FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4380729909> ?p ?o ?g. }

• W4380729909 endingPage "3" @default.
• W4380729909 startingPage "1" @default.
• W4380729909 abstract "THE NEED FOR A BROADER PERSPECTIVE Since the 21st Century Cures Act, real-world research has been increasingly valued as a source of evidence to guide clinical practice.1 Randomized control trials (RCTs) constitute the highest level of evidence obtainable in medical research and embody the fundamental driving force for advancing medical and surgical knowledge.2,3 However, RCTs are burdened by their inherently costly nature and need for precise establishment of clinical equipoise while also occasionally having disputable external validity.4,5 Databases and registries provide a unique insight into reflecting current clinical practice and consequently complement the medical literature with real-world and big data, thus enhancing the evidence provided by RCTs. This combination enables the application of advanced analytics, which facilitates the interpretation of highly interactive outcomes and the detection of higher-order correlations.6 In addition to generating evidence, databases and registries enable the evaluation of health care practices and create a framework for the identification of potential areas of improvement. Overall, databases and registries pose an integral component of real-world research and have gained increasing traction in the current evidence-based and value-based system by advancing science through multicenter collaborations, expediting the development of clinical guidelines and ultimately improving patient care. ADMINISTRATIVE DATABASES The Healthcare Cost and Utilization Project (HCUP) has created a family of databases funded by the Agency of Healthcare Research and Quality to identify, monitor, and analyze national trends in healthcare utilization, access, charges, quality, and outcomes.7 The National Inpatient Sample (NIS) constitutes the largest publicly available all-payer inpatient care database in the United States, enabling the study of rare conditions, uncommon treatments, and special populations according to patient demographics and International Classification of Diseases diagnosis and procedure codes. It includes clinical and resource use information from approximately 7 million hospital inpatient stays, representing 20% of discharge records from all HCUP participating hospitals, which cover 98% of the US population. The latest NIS version has eliminated hospital identifiers, protecting patient confidentiality but consequently preventing hospital volume analyses. However, HCUP's state-specific databases, such as the State Inpatient Database, State Ambulatory Surgery and Services Databases, and State Emergency Department Databases, can be used to investigate regional trends in health care.7 The National Ambulatory Surgery Sample is the national aggregate of State Ambulatory Surgery and Services Databases, and the Nationwide Emergency Department Sample is the aggregate of State Emergency Department Databases.7 HCUP has also designed the Kids' Inpatient Database, which embodies the pediatric equivalent to NIS, including 3 million pediatric discharges per year, representing roughly 7 million hospitalizations nationwide.7 Furthermore, HCUP has developed the National Readmission Database, which encompasses approximately 18 million readmissions, representing 60% of all US readmissions.7 Limitations that narrow the clinical applicability of the different HCUP databases include the lack of clinically impactful covariates to standardize outcomes, the lack of sufficient follow-up to assess the long-term effectiveness of interventions, and the lack of patient-reported outcomes. QUALITY REGISTRIES: NSQIP The National Surgical Quality Improvement Program (NSQIP) is a quality registry including cases from several surgical specialties. Inspired by the need to regulate perioperative morbidity and mortality in Veterans' Affairs hospitals, NSQIP currently includes data from and provides adjusted quality feedback to more than 700 hospitals. One of NSQIP's outstanding virtues is comprehensiveness: One should note the concise yet inclusive list of comorbidities and complications captured that are relevant to a vast spectrum of surgeries from thyroidectomy to knee joint replacement. Nevertheless, NSQIP's all-surgery nature and quality-oriented objective limit its bandwidth to 30-day outcomes and low granularity in surgical details (relying on Current Procedure Terminology codes). NEUROSURGERY QUALITY REGISTRIES: QOD Responding to the need for a highly granular neurosurgical subspecialty-specific quality registry with long-term follow-up, the NeuroPoint Alliance (NPA) launched the Quality Outcomes Database (QOD) in 2012.8 Since its early days focusing on spine surgery—currently represented by the American Spine Registry—the QOD has launched 6 different modules covering a broad spectrum of neurosurgical practice, from cerebrovascular and functional to neuro-oncology and stereotactic radiosurgery.9 These focused clinical registries have not only provided participating sites with practice-specific quality feedback incorporating patient-reported outcomes but they have also yielded some of the most valuable observational research resources in spine surgery.9,10 More specifically, their large scale, prospective nature, and high external validity have revolutionized outcomes benchmarking and allowed for clinically impactful real-world investigations to advance the fields of patient-reported outcomes and surgical decision-making across neurosurgery.11,12 The QOD has also fostered the generation of focused high-granularity subset databases. More specifically, the QOD allows querying the largest volume of clinical data in spine surgery nationally and collecting cohorts of significant size that are prospectively followed up without the confirmation and selection bias inherent to studies with a prespecified research purpose. Triggered by the need for high-quality evidence in clinical research on lumbar spondylolisthesis13 and cervical spondylotic myelopathy,14 14 of the most high-enrolling participating sites within the spinal modules of the QOD formed the QOD Study Group, which currently uses 5 of the largest and most granular pathology-specific databases in clinical spine research. Research efforts of the QOD Study Group aim to shed light on important clinical questions in surgical decision-making and prognostication of surgical outcomes using prospectively collected, multi-institutional with high medium and long-term follow-ups. INSIGHTS INTO THE FUTURE OF CLINICAL REGISTRIES The landscape of clinical registries in neurosurgery and spine surgery is changing, and one of the evolutions anticipated in the future is the increasing integration of imaging in registries. Neurosurgical practice is highly dependent on imaging studies; hence, for the ultimate establishment of clinical equipoise, adjusting for radiographical phenotypes is critical. In this context, the Stereotactic Radiosurgery Registry of the NPA is one of the first quality registries to require the submission of brain magnetic resonance imaging for each enrolled patient.15 Another notable effort to collect big data on radiological imaging is the NYUMets_Brain project, a publicly accessible single-institution clinical registry that underlines the feasibility and advantages of large-scale imaging sharing with a potential to scale up into a multi-institutional collaboration.16 The application of advancements in computer science promise significant relief of the logistical burden associated with national registries. Data collection and verification constitute the most burdensome processes for central organizations and participating sites and, hence, absorb most of the funds and human resources. The tempting prospect of automated data abstraction might be realized using utilization of natural language processing (NLP).17 NLP is the field of artificial intelligence that focuses on training algorithms to interpret human languages.18 Admittedly, the application of NLP in data abstraction is challenged by heterogeneity in documentation styles among physicians and institutions; nevertheless, some early biomedical text mining efforts in seizure classification and mental health evaluation have yielded promising results.19,20 Overall, clinical registries are increasingly used in neurosurgery to facilitate quality surveillance and real-world research. During the past decade, the need for specialization and increased granularity in information has been evident, and the future of registries in neurosurgery belongs to the investment in neurosurgery subspecialty-specific efforts. In pursuing higher-quality pragmatic evidence, large-scale quality improvement, and health policy advocacy, the neurosurgical community's efforts are empowered by a growing force: Strength in Numbers. Praveen V. Mummaneni, MD, MBA Assistant Editor, Neurosurgery Publications Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA Mohamad Bydon, MD Member, CNS Executive Committee Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota, USA" @default.
• W4380729909 created "2023-06-16" @default.
• W4380729909 creator A5024735928 @default.
• W4380729909 creator A5030704684 @default.
• W4380729909 date "2023-07-01" @default.
• W4380729909 modified "2023-10-18" @default.
• W4380729909 title "Clinical Databases in Spine Surgery: Strength in Numbers" @default.
• W4380729909 cites W1964172790 @default.
• W4380729909 cites W1968930694 @default.
• W4380729909 cites W2106952837 @default.
• W4380729909 cites W2118847203 @default.
• W4380729909 cites W2552330020 @default.
• W4380729909 cites W2810478122 @default.
• W4380729909 cites W3016564562 @default.
• W4380729909 cites W3022266653 @default.
• W4380729909 cites W3024357968 @default.
• W4380729909 cites W3150108817 @default.
• W4380729909 cites W3205590193 @default.
• W4380729909 cites W4200131143 @default.
• W4380729909 cites W4212898841 @default.
• W4380729909 cites W4225128959 @default.
• W4380729909 cites W4226127130 @default.
• W4380729909 cites W4286255110 @default.
• W4380729909 cites W4293194409 @default.
• W4380729909 cites W4307585054 @default.
• W4380729909 doi "https://doi.org/10.1227/neu.0000000000002465" @default.
• W4380729909 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37318222" @default.
• W4380729909 hasPublicationYear "2023" @default.
• W4380729909 type Work @default.
• W4380729909 citedByCount "0" @default.
• W4380729909 crossrefType "journal-article" @default.
• W4380729909 hasAuthorship W4380729909A5024735928 @default.
• W4380729909 hasAuthorship W4380729909A5030704684 @default.
• W4380729909 hasBestOaLocation W43807299091 @default.
• W4380729909 hasConcept C141071460 @default.
• W4380729909 hasConcept C17744445 @default.
• W4380729909 hasConcept C199539241 @default.
• W4380729909 hasConcept C205383261 @default.
• W4380729909 hasConcept C2779473830 @default.
• W4380729909 hasConcept C60644358 @default.
• W4380729909 hasConcept C71924100 @default.
• W4380729909 hasConcept C86803240 @default.
• W4380729909 hasConceptScore W4380729909C141071460 @default.
• W4380729909 hasConceptScore W4380729909C17744445 @default.
• W4380729909 hasConceptScore W4380729909C199539241 @default.
• W4380729909 hasConceptScore W4380729909C205383261 @default.
• W4380729909 hasConceptScore W4380729909C2779473830 @default.
• W4380729909 hasConceptScore W4380729909C60644358 @default.
• W4380729909 hasConceptScore W4380729909C71924100 @default.
• W4380729909 hasConceptScore W4380729909C86803240 @default.
• W4380729909 hasIssue "1" @default.
• W4380729909 hasLocation W43807299091 @default.
• W4380729909 hasLocation W43807299092 @default.
• W4380729909 hasLocation W43807299093 @default.
• W4380729909 hasOpenAccess W4380729909 @default.
• W4380729909 hasPrimaryLocation W43807299091 @default.
• W4380729909 hasRelatedWork W1506200166 @default.
• W4380729909 hasRelatedWork W1995515455 @default.
• W4380729909 hasRelatedWork W2048182022 @default.
• W4380729909 hasRelatedWork W2080531066 @default.
• W4380729909 hasRelatedWork W2604872355 @default.
• W4380729909 hasRelatedWork W2748952813 @default.
• W4380729909 hasRelatedWork W2899084033 @default.
• W4380729909 hasRelatedWork W3031052312 @default.
• W4380729909 hasRelatedWork W3032375762 @default.
• W4380729909 hasRelatedWork W3108674512 @default.
• W4380729909 hasVolume "93" @default.
• W4380729909 isParatext "false" @default.
• W4380729909 isRetracted "false" @default.
• W4380729909 workType "article" @default.