HexAI-TJAtxt: A textual dataset to advance open scientific research in total joint arthroplasty.

Total joint arthroplasty (TJA) is the most common and fastest inpatient surgical procedure in the elderly, nationwide. Due to the increasing number of TJA patients and advancements in healthcare, there is a growing number of scientific articles being published in a daily basis. These articles offer important insights into TJA, covering aspects like diagnosis, prevention, treatment strategies, and epidemiological factors. However, there has been limited effort to compile a large-scale text dataset from these articles and make it publicly available for open scientific research in TJA. Rapid yet, utilizing computational text analysis on these large columns of scientific literatures holds great potential for uncovering new knowledge to enhance our understanding of joint diseases and improve the quality of TJA care and clinical outcomes. This work aims to build a dataset entitled HexAI-TJAtxt, which includes more than 61,936 scientific abstracts collected from PubMed using MeSH (Medical Subject Headings) terms within "MeSH Subheading" and "MeSH Major Topic," and Publication Date from 01/01/2000 to 12/31/2022. The current dataset is freely and publicly available at https://github.com/pitthexai/HexAI-TJAtxt, and it will be updated frequently in bi-monthly manner from new abstracts published at PubMed.

Postoperative infection and bone sarcoma survival: systematic review of the role of infection in bone sarcoma prognosis.

Background: Osteosarcoma (OS) and chondrosarcoma (CS) are primary bone malignancies whose prognoses have stagnated despite advancements in surgical management, chemotherapy, radiation therapy, and immunotherapy. The role of the immune system in generating anti-cancer physiologic responses is critical to prognosis. Prior studies have explored if immune system activation via infection enhances survival in bone sarcomas without a clear consensus. Methods: This study sought to (I) retrospectively examine the effect of postoperative infection on survival in OS and CS and (II) systematically review the effect of postoperative infection on survival in primary bone malignancies. We performed a retrospective case-control study of 192 patients treated between 1/2000-12/2015 at a single academic sarcoma referral center. Patients with OS or CS undergoing operative resection were included. Eligible patients were grouped by presence of metastasis, and survival was compared between patients with or without postoperative infection. Furthermore, we performed a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines investigating the effect of infection on primary bone malignancy survival. Risk of bias assessment was performed utilizing the ROBINS-I (Risk of Bias in Non-randomized Studies-of Interventions) assessment tool. All presented studies included author information, study population, and overall or disease-free survival results. Results: One hundred and four patients were included, with 85 without infection (26 metastatic, 59 non-metastatic) and 19 with infection (10 metastatic, 9 non-metastatic). Five-year survival was greatest in patients without metastasis with a postoperative infection (100%), followed by patients without metastasis who were infection-free (80%). Five-year survival was comparatively lower in patients with metastasis who were infection-free (35%) and lowest in patients with metastasis with a postoperative infection (20%). No significant differences were present (P=0.17) on log-rank analysis. Our systematic review collected six studies exploring the impact of infection on primary bone malignancy survival, with two studies reporting significant findings of infection improving survival. Limitations of this review included risk of bias due to confounding, inconsistency comparing outcomes, and differences in patient populations. Conclusions: This retrospective study and systematic review suggests postoperative infection may play a role in modulating immune response to malignancy. Understanding the synergy between anti-pathogen and anti-cancer responses warrants further investigation as an alternative method of targeted cancer treatment.

Identifying Information Gaps in Electronic Health Records by Using Natural Language Processing: Gynecologic Surgery History Identification.

BACKGROUND: Electronic health records (EHRs) are a rich source of longitudinal patient data. However, missing information due to clinical care that predated the implementation of EHR system(s) or care that occurred at different medical institutions impedes complete ascertainment of a patient's medical history. OBJECTIVE: This study aimed to investigate information discrepancies and to quantify information gaps by comparing the gynecological surgical history extracted from an EHR of a single institution by using natural language processing (NLP) techniques with the manually curated surgical history information through chart review of records from multiple independent regional health care institutions. METHODS: To facilitate high-throughput evaluation, we developed a rule-based NLP algorithm to detect gynecological surgery history from the unstructured narrative of the Mayo Clinic EHR. These results were compared to a gold standard cohort of 3870 women with gynecological surgery status adjudicated using the Rochester Epidemiology Project medical records-linkage system. We quantified and characterized the information gaps observed that led to misclassification of the surgical status. RESULTS: The NLP algorithm achieved precision of 0.85, recall of 0.82, and F1-score of 0.83 in the test set (n=265) relative to outcomes abstracted from the Mayo EHR. This performance attenuated when directly compared to the gold standard (precision 0.79, recall 0.76, and F1-score 0.76), with the majority of misclassifications being false negatives in nature. We then applied the algorithm to the remaining patients (n=3340) and identified 2 types of information gaps through error analysis. First, 6% (199/3340) of women in this study had no recorded surgery information or partial information in the EHR. Second, 4.3% (144/3340) of women had inconsistent or inaccurate information within the clinical narrative owing to misinterpreted information, erroneous "copy and paste," or incorrect information provided by patients. Additionally, the NLP algorithm misclassified the surgery status of 3.6% (121/3340) of women. CONCLUSIONS: Although NLP techniques were able to adequately recreate the gynecologic surgical status from the clinical narrative, missing or inaccurately reported and recorded information resulted in much of the misclassification observed. Therefore, alternative approaches to collect or curate surgical history are needed.

Expression and prognosis analyses of BUB1, BUB1B and BUB3 in human sarcoma.

Budding Uninhibited By Benzimidazoles are a group of genes encoding proteins that play central roles in spindle checkpoint during mitosis. Improper mitosis may lead to aneuploidy which is found in many types of tumors. As a key mediator in mitosis, the dysregulated expression of BUBs has been proven to be highly associated with various malignancies, such as leukemia, gastric cancer, breast cancer, and liver cancer. However, bioinformatic analysis has not been applied to explore the role of the BUBs in sarcomas. Herein, we investigate the transcriptional and survival data of BUBs in patients with sarcomas using Oncomine, Gene Expression Profiling Interactive Analysis, Cancer Cell Line Encyclopedia, Kaplan-Meier Plotter, LinkedOmics, and the Database for Annotation, Visualization and Integrated Discovery. We found that the expression levels of BUB1, BUB1B and BUB3 were higher in sarcoma samples and cell lines than in normal controls. Survival analysis revealed that the higher expression levels of BUB1, BUB1B and BUB3 were associated with lower overall and disease-free survival in patients with sarcomas. This study implies that BUB1, BUB1B and BUB3 are potential treatment targets for patients with sarcomas and are new biomarkers for the prognosis of sarcomas.

Characterizing Chronic Pain Episodes in Clinical Text at Two Health Care Systems: Comprehensive Annotation and Corpus Analysis.

BACKGROUND: Chronic pain affects more than 20% of adults in the United States and is associated with substantial physical, mental, and social burden. Clinical text contains rich information about chronic pain, but no systematic appraisal has been performed to assess the electronic health record (EHR) narratives for these patients. A formal content analysis of the unstructured EHR data can inform clinical practice and research in chronic pain. OBJECTIVE: We characterized individual episodes of chronic pain by annotating and analyzing EHR notes for a stratified cohort of adults with known chronic pain. METHODS: We used the Rochester Epidemiology Project infrastructure to screen all residents of Olmsted County, Minnesota, for evidence of chronic pain, between January 1, 2005, and September 30, 2015. Diagnosis codes were used to assemble a cohort of 6586 chronic pain patients; people with cancer were excluded. The records of an age- and sex-stratified random sample of 62 patients from the cohort were annotated using an iteratively developed guideline. The annotated concepts included date, location, severity, causes, effects on quality of life, diagnostic procedures, medications, and other treatment modalities. RESULTS: A total of 94 chronic pain episodes from 62 distinct patients were identified by reviewing 3272 clinical notes. Documentation was written by clinicians across a wide spectrum of specialties. Most patients (40/62, 65%) had 1 pain episode during the study period. Interannotator agreement ranged from 0.78 to 1.00 across the annotated concepts. Some pain-related concepts (eg, body location) had 100% (94/94) coverage among all the episodes, while others had moderate coverage (eg, effects on quality of life) (55/94, 59%). Back pain and leg pain were the most common types of chronic pain in the annotated cohort. Musculoskeletal issues like arthritis were annotated as the most common causes. Opioids were the most commonly captured medication, while physical and occupational therapies were the most common nonpharmacological treatments. CONCLUSIONS: We systematically annotated chronic pain episodes in clinical text. The rich content analysis results revealed complexity of the chronic pain episodes and of their management, as well as the challenges in extracting pertinent information, even for humans. Despite the pilot study nature of the work, the annotation guideline and corpus should be able to serve as informative references for other institutions with shared interest in chronic pain research using EHRs.

Natural Language Processing for the Evaluation of Methodological Standards and Best Practices of EHR-based Clinical Research.

The effective use of EHR data for clinical research is challenged by the lack of methodologic standards, transparency, and reproducibility. For example, our empirical analysis on clinical research ontologies and reporting standards found little-to-no informatics-related standards. To address these issues, our study aims to leverage natural language processing techniques to discover the reporting patterns and data abstraction methodologies for EHR-based clinical research. We conducted a case study using a collection of full articles of EHR-based population studies published using the Rochester Epidemiology Project infrastructure. Our investigation discovered an upward trend of reporting EHR-related research methodologies, good practice, and the use of informatics related methods. For example, among 1279 articles, 24.0% reported training for data abstraction, 6% reported the abstractors were blinded, 4.5% tested the inter-observer agreement, 5% reported the use of a screening/data collection protocol, 1.5% reported that team meetings were organized for consensus building, and 0.8% mentioned supervision activities by senior researchers. Despite that, the overall ratio of reporting/adoption of methodologic standards was still low. There was also a high variation regarding clinical research reporting. Thus, continuously developing process frameworks, ontologies, and reporting guidelines for promoting good data practice in EHR-based clinical research are recommended.