The Ottawa SAH search algorithms: protocol for a multi- centre validation study of primary subarachnoid hemorrhage prediction models using health administrative data (the SAHepi prediction study protocol)

Abstract Background Conducting prospective epidemiological studies of hospitalized patients with rare diseases like primary subarachnoid hemorrhage (pSAH) are difficult due to time and budgetary constraints. Routinely collected administrative data could remove these barriers. We derived and validate...

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Main Authors:	S. W. English, L. McIntyre, V. Saigle, M. Chassé, D. A. Fergusson, A. F. Turgeon, F. Lauzier, D. Griesdale, A. Garland, R. Zarychanski, A. Algird, C. van Walraven
Format:	Article
Language:	English
Published:	BMC 2018-09-01
Series:	BMC Medical Research Methodology
Subjects:	Administrative health data Prediction rule Diagnosis Subarachnoid hemorrhage
Online Access:	http://link.springer.com/article/10.1186/s12874-018-0553-3

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spelling	doaj-876745e5563e4f178ff0b6f6c3cdb93a2020-11-24T21:35:12ZengBMCBMC Medical Research Methodology1471-22882018-09-011811910.1186/s12874-018-0553-3The Ottawa SAH search algorithms: protocol for a multi- centre validation study of primary subarachnoid hemorrhage prediction models using health administrative data (the SAHepi prediction study protocol)S. W. English0L. McIntyre1V. Saigle2M. Chassé3D. A. Fergusson4A. F. Turgeon5F. Lauzier6D. Griesdale7A. Garland8R. Zarychanski9A. Algird10C. van Walraven11Department of Medicine (Critical Care), University of OttawaDepartment of Medicine (Critical Care), University of OttawaClinical Epidemiology Program, Ottawa Hospital Research InstituteDepartment of Medicine, Division of Critical Care, Centre Hospitalier de l’Université de MontréalClinical Epidemiology Program, Ottawa Hospital Research InstituteCentre de recherche du CHU de Québec, Population Health and Optimal Health Practices Research Unit (Trauma – Emergency – Critical Care Medicine), Université LavalCentre de recherche du CHU de Québec, Population Health and Optimal Health Practices Research Unit (Trauma – Emergency – Critical Care Medicine), Université LavalDeparment of Anesthesiology, Pharmacology & Therapeutics, University of British ColumbiaDepartment of Internal Medicine, Sections of Critical Care and Respirology, University of ManitobaDepartment of Internal Medicine, Sections of Critical Care and Hematology/Medical Oncology, University of ManitobaDepartment of Neurosurgy, McMaster University, Hamilton Health SciencesClinical Epidemiology Program, Ottawa Hospital Research InstituteAbstract Background Conducting prospective epidemiological studies of hospitalized patients with rare diseases like primary subarachnoid hemorrhage (pSAH) are difficult due to time and budgetary constraints. Routinely collected administrative data could remove these barriers. We derived and validated 3 algorithms to identify hospitalized patients with a high probability of pSAH using administrative data. We aim to externally validate their performance in four hospitals across Canada. Methods Eligible patients include those ≥18 years of age admitted to these centres from January 1, 2012 to December 31, 2013. We will include patients whose discharge abstracts contain predictive variables identified in the models (ICD-10-CA diagnostic codes I60 (subarachnoid hemorrhage), I61 (intracranial hemorrhage), 162 (other nontrauma intracranial hemorrhage), I67 (other cerebrovascular disease), S06** (intracranial injury), G97 (other postprocedural nervous system disorder) and CCI procedural codes 1JW51 (occlusion of intracranial vessels), 1JE51 (carotid artery inclusion), 3JW10 (intracranial vessel imaging), 3FY20 (CT scan (soft tissue of neck)), and 3OT20 (CT scan (abdominal cavity)). The algorithms will be applied to each patient and the diagnosis confirmed via chart review. We will assess each model’s sensitivity, specificity, negative and positive predictive value across the sites. Discussion Validating the Ottawa SAH Prediction Algorithms will provide a way to accurately identify large SAH cohorts, thereby furthering research and altering care.http://link.springer.com/article/10.1186/s12874-018-0553-3Administrative health dataPrediction ruleDiagnosisSubarachnoid hemorrhage
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	S. W. English L. McIntyre V. Saigle M. Chassé D. A. Fergusson A. F. Turgeon F. Lauzier D. Griesdale A. Garland R. Zarychanski A. Algird C. van Walraven
spellingShingle	S. W. English L. McIntyre V. Saigle M. Chassé D. A. Fergusson A. F. Turgeon F. Lauzier D. Griesdale A. Garland R. Zarychanski A. Algird C. van Walraven The Ottawa SAH search algorithms: protocol for a multi- centre validation study of primary subarachnoid hemorrhage prediction models using health administrative data (the SAHepi prediction study protocol) BMC Medical Research Methodology Administrative health data Prediction rule Diagnosis Subarachnoid hemorrhage
author_facet	S. W. English L. McIntyre V. Saigle M. Chassé D. A. Fergusson A. F. Turgeon F. Lauzier D. Griesdale A. Garland R. Zarychanski A. Algird C. van Walraven
author_sort	S. W. English
title	The Ottawa SAH search algorithms: protocol for a multi- centre validation study of primary subarachnoid hemorrhage prediction models using health administrative data (the SAHepi prediction study protocol)
title_short	The Ottawa SAH search algorithms: protocol for a multi- centre validation study of primary subarachnoid hemorrhage prediction models using health administrative data (the SAHepi prediction study protocol)
title_full	The Ottawa SAH search algorithms: protocol for a multi- centre validation study of primary subarachnoid hemorrhage prediction models using health administrative data (the SAHepi prediction study protocol)
title_fullStr	The Ottawa SAH search algorithms: protocol for a multi- centre validation study of primary subarachnoid hemorrhage prediction models using health administrative data (the SAHepi prediction study protocol)
title_full_unstemmed	The Ottawa SAH search algorithms: protocol for a multi- centre validation study of primary subarachnoid hemorrhage prediction models using health administrative data (the SAHepi prediction study protocol)
title_sort	ottawa sah search algorithms: protocol for a multi- centre validation study of primary subarachnoid hemorrhage prediction models using health administrative data (the sahepi prediction study protocol)
publisher	BMC
series	BMC Medical Research Methodology
issn	1471-2288
publishDate	2018-09-01
description	Abstract Background Conducting prospective epidemiological studies of hospitalized patients with rare diseases like primary subarachnoid hemorrhage (pSAH) are difficult due to time and budgetary constraints. Routinely collected administrative data could remove these barriers. We derived and validated 3 algorithms to identify hospitalized patients with a high probability of pSAH using administrative data. We aim to externally validate their performance in four hospitals across Canada. Methods Eligible patients include those ≥18 years of age admitted to these centres from January 1, 2012 to December 31, 2013. We will include patients whose discharge abstracts contain predictive variables identified in the models (ICD-10-CA diagnostic codes I60 (subarachnoid hemorrhage), I61 (intracranial hemorrhage), 162 (other nontrauma intracranial hemorrhage), I67 (other cerebrovascular disease), S06** (intracranial injury), G97 (other postprocedural nervous system disorder) and CCI procedural codes 1JW51 (occlusion of intracranial vessels), 1JE51 (carotid artery inclusion), 3JW10 (intracranial vessel imaging), 3FY20 (CT scan (soft tissue of neck)), and 3OT20 (CT scan (abdominal cavity)). The algorithms will be applied to each patient and the diagnosis confirmed via chart review. We will assess each model’s sensitivity, specificity, negative and positive predictive value across the sites. Discussion Validating the Ottawa SAH Prediction Algorithms will provide a way to accurately identify large SAH cohorts, thereby furthering research and altering care.
topic	Administrative health data Prediction rule Diagnosis Subarachnoid hemorrhage
url	http://link.springer.com/article/10.1186/s12874-018-0553-3
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The Ottawa SAH search algorithms: protocol for a multi- centre validation study of primary subarachnoid hemorrhage prediction models using health administrative data (the SAHepi prediction study protocol)

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