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 post-operative outcomes. Additionally, we provide screening thresholds based on HU and VBQ for prediction of osteopenia/ osteoporosis and post-operative 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.

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.

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.

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 Cancer Treatment Experiences for Patients With Preexisting Mobility Disability.

OBJECTIVE: We explored the process of cancer care for patients with preexisting mobility disability, focusing on treatment decisions and experiences. DESIGN: We recruited 20 participants with preexisting mobility disability, requiring use of an assistive device or assistance with activities of daily living, subsequently diagnosed with cancer (excluding skin cancers). We conducted open-ended individual interviews, which reached data saturation and were transcribed verbatim for conventional content analysis. RESULTS: Concerns coalesced around 4 themes: disability-related healthcare experiences affect cancer treatment decisions; concerns about cancer treatment worsening functional impairments; access barriers; and limited provider awareness and biases about treating people with disability. Residual fear from previous medical interventions and concerns about exacerbating functional impairments influenced cancer treatment preferences. Participants also raised concerns that their underlying disability may be used to justify less aggressive treatment. Nevertheless, cancer treatment did exacerbate mobility difficulties for some participants. Inaccessible hospital rooms, lack of accessible medical equipment, and attitudinal barriers complicated treatments. CONCLUSIONS: People with preexisting mobility disability experience barriers to cancer treatment, compromising quality of care and potentially outcomes. Further training and proactive planning for accommodating disability during cancer treatment and rehabilitation are warranted. TO CLAIM CME CREDITS: Complete the self-assessment activity and evaluation online at http://www.physiatry.org/JournalCME. CME OBJECTIVES: Upon completion of the article, the reader should be able to: (1) Recognize inadequate accommodations that compromise the diagnosis and treatment of a new cancer in patients with preexisting disability; (2) Recommend involving rehabilitation specialists in the process of care and clinical decision making from the time of cancer diagnosis for patients with preexisting disability newly diagnosed with malignancy; and (3) In the setting of accessibility barriers, facilitate efforts to accommodate patients with preexisting disability to improve quality of care in diagnosing and treating cancer. LEVEL: Advanced. ACCREDITATION: The Association of Academic Physiatrists is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.The Association of Academic Physiatrists designates this Journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Why Disability Must Be Included in Medical School Diversification Efforts.

Individuals living with disabilities are underrepresented in the physician workforce, despite benefits of inclusion. This article describes how both ableism in admissions processes and expectations set by technical standards can perpetuate harm. The authors advocate for active attention to disability diversity and equity in medical school admissions.

Exploring attitudes about developing cancer among patients with pre-existing mobility disability.

OBJECTIVE: Mobility disability affects approximately 13.7% of the United States population, representing the most common disability type. People with mobility disability experience disparities in cancer screening and higher prevalence of some cancers compared to the general population. We sought to explore the attitudes of people with pre-existing mobility disability about their cancer diagnosis. METHODS: We conducted open-ended individual interviews with 20 participants who had pre-existing mobility disability requiring use of an assistive device or assistance with performance of activities of daily living (ADLs), subsequently diagnosed with cancer (excluding skin cancers). Interviews reached data saturation and were transcribed verbatim for conventional content analysis. RESULTS: Concerns coalesced around three major themes: sense of control over health conditions, seeking support, and recommendations for other people with disability seeking cancer care. Some participants described feeling a loss of control over their cancer diagnosis that they did not have regarding disability, while others suggested that disability presented greater challenges than their cancer diagnosis. Participants described seeking various forms of support, including emotional support (e.g., from friends and family), informational support (e.g., recommendations for seeking care), instrumental support (e.g., ADLs), and appraisal (e.g., self-reflection of personal qualities for fighting cancer). They provided recommendations, highlighting importance of self-advocacy and being attuned to changes in health status. CONCLUSIONS: We found that people with pre-existing mobility disability and cancer express complex attitudes towards their cancer diagnosis. Findings may inform efforts to improve quality of relevant supports to meet the psychosocial needs of this population.

Accommodating patients with obesity and mobility difficulties: Observations from physicians.

BACKGROUND: Given the growing population of U.S. adults with obesity and mobility disability, physicians will need to accommodate these patients. OBJECTIVE: To explore attitudes and practices of US physicians related to caring for patients with obesity and mobility disability. METHODS: Three open-ended, semi-structured, web-based focus group interviews with practicing physicians in selected specialties, which reached data saturation. Interviews were video recorded and transcribed for qualitative, conventional content analysis. Measurements included commonly expressed themes around caring for patients with obesity. RESULTS: Physicians recognized obesity as a disability that poses challenges to high quality, safe, and efficient patient care. Observations coalesced around four themes: (1) difficulty routinely tracking weight; (2) reluctance to transfer obese patients to exam tables; (3) barriers to diagnostic testing; and (4) weight stigma. Physicians described difficulties accurately assessing weight, performing complete physical examinations, arranging diagnostic imaging, and providing prenatal care for obese patients. Lack of accessible medical diagnostic equipment impeded care for patients with obesity. Other participants did not contest comments of individual participants' that suggested weight stigma. CONCLUSIONS: Our findings suggest that important gaps may remain in providing equitable access to care for patients with obesity, requiring additional training and accessible medical diagnostic equipment to safely accommodate these patients.

Associations Between Disability and Breast or Cervical Cancers, Accounting for Screening Disparities.

INTRODUCTION: Studies suggest that women with disability experience disparities in routine, high-value screening services, including mammograms and Papanicolaou (Pap) tests. However, few studies have explored whether women with disability have higher risks than other women of developing breast or cervical cancers. METHODS: The authors analyzed 2010, 2013, 2015, and 2018 National Health Interview Surveys, which involved civilian, noninstitutionalized US residents, and included supplemental surveys on cancer screening. The authors used self-reported functional status limitations to identify women without disability and women with movement difficulties (MDs) or complex activity limitations (CAL) predating breast or cervical cancer diagnoses. Multivariable models evaluated associations of disability status to cancer diagnosis, adjusting for other variables. Analyses used sampling weights, producing national estimates. RESULTS: The sample included 66,641 women; 24.4% reported MD and 14.5% reported CAL. Compared with women without disability, women with pre-existing MD or CAL had significantly higher rates of breast cancer (2.2% vs. 3.5% and 3.6%, respectively) and cervical cancer (0.6% vs. 0.8% and 1.0%, respectively). Women with disability had significantly lower recent mammography and Pap test rates than women without disability. After adjusting for all covariates, the values for odds ratio (95% confidence interval) of pre-existing CAL for cancer diagnoses were 1.21 (1.01-1.46; P=0.04) for breast cancer and 1.43 (1.04-1.99; P=0.03) for cervical cancer. CONCLUSIONS: Pre-existing disability is associated with a higher likelihood of breast and cervical cancer diagnoses, raising the urgency of eliminating disability disparities in mammography and Pap testing. Further research will need to explore the causes of these higher cancer rates.

Perspectives of Patients with Pre-existing Mobility Disability on the Process of Diagnosing Their Cancer.

BACKGROUND: Mobility disability is the most common disability among adult Americans, estimated at 13.7% of the US population. Cancer prevalence is higher among people with mobility disability compared with the general population, yet people with disability experience disparities in cancer screening and treatment. OBJECTIVE: We explored experiences of patients with mobility disability with the process of cancer diagnosis. DESIGN: Open-ended individual interviews, which reached data saturation. Interviews were transcribed verbatim for conventional content analysis. PARTICIPANTS: We interviewed 20 participants with pre-existing mobility disability that required the use of an assistive device or assistance with performance of activities of daily living and who were subsequently diagnosed with cancer (excluding melanoma). KEY RESULTS: Concerns coalesced around five broad categories: inaccessibility of medical diagnostic equipment affecting the process of cancer diagnosis, attitudes of clinical staff about accommodating disability, dismissal of cancer signs/symptoms as emotional responses to chronic health conditions, misattributing cancer signs/symptoms to underlying disability, and attitudes about pursuing legal action for substandard care. Participants provided examples of how erroneous assumptions and potentially biased attitudes among clinicians interfered with the process of their cancer diagnosis, sometimes contributing to an insufficient workup and diagnostic delays. CONCLUSIONS: Physical and attitudinal barriers affect the process of cancer diagnosis in people with mobility disability. Though people with mobility disability may be clinically complex, clinicians should be aware of the risks of diagnostic overshadowing (i.e., the misattribution of cancer signs/symptoms to underlying disability) and other erroneous assumptions that may affect timeliness of cancer diagnosis and quality of care. Further efforts, including educating clinicians about challenges in caring for persons with disability, should be considered to improve the process of cancer diagnosis for this population. TRIAL REGISTRATION: N/A.

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.

Cross-Sectional Analysis of the Associations Between Four Common Cancers and Disability.

BACKGROUND: Approximately 61 million Americans have a disability. Little research has explored whether disability is associated with subsequent diagnosis of cancer, the second-leading cause of death in the United States. The objective of this study was to explore associations between cancer and disability, focusing on 4 cancers that may present with nonspecific symptoms that could be conflated with aspects of disability, thus delaying cancer diagnoses. An analysis of a nationally representative survey using sampling weights to produce national estimates was performed. METHODS: Civilian, noninstitutionalized US residents responding to the 2010-2017 National Health Interview Surveys totaling 259,392 Sample Adult Core survey respondents were included. We used self-reported functional status limitations to identify persons with movement difficulties (MD), complex activity limitations (CAL), and no disability. Multivariable regressions predicting cancer diagnosis included sociodemographic characteristics, tobacco use, body mass index, access to care indicators, and disability status. RESULTS: Persons with preexisting disability had significantly higher rates of cancer (ranging from 0.40 [SE, 0.05] for ovarian to 3.38 [0.14] for prostate) than did those without disability (0.20 [0.02] and 1.26 [0.04] for the same cancers; all P<.0001). Multivariable analyses found strong associations of preexisting MD and CAL with colorectal cancer, with adjusted odds ratios (aORs) of 1.5 (95% CI, 1.2-1.9) and 1.9 (1.5-2.4), respectively. For non-Hodgkin's lymphoma, the aOR for CAL was 1.5 (1.1-2.1). For prostate cancer, aORs for MD were 1.2 (1.0-1.3) and 1.1 (1.0-1.3) for CAL. Using cross-sectional survey data, we could only identify statistical associations, not causality. CONCLUSIONS: Our population-based analyses suggest that persons with disability may constitute a high-risk population, with higher cancer incidence. Optimizing appropriate screening and fully investigating new signs and symptoms are therefore critical for patients with disability.

Challenges of Developing a Natural Language Processing Method With Electronic Health Records to Identify Persons With Chronic Mobility Disability.

OBJECTIVE: To assess the utility of applying natural language processing (NLP) to electronic health records (EHRs) to identify individuals with chronic mobility disability. DESIGN: We used EHRs from the Research Patient Data Repository, which contains EHRs from a large Massachusetts health care delivery system. This analysis was part of a larger study assessing the effects of disability on diagnosis of colorectal cancer. We applied NLP text extraction software to longitudinal EHRs of colorectal cancer patients to identify persons who use a wheelchair (our indicator of mobility disability for this analysis). We manually reviewed the clinical notes identified by NLP using directed content analysis to identify true cases using wheelchairs, duration or chronicity of use, and documentation quality. SETTING: EHRs from large health care delivery system PARTICIPANTS: Patients (N=14,877) 21-75 years old who were newly diagnosed with colorectal cancer between 2005 and 2017. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Confirmation of patients' chronic wheelchair use in NLP-flagged notes; quality of disability documentation. RESULTS: We identified 14,877 patients with colorectal cancer with 303,182 associated clinical notes. NLP screening identified 1482 (0.5%) notes that contained 1+ wheelchair-associated keyword. These notes were associated with 420 patients (2.8% of colorectal cancer population). Of the 1482 notes, 286 (19.3%, representing 105 patients, 0.7% of the total) contained documentation of reason for wheelchair use and duration. Directed content analysis identified 3 themes concerning disability documentation: (1) wheelchair keywords used in specific EHR contexts; (2) reason for wheelchair not clearly stated; and (3) duration of wheelchair use not consistently documented. CONCLUSIONS: NLP offers an option to screen for patients with chronic mobility disability in much less time than required by manual chart review. Nonetheless, manual chart review must confirm that flagged patients have chronic mobility disability (are not false positives). Notes, however, often have inadequate disability documentation.

Knowledge Of Practicing Physicians About Their Legal Obligations When Caring For Patients With Disability.

Disability civil rights laws require equitable treatment of the approximately sixty-one million Americans with disability. However, federal reports and numerous research studies indicate that this diverse and growing population often experiences health care disparities. To examine one possible contributing factor, we interviewed practicing physicians to explore their knowledge of their obligations to accommodate patients with disability under federal civil rights law. Interviewees reported having had little formal training about, and demonstrated superficial or incorrect understanding of, their obligations in three potentially problematic areas: deciding which accommodations their practices should implement, refusing patients with disability, and holding patients accountable for costs of accommodations. The fact that practicing physicians might not fully understand their legal responsibilities when caring for people with disability may contribute to persisting inequity in their care, and it suggests that further education in the Americans with Disabilities Act and other disability civil rights laws may be warranted.