chose it than when limb salvage was presented as means for increased functional gains (23% [23 of 100] versus 10% [12 of 118], odds ratio [OR], 2.26; p = 0.010). Older simulated patients were more likely to choose limb salvage when exposed to framing bias versus younger patients (mean age 33 years versus 30 years, p = 0.02). Respondents who were employed in healthcare more commonly chose amputation versus limb salvage when exposed to framing bias (24% [eight of 35] versus 9% [17 of 183]; OR, 2.46; p = 0.02). Those who chose amputation were more likely to score higher on scales that measured depression or negative affect. CONCLUSIONS: Shared decision-making in orthopaedic oncology represents a unique circumstance in which several variables may influence a patient's decision between limb salvage and amputation. Invoking cognitive bias in simulated patients appeared to affect treatment decisions. We cannot be sure that these findings translate to the experience of actual sarcoma patients; however, we can conclude that important treatment decisions may be affected by cognitive bias and that patient characteristics (in this study, age, healthcare profession, and mood) may be associated with an individual's susceptibility to cognitive bias. We hope these observations will assist providers in the thoughtful delivery of highly charged information to patients facing difficult decisions, and promote further study of this important concept. LEVEL OF EVIDENCE: Level III, economic and decision analyses.

Hospital and Surgeon Variation in Patient-reported Functional Outcomes After Lumbar Spine Fusion: A Statewide Evaluation.

STUDY DESIGN: Statewide retrospective cohort study using prospectively collected data from the Spine Care and Outcomes Assessment Program, capturing ∼75% of the state's spine fusion procedures. OBJECTIVE: The aim of this study was to estimate the variation in patient-reported outcomes (PROs) 1 year after elective lumbar fusion surgery across surgeons and hospitals; and to discuss the potential impact of guiding patient selection using a PRO prediction tool. SUMMARY OF BACKGROUND DATA: Despite an increasing interest in incorporating PROs as part of the move toward value-based payment and to improve quality, limited evidence exists on how PROs vary across hospitals and surgeons, a key aspect of using these metrics for quality profiling. METHODS: We examined patient-reported functional improvement (≥15-point reduction in the Oswestry Disability Index [ODI]) and minimal disability (reaching ≤22 on the ODI) 1 year after surgery in 17 hospitals and 58 surgeons between 2012 and 2017. Outcomes were risk-adjusted for patient characteristics with multiple logistic regressions and reliability-adjusted using hierarchical models. RESULTS: Of the 737 patients who underwent lumbar fusion (mean [SD] age, 63 [12] years; 60% female; 84% had stenosis; 70% had spondylolisthesis), 58.7% achieved functional improvement and 42.5% reached minimal disability status at 1 year. After adjusting for patient factors, there was little variation between hospitals and surgeons (maximum interclass correlation was 3.5%), and this variation became statistically insignificant after further reliability adjustment. Avoiding operation on patients with <50% chance of functional improvement may reduce current surgical volume by 63%. CONCLUSION: Variations in PROs across hospitals and surgeons were mainly driven by differences in patient populations undergoing lumbar fusion, suggesting that PROs may not be useful indicators of hospital or surgeon quality. Careful patient selection using validated prediction tools may decrease differences in outcomes across hospitals and providers and improve overall quality, but would significantly reduce surgical volumes. LEVEL OF EVIDENCE: 3.

Body mass index and the risk of deep surgical site infection following posterior cervical instrumented fusion.

BACKGROUND: Surgical site infection (SSI) following spine surgery is associated with increased morbidity, reoperation rates, hospital readmissions, and cost. The incidence of SSI following posterior cervical spine surgery is higher than anterior cervical spine surgery, with rates from 4.5% to 18%. It is well documented that higher body mass index (BMI) is associated with increased risk of SSI after spine surgery. There are only a few studies that examine the correlation of BMI and SSI after posterior cervical instrumented fusion (PCIF) using national databases, however, none that compare trauma and nontraumatic patients. PURPOSE: The purpose of this study is to determine the odds of developing SSI with increasing BMI after PCIF, and to determine the risk of SSI in both trauma and nontraumatic adult patients. STUDY DESIGN: This is a retrospective cohort study of a prospective surgical database collected at one academic institution. PATIENT SAMPLE: The patient sample is from a prospectively collected surgical registry from one institution, which includes patients who underwent PCIF from April 2011 to October 2017. OUTCOME MEASURES: A SSI that required return to the operating room for surgical debridement. METHODS: This is a retrospective cohort study using a prospectively collected database of all spine surgeries performed at our institution from April 2011 to October 2017. We identified 1,406 patients, who underwent PCIF for both traumatic injuries and nontraumatic pathologies using International Classification of Diseases 9 and 10 procedural codes. Thirty-day readmission data were obtained. Patient's demographics, BMI, presence of diabetes, preoperative diagnosis, and surgical procedures performed were identified. Using logistic regression analysis, the risk of SSI associated with every one-unit increase in BMI was determined. This study received no funding. All the authors in this study report no conflict of interests relevant to this study. RESULTS: Of the 1,406 patients identified, 1,143 met our inclusion criteria. Of those patients, 688 had PCIF for traumatic injuries and 454 for nontraumatic pathologies. The incidence of SSI for all patients, who underwent PCIF was 3.9%. There was no significant difference in the rate of SSI between our trauma group and nontraumatic group. There was a higher rate of infection in patients, who were diabetic and with BMI≥30 kg/m2. The presence of both diabetes and BMI≥30 kg/m2 had an added effect on the risk of developing SSI in all patients, who underwent PCIF. Additionally, logistic regression analysis showed that there was a positive difference measure between BMI and SSI. Our results demonstrate that for one-unit increase in BMI, the odds of having a SSI is 1.048 (95% CI: 1.007-1.092, p=.023). CONCLUSIONS: Our study demonstrates that our rate of SSI after PCIF is within the range of what is cited in the literature. Interestingly, we did not see a statistically significant difference in the rate of infection between our trauma and nontrauma group. Overall, diabetes and elevated BMI are associated with increased risk of SSI in all patients, who underwent PCIF with even a higher risk in patient, who are both diabetic and obese. Obese patients should be counseled on elevated SSI risk after PCIF, and those with diabetes should be medically optimized before and after surgery when possible to minimize SSI.

Complications and Outcomes After Medial Ulnar Collateral Ligament Reconstruction: A Meta-Regression and Systematic Review.

BACKGROUND: Evidence is limited regarding the complications and outcomes after medial ulnar collateral ligament (UCL) reconstruction. The published data regarding this procedure are composed predominantly of small Level-III and IV retrospective studies for which meta-analysis is not generally useful. Meta-regression is an alternative technique to identify variables across multiple publications that have an effect on published outcomes and complication rates. METHODS: We performed a systematic search of published literature for outcomes after UCL reconstruction. A random effects meta-regression model was constructed to identify the association of study characteristics with outcome proportions. Sensitivity analyses were performed to assess the impact of influential studies on the results. RESULTS: Fourteen studies (median sample size, 26 patients; range, 10 to 743 patients) were included in the systematic review and meta-regression. These studies included a total of 1,177 patients with a reported Conway outcome rating (a measure of a patient's return to play). The majority of patients (mean, 83.2%) achieved an excellent Conway rating. Studies involving the docking technique were more likely to have a higher percentage of patients with an excellent outcome (14% risk difference compared with the figure-of-8 technique, p = 0.002) and lower reported rates of ulnar neurapraxia (Spearman correlation = -0.83). A lower reported rate of ulnar neurapraxia was strongly associated with the likelihood of an excellent (p = 0.01) or good or excellent (p = 0.001) Conway outcome rating. CONCLUSIONS: Despite substantial heterogeneity among study outcomes, studies that involved a docking technique and that had lower reported rates of ulnar neurapraxia were associated with greater percentages of excellent or good-to-excellent reported Conway outcome ratings. LEVEL OF EVIDENCE: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Development and Validation of a Prediction Model for Pain and Functional Outcomes After Lumbar Spine Surgery.

Importance: Functional impairment and pain are common indications for the initiation of lumbar spine surgery, but information about expected improvement in these patient-reported outcome (PRO) domains is not readily available to most patients and clinicians considering this type of surgery. Objective: To assess population-level PRO response after lumbar spine surgery, and develop/validate a prediction tool for PRO improvement. Design, Setting, and Participants: This statewide multicenter cohort was based at 15 Washington state hospitals representing approximately 75% of the state's spine fusion procedures. The Spine Surgical Care and Outcomes Assessment Program and the survey center at the Comparative Effectiveness Translational Network prospectively collected clinical and PRO data from adult candidates for lumbar surgery, preoperatively and postoperatively, between 2012 and 2016. Prediction models were derived for PRO improvement 1 year after lumbar fusion surgeries on a random sample of 85% of the data and were validated in the remaining 15%. Surgical candidates from 2012 through 2015 were included; follow-up surveying continued until December 31, 2016, and data analysis was completed from July 2016 to April 2017. Main Outcomes and Measures: Functional improvement, defined as a reduction in Oswestry Disability Index score of 15 points or more; and back pain and leg pain improvement, defined a reduction in Numeric Rating Scale score of 2 points or more. Results: A total of 1965 adult lumbar surgical candidates (mean [SD] age, 61.3 [12.5] years; 944 [59.6%] female) completed baseline surveys before surgery and at least 1 postoperative follow-up survey within 3 years. Of these, 1583 (80.6%) underwent elective lumbar fusion procedures; 1223 (77.3%) had stenosis, and 1033 (65.3%) had spondylolisthesis. Twelve-month follow-up participation rates for each outcome were between 66% and 70%. Improvements were reported in function, back pain, and leg pain at 12 months by 306 of 528 surgical patients (58.0%), 616 of 899 patients (68.5%), and 355 of 464 patients (76.5%), respectively, whose baseline scores indicated moderate to severe symptoms. Among nonoperative patients, 35 (43.8%), 47 (53.4%), and 53 (63.9%) reported improvements in function, back pain, and leg pain, respectively. Demographic and clinical characteristics included in the final prediction models were age, sex, race, insurance status, American Society of Anesthesiologists score, smoking status, diagnoses, prior surgery, prescription opioid use, asthma, and baseline PRO scores. The models had good predictive performance in the validation cohort (concordance statistic, 0.66-0.79) and were incorporated into a patient-facing, web-based interactive tool (https://becertain.shinyapps.io/lumbar_fusion_calculator). Conclusions and Relevance: The PRO response prediction tool, informed by population-level data, explained most of the variability in pain reduction and functional improvement after surgery. Giving patients accurate information about their likelihood of outcomes may be a helpful component in surgery decision making.

Use of patient-reported outcomes and satisfaction for quality assessments.

OBJECTIVES: Recent focus on patient-reported outcomes (PROs) has created a new challenge as we learn how to integrate these outcomes into practice along with other quality metrics. We investigated the relationship between PROs and satisfaction among spine surgery patients. We hypothesized that there would be significant disparities between patient satisfaction and PROs at the 1-year postoperative time point. STUDY DESIGN: Retrospective cohort study of adults undergoing elective lumbar spine surgery at 12 hospitals participating in the Spine Surgical Care and Outcomes Assessment Program. METHODS: Satisfaction, pain, and function scores were collected at 1 year post operation, along with clinical information, to determine the relationship between PROs and satisfaction at the patient level. RESULTS: Among 520 patients (mean age = 63 ± 13 years; 47% male), the majority of patients (82%) reported being satisfied with surgery. Satisfaction was associated with both improvement in pain (odds ratio [OR], 1.33; 95% CI, 1.17-1.51) and function (OR, 1.06; 95% CI, 1.04-1.08). However, even among patients who did not improve in pain or function, more than half (59%) reported being satisfied. CONCLUSIONS: Overall, patients undergoing elective lumbar spine surgery reported being satisfied with outcomes, but the reported responses in PROs were much more variable. As the expectations increase to include PRO measures as valid quality indicators, it is necessary to dedicate time and consideration to understanding the relationships among these measures to support meaningful translations into healthcare policy.

Intra-Wound Antibiotics and Infection in Spine Fusion Surgery: A Report from Washington State's SCOAP-CERTAIN Collaborative.

BACKGROUND: Surgical site infection (SSI) after spine surgery is classified as a "never event" by the Centers for Medicare and Medicaid. Intra-wound antibiotics (IWA) have been proposed to reduce the incidence of SSI, but robust evidence to support its use is lacking. METHODS: Prospective cohort undergoing spine fusion at 20 Washington State hospitals (July 2011 to March 2014) participating in the Spine Surgical Care and Outcomes Assessment Program (Spine SCOAP) linked to a discharge tracking system. Patient, hospital, and operative factors associated with SSI and IWA use during index hospitalizations through 600 days were analyzed using a random effects logistic model (index), and a time-to-event analysis (follow-up) using Cox proportional hazards. RESULTS: A total of 9,823 patients underwent cervical (47%) or lumbar (53%) procedures (mean age, 58; 54% female) with an SSI rate of 1.1% during index hospitalization. Those with SSI were older, more often had diabetes mellitus, and more frequently underwent lumbar (versus cervical) fusion compared with those without SSI (all p < 0.01). Unadjusted rates of SSI during index hospitalization were lower in patients who received IWA (0.8% versus 1.5%). After adjustment for patient, hospital, and operative factors, no benefit was observed in those receiving IWA (odds ratio [OR] 0.65, 95% confidence interval [CI]: 0.42-1.03). At 12 mo, unadjusted rates of SSI were 2.4% and 3.0% for those who did and did not receive antibiotics; after adjustment there was no significant difference (hazard ratio [HR] 0.94, 95% CI: 0.62-1.42). CONCLUSIONS: Whereas unadjusted analyses indicate a nearly 50% reduction in index SSI using IWA, we did not observe a statistically significant difference after adjustment. Despite its size, this study is underpowered to detect small but potentially relevant improvements in rates of SSI. It remains to be determined if IWA should be promoted as a quality improvement intervention. Concerns related to bias in the use of IWA suggest the benefit of a randomized trial.

Quality of life measures in soft tissue sarcoma.

The assessment of health-related quality of life (HRQL) via patient-reported outcomes has the potential to answer critical questions and improve the care of soft tissue sarcoma (STS). This review outlines the rationale for quality of life measures in sarcoma, and details various instrument types: disease- and anatomic-specific, provider-generated, generic HRQL and health state utilities. Prior usage in STS populations, relative advantages of specific patient-reported outcome measures and a framework for selecting appropriate measures are discussed. Uniform incorporation of validated HRQL measures in STS clinical research would further the understanding of patient wellbeing beyond traditional clinical measures, and more widespread use of health state utilities measures in particular has the potential to facilitate comparative effectiveness research.

Medicaid status is associated with higher surgical site infection rates after spine surgery.

STUDY DESIGN: The Spine End Results Registry (2003-2004) is a registry of prospectively collected data of all patients undergoing spinal surgery at the University of Washington Medical Center and Harborview Medical Center. Insurance data were prospectively collected and used in multivariate analysis to determine risk of perioperative complications. OBJECTIVE: Given the negative financial impact of surgical site infections (SSIs) and the higher overall complication rates of patients with a Medicaid payer status, we hypothesized that a Medicaid payer status would have a significantly higher SSI rate. SUMMARY OF BACKGROUND DATA: The medical literature demonstrates lesser outcomes and increased complication rates in patients who have public insurance than those who have private insurance. No one has shown that patients with a Medicaid payer status compared with Medicare and privately insured patients have a significantly increased SSI rate for spine surgery. METHODS: The prospectively collected Spine End Results Registry provided data for analysis. SSI was defined as treatment requiring operative debridement. Demographic, social, medical, and the surgical severity index risk factors were assessed against the exposure of payer status for the surgical procedure. RESULTS: The population included Medicare (N = 354), Medicaid (N = 334), the Veterans' Administration (N = 39), private insurers (N = 603), and self-pay (N = 42). Those patients whose insurer was Medicaid had a 2.06 odds (95% confidence interval: 1.19-3.58, P = 0.01) of having a SSI compared with the privately insured. CONCLUSION: The study highlights the increased cost of spine surgical procedures for patients with a Medicaid payer status with the passage of the Patient Protection and Affordable Care Act of 2010. The Patient Protection and Affordable Care Act of 2010 provisions could cause a reduction in reimbursement to the hospital for taking care of patients with Medicaid insurance due to their higher complication rates and higher costs. This very issue could inadvertently lead to access limitations. LEVEL OF EVIDENCE: 3.

Validation of the SF-6D Health State Utilities Measure in Lower Extremity Sarcoma.

Aim. Health state utilities measures are preference-weighted patient-reported outcome (PRO) instruments that facilitate comparative effectiveness research. One such measure, the SF-6D, is generated from the Short Form 36 (SF-36). This report describes a psychometric evaluation of the SF-6D in a cross-sectional population of lower extremity sarcoma patients. Methods. Patients with lower extremity sarcoma from a prospective database who had completed the SF-36 and Toronto Extremity Salvage Score (TESS) were eligible for inclusion. Computed SF-6D health states were given preference weights based on a prior valuation. The primary outcome was correlation between the SF-6D and TESS. Results. In 63 pairs of surveys in a lower extremity sarcoma population, the mean preference-weighted SF-6D score was 0.59 (95% CI 0.4-0.81). The distribution of SF-6D scores approximated a normal curve (skewness = 0.11). There was a positive correlation between the SF-6D and TESS (r = 0.75, P < 0.01). Respondents who reported walking aid use had lower SF-6D scores (0.53 versus 0.61, P = 0.03). Five respondents underwent amputation, with lower SF-6D scores that approached significance (0.48 versus 0.6, P = 0.06). Conclusions. The SF-6D health state utilities measure demonstrated convergent validity without evidence of ceiling or floor effects. The SF-6D is a health state utilities measure suitable for further research in sarcoma patients.

Predicting surgical site infection after spine surgery: a validated model using a prospective surgical registry.

BACKGROUND CONTEXT: The impact of surgical site infection (SSI) is substantial. Although previous study has determined relative risk and odds ratio (OR) values to quantify risk factors, these values may be difficult to translate to the patient during counseling of surgical options. Ideally, a model that predicts absolute risk of SSI, rather than relative risk or OR values, would greatly enhance the discussion of safety of spine surgery. To date, there is no risk stratification model that specifically predicts the risk of medical complication. PURPOSE: The purpose of this study was to create and validate a predictive model for the risk of SSI after spine surgery. STUDY DESIGN: This study performs a multivariate analysis of SSI after spine surgery using a large prospective surgical registry. Using the results of this analysis, this study will then create and validate a predictive model for SSI after spine surgery. PATIENT SAMPLE: The patient sample is from a high-quality surgical registry from our two institutions with prospectively collected, detailed demographic, comorbidity, and complication data. OUTCOME MEASURES: An SSI that required return to the operating room for surgical debridement. MATERIALS AND METHODS: Using a prospectively collected surgical registry of more than 1,532 patients with extensive demographic, comorbidity, surgical, and complication details recorded for 2 years after the surgery, we identified several risk factors for SSI after multivariate analysis. Using the beta coefficients from those regression analyses, we created a model to predict the occurrence of SSI after spine surgery. We split our data into two subsets for internal and cross-validation of our model. We created a predictive model based on our beta coefficients from our multivariate analysis. RESULTS: The final predictive model for SSI had a receiver-operator curve characteristic of 0.72, considered to be a fair measure. The final model has been uploaded for use on SpineSage.com. CONCLUSIONS: We present a validated model for predicting SSI after spine surgery. The value in this model is that it gives the user an absolute percent likelihood of SSI after spine surgery based on the patient's comorbidity profile and invasiveness of surgery. Patients are far more likely to understand an absolute percentage, rather than relative risk and confidence interval values. A model such as this is of paramount importance in counseling patients and enhancing the safety of spine surgery. In addition, a tool such as this can be of great use particularly as health care trends toward pay for performance, quality metrics (such as SSI), and risk adjustment. To facilitate the use of this model, we have created a Web site (SpineSage.com) where users can enter patient data to determine likelihood for SSI.

Predicting medical complications after spine surgery: a validated model using a prospective surgical registry.

BACKGROUND CONTEXT: The possibility and likelihood of a postoperative medical complication after spine surgery undoubtedly play a major role in the decision making of the surgeon and patient alike. Although prior study has determined relative risk and odds ratio values to quantify risk factors, these values may be difficult to translate to the patient during counseling of surgical options. Ideally, a model that predicts absolute risk of medical complication, rather than relative risk or odds ratio values, would greatly enhance the discussion of safety of spine surgery. To date, there is no risk stratification model that specifically predicts the risk of medical complication. PURPOSE: The purpose of this study was to create and validate a predictive model for the risk of medical complication during and after spine surgery. STUDY DESIGN/SETTING: Statistical analysis using a prospective surgical spine registry that recorded extensive demographic, surgical, and complication data. Outcomes examined are medical complications that were specifically defined a priori. This analysis is a continuation of statistical analysis of our previously published report. METHODS: Using a prospectively collected surgical registry of more than 1,476 patients with extensive demographic, comorbidity, surgical, and complication detail recorded for 2 years after surgery, we previously identified several risk factor for medical complications. Using the beta coefficients from those log binomial regression analyses, we created a model to predict the occurrence of medical complication after spine surgery. We split our data into two subsets for internal and cross-validation of our model. We created two predictive models: one predicting the occurrence of any medical complication and the other predicting the occurrence of a major medical complication. RESULTS: The final predictive model for any medical complications had a receiver operator curve characteristic of 0.76, considered to be a fair measure. The final predictive model for any major medical complications had receiver operator curve characteristic of 0.81, considered to be a good measure. The final model has been uploaded for use on SpineSage.com. CONCLUSION: We present a validated model for predicting medical complications after spine surgery. The value in this model is that it gives the user an absolute percent likelihood of complication after spine surgery based on the patient's comorbidity profile and invasiveness of surgery. Patients are far more likely to understand an absolute percentage, rather than relative risk and confidence interval values. A model such as this is of paramount importance in counseling patients and enhancing the safety of spine surgery. In addition, a tool such as this can be of great use particularly as health care trends toward pay-for-performance, quality metrics, and risk adjustment. To facilitate the use of this model, we have created a website (SpineSage.com) where users can enter in patient data to determine likelihood of medical complications after spine surgery.