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Impact of Risk Adjustment Using Clinical vs Administrative Data on Hospital Sepsis Mortality Comparisons

BACKGROUND: A reliable risk-adjusted sepsis outcome measure could complement current national process metrics by identifying outlier hospitals and catalyzing additional improvements in care. However, it is unclear whether integrating clinical data into risk adjustment models identifies similar high-...

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Autores principales: Rhee, Chanu, Li, Zhonghe, Wang, Rui, Song, Yue, Kadri, Sameer S, Septimus, Edward J, Chen, Huai-Chun, Fram, David, Jin, Robert, Poland, Russell, Sands, Kenneth, Klompas, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320830/
https://www.ncbi.nlm.nih.gov/pubmed/32617377
http://dx.doi.org/10.1093/ofid/ofaa213
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author Rhee, Chanu
Li, Zhonghe
Wang, Rui
Song, Yue
Kadri, Sameer S
Septimus, Edward J
Chen, Huai-Chun
Fram, David
Jin, Robert
Poland, Russell
Sands, Kenneth
Klompas, Michael
author_facet Rhee, Chanu
Li, Zhonghe
Wang, Rui
Song, Yue
Kadri, Sameer S
Septimus, Edward J
Chen, Huai-Chun
Fram, David
Jin, Robert
Poland, Russell
Sands, Kenneth
Klompas, Michael
author_sort Rhee, Chanu
collection PubMed
description BACKGROUND: A reliable risk-adjusted sepsis outcome measure could complement current national process metrics by identifying outlier hospitals and catalyzing additional improvements in care. However, it is unclear whether integrating clinical data into risk adjustment models identifies similar high- and low-performing hospitals compared with administrative data alone, which are simpler to acquire and analyze. METHODS: We ranked 200 US hospitals by their Centers for Disease Control and Prevention Adult Sepsis Event (ASE) mortality rates and assessed how rankings changed after applying (1) an administrative risk adjustment model incorporating demographics, comorbidities, and codes for severe illness and (2) an integrated clinical and administrative model replacing severity-of-illness codes with laboratory results, vasopressors, and mechanical ventilation. We assessed agreement between hospitals’ risk-adjusted ASE mortality rates when ranked into quartiles using weighted kappa statistics (к). RESULTS: The cohort included 4 009 631 hospitalizations, of which 245 808 met ASE criteria. Risk-adjustment had a large effect on rankings: 22/50 hospitals (44%) in the worst quartile using crude mortality rates shifted into better quartiles after administrative risk adjustment, and a further 21/50 (42%) of hospitals in the worst quartile using administrative risk adjustment shifted to better quartiles after incorporating clinical data. Conversely, 14/50 (28%) hospitals in the best quartile using administrative risk adjustment shifted to worse quartiles with clinical data. Overall agreement between hospital quartile rankings when risk-adjusted using administrative vs clinical data was moderate (к = 0.55). CONCLUSIONS: Incorporating clinical data into risk adjustment substantially changes rankings of hospitals’ sepsis mortality rates compared with using administrative data alone. Comprehensive risk adjustment using both administrative and clinical data is necessary before comparing hospitals by sepsis mortality rates.
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spelling pubmed-73208302020-07-01 Impact of Risk Adjustment Using Clinical vs Administrative Data on Hospital Sepsis Mortality Comparisons Rhee, Chanu Li, Zhonghe Wang, Rui Song, Yue Kadri, Sameer S Septimus, Edward J Chen, Huai-Chun Fram, David Jin, Robert Poland, Russell Sands, Kenneth Klompas, Michael Open Forum Infect Dis Major Article BACKGROUND: A reliable risk-adjusted sepsis outcome measure could complement current national process metrics by identifying outlier hospitals and catalyzing additional improvements in care. However, it is unclear whether integrating clinical data into risk adjustment models identifies similar high- and low-performing hospitals compared with administrative data alone, which are simpler to acquire and analyze. METHODS: We ranked 200 US hospitals by their Centers for Disease Control and Prevention Adult Sepsis Event (ASE) mortality rates and assessed how rankings changed after applying (1) an administrative risk adjustment model incorporating demographics, comorbidities, and codes for severe illness and (2) an integrated clinical and administrative model replacing severity-of-illness codes with laboratory results, vasopressors, and mechanical ventilation. We assessed agreement between hospitals’ risk-adjusted ASE mortality rates when ranked into quartiles using weighted kappa statistics (к). RESULTS: The cohort included 4 009 631 hospitalizations, of which 245 808 met ASE criteria. Risk-adjustment had a large effect on rankings: 22/50 hospitals (44%) in the worst quartile using crude mortality rates shifted into better quartiles after administrative risk adjustment, and a further 21/50 (42%) of hospitals in the worst quartile using administrative risk adjustment shifted to better quartiles after incorporating clinical data. Conversely, 14/50 (28%) hospitals in the best quartile using administrative risk adjustment shifted to worse quartiles with clinical data. Overall agreement between hospital quartile rankings when risk-adjusted using administrative vs clinical data was moderate (к = 0.55). CONCLUSIONS: Incorporating clinical data into risk adjustment substantially changes rankings of hospitals’ sepsis mortality rates compared with using administrative data alone. Comprehensive risk adjustment using both administrative and clinical data is necessary before comparing hospitals by sepsis mortality rates. Oxford University Press 2020-06-25 /pmc/articles/PMC7320830/ /pubmed/32617377 http://dx.doi.org/10.1093/ofid/ofaa213 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Major Article
Rhee, Chanu
Li, Zhonghe
Wang, Rui
Song, Yue
Kadri, Sameer S
Septimus, Edward J
Chen, Huai-Chun
Fram, David
Jin, Robert
Poland, Russell
Sands, Kenneth
Klompas, Michael
Impact of Risk Adjustment Using Clinical vs Administrative Data on Hospital Sepsis Mortality Comparisons
title Impact of Risk Adjustment Using Clinical vs Administrative Data on Hospital Sepsis Mortality Comparisons
title_full Impact of Risk Adjustment Using Clinical vs Administrative Data on Hospital Sepsis Mortality Comparisons
title_fullStr Impact of Risk Adjustment Using Clinical vs Administrative Data on Hospital Sepsis Mortality Comparisons
title_full_unstemmed Impact of Risk Adjustment Using Clinical vs Administrative Data on Hospital Sepsis Mortality Comparisons
title_short Impact of Risk Adjustment Using Clinical vs Administrative Data on Hospital Sepsis Mortality Comparisons
title_sort impact of risk adjustment using clinical vs administrative data on hospital sepsis mortality comparisons
topic Major Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320830/
https://www.ncbi.nlm.nih.gov/pubmed/32617377
http://dx.doi.org/10.1093/ofid/ofaa213
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