Using advanced imaging to measure bone density, compression fracture risk, and risk for construct failure after spine surgery.

Low bone mineral density (BMD) can predispose to vertebral body compression fractures and postoperative instrumentation failure. DEXA is considered the gold standard for measurement of BMD, however it is not obtained for all spine surgery patients preoperatively. There is a growing body of evidence suggesting that more routinely acquired spine imaging studies such as computed tomography (CT) and magnetic resonance imaging (MRI) can be opportunistically used to measure BMD. Here we review available studies that assess the validity of opportunistic screening with CT-derived Hounsfield Units (HU) and MRI-derived vertebral vone quality (VBQ) to measure BMD of the spine as well the utility of these measures in predicting postoperative outcomes. Additionally, we provide screening thresholds based on HU and VBQ for prediction of osteopenia/ osteoporosis and postoperative outcomes such as cage subsidence, screw loosening, proximal junctional kyphosis, and implant failure.

Assessing performance of Geriatric Surgery Verification Program preoperative communication standards in spine surgery.

BACKGROUND: To assess performance of the American College of Surgeons Geriatric Surgery Verification (GSV) Program preoperative communication standards in older patients undergoing high risk spine surgery. METHODS: We performed an external validation of a natural language processing (NLP) method for identifying documentation meeting GSV communication standards. We then applied this method to a retrospective cohort of patients aged 65 and older who underwent spinal fusion procedures between January 2018-December 2020 in a large healthcare system in Massachusetts. Our primary outcome of interest was fulfillment of GSV communication domains: overall health goals, treatment goals, and patient-centered outcomes. Factors associated with the fulfillment of at least one domain were assessed using Poisson regression to adjust for confounding. RESULTS: External validation of the NLP method had a sensitivity of 88.6% and specificity of 99.0%. Our study population included 1294 patients, of whom only 0.8% (n = 10) patients contained documentation of all three GSV domains, and 33.7% (n = 436) had documentation fulfilling at least one GSV domain. The GSV domain with lowest frequency of documentation was overall health goals, with only 35 (2.7%) of patients meeting this requirement. Adjusted analysis suggested that patients with a Charlson comorbidity score of one or more had higher fulfillment of GSV criteria (CCI 1-3: prevalence rate ratio (PRR) 1.8, 95% confidence interval (CI) 1.5-2.1; CCI >3: PRR 1.5, 95% CI 1.2-1.9). CONCLUSION: A paucity of geriatric patients undergoing spine surgery had preoperative documentation consistent with GSV standards. Given that spine surgery is one of the highest risk surgeries in older adults and GSV standards are relevant to all surgical specialties, wider promulgation of these standards is warranted.

Development of a machine learning algorithm to identify surgical candidates for hip and knee arthroplasty without in-person evaluation.

INTRODUCTION: Arthroplasty care delivery is facing a growing supply-demand mismatch. To meet future demand for joint arthroplasty, systems will need to identify potential surgical candidates prior to evaluation by orthopaedic surgeons. MATERIALS AND METHODS: Retrospective review was conducted at two academic medical centers and three community hospitals from March 1 to July 31, 2020 to identify new patient telemedicine encounters (without prior in-person evaluation) for consideration of hip or knee arthroplasty. The primary outcome was surgical indication for joint replacement. Five machine learning algorithms were developed to predict likelihood of surgical indication and assessed by discrimination, calibration, overall performance, and decision curve analysis. RESULTS: Overall, 158 patients underwent new patient telemedicine evaluation for consideration of THA, TKA, or UKA and 65.2% (n = 103) were indicated for operative intervention prior to in-person evaluation. The median age was 65 (interquartile range 59-70) and 60.8% were women. Variables found to be associated with operative intervention were radiographic degree of arthritis, prior trial of intra-articular injection, trial of physical therapy, opioid use, and tobacco use. In the independent testing set (n = 46) not used for algorithm development, the stochastic gradient boosting algorithm achieved the best performance with AUC 0.83, calibration intercept 0.13, calibration slope 1.03, Brier score 0.15 relative to a null model Brier score of 0.23, and higher net benefit than the default alternatives on decision curve analysis. CONCLUSION: We developed a machine learning algorithm to identify potential surgical candidates for joint arthroplasty in the setting of osteoarthritis without an in-person evaluation or physical examination. If externally validated, this algorithm could be deployed by various stakeholders, including patients, providers, and health systems, to direct appropriate next steps in patients with osteoarthritis and improve efficiency in identifying surgical candidates. LEVEL OF EVIDENCE: III.

Osteochondroma of the Tibial Tubercle Masquerading as Osgood-Shlatter Disease: A Case Report.

Background: Osteochondromas are a relatively common primary bone tumor, which may share common clinical features with Osgood-Schlatter disease (OSD). A limited number of cases have described tumors misdiagnosed as OSD. Case Presentation: We report the case of an 11-year-old male with a sessile osteochondroma of the tibial tubercle and concomitant involvement of the distal extension and attachment of the patellar tendon into the tibial periosteum. A prior diagnosis OSD had been made. The lesion was resected and repair of the extensor mechanism was required at the time of surgery. The patient was followed for 20 months postoperatively and had restoration of knee function with minimal pain, as demonstrated by a PEDI-IKDC score of 94.6 at 19-month. Conclusion: This is a rarely reported case of benign tumor masquerading as OSD requiring excisional biopsy with extensor mechanism repair.

Implications of Physical Access Barriers for Breast Cancer Diagnosis and Treatment in Women with Mobility Disability.

Objective: More than 30 years since enactment of the Americans with Disabilities Act, people with disability continue to face physical access barriers, notably inaccessible medical diagnostic equipment, in clinical settings. Access barriers affect breast cancer screening and treatment for women with disability. Methods: We used standard diagnosis codes and natural language processing to screen electronic health records (EHRs) in a digital data repository from a large healthcare delivery system for patients with pre-existing mobility disability diagnosed with breast cancer between 2005-2017. We reviewed EHRs of 20 patients, using conventional content analysis to examine breast cancer diagnosis and treatment experiences. Results: Clinicians noted challenges positioning patients for routine procedures including manual breast exam, screening mammography, and breast biopsies. Given challenges accommodating disability for adjuvant therapies, mastectomy was favored over breast-conserving options despite early stages of diagnosis. Notations contained little information about proactive problem-solving for arranging accommodations. Conclusions: Notations described physical access barriers for breast cancer detection and treatment, with limited planning for mitigating barriers. Despite 2017 promulgation of federal Standards for Accessible Medical Diagnostic Equipment, implementing these standards requires further rulemaking.

Deep Learning for Cancer Symptoms Monitoring on the Basis of Electronic Health Record Unstructured Clinical Notes.

PURPOSE: Symptoms are vital outcomes for cancer clinical trials, observational research, and population-level surveillance. Patient-reported outcomes (PROs) are valuable for monitoring symptoms, yet there are many challenges to collecting PROs at scale. We sought to develop, test, and externally validate a deep learning model to extract symptoms from unstructured clinical notes in the electronic health record. METHODS: We randomly selected 1,225 outpatient progress notes from among patients treated at the Dana-Farber Cancer Institute between January 2016 and December 2019 and used 1,125 notes as our training/validation data set and 100 notes as our test data set. We evaluated the performance of 10 deep learning models for detecting 80 symptoms included in the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) framework. Model performance as compared with manual chart abstraction was assessed using standard metrics, and the highest performer was externally validated on a sample of 100 physician notes from a different clinical context. RESULTS: In our training and test data sets, 75 of the 80 candidate symptoms were identified. The ELECTRA-small model had the highest performance for symptom identification at the token level (ie, at the individual symptom level), with an F1 of 0.87 and a processing time of 3.95 seconds per note. For the 10 most common symptoms in the test data set, the F1 score ranged from 0.98 for anxious to 0.86 for fatigue. For external validation of the same symptoms, the note-level performance ranged from F1 = 0.97 for diarrhea and dizziness to F1 = 0.73 for swelling. CONCLUSION: Training a deep learning model to identify a wide range of electronic health record-documented symptoms relevant to cancer care is feasible. This approach could be used at the health system scale to complement to electronic PROs.

Can We Geographically Validate a Natural Language Processing Algorithm for Automated Detection of Incidental Durotomy Across Three Independent Cohorts From Two Continents?

BACKGROUND: Incidental durotomy is an intraoperative complication in spine surgery that can lead to postoperative complications, increased length of stay, and higher healthcare costs. Natural language processing (NLP) is an artificial intelligence method that assists in understanding free-text notes that may be useful in the automated surveillance of adverse events in orthopaedic surgery. A previously developed NLP algorithm is highly accurate in the detection of incidental durotomy on internal validation and external validation in an independent cohort from the same country. External validation in a cohort with linguistic differences is required to assess the transportability of the developed algorithm, referred to geographical validation. Ideally, the performance of a prediction model, the NLP algorithm, is constant across geographic regions to ensure reproducibility and model validity. QUESTION/PURPOSE: Can we geographically validate an NLP algorithm for the automated detection of incidental durotomy across three independent cohorts from two continents? METHODS: Patients 18 years or older undergoing a primary procedure of (thoraco)lumbar spine surgery were included. In Massachusetts, between January 2000 and June 2018, 1000 patients were included from two academic and three community medical centers. In Maryland, between July 2016 and November 2018, 1279 patients were included from one academic center, and in Australia, between January 2010 and December 2019, 944 patients were included from one academic center. The authors retrospectively studied the free-text operative notes of included patients for the primary outcome that was defined as intraoperative durotomy. Incidental durotomy occurred in 9% (93 of 1000), 8% (108 of 1279), and 6% (58 of 944) of the patients, respectively, in the Massachusetts, Maryland, and Australia cohorts. No missing reports were observed. Three datasets (Massachusetts, Australian, and combined Massachusetts and Australian) were divided into training and holdout test sets in an 80:20 ratio. An extreme gradient boosting (an efficient and flexible tree-based algorithm) NLP algorithm was individually trained on each training set, and the performance of the three NLP algorithms (respectively American, Australian, and combined) was assessed by discrimination via area under the receiver operating characteristic curves (AUC-ROC; this measures the model's ability to distinguish patients who obtained the outcomes from those who did not), calibration metrics (which plot the predicted and the observed probabilities) and Brier score (a composite of discrimination and calibration). In addition, the sensitivity (true positives, recall), specificity (true negatives), positive predictive value (also known as precision), negative predictive value, F1-score (composite of precision and recall), positive likelihood ratio, and negative likelihood ratio were calculated. RESULTS: The combined NLP algorithm (the combined Massachusetts and Australian data) achieved excellent performance on independent testing data from Australia (AUC-ROC 0.97 [95% confidence interval 0.87 to 0.99]), Massachusetts (AUC-ROC 0.99 [95% CI 0.80 to 0.99]) and Maryland (AUC-ROC 0.95 [95% CI 0.93 to 0.97]). The NLP developed based on the Massachusetts cohort had excellent performance in the Maryland cohort (AUC-ROC 0.97 [95% CI 0.95 to 0.99]) but worse performance in the Australian cohort (AUC-ROC 0.74 [95% CI 0.70 to 0.77]). CONCLUSION: We demonstrated the clinical utility and reproducibility of an NLP algorithm with combined datasets retaining excellent performance in individual countries relative to algorithms developed in the same country alone for detection of incidental durotomy. Further multi-institutional, international collaborations can facilitate the creation of universal NLP algorithms that improve the quality and safety of orthopaedic surgery globally. The combined NLP algorithm has been incorporated into a freely accessible web application that can be found at https://sorg-apps.shinyapps.io/nlp_incidental_durotomy/ . Clinicians and researchers can use the tool to help incorporate the model in evaluating spine registries or quality and safety departments to automate detection of incidental durotomy and optimize prevention efforts. LEVEL OF EVIDENCE: Level III, diagnostic study.

Impact of insurance type on patient-reported outcome measures in patients with lumbar disc herniation.

BACKGROUND: Lumbar disc herniations (LDH) are among the most common spinal conditions. Despite increased appreciation for the importance of social determinants of health, the role that these factors play in patients with lumbar disc herniations is poorly defined. PURPOSE: To elucidate the association between insurance status and baseline patient reported outcome measures (PROMs) in the setting of lumbar disc herniations. STUDY DESIGN/SETTING: Retrospective cohort study PATIENT SAMPLE: Baseline patient-reported outcome measures (PROMS) were reviewed from 924 adult patients presenting for treatment of lumbar disc herniation within our institutional healthcare system (2015-2020). OUTCOME MEASURES: The Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function Short Form 10a (PF10a), PROMIS Global-Mental, PROMIS Global-Physical, and visual analogue scale (VAS) for back and leg pain were assessed. METHODS: PROMIS scores at presentation were defined at the primary outcome and insurance status as the primary predictor. Differences in clinical and sociodemographic characteristics between our cohorts, stratified by insurance status, were evaluated using Wilcoxon rank-sum or chi-squared testing. We used multivariable negative binomial regression modeling to adjust for potential confounders including age, gender, race, language, ethnicity, comorbidity index, and median geospatial household income. RESULTS: We included 924 patients, with mean age of 58.4 +/- 15.2 years and 52.6% male prevalence. Patients insured through Medicaid were more likely to be Black, Hispanic, and non-English speaking patients compared with the commercially insured. The Charlson Comorbidity index was significantly higher in the Medicare group. Following adjusted analysis, patients with Medicaid insurance had significantly worse PF10a (IRR, 0.90, 95% CI 0.85-0.96), as well as PROMIS Global-Physical score (IRR 0.88, 95% CI 0.82-0.94), and VAS low back pain (IRR 1.20, 95% CI 1.04-1.40) when compared to the commercially insured. CONCLUSIONS: We encountered worse physical function, mental, and pain-related patient-reported outcomes for those with Medicaid insurance in a population of patients presenting for evaluation of lumbar disc herniation. These findings, including worse depression, anxiety, and higher axial back pain scores, merit further investigation into potential health system asymmetries, and should be accounted for by treating providers.

US Physicians' Knowledge About The Americans With Disabilities Act And Accommodation Of Patients With Disability.

More than thirty years since the enactment of the Americans with Disabilities Act (ADA), people with disability continue to experience health care disparities. The ADA mandates that patients with disability receive reasonable accommodations. In our survey of 714 US physicians in outpatient practices, 35.8 percent reported knowing little or nothing about their legal responsibilities under the ADA, 71.2 percent answered incorrectly about who determines reasonable accommodations, 20.5 percent did not correctly identify who pays for these accommodations, and 68.4 felt that they were at risk for ADA lawsuits. Physicians who felt that lack of formal education or training was a moderate or large barrier to caring for patients with disability were more likely to report little or no knowledge of their responsibilities under the law and were more likely to believe that they were at risk for an ADA lawsuit. To achieve equitable care and social justice for patients with disability, considerable improvements are needed to educate physicians and make health care delivery systems more accessible and accommodating.

Natural language processing for automated surveillance of intraoperative neuromonitoring in spine surgery.

We sought to develop natural language processing (NLP) methods for automated detection and characterization of neuromonitoring documentation from free-text operative reports in patients undergoing spine surgery. We included 13,718 patients who received spine surgery at two tertiary academic medical centers between December 2000 - December 2020. We first validated a rule-based NLP method for identifying operative reports containing neuromonitoring documentation, comparing performance to standard administrative codes. We then trained a deep learning model in a subset of 993 patients to characterize neuromonitoring documentation and identify events indicating change in status or difficulty establishing baseline signals. Performance of the deep learning model was compared to gold-standard manual chart review. In our patient population, 3,606 (26.3%) patients had neuromonitoring documentation identified using NLP. Our NLP method identified notes containing neuromonitoring documentation with an F1-score of 1.0, surpassing performance of standard administrative codes which had an F1-score of 0.64. In the subset of 993 patients used for training, validation, and testing a deep learning model, the prevalence of change in status was 6.5% and difficulty establishing neuromonitoring baseline signals was 6.6%. The deep learning model had an F1-score = 0.80 and AUC-ROC = 1.0 for identifying change in status, and an F1-score = 0.80 and AUC-ROC = 0.97 for identifying difficulty establishing baseline signals. Compared to gold standard manual chart review, our methodology has greater efficiency for identifying infrequent yet important types of neuromonitoring documentation. This method may facilitate large-scale quality improvement initiatives that require timely analysis of a large volume of EHRs.

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.

Natural Language Processing to Identify Advance Care Planning Documentation in a Multisite Pragmatic Clinical Trial.

CONTEXT: Large multisite clinical trials studying decision-making when facing serious illness require an efficient method for abstraction of advance care planning (ACP) documentation from clinical text documents. However, the current gold standard method of manual chart review is time-consuming and unreliable. OBJECTIVES: To evaluate the ability to use natural language processing (NLP) to identify ACP documention in clinical notes from patients participating in a multisite trial. METHODS: Patients with advanced cancer followed in three disease-focused oncology clinics at Duke Health, Mayo Clinic, and Northwell Health were identified using administrative data. All outpatient and inpatient notes from patients meeting inclusion criteria were extracted from electronic health records (EHRs) between March 2018 and March 2019. NLP text identification software with semi-automated chart review was applied to identify documentation of four ACP domains: (1) conversations about goals of care, (2) limitation of life-sustaining treatment, (3) involvement of palliative care, and (4) discussion of hospice. The performance of NLP was compared to gold standard manual chart review. RESULTS: 435 unique patients with 79,797 notes were included in the study. In our validation data set, NLP achieved F1 scores ranging from 0.84 to 0.97 across domains compared to gold standard manual chart review. NLP identified ACP documentation in a fraction of the time required by manual chart review of EHRs (1-5 minutes per patient for NLP, vs. 30-120 minutes for manual abstraction). CONCLUSION: NLP is more efficient and as accurate as manual chart review for identifying ACP documentation in studies with large patient cohorts.

Prospective comparison of the accuracy of the New England Spinal Metastasis Score (NESMS) to legacy scoring systems in prognosticating outcomes following treatment of spinal metastases.

BACKGROUND CONTEXT: We developed the New England Spinal Metastasis Score (NESMS) as a simple, informative, scoring scheme that could be applied to both operative and non-operative patients. The performance of the NESMS to other legacy scoring systems has not previously been compared using appropriately powered, prospectively collected, longitudinal data. PURPOSE: To compare the predictive capacity of the NESMS to the Tokuhashi, Tomita and Spinal Instability Neoplastic Score (SINS) in a prospective cohort, where all scores were assigned at the time of baseline enrollment. PATIENT SAMPLE: We enrolled 202 patients with spinal metastases who met inclusion criteria between 2017-2019. OUTCOME MEASURES: One-year survival (primary); 3-month mortality and ambulatory function at 3- and 6-months were considered secondarily. METHODS: All prognostic scores were assigned based on enrollment data, which was also assigned as time-zero. Patients were followed until death or survival at 365 days after enrollment. Survival was assessed using Kaplan-Meier curves and score performance was determined via logistic regression testing and observed to expected plots. The discriminative capacity (c-statistic) of the scoring measures were compared via the z-score. RESULTS: When comparing the discriminative capacity of the predictive scores, the NESMS had the highest c-statistic (0.79), followed by the Tomita (0.69), the Tokuhashi (0.67) and the SINS (0.54). The discriminative capacity of the NESMS was significantly greater (p-value range: 0.02 to <0.001) than any of the other predictive tools. The NESMS was also able to inform independent ambulatory function at 3- and 6-months, a function that was only uniformly replicated by the Tokuhashi score. CONCLUSIONS: The results of this prospective validation study indicate that the NESMS was able to differentiate survival to a significantly higher degree than the Tokuhashi, Tomita and SINS. We believe that these findings endorse the utilization of the NESMS as a prognostic tool capable of informing care for patients with spinal metastases.

The role of gender in academic productivity, impact, and leadership among academic spine surgeons.

BACKGROUND/CONTEXT: Women represent a small minority of practicing orthopedic surgeons and neurosurgeons, with spine surgery having a disproportionately low representation relative to other subspecialties. Previous efforts have attempted to characterize gender patterns in authorship amongst select spine journals. However, no study to our knowledge has done a comprehensive assessment of the influence of gender on academic productivity, impact, and leadership amongst academic spine faculty. PURPOSE: To evaluate the impact of gender on academic productivity, promotion to leadership positions, and career advancement among academic spine faculty in the United States. STUDY DESIGN: Cross-sectional study. PATIENT SAMPLE: Academic spine faculty associated with orthopedic residency, North American Spine Society spine fellowship programs, and American Association of Neurological Surgeons spine fellowship programs. OUTCOME MEASURES: Academic productivity as measured by publications counts, h-index, authorship ranking as well as academic rank and leadership roles METHODS: We identified all spine faculty across orthopedic residency, orthopedic spine fellowship, and neurosurgical spine fellowship programs in the United States, and abstracted academic performance characteristics, cumulative h-index, and complete publication records for each individual faculty member. Proportions of men and women by specialty, academic rank, and leadership were compared with Fisher's exact testing, and comparison of mean h-index and publication counts compared with Wilcoxon rank-sum testing. Adjusted analyses on publication count and h-index were achieved with poisson regression analysis with gender as the primary predictor adjusting for specialty, degrees, academic rank, and seniority based on time since fellowship completion. RESULTS: The representation of women among spine faculty associated with orthopedic residency and North American Spine Society spine fellowship programs was 5.6%. On average, women had 40% fewer total publications (p=.025), h-indices approximately 5 units lower than men (p=.006), 40% fewer total high-impact publications (p=.030), half the senior author publications (p=.005), and half the high-impact senior author publications (p=.007) compared to men. After adjusting for seniority and academic rank, the number of publications in high impact journals no longer differed between men and women, although differences persisted for total publication count and the h-index. Men were significantly more likely to occupy higher academic ranks, with 25.6% of men and 9.5% of women holding the rank of full professor (p=.031), although there was no significant difference in the rate of appointment to leadership positions. Similar findings were encountered among American Association of Neurological Surgeons spine fellowship faculty. CONCLUSIONS: The present study details the low rate of women in academic spine surgery. Furthermore, gender disparities exist in publication volume, impact, and h-indices. A much lower proportion of women hold higher-ranking academic positions compared to men, though appointment to leadership positions was similar between genders. Differences in seniority and publication metrics may in part be due to the relatively younger cohort of women faculty. These findings underscore the need for active investment in diversity and pipeline efforts that facilitate recruitment and support academic productivity of women in spine surgery.

Use of Accessible Weight Scales and Examination Tables/Chairs for Patients with Significant Mobility Limitations by Physicians Nationwide.

BACKGROUND: Mobility limitations are the most common disability type among the 61 million Americans with disability. Studies of patients with mobility limitations suggest that inaccessible medical diagnostic equipment poses significant barriers to care. METHODS: The study team surveyed randomly selected US physicians nationwide representing seven specialties about their reported use of accessible weight scales and exam tables/chairs when caring for patients with mobility limitations. A descriptive analysis of responses was performed, and multivariable logistic regression was used to examine associations between accessible equipment and participants' characteristics. RESULTS: The 714 participants (survey response rate = 61.0%) were primarily male, White, and urban, and had practiced for 20 or more years. Among those reporting routinely recording patients' weights (n = 399), only 22.6% (standard error [SE] = 2.2) reported always or usually using accessible weight scales for patients with significant mobility limitations. To determine weights of patients with mobility limitations, 8.1% always, 24.3% usually, and 40.0% sometimes asked patients. Physicians practicing ≥ 20 years were much less likely than other physicians to use accessible weight scales: odds ratio (OR) = 0.51 (95% confidence interval [CI] = 0.26-0.99). Among participants seeing patients with significant mobility limitations (n = 584), only 40.3% (SE = 2.2) always or usually used accessible exam tables or chairs. Specialists were much more likely than primary care physicians to use accessible exam tables/chairs: OR = 1.96 (95% CI = 1.29-2.99). CONCLUSION: More than 30 years after enactment of the Americans with Disabilities Act, most physicians surveyed do not use accessible equipment for routine care of patients with chronic significant mobility limitations.

Evaluating ambulatory function as an outcome following treatment for spinal metastases: a systematic review.

BACKGROUND CONTEXT: Studies regarding treatment of spinal metastases are critical to evidence-based decision-making. However, variation exists in how a key outcome, ambulatory function, is assessed. PURPOSE: To characterize the sources and tools investigators have used to evaluate ambulatory function as an outcome following treatment of spinal metastases. We also sought to understand the ways ambulatory function has been conceptualized in prior studies. STUDY DESIGN: Systematic review of the literature. PATIENT SAMPLE: We identified 44 published studies for inclusion. Samples within these investigations ranged from 20 to 2,096 subjects. OUTCOME MEASURES: We describe the methods investigators have used to evaluate ambulatory function following treatment for spinal metastases. METHODS: We conducted a systematic review through PubMed, Scopus and Web of Science following PRISMA guidelines. We included studies that consisted of adult patients receiving operative or non-operative treatment for spinal metastases. We also required that study investigators specified post-treatment ambulatory function as an outcome. We recorded year of publication, study design, types of spinal metastases included in the study, treatments employed, and sample size. We also described the source (medical record, study-specific observer and/or provider, patient and/or participant), tool (standardized measure, quantitative, qualitative) and concept (eg, ambulatory vs. non-ambulatory; independent ambulation vs. ambulatory with assistance vs. non-ambulatory) used to assess ambulatory function. RESULTS: We found the plurality of studies relied on medical record documentation as their source. Amongst prospective studies, only a minority used a quantitative measure (eg, prespecified degree of walking ability) to assess ambulatory function. Most studies conceptualized ambulatory function as a dichotomized outcome, typically ambulatory versus non-ambulatory or a similar equivalent. CONCLUSIONS: Wide variation exists in how ambulatory function is defined in studies involving patients with spinal metastases. We suggest several improvements that will allow a more robust assessment of the quality and quantity of ambulatory function among patients treated for spinal metastases.

Physicians' Perceptions Of People With Disability And Their Health Care.

More than sixty-one million Americans have disabilities, and increasing evidence documents that they experience health care disparities. Although many factors likely contribute to these disparities, one little-studied but potential cause involves physicians' perceptions of people with disability. In our survey of 714 practicing US physicians nationwide, 82.4 percent reported that people with significant disability have worse quality of life than nondisabled people. Only 40.7 percent of physicians were very confident about their ability to provide the same quality of care to patients with disability, just 56.5 percent strongly agreed that they welcomed patients with disability into their practices, and 18.1 percent strongly agreed that the health care system often treats these patients unfairly. More than thirty years after the Americans with Disabilities Act of 1990 was enacted, these findings about physicians' perceptions of this population raise questions about ensuring equitable care to people with disability. Potentially biased views among physicians could contribute to persistent health care disparities affecting people with disability.

Exploring the Process of Cancer Care for Patients With Pre-Existing Mobility Disability.

PURPOSE: Approximately 13% of the US population report mobility disability. People with mobility disability experience healthcare disparities, including lower rates of cancer screening and substandard cancer care compared with nondisabled people. We explored clinicians' reports of aspects of diagnosing and treating three common cancer types among persons with pre-existing mobility disability. METHODS: We used standard diagnosis codes and natural language processing to screen electronic health records (EHR) in the Research Patient Data Repository for patients with pre-existing chronic mobility impairment who were newly diagnosed with one of three common cancers (colorectal, prostate, and non-Hodgkin lymphoma) between 2005 and 2017. We eliminated numerous cases whose EHRs lacked essential information. We reviewed EHRs of 27 cases, using conventional content analysis to identify themes concerning their cancer diagnoses and treatments. RESULTS: Clinicians' notations coalesced around four major themes: (1) patients' health risks raise concerns about diagnostic processes; (2) cancer signs or symptoms can be erroneously attributed to the patient's underlying disabling condition, delaying diagnosis; (3) disability complicates cancer treatment decisions; and (4) problems with equipment accessibility and disability accommodations impede cancer diagnoses. DISCUSSION: Clinicians view patients with pre-existing mobility disability as often clinically complex, presenting challenges for diagnosing and treating their cancer. Nonetheless, these patients may experience substandard care because of disability-related problems. Given the growing population of people with mobility disability, further efforts to improve care quality and timeliness of diagnosis are warranted.