Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations.

TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.

An analysis of ChatGPT recommendations for the diagnosis and treatment of cervical radiculopathy.

OBJECTIVE: The objective of this study was to assess the safety and accuracy of ChatGPT recommendations in comparison to the evidence-based guidelines from the North American Spine Society (NASS) for the diagnosis and treatment of cervical radiculopathy. METHODS: ChatGPT was prompted with questions from the 2011 NASS clinical guidelines for cervical radiculopathy and evaluated for concordance. Selected key phrases within the NASS guidelines were identified. Completeness was measured as the number of overlapping key phrases between ChatGPT responses and NASS guidelines divided by the total number of key phrases. A senior spine surgeon evaluated the ChatGPT responses for safety and accuracy. ChatGPT responses were further evaluated on their readability, similarity, and consistency. Flesch Reading Ease scores and Flesch-Kincaid reading levels were measured to assess readability. The Jaccard Similarity Index was used to assess agreement between ChatGPT responses and NASS clinical guidelines. RESULTS: A total of 100 key phrases were identified across 14 NASS clinical guidelines. The mean completeness of ChatGPT-4 was 46%. ChatGPT-3.5 yielded a completeness of 34%. ChatGPT-4 outperformed ChatGPT-3.5 by a margin of 12%. ChatGPT-4.0 outputs had a mean Flesch reading score of 15.24, which is very difficult to read, requiring a college graduate education to understand. ChatGPT-3.5 outputs had a lower mean Flesch reading score of 8.73, indicating that they are even more difficult to read and require a professional education level to do so. However, both versions of ChatGPT were more accessible than NASS guidelines, which had a mean Flesch reading score of 4.58. Furthermore, with NASS guidelines as a reference, ChatGPT-3.5 registered a mean ± SD Jaccard Similarity Index score of 0.20 ± 0.078 while ChatGPT-4 had a mean of 0.18 ± 0.068. Based on physician evaluation, outputs from ChatGPT-3.5 and ChatGPT-4.0 were safe 100% of the time. Thirteen of 14 (92.8%) ChatGPT-3.5 responses and 14 of 14 (100%) ChatGPT-4.0 responses were in agreement with current best clinical practices for cervical radiculopathy according to a senior spine surgeon. CONCLUSIONS: ChatGPT models were able to provide safe and accurate but incomplete responses to NASS clinical guideline questions about cervical radiculopathy. Although the authors' results suggest that improvements are required before ChatGPT can be reliably deployed in a clinical setting, future versions of the LLM hold promise as an updated reference for guidelines on cervical radiculopathy. Future versions must prioritize accessibility and comprehensibility for a diverse audience.

Diet quality from mid to late life and its association with physical frailty in late life in a cohort of Chinese adults.

BACKGROUND: It is unclear if improving diet quality after midlife could reduce the risk of physical frailty at late life. We aimed to associate changes in diet quality after midlife with physical frailty at late life. METHODS: Diet quality in 12,580 participants from the Singapore Chinese Health Study was assessed with the Dietary Approaches to Stop Hypertension (DASH) scores at baseline (1993-1998; mean age 53 years) and follow-up 3 (2014-2016; mean age 73 years). Physical frailty was assessed using the modified Cardiovascular Health Study phenotype at follow-up 3. Multivariable logistic regressions examined associations between DASH scores and physical frailty. RESULTS: Comparing participants in extreme quartiles of DASH scores, the odds ratios (OR) [95% confidence interval (CI)] for physical frailty were 0.85 (0.73,0.99) at baseline and 0.49 (0.41, 0.58) at follow-up 3. Compared to participants with consistently low DASH scores, participants with consistently high scores (OR 0.74, 95% CI: 0.59, 0.94) and those with > 10% increase in scores (OR 0.78, 95% CI: 0.64, 0.95) had lower odds of frailty. Compared to those in the lowest DASH tertiles at both time-points, significantly lower odds of physical frailty were observed in those who were in the highest DASH tertiles at both time points [0.59 (0.48, 0.73)], and in those who improved their scores from the lowest [0.68 (0.51, 0.91)] or second tertile at baseline [0.61 (0.48, 0.76)] to the highest tertile at follow-up 3. CONCLUSIONS: Maintaining a high diet quality or a substantial improvement in diet quality after midlife could lower the risk of physical frailty at late life.

The Effect of Intraoperative Overdistraction on Subsidence Following Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: Retrospective cohort. OBJECTIVE: The purpose of this study was to evaluate the effect of overdistraction on interbody cage subsidence. BACKGROUND: Vertebral overdistraction due to the use of large intervertebral cage sizes may increase the risk of postoperative subsidence. METHODS: Patients who underwent anterior cervical discectomy and fusion between 2016 and 2021 were included. All measurements were performed using lateral cervical radiographs at 3 time points - preoperative, immediate postoperative, and final follow-up >6 months postoperatively. Anterior and posterior distraction were calculated by subtracting the preoperative disc height from the immediate postoperative disc height. Cage subsidence was calculated by subtracting the final follow-up postoperative disc height from the immediate postoperative disc height. Associations between anterior and posterior subsidence and distraction were determined using multivariable linear regression models. The analyses controlled for cage type, cervical level, sex, age, smoking status, and osteopenia. RESULTS: Sixty-eight patients and 125 fused levels were included in the study. Of the 68 fusions, 22 were single-level fusions, 35 were 2-level, and 11 were 3-level. The median final follow-up interval was 368 days (range: 181-1257 d). Anterior disc space subsidence was positively associated with anterior distraction (beta = 0.23; 95% CI: 0.08, 0.38; P = 0.004), and posterior disc space subsidence was positively associated with posterior distraction (beta = 0.29; 95% CI: 0.13, 0.45; P < 0.001). No significant associations between anterior distraction and posterior subsidence (beta = 0.07; 95% CI: -0.06, 0.20; P = 0.270) or posterior distraction and anterior subsidence (beta = 0.06; 95% CI: -0.14, 0.27; P = 0.541) were observed. CONCLUSIONS: We found that overdistraction of the disc space was associated with increased postoperative subsidence after anterior cervical discectomy and fusion. Surgeons should consider choosing a smaller cage size to avoid overdistraction and minimize postoperative subsidence.

Practical guidance and workflows for identifying fast evolving non-coding genomic elements using PhyloAcc.

Comparative genomics provides ample ways to study genome evolution and its relationship to phenotypic traits. By developing and testing alternate models of evolution throughout a phylogeny, one can estimate rates of molecular evolution along different lineages in a phylogeny and link these rates with observations in extant species, such as convergent phenotypes. Pipelines for such work can help identify when and where genomic changes may be associated with, or possibly influence, phenotypic traits. We recently developed a set of models called PhyloAcc, using a Bayesian framework to estimate rates of nucleotide substitution on different branches a phylogenetic tree and evaluate their association with pre-defined or estimated phenotypic traits PhyloAcc-ST and PhyloAcc-GT both allow users to define a priori a set of target lineages and then compare different models to identify loci accelerating in one or more target lineages. Whereas ST considers only one species tree across all input loci, GT considers alternate topologies for every locus. PhyloAcc-C simultaneously models molecular rates and rates of continuous trait evolution,allowing the user to ask whether the two are associated. Here we describe these models and provide tips and workflows on how to prepare the input data and run PhyloAcc.

Structural Equation Modelling to Identify Psychometric Determinants of Medication Adherence in a Survey of Kidney Dialysis Patients.

Purpose: Medication non-adherence in dialysis patients is associated with increased mortality and higher healthcare costs. We assessed whether medication adherence is influenced by specific psychometric constructs measuring beliefs about the necessity for medication and concerns about them. We also tested whether medication knowledge, health literacy, and illness perceptions influenced this relationship. Patients and Methods: This study is based on data from a cross-sectional in-person questionnaire, administered to a random sample of all adult dialysis patients at a teaching hospital. The main outcome was self-assessed medication adherence (8-Item Morisky Medication Adherence Scale). The predictors were: concerns about medications and necessity for medication (Beliefs About Medication Questionnaire); health literacy; medication knowledge (Medication Knowledge Evaluation Tool); cognitive, emotional, and comprehensibility Illness perceptions (Brief Illness Perception Questionnaire). Path analysis was performed using structural equations in both covariance and variance-based models. Results: Necessity for medication increased (standardized path coefficient [ß] 0.30 [95% CI 0.05, 0.54]) and concerns about medication decreased (standardized ß -0.33 [-0.57, -0.09]) medication adherence, explaining most of the variance in outcome (r2=0.95). Medication knowledge and cognitive illness perceptions had no effects on medication adherence, either directly or indirectly. Higher health literacy, greater illness comprehension, and a more positive emotional view of their illness had medium-to-large sized effects in increasing medication adherence. These were indirect rather and direct effects mediated by decreases in concerns about medications (standardized ß respectively -0.40 [-0.63,-0.16], -0.60 [-0.85, -0.34], -0.33 [-0.52, -0.13]). Conclusion: Interventions that reduce patients' concerns about their medications are likely to improve adherence, rather than interventions that increase patients' perceived necessity for medication. Improving patients' general health literacy and facilitating a better understanding and more positive perception of the illness can probably achieve this. Our study is potentially limited by a lack of generalizability outside of the population and setting in which it was conducted.

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.

A phylogenetic method linking nucleotide substitution rates to rates of continuous trait evolution.

Genomes contain conserved non-coding sequences that perform important biological functions, such as gene regulation. We present a phylogenetic method, PhyloAcc-C, that associates nucleotide substitution rates with changes in a continuous trait of interest. The method takes as input a multiple sequence alignment of conserved elements, continuous trait data observed in extant species, and a background phylogeny and substitution process. Gibbs sampling is used to assign rate categories (background, conserved, accelerated) to lineages and explore whether the assigned rate categories are associated with increases or decreases in the rate of trait evolution. We test our method using simulations and then illustrate its application using mammalian body size and lifespan data previously analyzed with respect to protein coding genes. Like other studies, we find processes such as tumor suppression, telomere maintenance, and p53 regulation to be related to changes in longevity and body size. In addition, we also find that skeletal genes, and developmental processes, such as sprouting angiogenesis, are relevant.

Relation of preconception eating behaviours to dietary pattern trajectories and gestational weight gain from preconception to late pregnancy.

Studies examining preconception eating behaviours with longitudinal dietary patterns from preconception to late pregnancy as well as gestational weight gain (GWG) are limited. We derived dietary pattern trajectories from preconception to late-pregnancy, and related preconception eating behaviours to these trajectories and GWG. Preconception eating behaviours were assessed using the Three-Factor Eating Questionnaire measuring cognitive restraint (CR) - conscious restriction of food intake, emotional eating (EE) - overeating in response to negative emotions, and uncontrolled eating (UE) - overeating with a feeling of lack of control. Dietary intakes were measured at preconception, 20-21 and 34-36 weeks' gestation with food frequency questionnaires. Dietary patterns were determined using factor analysis, and trajectories derived using group-based trajectory modelling. Inadequate and excessive GWG were defined according to Institute of Medicine guidelines based on weights at preconception and the last antenatal visit (median: 38 weeks' gestation). Two dietary patterns were derived: 'Fast Food, Fried Snacks and Desserts (FFD)' and 'Soup, Fish and Vegetables (SFV)'. Adherence trajectories from preconception to late-pregnancy were characterised as consistently high ("stable-high") and low ("stable-low"). Women with higher UE scores had higher odds of being in the "stable-high" trajectory (n = 34) of the FFD pattern [Odds Ratio (OR): 1.25, 95% Confidence Interval (CI): 1.03, 1.51], compared to "stable-low" (n = 260). Percentages of women with inadequate, adequate or excessive GWG were 21.7% (n = 70), 25.8% (n = 83), and 52.5% (n = 169), respectively; women with higher EE scores had a higher likelihood of excessive GWG [Relative Risk Ratio (RRR): 1.35, 95% CI: 1.02, 1.80], but this association was attenuated after adjusting for preconception body mass index. Eating behaviour interventions to improve dietary patterns among pregnant women may need to start as early as preconception, incorporating strategies to manage UE.

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.

Corrigendum: Chromatin structure and context-dependent sequence features control prime editing efficiency.

[This corrects the article DOI: 10.3389/fgene.2023.1222112.].

KDM3B inhibitors disrupt the oncogenic activity of PAX3-FOXO1 in fusion-positive rhabdomyosarcoma.

Fusion-positive rhabdomyosarcoma (FP-RMS) is an aggressive pediatric sarcoma driven primarily by the PAX3-FOXO1 fusion oncogene, for which therapies targeting PAX3-FOXO1 are lacking. Here, we screen 62,643 compounds using an engineered cell line that monitors PAX3-FOXO1 transcriptional activity identifying a hitherto uncharacterized compound, P3FI-63. RNA-seq, ATAC-seq, and docking analyses implicate histone lysine demethylases (KDMs) as its targets. Enzymatic assays confirm the inhibition of multiple KDMs with the highest selectivity for KDM3B. Structural similarity search of P3FI-63 identifies P3FI-90 with improved solubility and potency. Biophysical binding of P3FI-90 to KDM3B is demonstrated using NMR and SPR. P3FI-90 suppresses the growth of FP-RMS in vitro and in vivo through downregulating PAX3-FOXO1 activity, and combined knockdown of KDM3B and KDM1A phenocopies P3FI-90 effects. Thus, we report KDM inhibitors P3FI-63 and P3FI-90 with the highest specificity for KDM3B. Their potent suppression of PAX3-FOXO1 activity indicates a possible therapeutic approach for FP-RMS and other transcriptionally addicted cancers.

In Situ Photo-Crosslinkable Protein Bioadhesive for Bone Graft Fixation.

Bone grafting is a fundamental dental surgery procedure widely used for implant placement and periodontal disease management treatments. Despite its broad applications, vertical bone augmentation presents unique challenges, including the risk of graft displacement due to gravitational and masticatory forces. Traditional physical stabilization methods introduce additional complexities and risks, underscoring the need for innovative fixation technologies. This study aimed to develop an in situ photo-crosslinkable bioadhesive hydrogel (iPBAH) as a multifunctional bone graft binder to enhance the process of bone reconstruction. The bioadhesive is composed of mussel-derived adhesive protein (MAP) fused with the cell-adhesive peptide RGD. The numerous tyrosine residues in MAP facilitate rapid photo-crosslinking, enabling efficient hydrogel formation using visible blue light. Subsequently, iPBAH underwent comprehensive characterization to evaluate its suitability as a multifunctional bone graft binder. iPBAH efficiently underwent in situ crosslinking through harmless exposure to visible light within minutes and displayed several exceptional properties, including a microporous structure, underwater adhesion, extended durability, high compressive strength, and biocompatibility. In vivo assessments, using male Sprague-Dawley rats, demonstrated that iPBAH binder significantly enhanced bone regeneration in a rat calvarial bone defect model. The in situ crosslinking of the iPBAH binder during bone graft transplantation can effectively fill irregular and complex defect shapes while simultaneously preventing graft material leakage. The improved physical attributes of the bound graft material can enhance its resistance to external forces, thereby ensuring sustained retention over time. Moreover, the interaction between iPBAH and surrounding tissues promotes adhesion and integration of the graft material with host tissues in the defect area. In addition, the included RGD peptide in iPBAH can augment inherent cell recruitment, adhesion, and growth, consequently expediting osteogenesis.

Neopetrotaurines A-C, Isoquinoline Alkaloids with an Unprecedented Taurine Bridge from the Sponge Neopetrosia sp.

Neopetrotaurines A-C (1-3), unusual alkaloids possessing two isoquinoline-derived moieties that are linked via a unique taurine bridge, were isolated from a Neopetrosia sp. marine sponge. These new compounds have proton-deficient structural scaffolds that are difficult to unambiguously assign using only conventional 2- and 3-bond 1H-13C and 1H-15N heteronuclear correlation data. Thus, the application of LR-HSQMBC and HMBC NMR experiments optimized to detect 4- and 5-bond long-range 1H-13C heteronuclear correlations facilitated the structure elucidation of these unusual taurine-bridged marine metabolites. Neopetrotaurines A-C (1-3) showed significant inhibition of transcription driven by the oncogenic fusion protein PAX3-FOXO1, which is associated with alveolar rhabdomyosarcoma, and cytotoxic activity against PAX3-FOXO1-positive cell lines.

Reliable Prediction of Discharge Disposition Following Cervical Spine Surgery With Ensemble Machine Learning and Validation on a National Cohort.

STUDY DESIGN: A retrospective cohort study. OBJECTIVE: The purpose of this study is to develop a machine learning algorithm to predict nonhome discharge after cervical spine surgery that is validated and usable on a national scale to ensure generalizability and elucidate candidate drivers for prediction. SUMMARY OF BACKGROUND DATA: Excessive length of hospital stay can be attributed to delays in postoperative referrals to intermediate care rehabilitation centers or skilled nursing facilities. Accurate preoperative prediction of patients who may require access to these resources can facilitate a more efficient referral and discharge process, thereby reducing hospital and patient costs in addition to minimizing the risk of hospital-acquired complications. METHODS: Electronic medical records were retrospectively reviewed from a single-center data warehouse (SCDW) to identify patients undergoing cervical spine surgeries between 2008 and 2019 for machine learning algorithm development and internal validation. The National Inpatient Sample (NIS) database was queried to identify cervical spine fusion surgeries between 2009 and 2017 for external validation of algorithm performance. Gradient-boosted trees were constructed to predict nonhome discharge across patient cohorts. The area under the receiver operating characteristic curve (AUROC) was used to measure model performance. SHAP values were used to identify nonlinear risk factors for nonhome discharge and to interpret algorithm predictions. RESULTS: A total of 3523 cases of cervical spine fusion surgeries were included from the SCDW data set, and 311,582 cases were isolated from NIS. The model demonstrated robust prediction of nonhome discharge across all cohorts, achieving an area under the receiver operating characteristic curve of 0.87 (SD=0.01) on both the SCDW and nationwide NIS test sets. Anterior approach only, age, elective admission status, Medicare insurance status, and total Elixhauser Comorbidity Index score were the most important predictors of discharge destination. CONCLUSIONS: Machine learning algorithms reliably predict nonhome discharge across single-center and national cohorts and identify preoperative features of importance following cervical spine fusion surgery.

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.

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.

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.