Literature DB >> 26515215

Variation and Trends in the Documentation of National Institutes of Health Stroke Scale in GWTG-Stroke Hospitals.

Mathew J Reeves1, Eric E Smith2, Gregg C Fonarow2, Xin Zhao2, Michael Thompson2, Eric D Peterson2, Lee H Schwamm2, DaiWai Olson2.   

Abstract

BACKGROUND: Although National Institutes of Health Stroke Scale (NIHSS) is an important prognostic variable, it is often incompletely documented in clinical registries, such as Get With The Guidelines (GWTG)-Stroke. We describe trends in NIHSS documentation by GWTG-Stroke hospitals, identify patient-level and hospital-level factors associated with documentation, and determine the degree to which the reporting of NIHSS is potentially biased. METHODS AND
RESULTS: We analyzed NIHSS documentation in 1 184 288 patients with acute ischemic stroke admitted to 1704 GWTG-Stroke hospitals between 2003 and 2012. We used multivariable logistic regression models to identify hospital-level and patient-level predictors of NIHSS documentation. We examined the relationship between hospital-level NIHSS documentation rates and observed NIHSS scores to determine whether the reporting of NIHSS data was subject to selection bias. The overall NIHSS documentation rate was 56.1%; the median NIHSS was 4 (interquartile range, 2-9). Between 2003 and 2012, mean hospital-level NIHSS documentation increased dramatically from 27% to 70% (P<0.0001). Documentation was higher in patients who arrived by ambulance, who arrived soon after onset, and were treated at larger, primary stroke centers. Hospital-level NIHSS documentation rates and NIHSS scores were modestly inversely correlated (r=-0.207; P<0.0001), suggesting that NIHSS data from hospitals with low documentation were shifted toward higher values. In sensitivity analysis, the degree of bias in NIHSS reporting was reduced in more recent years (2011-2012) when NIHSS documentation was noticeably better.
CONCLUSIONS: Documentation of NIHSS is higher in patients who are thrombolysis candidates. Evidence of modest bias in NIHSS scores was observed but this has lessened as the documentation of NIHSS has improved in recent years.
© 2015 American Heart Association, Inc.

Entities:  

Keywords:  documentation; hospitals; registries; selection bias; stroke

Mesh:

Substances:

Year:  2015        PMID: 26515215     DOI: 10.1161/CIRCOUTCOMES.115.001775

Source DB:  PubMed          Journal:  Circ Cardiovasc Qual Outcomes        ISSN: 1941-7713


  10 in total

1.  Comparison of Acute Ischemic Stroke Care and Outcomes Between Comprehensive Stroke Centers and Primary Stroke Centers in the United States.

Authors:  Shumei Man; Xin Zhao; Ken Uchino; M Shazam Hussain; Eric E Smith; Deepak L Bhatt; Ying Xian; Lee H Schwamm; Shreyansh Shah; Yosef Khan; Gregg C Fonarow
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2018-06

2.  Adherence to American Heart Association/American Stroke Association Clinical Performance Measures in a Peruvian Neurological Reference Institute.

Authors:  Carlos Abanto; Angela K Ulrich; Ana Valencia; Víctor Dueñas; Silvia Montano; David Tirschwell; Joseph Zunt
Journal:  J Stroke Cerebrovasc Dis       Date:  2020-09-09       Impact factor: 2.136

3.  Distributional Validity and Prognostic Power of the National Institutes of Health Stroke Scale in US Administrative Claims Data.

Authors:  Hamidreza Saber; Jeffrey L Saver
Journal:  JAMA Neurol       Date:  2020-05-01       Impact factor: 18.302

4.  Impact of an Expanded Hospital Recognition Program for Stroke Quality of Care.

Authors:  Paul A Heidenreich; Xin Zhao; Adrian F Hernandez; Lee H Schwamm; Eric Smith; Mat Reeves; Eric D Peterson; Gregg C Fonarow
Journal:  J Am Heart Assoc       Date:  2017-01-21       Impact factor: 5.501

Review 5.  The American Heart Association's Get With the Guidelines (GWTG)-Stroke development and impact on stroke care.

Authors:  Cora H Ormseth; Kevin N Sheth; Jeffrey L Saver; Gregg C Fonarow; Lee H Schwamm
Journal:  Stroke Vasc Neurol       Date:  2017-05-29

6.  The impact of disease severity adjustment on hospital standardised mortality ratios: Results from a service-wide analysis of ischaemic stroke admissions using linked pre-hospital, admissions and mortality data.

Authors:  Melina Gattellari; Chris Goumas; Bin Jalaludin; John Worthington
Journal:  PLoS One       Date:  2019-05-21       Impact factor: 3.240

7.  Assessing stroke severity using electronic health record data: a machine learning approach.

Authors:  Emily Kogan; Kathryn Twyman; Jesse Heap; Dejan Milentijevic; Jennifer H Lin; Mark Alberts
Journal:  BMC Med Inform Decis Mak       Date:  2020-01-08       Impact factor: 2.796

8.  Identifying Gaps and Missed Opportunities for Intravenous Thrombolytic Treatment of Inpatient Stroke.

Authors:  Karan Topiwala; Karan Tarasaria; Ilene Staff; Dawn Beland; Erica Schuyler; Amre Nouh
Journal:  Front Neurol       Date:  2020-02-26       Impact factor: 4.003

9.  Association of Socioeconomic Status and Infarct Volume With Functional Outcome in Patients With Ischemic Stroke.

Authors:  Ahmed Ghoneem; Michael T Osborne; Shady Abohashem; Nicki Naddaf; Tomas Patrich; Tawseef Dar; Amr Abdelbaky; Adeeb Al-Quthami; Jason H Wasfy; Katrina A Armstrong; Hakan Ay; Ahmed Tawakol
Journal:  JAMA Netw Open       Date:  2022-04-01

10.  Association Between 2010 Medicare Reforms and Utilization of Postacute Inpatient Rehabilitation in Ischemic Stroke.

Authors:  Nneka L Ifejika; Farhaan Vahidy; Mathew Reeves; Ying Xian; Li Liang; Roland Matsouaka; Gregg C Fonarow; Sean I Savitz
Journal:  Am J Phys Med Rehabil       Date:  2021-07-01       Impact factor: 3.412

  10 in total

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