Standard Query Language Research Articles

Concurrent Access Performance Comparison Between Relational Databases and Graph NoSQL Databases for Complex Algorithms

Databases are a fundamental element of contemporary software applications. The most widely used and recognized type in practice is the relational database, valued for its ability to store and organize data in tabular structures, its emphasis on data consistency and integrity, and its use of a standardized query language, SQL. However, with the rapid increase in both the volume and complexity of data, relational databases have recently encountered challenges in effectively modeling this expanding information. To address performance challenges, new database systems have emerged, offering alternative approaches to data modeling—these are known as NoSQL databases. In this paper, we present an indoor navigation application designed to operate on both a relational database, Microsoft SQL Server, and a graph-based NoSQL database, Neo4j. We describe the algorithms implemented for testing and the performance metrics analyzed to draw our conclusions. The results revealed Neo4j’s strength in managing data with complex relationships but also exposed its limitations in handling concurrent access, where SQL Server demonstrated significantly greater stability.

Developing a Geo-Spatial Database and Standard Queries for Election Management by Stakeholders within Jos South, Nigeria

One of the major set-backs of the 2023 general elections in Nigeria as highlighted by YIAGA Africa (2024) was the failure of the body saddled with election management which is Independent Electoral Commission (INEC), to deploy Geo-spatial Database (GSDB) as a repository in the efficient management processes which enable all election stakeholder participate seamlessly and interactively. The modus of oparandy for the geo-spatial database was hierarchical by dis-aggregating GSDB to the lowest geographical unit. The recent court verdict on re-delineation of electoral wards in Rivers State did not go down well with the political pundits prior to the forth coming local government election in the state. It surffices to say that, INEC from the on-set never had a geo-spatial database from which delineation and update of electoral wards, polling units can be carried out This will further build confidence and integrity, then reducing litigations that characterized the 2023 elections. The GSDB used was predicated on file geo-database of ArcGIS, where both spatial and attribute data of the study were stored; and standard query languages that are pertinent to elections were issued at prompt which generated maps and texts for decision making by all stakeholders.

National Experience of Technology-based Eye Care Services: A Comprehensive Ophthalmology Telemedicine Initiative

Technology-Based Eye Care Services (TECS) is a tele-ophthalmology program operating in the Veterans' Administration since 2015. This study explores characteristics of the national TECS population, evaluates implementation and sustainability of TECS, and analyzes possible associations and effects of demographic characteristics and social determinants of health (SDOH) on being diagnosed with a vision threatening (VT) disease. Implementation and sustainability of TECS from 2015-2022 was examined along with the sociodemographic characteristics of Veterans served through TECS in 2021. Veteran patients seen in TECS nationwide. Characteristics, disease prevalence and diagnoses, implementation success rate, sustainability rate, preliminary analysis of outcomes and disparity. Per quarter from 2015-2022, TECS sites were classified as implementing, active, or sustained. Standard Query Language (SQL) was used to determine sociodemographic data and logistic regression models were used to identify risk factors associated with VT eye diagnosis. 21,712 Veterans, 52.1% rural or highly rural, were served by TECS in 2021. The average age was 64.7 years, with females comprising 10.9% of the population served. From 2015-2022, of the 67 TECS sites initiated, 6 were implementing with 51 of 61 initiated sites still operational in the first quarter (Q1) of 2022 (83.6% success rate).Macular degeneration (AMD) and cataracts were more prevalent in rural and highly rural populations (7.6% and 11.3%, and 48.8% and 55.0%, respectively) vs. urban populations; glaucoma and diabetic retinopathy (DR) had the opposite association. The prevalence of any type of VT eye disease was lowest in the Mountains/Central region (0.54%) and highest in the Southeast region (3.2%) of the US. Rural and highly rural residents were 1.3 and 2.5 times as likely, respectively, to be diagnosed with a VT eye disease than urban residents. Implementation and sustainability of TECS has been promising. The data provides key information that can be utilized to improve the deployment of TECS and similar programs, along with the possible future direction of TECS services. Moreover, experience from one national ocular telehealth program clearly illustrates that telemedicine can address eye care disparities in the Veteran population and may be utilized for other vulnerable groups as well.

τSQWRL: A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

Temporal ontologies allow to represent not only concepts, their properties, and their relationships, but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view. They are widely used to formally represent temporal data semantics in several applications belonging to different fields (e.g., Semantic Web, expert systems, knowledge bases, big data, and artificial intelligence). They facilitate temporal knowledge representation and discovery, with the support of temporal data querying and reasoning. However, there is no standard or consensual temporal ontology query language. In a previous work, we have proposed an approach named τJOWL (temporal OWL 2 from temporal JSON, where OWL 2 stands for “OWL 2 Web Ontology Language” and JSON stands for “JavaScript Object Notation”). τJOWL allows (1) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (2) to manage its incremental maintenance accommodating their evolution, in a temporal and multi-schema-version environment. In this paper, we propose a temporal ontology query language for rJOWL, named rSQWRL (temporal SQWRL), designed as a temporal extension of the ontology query language-Semantic Query-enhanced Web Rule Language (SQWRL). The new language has been inspired by the features of the consensual temporal query language TSQL2 (Temporal SQL2), well known in the temporal (relational) database community. The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any (complex) temporal query on time-varying ontologies generated from time-varying big data. Some examples, in the Internet of Healthcare Things (IoHT) domain, are provided to motivate and illustrate our proposal.

PG-Schema: Schemas for Property Graphs

Property graphs have reached a high level of maturity, witnessed by multiple robust graph database systems as well as the ongoing ISO standardization effort aiming at creating a new standard Graph Query Language (GQL). Yet, despite documented demand, schema support is limited both in existing systems and in the first version of the GQL Standard. It is anticipated that the second version of the GQL Standard will include a rich DDL. Aiming to inspire the development of GQL and enhance the capabilities of graph database systems, we propose PG-Schema, a simple yet powerful formalism for specifying property graph schemas. It features PG-Schema with flexible type definitions supporting multi-inheritance, as well as expressive constraints based on the recently proposed PG-Keys formalism. We provide the formal syntax and semantics of PG-Schema, which meet principled design requirements grounded in contemporary property graph management scenarios, and offer a detailed comparison of its features with those of existing schema languages and graph database systems.

A Cost-Driven Top-K Queries Optimization Approach on Federated RDF Systems

RDF (Resource Description Framework) is a model widely used to construct knowledge bases, while SPARQL (SPARQL Protocol and RDF Query Language) is the standardized structured query language to manipulate RDF data. Recently, many data providers have published their RDF datasets in their own autonomous sites and provided SPARQL query interfaces, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RDF sources</i> . In order to integrate multiple RDF sources, researchers put forward the federated RDF system to support the federated SPARQL queries. However, existing studies can only support efficient basic queries but not top-k queries. Toward this end, we propose a cost-driven top-k queries optimization approach in federated RDF systems, which can support both top-k queries for single variable ordering and expression ordering. Firstly, we propose an optimized query decomposition method to decompose the federated query into multiple subqueries. Secondly, while considering the top-k operator, we propose a cost model to evaluate the query cost and join cost of subqueries. The optimal query plan can be obtained by the costed-based query plan generation algorithm. Finally, combined with the characteristics of top-k queries, an incremental query plan execution strategy is developed to minimize the total query cost. Experimental results show that the proposed method is effective, efficient and scalable.

SEIF: A Semantic-enabled IoT Service Framework for Realizing Interoperable Data and Knowledge Retrieval

The Internet of Things (IoT) has enabled interconnection of embedded computing devices comprised of their own data models in existing infrastructure. Currently, many IoT platforms provide data modeling and information retrieval services. However, the use of various data models and standards causes interoperability issues. Semantic technologies ease such barriers by providing the means to annotate semantic information as IoT data based on common data models. For this reason, semantics is becoming a growing requirement and a major challenge for IoT service layer platforms. The addition of semantic support empowers IoT systems with enhanced meaning in the data gathered from various devices and vertical silos. This paper presents a semantic IoT framework that includes agile support for adding semantic meaning to data, and for knowledge discovery, semantic validation, and accessibility to IoT data through a standardized semantic query language. The proposed evaluation matrix for a possible enhancement-anddevelopment guideline based on a global IoT standard is measured through smart office use cases comprising different sensor information. This paper provides a detailed description of semantic enablement on IoT platforms, including details of its implementation. The results provide a clear guideline for integrating semantic features into the IoT service layer and for data management.

Izrada OWL ontologije za prikaz, povezivanje i pretraživanje SemAF diskursnih oznaka

Linguistic Linked Open Data (LLOD) are technologies that provide a powerful instrument for representing and interpreting language phenomena on a web-scale. The main objective of this paper is to demonstrate how LLOD technologies can be applied to represent and annotate a corpus composed of multiword discourse markers, and what the effects of this are. In particular, it is our aim to apply semantic web standards such as RDF and OWL for publishing and integrating data. We present a novel scheme for discourse annotation that combines ISO standards describing discourse relations and dialogue acts – ISO DR-Core (ISO 24617-8) and ISO-Dialogue Acts (ISO 24617-2) in 9 languages (cf. Silvano and Damova 2022; Silvano, et al. 2022). We develop an OWL ontology to formalize that scheme, provide a newly annotated dataset and link its RDF edition with the ontology. Consequently, we describe the conjoint querying of the ontology and the annotations by means of SPARQL, the standard query language for the web of data. The ultimate result is that we are able to perform queries over multiple, interlinked datasets with complex internal structure. This is a first, but essential step, in developing novel, powerful, and groundbreaking means for the corpus-based study of multilingual discourse, communication analysis, or attitudes discovery.

Informatics tools to implement late cardiovascular risk prediction modeling for population health management of high-risk childhood cancer survivors.

10048 Background: Marked improvements in outcomes for children with cancer and robust cohort studies with longitudinal follow-up inform evidence-based guidelines for survivors at risk for late cardiomyopathy. Clinical informatics tools to integrate data from multiple sources have the potential to catalyze population health management. Methods: The Oklahoma Childhood Cancer Survivor cohort was constructed from an institutional cancer registry of survivors diagnosed between 2005 and 2014 (n=382). Data elements (cumulative anthracycline, cumulative chest-directed radiotherapy, alkylator, and platinum exposures) were extracted from Passport for Care (PFC) to implement previously validated late cardiovascular risk prediction modeling from the Childhood Cancer Survivor Study (CCSS) for cardiomyopathy. Risk groups were compared to the Children’s Oncology Group (COG) Long-Term Follow-up Guidelines. Standard query language facilitated extraction of echocardiogram data from the electronic health record to determine adherence, defined as an echocardiogram within 27 months and 63 months from the last day of therapy for high and moderate risk survivors, respectively. Results: Sixty-nine percent (n=264) of survivors from the cancer registry were documented in PFC, of whom 29%, 56%, and 15% were classified as low, moderate, high risk, respectively, based on the CCSS late cardiomyopathy risk calculator. Concordance was modest for high and moderate risk groups (Kappa = 0.42 and 0.46, respectively) and good for the low risk group (Kappa = 0.77) compared to COG risk groups. There was excellent adherence to echocardiogram guidelines with 93% and 81% of moderate and high-risk survivors, respectively. There were significant differences based on risk group (p=0.02) and age at diagnosis (p&lt;0.01). Conclusions: Clinical informatics tools represent a feasible approach to leverage discrete data elements regarding key treatment exposures from PFC to successfully implement previously validated late cardiovascular risk prediction models on a population health level. PFC promotes adherence to echocardiogram surveillance and serves as a platform for future interoperability to generate real-world data to improve survivorship-focused care. [Table: see text]

A Fuzzy Query Method Oriented to Knowledge Graph

With the widespread application of Knowledge Graphs, Knowledge Graph query technology has received widespread attention. The daily use of natural language usually contains fuzzy terms. The use of fuzzy terms in queries is more in line with natural language habits. Therefore, fuzzy questions have been widely discussed and applied in relational data models, and their use in NoSQL data models has also begun to be affected in recent years. RDF (Resource Description Framework) is a representative model of the knowledge graph, and SPARQL is the standard query language of RDF. To realize the fuzzy query of the knowledge graph, this paper introduces the unclear expansion syntax of aggregate function based on summarizing and analyzing the existing SPARQL fuzzy expansion. Supplements the qualitative preference analysis method then proposes and SPARQL1. 1 Corresponding fuzzy SPARQL1.1 (f-SPARQL1.1) simultaneously provides strategies and techniques for rewriting ambiguous queries into classic SPARQL queries. In addition, this article also provides a visual interface for defining the membership functions of fuzzy terms and provides three interactive methods for setting fuzzy membership functions. Compared with the previous fuzzy query methods for RDF models, the method proposed in this paper has more vital fuzzy query ability and more flexible universality. The experimental results show the usability of the method proposed in this paper.

Instrumentation Across the Cervicothoracic Junction Does Not Improve Patient-reported Outcomes in Multilevel Posterior Cervical Decompression and Fusion.

This was a retrospective cohort. The objective of this study was to determine if instrumentation across the cervicothoracic junction (CTJ) in elective multilevel posterior cervical decompression and fusion (PCF) is associated with improved patient-reported outcome measures (PROMs). Fusion across the CTJ may result in lower revision rates at the expense of prolonged operative duration. However, it is unclear whether constructs crossing the CTJ affect PROMs. Standard Query Language (SQL) identified patients with PROMs who underwent elective multilevel PCF (≥3 levels) at our institution. Patients were grouped based on anatomic construct: crossing the CTJ (crossed) versus not crossing the CTJ (noncrossed). Subgroup analysis compared constructs stopping at C7 or T1. Independent t tests and χ 2 tests were utilized for continuous and categorical data, respectively. Regression analysis controlled for baseline demographics. The α was set at 0.05. Of the 160 patients included, the crossed group (92, 57.5%) had significantly more levels fused (5.27 vs. 3.71, P &lt;0.001), longer operative duration (196 vs. 161 min, P =0.003), greater estimated blood loss (242 vs. 160 mL, P =0.021), and a decreased revision rate (1.09% vs. 10.3%, P =0.011). Neither crossing the CTJ (vs. noncrossed) nor constructs spanning C3-T1 (vs. C3-C7) were independent predictors of ∆PROMs (change in preoperative minus postoperative patient-reported outcomes) on regression analysis. However, C3-C7 constructs had a greater revision rate than C3-T1 constructs (15.6% vs. 1.96%, P =0.030). Crossing the CTJ in patients undergoing elective multilevel PCF was not an independent predictor of improvement in PROMs at 1 year, but they experienced lower revision rates. Level III.

Development of an Automated Hospital Infection Control Surveillance Toolkit

Background: Infection control systems allow the healthcare facility to detect, manage, prevent and control the infection or risk of infection. It helps to improve healthcare quality by allowing hospital infection control (HIC) team to focus on prevention and intervention efforts during the outbreaks. Objectives: To design, develop and implement an automated HIC surveillance Toolkit for Infection Prevention and Control Department based on user requirements. Methods: This study was carried out in a multi-speciality hospital in Southern India. The study was carried out in four phases; the first phase included the observation of current documentation practice of Infection Prevention and Control Department. During the second phase, a formal discussion was conducted with the infection control team to collect the data to add to the HIC surveillance toolkit. The third phase included the design and development of the toolkit using ASP.NET and Standard Query Language (SQL) database server. During the fourth phase, a prospective cross-sectional survey was conducted to assess the end-user satisfaction towards the Toolkit using a structured and validated checklist based on 5-point Likert scale from extremely satisfied to not satisfied (score 5–1). Results: Based on the observation of existing documentation and reporting practice, a discussion was carried out with the infection control team to identify the templates to be added to the toolkit. Once identified, a web-based toolkit was designed and developed using ASP.NET and stored using SQL database server. The developed toolkit was implemented in the hospital and provided to the HIC team to use. The finding of the assessment of 15 features of HIC surveillance toolkit indicated that the end-users were very satisfied with the support of toolkit in documenting and reporting of infection-related data. Conclusion: HIC surveillance toolkit, allows the HIC team to capture, store, manage data, compare, and calculate the infection control rate and compliance rate. It also increases the ease of surveillance and reporting. It aids in reducing hospital-acquired infection (HAI) rates, simplifies the workflow of HIC and improves healthcare quality.

Natural language query formalization to SPARQL for querying knowledge bases using Rasa

The idea of data to be semantically linked and the subsequent usage of this linked data with modern computer applications has been one of the most important aspects of Web 3.0. However, the actualization of this aspect has been challenging due to the difficulties associated with building knowledge bases and using formal languages to query them. In this regard, SPARQL, a recursive acronym for standard query language and protocol for Linked Open Data and Resource Description Framework databases, is a most popular formal querying language. Nonetheless, writing SPARQL queries is known to be difficult, even for experts. Natural language query formalization, which involves semantically parsing natural language queries to their formal language equivalents, has been an essential step in overcoming this steep learning curve. Recent work in the field has seen the usage of artificial intelligence (AI) techniques for language modelling with adequate accuracy. This paper discusses a design for creating a closed domain ontology, which is then used by an AI-powered chat-bot that incorporates natural language query formalization for querying linked data using Rasa for entity extraction after intent recognition. A precision–recall analysis is performed using in-built Rasa tools in conjunction with our own testing parameters, and it is found that our system achieves a precision of 0.78, recall of 0.79 and F1-score of 0.79, which are better than the current state of the art.

Leveraging Clinical Informatics Tools to Extract Cumulative Anthracycline Exposure, Measure Cardiovascular Outcomes, and Assess Guideline Adherence for Children With Cancer.

Cardiovascular disease is a significant cause of late morbidity and mortality in survivors of childhood cancer. Clinical informatics tools could enhance provider adherence to echocardiogram guidelines for early detection of late-onset cardiomyopathy. Cancer registry data were linked to electronic health record data. Structured query language facilitated the construction of anthracycline-exposed cohorts at a single institution. Primary outcomes included the data quality from automatic anthracycline extraction, sensitivity of International Classification of Disease coding for heart failure, and adherence to echocardiogram guideline recommendations. The final analytic cohort included 385 pediatric oncology patients diagnosed between July 1, 2013, and December 31, 2018, among whom 194 were classified as no anthracycline exposure, 143 had low anthracycline exposure (< 250 mg/m2), and 48 had high anthracycline exposure (≥ 250 mg/m2). Manual review of anthracycline exposure was highly concordant (95%) with the automatic extraction. Among the unexposed group, 15% had an anthracycline administered at an outside institution not captured by standard query language coding. Manual review of echocardiogram parameters and clinic notes yielded a sensitivity of 75%, specificity of 98%, and positive predictive value of 68% for International Classification of Disease coding of heart failure. For patients with anthracycline exposure, 78.5% (n = 62) were adherent to guideline recommendations for echocardiogram surveillance. There were significant association with provider adherence and race and ethnicity (P = .047), and 50% of patients with Spanish as their primary language were adherent compared with 90% of patients with English as their primary language (P = .003). Extraction of treatment exposures from the electronic health record through clinical informatics and integration with cancer registry data represents a feasible approach to assess cardiovascular disease outcomes and adherence to guideline recommendations for survivors.

Efficient distributed path computation on RDF knowledge graphs using partial evaluation

A key property of Linked Data is the representation and publication of data as interconnected labelled graphs where different resources linked to each other form a network of meaningful information. Searching these important relationships between resources – within single or distributed graphs – can be reduced to a pathfinding or navigation problem, i.e., looking for chains of intermediate nodes. SPARQL1.1, the current standard query language for RDF-based Linked Data defines a construct – called Property Paths (PPs) – to navigate between entities within a single graph. Since Linked Data technologies are naturally aimed at decentralised scenarios, there are many cases where centralising this data is not feasible or even not possible for querying purposes. To address these problems, we propose a SPARQL PP-based graph processing approach – dubbed DpcLD – where users can execute SPARQL PP queries and find paths distributed across multiple, connected graphs exposed as SPARQL endpoints. To execute the distributed path queries we implemented an index-free, cache-based query engine that communicates with a shared algorithm running on each remote endpoint, and computes the distributed paths. In this paper, we highlight the way in which this approach exploits and aggregates partial paths, within a distributed environment, to produce complete results. We perform extensive experiments to demonstrate the performance of our approach on two datasets: One representing 10 million triples from the DBPedia SPARQL benchmark, and another full benchmark dataset corresponding to 124 million triples. We also perform a scalability test of our approach using real-world genomics datasets distributed across multiple endpoints. We compare our distributed approach with other distributed and centralized pathfinding approaches, showing that it outperforms other distributed approaches by orders of magnitude, and provides a good trade-off for cases when the data cannot be centralised.

COVID vaccination efficiency in nursing homes: from nursing home-centred to resident-centred

Abstract Background Korian® group is specialized in care and support for fragile people. Its data warehouse manages 304 French nursing homes (NH). The transmissions' table (TT), daily-fed by caregivers, contains key data about residents' care and health. Following GDPR rules, we could still build residents' life trajectories and detail all the residents' health events using indexes. In the past, we designed syndromes such as falls, flu, and last year, COVID with the standard query language. Now we want to combine ‘hard' data such as sociodemographic profiles and COVID vaccines with ‘soft' textual data such as COVID and emergency calls syndromes to get a better residents vaccine monitoring. Methods First, we selected all residents with at least one transmission, from December 21, to January 11, just before the COVID vaccination start, from 34 NH chosen for their variety of size and geographic location. We followed them for eleven weeks starting January 4, 2021. We selected the residents' age, sex, autonomy level, comorbidities and risks and then, vaccinations (0, 1 or 2), syndromes, hospitalizations and deaths date events. We searched for sample data inconsistencies by filtering COVID textual information in the TT and comparing it with the vaccination table. We described the sample according to the NH size, and then according to the number of vaccinations. Finally, we assessed COVID syndromes, falls, hospitalizations, emergency calls and deaths according to vaccinations followed by Poisson and logistic regressions. Results We built a 2,051 residents cohort with 1,401 vaccinated at least once and 650 not yet vaccinated. Data corrections then added 60 vaccinations. Residents in big structures were more masculine (72,9% vs 75,1%), more vaccinated (77,2% vs 67,2%), younger (86,7 vs 87,5), more autonomous (2,6, vs 2,4). Vaccination rates were also greater among those with comorbidities and risks. Conclusions By focusing on the TT resident's health events, we improved health modelization Key messages By using all the COVID information available in the database, we were able to show both, the beneficial effect of vaccination and the nefarious effect of COVID in a same model with machine learning. Aggregating hard categorical and numerical data with soft textual information about the resident’s health events, dates included, gives a precise and complete resident’s profile and health trajectory.

Practical and comprehensive formalisms for modelling contemporary graph query languages

The industry-wide adoption of graph databases has been hindered due to the fragmentation in syntax and semantics of available graph query languages. As a result, several projects have been proposed by industry and academia to develop a standard query language by integrating features from existing practical graph query languages. A significant factor that can impact query language integration is the lack of common theoretical language formalisms. We propose common formalisms by extending conjunctive queries and union of conjunctive queries with Tarski’s relation algebra (CQT/UCQT). We use common graph query patterns to compare the expressive power of (CQT/UCQT) with two practical graph query languages - Cypher and PGQL. The query languages are analysed on the core features of graph pattern matching and graph navigation, revealing the common and exclusive characteristics for these languages. Overall, our study serves as a formal basis for comparing existing graph query languages and assists the move towards query language integration and interoperability between available graph database technologies.

Moving out of the silos: from nursing homes clinical narratives to salient features of care

Abstract Background Korian® group is specialized in care and support for fragile people. Its professional data warehouse (DWH) is operational today in 300 French nursing homes (NH). The transmissions' table, daily-fed by caregivers, contains key data about residents' care and health. Thanks to long-term stays and substantial available data, we can build residents' life trajectories. In the past, we designed syndromes such as falls, flu, acute gastro enteritis and emergency calls by using the standard query language (SQL) but now we want to go further, analyzing the caregiver's feelings about residents' health. We will show here that our method involving syndromes' approach and then, sentiment analysis can improve residents' life prediction and achieve transparency and interpretability of the whole data flow process. Methods First, we selected all residents with at least one transmission over the last six weeks of 2014. Second, we extracted among them those having at least one transmission over the last six weeks of 2018, the 'survivors', and those found dead before, the 'deceased'. Third, we extracted both groups' transmissions over a 60 days period, removed the 'syndromic' ones and gave small subsets of the remaining ones, after anonymization and random sampling, to the Korian® physicians' network to classify into four features. Then, we completed this qualitative data with scores such as the autonomy level (AL = 1-6 higher the better), to build sentiment analysis but also new 'opinion' syndromes with SQL. Results We found, among 12,278 residents living in 155 NH in 2014: 3,075 survivors with 82% women (mean age = 85.3, AL = 3), 18% men (mean age=79.7, AL = 3.2) with 9,687 syndromes among 84,416 transmissions.2,900 deceased with 72% women (mean age = 88.7, AL = 2.3), 28% men (mean age=86.5, AL = 2.3) with 11,305 syndromes among 109,073transmissions. Conclusions We are confident that our interdisciplinary sentiment analysis will deepen the quantitative aging process understanding. Key messages After SQL procedures to extract syndromes, we moved health-transmissions data out of the silos and gave it to our physicians’ network for further analysis designing a truly interdisciplinary approach. The transmissions’ table with its nursing care narratives textual content can help us track every resident through time and space, define health features and deepen residents’ aging process knowledge.

225. Severity of cervical disc degeneration and health-related quality of life outcomes following anterior cervical discectomy and fusion

BACKGROUND CONTEXT Cervical disc degeneration is a common radiographic finding among patients with cervical spine pathology. While not all patients with radiographic evidence of disc degeneration are symptomatic, there has been little literature exploring the relationship between severity of cervical disc degeneration and postoperative health-related quality of life (HRQOL) outcomes in patients following anterior cervical discectomy and fusion (ACDF). PURPOSE The purpose of this study was to explore the relationship between the severity of cervical disc degeneration and postoperative HRQOL outcomes in patients’ status post one- to three-level ACDFs. STUDY DESIGN/SETTING Retrospective cohort study. PATIENT SAMPLE A cohort of patients status post ACDF with available preoperative/postoperative cervical radiographic imaging was obtained using Standardized Query Language (SQL) of a hospital database from a single-center, high-volume academic hospital from January 2013 to December 2018. OUTCOME MEASURES The primary outcomes of interest were HRQOL outcomes: Short Form-12 Physical/Mental Component Scores (SF-12 PCS/MCS), neck disability index (NDI), and visual analog scale arm/neck scores (VAS Arm/Neck). METHODS Patient demographics and case characteristics were obtained via SQL query and electronic medical record (EMR) chart reviews. HRQOL outcomes were obtained via SQL query with at least 1-year follow-up. Cervical disc degeneration was graded using a previously established scoring system incorporating: middle intervertebral disc height loss, anterior osteophyte length, and endplate sclerosis. Patients were then categorized into mild, moderate, or severe degeneration groups. Statistical analysis was performed with regression to examine the relationship between severity of disc degeneration and patient/clinical outcomes. RESULTS Of the 211 patients in the final cohort, 27 had mild degeneration, 100 moderate degeneration and 84 severe degeneration. Univariate analysis showed a significantly higher age in patients with severe disc degeneration compared to mild and moderate (58.4 vs 55.5 vs 52.5; p: 0.034). among HRQOL outcomes, patients with severe disc degeneration reported significant improvement in SF-12 PCS (9.4 vs 3.6 vs 4.9; p: 0.024) and VAS neck scores (-4.6 vs -2.6 vs -3.8; p: 0.036). This was further supported with multiple linear regression analysis, showing significant improvement in SF-12 PCS (β: 3.59; p: 0.005) and VAS neck (β: -1.13; p: 0.005) scores in the severe degeneration group when compared with the mild and moderate disc degeneration groups. Patients among all three groups showed significant improvement in SF-12 PCS, NDI, VAS neck, and VAS arm scores after surgical intervention. CONCLUSIONS Overall, our data validates that patients suffering from preoperative severe disc degeneration report the greatest improvement in symptoms and functional status when compared to patients with mild and moderate disease. Cervical disc degeneration has been meticulously studied at a basic science level, however clinical data regarding its effect on patient clinical outcomes has been lacking. Our study provides evidence that the preoperative degree of cervical disc degeneration has a significant effect on postoperative patient-reported outcomes, and discussion of these findings can help physicians and patients make informed decisions during the preoperative period. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

272. Timing of preoperative surgical antibiotic prophylaxis prior to one- to three-level elective lumbar fusion

BACKGROUND CONTEXT Surgical site infections (SSIs) in patients who have undergone lumbar fusion surgery can be a devastating complication and a common indication for revision surgery. The functional decline and economic burden put on the patient as a result of infection can drastically affect postoperative outcomes. As such, studies have identified potential areas of intervention, including: optimizing patient comorbidities, thoroughly supervising the operating room, and administering antibiotic prophylaxis. Guidelines have established a goal to infuse antibiotics within one hour prior to incision. PURPOSE The aim of this study was to examine the relationship between timing of preoperative surgical antibiotic prophylaxis and adverse outcomes (e.g. SSIs, 90-day readmissions, and revision surgery) with subgroup analysis stratified by type of antibiotic. While most antibiotics are administered within one hour of surgery, certain circumstances (ie, prolonged case duration, long operating room turnover) may delay the time from antibiotic administration to incision. STUDY DESIGN/SETTING Retrospective cohort study. PATIENT SAMPLE A cohort of patients status post elective one- to three-level lumbar fusion performed between January 2013 and December 2018 was identified at a single, high-volume academic medical center using a database query via Standardized Query Language (SQL). OUTCOME MEASURES The primary outcome of interest was surgical site infections (SSIs), with secondary outcomes of interest including: 90-day readmission and revision surgery. METHODS SQL query and electronic medical record (EMR) chart reviews were used to identify patient demographics, surgical case characteristics, and postoperative complications. Patients were allocated into 5 groups based on the time period from preoperative antibiotic administration to incision (Group A = 0-15 min, Group B = 16-30 min, Group C = 31-45 min, Group D = 46 – 60 min, and Group E = 61+ min). SSIs were identified by the definition set forth by the CDC. Statistical analysis, including a logistic regression model, was used to examine the relationship between patient/case characteristics, preoperative antibiotic timing, and postoperative outcomes. RESULTS From 1,131 patients included in the final analysis, 26 SSIs were identified. Univariate analysis revealed patients in Group E had significantly longer lengths of operation (p: 0.044) and higher mean comorbidity scores (p: 0.012) compared to all other groups. Logistic regression analysis revealed that Group E patients were 1.4 times more likely to develop an SSI and 7.2 times more likely to be readmitted within 90 days compared to Group A. Furthermore, Groups B-D did not show evidence of significantly higher odds of infection compared to Group A. When stratified by antibiotic type, patients treated with Vancomycin showed the lowest incidence of SSI when administered between 16-30 minutes, however Cefazolin showed the lowest incidence when administered between 0-15 minutes. Among 26 SSIs, nine cultures showed evidence of multi-drug resistance, with two treated preoperatively with vancomycin and seven treated with cefazolin. CONCLUSIONS Our results present that preoperative antibiotic administration beyond one hour in patients who have undergone lumbar fusion is associated with higher rates of SSI. Furthermore, the utility of vancomycin to minimize the risk of SSI is optimal when administered between 16 and 30 minutes prior to incision. An effective antibiotic prophylaxis regiment depends on patient characteristics and the pharmacokinetics of antibiotic selection, while minimizing side effects and multi-drug resistant organisms. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.