Identification of Patients with Nontraumatic Intracranial Hemorrhage Using Administrative Claims Data-Reference-Cited by-同舟云学术

Identification of Patients with Nontraumatic Intracranial Hemorrhage Using Administrative Claims Data

Published:2020-12 Issue:12 Volume:29 Page:105306
ISSN:1052-3057
Container-title:Journal of Stroke and Cerebrovascular Diseases
language:en
Short-container-title:Journal of Stroke and Cerebrovascular Diseases

Author:

Sangal Rohit B.^ORCID,Fodeh Samah,Taylor Andrew,Rothenberg Craig^ORCID,Finn Emily B.^ORCID,Sheth Kevin,Matouk Charles^ORCID,Ulrich Andrew,Parwani Vivek,Sather John,Venkatesh Arjun

Funder

National Center for Advancing Translational Sciences

Agency for Healthcare Research and Quality

Publisher

Elsevier BV

Subject

Cardiology and Cardiovascular Medicine,Neurology (clinical),Rehabilitation,Surgery

Reference22 articles.

1. Validity of hospital discharge diagnosis codes for stroke: the atherosclerosis risk in communities study;Jones;Stroke,2014

2. Sensitivity of administrative coding in identifying inpatient acute strokes complicating procedures or other diseases in uk hospitals;Li;Journal of the American Heart Association,2019

3. Validating administrative data in stroke research;Tirschwell;Stroke,2002

4. Using administrative diagnostic data to assess the quality of hospital care. Pitfalls and potential of icd-9-cm;Iezzoni;International Journal of Technology Assessment in Health Care,1990

5. Inaccuracy of the international classification of diseases (icd-9-cm) in identifying the diagnosis of ischemic cerebrovascular disease;Benesch;Neurology,1997

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