Estimating the Effect of Healthcare-Associated Infections on Excess Length of Hospital Stay Using Inverse Probability-Weighted Survival Curves.
bacteremia
causal inference
confounding
excess length of stay
infection
Journal
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
ISSN: 1537-6591
Titre abrégé: Clin Infect Dis
Pays: United States
ID NLM: 9203213
Informations de publication
Date de publication:
03 12 2020
03 12 2020
Historique:
received:
15
11
2019
accepted:
07
02
2020
pubmed:
13
2
2020
medline:
28
4
2021
entrez:
13
2
2020
Statut:
ppublish
Résumé
Studies estimating excess length of stay (LOS) attributable to nosocomial infections have failed to address time-varying confounding, likely leading to overestimation of their impact. We present a methodology based on inverse probability-weighted survival curves to address this limitation. A case study focusing on intensive care unit-acquired bacteremia using data from 2 general intensive care units (ICUs) from 2 London teaching hospitals were used to illustrate the methodology. The area under the curve of a conventional Kaplan-Meier curve applied to the observed data was compared with that of an inverse probability-weighted Kaplan-Meier curve applied after treating bacteremia as censoring events. Weights were based on the daily probability of acquiring bacteremia. The difference between the observed average LOS and the average LOS that would be observed if all bacteremia cases could be prevented was multiplied by the number of admitted patients to obtain the total excess LOS. The estimated total number of extra ICU days caused by 666 bacteremia cases was estimated at 2453 (95% confidence interval [CI], 1803-3103) days. The excess number of days was overestimated when ignoring time-varying confounding (2845 [95% CI, 2276-3415]) or when completely ignoring confounding (2838 [95% CI, 2101-3575]). ICU-acquired bacteremia was associated with a substantial excess LOS. Wider adoption of inverse probability-weighted survival curves or alternative techniques that address time-varying confounding could lead to better informed decision making around nosocomial infections and other time-dependent exposures.
Sections du résumé
BACKGROUND
Studies estimating excess length of stay (LOS) attributable to nosocomial infections have failed to address time-varying confounding, likely leading to overestimation of their impact. We present a methodology based on inverse probability-weighted survival curves to address this limitation.
METHODS
A case study focusing on intensive care unit-acquired bacteremia using data from 2 general intensive care units (ICUs) from 2 London teaching hospitals were used to illustrate the methodology. The area under the curve of a conventional Kaplan-Meier curve applied to the observed data was compared with that of an inverse probability-weighted Kaplan-Meier curve applied after treating bacteremia as censoring events. Weights were based on the daily probability of acquiring bacteremia. The difference between the observed average LOS and the average LOS that would be observed if all bacteremia cases could be prevented was multiplied by the number of admitted patients to obtain the total excess LOS.
RESULTS
The estimated total number of extra ICU days caused by 666 bacteremia cases was estimated at 2453 (95% confidence interval [CI], 1803-3103) days. The excess number of days was overestimated when ignoring time-varying confounding (2845 [95% CI, 2276-3415]) or when completely ignoring confounding (2838 [95% CI, 2101-3575]).
CONCLUSIONS
ICU-acquired bacteremia was associated with a substantial excess LOS. Wider adoption of inverse probability-weighted survival curves or alternative techniques that address time-varying confounding could lead to better informed decision making around nosocomial infections and other time-dependent exposures.
Identifiants
pubmed: 32047916
pii: 5734540
doi: 10.1093/cid/ciaa136
pmc: PMC7713691
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e415-e420Subventions
Organisme : Department of Health
ID : HPRU-2012–10041
Pays : United Kingdom
Investigateurs
Philip E Anyanwu
(PE)
Aleksandra Borek
(A)
Nicole Bright
(N)
James Buchanan
(J)
Christopher Butler
(C)
Anne Campbell
(A)
Ceire Costelloe
(C)
Benedict Hayhoe
(B)
Alison Holmes
(A)
Susan Hopkins
(S)
Azeem Majeed
(A)
Monsey McLeod
(M)
Michael Moore
(M)
Liz Morrell
(L)
Koen B Pouwels
(KB)
Julie V Robotham
(JV)
Laurence S J Roope
(LSJ)
Sarah Tonkin-Crine
(S)
Ann Sarah Walker
(AS)
Sarah Wordsworth
(S)
Anna Zalevski
(A)
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America.
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