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Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise

BACKGROUND: Intensive care unit (ICU) length of stay (LOS) and the risk adjusted equivalent (RALOS) have been used as quality metrics. The latter measures entail either ratio or difference formulations or ICU random effects (RE), which have not been previously compared. METHODS: From calendar year 2...

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Autores principales: Moran, John L., Duke, Graeme J., Santamaria, John D., Linden, Ariel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500937/
https://www.ncbi.nlm.nih.gov/pubmed/37710162
http://dx.doi.org/10.1186/s12874-023-02028-x
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author Moran, John L.
Duke, Graeme J.
Santamaria, John D.
Linden, Ariel
author_facet Moran, John L.
Duke, Graeme J.
Santamaria, John D.
Linden, Ariel
author_sort Moran, John L.
collection PubMed
description BACKGROUND: Intensive care unit (ICU) length of stay (LOS) and the risk adjusted equivalent (RALOS) have been used as quality metrics. The latter measures entail either ratio or difference formulations or ICU random effects (RE), which have not been previously compared. METHODS: From calendar year 2016 data of an adult ICU registry-database (Australia & New Zealand Intensive Care Society (ANZICS) CORE), LOS predictive models were established using linear (LMM) and generalised linear (GLMM) mixed models. Model fixed effects quality-metric formulations were estimated as RALOSR for LMM (geometric mean derived from log(ICU LOS)) and GLMM (day) and observed minus expected ICU LOS (OMELOS from GLMM). Metric confidence intervals (95%CI) were estimated by bootstrapping; random effects (RE) were predicted for LMM and GLMM. Forest-plot displays of ranked quality-metric point-estimates (95%CI) were generated for ICU hospital classifications (metropolitan, private, rural/regional, and tertiary). Robust rank confidence sets (point estimate and 95%CI), both marginal (pertaining to a singular ICU) and simultaneous (pertaining to all ICU differences), were established. RESULTS: The ICU cohort was of 94,361 patients from 125 ICUs (metropolitan 16.9%, private 32.8%, rural/regional 6.4%, tertiary 43.8%). Age (mean, SD) was 61.7 (17.5) years; 58.3% were male; APACHE III severity-of-illness score 54.6 (25.7); ICU annual patient volume 1192 (702) and ICU LOS 3.2 (4.9). There was no concordance of ICU ranked model predictions, GLMM versus LMM, nor for the quality metrics used, RALOSR, OMELOS and site-specific RE for each of the ICU hospital classifications. Furthermore, there was no concordance between ICU ranking confidence sets, marginal and simultaneous for models or quality metrics. CONCLUSIONS: Inference regarding adjusted ICU LOS was dependent upon the statistical estimator and the quality index used to quantify any LOS differences across ICUs. That is, there was no “one best model”; thus, ICU “performance” is determined by model choice and any rankings thereupon should be circumspect. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-02028-x.
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spelling pubmed-105009372023-09-15 Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise Moran, John L. Duke, Graeme J. Santamaria, John D. Linden, Ariel BMC Med Res Methodol Research BACKGROUND: Intensive care unit (ICU) length of stay (LOS) and the risk adjusted equivalent (RALOS) have been used as quality metrics. The latter measures entail either ratio or difference formulations or ICU random effects (RE), which have not been previously compared. METHODS: From calendar year 2016 data of an adult ICU registry-database (Australia & New Zealand Intensive Care Society (ANZICS) CORE), LOS predictive models were established using linear (LMM) and generalised linear (GLMM) mixed models. Model fixed effects quality-metric formulations were estimated as RALOSR for LMM (geometric mean derived from log(ICU LOS)) and GLMM (day) and observed minus expected ICU LOS (OMELOS from GLMM). Metric confidence intervals (95%CI) were estimated by bootstrapping; random effects (RE) were predicted for LMM and GLMM. Forest-plot displays of ranked quality-metric point-estimates (95%CI) were generated for ICU hospital classifications (metropolitan, private, rural/regional, and tertiary). Robust rank confidence sets (point estimate and 95%CI), both marginal (pertaining to a singular ICU) and simultaneous (pertaining to all ICU differences), were established. RESULTS: The ICU cohort was of 94,361 patients from 125 ICUs (metropolitan 16.9%, private 32.8%, rural/regional 6.4%, tertiary 43.8%). Age (mean, SD) was 61.7 (17.5) years; 58.3% were male; APACHE III severity-of-illness score 54.6 (25.7); ICU annual patient volume 1192 (702) and ICU LOS 3.2 (4.9). There was no concordance of ICU ranked model predictions, GLMM versus LMM, nor for the quality metrics used, RALOSR, OMELOS and site-specific RE for each of the ICU hospital classifications. Furthermore, there was no concordance between ICU ranking confidence sets, marginal and simultaneous for models or quality metrics. CONCLUSIONS: Inference regarding adjusted ICU LOS was dependent upon the statistical estimator and the quality index used to quantify any LOS differences across ICUs. That is, there was no “one best model”; thus, ICU “performance” is determined by model choice and any rankings thereupon should be circumspect. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-02028-x. BioMed Central 2023-09-14 /pmc/articles/PMC10500937/ /pubmed/37710162 http://dx.doi.org/10.1186/s12874-023-02028-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Moran, John L.
Duke, Graeme J.
Santamaria, John D.
Linden, Ariel
Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise
title Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise
title_full Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise
title_fullStr Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise
title_full_unstemmed Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise
title_short Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise
title_sort modelling of intensive care unit (icu) length of stay as a quality measure: a problematic exercise
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500937/
https://www.ncbi.nlm.nih.gov/pubmed/37710162
http://dx.doi.org/10.1186/s12874-023-02028-x
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