Predicting Hospital Length of Stay at Admission Using Global and Country-Specific Competing Risk Analysis of Structural, Patient, and Nutrition-Related Data from nutritionDay 2007–2015

Hospital length of stay (LOS) is an important clinical and economic outcome and knowing its predictors could lead to better planning of resources needed during hospitalization. This analysis sought to identify structure, patient, and nutrition-related predictors of LOS available at the time of admis...

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Autores principales:	Noemi Kiss, Michael Hiesmayr, Isabella Sulz, Peter Bauer, Georg Heinze, Mohamed Mouhieddine, Christian Schuh, Silvia Tarantino, Judit Simon
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	length of stay nutrition hospital survey discharge transfer Nutrition. Foods and food supply TX341-641
Acceso en línea:	https://doaj.org/article/c45e6ab86fcd40ddab91b4a3e3366de8
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spelling	oai:doaj.org-article:c45e6ab86fcd40ddab91b4a3e3366de82021-11-25T18:36:51ZPredicting Hospital Length of Stay at Admission Using Global and Country-Specific Competing Risk Analysis of Structural, Patient, and Nutrition-Related Data from nutritionDay 2007–201510.3390/nu131141112072-6643https://doaj.org/article/c45e6ab86fcd40ddab91b4a3e3366de82021-11-01T00:00:00Zhttps://www.mdpi.com/2072-6643/13/11/4111https://doaj.org/toc/2072-6643Hospital length of stay (LOS) is an important clinical and economic outcome and knowing its predictors could lead to better planning of resources needed during hospitalization. This analysis sought to identify structure, patient, and nutrition-related predictors of LOS available at the time of admission in the global nutritionDay dataset and to analyze variations by country for countries with <i>n</i> > 750. Data from 2006–2015 (<i>n</i> = 155,524) was utilized for descriptive and multivariable cause-specific Cox proportional hazards competing-risks analyses of total LOS from admission. Time to event analysis on 90,480 complete cases included: discharged (<i>n</i> = 65,509), transferred (<i>n</i> = 11,553), or in-hospital death (<i>n</i> = 3199). The median LOS was 6 days (25th and 75th percentile: 4–12). There is robust evidence that LOS is predicted by patient characteristics such as age, affected organs, and comorbidities in all three outcomes. Having lost weight in the last three months led to a longer time to discharge (Hazard Ratio (HR) 0.89; 99.9% Confidence Interval (CI) 0.85–0.93), shorter time to transfer (HR 1.40; 99.9% CI 1.24–1.57) or death (HR 2.34; 99.9% CI 1.86–2.94). The impact of having a dietician and screening patients at admission varied by country. Despite country variability in outcomes and LOS, the factors that predict LOS at admission are consistent globally.Noemi KissMichael HiesmayrIsabella SulzPeter BauerGeorg HeinzeMohamed MouhieddineChristian SchuhSilvia TarantinoJudit SimonMDPI AGarticlelength of staynutritionhospitalsurveydischargetransferNutrition. Foods and food supplyTX341-641ENNutrients, Vol 13, Iss 4111, p 4111 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	length of stay nutrition hospital survey discharge transfer Nutrition. Foods and food supply TX341-641
spellingShingle	length of stay nutrition hospital survey discharge transfer Nutrition. Foods and food supply TX341-641 Noemi Kiss Michael Hiesmayr Isabella Sulz Peter Bauer Georg Heinze Mohamed Mouhieddine Christian Schuh Silvia Tarantino Judit Simon Predicting Hospital Length of Stay at Admission Using Global and Country-Specific Competing Risk Analysis of Structural, Patient, and Nutrition-Related Data from nutritionDay 2007–2015
description	Hospital length of stay (LOS) is an important clinical and economic outcome and knowing its predictors could lead to better planning of resources needed during hospitalization. This analysis sought to identify structure, patient, and nutrition-related predictors of LOS available at the time of admission in the global nutritionDay dataset and to analyze variations by country for countries with <i>n</i> > 750. Data from 2006–2015 (<i>n</i> = 155,524) was utilized for descriptive and multivariable cause-specific Cox proportional hazards competing-risks analyses of total LOS from admission. Time to event analysis on 90,480 complete cases included: discharged (<i>n</i> = 65,509), transferred (<i>n</i> = 11,553), or in-hospital death (<i>n</i> = 3199). The median LOS was 6 days (25th and 75th percentile: 4–12). There is robust evidence that LOS is predicted by patient characteristics such as age, affected organs, and comorbidities in all three outcomes. Having lost weight in the last three months led to a longer time to discharge (Hazard Ratio (HR) 0.89; 99.9% Confidence Interval (CI) 0.85–0.93), shorter time to transfer (HR 1.40; 99.9% CI 1.24–1.57) or death (HR 2.34; 99.9% CI 1.86–2.94). The impact of having a dietician and screening patients at admission varied by country. Despite country variability in outcomes and LOS, the factors that predict LOS at admission are consistent globally.
format	article
author	Noemi Kiss Michael Hiesmayr Isabella Sulz Peter Bauer Georg Heinze Mohamed Mouhieddine Christian Schuh Silvia Tarantino Judit Simon
author_facet	Noemi Kiss Michael Hiesmayr Isabella Sulz Peter Bauer Georg Heinze Mohamed Mouhieddine Christian Schuh Silvia Tarantino Judit Simon
author_sort	Noemi Kiss
title	Predicting Hospital Length of Stay at Admission Using Global and Country-Specific Competing Risk Analysis of Structural, Patient, and Nutrition-Related Data from nutritionDay 2007–2015
title_short	Predicting Hospital Length of Stay at Admission Using Global and Country-Specific Competing Risk Analysis of Structural, Patient, and Nutrition-Related Data from nutritionDay 2007–2015
title_full	Predicting Hospital Length of Stay at Admission Using Global and Country-Specific Competing Risk Analysis of Structural, Patient, and Nutrition-Related Data from nutritionDay 2007–2015
title_fullStr	Predicting Hospital Length of Stay at Admission Using Global and Country-Specific Competing Risk Analysis of Structural, Patient, and Nutrition-Related Data from nutritionDay 2007–2015
title_full_unstemmed	Predicting Hospital Length of Stay at Admission Using Global and Country-Specific Competing Risk Analysis of Structural, Patient, and Nutrition-Related Data from nutritionDay 2007–2015
title_sort	predicting hospital length of stay at admission using global and country-specific competing risk analysis of structural, patient, and nutrition-related data from nutritionday 2007–2015
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c45e6ab86fcd40ddab91b4a3e3366de8
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Predicting Hospital Length of Stay at Admission Using Global and Country-Specific Competing Risk Analysis of Structural, Patient, and Nutrition-Related Data from nutritionDay 2007–2015

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