Literature DB >> 27179057

Can Creatinine Height Index Predict Weaning and Survival Outcomes in Patients on Prolonged Mechanical Ventilation After Critical Illness?

Debapriya Datta1, Raymond Foley1, Rong Wu2, James Grady2, Paul Scalise3.   

Abstract

OBJECTIVE: Malnutrition is common in chronic critically ill patients on prolonged mechanical ventilation (PMV) and may affect weaning. The creatinine height index (CHI), which reflects lean muscle mass, is regarded as the most accurate indicator of malnutrition. The objective of this study was to determine the impact of CHI in comparison with other traditional nutritional indices on successful weaning and survival in patients on PMV after critical illness.
METHODS: Records of 167 patients on PMV following critical illness, admitted for weaning, were reviewed. Parameters studied included age, gender, body mass index (BMI), percentage ideal body weight (%IBW), total protein, albumin, prealbumin, hemoglobin (Hb), and cause of respiratory failure. Number successfully weaned and number discharged alive and time to wean and time to discharge alive were determined from records. The CHI was calculated from 24-hour urine creatinine using a standard formula. Unpaired 2-sample t test was performed to determine the association between the studied nutritional parameters and outcomes. Predictive value of studied parameters for successful weaning and survival was determined by multivariate logistic regression analysis to model dichotomous outcome of successful weaning and survival.
RESULTS: Mean age was 68 ± 14 years, 49% were males, 64% were successfully weaned, and 65.8% survived. Total protein, Hb, and CHI had a significant impact on successful weaning. Weight, %IBW, BMI, and CHI had a significant effect on survival. Of all parameters, CHI was most strongly predictive of successful weaning and survival.
CONCLUSIONS: The CHI is a strong predictor of successful weaning and survival in patients on PMV.

Entities:  

Keywords:  creatinine height index; nutrition; prolonged mechanical ventilation; respiratory failure; survival; weaning

Mesh:

Substances:

Year:  2016        PMID: 27179057     DOI: 10.1177/0885066616648133

Source DB:  PubMed          Journal:  J Intensive Care Med        ISSN: 0885-0666            Impact factor:   3.510


  7 in total

1.  ROUNDS Studies: Relation of OUtcomes with Nutrition Despite Severity-Round One: Ultrasound Muscle Measurements in Critically Ill Adult Patients.

Authors:  Carlos Alfredo Galindo Martín; Reyna Del Carmen Ubeda Zelaya; Enrique Monares Zepeda; Octavio Augusto Lescas Méndez
Journal:  J Nutr Metab       Date:  2018-04-01

2.  Prediction of extubation outcome in mechanically ventilated patients: Development and validation of the Extubation Predictive Score (ExPreS).

Authors:  Antuani Rafael Baptistella; Laura Maito Mantelli; Leandra Matte; Maria Eduarda da Rosa Ulanoski Carvalho; João Antonio Fortunatti; Iury Zordan Costa; Felipe Gabriel Haro; Vanda Laís de Oliveira Turkot; Shaline Ferla Baptistella; Diego de Carvalho; João Rogério Nunes Filho
Journal:  PLoS One       Date:  2021-03-18       Impact factor: 3.240

Review 3.  Nutritional support in chronic liver disease and cirrhotics.

Authors:  Ravi Shergill; Wajahat Syed; Syed Ali Rizvi; Ikjot Singh
Journal:  World J Hepatol       Date:  2018-10-27

Review 4.  Optimizing Nutrition Assessment to Create Better Outcomes in Lung Transplant Recipients: A Review of Current Practices.

Authors:  Mara Weber Gulling; Monica Schaefer; Laura Bishop-Simo; Brian C Keller
Journal:  Nutrients       Date:  2019-11-27       Impact factor: 5.717

5.  The Combination of SOFA Score and Urinary NGAL May Be an Effective Predictor for Ventilator Dependence among Critically Ill Surgical Patients: A Pilot Study.

Authors:  Hsin-I Tsai; Yu-Chieh Lu; Hao-Wei Kou; Heng-Yuan Hsu; Song-Fong Huang; Chun-Wei Huang; Chao-Wei Lee
Journal:  Diagnostics (Basel)       Date:  2021-06-30

6.  A Simple Algorithm Using Ventilator Parameters to Predict Successfully Rapid Weaning Program in Cardiac Intensive Care Unit Patients.

Authors:  Wei-Teing Chen; Hai-Lun Huang; Pi-Shao Ko; Wen Su; Chung-Cheng Kao; Sui-Lung Su
Journal:  J Pers Med       Date:  2022-03-21

7.  The Feasibility of a Machine Learning Approach in Predicting Successful Ventilator Mode Shifting for Adult Patients in the Medical Intensive Care Unit.

Authors:  Kuang-Hua Cheng; Mei-Chu Tan; Yu-Jen Chang; Cheng-Wei Lin; Yi-Han Lin; Tzu-Min Chang; Li-Kuo Kuo
Journal:  Medicina (Kaunas)       Date:  2022-03-01       Impact factor: 2.430

  7 in total

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