Vision loss reduction with avacincaptad pegol for geographic atrophy: a 12-month post hoc analysis of the GATHER1 and GATHER2 trials.

PURPOSE: To evaluate the impact of reduction in geographic atrophy (GA) lesion growth on visual acuity in the GATHER trials using categorical outcome measures. DESIGN: Randomized, double-masked, sham-controlled phase 3 trials. PARTICIPANTS: Aged ≥50 years with non-center point involving GA and best corrected visual acuity (BCVA) of 25-80 Early Treatment Diabetic Retinopathy Study (ETDRS) letters in the study eye. METHODS: GATHER1 consisted of 2 parts. In Part 1, 77 patients were randomized 1:1:1 to avacincaptad pegol (ACP) 1 mg, ACP 2 mg, and sham. In Part 2, 209 patients were randomized 1:2:2 to ACP 2 mg, ACP 4 mg, and sham. In GATHER2, patients were randomized 1:1 to ACP 2 mg (n=225) and sham (n=223). A post hoc analysis of 12-month data for pooled ACP 2 mg and sham groups is reported. MAIN OUTCOME MEASURES: Proportion of study eyes that experienced ≥10-, ≥15-, or ≥20-BCVA ETDRS letter loss from baseline to month 12; time-to-event analysis of persistent vision loss of ≥10-, ≥15-, or ≥20-BCVA letters from baseline at ≥2 consecutive visits over 12 months; proportion of study eyes with BCVA loss to a level below driving eligibility threshold at month 12 among those eligible to drive at baseline. RESULTS: Lower proportions of study eyes experienced ≥10-, ≥15-, or ≥20-BCVA letter loss from baseline over 12 months with ACP 2 mg (11.6%, 4.0%, and 1.6%, respectively) vs sham (14.1%, 7.6%, and 4.5%, respectively). There was a reduction in the risk of persistent loss of ≥15-BCVA ETDRS letters with ACP 2 mg (3.4%) vs sham (7.8%) through 12 months. A lower proportion of study eyes treated with ACP 2 mg reached the threshold for driving ineligibility vs sham by 12 months. CONCLUSIONS: Treatment with ACP 2 mg delayed the risk of progression to persistent vision loss (ie, ≥10-, ≥15-, and ≥20-BCVA letter loss or BCVA loss to a level below driving eligibility threshold) vs sham over 12 months.

Performance of a Large Language Model in the Generation of Clinical Guidelines for Antibiotic Prophylaxis in Spine Surgery.

OBJECTIVE: Large language models, such as chat generative pre-trained transformer (ChatGPT), have great potential for streamlining medical processes and assisting physicians in clinical decision-making. This study aimed to assess the potential of ChatGPT's 2 models (GPT-3.5 and GPT-4.0) to support clinical decision-making by comparing its responses for antibiotic prophylaxis in spine surgery to accepted clinical guidelines. METHODS: ChatGPT models were prompted with questions from the North American Spine Society (NASS) Evidence-based Clinical Guidelines for Multidisciplinary Spine Care for Antibiotic Prophylaxis in Spine Surgery (2013). Its responses were then compared and assessed for accuracy. RESULTS: Of the 16 NASS guideline questions concerning antibiotic prophylaxis, 10 responses (62.5%) were accurate in ChatGPT's GPT-3.5 model and 13 (81%) were accurate in GPT-4.0. Twenty-five percent of GPT-3.5 answers were deemed as overly confident while 62.5% of GPT-4.0 answers directly used the NASS guideline as evidence for its response. CONCLUSION: ChatGPT demonstrated an impressive ability to accurately answer clinical questions. GPT-3.5 model's performance was limited by its tendency to give overly confident responses and its inability to identify the most significant elements in its responses. GPT-4.0 model's responses had higher accuracy and cited the NASS guideline as direct evidence many times. While GPT-4.0 is still far from perfect, it has shown an exceptional ability to extract the most relevant research available compared to GPT-3.5. Thus, while ChatGPT has shown far-reaching potential, scrutiny should still be exercised regarding its clinical use at this time.

The potential role of precision medicine to alleviate racial disparities in prostate, bladder and renal urological cancer care.

Background: Racial disparities in oncological outcomes resulting from differences in social determinants of health (SDOH) and tumour biology are well described in prostate cancer (PCa) but similar inequities exist in bladder (BCa) and renal cancers (RCCs). Precision medicine (PM) aims to provide personalized treatment based on individual patient characteristics and has the potential to reduce these inequities in GU cancers. Objective: This article aims to review the current evidence outlining racial disparities in GU cancers and explore studies demonstrating improved oncological outcomes when PM is applied to racially diverse patient populations. Evidence acquisition: Evidence was obtained from Pubmed and Web of Science using keywords prostate, bladder and renal cancer, racial disparity and precision medicine. Because limited studies were found, preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were not applied but rather related articles were studied to explore existing debates, identify the current status and speculate on future applications. Results: Evidence suggests addressing SDOH for PCa can reverse racial inequities in oncological outcomes but differences in incidence remain. Similar disparities in BCa and RCC are seen, and it would be reasonable to suggest achieving parity in SDOH for all races would do the same. Research applying a PM approach to different ethnicities is lacking although in African Americans (AAs) with metastatic castrate-resistant prostate cancer (mCRPCa) better outcomes have been shown with androgen receptor inhibitors, radium-223 and sipuleucel. Exploiting the abscopal effect with targeted radiation therapy (RT) and immunotherapy has promise but requires further study, as does defining actionable mutations in specific patient groups to tailor treatments as appropriate. Conclusion: For all GU cancers, the historical underrepresentation of ethnic minorities in clinical trials still exists and there is an urgent need for recruitment strategies to address this. PM is a promising development with the potential to reduce inequities in GU cancers, however, both improved understanding of race-specific tumour biology, and enhanced recruitment of minority populations into clinical trials are required. Without this, the benefits of PM will be limited.

Can generative artificial intelligence pass the orthopaedic board examination?

Background: Resident training programs in the US use the Orthopaedic In-Training Examination (OITE) developed by the American Academy of Orthopaedic Surgeons (AAOS) to assess the current knowledge of their residents and to identify the residents at risk of failing the Amerian Board of Orthopaedic Surgery (ABOS) examination. Optimal strategies for OITE preparation are constantly being explored. There may be a role for Large Language Models (LLMs) in orthopaedic resident education. ChatGPT, an LLM launched in late 2022 has demonstrated the ability to produce accurate, detailed answers, potentially enabling it to aid in medical education and clinical decision-making. The purpose of this study is to evaluate the performance of ChatGPT on Orthopaedic In-Training Examinations using Self-Assessment Exams from the AAOS database and approved literature as a proxy for the Orthopaedic Board Examination. Methods: 301 SAE questions from the AAOS database and associated AAOS literature were input into ChatGPT's interface in a question and multiple-choice format and the answers were then analyzed to determine which answer choice was selected. A new chat was used for every question. All answers were recorded, categorized, and compared to the answer given by the OITE and SAE exams, noting whether the answer was right or wrong. Results: Of the 301 questions asked, ChatGPT was able to correctly answer 183 (60.8%) of them. The subjects with the highest percentage of correct questions were basic science (81%), oncology (72.7%, shoulder and elbow (71.9%), and sports (71.4%). The questions were further subdivided into 3 groups: those about management, diagnosis, or knowledge recall. There were 86 management questions and 47 were correct (54.7%), 45 diagnosis questions with 32 correct (71.7%), and 168 knowledge recall questions with 102 correct (60.7%). Conclusions: ChatGPT has the potential to provide orthopedic educators and trainees with accurate clinical conclusions for the majority of board-style questions, although its reasoning should be carefully analyzed for accuracy and clinical validity. As such, its usefulness in a clinical educational context is currently limited but rapidly evolving. Clinical relevance: ChatGPT can access a multitude of medical data and may help provide accurate answers to clinical questions.

ChatGPT versus NASS clinical guidelines for degenerative spondylolisthesis: a comparative analysis.

BACKGROUND CONTEXT: Clinical guidelines, developed in concordance with the literature, are often used to guide surgeons' clinical decision making. Recent advancements of large language models and artificial intelligence (AI) in the medical field come with exciting potential. OpenAI's generative AI model, known as ChatGPT, can quickly synthesize information and generate responses grounded in medical literature, which may prove to be a useful tool in clinical decision-making for spine care. The current literature has yet to investigate the ability of ChatGPT to assist clinical decision making with regard to degenerative spondylolisthesis. PURPOSE: The study aimed to compare ChatGPT's concordance with the recommendations set forth by The North American Spine Society (NASS) Clinical Guideline for the Diagnosis and Treatment of Degenerative Spondylolisthesis and assess ChatGPT's accuracy within the context of the most recent literature. METHODS: ChatGPT-3.5 and 4.0 was prompted with questions from the NASS Clinical Guideline for the Diagnosis and Treatment of Degenerative Spondylolisthesis and graded its recommendations as "concordant" or "nonconcordant" relative to those put forth by NASS. A response was considered "concordant" when ChatGPT generated a recommendation that accurately reproduced all major points made in the NASS recommendation. Any responses with a grading of "nonconcordant" were further stratified into two subcategories: "Insufficient" or "Over-conclusive," to provide further insight into grading rationale. Responses between GPT-3.5 and 4.0 were compared using Chi-squared tests. RESULTS: ChatGPT-3.5 answered 13 of NASS's 28 total clinical questions in concordance with NASS's guidelines (46.4%). Categorical breakdown is as follows: Definitions and Natural History (1/1, 100%), Diagnosis and Imaging (1/4, 25%), Outcome Measures for Medical Intervention and Surgical Treatment (0/1, 0%), Medical and Interventional Treatment (4/6, 66.7%), Surgical Treatment (7/14, 50%), and Value of Spine Care (0/2, 0%). When NASS indicated there was sufficient evidence to offer a clear recommendation, ChatGPT-3.5 generated a concordant response 66.7% of the time (6/9). However, ChatGPT-3.5's concordance dropped to 36.8% when asked clinical questions that NASS did not provide a clear recommendation on (7/19). A further breakdown of ChatGPT-3.5's nonconcordance with the guidelines revealed that a vast majority of its inaccurate recommendations were due to them being "over-conclusive" (12/15, 80%), rather than "insufficient" (3/15, 20%). ChatGPT-4.0 answered 19 (67.9%) of the 28 total questions in concordance with NASS guidelines (P = 0.177). When NASS indicated there was sufficient evidence to offer a clear recommendation, ChatGPT-4.0 generated a concordant response 66.7% of the time (6/9). ChatGPT-4.0's concordance held up at 68.4% when asked clinical questions that NASS did not provide a clear recommendation on (13/19, P = 0.104). CONCLUSIONS: This study sheds light on the duality of LLM applications within clinical settings: one of accuracy and utility in some contexts versus inaccuracy and risk in others. ChatGPT was concordant for most clinical questions NASS offered recommendations for. However, for questions NASS did not offer best practices, ChatGPT generated answers that were either too general or inconsistent with the literature, and even fabricated data/citations. Thus, clinicians should exercise extreme caution when attempting to consult ChatGPT for clinical recommendations, taking care to ensure its reliability within the context of recent literature.

Combining theranostic/particle therapy with immunotherapy for the treatment of GU malignancies.

Particle therapy and radiopharmaceuticals are emerging fields in the treatment of genitourinary cancers. With these novel techniques and the ever-growing immunotherapy options, the combinations of these therapies have the potential to improve current cancer cure rates. However, the most effective sequence and combination of these therapies is unknown and is a question that is actively being explored in multiple ongoing clinical trials. Here, we review the immunological effects of particle therapy and the available radiopharmaceuticals and discuss how best to combine these therapies.

Pannexin 3 deletion in mice results in knee osteoarthritis and intervertebral disc degeneration after forced treadmill running.

Pannexin 3 (Panx3) is a glycoprotein that forms mechanosensitive channels expressed in chondrocytes and annulus fibrosus cells of the intervertebral disc (IVD). Evidence suggests Panx3 plays contrasting roles in traumatic versus aging osteoarthritis (OA) and intervertebral disc degeneration (IDD). However, whether its deletion influences the response of joint tissue to forced use is unknown. The purpose of this study was to determine if Panx3 deletion in mice causes increased knee joint OA and IDD after forced treadmill running. Male and female wildtype (WT) and Panx3 knockout (KO) mice were randomized to either a no-exercise group (sedentary; SED) or daily forced treadmill running (forced exercise; FEX) from 24 to 30 weeks of age. Knee cartilage and IVD histopathology were evaluated by histology, while tibial secondary ossification centers were analyzed using microcomputed tomography (µCT). Both male and female Panx3 KO mice developed larger superficial defects of the tibial cartilage after forced treadmill running compared with SED WT mice. Additionally, Panx3 KO mice developed reduced bone volume, and female PANX3 KO mice had lengthening of the lateral tubercle at the intercondylar eminence. In the lower lumbar spine, both male and female Panx3 KO mice developed histopathological features of IDD after running compared to SED WT mice. These findings suggest that the combination of deleting Panx3 and forced treadmill running induces OA and causes histopathological changes associated with the degeneration of the IVDs in mice.

Performance of ChatGPT on NASS Clinical Guidelines for the Diagnosis and Treatment of Low Back Pain: A Comparison Study.

STUDY DESIGN: Comparative analysis. OBJECTIVE: To evaluate Chat Generative Pre-trained Transformer (ChatGPT's) ability to predict appropriate clinical recommendations based on the most recent clinical guidelines for the diagnosis and treatment of low back pain. BACKGROUND: Low back pain is a very common and often debilitating condition that affects many people globally. ChatGPT is an artificial intelligence model that may be able to generate recommendations for low back pain. MATERIALS AND METHODS: Using the North American Spine Society Evidence-Based Clinical Guidelines as the gold standard, 82 clinical questions relating to low back pain were entered into ChatGPT (GPT-3.5) independently. For each question, we recorded ChatGPT's answer, then used a point-answer system-the point being the guideline recommendation and the answer being ChatGPT's response-and asked ChatGPT if the point was mentioned in the answer to assess for accuracy. This response accuracy was repeated with one caveat-a prior prompt is given in ChatGPT to answer as an experienced orthopedic surgeon-for each question by guideline category. A two-sample proportion z test was used to assess any differences between the preprompt and postprompt scenarios with alpha=0.05. RESULTS: ChatGPT's response was accurate 65% (72% postprompt, P =0.41) for guidelines with clinical recommendations, 46% (58% postprompt, P =0.11) for guidelines with insufficient or conflicting data, and 49% (16% postprompt, P =0.003*) for guidelines with no adequate study to address the clinical question. For guidelines with insufficient or conflicting data, 44% (25% postprompt, P =0.01*) of ChatGPT responses wrongly suggested that sufficient evidence existed. CONCLUSION: ChatGPT was able to produce a sufficient clinical guideline recommendation for low back pain, with overall improvements if initially prompted. However, it tended to wrongly suggest evidence and often failed to mention, especially postprompt, when there is not enough evidence to adequately give an accurate recommendation.

ChatGPT and its Role in the Decision-Making for the Diagnosis and Treatment of Lumbar Spinal Stenosis: A Comparative Analysis and Narrative Review.

STUDY DESIGN: Comparative Analysis and Narrative Review. OBJECTIVE: To assess and compare ChatGPT's responses to the clinical questions and recommendations proposed by The 2011 North American Spine Society (NASS) Clinical Guideline for the Diagnosis and Treatment of Degenerative Lumbar Spinal Stenosis (LSS). We explore the advantages and disadvantages of ChatGPT's responses through an updated literature review on spinal stenosis. METHODS: We prompted ChatGPT with questions from the NASS Evidence-based Clinical Guidelines for LSS and compared its generated responses with the recommendations provided by the guidelines. A review of the literature was performed via PubMed, OVID, and Cochrane on the diagnosis and treatment of lumbar spinal stenosis between January 2012 and April 2023. RESULTS: 14 questions proposed by the NASS guidelines for LSS were uploaded into ChatGPT and directly compared to the responses offered by NASS. Three questions were on the definition and history of LSS, one on diagnostic tests, seven on non-surgical interventions and three on surgical interventions. The review process found 40 articles that were selected for inclusion that helped corroborate or contradict the responses that were generated by ChatGPT. CONCLUSIONS: ChatGPT's responses were similar to findings in the current literature on LSS. These results demonstrate the potential for implementing ChatGPT into the spine surgeon's workplace as a means of supporting the decision-making process for LSS diagnosis and treatment. However, our narrative summary only provides a limited literature review and additional research is needed to standardize our findings as means of validating ChatGPT's use in the clinical space.

Association Between Age-stratified Cohorts and Perioperative Complications and 30-day and 90-day Readmission in Patients Undergoing Single-level Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: Retrospective analysis. OBJECTIVE: To assess perioperative complication rates and readmission rates after ACDF in a patient population of advanced age. SUMMARY OF BACKGROUND DATA: Readmission rates after ACDF are important markers of surgical quality and, with recent shifts in reimbursement schedules, they are rapidly gaining weight in the determination of surgeon and hospital reimbursement. METHODS: Patients 18 years of age and older who underwent elective single-level ACDF were identified in the National Readmissions Database (NRD) and stratified into 4 cohorts: 18-39 ("young"), 40-64 ("middle"), 65-74 ("senior"), and 75+ ("elderly") years of age. For each cohort, the perioperative complications, frequency of those complications, and number of patients with at least 1 readmission within 30 and 90 days of discharge were analyzed. χ 2 tests were used to calculate likelihood of complications and readmissions. RESULTS: There were 1174 "elderly" patients in 2016, 1072 in 2017, and 1010 in 2018 who underwent ACDF. Their rate of any complication was 8.95%, 11.00%, and 13.47%, respectively ( P <0.0001), with dysphagia and acute posthemorrhagic anemia being the most common across all 3 years. They experienced complications at a greater frequency than their younger counterparts (15.80%, P <0.0001; 16.98%, P <0.0001; 21.68%, P <0.0001). They also required 30-day and 90-day readmission more frequently ( P <0.0001). CONCLUSION: It has been well-established that advanced patient age brings greater risk of perioperative complications in ACDF surgery. What remains unsettled is the characterization of this age-complication relationship within specific age cohorts and how these complications inform patient hospital course. Our study provides an updated analysis of age-specific complications and readmission rates in ACDF patients. Orthopedic surgeons may account for the rise in complication and readmission rates in this population with the corresponding reduction in length and stay and consider this relationship before discharging elderly ACDF patients.

Impact of tobacco usage on readmission and complication rates following shoulder replacement surgery: A study of 164,527 patients.

Background: Tobacco carcinogens have adverse effects on bone health and are associated with inferior outcomes following orthopedic procedures. The purpose of this study was to assess the impact tobacco use has on readmission and complication rates following shoulder arthroplasty. Methods: The 2016-2018 National Readmissions Database was queried to identify patients who underwent anatomical, reverse, and hemi-shoulder arthroplasty. ICD-10 codes Z72.0 × (tobacco use disorder) and F17.2 × (nicotine dependence) were used to define "tobacco-users." Demographic, 30-/90-day readmission, surgical complication, and medical complication data were collected. Inferential statistics were used to analyze complications for both the cohort as a whole and for each procedure separately (i.e. anatomical, reverse, and hemiarthroplasty). Results: 164,527 patients were identified (92% nontobacco users). Tobacco users necessitated replacement seven years sooner than nonusers (p < 0.01) and were more likely to be male (52% vs. 43%; p < 0.01). Univariate analysis showed that tobacco users had higher rates of readmission, revisions, shoulder complications, and medical complications overall. In the multivariate analysis for the entire cohort, readmission, revision, and complication rates did not differ based on tobacco usage; however, smokers who underwent reverse shoulder arthroplasty in particular were found to have higher 90-day readmission, dislocation, and prosthetic complication rates compared to nonsmokers. Conclusion: Comparatively, tobacco users required surgical correction earlier in life and had higher rates of readmission, revision, and complications in the short term following their shoulder replacement. However, when controlling for tobacco usage as an independent predictor of adverse outcomes, these aforementioned findings were lost for the cohort as a whole. Overall, these findings indicate that shoulder replacement in general is a viable treatment option regardless of patient tobacco usage at short-term follow-up, but this conclusion may vary depending on the replacement type used.

An 11-Year-Long Analysis of the Risks Associated With Age in Patients Undergoing Anterior Cervical Discectomy and Fusion in a Large, Urban Academic Hospital.

STUDY DESIGN: A retrospective database study of patients at an urban academic medical center undergoing an Anterior Cervical Discectomy and Fusion (ACDF) surgery between 2008 and 2019. OBJECTIVE: ACDF is one of the most common spinal procedures. Old age has been found to be a common risk factor for postoperative complications across a plethora of spine procedures. Little is known about how this risk changes among elderly cohorts such as the difference between elderly (60+) and octogenarian (80+) patients. This study seeks to analyze the disparate rates of complications following elective ACDF between patients aged 60-69 or 70-79 and 80+ at an urban academic medical center. METHODS: We identified patients who had undergone ACDF procedures using CPT codes 22,551, 22,552, and 22,554. Emergent procedures were excluded, and patients were subdivided on the basis of age. Then each cohort was propensity matched for univariate and univariate logistic regression analysis. RESULTS: The propensity matching resulted in 25 pairs in both the 70-79 and 80+ y.o. cohort comparison and 60-69 and 80+ y.o. cohort comparison. None of the cohorts differed significantly in demographic variables. Differences between elderly cohorts were less pronounced: the 80+ y.o. cohort experienced only significantly higher total direct cost (P = .03) compared to the 70-79 y.o. cohort and significantly longer operative time (P = .04) compared to the 60-69 y.o. cohort. CONCLUSIONS: Octogenarian patients do not face much riskier outcomes following elective ACDF procedures than do younger elderly patients. Age alone should not be used to screen patients for ACDF.

Efficacy and safety of avacincaptad pegol in patients with geographic atrophy (GATHER2): 12-month results from a randomised, double-masked, phase 3 trial.

BACKGROUND: Geographic atrophy is an advanced form of dry age-related macular degeneration that can lead to irreversible vision loss and high burden of disease. We aimed to assess efficacy and safety of avacincaptad pegol 2 mg in reducing geographic atrophy lesion growth. METHODS: GATHER2 is a randomised, double-masked, sham-controlled, 24-month, phase 3 trial across 205 retina clinics, research hospitals, and academic institutions globally. To be eligible, patients had to be aged 50 years or older with non-centrepoint-involving geographic atrophy and best corrected visual acuity between 20/25 and 20/320 in the study eye. Eligible patients were randomly assigned (1:1) to monthly avacincaptad pegol 2 mg administered as a 100 µL intravitreal injection or sham for the first 12 months. Randomisation was performed using an interactive response technology system with stratification by factors known to be of prognostic importance in age-related macular degeneration. Patients, investigators, study centre staff, sponsor personnel, and data analysts were masked to treatment allocation. The primary endpoint was geographic atrophy lesion size measured by fundus autofluorescence at baseline, month 6, and month 12. Efficacy and safety analyses were done in the modified intention-to-treat and safety populations, respectively. This trial is registered with ClinicalTrials.gov, NCT04435366. FINDINGS: Between June 22, 2020, and July 23, 2021, 1422 patients were screened for eligibility, of whom 448 were enrolled and randomly assigned to avacincaptad pegol 2 mg (n=225) or sham (n=223). One patient in the sham group did not receive study treatment and was excluded from analyses. There were 154 (68%) female patients and 71 (32%) male patients in the avacincaptad pegol 2 mg group, and 156 (70%) female patients and 66 (30%) male patients in the sham group. From baseline to month 12, the mean rate of square-root-transformed geographic atrophy area growth was 0·336 mm/year (SE 0·032) with avacincaptad pegol 2 mg and 0·392 mm/year (0·033) with sham, a difference in growth of 0·056 mm/year (95% CI 0·016-0·096; p=0·0064), representing a 14% difference between the avacincaptad pegol 2 mg group and the sham group. Ocular treatment-emergent adverse events in the study eye occurred in 110 (49%) patients in the avacincaptad pegol 2 mg group and 83 (37%) in the sham group. There were no endophthalmitis, intraocular inflammation, or ischaemic optic neuropathy events over 12 months. To month 12, macular neovascularisation in the study eye occurred in 15 (7%) patients in the avacincaptad pegol 2 mg group and nine (4%) in the sham group, with exudative macular neovascularisation occurring in 11 (5%) in the avacincaptad pegol 2 mg group and seven (3%) in the sham group. INTERPRETATION: Monthly avacincaptad pegol 2 mg was well tolerated and showed significantly slower geographic atrophy growth over 12 months than sham treatment, suggesting that avacincaptad pegol might slow disease progression and potentially change the trajectory of disease for patients with geographic atrophy. FUNDING: Iveric Bio, An Astellas Company.

How Are We Doing? A Scoping Review of Published Patient-Centered Outcomes Research in United States Student-Run Free Clinics.

Phenomenon: Student-run free clinics (SRFCs) serve an integral role in most United States (US) medical schools and contribute substantially to literature on the quality of care to uninsured persons. There has been substantial growth over the past decade of scholarly work produced by SRFCs as they have increased in size and number. Research on patient care outcomes informs better care structures for patients, however there is no current synthesis of patient care outcomes research among SRFCs. This article provides an overview of SRFC research on patient outcomes to understand current research domains and to identify gaps in the literature. Approach: We completed a scoping review by searching Scopus, PubMed, and Journal of Student Run Clinics in June 2021. All peer-reviewed, English-language articles focused on patient-centered outcomes at SRFCs in the US were included. Two independent reviewers performed title, abstract, and full-text screening of relevant works, and eight reviewers conducted data extraction. Descriptive data analysis was performed along with relevant content analysis of patient-centered outcomes. Findings: The search strategy identified 784 studies, of which 87 met inclusion criteria. Most studies were published within the last six years (81.6%), located in California, New York, or Florida (43.7%), and intervention based (33.3%). Many studies (46.0%) had a specific disease of focus of which diabetes was the most researched(19.5%). Patient-centered studies were the leading focus of the study aims (40.2%), where key findings demonstrated primarily improved outcomes in clinic metrics post-intervention (36.8%) or equivalent/better clinical performance than national metrics (20.7%). Insights: This review brings to light gaps in the literature reporting research in SRFCs and can be applied to other low-resource settings. Future efforts to expand SRFC outcomes research should focus on community relationship building, understanding institutional support, and ensuring education on best practices for research within SRFCs. Doing so informs patient care improvement as SRFCs continue to operate as safety net clinics for marginalized populations.

Thromboembolic prophylaxis in spine surgery: an analysis of ChatGPT recommendations.

BACKGROUND CONTEXT: Venous thromboembolism is a negative outcome of elective spine surgery. However, the use of thromboembolic chemoprophylaxis in this patient population is controversial due to the possible increased risk of epidural hematoma. ChatGPT is an artificial intelligence model which may be able to generate recommendations for thromboembolic prophylaxis in spine surgery. PURPOSE: To evaluate the accuracy of ChatGPT recommendations for thromboembolic prophylaxis in spine surgery. STUDY DESIGN/SETTING: Comparative analysis. PATIENT SAMPLE: None. OUTCOME MEASURES: Accuracy, over-conclusiveness, supplemental, and incompleteness of ChatGPT responses compared to the North American Spine Society (NASS) clinical guidelines. METHODS: ChatGPT was prompted with questions from the 2009 NASS clinical guidelines for antithrombotic therapies and evaluated for concordance with the clinical guidelines. ChatGPT-3.5 responses were obtained on March 5, 2023, and ChatGPT-4.0 responses were obtained on April 7, 2023. A ChatGPT response was classified as accurate if it did not contradict the clinical guideline. Three additional categories were created to further evaluate the ChatGPT responses in comparison to the NASS guidelines: over-conclusiveness, supplementary, and incompleteness. ChatGPT was classified as over-conclusive if it made a recommendation where the NASS guideline did not provide one. ChatGPT was classified as supplementary if it included additional relevant information not specified by the NASS guideline. ChatGPT was classified as incomplete if it failed to provide relevant information included in the NASS guideline. RESULTS: Twelve clinical guidelines were evaluated in total. Compared to the NASS clinical guidelines, ChatGPT-3.5 was accurate in 4 (33%) of its responses while ChatGPT-4.0 was accurate in 11 (92%) responses. ChatGPT-3.5 was over-conclusive in 6 (50%) of its responses while ChatGPT-4.0 was over-conclusive in 1 (8%) response. ChatGPT-3.5 provided supplemental information in 8 (67%) of its responses, and ChatGPT-4.0 provided supplemental information in 11 (92%) responses. Four (33%) responses from ChatGPT-3.5 were incomplete, and 4 (33%) responses from ChatGPT-4.0 were incomplete. CONCLUSIONS: ChatGPT was able to provide recommendations for thromboembolic prophylaxis with reasonable accuracy. ChatGPT-3.5 tended to cite nonexistent sources and was more likely to give specific recommendations while ChatGPT-4.0 was more conservative in its answers. As ChatGPT is continuously updated, further validation is needed before it can be used as a guideline for clinical practice.

The Role of Triglyceride-Glucose Index in the Prediction of the Development of Hypertension - Findings from a Community Cohort in Singapore.

Objectives: Triglyceride-glucose index (TyGI) is an emerging surrogate marker of insulin resistance. We aim to explore the role of triglyceride-glucose index in the prediction of the development of hypertension. Methodology: We conducted a retrospective cohort study that included 3,183 study participants identified from a community health screening programme who had no baseline hypertension and were then followed up after an average of 1.7 years. Cox proportional-hazard model was used to assess the association between risk of incident hypertension and TyGI in quartiles, while adjusting for demographics and clinical characteristics. Results: Hypertension occurred in 363 study participants (11.4%). Those who developed hypertension had higher TyGI [8.6 (IQR 8.2-9.0)] than those who did not [8.2 (IQR 8.0-8.7)] (p<0.001). Significant association between TyGI and hypertension was observed in both the unadjusted and proportional hazard model [Quartile (Q)2, p=0.010; Q3, p<0.001 and Q4, p<0.001] and the model that adjusted for demographics (Q2, p=0.016; Q3, p=0.003; Q4, p<0.001). In the model adjusted for clinical covariates, the hazard of developing hypertension remained higher in TyGI Q4 compared to TyGI Q1(Hazard Ratio=2.57; 95% Confidence Interval: 1.71, 3.87). Increasing triglyceride-glucose index accounted for 16.4% of the association between increasing BMI and incident hypertension, after adjusting for age, gender, ethnicity and baseline HDL cholesterol (p<0.001). Conclusion: Triglyceride-glucose index was an independent predictor of the development of hypertension. It may potentially be used as an inexpensive indicator to predict the development of hypertension and risk-stratify individuals to aid management in clinical practice.

Robust prediction of nonhome discharge following elective anterior cervical discectomy and fusion using explainable machine learning.

PURPOSE: Predict nonhome discharge (NHD) following elective anterior cervical discectomy and fusion (ACDF) using an explainable machine learning model. METHODS: 2227 patients undergoing elective ACDF from 2008 to 2019 were identified from a single institutional database. A machine learning model was trained on preoperative variables, including demographics, comorbidity indices, and levels fused. The validation technique was repeated stratified K-Fold cross validation with the area under the receiver operating curve (AUROC) statistic as the performance metric. Shapley Additive Explanation (SHAP) values were calculated to provide further explainability regarding the model's decision making. RESULTS: The preoperative model performed with an AUROC of 0.83 ± 0.05. SHAP scores revealed the most pertinent risk factors to be age, medicare insurance, and American Society of Anesthesiology (ASA) score. Interaction analysis demonstrated that female patients over 65 with greater fusion levels were more likely to undergo NHD. Likewise, ASA demonstrated positive interaction effects with female sex, levels fused and BMI. CONCLUSION: We validated an explainable machine learning model for the prediction of NHD using common preoperative variables. Adding transparency is a key step towards clinical application because it demonstrates that our model's "thinking" aligns with clinical reasoning. Interactive analysis demonstrated that those of age over 65, female sex, higher ASA score, and greater fusion levels were more predisposed to NHD. Age and ASA score were similar in their predictive ability. Machine learning may be used to predict NHD, and can assist surgeons with patient counseling or early discharge planning.

Diabetes increases risk for readmission and infection after shoulder arthroplasty: A national readmissions study of 113,713 patients.

Background: The recent increasing popularity of shoulder arthroplasty has been paralleled by a rise in prevalence of diabetes in the United States. We aimed to evaluate the impact of diabetes status on readmission and short-term complications among patients undergoing shoulder arthroplasty. Methods: We analyzed the Healthcare Cost and Utilization Project National Readmissions Database (NRD) between the years 2016-2018. Patients were included in the study if they underwent anatomic total shoulder arthroplasty (aTSA) or reverse total shoulder arthroplasty (rTSA) according to ICD-10 procedure codes. Postoperative complications including surgical site/joint infection, dislocation, prosthetic complications, hardware-related complications, non-infectious wound complications, 30-day, and 90-day readmission were collected. Results: A total of 113,713 shoulder arthroplasty patients were included. 23,749 (20.9%) had a diagnosis of diabetes and 89,964 (79.1%) did not. On multivariate analysis, a diagnosis of diabetes led to an increased risk of 30-day (OR: 1.24; 95% CI: [1.14, 1.34]; p < 0.001) and 90-day (OR: 1.18; 95% CI: [1.12, 1.25]; p < 0.001) readmission, surgical site/joint infection (OR: 1.21; 95% CI: [1.06, 1.38]; p = 0.005), respiratory complication (OR: 1.34; 95% CI: [1.09, 1.64]; p = 0.005), postoperative infection (OR: 1.22; 95% CI [1.07, 1.39]; p = 0.003), and deep vein thrombosis (OR: 1.38; 95% CI: [1.09, 1.74]; p = 0.007). Conclusions: Our findings suggest that patients with diabetes may be at an increased risk of readmission, infection, respiratory complication, and deep vein thrombosis following shoulder arthroplasty. Shoulder surgeons should consider these potential adverse events when planning postoperative care for patients with diabetes.

Can a Novel Natural Language Processing Model and Artificial Intelligence Automatically Generate Billing Codes From Spine Surgical Operative Notes?

STUDY DESIGN: Retrospective cohort. OBJECTIVE: Billing and coding-related administrative tasks are a major source of healthcare expenditure in the United States. We aim to show that a second-iteration Natural Language Processing (NLP) machine learning algorithm, XLNet, can automate the generation of CPT codes from operative notes in ACDF, PCDF, and CDA procedures. METHODS: We collected 922 operative notes from patients who underwent ACDF, PCDF, or CDA from 2015 to 2020 and included CPT codes generated by the billing code department. We trained XLNet, a generalized autoregressive pretraining method, on this dataset and tested its performance by calculating AUROC and AUPRC. RESULTS: The performance of the model approached human accuracy. Trial 1 (ACDF) achieved an AUROC of .82 (range: .48-.93), an AUPRC of .81 (range: .45-.97), and class-by-class accuracy of 77% (range: 34%-91%); trial 2 (PCDF) achieved an AUROC of .83 (.44-.94), an AUPRC of .70 (.45-.96), and class-by-class accuracy of 71% (42%-93%); trial 3 (ACDF and CDA) achieved an AUROC of .95 (.68-.99), an AUPRC of .91 (.56-.98), and class-by-class accuracy of 87% (63%-99%); trial 4 (ACDF, PCDF, CDA) achieved an AUROC of .95 (.76-.99), an AUPRC of .84 (.49-.99), and class-by-class accuracy of 88% (70%-99%). CONCLUSIONS: We show that the XLNet model can be successfully applied to orthopedic surgeon's operative notes to generate CPT billing codes. As NLP models as a whole continue to improve, billing can be greatly augmented with artificial intelligence assisted generation of CPT billing codes which will help minimize error and promote standardization in the process.

Predictors of Financial Toxicity in Patients Receiving Concurrent Radiation Therapy and Chemotherapy.

Purpose: Financial toxicity (FT) is a significant concern for patients with cancer. We reviewed prospectively collected data to explore associations with FT among patients undergoing concurrent, definitive chemoradiation therapy (CRT) within a diverse, urban, academic radiation oncology department. Methods and Materials: Patients received CRT in 1 of 3 prospective trials. FT was evaluated before CRT (baseline) and then weekly using the European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire Core-30 questionnaire. Patients were classified as experiencing FT if they answered ≥2 on a Likert scale question (1-4 points) asking if they experienced FT. Rate of change of FT was calculated using linear regression; worsening FT was defined as increase ≥1 point per month. χ2, t tests, and logistic regression were used to assess predictors of FT. Results: Among 233 patients, patients attended an average of 9 outpatient and 4 radiology appointments over the 47 days between diagnosis and starting CRT. At baseline, 52% of patients reported experiencing FT. Advanced T stage (odds ratio, 2.47; P = .002) was associated with baseline FT in multivariate analysis. The mean rate of FT change was 0.23 Likert scale points per month. In total, 26% of patients demonstrated worsening FT during CRT. FT at baseline was not associated with worsening FT (P = .98). Hospitalization during treatment was associated with worsening FT (odds ratio, 2.30; P = .019) in multivariate analysis. Conclusions: Most patients reported FT before CRT. These results suggest that FT should be assessed (and, potentially, addressed) before starting definitive treatment because it develops early in a patient's cancer journey. Reducing hospitalizations may mitigate worsening FT. Further research is warranted to design interventions to reduce FT and avoid hospitalizations.