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Performance of in-hospital mortality prediction models for acute hospitalization: Hospital Standardized Mortality Ratio in Japan

OBJECTIVE: In-hospital mortality is an important performance measure for quality improvement, although it requires proper risk adjustment. We set out to develop in-hospital mortality prediction models for acute hospitalization using a nation-wide electronic administrative record system in Japan. MET...

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Autores principales: Miyata, Hiroaki, Hashimoto, Hideki, Horiguchi, Hiromasa, Matsuda, Shinya, Motomura, Noboru, Takamoto, Shinichi
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2606685/
https://www.ncbi.nlm.nih.gov/pubmed/18990251
http://dx.doi.org/10.1186/1472-6963-8-229
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author Miyata, Hiroaki
Hashimoto, Hideki
Horiguchi, Hiromasa
Matsuda, Shinya
Motomura, Noboru
Takamoto, Shinichi
author_facet Miyata, Hiroaki
Hashimoto, Hideki
Horiguchi, Hiromasa
Matsuda, Shinya
Motomura, Noboru
Takamoto, Shinichi
author_sort Miyata, Hiroaki
collection PubMed
description OBJECTIVE: In-hospital mortality is an important performance measure for quality improvement, although it requires proper risk adjustment. We set out to develop in-hospital mortality prediction models for acute hospitalization using a nation-wide electronic administrative record system in Japan. METHODS: Administrative records of 224,207 patients (patients discharged from 82 hospitals in Japan between July 1, 2002 and October 31, 2002) were randomly split into preliminary (179,156 records) and test (45,051 records) groups. Study variables included Major Diagnostic Category, age, gender, ambulance use, admission status, length of hospital stay, comorbidity, and in-hospital mortality. ICD-10 codes were converted to calculate comorbidity scores based on Quan's methodology. Multivariate logistic regression analysis was then performed using in-hospital mortality as a dependent variable. C-indexes were calculated across risk groups in order to evaluate model performances. RESULTS: In-hospital mortality rates were 2.68% and 2.76% for the preliminary and test datasets, respectively. C-index values were 0.869 for the model that excluded length of stay and 0.841 for the model that included length of stay. CONCLUSION: Risk models developed in this study included a set of variables easily accessible from administrative data, and still successfully exhibited a high degree of prediction accuracy. These models can be used to estimate in-hospital mortality rates of various diagnoses and procedures.
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spelling pubmed-26066852008-12-23 Performance of in-hospital mortality prediction models for acute hospitalization: Hospital Standardized Mortality Ratio in Japan Miyata, Hiroaki Hashimoto, Hideki Horiguchi, Hiromasa Matsuda, Shinya Motomura, Noboru Takamoto, Shinichi BMC Health Serv Res Research Article OBJECTIVE: In-hospital mortality is an important performance measure for quality improvement, although it requires proper risk adjustment. We set out to develop in-hospital mortality prediction models for acute hospitalization using a nation-wide electronic administrative record system in Japan. METHODS: Administrative records of 224,207 patients (patients discharged from 82 hospitals in Japan between July 1, 2002 and October 31, 2002) were randomly split into preliminary (179,156 records) and test (45,051 records) groups. Study variables included Major Diagnostic Category, age, gender, ambulance use, admission status, length of hospital stay, comorbidity, and in-hospital mortality. ICD-10 codes were converted to calculate comorbidity scores based on Quan's methodology. Multivariate logistic regression analysis was then performed using in-hospital mortality as a dependent variable. C-indexes were calculated across risk groups in order to evaluate model performances. RESULTS: In-hospital mortality rates were 2.68% and 2.76% for the preliminary and test datasets, respectively. C-index values were 0.869 for the model that excluded length of stay and 0.841 for the model that included length of stay. CONCLUSION: Risk models developed in this study included a set of variables easily accessible from administrative data, and still successfully exhibited a high degree of prediction accuracy. These models can be used to estimate in-hospital mortality rates of various diagnoses and procedures. BioMed Central 2008-11-07 /pmc/articles/PMC2606685/ /pubmed/18990251 http://dx.doi.org/10.1186/1472-6963-8-229 Text en Copyright © 2008 Miyata et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Miyata, Hiroaki
Hashimoto, Hideki
Horiguchi, Hiromasa
Matsuda, Shinya
Motomura, Noboru
Takamoto, Shinichi
Performance of in-hospital mortality prediction models for acute hospitalization: Hospital Standardized Mortality Ratio in Japan
title Performance of in-hospital mortality prediction models for acute hospitalization: Hospital Standardized Mortality Ratio in Japan
title_full Performance of in-hospital mortality prediction models for acute hospitalization: Hospital Standardized Mortality Ratio in Japan
title_fullStr Performance of in-hospital mortality prediction models for acute hospitalization: Hospital Standardized Mortality Ratio in Japan
title_full_unstemmed Performance of in-hospital mortality prediction models for acute hospitalization: Hospital Standardized Mortality Ratio in Japan
title_short Performance of in-hospital mortality prediction models for acute hospitalization: Hospital Standardized Mortality Ratio in Japan
title_sort performance of in-hospital mortality prediction models for acute hospitalization: hospital standardized mortality ratio in japan
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2606685/
https://www.ncbi.nlm.nih.gov/pubmed/18990251
http://dx.doi.org/10.1186/1472-6963-8-229
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