Literature DB >> 27863971

Systemic inaccuracies in the National Surgical Quality Improvement Program database: Implications for accuracy and validity for neurosurgery outcomes research.

John D Rolston1, Seunggu J Han2, Edward F Chang2.   

Abstract

The American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) provides a rich database of North American surgical procedures and their complications. Yet no external source has validated the accuracy of the information within this database. Using records from the 2006 to 2013 NSQIP database, we used two methods to identify errors: (1) mismatches between the Current Procedural Terminology (CPT) code that was used to identify the surgical procedure, and the International Classification of Diseases (ICD-9) post-operative diagnosis: i.e., a diagnosis that is incompatible with a certain procedure. (2) Primary anesthetic and CPT code mismatching: i.e., anesthesia not indicated for a particular procedure. Analyzing data for movement disorders, epilepsy, and tumor resection, we found evidence of CPT code and postoperative diagnosis mismatches in 0.4-100% of cases, depending on the CPT code examined. When analyzing anesthetic data from brain tumor, epilepsy, trauma, and spine surgery, we found evidence of miscoded anesthesia in 0.1-0.8% of cases. National databases like NSQIP are an important tool for quality improvement. Yet all databases are subject to errors, and measures of internal consistency show that errors affect up to 100% of case records for certain procedures in NSQIP. Steps should be taken to improve data collection on the frontend of NSQIP, and also to ensure that future studies with NSQIP take steps to exclude erroneous cases from analysis.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Complications; Database; NSQIP; Patient safety; Quality improvement; Registry

Mesh:

Year:  2016        PMID: 27863971     DOI: 10.1016/j.jocn.2016.10.045

Source DB:  PubMed          Journal:  J Clin Neurosci        ISSN: 0967-5868            Impact factor:   1.961


  6 in total

1.  Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis.

Authors:  Paul T Ogink; Aditya V Karhade; Quirina C B S Thio; Stuart H Hershman; Thomas D Cha; Christopher M Bono; Joseph H Schwab
Journal:  Eur Spine J       Date:  2019-03-27       Impact factor: 3.134

2.  CORR Insights®: What Is the Timing of General Health Adverse Events That Occur After Total Joint Arthroplasty?

Authors:  Arun B Mullaji
Journal:  Clin Orthop Relat Res       Date:  2017-04-18       Impact factor: 4.176

3.  Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods.

Authors:  Paul T Ogink; Aditya V Karhade; Quirina C B S Thio; William B Gormley; Fetullah C Oner; Jorrit J Verlaan; Joseph H Schwab
Journal:  Eur Spine J       Date:  2019-04-02       Impact factor: 3.134

4.  Variation in Coding Practices for Vestibular Schwannoma Surgery.

Authors:  Wenya Linda Bi; Michael A Mooney; Seungwon Yoon; Saksham Gupta; Michael T Lawton; Kaith K Almefty; C Eduardo Corrales; Ian F Dunn
Journal:  J Neurol Surg B Skull Base       Date:  2018-07-16

5.  Status epilepticus after intracranial neurosurgery: incidence and risk stratification by perioperative clinical features.

Authors:  Michael C Jin; Jonathon J Parker; Michael Zhang; Zack A Medress; Casey H Halpern; Gordon Li; John K Ratliff; Gerald A Grant; Robert S Fisher; Stephen Skirboll
Journal:  J Neurosurg       Date:  2021-05-14       Impact factor: 5.115

6.  The association of preoperative blood markers with postoperative readmissions following arthroplasty.

Authors:  Amir Khoshbin; Graeme Hoit; Lauren Leone Nowak; Anser Daud; Martine Steiner; Peter Juni; Bheeshma Ravi; Amit Atrey
Journal:  Bone Jt Open       Date:  2021-06
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.