Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements

Background: Predictive equations (PEs) for estimating resting energy expenditure (REE) that have been developed from acute phase data may not be applicable in the late phase and vice versa. This study aimed to assess whether separate PEs are needed for acute and late phases of critical illness and t...

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Published in:European Journal of Clinical Nutrition
Main Author: Tah P.C.; Poh B.K.; Kee C.C.; Lee Z.-Y.; Hakumat-Rai V.-R.; Mat Nor M.B.; Kamarul Zaman M.; Majid H.A.; Hasan M.S.
Format: Article
Language:English
Published: Springer Nature 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114001311&doi=10.1038%2fs41430-021-00999-y&partnerID=40&md5=22dbe5c5b1493168ae1ff44e88ecd282
id 2-s2.0-85114001311
spelling 2-s2.0-85114001311
Tah P.C.; Poh B.K.; Kee C.C.; Lee Z.-Y.; Hakumat-Rai V.-R.; Mat Nor M.B.; Kamarul Zaman M.; Majid H.A.; Hasan M.S.
Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements
2022
European Journal of Clinical Nutrition
76
4
10.1038/s41430-021-00999-y
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114001311&doi=10.1038%2fs41430-021-00999-y&partnerID=40&md5=22dbe5c5b1493168ae1ff44e88ecd282
Background: Predictive equations (PEs) for estimating resting energy expenditure (REE) that have been developed from acute phase data may not be applicable in the late phase and vice versa. This study aimed to assess whether separate PEs are needed for acute and late phases of critical illness and to develop and validate PE(s) based on the results of this assessment. Methods: Using indirect calorimetry, REE was measured at acute (≤5 days; n = 294) and late (≥6 days; n = 180) phases of intensive care unit admission. PEs were developed by multiple linear regression. A multi-fold cross-validation approach was used to validate the PEs. The best PEs were selected based on the highest coefficient of determination (R2), the lowest root mean square error (RMSE) and the lowest standard error of estimate (SEE). Two PEs developed from paired 168-patient data were compared with measured REE using mean absolute percentage difference. Results: Mean absolute percentage difference between predicted and measured REE was <20%, which is not clinically significant. Thus, a single PE was developed and validated from data of the larger sample size measured in the acute phase. The best PE for REE (kcal/day) was 891.6(Height) + 9.0(Weight) + 39.7(Minute Ventilation)−5.6(Age) – 354, with R2 = 0.442, RMSE = 348.3, SEE = 325.6 and mean absolute percentage difference with measured REE was: 15.1 ± 14.2% [acute], 15.0 ± 13.1% [late]. Conclusions: Separate PEs for acute and late phases may not be necessary. Thus, we have developed and validated a PE from acute phase data and demonstrated that it can provide optimal estimates of REE for patients in both acute and late phases. Trial registration: ClinicalTrials.gov NCT03319329. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.
Springer Nature
9543007
English
Article
All Open Access; Bronze Open Access
author Tah P.C.; Poh B.K.; Kee C.C.; Lee Z.-Y.; Hakumat-Rai V.-R.; Mat Nor M.B.; Kamarul Zaman M.; Majid H.A.; Hasan M.S.
spellingShingle Tah P.C.; Poh B.K.; Kee C.C.; Lee Z.-Y.; Hakumat-Rai V.-R.; Mat Nor M.B.; Kamarul Zaman M.; Majid H.A.; Hasan M.S.
Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements
author_facet Tah P.C.; Poh B.K.; Kee C.C.; Lee Z.-Y.; Hakumat-Rai V.-R.; Mat Nor M.B.; Kamarul Zaman M.; Majid H.A.; Hasan M.S.
author_sort Tah P.C.; Poh B.K.; Kee C.C.; Lee Z.-Y.; Hakumat-Rai V.-R.; Mat Nor M.B.; Kamarul Zaman M.; Majid H.A.; Hasan M.S.
title Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements
title_short Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements
title_full Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements
title_fullStr Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements
title_full_unstemmed Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements
title_sort Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements
publishDate 2022
container_title European Journal of Clinical Nutrition
container_volume 76
container_issue 4
doi_str_mv 10.1038/s41430-021-00999-y
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114001311&doi=10.1038%2fs41430-021-00999-y&partnerID=40&md5=22dbe5c5b1493168ae1ff44e88ecd282
description Background: Predictive equations (PEs) for estimating resting energy expenditure (REE) that have been developed from acute phase data may not be applicable in the late phase and vice versa. This study aimed to assess whether separate PEs are needed for acute and late phases of critical illness and to develop and validate PE(s) based on the results of this assessment. Methods: Using indirect calorimetry, REE was measured at acute (≤5 days; n = 294) and late (≥6 days; n = 180) phases of intensive care unit admission. PEs were developed by multiple linear regression. A multi-fold cross-validation approach was used to validate the PEs. The best PEs were selected based on the highest coefficient of determination (R2), the lowest root mean square error (RMSE) and the lowest standard error of estimate (SEE). Two PEs developed from paired 168-patient data were compared with measured REE using mean absolute percentage difference. Results: Mean absolute percentage difference between predicted and measured REE was <20%, which is not clinically significant. Thus, a single PE was developed and validated from data of the larger sample size measured in the acute phase. The best PE for REE (kcal/day) was 891.6(Height) + 9.0(Weight) + 39.7(Minute Ventilation)−5.6(Age) – 354, with R2 = 0.442, RMSE = 348.3, SEE = 325.6 and mean absolute percentage difference with measured REE was: 15.1 ± 14.2% [acute], 15.0 ± 13.1% [late]. Conclusions: Separate PEs for acute and late phases may not be necessary. Thus, we have developed and validated a PE from acute phase data and demonstrated that it can provide optimal estimates of REE for patients in both acute and late phases. Trial registration: ClinicalTrials.gov NCT03319329. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.
publisher Springer Nature
issn 9543007
language English
format Article
accesstype All Open Access; Bronze Open Access
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