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Validation of an algorithm to identify heart failure hospitalisations in patients with diabetes within the veterans health administration

OBJECTIVES: We aimed to validate an algorithm using both primary discharge diagnosis (International Classification of Diseases Ninth Revision (ICD-9)) and diagnosis-related group (DRG) codes to identify hospitalisations due to decompensated heart failure (HF) in a population of patients with diabete...

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Autores principales: Presley, Caroline A, Min, Jea Young, Chipman, Jonathan, Greevy, Robert A, Grijalva, Carlos G, Griffin, Marie R, Roumie, Christianne L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5875613/
https://www.ncbi.nlm.nih.gov/pubmed/29581206
http://dx.doi.org/10.1136/bmjopen-2017-020455
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author Presley, Caroline A
Min, Jea Young
Chipman, Jonathan
Greevy, Robert A
Grijalva, Carlos G
Griffin, Marie R
Roumie, Christianne L
author_facet Presley, Caroline A
Min, Jea Young
Chipman, Jonathan
Greevy, Robert A
Grijalva, Carlos G
Griffin, Marie R
Roumie, Christianne L
author_sort Presley, Caroline A
collection PubMed
description OBJECTIVES: We aimed to validate an algorithm using both primary discharge diagnosis (International Classification of Diseases Ninth Revision (ICD-9)) and diagnosis-related group (DRG) codes to identify hospitalisations due to decompensated heart failure (HF) in a population of patients with diabetes within the Veterans Health Administration (VHA) system. DESIGN: Validation study. SETTING: Veterans Health Administration—Tennessee Valley Healthcare System PARTICIPANTS: We identified and reviewed a stratified, random sample of hospitalisations between 2001 and 2012 within a single VHA healthcare system of adults who received regular VHA care and were initiated on an antidiabetic medication between 2001 and 2008. We sampled 500 hospitalisations; 400 hospitalisations that fulfilled algorithm criteria, 100 that did not. Of these, 497 had adequate information for inclusion. The mean patient age was 66.1 years (SD 11.4). Majority of patients were male (98.8%); 75% were white and 20% were black. PRIMARY AND SECONDARY OUTCOME MEASURES: To determine if a hospitalisation was due to HF, we performed chart abstraction using Framingham criteria as the referent standard. We calculated the positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity for the overall algorithm and each component (primary diagnosis code (ICD-9), DRG code or both). RESULTS: The algorithm had a PPV of 89.7% (95% CI 86.8 to 92.7), NPV of 93.9% (89.1 to 98.6), sensitivity of 45.1% (25.1 to 65.1) and specificity of 99.4% (99.2 to 99.6). The PPV was highest for hospitalisations that fulfilled both the ICD-9 and DRG algorithm criteria (92.1% (89.1 to 95.1)) and lowest for hospitalisations that fulfilled only DRG algorithm criteria (62.5% (28.4 to 96.6)). CONCLUSIONS: Our algorithm, which included primary discharge diagnosis and DRG codes, demonstrated excellent PPV for identification of hospitalisations due to decompensated HF among patients with diabetes in the VHA system.
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spelling pubmed-58756132018-04-02 Validation of an algorithm to identify heart failure hospitalisations in patients with diabetes within the veterans health administration Presley, Caroline A Min, Jea Young Chipman, Jonathan Greevy, Robert A Grijalva, Carlos G Griffin, Marie R Roumie, Christianne L BMJ Open Epidemiology OBJECTIVES: We aimed to validate an algorithm using both primary discharge diagnosis (International Classification of Diseases Ninth Revision (ICD-9)) and diagnosis-related group (DRG) codes to identify hospitalisations due to decompensated heart failure (HF) in a population of patients with diabetes within the Veterans Health Administration (VHA) system. DESIGN: Validation study. SETTING: Veterans Health Administration—Tennessee Valley Healthcare System PARTICIPANTS: We identified and reviewed a stratified, random sample of hospitalisations between 2001 and 2012 within a single VHA healthcare system of adults who received regular VHA care and were initiated on an antidiabetic medication between 2001 and 2008. We sampled 500 hospitalisations; 400 hospitalisations that fulfilled algorithm criteria, 100 that did not. Of these, 497 had adequate information for inclusion. The mean patient age was 66.1 years (SD 11.4). Majority of patients were male (98.8%); 75% were white and 20% were black. PRIMARY AND SECONDARY OUTCOME MEASURES: To determine if a hospitalisation was due to HF, we performed chart abstraction using Framingham criteria as the referent standard. We calculated the positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity for the overall algorithm and each component (primary diagnosis code (ICD-9), DRG code or both). RESULTS: The algorithm had a PPV of 89.7% (95% CI 86.8 to 92.7), NPV of 93.9% (89.1 to 98.6), sensitivity of 45.1% (25.1 to 65.1) and specificity of 99.4% (99.2 to 99.6). The PPV was highest for hospitalisations that fulfilled both the ICD-9 and DRG algorithm criteria (92.1% (89.1 to 95.1)) and lowest for hospitalisations that fulfilled only DRG algorithm criteria (62.5% (28.4 to 96.6)). CONCLUSIONS: Our algorithm, which included primary discharge diagnosis and DRG codes, demonstrated excellent PPV for identification of hospitalisations due to decompensated HF among patients with diabetes in the VHA system. BMJ Publishing Group 2018-03-25 /pmc/articles/PMC5875613/ /pubmed/29581206 http://dx.doi.org/10.1136/bmjopen-2017-020455 Text en © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Epidemiology
Presley, Caroline A
Min, Jea Young
Chipman, Jonathan
Greevy, Robert A
Grijalva, Carlos G
Griffin, Marie R
Roumie, Christianne L
Validation of an algorithm to identify heart failure hospitalisations in patients with diabetes within the veterans health administration
title Validation of an algorithm to identify heart failure hospitalisations in patients with diabetes within the veterans health administration
title_full Validation of an algorithm to identify heart failure hospitalisations in patients with diabetes within the veterans health administration
title_fullStr Validation of an algorithm to identify heart failure hospitalisations in patients with diabetes within the veterans health administration
title_full_unstemmed Validation of an algorithm to identify heart failure hospitalisations in patients with diabetes within the veterans health administration
title_short Validation of an algorithm to identify heart failure hospitalisations in patients with diabetes within the veterans health administration
title_sort validation of an algorithm to identify heart failure hospitalisations in patients with diabetes within the veterans health administration
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5875613/
https://www.ncbi.nlm.nih.gov/pubmed/29581206
http://dx.doi.org/10.1136/bmjopen-2017-020455
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